The International Council for Harmonization (ICH) M7 guideline describes a hazard assessment process for impurities that have the potential to be present in a drug substance or drug product. In the absence of adequate experimental bacterial mutagenicity data, (Q)SAR analysis may be used as a test to predict impurities’ DNA reactive (mutagenic) potential. However, in certain situations, (Q)SAR software is unable to generate a positive or negative prediction either because of conflicting information or because the impurity is outside the applicability domain of the model. Such results present challenges in generating an overall mutagenicity prediction and highlight the importance of performing a thorough expert review. The following paper reviews pharmaceutical and regulatory experiences handling such situations. The paper also presents an analysis of proprietary data to help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results. This information may be taken into consideration when supporting the (Q)SAR results with an expert review, especially when out-of-domain results are generated during a (Q)SAR evaluation. The guigenicity . Based ogenicity  and the t safety . The (Q)t safety . This clThe guideline recommends such (Q)SAR assessments to be based on the results from two complementary (Q)SAR methodologies: expert rule-based and statistical-based . The resindeterminate predictions in this publication .(Q)SAR models use datasets of historical data as well as general scientific knowledge  from the literature based on known mechanisms of DNA reactive mutagenicity to generate a prediction. Since the models are based on what is known, they may not be able to predict with sufficient confidence a clear positive or negative outcome for novel chemicals. This may be due to conflicting evidence such that the influence of substituents on the reactivity of an alerting chemical moiety is not fully understood. These results will be referred to as out-of-domain throughout this publication; however, different systems may use other terms such as not-in-domain).Another area where (Q)SAR models can present a challenge to users is when the structure being assessed falls outside the training or reference set used to generate the model. Such domain analysis is required as part of the (Q)SAR assessment since the ICH M7 guideline states that both methodologies should follow the general validation principles set forth by the Organization for Economic Co-operation and Development (OECD) . The thiin silico toxicology workflow that can be utilized under ICH M7 to generate predictions with improved accuracy that are consistent between different experts (The ICH M7 guideline describes that the (Q)SAR analysis may be supported by an expert review, especially in situations where the results are inconclusive  as well as where there are valid reasons to overturn or refute a prediction. The standardized use of expert review has been detailed in several publications  includin experts .The principles of the ICH M7 guideline are now routinely followed by the pharmaceutical industry and international regulatory agencies. Although the (Q)SAR assessment and expert review of the results has been discussed in a number of publications, there are some specific challenges associated with managing out-of-domain and indeterminate results, which have not been fully addressed. The following paper outlines current regulatory and industry approaches for handling out-of-domain and indeterminate results based on an industry survey. A series of case studies from regulatory submissions are provided to illustrate how out-of-domain or indeterminate results can be put into context. The paper also includes an assessment of the likelihood of misclassifying a mutagenic impurity as not mutagenic based on different combinations of (Q)SAR results . How this information can be taken into consideration as part of an overall assessment is discussed.2.A general request was made to the pharmaceutical industry and regulatory authorities to outline current practices for handling out-of-domain and indeterminate results. This information was collated and summarized in To help understand the likelihood of misclassifying a mutagenic impurity as non-mutagenic based on different combinations of (Q)SAR results, a request was made to run the (Q)SAR models generally used for ICH M7 assessment over proprietary chemicals for which bacterial reverse mutation assay (Ames) data were available and provide a table containing the fields shown in The results were compiled into a single consolidated table for analysis. This involved a step to harmonize the results from different systems  into the following calls for each methodology:Positive: A positive call .Negative: A negative call that is within the applicability domain of the model .Indeterminate: An indeterminate or equivocal call that is within the applicability domain.Out-of-domain: The (Q)SAR model considered the chemical outside the applicability domain.In order to assess if additional model output that reflects the probability of a positive response can be used to support an overall assessment where the statistical-based model is out-of-domain, the following two situations are also considered when the system generates a probability score , as outlined in the Out-of-domain with probability of being positive < 0.2: The compound is considered outside the applicability domain of the statistical-based model; however, a probability score of less than 0.2 is generatedOut-of-domain with probability of being positive 0.2–0.4: The compound is considered outside the applicability domain of the statistical-based model; however, a probability score of between 0.2 and 0.4 is generatedThe rules used to harmonize the results across the different systems are included in the 3.The following analysis of proprietary data was performed to help understand the likelihood of misclassifying a mutagenic impurity as not mutagenic  using different combinations of (Q)SAR results. The analysis is based on historical data from proprietary collections that include similar chemicals to those in a typical assessment of impurities such as low molecular weight chemicals used as starting materials and API -like chemicals similar to the synthetic intermediates. The results were generated based on the methodology outlined in 4.4.1.Out-of-domain and/or indeterminate results are often encountered as part of an ICH M7 impurity assessment. This has been quantified, in part, through an analysis of new drugs approved in 2016 and 2017 that showed 18% of the impurities had an out-of-domain result . These oA variety of approaches for handling out-of-domain and/or indeterminate results are being used across pharmaceutical companies as well as regulatory agencies to support an overall prediction . This is reflected in a further breakdown of drugs approved in 2016 and 2017 which quantified the different approaches for handling such situations including applying expert knowledge (for 70% of the submitted impurities), applying an additional model (for 6% of the submitted impurities), test/control (for 21% of the submitted impurities), and no follow-up (for 3% of the submitted impurities) . The fol4.2.4.2.1.The application of expert knowledge has been shown to improve (Q) SAR predictive performance, particularly in resolving ambiguous outcomes such as out-of-domain results or indeterminate predictions, which is consistent with other published accounts . To suppTo quantify the actions resulting from such a review, the US Food and Drug Administration’s (FDA) Center for Drug Evaluation and Research (CDER) between May 2016 and April 2017 analyzed 519 impurities for bacterial mutation using software from 2.1% of the negative consensus predictions changed to positive after the expert review4.2% of the positive consensus predictions changed to negative after the expert review61% of the indeterminate consensus predictions changed to negative after the expert review11% of the indeterminate consensus predictions changed to positive after the expert review28% of the indeterminate consensus predictions were not changedThese results  support the observation that the ICH M7 classification paradigm  risks generating a number of false positive predictions that can be corrected through the application of expert knowledge. Furthermore, it supports the conclusion that the majority of indeterminate predictions are not meaningful signals and can be readily downgraded to negative through a review of the model output and supporting analogs from the public domain.4.2.2.Structurally and/or toxicologically meaningful non-mutagenic analog(s) from public or proprietary databases or chemicals related to the impurity (such as the API or synthetic intermediate) are often used to support an overall prediction for an impurity when additional evidence is needed. This approach is sometimes referred to as read-across . As partThe following examples illustrate expert reviews of out-of-domain results based on structural analogs.An impurity shown in  was predCyclohexyldiphenylphosphine oxide  and cyclohexyldiphenylphosphine  are impurities shown in  occurrin4.2.3.For out-of-domain or indeterminate (Q)SAR results, additional supporting analysis to confirm that the impurity lacks any DNA-reactive potential has also been used . This inIn addition, specific searches for significant functional groups and other substructures present in the impurity  against a database containing mutagenicity data (including proprietary data) is often performed. This may indicate that the impurity contains a potentially reactive group when the results contain significantly more positive examples linked to that particular substructure than would be expected by chance . Additionally, the (Q) SAR model may be applied to any substructure of the impurity to help determine the reactive potential for the components, when the whole chemical is out-of-domain.There are several substructures, such as protection groups , where their presence within an impurity may change the prediction from negative to out-of-domain. In these cases, such substructures are specifically known to block that portion of the molecule from chemical reactivity . TherefoAs shown in E. coli (As shown in E. coli .), the i4.2.4.E. coli WP2 or E. coli WP2 uvrA, or E. coli WP2 uvrA (pKM101), or S. typhimurium TA102). The database of compounds used to build the model for the GC base pair mutations is typically larger than that used for the AT reversion site. Therefore, it is more likely that a compound will be out-of-domain in the model for the AT reversion site. A prediction just at the GC primary reversion site may be sufficient to support a valid prediction in many cases. However, where the impurities contain specific AT alerting fragment(s), such as oxidizing mutagens, cross-linking agents, and hydrazines, the four strains in the traditional Ames would not be able to detect the mutagenicity of an impurity. In this case, further interrogation of the impurity in strains that detect the AT base pair reversion site may be warranted.Some (Q)SAR systems include a battery of models including those for the traditional Ames  and an additional model for the AT base pair reversion site  to increase the domain of the original model without substantially changing the original model. This has been particularly useful when many related compounds are out-of-domain and the expansion of the model includes one or more chemicals  that are structurally related to the impurities. The addition of these structures is often sufficient to bring the impurity within the applicability domain but might change the probability score  and, in limited situations, the prediction of mutagenic potential for the impurity. Another approach is to create a new model using a training set built either exclusively from proprietary data or proprietary data combined with publicly available data . Such moFor the impurity shown in An impurity shown in  was predThis impurity shown in  was nega4.4.Any situation where one or more of the results are out-of-domain or indeterminate, in general, requires an expert review to provide support for an overall negative prediction (as outlined previously). In the absence of any supportive evidence for out-of-domain or indeterminate call(s) , the prediction would be considered uncertain and an Ames test may be prudent to make a final conclusion. Alternatively, it can be treated as mutagenic (class 3) and controlled to the Threshold of Toxicological Concern (TTC) defined in the ICH M7 guideline. In situations where there is sufficient supportive evidence and any positive signals from the models are refuted as part of an expert review, the impurity is generally assigned to class 5 or class 4 if any present alert is shared with an empirically non-mutagenic chemical such as an API.4.5.ICH M7 submissions are handled by the individual review divisions at US FDA/CDER. The reviewers assess the information provided by the pharmaceutical applicant, including information on the software and models used, the results from the software, the overall conclusions and any associated expert review documentation for consistency with the ICH M7 guideline and to ensure the results and expert review are valid. In cases where the reviewer has questions or concerns, the (Q)SAR submission is provided to FDA/CDER’s internal Computational Toxicology Consulting Service (CTCS) for evaluation . It shoupoorly documented evaluations, unfamiliar software, software that does not allow for prediction interpretation consistent with ICH M7, or models that are not compliant with the OECD validation principlessituations when only a single methodology was used or only read-across  was usedwhen the overall conclusions conflict with the individual model predictions, without an explanationwhen the most recent version of the software was not run and a change in the prediction is anticipatedIn 2016, 217 consultation requests  were related to ICH M7 submissions, with 90% for generic drug applications and 10% for new drug applications . In casea review of the (Q)SAR data submitteda further structure-based search  for additional experimental data on the impurities or any analogsan independent (Q)SAR assessment using models from Leadscope Inc. , Lhasa Lan expert review of the results and related literature4.6.A computational assessment of impurities should ideally balance the need for a rapid analysis of multiple compounds while limiting false negative and false positive predictions as much as practically possible. The information in As discussed previously, there are a variety of approaches to resolve out-of-domain results. For example, as part of an expert review additional supportive evidence may be provided, including suitable analogs, an analysis of the lack of reactive potential as well as running another model (as discussed earlier). The likelihood of misclassifying a mutagenic impurity as non-mutagenic when one of the methodologies generates an out-of-domain result is in large part dependent on the result from the other model. Based on the analysis shown in It is also interesting to note that when the statistical-based model is out-of-domain and the expert rule-based model is positive then the percentage of Ames mutagens is 36.1%, which is similar to the situation when the statistical-based model is negative and the expert rule-based model is positive (37.5%). This may indicate that many of the chemicals predicted to be out-of-domain by the statistical-based model are related to novel APIs lacking reactive features. It is also worth pointing out that when an expert rule-based result is indeterminate, then the results from the statistical-based model are correlated with the percentage of Ames positive. When the statistical-based result is negative and the expert rule-based result it indeterminate, the percentage of Ames positives is 11.8% whereas when the statistical-based result is positive the percentage of Ames positives is 27.7% which illustrates the value of using the two complementary methodologies.There are a number of limitations with this type of cross-pharmaceutical company analysis of proprietary data. It is not possible to see the individual chemicals to verify that there are no duplicate chemicals; however, this number will be low since different proprietary collections generally cover separate areas of the chemical space. The analysis is also based on a harmonized evaluation of different models and algorithms. In addition, the overall proportion of positive compounds differs across the individual proprietary collections; however, the general analysis is based on chemicals similar to APIs as well as chemicals typically used in chemical synthesis. Finally, there may be bias introduced since historical testing of impurities is generally performed on only class 3.5.As part of any ICH M7 analysis, an essential step is to combine the results from the two (Q)SAR methodologies consistent with the language in the ICH M7 guideline . Out-of-domain results present particular challenges as to how this overall prediction should be generated. The underlying basis for this applicability domain analysis is often based upon a structural assessment, using similarity measures or an analysis of unknown fragments, potentially in combination with other information. The results from such applicability domain analysis may be presented as either inside or outside the applicability domain. However, additional information may be taken into consideration as part of an expert review that includes a weight-of-evidence re-assessment of the applicability domain, including relevant non-mutagenic analogs or additional model output . The likelihood of misclassifying a mutagenic impurity as non-mutagenic in situations with out-of-domain calls when no alerts or other positive/indeterminate signals are identified is similar to when there are two clear negative results based on the analysis in this paper.The current paper has reviewed different ways of handling out-of-domain and indeterminate results, including how to generate an overall prediction and any follow-up expert review or additional analysis  that is considered adequate based on the likelihood of misclassifying a mutagenic impurity. Over time, models that are routinely updated with the latest information, including the incorporation of knowledge from propriezry databases whenever possible, will reduce the number of false positives and false negatives, as well as out-of-domain and indeterminate results, further streamlining the ICH M7 (Q)SAR assessment process.12


Connective tissue growth factor (CTGF) is a key signaling and regulatory molecule involved in different biological processes, such as cell proliferation, angiogenesis, and wound healing, as well as multiple pathologies, such as tumor development and tissue fibrosis. Although the underlying mechanisms of CTGF remain incompletely understood, a commonly accepted theory is that the interactions between different protein domains in CTGF and other various regulatory proteins and ligands contribute to its variety of functions. Here, we highlight the structure of each domain of CTGF and its biology functions in physiological conditions. We further summarized main diseases that are deeply influenced by CTGF domains and the potential targets of these diseases. Finally, we address the advantages and disadvantages of current drugs targeting CTGF and provide the perspective for the drug discovery of the next generation of CTGF inhibitors based on aptamers. Cyr61 (cysteine-rich protein 61), CTGF (connective tissue growth factor) and NOV (nephroblastoma overexpressed gene)  , extraceed gene) . Expresshildhood , for exaethality . Also, a tissues .CTGF in different organisms based on gene expression data from the Genotype-Tissue Expression (GTEx) project  is a relatively short gene and consists of 5 exons that code for a 349-amino acid protein, the first exon codes for a signal peptide (for secretion) and exons 2–5 code for each of the four different domains  except for a cysteine free-region between Asp167 and Asn198 (‘hinge region’) . DisulfiCAG46534.1’ was selected for the present study. The templates for predicting the structure of domains were searched by Swiss-model server and then listed in Three-dimensional (3D) structure of the protein is crucial for its interaction with other molecules and biological functions. Currently, there is no accurate structural information about the CTGF. Homology modeling is a computational method and predicts reliable 3D structure of a query protein through the sequence alignment of template proteins . In currCTGF and located in the sequence of Gln27-Lys98. The name of IGFBP domain comes from its similar structure with IGFBP family proteins which contain a conserved motif ‘GCGCCXXC,’ thus CTGF has been characterized as a member of the IGFBP superfamily  repeat which has been shown to bind members of the transforming growth factor-β (TGFβ) superfamily, is found in more than 500 ECM proteins  . CTGF biTaken together, the major functions of VWC domain of CTGF may include: (a) To interact with TGFβ to induce fibrosis and apoptosis of newborn neurons; (b) To bind with BMPs to suppress the differentiation of chondrocytes and osteoblasts.CTGF not only encodes the VWC domain but also a region devoid of cysteines that may serve as a ‘hinge’ connecting the N-terminal and C-terminal halves of CTGF. This ‘hinge’ region is unique to each CCN family member and contains an abundance of charged amino acids. Matrix metalloproteinases (MMPs) have been shown to cleave CTGF in the unstructured hinge region between VWC domains and TSP1 domain , vascular endothelial growth factor (VEGF). CTGF could bind with integrin α6β1 by ‘CLVQTTEWSACSKTCGM’ (Cys199-Met215) in TSP1 domain, evidenced by the inhibited collagen deposition in fibroblasts caused by exogenous CTGF peptide (Cys199-Met215) treatments . In addiIntegrin α6β1 is a well characterized laminin receptor and participates in ECM interactions . CTGF meCTGF. This domain contains a ‘cystine knot’ (CT) structure, therefore this domain is named as CT domain. The CT domain is also presented in growth factors including the TGFβ superfamily, platelet derived growth factor (PDGF) and nerve growth factors (NGFs) (The fourth domain which is located in Cys256-Pro330 and encoded by exon 5 of s (NGFs) . Cysteins (NGFs) . In the s (NGFs) . The CT s (NGFs) . The CT s (NGFs) .Cystine knot domain has been reported to bind with ECM proteins , integrins  and LRP6. CT domain enables the linkage of CTGF to fibronectin, and promotes cell adhesion and migration . IntegriHomology modeling of full length CTGF (FL-CTGF) could not be performed due to the lack of acceptable templates for FL-CTGF. Thus, a strategy of predicting the structure of FL-CTGF by assembling the structures of domains  was adopAs introduced above, CTGF has important roles in many biological processes, including cell adhesion, migration, proliferation, angiogenesis, skeletal development, and tissue wound repair. Meanwhile, it is critically involved in various diseases, including cancers, fibrotic diseases, and inflammatory diseases.Key roles for CTGF are to promote myofibroblast differentiation and angiogenesis. Similar mechanisms are active in different cancers where CTGF is expressed. To date, 30 types of human cancers have been linked to deregulated aberrant expression of CTGF . CancersIn Group I, CTGF expression is positively associated with poor survival outcomes and clinicopathological findings, including advanced stage, larger tumor size, and increased metastasis. Epithelial to mesenchymal transition (EMT) is the process where epithelial cells acquire a mesenchymal cell phenotype which is more prone to migrate and invade . CTGF anin vitro and in vivo by reducing VEGF gene expression and its subsequent angiogenic effects  than earlier stages (stages I and II) . The expTherefore, whether CTGF has a positive or negative correlation with tumor growth is related to the type and stage of cancer. The bindings between proteins with CTGF domains which exert distinct effects on tumor development may lead to the different functions of CTGF in cancers . For exaDespite CTGF shows different functions in tumorigenesis, targeting CTGF has shown therapeutic promises in specific cancers such as breast cancer and pancreatic cancer . In breaFibrosis is defined by the pathological accumulation of ECM proteins, which is in essence an exaggerated wound healing response that interferes with normal organ function . FibrosiIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal pulmonary fibrotic disease with unknown etiology . ExcessiCTGF and TGFβ were found to be increased in the alveolar epithelial cells inoculated with SARS-CoV-2 (COVID-19 (Coronavirus disease 2019) is caused by a novel coronavirus, named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and has emerged as a pandemic and a public health crisis of global proportions . The patRS-CoV-2 . As a poChronic liver disease can lead to the permanent loss of hepatocyte mass and replacement by fibrotic tissue, which can ultimately lead to severe architectural disturbance . CTGF isCTGF is positively correlated with myocardial fibrosis areas in diastolic dysfunction patients  irrespective of etiology. The excessive deposition of ECM in the interstitial compartment leads to scar tissue formation. Upon injury, tubule epithelial cells may undergo EMT which results in tubular function impairment, triggers cell cycle arrest and promotes the release of CTGF . Thus, oin vitro . CTGF in. The inflammation enables the immune system to efficiently remove the injurious stimuli and initiate the healing process . In contRheumatoid arthritis is a chronic inflammatory disorder and characterized by leukocyte infiltration, neovascularization, articular cartilage destruction, and synovial membrane inflammation associated with pain and loss of joint function . PreviouIn vivo, deletion of CTGF increased the thickness of the articular cartilage and protected mice from OA  is characterized by the deposition of amyloid-β peptide (Aβ) which triggers pro-inflammatory pathways and secrete inflammatory cytokines, and this inflammatory response can become toxic and harmful to neuronal cells . ElevateIn inflammatory disorders, CTGF exerts pro-inflammatory functions mostly via interaction with integrin αvβ3 and αvβ5, in which CT domain might be responsible for this function.Connective tissue growth factor is considered as a therapeutic target to combat cancer, fibrosis and other related disorders in a variety of organs, and tissues. There are many approaches such as antibodies, synthetic peptides, small interfering RNAs (siRNAs), and antisense oligonucleotides (ASOs), targeting CTGF to exert therapeutic effect .Humanized monoclonal antibodies are particularly well suited as blocking agents for CTGF activities and thus hold promise as potential therapeutics. Currently, multiple antibodies targeting different domains of CTGF have been studied. FG-3019 (pamrevlumab), the most widely studied antibody, is a fully human recombinant DNA-derived CTGF-targeted monoclonal antibody . FG-3019It has been reported that CTGF could be inhibited by other CCN family members such as CCN3, and CCN3 could be a potential anti-fibrotic treatment . AlthougCTGF also impacts both fibrotic markers, myofibroblast differentiation, and collagen deposition  has been developed to inhibit CTGF production and reduce CTGF-driven collagen deposition and scar formation . The mec EXC-001 . EXC-001 EXC-001 .in vitro (Current therapeutic strategies targeting CTGF mentioned above have shown moderate to promising results in different diseases. However, there still have some concerns for these strategies. Firstly, anti-CTGF monoclonal antibodies are likely to be rapidly cleared and therefore need to be administered at higher doses and/or more frequently . Secondlin vitro . In termin vitro . AlthougThrough the interaction of CTGF domains with a variety of proteins, the deregulation of CTGF expression or activity contributes to the pathobiology of various diseases including cancers, fibrotic disorders, and inflammatory diseases. In cancers and fibrotic disorders, both CT domain and VWC domain contribute to the development of diseases, and these two domains may have a synergic role. For example, tumor migration can be activated by the EMT process from the binding between VWC domain with TGFβ , and thein vivo production, and typically undefined cross-reactivity are challenges to overcome (Although antibodies are now established as a key therapeutic modality for a range of diseases, their limited stability, complicated overcome . Aptamerovercome . The aptovercome . Aptamerovercome . Moreoveovercome .In summary, CTGF has four domains that interact with a variety of proteins such as growth factors, integrins, and matrix proteins. These interactions affect multiple signaling pathways and make CTGF a key regulator and potential therapeutic target in fibrotic, inflammatory diseases, and cancers. A variety of CTGF antagonistic strategies have been developed in numerous experimental systems to reverse these diseases. Novel CTGF-targeted therapeutic strategies, such as aptamer-based CTGF-targeted strategies, are desired for the treatment of these diseases.ZC did the literature research and wrote the manuscript. NZ, HC, and YY helped in polishing the manuscript. Z-KZ, GZ, and B-TZ revised and approved the manuscript. All the authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In paper we compared the power of these methods, their complementing each other and which of them is the most powerful.Due to the advent of SNP array technology, a genome-wide analysis of genetic differences between populations and breeds has become possible at a previously unattainable level. The Wright’s fixation index  while for rare SNPs with MAF 0.0001–0.005 they were even smaller in the range of 0.001–0.01 (mean 0.0027). Genetic relatedness of the cows in the herds was the cause of such small Fst values. The contribution of rare alleles with MAF 0.0001–0.01 to the Fst values was much less than common alleles and this effect depends on linkage disequilibrium (LD). Despite of substantial change in the MAF spectrum and the number of SNPs we observed small effect size of LD - based pruning on Fst data. PCA analysis confirmed the mutual admixture and small genetic difference between herds. Moreover, PCA analysis of the herds based on the visualization the results of a single eigenvector cannot be used to significantly differentiate herds. Only summed eigenvectors should be used to realize full power of PCA to differentiate small between herds genetic difference. Finally, we presented evidences that the significance of Fst data far exceeds the significance of PCA data when these methods are used to reveal genetic differences between herds.Comparative analysis of the power Principal Components Analysis (PCA) and Fst and PCA analyzes. Therefore, for weakly structured populations the LD - based pruning is not effective. In addition, our results show that the significance of genetic differences between herds obtained by Fst analysis exceeds the values of PCA. Proposed, to differentiate herds or low structured populations we recommend primarily using the Fst approach and only then PCA.LD - based pruning had a small effect on findings of F Farmed animals should have large genetic variation in exterior, production and fitness traits. Genetic variation is the basis for survival and maintaining of cattle populations. On genome level variation appears as considerable allelic diversity and heterozygosity. Genomic data help to track herds’ genetic divergence at molecular level. Knowledge of genetic diversity is also important for small breed conservation and crossbreeding strategies , 2. Contst) analysis. Both methods are widely used to estimate genetic difference between populations. Genomic PCA finds the eigenvectors of the covariance matrix derived from the genotypes of animals. These eigenvectors provide the efficient linear combination of marker data which the most effectively differentiate of various samples, without requiring apriori sample classification information. The resolution of highly structured populations with PCA depends on non-random patterns of genetic variation. To reduce the impact of this factor, one should filter the data by removing a marker from every pair of markers which are in tight LD [Several tools could be used to estimate genetic diversity in populations. The most effective and commonly used are principal component (PCA) and Wright’s fixation index  in the whole genome resequencing (WGS) data, and overestimated this parameter for high WGS Fst (> 0.15) [st value can depend heavily on the level of variation present in a sample and the frequency of the most frequent allele [st may be so affected by genetic diversity that it should not be used as a measure of population differentiation, gene flow or relatedness.Generally accepted that the rare alleles play an important role in evolution. Analysis performed by Gorlov et al.  suggestsculation –20. In ot values , 21. Clat values  on humanFst < 0.1 in the wt allele . Indeed,t allele  argued tThe Leningrad region is the highest average milk yield producing region in Russia, with approximately 60,000 cows of Holsteinized Black and White cattle. Dutch, Danish, and Swedish Black and White bulls and heifers were imported to Russia during the 1930s. The Black and White breed was officially registered in Russia in 1959. To improve milk traits of Black and White cattle, local farmers started to use imported from USA (since 1978) and the Netherlands (since 2002) Holstein bulls and semen. Currently, the commercial Russian Black and White cattle population can be considered as Holstein due to long-term crossing only with Holstein bulls.st data. 3. Evaluate LD based pruning methodology on Fst values. 4. Evaluate impact the MAF of SNPs on Fst values. 5. Evaluate significance of Fst values. 6. Evaluate PCA analysis data. 7. Assess the power of Fst and PCA analyzes.In presented study, we tried to evaluate the following objective. 1. Evaluate the correspondence of MAF and linkage disequilibrium. 2. Assess the impact of outliers removal on FThe effect of LD - based pruning on the number of SNPs was large  and their proportion in the SNPs bins  had the same effect size on F had the st values, we divided the entire MAF interval 0.0001–0.5 into 6 bins and calculated for each of them the mean Fst value across Pairwise sets formed from complete and pruned data  across all herds than those for remaining SNPs  in which, as a rule, the differences between the herds at Fst values 0.002–0.003 should be insignificant. The results are shown in the Table st values 0.002–0.003  for ten eigenvectors calculated for complete and pruned data were 2.8e-15 (1.16), 0.20 (1.05), 3.9e-14 (1.02), 1.9e-08 (0.88), 9.7e-03 (0.76), 2.3e-03 (0.72), 8.2e-03 (0.71), 6.0e-09 (0.66), 4.9e-05 (0.62), 5.6e-04 (0.59) (1) and 3.3e-16 (0.84), 6.4e-06 (0.79), 2.0e-04 (0.76), 3.4eE-06 (0.70), 2.6e-05 (0.67), 3.2e-08 (0.58), 2.0e-03 (0.55), 4.0e-04 (0.54), 2.2e-07 (0.53), 3.0e-03 (0.51) (2) respectively, i.e. they were similar. However the overall P - value for the second eigenvector of pruned data has become significant (6.4e-06) and at the same time overall P - value for third eigenvector on many orders of magnitude decreased (3.9e-14 vs. 2.0e-04). Such was the effect of LD - based pruning on overall P - values. From the list of overall P – values should be clear what main significant “axes of variation” were. From the list of variances for each eigenvector (1) and (2) can be calculated the variances to be used after summing ten eigenvectors. It were 8.17% for complete data and 6.47% for pruned data. Whence, the more eigenvectors will be summed, the more value of variance will be used.st values and gradual decrease of the eigenvalues we calculated the mean for every herd in the PC scales to statistical description between herds genetic differences revealed by PCA. The plot of the means for all herds along PC 1 and PC 3 is shown on Fig. P ≤ 0.05 but given the Bonferroni correction we get P ≤ 6.4e-4. Further, for brevity, we write P ≤ 0.05 instead P ≤ 6.4e-4. For PC 1 among 78 pairs of the herds there were 14 significant pairs of the herds. Most often significant data were observed for herds 4 and 13. Some significant results obtained with Fst statistic also confirmed with PCA for eigenvectors 1. For example, the greatest pairwise Fst – values for herd 4 were confirmed by noticeably higher level of significance revealed by PCA  of second eigenvector for complete data and insignificance of the most pairs of the herds in Pairwise set indicates that there are not between herds genetic difference for this axis. Therefore, these data were excluded from consideration.The lack of overall significance  below which the population structure was essentially undetectable. They proposed that for two equal size subpopulations, there is a threshold value of Fst calculated as 1/ √nm , below which there will be essentially no evidence of the populations structure. In our study this threshold is within 0.0005–0.0006 for complete data which is considerably lower obtained minimal Fst value 0.002 and threshold 0.0015–0.0018 for pruned data which is comparable with minimal Fst value 0.002  [Therefore, we predict that the power of our PCA analysis would be sufficient to reveal detectable across herds genetic differences. But, PCA calculates correctly if the markers are independent (between them have not sizable LD) , 17. Sev6–0.068) . However6–0.068) , 17.). P6–0.068) . We prop6–0.068) .st analysis. The pattern of MAF before and after LD - based pruning changes considerably  [The PCA plots of the herds means along first, third and fourth PC for the cows from complete data are shown on Figs. –0.0063) . We assust – values with other 12 herds 0.0038 and 0.0039 [P ≤ 0.05 pairwise herds combinations with other herds revealed by PCA 1–20  [st value for Black and White and Holstein breeds was 0.035 and Black and White breed formed a cluster with the breeds from Northern Europe on multi dimentional scaling (MDS) images [st value 0.004 and they are indistinguishable across PC 1 – PC 2 [st across the genome for AU versus US cows was 0.008 (0.12) and the average (max) for US versus AU, US versus NZ, and AU versus NZ was 0.006 (0.08), 0.029 (0.21) and 0.009 (0.07), respectively. Authors suggest that although some differentiation based on Fst was seen, especially for US versus NZ cows, the other populations appear to be similar. Noteworthy, differentiation between Australian and the USA Jersey cow populations was marginal in comparison with populations of the bulls. On PC 1 – Pc 2 image it was impossible to differentiate them geographically. Taken together the PCA and Fst results show that two artificially unselected breeds were not well differentiated and still cover a considerable part of the original genetic diversity [st = 0.037). This breeds were very close to each other in the PC 1 - PC 2 and PC 1 - PC 3 images and statistical prove on genetic differences between them are not given. Most of the remaining breeds also had their smallest Fst value (Fst = 0.037–0.096) when compared to Illyrian Mountain Buša. In indigenous six cattle populations of Ethiopia and Korea, PCA evidently distinguishes Ethiopian cattle populations from Hanwoo breed [st 0.002 (P < 0.01) and they are very close to each other on PC 1 – PC 2 image but statistical data are not shown. Ancestry analysis of the new world cattle evidences that the first axis of PC was associated with the indicine – taurine split and the second PC axis was associated with the divergence between European and African taurine cattle [P-values based on TracyWidom test and shown that 154 axes in the 50 k dataset were statistically significant. In another research PCA and Fst showed minimal structure within the Guernsey breed, with no complete segregation of animals reflecting geographic origin (Fst = 0.006–0.011) and PCA with no distinct visual animal separation [For the large – scale SNP data the PCA and F = 0.02) . The aut) images . PCA ana) images . The deg1 – PC 2 . Authors1 – PC 2 . The meaiversity . On the oo breed . The mose cattle . The autparation .It is important to note that as a rule in above mention publications the eigenvalues (variance) decrease faster than in our study. Apparently this is the result a considerably low between herds genetic difference comparing to the breeds. We should keep in mind that visual evaluations of the genetic distance between herds on PCA images may be incorrect without of statistical prove. As a rule, any statistical treatment of PCA between populations’ results was not given and such images are only illustrative.st and PCA analyzes? Wright’s Fst is based on maximization of allele frequencies differences between populations through variance component. Used by us Hudson’s estimator provides the genetic difference between populations compared to the genetic difference within populations through variance component as well. Applied in our research PCA relies on covariance matrix of SNPs alleles among animals [st and PCA based on similar mathematical approaches and additional simulation analysis is needed to determine why Fst analysis gives more significant data. Summarizing the results of Fst and PCA analyzes should be noted that the power of both analyzes similar but probability of making a type I error is much less for Fst approach. It can be concluded at that point the Fst analysis is superior to PCA.What conclusions from obtained results should be done regarding the power of F animals  and it ast and PCA analyzes are able to differentiate between herds genetic differences. But, PCA applied to the herds might only be efficient when summed results of several eigenvectors will be used. Secondly, despite of considerable change in the number of SNPs and their MAF spectrum due to LD - based pruning, it has a small effect on the results of Fst and PCA analyzes. We suggest that this is a consequence of homogeneous genetic structure of the herds. Our findings show that Fst method give the more significant data than PCA but PCA approach might be useful due to visualization of some genetics features of the herds.Firstly, despite of genetic relatedness of the cows in the herds, FData and genotypes were obtained from Committee on Agro-Industrial Complex of Leningrad region. Cows genotypes were available from 13 breeding herds locating in Leningrad region (Russia) born in range from 2010 to 2013. Animals for genotyping were selected by farmers with respect to pedigree structure of the herd. Number of animals genotyped depends on number of sires used in herd at least one daughter was presented by sire. In case of multiple daughters were presented per single sire, sire of dams were different. Sampled animals are presented 8–15% of total number of milking cows (see Additional file st values were estimated with EIGENSOFT 6.0.1 software [st estimates were calculated using a block-jackknife approach implemented in EIGENSOFT. To estimate significance of Fst values the permutations of the cows between each pair of the herds was carried out to get the distribution under H0 hypothesis. We used PLINK commands –make –pheno and –fiter –mfilter 5 to perform 5 pairwise permutations. Then within each of permuted pair of the herds, the cows were allocated into two groups with the same size as the original pairs of the herds had and they were coded as case and control. Further, we calculated Fst value for each of 5 permuted pairs of the herds. Finally the mean Fst value and standard error for each 5 permuted pair of the herds was calculated. Altogether, 78 mean Fst values under H0 distribution were calculated and then 78 P – values were estimated using one-sided Student’s t-test. Power of t-test was calculated with «powerAnalysis» program in R software environment [st for MAF in the range 0.0001–0.5 was calculated as MSE = st value in each bin, m was equal to 78 pairs of the herds.The Fsoftware , where Hironment . The staP-values between each pair of the herds along principal components were calculated. For each pair of the herds, the above mentioned ANOVA statistics are summed across 10, 20 up to 100 eigenvectors. The distributions were approximately chisq with d.f. equal to the number of eigenvectors minus one. Likewise P-values were calculated for each 78 pairs of herds including summed PC 1–10, PC 1–20 and PC 1–100. For each of leading component PC 1, PC 3 and PC 4 the mean herd value was calculated. Then, they were plotted with R software environment [«powerAnalysis» software in R software environment [For calculation of PCA the EIGENSOFT 6.0.1 software was used . The outironment . Power oironment . When coAdditional file 1: Table S1. Effect of outliers on estimates of Fst values for complete data. a - Fst values for complete data corrected on the outliers are above the diagonal and Fst values for complete data does not corrected on the outliers are below the diagonal. b - Increased Fst values are in bold and decreased Fst values are in bold Italic. Table S2. Effect of rare alleles with MAF < 0.01 on estimates of Fst values. a - Fst values for complete data after removal of the alleles with MAF < 0.01 are below the diagonal and Fst values for complete data does not corrected on MAF < 0.01 are above the diagonal. b - increased Fst values are in bold and decreased Fst values are in bold Italic. Table S3. Mean Fst values across Pairwise set of the complete data in MAF bins. * - In each MAF bin 78 Fst values was averaged. Statistical estimates were obtained with t-test. ** - MSE calculation see at materials and methods. Table S4. Estimates of Fst values calculated for H0 distribution. Fst values should be multiplied by 10− 4. Table S5. Standard errors of the Fst – values computed by EIGENSOFT 6.0.1. Standard errors of Fst obtained from complete data are above diagonal and from pruned data are below diagonal. SE values should be multiplied by 10− 4. Table S6. Description of the herds and number of the genotyped cows. * - Country of origin of the sires of the genotyped cows, NL – the Netherlands. Figure S1. Effect of LD - based pruning on the number of SNP in the complete data.


Neurodegenerative diseases such as Alzheimer's (AD), Huntington's (HD), and Parkinson's diseases (PD) are a group of progressive disorders that feature degeneration of the structure and function of the human nervous system. Impaired mitochondrial function, excessive oxidative stress in human brain, genetic factors, and malfunction in human brain metabolism contribute to the progression of neurodegenerative diseases . MultitaWe received articles from across the globe featuring interesting research in this area. A case-control study in this special issue demonstrated the potential role of peripheral immune disorders in the pathological progression of late-onset Parkinson's disease (LOPD). Moreover, development of phytomedicines as potential therapeutics for AD is discussed as well. The role of gut microbiota in progression of neurological disorders and approaches for regulation of this effect is featured. In addition, the guest editorial team contributed a detailed review on rational design of MTDLs for neurodegenerative diseases with special focus on Alzheimer's disease. This review explored a large number of promising MTDLs obtained based on different target combinations strategies.In conclusion, there is a growing interest from researchers from different disciplines to identify efficient multitargeted strategies for neurodegenerative diseases. Comprehension of ongoing efforts in development of multitargeted strategies would enable scientists to identify the most successful approaches in the field and eventually lead to discovery of efficient therapeutics. The multidisciplinary nature of research in this area is evident in this special issue as it features research from various disciplines. The guest editorial team hopes that this special issue will help in featuring the multidisciplinary aspect of this research area and encourage future collaborative efforts.


Impressively, COF–alkene achieves an AQE of 6.7% at 420 nm. Combined femtosecond transient absorption spectroscopy and theoretical calculation disclose the critical role of cyano‐substituted alkene linkages toward high efficiency of charge separation and transfer in the presence of sacrificial electron donors—the decisive key to the superior PHE performance. Such alkene linkages can also be extended to design a series of high‐performance polymeric photocatalysts, highlighting a general design idea for efficient PHE.Covalent organic frameworks (COFs) are potential photocatalysts for artificial photosynthesis but they are much less explored for photocatalytic hydrogen evolution (PHE). COFs, while intriguing due to crystallinity, tunability, and porosity, tend to have low apparent quantum efficiency (AQE) and little is explored on atomistic structure–performance correlation. Here, adopting triphenylbenzene knots and phenyl linkers as a proof of concept, three structurally related COFs with different linkages are constructed to achieve a tunable COF platform and probe the effect of the linkage chemistry on PHE. Cyano‐substituted alkene‐linked COF  yields a stable 2330 µmol h Cyano‐substituted alkene linkages enable high‐efficiency of charge separation and transfer in covalent organic frameworks in the presence of sacrificial electron donors, which not only results in much superior photocatalytic hydrogen evolution performance to imide‐ and imine‐linked counterparts together with the apparent quantum efficiency of 6.7% at 420 nm, but also renders a general design idea for advanced polymeric photocatalysts. Organic semiconductors are regarded as intriguing catalysts for photocatalytic solar‐to‐hydrogen production or water splitting owing to their synthetic tunability and readily tailored optical and electronic properties. Carbon nitrides, conjugated microporous polymers, linear conjugated polymers, and covalent triazine frameworks have been explored as photocatalysts for water splitting. However, they are in general amorphous or semicrystalline, constraining the transport of photogenerated charges to the catalyst surface. Furthermore, it is still a challenging task to build atomistic structure–performance correlation. Recently, covalent organic frameworks (COFs) have been emerged as promising photoactive materials for artificial photosynthesis due to their multiple merits including long‐range order, broad light harvesting and, sometimes, high‐carrier mobility and good stability. In particular, many COFs possess excellent visible light response in a broad region of the solar spectrum (450–700 nm). Despite huge potential, however, much less was exploited for photocatalytic hydrogen evolution (PHE) except for only a few COFs. These reported COFs still suffer from unsatisfactory apparent quantum efficiency (AQE) (0.1–4.8% at 420 nm), encumbering the full demonstration of their photocatalytic potential. On the other hand, the existing COFs for PHE are mainly based on imine/enamine, hydrazone, and azine linkages. Recently, several groups developed a few sp2 carbon‐conjugated COFs via a C–C linkage, which enable a full π conjugation, desirable for optoelectronics and electrochemistry. While imine and imide linkages are less efficient in supporting π delocalization due to high polarization of heteroatom N in the skeletons by comparison. Yet, owing to great synthesis difficulty, sp2 carbon‐linked COFs were scantly explored for PHE expect for only two examples.Artificial photosynthesis by converting solar energy into hydrogen energy is an appealing and sustainable strategy to address the global environmental and energy issues.1 Orga To date, very little attention was paid to the role of the linkage chemistry in the rational design of COF‐based photocatalysts, while the linkage tends to determine the chemical stability and electronic communication of COFs, which is of significance for the photochemical process. Furthermore, photoexcited charge transfer in COFs is critically important for photocatalysis but there still lacks a lucid understanding on the dynamic.To construct advanced COF‐based photoactive materials for water splitting, previous strategies focus on the molecular design of the knot or linker with a specific linkage. For example, several heteronuclear molecular functionalities such as sulfone‐, triazine‐ or heptazine‐based units or diacetylene moieties were incorporated into the knots or linkers to effectively tune the physicochemical properties of COFs and hence promote PHE.9 To d2 evolution rate of 2330 µmol h−1 g−1 relative to the imine‐ and imide‐linked analogues (<40 µmol h−1 g−1). Noteworthy, we adopt identical alkene linkage to design other two new conjugated polymers, demonstrating high PHE activities. This highlights a general design idea for advanced polymeric photocatalysts.Here, using triphenylbenzene knots and phenyl linkers as a proof of concept, we build a tunable COF platform by constructing three structurally related COFs with imine‐, imide‐, and cyano‐substituted alkene‐linkages, respectively, and probe how linkages influence photocatalysis via combined experimental and theoretical studies. These constructed COFs while adopting different linkages, display similar topologies and stacking modes. We observe a PHE activity trend: COF–alkene >> COF–imide > COF–imine. Femtosecond transient absorption (fs‐TA) spectroscopy and theoretical calculation render a lucid understanding on such trend, disclosing the critical role of the linkage in tuning charge separation and transport for efficient photocatalysis. We discover that cyano‐substituted alkene linkages with not only stronger electron‐withdrawing ability but also stronger electron delocalization enable the formation of favorable molecular heterojunctions through the framework to facilitate charge separation and transfer especially in the presence of sacrificial electron donors relative to other two linkages. As such, COF–alkene yields an AQE of 6.7% at 420 nm, together with a notably higher HFigure  We applied 1,3,5‐tris‐(4‐formylphenyl)benzene  and p‐phenylenediacetonitrile to produce COF–alkene via a Knoevenagel condensation reaction. We used 1,3,5‒tris(4‒aminophenyl)benzene (TAPB) and pyromellitic dianhydride to construct COF–imide. We also performed a Schiff‐base condensation reaction between TAPB and terephthaladehyde (TPAL) for the formation of COF–imine. As indicated by powder X‐ray diffraction (PXRD) patterns in Figure 13C nuclear magnetic resonance (NMR) spectroscopty. As revealed by FT‐IR spectra, the characteristic absorption peak at 2213 cm−1 is ascribed to the cyano side group (C=C–CN) for COF–alkene and the observed peaks at 1776, 1723, and 1365 cm−1 in COF–imide prove the existence of the five‐membered imide rings, while the peak at 1620 cm−1 is attributed to the stretching vibration of C=N for COF–imine . In the 13C NMR spectroscopy, the chemical shift of 110.7 ppm in COF–alkene, 162.9 ppm in COF–imide, and 157.7 ppm in COF–imine can be attributed to the formation of C=C bonds, imide linkages, and C=N bonds, respectively . Thermogravimetric analysis indicates that all three COFs have excellent thermal stability (>400 °C) . Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) observation in Figure 2 carbon‐linked 2DPPV with sheet‐like morpholgy, while COF–imine and COF–imide are irregular agglomerates. We tried to observe the lattice structure by TEM, but failed due to the electron beam damage. The porosity of these COFs was evaluated by nitrogen sorption tests at 77.3 K. The calculated Brunauer–Emmett–Teller (BET) surface areas for COF–alkene, COF–imide, and COF–imine were 185, 1664, and 248 m2 g−1, respectively . Of note, the surface areas for three COFs are lower than theoritically predicted values with a perfectly crystalline structure . Such differences are often observed in some previously reported COF materials in the literatures. Especially for many previously reported sp2 carbon‐linked COFs, the BET surface areas tend to be lower than the theoretically predicted values (2DPPV (472 m2 g−1), TP‐COF (232 m2 g−1), 2D CCP‐HATN (317 m2 g−1), sp2c‐COF‐2 (322 m2 g−1), and Bpy‐sp2c‐COF (432 m2 g−1).) This could be caused by the intrinsic low reversibility of Knoevenagel condensation reaction accompanied by high‐density stacking faults in the structure according to the literature. Notably, the surface areas for our COF–imide and COF–imine are larger than those of previously reported imide‐linked PI‐COF‐2 (1297 m2 g−1) and imine‐linked TPB‐TP‐COF (16 m2 g−1) with similar molecular structures but different synthesis conditions. On the other hand, water wetting generally matters during PHE because it is vital to afford excellent catalyst dispersiblity and favorable interaction with water and the sacrificial donor. Also, the linkages have great influence on the hydrophobicity, the contact angles of three COFs with pure water are in the following order: COF–imide (0°) < COF–alkene (35°) < COF–imine (120°) .In our experiments, all three COFs were synthesized similar to literature‐reported methods but with some modification, particularly for COF–alkene, different solvents and catalysts from the literature were adopted in our case, which results in distinct morphologies and physicochemical properties . As shown in Figure 1, 9λ > 420 nm) using triethanolamine (TEOA) as a sacrificial electron donor and photodeposited the optimal amount 3% Pt from H2PtCl6 as a cocatalyst. According to the steady‐state photoluminescence spectroscopy , COF–alkene/Pt exhibits much lower emission intensity as compared to COF–alkene, indicating much weaker charge recombination rate after Pt deposited on the surface of COF–alkene. Consequently, COF–alkene yields continuous and stable hydrogen production with an average rate of 2330 µmol h−1 g−1. In sharp contrast, COF–imide and COF–imine, structurally analogous to COF–alkene, present considerably low PHE rates of 34 and 12 µmol h−1 g−1 . COF–imide and COF–imine display similar photostability within 5 h photocatalytic experiment . Furthermore, ex situ SEM imaging was conducted to probe the particle sizes of three COFs under photocatalytic condictions. It was found that three COFs appear in the aggregated form and have similar particle size range of about 2–7 µm , thus largely excluding the effect of particle sizes on the observed huge PHE performance difference of three COF materials. The AQE was tested under monochromatic incident light of 420, 475, 550, and 650 nm, respectively. Clearly, the AQE increases with the reduced wavelength in accordance with the optical absorption spectrum  structure, while the reduced transient absorption in 2.5–10 ps represents the electron–hole recombination process. As for COF–imide and COF–imine, ignorable excited state absorption or only weak absorption on shorter timescales of 1.5–2.1 ps was observed , while the negligible absorption reflects a weak absorption corss‐section for trapped electrons. In addition, the fs‐TA spectroscopy of three COFs dispersed in water containing 30% TEOA was carried out to explore the charge carrier dynamics of COFs in the presence of the electron donor of the scavenger. As can be seen from Figure  Based on the analysis from the kinetics of three COFs  and P12 (LUMO) peaks for alkene linkage center at −1.65 and 0.74 eV, determining the gap of 2.39 eV while the P21 and P22 peaks at −2.01 and 1.02 eV for imide linkage correspond to the gap of 3.03 eV and the P31 and P32 peaks at −1.85 and 0.86 eV for imine linkage give the gap of 2.71 eV. Apparently, the alkene linkage affords the smallest gap , indicating more efficient charge transport over the molecular skeleton with respect to the imine and imide linkages.For three COFs, the identical knot and linker are bridged by different linkages, forming three molecular junctions. To probe the charge transport capability across three linkages, we sandwiched three molecular junctions between two electrodes and calculated the transmission functions by density functional theory calculation Figure 4a. As obransport.14 The Taken together, the superior PHE activity of COF–alkene should benefit from the strong withdrawing ability and electron delocalization of cyano‐substituted alkene linkages, which creates favorable molecular heterojunctions for highly efficient charge separation and transfer especially in the presence of sacrificial electron donors.Generally, the PHE of COFs is ascribed to a complex interplay of multiple electronic and structural factors. As demonstrated above, we explored the correlation of the optical bandgaps, crystallinity, stacking modes, porosity, and water‐wetting property among three COFs. With the similar stacking mode, particle size, lowest crystallinity and surface area, moderate light harvesting, and moderate water‐wetting property, the COF–alkene achieved the highest PHE activity. As such, we reason that among the above‐mentioned multiple factors the charge separation and transfer efficiency in the presence of TEOA is the central factor determining the PHE event of COFs.12b Ta−1 g−1 under visible light irradiation (λ > 420 nm)  building blocks . Noteworthy, although the AQE of COF–alkene is still lower than that of some state‐of‐the‐art conjugated porous polymer PHE catalysts, the PHE activity of alkene‐linked COF still holds great room for further improvement by introducing heteronuclear molecular functionalities such as triazine moieties in the linkers. Combined fs‐TA spectroscopy and theoretical calculation reveal the vital role of cyano‐substituted alkene linkages toward high efficiency of charge separation and transfer especially in the presence of sacrificial electron donors enabled by its strong electron withdrawing ability and electron delocalization, which is the decisive key to superior PHE performance of COF–alkene. Interestingly, alkene linkages can be used to design a series of other conjugated polymers with high PHE activities. Our finding renders some fundamental insight into the correlation between the linkage chemistry and photochemical event. This study highlights a general guideline toward the rational design of advanced polymeric photocatalysts for solar‐to‐fuel conversion.In summary, we have established a tunable COF platform using triphenylbenzene as knots and phenyl as linkers but with different linkages and elucidated the role of the linkage chemistry on visible light photocatalysis. Systematic property variation of several structurally related COFs indicates that the linkage chemistry can effectively tune the structural and optoelectronic properties of COFs and hence PHE activity. A PHE activity trend COF–alkene >> COF–imide > COF–imine is achieved. COF–alkene yields an AQE of 6.7% at 420 nm together with a drastically higher PHE rate of 2330 µmol hatalysts,15 theThe authors declare no conflict of interest.Supporting InformationClick here for additional data file.


Macrophage-expressed gene 1 [MPEG1/Perforin-2 (PRF2)] is an ancient metazoan protein belonging to the Membrane Attack Complex/Perforin (MACPF) branch of the MACPF/Cholesterol Dependent Cytolysin (CDC) superfamily of pore-forming proteins (PFPs). MACPF/CDC proteins are a large and extremely diverse superfamily that forms large transmembrane aqueous channels in target membranes. In humans, MACPFs have known roles in immunity and development. Like perforin (PRF) and the membrane attack complex (MAC), MPEG1 is also postulated to perform a role in immunity. Indeed, bioinformatic studies suggest that gene duplications of MPEG1 likely gave rise to PRF and MAC components. Studies reveal partial or complete loss of MPEG1 causes an increased susceptibility to microbial infection in both cells and animals. To this end, MPEG1 expression is upregulated in response to proinflammatory signals such as tumor necrosis factor α (TNFα) and lipopolysaccharides (LPS). Furthermore, germline mutations in MPEG1 have been identified in connection with recurrent pulmonary mycobacterial infections in humans. Structural studies on MPEG1 revealed that it can form oligomeric pre-pores and pores. Strikingly, the unusual domain arrangement within the MPEG1 architecture suggests a novel mechanism of pore formation that may have evolved to guard against unwanted lysis of the host cell. Collectively, the available data suggest that MPEG1 likely functions as an intracellular pore-forming immune effector. Herein, we review the current understanding of MPEG1 evolution, regulation, and function. Furthermore, recent structural studies of MPEG1 are discussed, including the proposed mechanisms of action for MPEG1 bactericidal activity. Lastly limitations, outstanding questions, and implications of MPEG1 models are explored in the context of the broader literature and in light of newly available structural data. Arabidopsis thaliana; CAD1, NSL1)     and complement component 9 (C9)]  , microbi1, NSL1) –6, ingreT, MAOP) , 8, as vellin-2) –12, and tolysin) , 14. In  9 (C9)] –18.via the recruitment of C5, C6, C7, C8, and, finally, multiple copies of C9 , was identified in the mid 1980s through the work of Podack, Tschopp and colleagues , 40. PRFOutside the realm of immunology, several MACPFs appear to govern developmental processes within humans, namely the astrotactins (ASTN1 and ASTN2) and the bone morphogenetic protein/retinoic acid inducible neural-specific proteins  . While tβ-barrel pores (An intriguing family of pore-forming proteins (PFPs), the gasdermins (GSDMs), represents a class of intracellular immune effectors which like MACPFs form giant el pores , 58. Speel pores . Cleavedel pores , 61. Theel pores , 63.in situ analysis of differential gene expression comparing mature with immature cell lines of mouse macrophages , sea anemones (Cnidaria)  , comb jenidaria) , flatwornidaria) , molluscnidaria) –76 and anidaria) . These snidaria) .via the ER, Golgi, and secretory vesicles to localize to early endosomes and phagosomes/lysosomes  studies revealed that MPEG1 is able to form oligomers of a size and shape typical for members of the MACPF/CDC superfamily . MPEG1 traffics ysosomes –81. It ierfamily . The firerfamily . This iserfamily –81. Conserfamily . Finallyerfamily . Thus, tIn this review, we discuss the current understanding of MPEG1 evolution, regulation, and structure–function. We furthermore evaluate the contrasting proposed mechanisms of bactericidal activity. Taken together, we explore the implications of cellular and whole-organism research in the context of new MPEG1 structure–function studies and discuss the outstanding questions surrounding this ancient immune effector.MVB12)-associated β-prism (MABP) domain  vesicles  and β-Transducin Repeat Containing Protein (βTrCP) complex   , 81  –81  compared to wild-type counterparts  becomes upregulated. Of the remaining two, one is thought to be a pseudogene (mpeg1.3), while the other (mpeg1) is surprisingly suppressed. Knock-down experiments of mpeg1.2 resulted in an increased bacterial burden on zebrafish challenged with M. marinum, while knock-down of mpeg1 gave a survival advantage compared to wild type fish, suggesting an altered immune response  after treatment with MPEG1. The addition of minute quantities of lysozyme, however, were sufficient to kill pre-treated M. smegmatis, putatively indicating MPEG1 perforates the outer membrane but does not affect the integrity of the peptidoglycan layer  proximal complexes  may prevent unintended lytic function. Lastly, sequestration of MPEG1 by the transmembrane region is important for trafficking  have been identified in bacterial membranes treated with MPEG1 . NotablyThese findings suggest that spatio-temporal regulation of MPEG1 proteolysis is associated with normal function. The mechanism of this proteolysis is still being determined and the endogenous protease responsible for MPEG1 cleavage has not been identified. Further studies will be required to confirm the sites of MPEG1 cleavage and their functional role within the cellular context. While the relevance and functional importance of MPEG1 proteolysis is unclear, it is notable that the GSDMs require the specific and coordinated proteolytic processing by caspases for subsequent activation and pore-forming function , 60. In Pore-forming MACPF proteins generally adopt two different stable conformations—a soluble, metastable monomeric form and a membrane inserted hyper-stable oligomeric pore form , 98–102.β-sheet that features an L-shaped bend  and TMH-2 (α-helices (in the monomeric form) undergo structural rearrangement, unfurling to form a set of amphipathic β-hairpins  . As they unwind the TMH regions are postulated to concurrently zipper up into β-hairpins, these protrude below the MACPF domain and thereby form a giant, amphipathic β-barrel that inserts into the membrane. Together these two β-hairpins each contribute four β-strands to the final oligomeric β-barrel pore  , 83, 105units  . A short studies , 22. A sate TMH2 , 82. Theate TMH2 , 101.bona fide homolog of MAPCFs  is located in close proximity to the lipid bilayer and, in the primary sequence, directly precedes the transmembrane helix and cytosolic tail  (β-strand swapping to form tightly associated double-ring complexes (denoted the β-conformation) , the β-hairpin of the MABP domain interacts with the L-domain of the neighboring subunit , and hence these inter-subunit contacts anchor adjacent subunits in place. These interactions presumably provide stability to the complex and, therefore, were suggested to mediate oligomerization in solution. Truncation of the L-domain was reported not to affect membrane binding, however oligomerization in solution was not reported . The large rotation of the MABP domain results in the membrane-binding β-hairpin region oriented in the same direction with the β-barrel of the MACPF domain. Unlike most MACPF mechanisms studied to date, the peripheral ancillary domains typically do not undergo such drastic conformational changes, and hence these data suggest MPEG1 adopts an entirely unique mechanism. It remains unclear how and when this conformational change occurs, i.e. from a structural perspective and at which point of the MPEG1 assembly pathway. Higher resolution structures, as well as studies in the presence of lipid bilayers, will be required to fully understand the mechanism and structural motifs that mediate this unique transition.Strikingly, the MPEG1 pore revealed significant structural rearrangement of the MABP domain relative to the MACPF domain , E. coli lipid extract, LPS) but not neutrally charged lipids   , 83. Theomyelin) , 104, thi.e. the assembly and target membrane are the same    . While tin vitro high speed AFM imaging suggests these prepores could undergo a conformational change upon acidification that can be interpreted as pore formation . Both the ability to form soluble active prepore oligomers and the unique domain arrangement distinguish MPEG1 from other characterized MACPF/CDCs. Indeed, the canonical mechanism of pore formation and the notion of a single assembly/target membrane may not be consistent with the MPEG1 system.These features suggest that MPEG1, unlike other MACPF/CDC systems studied to date, may follow a distinct assembly pathway with independent assembly and target membranes  of MPEG1 in cells, will be important to determine the final pore state, mechanism of bactericidal activity and whether the prepore observed thus far represents a productive intermediate on the way to pore formation.The archetypal model of pore formation has largely arisen to describe PRF and CDCs   , 119.β-hairpin motif is responsible for recognition of the target membrane  are absent in GSDMs. Thus, some controversy exists with respect to whether the similarities between GSDMs and MACPF proteins have arisen through convergent or divergent evolution. Based on bioinformatic studies of other protein families , and hence, its role is poorly understood. Overall, the structural advances to date will provide a strong foundation for future work by guiding experiments and Drafting of manuscript by CB-J, JW and MD. Figures made by CB-J. Critical review and editing of manuscript by BS and SP. All authors contributed to the article and approved the submitted version.CB-J acknowledges the support of the Australian Government RTP scholarship. JW is an Australian Research Council (ARC) Laureate Fellow (FL180100019) and an Honorary National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow (APP1127593). He acknowledges the previous support of an Australian Research Council (ARC) Federation Fellowship. MD acknowledges the support of an ARC Future Fellowship (FT150100049) and ARC Discovery Project (DP180100040). We thank the staff of the Monash Ramaciotti Centre for Electron Microscopy, the Monash protein production and proteomics platforms, and the support of the MASSIVE supercomputer team.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


MYO16 has been linked to neurological disorders. Although the role of Myo16 as a generic actin-associated motor is still enigmatic, the N-, and C-terminal extensions that flank the motor domain seem to confer unique structural features and versatile interactions to the protein. Recent biochemical and physiological examinations portray Myo16 as a signal transduction element that integrates cell signaling pathways to actin cytoskeleton reorganization. This review discusses the current knowledge of the structure-function relation of Myo16. In light of its prevalent localization, the emphasis is laid on the neural aspects.The myosin family is a large inventory of actin-associated motor proteins that participate in a diverse array of cellular functions. Several myosin classes are expressed in neural cells and play important roles in neural functioning. A recently discovered member of the myosin superfamily, the vertebrate-specific myosin XVI (Myo16) class is expressed predominantly in neural tissues and appears to be involved in the development and proper functioning of the nervous system. Accordingly, the alterations of  Myosins are actin-based mechanochemical machines that convert the chemical energy of ATP hydrolysis to mechanical work and molecular movement along actin filaments ,2. MyosiMYO16 (cytogenetic location: 13q33.3) encodes two distinct protein isoforms generated by alternative splicing that differ in the length of their C-terminus; the shorter Myo16a and the longer, predominant Myo16b [Myosin XVI (Myo16) as an unconventional myosin was discovered in a search for molecular motors associated to the motile events of nerve cells . Myo16 it Myo16b .MYO16; either single nuclear polymorphisms (SNPs), deletions or epigenetic modifications are associated with neurodegenerative and neuropsychiatric disorders, including schizophrenia, autism spectrum disorder (ASD), bipolar disorder subtype II (BP-II) and major depressive disorder (MDD)   [21,22,2ctioning . This imMyosins. A Superfamily of Molecular Motors [Based on the recent biochemical and physiological analyses, Myo16 is emerging as an important regulator of the proper functioning of neural cells, although the exact molecular mechanisms underlying its role in the nervous system remain to be elucidated. Here, we provide an overview of the current understanding of the structural features, molecular interactions and cellular roles of Myo16 in the nervous system focusing on its unique domains: the N-terminal Myo16Ank and the C-terminal Myo16Tail. A more general overview of Myo16 can be found in r Motors .myosin phosphatase N-terminal element (MyPhoNE), a KVxF motif and ankyrin repeats composed of tandem ankyrin motifs  possess a pre-motor extension . There an motifs . These rprotein phosphatase 1catalytic subunit (PP1c) binding site of the myosin phosphatase target/regulatory subunit 1 (MYPT1) of myosin light chain phosphatase (MLCP) [surface plasmon resonance (SPR) studies ,35. Upon binding MYPT1 increases the dephosphorylation activity of PP1c towards the myosin regulatory light chain (RLC) target . In contrast, the association of Myo16Ank to PP1c results in an opposite functional outcome; the phosphatase activity of the catalytic subunit towards the myosin RLC is significantly decreased in the Myo16Ank:PP1c complex in vitro [Based on its composition, Myo16Ank resembles the e (MLCP) . MYPT1 ie (MLCP) . The MYPe (MLCP) . Indeed,The physiological relevance of this interaction and activity of Myo16Ank awaits further investigations, also targets of the Myo16Ank:PP1c have not been identified yet. PP1 is one of the most abundant protein phosphatases in eukaryotes; its catalytic subunit PP1c can interact with a large set of regulatory subunits in a mutually exclusive manner . This reWAVE1 interacting region (WIR) followed by a neuronal tyrosine-phosphorylated adaptor for phosphoinositide 3-kinase (NYAP) homology motif (NHM) with two phosphorylation sites (Tyr1416 and Tyr1441) [The C-terminal tails are highly versatile extensions among myosin classes containing different functional domains leading to diversified intracellular functions, such as anchoring, autoregulation, dimerization, cargo binding, cellular localization or protein-protein interaction ,40,41,42Tyr1441) . The dowTyr1441) ,30,31,43neuronal tyrosine-phosphorylated adaptor for phosphoinositide 3-kinase proteins (NYAP 1-3) found in a screen for Src non-receptor tyrosine kinase family substrates by using a human hippocampus cDNA library [NYAP homology motif (NHM) containing conserved tyrosine residues (YxxM) and a proline-rich stretch. Sequence analysis reveals the characteristic NHM motif in the C-terminal tail region of Myo16. It is specific for the Myo16b splice variant, the shorter Myo16a lacks this motif (Myo16 was recently identified as NYAP3 amongst the  library . The defis motif . In cont1416xxM and Y1441xxM) in the NHM by the Src tyrosine kinase Fyn in the mouse brain [glycosylphosphatidylinositol (GPI) anchored membrane protein. Contactins, a subfamily of the immunoglobulin superfamily of neural cell-adhesion molecules (Ig-CAMs) are important for neuritogenesis and synapse formation [protein tyrosine phosphatase α (PTPα) suggesting a potential pathway of Fyn-induced Myo16 phosphorylation [phosphoinositide 3-kinase (PI3K). Of note, this mode of activation is reminiscent of the direct recruitment and activation of PI3K by receptor tyrosine kinases [WAVE regulatory complex (WRC) that activates the Arp2/3 complex, which, in turn, catalyzes the assembly of branched actin meshwork [WAVE1 interacting region (WIR) intercalated between the motor domain and the NHM . In the ternary complex, Myo16 can link and potentiate PI3K-Rac1 to its downstream WRC, thereby it can contribute to the activation of the WRC-Arp2/3 complex and consequently to the regulation of the actin cytoskeleton. Indeed, transient transfection of HeLa cells with Myo16Tail resulted in the collapse of actin structures that required both the functional WIR and NHM [Myo16/NYAP3 was shown to be involved in signal transduction by linking the PI3K pathway to the remodeling of the actin cytoskeleton  Figure . Myo16 ise brain . Fyn is ormation . Contactrylation ,47. Tyro kinases ,49. The  kinases . The Rhomeshwork ,51. Inte and NHM .−/−MYO16 mice [Fluorescence recovery after photobleaching (FRAP) analysis of postsynaptic dendritic spines of Purkinje cells demonstrated that MYO16 depletion leads to a faster F-actin turnover rate. A similar effect was observed upon inhibiting either the WRC or the Arp2/3 complex both in wild-type and −/−MYO16 mice implying that Myo16 controls actin dynamics through this pathway. Altogether, both in vitro and in vivo data indicate that Myo16 by activating the WRC-Arp2/3 complex machinery functions to attenuate actin cytoskeleton dynamics. The data are consistent with a hypothesis in which Myo16 integrates signaling events to actin network reorganization, thereby it acts as an indirect regulator of the actin cytoskeleton in the dendritic spines of Purkinje cells. On the other hand, the phenotypic analysis of the cerebellar molecular layer of −/−MYO16 mice revealed a significant decrease in the number of synaptic vesicles and the area of presynaptic terminals in parallel fibers of granule cells [The physiological importance of the control of actin dynamics by Myo16 is further supported by studies in −/− mice . Fluoresle cells . ComprehMYO16 mice neither apparent abnormalities in the cerebellar anatomy nor change in selected synaptic markers were detected [−/−MYO16 mice revealed no phenotypes and dysfunctions in locomotor activity, motor learning and social interaction; behaviors altered in the mouse model of ASD. Although the epigenetic control of MYO16 was described upon experience-based learning  in mouse [The physiological relevance of Myo16 in neural functioning was assayed in animal studies ,52,53. IThe NYAP1-3 proteins are encoded by different genes  . The thrIn support of being a generic motor protein, Myo16b interacts with F-actin in an ATP-dependent fashion in rat brain (P8) extracts . The F-abackfolding’ [The mechanics of myosin motors can be tuned by many different mechanisms that provides myosin-specific regulation; including phosphorylation of the motor domain, interaction between myosin heavy and light chains, as well as ‘folding’ ,56,57.Acanthaomeba [Dictyostelium [Acanthamoeba myosin II is down-regulated by phosphorylation of the motor domain at the motor-actin interface [660Ex16DAMAK680). Although it is autophosphorylated by its kinase domain at two threonines in the loop 2 region (T908 and T919) [40616DALAK426Tx) is phosphorylated, although it does not influence the ATPase activity of the motor domain [72216DLLAK742Dx) [Phosphorylation of the heavy chain of the motor domain can control the enzymatic and mechanochemical activities of myosins. The consensus phosphorylation site designated as the TEDS site ,57,58. Tthaomeba ,60, Dictostelium ,62 and yostelium . By contnterface . The motnd T919) ,66,67. TLLAK742) . Althoug1029. Ubiquitination triggers proteolysis and protein degradation through the ubiquitin-proteasome pathway in which they are recognized by the 26S proteasome and degraded [Besides the predicted phosphoresidues a putative ubiquitination site is found at Lysdegraded . In linedegraded .The association between the IQ motif(s) of myosin heavy chain in the neck domain and the members of the calmodulin family; calmodulin or calmodulin-like  light chains can regulate both the structural properties and the mechanical adaptation of the myosin holoenzyme ,73. In tbackfolding’ mechanism described in class IIa, V, VI, VII and X myosins provides an inhibitory regulation manifested through intramolecular interactions between the N-, and C-terminus of myosin adjoining the motor domain [skeletal heavy meromyosin II (skHMM) or non-muscle myosin 2B [2+-ATPase of the motor domain [The ‘r domain ,76,77,78yosin 2B . Cosedimr domain . Besidesr domain . The abor domain . In lighintrinsically disordered proteins or protein regions  lacking a well-defined structural organization [Src-homology 3 (SH3) and WW domain proteins, as well as Enabled (Ena)/vasodilator-stimulated phosphoprotein (VASP) homology 1 (Ena-EVH1), Homer-EVH1 and profilin [,86,87.Polyproline (PP) proteins comprise proline-rich motifs that tend to form polyproline helix II (PP II helix) . PP II hnization ,83. The nization ,84. In sprofilin . On the 1416xxM and Y1441xxM) found in the NHM region of Myo16Tail [PxxM phosphopeptide consensus sequence, indicating that the association of Myo16 and PI3K is likely to be mediated by this region [From this aspect, Myo16b possesses a considerable amount of structure-breaker proline residues, phosphosites and disorder-promoting amino acids in the C-terminal Myo16Tail ,30,31. Iyo16Tail . The SH3yo16Tail ,90. Myo1s region . In suppPFN1-4). PFN1 is a ubiquitous isoform, while PFN2 shows predominant expression in the brain. Both PFN1 and 2 are important for actin-based neuronal processes; PFN1 regulates axonal growth and regeneration and PFN2 is associated with synaptic plasticity [5 , either as a single copy or present in tandem repeats [5 motif preceded by an alanine. Albeit, no direct interaction was found between recombinant Myo16Tail and PFN1 in steady-state anisotropy experiments [RhoA-associated coiled-coil containing protein kinase 1 (ROCK1) at Ser137 in vivo that is conserved between the PFN1 and 2 isoforms [137 [in vitro, suggesting that profilin is targeted by a PP1-interacting protein; however, regulatory subunits of PP1c for profilin modification have not been identified yet.Profilin is an actin-binding protein that also binds proline-rich ligands, such as formins, VASP and the WAVE family proteins ,80,91,92asticity ,94. Sinceriments . This sueriments . On the isoforms . ROCK phisoforms . Interesrms [137 . No direMYO16 on the function of the protein, as well as the molecular networks linking Myo16 to diseases, remain to be elucidated. Myo16 has a peculiar domain architecture that endows the protein with unique structural characteristics. As inherent for classic motor proteins, the direct actin interactions and the enzymatic characteristics of the isolated motor domain of Myo16 are still obscure. The generic motor domain of Myo16 is flanked by the N-terminal Myo16Ank and the C-terminal Myo16Tail extensions that appear to provide a molecular scaffold for versatile interactions. Myo16Ank interacts with the PP1c subunit and regulates its phosphatase activity [The confined expression in neural tissues, the structural characteristics and interactions of the vertebrate-specific unconventional myosin XVI class suggest that it has an important role in neural development and functioning. As a support of this, the genetic examinations underscore the significance of Myo16 in the pathomechanism of neurological disorders, such as schizophrenia, autism spectrum disorder, bipolar disorder subtype II and major depressive disorder. The effects of the genetic alterations of activity ,35. Thisactivity .Altogether these findings indicate that Myo16 comprises a mechanism in which it acts as a signal transduction element. From this aspect, Myo16 resembles class III and IX myosins . Signifi


In computer security, botnets still represent a significant cyber threat. Concealing techniques such as the dynamic addressing and the domain generation algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labeled algorithmically generated domain names decorated with a feature set ready-to-use for machine learning (ML) analysis. This proposed dataset has been co-submitted with the research article ”UMUDGA: a dataset for profiling DGA-based botnet” [1], and it aims to enable researchers to move forward the data collection, organization, and pre-processing phases, eventually enabling them to focus on the analysis and the production of ML-powered solutions for network intrusion detection. In this research, we selected 50 among the most notorious malware variants to be as exhaustive as possible. Inhere, each family is available both as a list of domains  and as a collection of features . Specification table•The proposed dataset aims to overcome the shortage of standard and publicly available data regarding DGA-based malwares. Its value resides in serving as a foundation for benchmarks that eventually might lead to replicable and comparable experiments.•The primary recipients of the data are the academic scientists that focus on machine-learning-driven network security researches. They might greatly benefit from these freshly generated and carefully reviewed data.•By shifting the researchers’ attention from the data to the possible solutions, this work aims to ease the development of further experiments, which might eventually lead to innovation in the field of network cybersecurity.•These data, methods, and code sources are distributed under an open license. We guarantee essential properties such as the comparability and testability of each component.1•The domain generation algorithms – in this folder, for each malware variant, there are the DGA executable, the source code, and the reference to the analysis.•The actual data folder  – in this folder, for each malware variant plus the legitimate domains, there are three subfolders:-Raw list – includes the TXT lists of Fully Qualified Domain Names (FQDNs) in different tiers ;-ARFF features – includes the data processed and exported in the TXT .•The utility folder – in this folder, there are the executables and the source codes for any relevant package that might be helpful for the researchers, e.g., the collision checker.The proposed dataset is publicly available through Mendeley Data •-Dataset structure – the figure mentioned above  reports -Framework architecture – from the main co-submitted article Figures:•-nGrams features are described in The list of features generated by the NLP Processor for each FQDN  – presen-nGrams set.the general feature statistics  – presenTables:•---Algorithms:In the following sections, we will refer to several figures and tables. Specifically:Alongside with the Mendeley Data 2botnet we identify an group of infected machines, called bots or zombies, that communicates with of one or more of the Command & Control (C&C) servers that act as a relay for the commands issued by the botmaster (botnet owner). Bots often use pseudo-random domain generators, called domain generation algorithms (DGAs), to communicate with the C&C servers. These DGAs generate thousands of domain names, called algorithmically generated domains (AGDs). A deep dive on the subject, with specific attention to machine learning (ML) techniques, is offered by Plohmann et al. Before introducing the dataset, it is worth mentioning a few terms and definitions that will be used throughout the article. Firstly, with The primary research article i.e., the number of AGDs generated for that specific malware variant. It is important to remark that several variants such as Pizd, Gozi, or Rovnix have wordlist-based DGAs; thus, their possible AGDs are limited.A selected list of 50 malware variants has been collected, analyzed, processed, and included in the proposed dataset to be as complete as possible. The primary research article Domain List Generation. Their fixed initialization parameters are described in the following dedicated subsection. To be more precise, whenever a malware variant, such as Gozi, needs one or more wordlists in order to generate the domain names, we have considered each wordlist as a separate variant and memorized the wordlist itself in the corresponding DGA folder.Firstly, each of the 50 malware variant DGAs included in the dataset has been collected from online sources Feature Extraction, that calculates the features according to their formal definitions as described in the following dedicated subsection.Secondly, the raw lists of AGDs are then processed by the secondary module, named •-14 AGDs with the variant Matsnu-5 AGDs with the variant Gozi (RFC 4343 wordlist)The variant Gozi (Nasa wordlist) shares•-5 AGDs with the variant Gozi (Nasa wordlist)-1 AGD with the variant Nymaim-24 AGDs with the variant MatsnuThe variant Gozi (RFC 4343 wordlist) shares•-14 AGDs with the variant Gozi (Nasa wordlist)-24 AGDs with the variant Gozi (RFC 4343 wordlist)-53 AGDs with the variant NymaimThe variant Matsnu shares•-1 AGD with the variant Gozi (RFC 4343 wordlist)-53 AGDs with the variant Matsnu-3 AGDs with the variant Suppobox (1st version)-5 AGDs with the variant Suppobox (2st version)The variant Nymaim shares•-441 AGDs with the variant Suppobox (1st version)The variant Pizd shares•-1 AGD with the variant Simda-1 AGD with the variant Pykspa (noise)The variant Proslikefan shares•-1 AGD with the variant Proslikefan-3 AGDs with the variant SimdaThe variant Pykspa (noise) shares•-1 AGD with the variant Proslikefan-3 AGDs with the variant Pykspa (noise)The variant Simda shares•-3 AGDs with the variant Nymaim-441 AGDs with the variant PizdThe variant Suppobox (1st version) shares•-5 AGDs with the variant NymaimThe variant Suppobox (2st version) sharesThe generated AGDs lists present 551 collisions, which are available in a separate file in the root of the project. To be more precise:2.1Domain List Generation module. The main output of this module is a list of AGDs generated by the malware variants, and to be as precise as possible, each DGA implementation utilizes a fixed seed for the pseudorandom number generator (PRNG) and firstly analyzes, whenever available, the original initialization vectors for the specific malware sample analyzed. Each malware family also includes the links fo the source code and the related analysis.Several independent executables that implement each malware variant DGA constitute the backbone of the 2.2Feature Extraction module is composed by two independent processes, namely the NLP Processor and the nGrams Processor. The features extracted are the ones belonging to Context-Free family, defined as specified in et al. Family 1A feature that is related only to a Fully Qualified Domain Name (FQDN) and thus is independent of contextual information, including, but not limited to, timing, origin or any other environment configuration. First and foremost example of this family is the lexical analysis of the domain name.The Domain Inspector processes each AGD generated, as presented in nGrams sets. Specifically, as reported in nGrams  extracts a total of 22 features by analyzing the domain name as a string. The first process , compares the different sets of nGrams generated by the Domain Inspector with the ones provided by the Leipzig Corpora nGrams. The second process  be the absolute frequency for all the nGrams g ∈ G of the domain d.Let  set See , n the sρ the linear application that associate each element of G of the domain d with a real number, in form of a vector of absolute frequencies:Then we define as Definition 3d be a FQDN. Then we define as dw the vector resulting of applying ρ( · ) to the nGrams set G obtained from the domain d. Formally:Let Definition 4dw by the total sum. Mathematically:Let Example 1dw has g ∈ G. It also holds that Let Having the definition of then nGrams vector can be compared with virtually any language data, namely the nGrams relative frequency, i.e., the frequency of the nGrams in the target language.Definition 5d be a FQDN, G its sorted nGrams set  be the absolute frequency in the target language dictionary T for all the nGrams g ∈ G of the domain d. Within the scope of this article, T is the English language dictionary Let  set See , n the sσ the linear application that associate each element of G of the domain d with a real number, in form of a vector of absolute frequencies::Then we define as Definition 6d be a FQDN. Then we define as dϕ the vector resulting of applying σ( · ) to the nGrams set G obtained from the domain d. Formally:Let Definition 7d be a FQDN, G its sorted nGrams set  be the relative frequency in the target language dictionary T for all the nGrams g ∈ G of the domain d. Within the scope of this article, T is the English language dictionary Let  set See , n the sσ′ the linear application that transforms the domain d in a vector of relative frequencies:Then we define as Definition 8d be a FQDN. Then we define as σ′( · ) to the domain d. Formally:Let The obtained Example 2Let dϕ has g ∈ G.Then, g ∈ G.Moreover, Using 2.3.1nGrams, and they are presented in Longest Consecutive Sequence  = standard deviation of “ · ”.nG-SPE: Spearman’s CorrelationFeature d with respect to the English language. It is implemented with Apache Commons Math SpearmansCorrelation class Computes Spearman’s rank correlation of the domain nG-MEAN: Mean of frequencies Represents the arithmetic mean of the relative frequencies for the domain dFeature nGrams relative vector  of the relative frequencies for the domain ctor See  of the dnG-SUMSQ: Squared sum of frequenciesFeature d. Mathematically, let nGrams relative vector  of the relative frequencies Calculates the Earth Movers distance  variant, then the entropy is defined as zero.•Having standard deviation value equal to zero – In order to have zero standard deviation, all the values of the features must be equals. This is the case of a group of feature calculated over 2Grams and 3Grams, namely NLP-nG-25P, NLP-nG-50P, NLP-nG-75P and NLP-nG-MED, where By looking at 2.4Domain List Generation and the Feature Extraction modules. The dataset with the released code has been published on the well-known platform Mendeley Data As specified in the previous section, there are two main code components that interact to generate the proposed dataset, namely the 2.4.1This module is mainly realized in Python 2.7 and it has been released under the MIT license.As specified before, the PRNGs have been initialized with a specific seed (either integer or string), available within each DGA source code.•PRNG Seed – Each random generator has been initialized with the hardcoded integer value “521496385”.•String Seed – Whenever a DGA requires a string seed as initialization vector, the module uses the string:“3138C81ED54AD5F8E905555A6623C9C9”.•Malware variant specific seeds – Security vendors often release, along with the relative signatures, also the initialization vectors for each variant discovered in the wild . In such cases, the initialization vectors are coded in the generator and marked with online source for reference.•Random date range – Most of the DGAs require a random date in order to generate the AGDs. When not fixed by some internal constraint, the dates are generated randomly from 01/01/1970 01:00 AM to 01/01/3000 01:10 AM.Specifically, the fixed parameters for each DGA are:2.4.2This module implements the feature definitions as described in The code, however, is closed source and is not, and will not released to the general public.2.4.3•38 of them use one of the new generic top level domains (gTLDs) which are still not included in the list of accepted gTLDs as per the last update of the library . Namely, .africa (delegated on 14/02/2017), .charity (04/06/2018), .hotels (03/04/2017), .inc (16/07/2018), .sport (08/01/2018);•140 domains are technically invalid because of the presence of at least one underscore character (“_”): the validation library checks the domains against the RFC 1123 When considering the list of FQDNs that we assume legitimate, two main problems are to be considered. As specified before, each domain is firstly validated by the Apache Domain Validator library. A total of 178 FQDNs fail to pass the validation procedure. To be more precise: