Given the increasing amount of biomedical resources that are being annotated with concepts from more than one ontology and covering multiple domains of knowledge, it is important to devise mechanisms to compare these resources that take into account the various domains of annotation. For example, metabolic pathways are annotated with their enzymes and their metabolites, and thus similarity measures should compare them with respect to both of those domains simultaneously.aggregative approach compares domains independently and averages the various similarity values into a final score. The integrative approach integrates all the relevant ontologies into a single one, calculating similarity in the resulting multi-domain ontology using the single-ontology measure.In this paper, we propose two approaches to lift existing single-ontology semantic similarity measures into multi-domain measures. The We evaluated the two approaches in a multidisciplinary epidemiology dataset by evaluating the capacity of the similarity measures to predict new annotations based on the existing ones. The results show a promising increase in performance of the multi-domain measures over the single-ontology ones in the vast majority of the cases. These results show that multi-domain measures outperform single-domain ones, and should be considered by the community as a starting point to study more efficient multi-domain semantic similarity measures. Contrast this with other similarity measures that use structural and/or physical properties of the entities: e.g. proteins have traditionally been compared based on their aminoacid sequence, chemical compounds on the graph representing their molecular structure, etc. While non-semantic measures are effective to a certain degree, they fail in some edge cases, such as proteins with similar functions having different sequences, or chemical compounds with similar molecular structure having disparate biological roles.Ontology-based semantic similarity uses the machine-readable definitions of concepts provided by ontologies to compare annotated entities based on their etc. These multidisciplinary entities, along with their multi-ontology annotations, can be regarded as biomedical digital resources that describe complex real-world phenomena.Semantic similarity between annotated biomedical resources has been a topic of research since Lord et al.  applied etc., we argue that semantic similarity measures need to be developed to handle the multidisciplinarity of these types of resources; nevertheless, research in this field is still stalled in the single-domain world. For example, to compare metabolic pathways, Clemente et al. [Given the success of single-ontology semantic similarity measures in the past, for example, to assist text-mining –12, mache et al.  used seme et al.  used semNing et al.  propose Cheng et al.  designedHowever, to the best of our knowledge, this type of algorithm has yet to be fully studied within this community. Two recent papers have been presented that tackle them , 23: Ninlift single-ontology measures into multi-domain measures. The “aggregative” approach compares each of the domains of relevance independently using existing single-ontology measures and then aggregates the several calculated values; the “integrative” approach integrates all the ontologies under the same common root and then applies single-ontology measures on it.Instead of a new measure of semantic similarity designed from scratch to handle multidisciplinarity, we propose two approaches that can To assess the performance of the different approaches, we selected as case study a dataset of epidemiology resources, an inherently multidisciplinary field of research.The results obtained with this dataset are meant to achieve two goals: (a) we show that the proposed approaches to the multi-domain similarity problem are effective, at least in comparison with the single-ontology counterpart; and (b) we hope to stimulate the community to think about the problematic of multidisciplinary similarity surrounding the ideas of knowledge representation and ontologies in the biomedical domain.i.e. the ontologies have common concepts that represent the same real-world ideas, for example two ontologies of anatomy. The existence of these various ontologies that represent the same domain can result from the ontologies offering complementary views of the reality. Some previous work has been carried out with respect to this type of semantic similarity [i.e. they represent different domains of reality, and thus rarely have concepts in common, and when they do, the overlapping concepts are very general. This type of measure is able to compare resources annotated with concepts from multiple domains of knowledge, as the biomedical entities mentioned above.Multi-ontology semantic similarity comes in two flavours: “single-domain” and “multi-domain”. “Single-domain multi-ontology” semantic similarity is a technique that takes into account multiple ontologies that try to represent the same domain of knowledge, milarity –27. Conte.g. Pederson et al. [near the concept in scientific literature).Notice that we are considering the multidisciplinarity of biomedical resources from the point of view of knowledge representation (KR): we propose a means to explore the ontology-provided definition of concepts to compare multidisciplinary entities annotated with concepts from more than one ontology .i.e. the concepts from one domain in the first resource are compared to the concepts from the same domain in the second resource. We do this for all the domains used to annotate the resources and aggregate these single-ontology results into a single value by using an aggregating function such as the raw average, where all domains weight the same, or the weighted average, where each domain is weighted proportionally to the number of concepts used to annotate the resources in that domain. Schliker & Albrecht [The “aggregative” approach is depicted in Fig. Albrecht  propose Albrecht  also useThe “integrative” approach consists in merging the relevant domain-specific ontologies into a single multi-domain ontology. In case the ontologies share a common upper ontology , this me.g. both “Native cell” <CL:0000003> from the domain of cellular lines, and “Balancer cell” <CTENO:0000057> from the domain of Ctenophore anatomy). In this case, given the common ancestor, our measure is able to provide a similarity between concepts from the different ontologies greater than 0.The integrative approach has the advantage of being easy to implement and to straightforwardly enable the application of existing measures that have been proved useful in other endeavours. Additionally, it does not make use of arbitrary parameters for the domain weights. It also has the advantage that it inherently takes care of equivalences in multiple ontologies. For example, if several ontologies contain the concept “Cell” <GO:0005623>, the integrative approach automatically considers the concept as a single one; and as such the similarity between subclasses of this concept can make use of their common ancestor even if the subclasses come from different domains  All the single-ontology values obtained with the baseline setting are averaged with equal weights.Aggregative (weighted) This is the same as last setting, except that the average of the values obtained for each domain are weighted in proportion to the number of annotations in that domain.Integrative All the ontologies relevant for the similarity calculation are merged into one ontology and then the single-ontology measure is applied to it.In a multi-domain context, therefore, we can separate our measures of semantic similarity in four different settings: etc. A multi-domain semantic similarity measure would enhance information retrieval mechanisms on a repository of epidemiology data. To support a meaningful search functionality, the repository has to show to the user a set of resources similar to their query, which requires a means to compare resources based not only on one domain of interest (such as diseases), but on all the domains of annotations of the resources. It is conceivable, for example, to imagine a user in need of data related to “flu” in “Europe” with “fever” and “sneezing” symptoms. A search engine needs to be able to deal with these domains in order to properly return to the user the set of resources they are requesting, in an order that meaningfully reflects the their relevance to the query.Epidemiology is an inherently multidisciplinary subject, relying on areas of knowledge as diverse as medicine, biology, statistics, sociology and geography . Even unIn fact, the multidisciplinarity of epidemiology has been previously explored and a network of epidemiology-related ontologies has been created, which contains ontologies that represent most of the epidemiology domains mentioned above . The netThe full set of 204 resources were extracted from the Epidemic Marketplace, each corresponding to a scientific paper published in an epidemiology journal and annotated with concepts from the aforementioned network of ontologies.i.e. they resemble ad-hoc vocabularies more than formal ontologies, where the relationships between class and subclass do not always reflect subsumption . Additionally, they are used in this dataset mainly to provide non-biomedical-specific concepts, such as “Family characteristics”, which belong to the socio-economic sub-domain of epidemiology. For these reasons, these annotations were not included in our analysis.Among the annotations for these resources, some use concepts from the NCIT  and MeSH , which are on the less formal end of the ontology spectrum, Coverage is the fraction of resources that have at least one annotation in the specified domain, Volume is the average number of annotations from that domain within those resources, Diversity is the number of distinct concepts in that domain used in those annotations, and Isolation is the fraction of those resources that have annotations only in that domain. The figure shows that while a lot of resources are annotated with concepts from a single domain, the majority contain concepts from multiple domains. It also shows that the maximum number of domains is 5.A summary of the relevant annotations for these resources is given in Table e.g. 21.5% of the resources have DOID annotations only), it is not the same domain that covers those resources. As such, to compare the resources in this dataset using the classical single-ontology semantic similarity, it would be necessary to select one domain, which means that several of the resources would need to be left out of the analysis and that a high volume of information would be disregarded, as it belongs to some other domain. Multi-domain semantic similarity seems to be essential in this case study to enable a proper comparison of the resources.As can be seen from these results, each domain contributes with a partial description of the resources: there is a sparseness in the annotation profile, with many resources having annotations in only a few domains, and not always on the same domains. No domain covers the whole dataset, and most resources are annotated with more than one domain. Additionally, even though 37.3% of the resources are annotated in a single domain  and is, therefore, one of the most important problems in biomedical informatics. In other words, we aim at predicting diseases that are related to a resource characterized by chemical compounds, environmental conditions, phenotype qualities, symptoms, modes of transmission and vaccines.k-nearest neighbours (k-NN) approach to assign, to each resource, a set of DOID concepts. Using a k-NN-based algorithm is appropriate, since its performance highly depends on the performance of the similarity measure used to find the neighbours. The following steps describe ML-KNN: r to the other resources, and determine the k most similar ones (this is the neighbourhood of r);Compare each resource k resources, build a Bayesian model to calculate the probability that each DOID concept  is also one of the annotations, based on the frequency with which each distinct possible concept appears in the k neighbours.With these Compute a metric of performance based on the probabilities derived in the previous step. The ML-KNN paper suggests five different metrics, which we use here.For this purpose, we used a multi-label machine learning algorithm, described by Zhang & Zhou . This alWe executed these steps with each of the settings delineated in the previous section ; we also ran the calculations using several different groupwise single-ontology measures  the overall behaviour of the other groupwise measures does not differ significantly from the results that we are about to show, and (ii) this shows the best performance on this dataset. As can be observed, the integrative approach almost always outperforms the other settings, irrespective of evaluation metric and the value of k, which suggests that this measure is indeed superior to single-ontology measures in this dataset.Figure k, the performance of this baseline is either on par or above the performance of the aggregative approaches.We can observe that the single-ontology measure performed on the “Symptoms” domain is the most successful baseline. This is justified by taking into consideration the annotation profile shown in Table However, 88% of the resources have annotations to concepts from ontologies other than SYMP, which means that using only the “Symptoms” domain to measure similarity leaves out information; the results suggest that, in fact, incorporating other domains into the comparison algorithm increases the accuracy of the measure, as the evaluation metrics increase when we go from the SYMP baseline to the multi-domain measures.k increases, the performance of the ML-KNN algorithm in the baseline settings decreases, especially for the SYMP baseline. This may be related to the fact that as we keep incorporating more and more neighbouring resources to predict DOID labels, we start including resources annotated with irrelevant symptoms, to the point where the extraneous information leads to a decrease in the algorithm’s performance. However, the performance for the TRANS baseline increases with k, and it appears that its incorporation in the multi-domain approaches helps keep the multi-domain performance either at a plateau or even to increase (for the aggregative measures). It is not immediately obvious why this difference in behaviour manifests in this dataset, and further studies would be needed to attribute a reason for this. For the moment, we believe that this can be explained by one of two reasons: There are only 9 distinct TRANS concepts used throughout the dataset, and only 48% of the resources have annotations in this domain. The increase suggests, then, that a similar mode of transmission is not directly indicative of similar diseases and only by probing further can this baseline be able to predict the correct DOID labels.The TRANS baseline has a generally low performance and the observed increase is not significant and can be mostly attributed to random chance.It is interesting to notice the following overall trend: as Overall, the results consolidate the idea that taking into account multiple domains of annotation is relevant for obtaining useful similarity values.p-highest performance are all multi-domain approaches, which we call Hp. Since there are three different multi-domain approaches, it makes sense to compute Hp for p∈{1,2,3}. Also, since we performed an assessment with 5 distinct metrics and 10 different values of k, there is a total of 50 runs, which is the maximum value for Hp.One way to summarize the results in the figure is by counting how many times the approaches with the H1, H2 and H3 obtained for the problem of predicting DOID labels .First, we feel necessary to provide a few proposals for future work. First, while our results show the superiority of the integrative approach over the baselines, they were obtained by a validation method that cannot be directly applied to create new knowledge or to perform information retrieval. We would like to test these multi-domain approaches with actual data repository users (e.g. weighting the average on the information content of concepts rather than the amount of concepts in each domain).Other possible avenues to pursue include (i) trying new groupwise single-ontology measures in evaluating the behaviour of the aggregative and integrative approaches, and (ii) defining new aggregation methods for the aggregative approach  to the CHEBI concept “carbohydrate”. Given that current single ontology measures are not able to exploit this inter-domain relationships directly, the “aggregative” and “integrative” approaches are also unable to use the cross-references. To solve this issue, we need to create new measures that properly explore such relationships. While such measures have still not been developed, we think that a measure proposed by us in the past could be a starting point to tackle this problem . This meIn conclusion, we present evidence to support the hypothesis that multi-domain semantic similarity is both necessary and feasible, and propose two approaches to lift single-ontology measures (which have been actively developed throughout the last two decades) into multi-domain measures. Therefore, this manuscript presents two main contributions: in multidisciplinary context we should not limit ourselves to single-ontology similarity, as that has negative implications on the overall performance of semantic similarity; and, by extension, we provide a baseline for future multi-domain measures.


Recently, there has been great progress in defining a complete taxonomy of bacteria and archaea, which has been enabled by improvements in DNA sequencing technology and new bioinformatic techniques. A new, algorithmically defined microbial tree of life describes those linkages, relying solely on genetic data, which raises the issue of how microbial traits relate to taxonomy. Here, we adopted cluster of orthologous group functional categories as a scheme to describe the genomic contents of microbes, a method that can be applied to any microbial lineage for which genomes are available. This simple approach allows quantitative comparisons between microbial genomes with different gene compositions from across the microbial tree of life. Our observations demonstrate statistically significant patterns in cluster of orthologous group functional categories at taxonomic levels that span the range from domain to genus. P < 0.001 for all). To our knowledge, this is the first work to quantify the variance in metabolic potential contributed by individual taxonomic ranks. A qualitative comparison between the COG-FC relative abundances and genus-level phylogenies, generated from published concatenated protein sequence alignments, further supports the idea that metabolic potential is taxonomically coherent at higher taxonomic ranks. The quantitative analyses presented here characterize the integral relationship between diversification of microbial lineages and the metabolisms which they host.Widely used microbial taxonomies, such as the NCBI taxonomy, are based on a combination of sequence homology among conserved genes and historically accepted taxonomies, which were developed based on observable traits such as morphology and physiology. A recently proposed alternative taxonomy database, the Genome Taxonomy Database (GTDB), incorporates only sequence homology of conserved genes and attempts to partition taxonomic ranks such that each rank implies the same amount of evolutionary distance, regardless of its position on the phylogenetic tree. This provides the first opportunity to completely separate taxonomy from traits and therefore to quantify how taxonomic rank corresponds to traits across the microbial tree of life. We quantified the relative abundances of clusters of orthologous group functional categories (COG-FCs) as a proxy for traits within the lineages of 13,735 cultured and uncultured microbial lineages from a custom-curated genome database. On average, 41.4% of the variation in COG-FC relative abundance is explained by taxonomic rank, with domain, phylum, class, order, family, and genus explaining, on average, 3.2%, 14.6%, 4.1%, 9.2%, 4.8%, and 5.5% of the variance, respectively (IMPORTANCE Recently, there has been great progress in defining a complete taxonomy of bacteria and archaea, which has been enabled by improvements in DNA sequencing technology and new bioinformatic techniques. A new, algorithmically defined microbial tree of life describes those linkages, relying solely on genetic data, which raises the issue of how microbial traits relate to taxonomy. Here, we adopted cluster of orthologous group functional categories as a scheme to describe the genomic contents of microbes, a method that can be applied to any microbial lineage for which genomes are available. This simple approach allows quantitative comparisons between microbial genomes with different gene compositions from across the microbial tree of life. Our observations demonstrate statistically significant patterns in cluster of orthologous group functional categories at taxonomic levels that span the range from domain to genus. The relationship between microbial taxonomy and function is a longstanding problem in microbiology –3. Prior–Comparing phenotypic characteristics of microorganisms across the tree of life is not currently possible, because most organisms and lineages currently lack cultured representatives , 7. We thttps://zenodo.org/record/3361565). Among these genomes, we included only those that satisfied a set of criteria designed to ensure that each genus contained enough genomes to allow statistically robust analysis . This resulted in a set of 13,735 lineages, representing 22 bacterial phyla and 4 archaeal phyla, 67% of which have been grown in culture  database, and GenBank, in order to include genomes created using diverse sequencing and assembly techniques. The integration of the RefSeq v92, JGI IMG/M, and GenBank databases resulted in a total of 119,852 genomes within the custom-curated database. Raw data, GTDB taxonomy, and associated accession numbers are provided in Data Set S1 , and all but five COG-FCs had deviance-explained values  of more than 50%. We interpret analyses of these genome size-normalized data sets as reflecting the enrichment or depletion of COG-FC abundance, relative to that expected for a given genome size, and thus, the data are defined as COG-FC relative abundances. PCA of these COG-FC relative abundances showed that species-level lineages still tended to group by phylum, even though the interphylum gradients in genome size were no longer apparent  as a function of genome size. Solid red lines correspond to mean fit. Upper and lower red dashed lines correspond to 95th percentile confidence intervals. Download Copyright © 2019 Royalty and Steen.2019Royalty and SteenCreative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00446-19.3FIG S3FIG S3, PDF file, 0.9 MB.Contour plots similar to those shown in Copyright © 2019 Royalty and Steen.2019Royalty and SteenCreative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00446-19.4TABLE S1Table S1, XLSX file, 0.01 MB.Fit statistics for GAM regressions modeling COG-FC abundance as a function of genome size. Download Copyright © 2019 Royalty and Steen.2019Royalty and SteenCreative Commons Attribution 4.0 International license.This content is distributed under the terms of the P < 0.001), but the fractions of variance that the various ranks explained differed substantially: phylum explained the most variance (14.6%), followed by order (9.2%), genus (5.5%), family (4.8%), and class (4.1%). Domain explained only 3.1% of the variances in COG-FC relative abundances, the least of any taxonomic rank. Culture status was a significant correlate of COG-FC abundance (P < 0.001) but had virtually no explanatory power, accounting for only <0.001% of the variance. This observation is consistent with the idea that no particular COG-FC relative abundance level was systematically higher or lower in uncultured microbes relative to cultured microbes.To quantify the degree to which taxonomic rank explains the distribution of COG-FC relative abundances among individual genomes, we performed permutation multivariate analysis of variance (PERMANOVA) using the taxonomic ranks of domain, phylum, class, order, family, and genus, as well as culture status (cultured versus uncultured lineage). The rank of species was excluded from the analysis as every lineage was unique, and thus, species would explain 100% of the data. Every rank significantly influenced the distribution of COG-FC relative abundances . Coefficient B, which represents how quickly the function approaches the asymptote, was 0.43 ± 0.30 (P = 0.17). Coefficient C, representing an offset employed to address the fact that all the log-transformed distances have negative values, was −1.63 ± 0.14 (P < 0.001). This means that observing approximately 100 lineages in a phylum is sufficient to assess the variance in trait space representing half of all potential variance for that phylum (0.13). Note that this represents the effect of incorporation of the shift parameter, coefficient C.The variability in COG-FC relative abundances across different phyla was explored in addition to mean COG-FC composition for individual phyla see . The evovely see . We explvely see . We modevely see  and a liThermoplasmatota appears unique, with high COG-FC relative abundances in cell motility and depletion in every other category. In general, the COG-FC content of bacterial lineages appeared more variable than that of the archaeal lineages at all taxonomic resolutions. The clade consisting of Bacteroidota, Spirochaetota, and Verrucomicrobiota was notably depleted in the less variable COG-FCs, including energy production and conversion, amino acid transport and metabolism, and carbohydrate transport and metabolism, among others. Another prominent feature is the nearly ubiquitous elevation in COG-FC relative abundances of cell motility; secondary metabolite biosynthesis, transport, and catabolism; lipid transport and metabolism; and intracellular trafficking, secretion, and vesicular transport COGs in Proteobacteria. A notable dichotomy in the COG-FC relative abundances of RNA processing and modification within the Proteobacteria mirrors the division of the two largest clades within the Proteobacteria. Overall, the relative abundance data appear qualitatively consistent with phylogenetic relationships, albeit they occur on different taxonomic levels.We sought a qualitative determination of how the distribution of COG-FC relative abundances related to phylogeny. To achieve this, we quantified the average COG-FC relative abundances for all COG-FCs in each genus These values were then visualized on a genus-level phylogenetic tree see  utilizin10.1128/mSphere.00446-19.5revCORRECTED TABLE S2Table S2, CSV file, 0.2 MB.Individual rows correspond to individual genus-level lineages . Columns 1, 2, 3, 4, and 5 correspond to domain, phylum, order, family, and genus, respectively. Columns 6 through 28 correspond to average enrichments for the respective lineages and COG functional categories. Download Copyright © 2019 Royalty and Steen.2019Royalty and SteenCreative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSphere.00446-19.5revCORRECTED TABLE S2Table S2, CSV file, 0.2 MB.Individual rows correspond to individual genus-level lineages . Columns 1, 2, 3, 4, and 5 correspond to domain, phylum, order, family, and genus, respectively. Columns 6 through 28 correspond to average enrichments for the respective lineages and COG functional categories. Download Copyright © 2019 Royalty and Steen.2019Royalty and SteenCreative Commons Attribution 4.0 International license.This content is distributed under the terms of the The relationship between individual COG-FC relative abundances and taxonomic ranks appeared largely variable see . For insP < 0.05; based on a 105 iteration bootstrap analysis) greater or lesser than those calculated for the 85th or 15th percentile, respectively, are shown in Fibrobacterota (depleted in eight COG-FCs), Nitrospirota A (enriched in four and depleted in five), and Proteobacteria (the only phylum not heavily enriched or depleted in any COG-FCs). Relative abundance data, along with associated GTDB taxonomic assignments used for generating data presented here . Furthermore, PCA scores along PC1 correlated strongly with genome size  exhibited comparable levels of variability in COG-FC distributions . This clade is known to contain many host-dependent pathogens and symbionts , the lowest taxonomic rank considered during our analysis was species. The COG-FC relative abundances (see below) were averaged for all strains within a given species. An exception was made for lineages which shared a genus classification but lacked a species classification. In this scenario, each genome was treated as an independent lineage. In total, 10.9% of the total number of genomes analyzed  met this condition. Last, only genomes belonging to genera with at least 10 unique species in the database were retained. This criterion ensured the availability of enough data to generate meaningful statistics during our PERMANOVA. The final database is summarized in All bacterial and archaeal genomes from the RefSeq database v92 , all uncreverse-position-specific BLAST (RPS-BLAST) algorithm  with a smoothing term employed due to the pairwise response to genome size see . In someWe performed PCA on the normalized COG-FC abundances and relative abundances. Prior to PCA, assumptions of normality were achieved by performing a boxcox transformation on individual COG-FC abundance and relative abundance distributions with the boxcox function from the R package MASS . The resWe performed permutational multivariate analysis of variance (PERMANOVA) using the adonis function from the R package vegan . The tax10 distance from the phylum centroid was determined for each phylum and modeled with the following equation, which represents a hyperbola shifted on the x axis to ensure that the mean distance value is zero when n = 1:A, B, and C represent fit coefficients and n represents the total number of lineages in the given phylum. The Akaike information criterion value was calculated with the fit from The mean logA variance component model was performed using the lme function from the R package nlme . The proftp://ftp.ncbi.nlm.nih.gov/refseq/release/). UBA MAGs used for the current study are available under NCBI’s BioProject PRJNA417962 and PRJNA348753. Publicly available JGI IMG/M genomes can be downloaded from the Genome Portal (https://img.jgi.doe.gov/), while the private genomes were acquired from Chad Burdyshaw. Associated genome accession numbers for genomes in the described data sets are available in Data set S1 at https://zenodo.org/record/3361565 (https://doi.org/10.5281/zenodo.3361565).The genomes analyzed for the current study are available in the NCBI RefSeq database (


Puffinus opisthomelas), on Natividad Island, Mexico. Our results provide a census of burrows in the colony, with very low detection error (5.6%). This is greater accuracy compared to other methods based on extrapolating results from sample plots to total colony area. We then combined this burrow census with ground truth data on occupancy to estimate population size. We obtained a population estimate of 37,858 and 46,322 breeding pairs for 2016 and 2017 respectively. The proposed method provides a cost effective and repeatable approach for monitoring numbers of burrows occupied in a colony, thereby enabling easier and faster estimates of population trends. We suggest this method can be valid for other burrow-nesting species in habitats without dense vegetation cover.Estimating the population of burrow-nesting seabirds is a challenging task, as human presence in the colony creates disturbances and can damage burrows and occupants. Here, we present a novel method using aerial photographs taken with Unmanned Aerial Vehicles (UAVs) to estimate the population size of a burrow-nesting seabird, the Black-vented Shearwater ( Seabirds are among the most threatened of bird taxa, with 42% of 365 species listed as threatened on the IUCN Red List . AccuratSeveral different methods have been used to determine population sizes of burrowing seabirds –6. For b2 detectors [In recent years, various field methodologies, technologies, and mathematical models have evolved, allowing researchers to detect, with increased accuracy, variation and changes in population size or demographic parameters, including distance sampling combined with burrow-scoping , thermaletectors , and acoetectors . In receetectors , 15 for etectors  and wateetectors . Advantaetectors , 19. HowPuffinus opisthomelas), on Natividad Island, Mexico. The UAV collected imagery, allowing a complete count of burrows in the colony, which we supplemented with on-the-ground estimates of burrow occupancy to determine colony population size. We compared the results of our UAV approach and other population estimates methods to highlight the applicability of using aerial-based methods, and potential application to other species.We used a UAV-based method to estimate the population size of burrow-nesting seabird, the Black-vented Shearwater , which was flown in all four visits. The UAV is 49 x 49 cm, with the props mounted, and approximately 20 cm in height. It is white and, when in flight, it has two green lights and two red/orange lights. The UAV weighs 1216 g. The noise produced by the UAV at 2 m distance is 60 dB . PhotogrThe camera is the standard camera for the DJI Phantom 3 Standard, with a f/2.8 focal point, exposure time: 1/2,000 (at least), and ISO = 100. Each photo is 4,000 x 3,000 pixels, with a resolution of 72 DPI and 32 bits of color. The camera was supported on a gimbal to avoid vibration effects and was pointed 90 degrees downwards, parallel to flat ground.The first two field trips were used for calibration of the UAV method. Data collected from the UAV during the June 2016 visit were used for the burrow census. As the number of burrows was expected to remain constant within one breeding season , we did not compare the variation among the three trips. However, occupancy was monitored during all three visits to account for within-season variability. We selected the highest occupancy figure of the three trips to determine the population size. Non-breeders were considered in the population estimate.2) chosen randomly in the colony. Concurrently, we collected aerial photographs of these same plots with the UAV at a height of 30 m, and burrows identified later were counted. Each circular calibration plot was marked on the ground with a flag in the center and a metric strip marking the diameter to aid comparison between methods. Comparisons were then made between ground and aerial burrow counts to determine the detection error. Two different persons counted either the burrows from the aerial photograph or the plots on the ground, without exchanging information.To estimate detection error between aerial surveys and on-ground counts, we visited the colony and counted burrows in 20 circular plots  that were used by the imaging software to locate and arrange all photos. Photographs were taken between 11 AM and 3 PM local time to reduce shadows, which could be mistaken for burrows (https://flylitchi.com/) to establish the flight path and avoid excessive overlap and large areas without burrows on the edge of the colony. Each flight was programmed to last for 13 minutes and fly a total distance of 3100 m on parallel paths of 300 m long and 20 m apart; each flight covered between 9 and 12 hectares. Weather permitting, the UAV was flown daily. When wind was over 40 km/h, we did not fly the UAV.To establish a complete count of burrows in the colony, aerial photographs of the entire colony were taken between 25 and 60 m above ground level. Altitude variation was largely an artifact of navigating in the hilly terrain. Given the high resolution of the photographs , this height difference did not appear to affect detection probability, as we were able to zoom in considerably before image resolution limited burrow detection. The center point of each photograph was approximately 10–15 m apart, with each photograph overlapped at least 25%. The UAV was flown at a speed of ≈2 m s burrows . During 2) to use as a reference point when counting the burrows. The grid size is the minimum set by QGIS. We found the optimum scale before image resolution limited burrow detection to be 1:40. Subsequently, all burrows were counted and marked on QGIS. We created a layer on QGIS and marked each burrow with a point over the burrow entrance. Two people carried out the counting independently over a period of 20 working days (8 hr per day). When in doubt, the counters consulted each other to reach a consensus on whether it was a burrow or not. To estimate burrow density, we divided the number of burrows per 124.6 m2 grid square.We used the photogrammetry software, Agisoft Photoscan Professional , to gene2 (>18 burrows per 124.6 m2 quadrat), medium density areas had between 0.10 and 0.15 burrows per m2 (between 12 and 18 burrows per quadrat), and low density had <0.10 burrows per m2 (<12 burrows per quadrat). Burrows were monitored in April, May, and June 2016, and June 2017, and occupancy was estimated using toothpicks placed at the entrance of the burrows. We monitored 1,342 burrows with toothpicks. The toothpicks were deployed late in the evening, when ravens (Corvus corax) and gulls  were known to reduce or stop their activity; therefore, they would not interfere with toothpicks. The toothpicks were recovered early in the morning, before ravens and gulls became active. If the toothpick was down, we assumed the burrow to be occupied [Burrow occupancy was estimated using ground-based methods. We used 25 quadrants of 20 m x 20 m randomly selected. Each corner was marked with wooden stakes and locations recorded with GPS. We separated the quadrats in three categories, based on burrow density—low, medium, and high, following Keitt et al.  for compoccupied . The tooBurrow scopes were used to detect whether there were signs of reproduction, “real occupancy”, and to discriminate from nests visited randomly or used by non-breeders, “apparent occupancy”. This method was used to calibrate our occupancy estimates, using the toothpick method. Twenty burrows were inspected inside four of the 20 x 20 m quadrats, for a total of 80 burrows. We used a burrow scope with a SHARP CMOS sensor, 6 white LEDs, 640 x 480 resolution and a 7 mm lens. The burrow scope was connected to a smart phone and photographs taken were stored on the internal memory of the phone.We could not choose our sample plots randomly because the burrow scope limited our selection to burrows that had shorter and straighter access tunnels (<1 m) dug in solid soil. The burrows were repeatedly inspected in May and June to record activity histories for each nest. We had a similar number of burrows in each of the burrow density groups—low (27), medium (27), and high (26).We used multi-event capture-recapture models to estimate the proportion of breeders and non-breeders and the probability of detecting evidence of reproduction. For species that have asynchronous laying, such as the Black-vented Shearwater, capture-mark-recapture (CMR) was used to estimate the breeding population size in a given year . OccupanLarus occidentalis) nest near the Black-vented Shearwater colony and would be disturbed by the UAV. Common Ravens (Corvus corax) and Osprey  also fly over the colony during the day. Ravens frequently land in the burrow areas and walk among burrow entrances to prey upon individuals close to the entrance. The gull colony is dispersed throughout the island, with no preferred nesting areas. The population is estimated of over 15,000 individuals (unpubl. data). To determine the effect of the UAV on Western Gulls, we flew the UAV in three randomly selected areas directly over the gull colony in July 2017. We counted the initial number of gulls present in the area and recorded the behavior of the gulls when the UAV flew at elevations of 60 m, 50 m, 40 m and 30 m in a 200 m line transect, with flight time between 100 and 130 seconds. We recorded the time when the first individuals were flushed, the time to return to the nest, the number of reacting birds, and the kind of reaction: alarm  and/or mobbing behavior. All flights were done between 1100 and 1500 hours, the same time the UAV was flown to collect images for burrow counts. During the disturbance study, there was one UAV operator and one independent observer of the gull behavior. The observer was no closer than 30 m from the nearest nest, and gull behavior was not noticeably influenced by the observer. We concentrated on behavioral and not on physiological responses [The use of UAVs over a colony can cause stress and evasion behavior by birds , 32 depeesponses .To make a qualitative comparison between the cost of the UAV method and a traditional population assessment, we budgeted a survey on Isla Natividad from our headquarters in La Paz, Mexico, in days or number of pieces of equipment, including traveling, lodging, days/person, and field material costs. We estimated the cost of a survey, using on-ground only methods and assuming three survey trips to the island, which included mapping 181 sample plots of 60 m2 (the same number used for previous population estimates), counting all burrows within each plot, and measuring burrow occupancy in half of these, plus measuring the perimeter of the island.2. From the quadrats with at least one burrow, we estimated the area occupied by the colony to be 148 ha , 565 having medium density (between 0.10 and 0.15 burrows/m2), and 83 having high density (>0.15 burrows/m2).We flew two to six flights each day with the UAV on most days, depending on wind speed. We covered a total of 300 hectares with aerial photographs, resulting in more than 40,000 photographs. These photographs produced 30 overlapping orthomosaic images extending approximately 150 m beyond the edge of the colony. Within these orthomosaics, the entire colony was represented by 11,963 quadrats of 124.6 m2 circular calibration plots. The corrected total from the aerial photographs was 56,395 burrows. The area within the colony perimeter was 261 ha, of which 148 ha contained burrows, an increase of 10 ha from the previous estimate that used the perimeter method to estimate colony size [We estimated detection error of the aerial counts of –5.6%, meaning the aerial counts missed 5.6 burrows for every 100, after comparing aerial and ground-based counts within the 60 mony size .–2 in low density clusters, 0.12  burrows m–2 in medium density clusters, and 0.18  burrows m–2 in high density clusters , we estimated that burrow densities vary from an average of 0.07  burrows mclusters .We compared our toothpick method for estimating occupancy against burrow scope results of true occupancy to determine error in the toothpick method for detecting actual occupancy. The toothpick method resulted in an overestimate of 22% in May (when there were only eggs in the nests) and an underestimate of 16% in June when eggs or chicks were in the nests. Based on burrow depths and soil characteristics, only a small portion of nests could be inspected with the burrow scope, potentially biasing our occupancy estimates. Consequently, for occupancy, we used only the toothpick method.The average occupancy, based on the toothpick method and calibrated with the burrow scope, was variable in low density zones with 65% (±26% SD) of burrows occupied, and less variable in medium and high-density zones, with 78% (±7% SD) and 77% (±10% SD) of burrows occupied, respectively. In June 2016, following an apparent widespread breeding failure, we observed a decrease of occupancy, compared to earlier in the year, resulting in estimates of: 37% (±16% SD) in low-density clusters, 56% (±17% SD) in medium density clusters, and 37% (±12% SD) in high-density clusters. The occupancy rate in 2016 is similar to the 1997 estimates, but much higher than in 1998 [When applying multi-state occupancy models  during tThe Natividad Black-vented Shearwater population was 37,858  breeding pairs in 2016, and 46,322 (±4931 SD) in 2017. When adding non-breeders, the estimated number of individuals visiting the colony was 84,802 in 2016 and 103,761 in 2017.Flights were conducted after Western Gulls chicks had already hatched in three sample areas  of the colony with 90, 75, and 98 nesting adult individuals, respectively. We saw little evidence of behavioral reaction to the UAV flying at 50 m above ground and no evidence of reaction at 60 m. Behavioral reactions in response to lower altitude flights consisted of alarm calls and increased vigilance, observed in ≈1 to 5 of 263 birds. Between 0 and 2 birds flushed in response . Based oWe estimated that our UAV-based survey of burrowing seabirds represented a 68% savings in man-hours over traditional ground counts, even when accounting for the additional time required for UAV image processing  a complete burrow census and thus greater power in detecting changes over time, (b) archivable, as images are available for future reference, (c) data can be collected without damaging burrows, i.e., only limited walking is needed in the colony, (d) data are reproducible and relatively inexpensive to collect.Our nest census showed 56,395 burrows, which combined with an occupancy estimate, provided an estimate of 84,802 individuals in 2016. These results are not directly comparable to the Keitt et al.  estimateIt is notable that the colony area, when using the method of including all the area within the bounds of the colony , is simiFregata minor), Crested Terns , and Royal Penguins (Eudyptes schlegeli), the estimation error was ±10% [Unmanned aerial vehicles have been used to estimate population of ground-nesting seabirds . For Leswas ±10% . Howeverwas ±10% , comparaWe found that the toothpick method underestimated Black-vented Shearwater burrow occupancy, compared to actual occupancy, as determined with the burrow scope. This is likely because of the Black-vented Shearwater nest- attending behaviour, where parents leave the nest unattended for two or more days or remain inside for several days. Toothpicks would not be knocked down on those survey days, and nests would be classified as unoccupied. We chose to use the toothpick data in the population estimate because it provided a larger and more variable sample of nests, including many types of soil, vegetation cover, and different areas within the colony. Burrow scope inspection was limited only to suitable nests in terms of soil type and length of the nest chamber.We included a non-breeder state in our models  to accouOccupancy rates can change dramatically under El Niño conditions, compared to other years . Thus, eThe time spent in the field to carry out the UAV survey is much less than the time spent in a traditional survey  and willWe suspected that gulls would assess the UAVs as a potential predation risk, making subsequent decisions about fight or flight . We obseSimilar behaviour has been observed in other surface nesters when approached by a UAV, in some cases passing as close as 4 m from the individual, without causing the bird to flush . While wPelecanoides garnotii) or the African penguin (Spheniscus demersus). For colonies where part of the area is obscured by vegetation, a subsample of an accessible part of a colony could be monitored to detect changes. For future research, we suggest determing the best times for taking photographs. We decided to take them between 11 AM and 3 PM to have azimuthal light and reduced shadows. However, this may not be the best approach, as slope or latitude may influence the resulting photographs. To monitor changes in colony population over time, the method we are proposing is less accurate than a census of the whole colony, but it offers a much cheaper option with less likelihood of disturbance.The UAV method could also be applied to populations of other burrowing seabird species, especially those that breed in sandy soil free of heavy vegetation, such as the Peruvian Diving-petrel (


Bridging integrator 1 (BIN1) has showed outstanding tumor-suppressive potential via inhibiting c-MYC-mediated tumorigenesis. However, a frequent phosphorylation of c-MYC at Ser-62 site could block the BIN1/c-MYC interaction and limits the tumor-suppressive effect of BIN1. Cyclin-dependent kinase 5 (CDK5), a generally dysregulated protein in various carcinomas, can mediate c-MYC phosphorylation at Ser-62 site. However, whether the existence of CDK5 could block the BIN1/c-MYC interaction remains unclear.The expression of CDK5 and BIN1 in non-small cell lung cancer (NSCLC) cell lines were measured. CDK5 was knocked down and overexpressed in H460 and PC9 cells, respectively. CCK-8, wound healing and transwell were used to detect the proliferation, migration and invasion ability of NSCLC cells. Tumor-bearing nude mouse model was built with H460 cells. Dinaciclib was added to realize the effect of CDK5 inhibition in vivo. NSCLC and matched para-carcinoma specimens were collected from 153 patients who underwent radical operation. IHC was performed to determine the expression of CDK5 in the specimens. Kaplan–Meier analysis was used to analyze the correlation between the postoperative survival and CDK5 expression.CDK5 was highly expressed in H460 cells, and knockdown of CDK5 could restore the BIN1/c-MYC interaction. Meanwhile, low expression of CDK5 was observed in PC9 cells, and overexpression of CDK5 blocked the BIN1/c-MYC interaction. Consequently, the growth, migration, invasion and epithelial mesenchymal transition (EMT) ability of H460 and PC9 cells could be facilitated by CDK5. The addition of CDK5 inhibitor Dinaciclib significantly suppressed the tumorigenesis ability of NSCLC cells in tumor-bearing mouse model. Furthermore, high expression of CDK5, along with low expression of BIN1, could predict poor postoperative prognosis of NSCLC patients. The patients with high expression of CDK5 and low expression of BIN1 showed similar prognosis, indicating that CDK5 could neutralize the tumor suppressing effect of BIN1 in clinical situation.CDK5 blocked the interaction of BIN1 and c-MYC via promoting phosphorylation of c-MYC at ser-62 site, ultimately facilitated the progression of NSCLC. Non-small cell lung cancer (NSCLC) is one of the most common malignant tumor with high mortality rate worldwide . DespiteBridging integrator 1 (BIN1), also known as Myc box-dependent-interacting protein 1, was identified as a tumor suppressor interacting with MYC box 1, a highly conversed region of the c-MYC N terminus . By bindCyclin-dependent kinase 5 (CDK5) is a proline-directed serine/threonine kinase that functions as tumor promoter in the development and progression of multiple cancers by regulating cell proliferation, apoptosis, DNA repair and immune escape –16. VariHere, we showed that the high expression of CDK5 neutralized the tumor suppressing effect of BIN1 by phosphorylating c-MYC at Ser-62 site. In addition, knockdown of CDK5 inhibited clone formation, proliferation, invasion and migration ability of NSCLC cells by restoring BIN1/c-MYC interaction. Meanwhile, CDK5 inhibitor Dinaciclib restored the function of BIN1 to suppress cell proliferation via interrupting phosphorylation of c-MYC at Ser-62 site. Furthermore, high expression of CDK5 along with low expression of BIN1 associated with worse prognosis of NSCLC patients, indicating a novel role of CDK5 in NSCLC.The specimens of NSCLC tissues and matched para-carcinoma tissues were collected from 153 patients who underwent radical operation at the Fourth Hospital of Hebei Medical University  between Jan. 2015 and Feb. 2016. The median patient age at the time of surgery was 54 years (range: 25–72 years). None of the NSCLC patients received preoperative radiotherapy, chemotherapy and immunotherapy. The clinical stage and histological tumor type were determined according to the Uion of International Cancer Control (UICC) Classification of 2017 (8th edition). Patient’s clinical information, such as gender, age, TNM stage, invasion range, lymph node metastasis, was collected and stored in a database. All participant information was updated every 3 months by telephone follow-up. Complete follow-up was updated until death or Mar. 2019. Carcinoma tissue and para-carcinoma tissue specimens were collected and treated promptly after the surgery. The data of this research was approved for use by the ethic committee of the Hebei Medical University  and all informed consents were signed by patients.Antibodies to CDK5 (EP715), c-MYC (ab32072), phosphorylated c-MYC at Ser-62 (ab185656), GAPDH (ab9485) were purchased from Abcam. . Antibodies to CDK5/p35 (PR4026C) was purchased from Thermo Fisher . The CDK5-expressing lentivirus vector pCDH-CDK5 (pCDH-CMV-MCS-EF1-copGFP) and the control vector pCDH (pCDH-CMV-MCS-EF1-puro) and CDK5-siRNA were purchased from Gene Pharma Company . GoTaq qPCR Master Mix was purchased from Promega . Revert Aid First Strand cDNA Synthesis Kits was purchased from MBI Fermentas . CDK5 inhibitor Dinaciclib (sc-364483A) was purchased from Santa Cruz Biotechnology .−∆∆Ct .Reverse-transcriptase PCR primers for human CDK5 mRNA on the basis of the sequence in NCBI (accession number U68485). CDK5: forward: 5′-GTCCATCGACATGTGGTCAG-3′; reverse: 5′-CTGGTCATCCACATCATTGC-3′, GAPDH forward: 5′-ACCACAGTCCATGCCATCACT-3′, reverse: 5′-TCCACCACCCTGTTGCTGTA-3′. The experiments were triplicated. The comparative threshold cycle (Ct) method was used to calculate the relative expression. The amount of target relative to a calibrator is given by 2Cells were lysed in lysis buffer, and protein concentrations were measured with the BCA protein assay kit (ThermoFisher). Proteins were separated by 10% SDS-PAGE and transferred electrophoreticly onto polyvinylidene difluoride membranes . The membranes were incubated in PBS containing 5% bovine serum albumin for 2 h at room temperature, followed by overnight incubation at 4 °C with different dilutions of the primary antibodies, including antibodies to CDK5, c-MYC, p-c-MYC (Ser-62). The membranes were developed with the Odyssey infrared imaging system according to the manufacturer’s instructions. The levels of protein in each sample were normalized relative to those of GAPDH. Each experiment contained triplicate wells of each sample, and all experiments were repeated at least three times.A eukaryotic expression plasmid of the human CDK5 gene was constructed using a pCDH vector . We constructed CDK5 cDNA expression vector on the basis of the sequence in NCBI. The empty vector was used as negative control. PC9 cells were cultured in six-well plates until they reached 80–90% confluence, and then transient transfections were performed using Lipofectamine 2000  according to the manufacturer instructions. At 48 h after transfection, gene expression was confirmed via immunoblotting analysis and qRT-PCR.5 H460 cells using Lipofectamine RNAi MAX reagent  according to the manufacturer’s protocol.To further investigate the mechanisms of immunomodulation by CDK5, the CDK5 gene was silenced with siRNA. The siRNA sequences were as follows: CDK5-siRNA-1: 5′-GCGUCCAGAAUUUCAACAATT-3′; CDK5-siRNA-2: 5′-CCACUACGAGUCCCUUCAATT-3′; CDK5-siRNA-3: 5′-GCGUAGGUUUCUACGUCAATT-3′; negative control: 5′-UUCUCCGAACGUGUCACGUTT-3′. The siRNAs were synthesized by GenePharma Company . A total of 400 pmol siRNA was transfected into 4 × 10In order to obtain total protein, transfected cells were harvested and lysed using lysis buffer containing protease inhibitors. Co-immunoprecipitation (Co-IP) assay was performed using a Co-IP kit  according to the manufacturer’s instructions. Briefly, 3 mg of total protein for each treatment was used in this study, pre-cleared with Sepharose resins. The supernatants were divided equally into 2 tubes and incubated into columns containing 1 µg immobilized anti-c-MYC or anti-BIN1 antibody (Abcam). The immunocomplexes were covalently associated with Sepharose resins. The resins were eluted 5 times and the co-immunoprecipitate were separated on an SDS-PAGE gel before transferring to a PVDF membrane for analysis with anti-c-MYC or anti-BIN1 antibodies.3 cells were plated into 96-well plates. When cells adhered, 10 μl of CCK8  was added to each well and incubated for 2 h in a humidified incubator containing 5% CO2 at 37 °C. The absorbance of each well was detected at a wavelength of 450 nm. Proliferation rates were determined at 0, 24, 48, 72, 96 h after transfection. Experiments were performed in triplicate.The proliferation of cells was measured by cell-counting kit-8 (CCK-8) assay according to the manufacturer’s protocol. Briefly, approximately 2 × 105 H460 and PC9 cells were seeded in 24-well plates. After scraping the cell monolayer with a sterile micropipette tip, the wells were washed with serum-free medium in triplicate. The first image of each scratch was acquired at time zero. After 24 h, each scratch was examined and captured at the same location. Then, the healed area was measured for calculating the index of cell migration.5 × 102 humidified incubator at 37 °C. Cells on the upper side of the filters were removed by cotton-tipped swabs, and the filters were washed with PBS. Then the cells on the lower side were fixed in 4% formaldehyde and stained with 1% crystal violet in PBS for 5 min at room temperature. The cells on the lower side of the filters were defined as migration cells and counted at 200× magnification in 5 random fields of each filter.Tumor cell migration assay was performed in a 24-well transwell chamber , which contained an 8 μm pore size polycarbonate membrane filter and was precoated with 100 μg Matrigel for invasion assay . Briefly, the cells were seeded in the upper chambers and incubated in 500 μl RPMI 1640 medium without FBS, while 500 μl medium with 10% FBS was placed in the lower chambers. The plates were incubated for 24 h in a 5% CONSCLC cell lines H460 and PC9 were grown in RPMI supplemented with 10% FBS. A stock solution of Dinaciclib (1 nM) or vehicle control-DMSO was diluted in fresh tumor medium and added to samples to achieve a final concentration of 10 μM or 20 μM. After 4 days of treatment for 24 h and then harvested for cell proliferation analysis.6 cells/mouse) in 0.1 ml were injected subcutaneously into BALB/c nude mice. The mice were divided randomly to 2 groups: Dinaciclib treated group and control group. All mice were sacrificed by cervical dislocation on the day 32.BALB/c nude mice were used for tumor growth assay in vivo. Care was performed according to the National Research Council Guide for the Care and Use of Laboratory Animals, and was approved by the Institutional Animal Care and Use Committee (IACUC) of Hebei Medical University, Shijiazhuang, China. Tumor cells were harvested with trypsin solution and resuspended in PBS. Cells (1 × 10Immunohistochemistry (IHC) analysis was performed as we previously described . The rabPC9 and H460 cells harvested and incubated with rabbit to human BIN1, mouse to human CDK5 mAb at 4 °C overnight. The cells were then stained by FITC conjugated goat anti-mouse and PE conjugated goat anti-rabbit antibody, followed by DAPI staining of the nucleus. The fluorescence was observed and analyzed with a fluorescence microscope at high magnification (200×).Statistical analysis was performed by using SPSS statistics software, version 25.0 . Spearman rank correlation was used to analyze the association of CDK5 and BIN1 expression in NSCLC specimens. Survival analysis was carried out using the log-rank test in association with Kaplan–Meier analysis and Cox proportional hazards model analysis. A P value < 0.05 was considered to be statistically significant, and all P values were two-tailed.To preliminarily clarify whether presence of CDK5 could act as tumor promoter by effecting the interaction of BIN1/c-MYC, we detected both CDK5 and BIN1 expression status in NSCLC cells. First, we used western blot to detect BIN1 expression in five NSCLC cells with high c-MYC expression  and human embryo lung cell line 2BS. As shown in Fig. Aiming at confirming the potential of CDK5 in neutralizing BIN1/c-MYC interaction, we next determined the co-expression status of CDK5 and BIN1 in PC9 and H460 cells. The immunofluorescent results showed that CDK5 and BIN1 could be simultaneously observed in H460 cells, while only BIN1 could be observed in PC9 cells Fig. .Fig. 2ScAs BIN1 has been proved to play a pivotal role in proliferation, invasion and migration abilities of cancer cells , we thenParallelly, we detected the above malignant behaviors of PC9 cells after CDK5 overexpression. The results showed that the growth, migration and invasion ability were all increased in PC9 cells after CDK5 overexpression, compared to control cells Fig. h.Then we determined whether CDK family inhibitor Dinaciclib had an effect on the BIN1/c-MYC interaction in NSCLC cells. Parry et al. demonstrated that Dinaciclib, a CDK family inhibitor without CDK5 specificity, showed the most efficient suppressing effect on the activity of CDK5 with little effect on other members of CDKs including CDK1, CDK2 and CDK9 at the concentration of 1 nM . The resTo clarify the tumorigenesis mechanisms of CDK5, we constructed a tumor-bearing mouse model by injecting H460 cells into BALB/c nude mice. The tumor size was measured after modeling and randomly divided into 3 groups: Dinaciclib-treated group, placebo group, untreated group. Tumor volume in Dinaciclib-treated mice was significantly inhibited compared with placebo and untreated groups, indicating that loss of CDK5 could inhibit the tumorigenesis of NSCLC (Fig. To reveal the expression pattern of BIN1 and CDK5, we detected their expressions in 153 NSCLC tissues by using IHC. As Fig. NSCLC is the one of the most common malignant tumor with unsatisfactory prognosis . AberranIn the present study, we evaluated the effect of CDK5 on BIN1/c-MYC interaction in NSCLC. CDK5, an aberrantly overexpressed protein in multiple cancers, could significantly promote proliferation, invasion and migration abilities of tumor cells , 15. In Knockdown of CDK5 expression in H460 cells significantly promoted the interaction of BIN1 and c-MYC. We found that knockdown of CDK5 could inhibit cell growth, invasion, migration and EMT ability of H460 cells. Meanwhile, we transfected CDK5 into PC9 cell which highly expressed BIN1, and the results showed that the growth, invasion, migration and EMT ability could be significantly enhanced by CDK5 overexpression. Furthermore, with Co-IP experiment, we found that CDK5 was correlated with the anergy of BIN1/c-MYC interaction, suggesting that CDK5 could neutralize the tumor-suppressing effect of BIN1 via inducing phosphorylation of c-MYC at Ser-62 site.Due to the oncogenic function of CDK5, Dinaciclib, an effective inhibitor of CDK5, exerted potent tumor-suppressing effect in relapsed multiple myeloma and hepatocellular carcinoma , 36. HowTaken together, to our best knowledge, despite of the studies on the carcinogenesis role of CDK5, this is the first study to analyze its effect on BIN1/c-MYC interaction. Knockdown of CDK5 significantly inhibited the phosphorylation of c-MYC at Ser-62 site to facilitate the interaction of BIN1 and c-MYC, and consequently suppressed the invasion and migration ability of NSCLC cells. High expression of CDK5 along with low expression of BIN1 could predict poor postoperative prognosis for NSCLC patients. Taken above, CDK5 might be used as a potential biomarker in predicting prognosis of NSCLC patients, and regarded as a novel target for anti-cancer therapies.


However, proper pre-deformation is an essential part of repeatedly obtaining large and stable shape change. In this paper, one-time uniaxial tensile pre-deformation was carried out at the temperature range −70–−80 °C. Then, the two-way shape memory effect (TWSME) of the alloy was evaluated by the martensitic transformation strain (εM) which was measured by a thermal expansion test to investigate the relationship between the pre-deformation strain (εT) and the TWSME. The results showed that εM of the columnar-grained Cu71.7Al18.1Mn10.2 alloy increased at first and then decreased with the increase of εT. The maximum value 2.91% of the εM could be reached when εT was 6%. The effects of the εT on transformation temperatures were also measured by differential scanning calorimetry. Based on the variations of transformation temperatures, the relationship between the internal stress induced by the pre-deformation process and the εM, and the influence mechanism of the pre-deformation strain on the TWSME in columnar-grained Cu71.7Al18.1Mn10.2 alloy, were discussed. The results obtained from this work may provide reference for potential applications of Cu-based SMAs, such as self-control components, fasteners, etc.Columnar-grained Cu Mf) which is often called cryogenic pre-deformation, and then restoring the original shape by subsequently increasing the temperature to a high temperature  without any external stress [εT). If the uniaxial tensile loading is chosen for the cryogenic pre-deformation, the TWSME can be evaluated by the elongation induced by the martensite transformation strain (εM) upon cooling or the contraction induced by the reverse/austenite transformation strain (εA) upon heating. For a fully recovered deformation, the transformation strain for the thermal cycle (εTW) can be used as εTW = εM = εA.The two-way shape memory effect (TWSME) associated with the forward and reverse transformations of martensite is a phenomenon in which the reversible and spontaneous shape change during heating and cooling occur without any external stress . The TWSl stress . In thisεT applied on the sample is usually 2–5 times as large as the εTW finally obtained [εTW of 4.1% and 2% were obtained in Ni–Ti alloy and Cu–Zn–Al alloy after pre-deformation with εT of 13.2% and 7%, respectively [εTW in SMAs, which could guarantee a sufficient εT to apply onto the SMAs without cracking or fracturing. For Cu-based SMAs, single crystal alloys exhibit good ductility [εTW due to their low ductility. The development of ductile polycrystalline Cu-based SMAs becomes a key to realizing wide industrial application. In the 1990s, Kainuma et al. developed ductile Cu–Al–Mn alloys with a high Mn content of about 10 at.% and a low Al content of 16–18.5 at.% due to an appropriate decrease of the degree of order in the L21 austenite phase, in which the martensite phase has the 18R structure [εTW = 3.2% was obtained after ~12.5% pre-deformation strain [During thermomechanical treatment of SMAs, much research indicated that the obtained ,3,4,5. Fuctility ,7. Neveructility . Meanwhiuctility , while ouctility ) and laructility  which letructure ,12. Omorn strain ,15 prepaεTW/εT) is another way to obtain higher εTW under a certain pre-deformation strain. Considering the energy dissipation, minimization of the energy dissipation induced by microstructure defects, grain boundaries, etc. during thermomechanical pre-deformation of SMAs, could increase the pre-deformation efficiency [71Al18Mn11 alloys at room temperature [εTW in columnar-grained Cu–Al–Mn alloys was experimentally investigated. The internal stresses induced by tensile pre-deformation were also analyzed by transformation temperature measurement to discuss the effect mechanism of pre-deformation strain on the TWSME of columnar-grained Cu–Al–Mn alloys.On the other hand, improving pre-deformation efficiency , electrolytic aluminum (99.99 wt.% purity) and electrolytic manganese (99.9 wt.% purity), which were cast into precast ingots by vacuum induction melting. Subsequently, the precast ingots were re-melted and directionally solidified to obtain columnar-grained ingots with a dimension of Φ50 × 150 mm . The ing3 hydrochloric solution. The phase and microstructure were analyzed by Rigaku D/MAX-RB12KW X-ray diffraction  with Cu Kα radiation and a Tecnai G2 F20 field emission transmission electron microscope . The orientation of the grains was analyzed by electron backscatter diffraction (EBSD).The metallographic samples were cut from the middle part of the ingot by electrical-discharge machining, which was observed by Nikon Eclipse LV150 optical microscope  after being mechanically polished and etched by a FeCl2. During deformation a local strain was measured using an Instron extensometer with a gauge length of 25 mm. The longitudinal direction of the samples was parallel to the solidification direction (SD). Superelasticity was examined by a cycle tensile test with a strain rate of 5 × 10−4 s−1 at room temperature in an austenite phase state [The SE test and cryogenic pre-deformation experiments were conducted using dog-bone-shaped samples with a gauge length of 30 mm and a section area of 8 × 3 mmse state , that is−4 s−1 in a nearly isothermal condition, held for 300 s, and then unloaded at a strain rate of 1 × 10−4 s−1. Subsequently, the cryogenic pre-deformation samples were taken into ambient temperature and L0 = 20 mm rectangular plate samples for the thermal expansion test were cut from the middle of the cryogenic pre-deformation samples with a 25 mm gauge length and examined by a laser thermal expansion instrument  with an accuracy of 1 × 10−3 mm at a heating and cooling rate of 2 °C/min. It should be noted that during the thermal expansion measurement, a very small pressure of about 0.1–1 N was applied to the samples.The cryogenic pre-deformation experiment was conducted by Instron universal mechanical test systems with a thermostatic chamber, and nine specimens were used to load to 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 14%, respectively. All cryogenic pre-deformation specimens were cut from the same ingot to avoid the effects of deviation from the composition and casting processes on the comparability of test results. The detailed procedure of cryogenic pre-deformation experiment was as follows. First, the cryogenic pre-deformation samples were cooled to −70–−80 °C into martensite phase state for 1 h using liquid nitrogen . The cryogenic pre-deformation samples in the martensite phase state were loaded to a specific strain with a strain rate of 5 × 1071.7Al18.1Mn10.2 sample are shown in Ms = −10.3 °C, the martensite transformation finish temperature Mf = −29.3 °C, the austenite transformation starting temperature As = −3.9 °C, and the austenite transformation finish temperature Af = 7.6 °C. The transformation heat was also measured based on the DSC curves as 5.7 J/g for the martensitic transformation on cooling and 6.5 J/g for the reverse transformation on heating.The DSC curves of the Cu71.7Al18.1Mn10.2 alloy sample was composed of a single β1 phase (austenite phase) at room temperature as proved by XRD  of 18%. With the increase of cycles and total strain, the SE strain (εSE) increased at first and then decreased, where εSE= εt − εr − εe, εr was unrecovered strain and εe was elastic strain, as shown in εt was up to 10%, the maximum εSE was 8.18% (σM) at the first cyclic strain was about 60 MPa and decreased with the increase in the number of cycles. The stress plateau slope dσSE/dε of the stress induced martensitic transformation stage was 7.3 MPa. The cyclic stress-strain curves showed that the columnar-grained Cu71.7Al18.1Mn10.2 alloy exhibited excellent superelasticity along the SD. The normal tensile test was also conducted and showed that the elongation reached more than 40%, which indicated a high ductility of the columnar-grained Cu71.7Al18.1Mn10.2 alloy sample.The SE cyclic stress-strain curve tested at room temperature was plotted in as 8.18% a. The cr71.7Al18.1Mn10.2 alloy samples extended at −70–−80 °C (<Ms −50 °C) are shown in σMR) [σ) could be described as Δσ = σs − σMR, where σs was the maximum loading stress for each stress-strain curve in The cryogenic pre-deformation tensile curves of 2%, 3%, 4%, 5%, 6%, 8%, 10%, 12%, 14% strains respectively for nine columnar-grained Cu in σMR) . FurtherεTW is calculated by L is the average absolute value of expansion during the cooling process and of contraction during the heating process along the longitudinal direction of the samples. εTW are shown in εTW increased with increasing εT to a maximum of 2.91% when εT = 6% and then decreased as shown by black dots in εT reached 12%, the εTW decreased to nearly zero, which indicated TWSME disappeared when undergoing a pre-deformation strain that was equal to or higher than 12%. The polynomial fitting  done by pre-deformation corresponding to the WT than their polycrystalline counterparts. The value of 71.7Al18.1Mn10.2 alloy was ~55.0% corresponding to ~3% εTW. Comparatively, the εTW obtained by bending pre-deformation in ordinary polycrystalline Cu–Al–Mn alloys [WT for columnar-grained Cu71.7Al18.1Mn10.2 alloy was only ~3 MJ/m3, while for the Ti–Ni alloy, it was ~15 MJ/m3. 71.7Al18.1Mn10.2 alloy were better than ordinary polycrystalline Cu-based SMAs and reached a single crystal level. In terature ,27,28,29εTW was much larger than the thermal expansion. 71.7Al18.1Mn10.2 alloy sample without deformation pre-deformation. The thermal expansion coefficient was measured to be ~12.9 × 10−6/°C for both the austenite and martensite. It was seen that a near-linear dimensional expansion of ~0.2% was involved in transformation from −80 to 80 °C without deformation pre-deformation. Hence, when compared to the εTW mentioned above, the inherent thermal expansion of columnar-grained Cu71.7Al18.1Mn10.2 alloy could be ignored.For SMAs, the 71.7Al18.1Mn10.2 alloy samples. The cryogenic pre-deformation was attributed to the variants of self-accommodation and reorientation with an internal elastic energy  ΔG=ΔIn this equation, the superscript “A–M” denotes the reversible transformation between the austensite and the martensite. The critical temperature for the reversible transformation can be determined at T0 was directly influenced by It was seen that The 71.7Al18.1Mn10.2 alloy samples with the increase of pre-deformation strain could be obtained and plotted in 71.7Al18.1Mn10.2 alloy samples decreased at first and then increased with the increase of the pre-deformation strain. The change of transformation temperatures could be attributed to the internal stress created during cryogenic pre-deformation. When the sample was loaded with small strains during cryogenic pre-deformation, the reorientation of self-accommodation martensite variants became the dominative deformation mechanism. The internal stress of the sample decreased with the completion of reorientation. When the reorientation completely finished, the plastic deformation was induced by a dislocation slip mechanism, which would increase the internal stress by interaction of dislocations and other microstructure defects such as grain boundary and phase interface. 71.7Al18.1Mn10.2 samples after cryogenic pre-deformation at room temperature, which illustrated that martensites formed preferentially in some grains with little resistance during transformation. The martensite plates were thin when pre-deformation strain was ≤6% as shown in As shown in 71.7Al18.1Mn10.2 alloy. With the raising of pre-deformation strain, TWSME increased at first and then decreased, and the maximum transformation strain of 2.91% was measured under 6% pre-deformation strain. Meanwhile, the high pre-deformation efficiency of 57% and low applied pre-deformation work of ~3 MJ/m3 were found in the columnar-grained Cu71.7Al18.1Mn10.2 alloy, which indicated that the columnar-grained Cu-based SMAs show a better shape memory property than ordinary polycrystalline SMAs.In this paper, we have investigated the relationship between pre-deformation strain and TWSME in the columnar-grained Cu


In this paper we provide a detailed description of the amino acid residues stabilizing the structure and taking part in conformational change of VDR LBD according to functional domains.Vitamin D Receptor (VDR) belongs to the nuclear receptor (NR) superfamily. Whereas the structure of the ligand binding domain (LBD) of VDR has been determined in great detail, the role of its amino acid residues in stabilizing the structure and ligand triggering conformational change is still under debate. There are 13 α-helices and one β-sheet in the VDR LBD and they form a three-layer sandwich structure stabilized by 10 residues. Thirty-six amino acid residues line the ligand binding pocket (LBP) and six of these residues have hydrogen-bonds linking with the ligand. In 1α,25-dihydroxyvitamin D Vitamin D Receptor (VDR) belongs to the nuclear receptor (NR) superfamily and consists of 427 amino acid residues. It is composed of four major functional domains: A/B domain, a highly variable amino-terminal; C domain , a highly conserved zinc finger DNA-binding domain; D domain, a hinge domain; and E domain (Ligand Binding Domain or LBD). The VDR LBD plays an important role in facilitating ligand binding, nuclear localization, dimerization, and interaction with coactivator (CoA) and corepressor (CoR) proteins .Like other NRs, VDR LBD includes 13 α-helical (H) structures, of which the H12 contain a short activation function-2 (AF-2) domain. It is folded into a three-layered, antiparallel α-helical sandwich that creates a ligand binding pocket (LBP). VDR LBD presents a Ω loop between H2 and H3, and its conformation is flexible . Th. Th33]. 2D3 binding, VDRE interaction, or nuclear localization, but the mutant is transcriptionally silent, because the distance between K246 (H3) and E420 (H12) is essential for the VDR–CoA interaction mentioned above  does not abolish interaction with hTAFII 28, as expected from the fact that hTAFII 28 also interacts with the VDR H8 region. However, the interaction with the mutated VDR LBD is no longer ligand-reversible and is unable to activate transcription. The determinants of hTAFII55 and hTAFII 28, for their respective interactions, may not be identical and the key domain needs to be certified [hTAFertified .2D3 signaling, H3 and H12 play an important role in the course of conformational change resulting in the provision of interfaces for dimerization, CoA, CoR, and hTAFII 28. In summary, this paper provides a detailed description of the amino acid residues stabilizing the structure and taking part in conformational change of VDR LBD according to functional domains.The function of VDR LBD is related with ligand binding, nuclear localization, forming different surfaces for dimerization and interaction with CoA and CoR proteins . VDR as


Macrobrachium rosenbergii nodavirus (MrNV) is the causative agent of white tail disease (WTD) which seriously impedes the production of the giant freshwater prawn and has a major economic impact. MrNV contains two segmented RNA molecules, which encode the RNA dependent RNA polymerase (RdRp) and the capsid protein (MrNV-CP) containing 371 amino acid residues. MrNV-CP comprises of the Shell (S) and the Protruding (P) domains, ranging from amino acid residues 1–252 and 253–371, respectively. The P-domain assembles into dimeric protruding spikes, and it is believed to be involved in host cell attachment and internalization. In this study, the recombinant P-domain of MrNV-CP was successfully cloned and expressed in Escherichia coli, purified with an immobilized metal affinity chromatography (IMAC) and size exclusion chromatography (SEC) up to ~90% purity. Characterization of the purified recombinant P-domain with SEC revealed that it formed dimers, and dynamic light scattering (DLS) analysis demonstrated that the hydrodynamic diameter of the dimers was ~6 nm. Circular dichroism (CD) analysis showed that the P-domain contained 67.9% of beta-sheets, but without alpha-helical structures. This is in good agreement with the cryo-electron microscopic analysis of MrNV which demonstrated that the P-domain contains only beta-stranded structures. Our findings of this study provide essential information for the production of the P-domain of MrNV-CP that will aid future studies particularly studies that will shed light on anti-viral drug discovery and provide an understanding of virus-host interactions and the viral pathogenicity. Macrobrachium rosenbergii (de Man) is one of the most commercially important farmed aquatic invertebrates in subtropical countries including Malaysia, Indonesia and Thailand , freshly prepared lysozyme (0.2 mg/ml), DNase 1 (0.02 mg/ml) and phenylmethylsulfonyl fluoride  for 2 hours at room temperature. The cell suspension was then sonicated at 20 kHz for 25 seconds for 10 cycles with 15 seconds intervals between pulses using an ultrasonicator . The crude lysate was recovered by centrifugation at 4°C at 16,200 xg for 20 minutes.Expression of the h et al.  with som2+-charged resin (2 ml) and equilibrated with Tris-HCl buffer . The crude lysate was loaded onto the column. Weak binders were removed by Tris-HCl buffer containing imidazole (10 mM) and bound proteins were eluted using Tris-HCl buffer containing Imidazole (100 mM). Eluted fractions were collected for protein analysis using SDS-PAGE [12% (w/v)] and western blot (see below). The positive fractions containing the P-domain of MrNV-CP were pooled, concentrated to ~1.5 ml using a centrifugal protein concentrator with molecular mass cut-off 3 kDa  prior to gel filtration chromatography. The concentrated sample was loaded onto a Tris-HCl buffer-equilibrated HiPrep 16/60 Sephacryl S-200 HR column  mounted on an AKTA Purifier . Eluents of the column were collected (2 ml per fraction) using a Frac-950 fraction collector at a constant flow rate of 0.5 ml/min. Eluents were analyzed using SDS-PAGE and western blotting. The positive fractions containing the P-domain of MrNV-CP were pooled and concentrated.The poly-prep chromatography column  was packed by adding the profinity IMAC Ni2]. Color development was terminated by incubating the nitrocellulose membrane in distilled water and the membrane was allowed to dry at room temperature.The separated proteins on an SDS-polyacrylamide gel were transferred onto a nitrocellulose membrane using a semi dry blotter . The nitrocellulose membrane was then blocked with skimmed milk [10% (w/v)] for 1 hour at room temperature. After three times of washing with TBS-T , the nitrocellulose membrane was incubated with the anti-His monoclonal antibody  for 1 hour at room temperature. Afterwards, the nitrocellulose membrane was washed with TBS-T and incubated with the alkaline phosphatase conjugated goat anti-mouse antibody  for 1 hour at room temperature. The immunoblotted bands were developed by adding 5-bromo-4-chloro-3-indolyl phosphate/nitro blue tetrazolium (BCIP/NBT) in alkaline phosphatase buffer  were overlaid. The molecular mass of the P-domain of MrNV at each step of the purification were determined according to Yoon et al. [MrNV-CP was quantified with the Bradford assay [MrNV-CP from SDS-PAGE gels was measured using the Image J software [The quantity and quality of the P-domain of n et al. . Brieflyrd assay . The amosoftware  by calcuMrNV-CP. All Zetasizer data were then analyzed using the Zetasizer Software 7.12.The purified protein sample (0.3 mg/ml) was filtered using a 0.2 μm-syringe filter membrane and then loaded into a folded capillary Zeta cell and placed into a Zetasizer Nano ZS  equipped with He-Ne laser (633 nm) to measure the diameter of the P-domain of MrNV-CP was estimated using circular dichroism (CD). The buffer of the purified protein sample was exchanged with HEPES buffer . The protein sample (5 μM) was loaded into a cuvette with 0.1 cm path length and placed in the measurement chamber of a JASCO J-815 CD spectropolarimeter  after being filtered through a 0.2 μm-syringe filter membrane. The sample was scanned from wavelengths 190 nm to 260 nm, with a speed of 10 nm/min for three times. The data were collected and analysed using the Secondary Structure Estimation Software which was installed in the Spectra Manager version 2. The calculations of the secondary structure elements were carried out based on Yang et al [The percentage of the secondary structure of the P-domain of ng et al  and Reedng et al .MrNV-CP was ligated to the expression vector pTrcHis2-TOPO at NcoI and EcoRI endonuclease restriction sites  were compared. The expression of MrNV-CP P-domain was at its highest expression level at 37°C, followed by 30°C, and the lowest at 25°C. In the expression of full-length MrNV-CP, Goh et al. [MrNV propagates in the giant freshwater prawn. As a result, we chose 30°C as the most appropriate induction temperature in order to maintain the protein solubility, and at the same time, not compromising the protein expression level.The coding sequence of the P-domain of on sites . The recterminus . Proteinh et al.  induced MrNV-CP inclusive of a myc-tag and a cleavable 6xHis-tag was expressed as a soluble protein ~18 kDa at 30°C for 2 hours  immobilized metal ion affinity chromatography (IMAC), a 6xHis-tag was fused to the C-terminal region of the recombinant P-domain of MrNV-CP. As shown in MrNV-CP P-domain was eluted later with 100 mM imidazole, producing a partially purified protein with protein purity of ~77%  which did not give a protein band when analyzed with SDS-PAGE whereas the second peak (red box) of the profile gave a protein band with a molecular weight ~18 kDa, which is in good agreement with the molecular mass of the monomer of MrNV-CP P-domain  staining on the polyacrylamide gel . As shown in MrNV-CP has a hydrodynamic diameter ~6 nm. The measured diameter of the dimeric particle corresponded well with the cryo-EM structure showing that the blade-shaped spike formed by the P-domain of MrNV-CP is around 4.7 nm in both width and height. It is believed that in the absence of the S-domain, the dimeric conformation of the P-domain alone is possibly distinct from that observed in cryo-EM analysis. In addition, the diameter of a particle determined by DLS is normally bigger than that measured by EM due to the fact that ions in solution associate with the particle and contribute to the size determined by DLS [The molecular mass and multimeric state of the P-domain of atograms  shows thd by DLS .In silico analysis performed using Phyre 2 identified the crystal structure of cucumber necrosis virus (CNV)  showed 19% identity and 27.5% similarity, respectively  analysis was carried out to estimate the content of the secondary structures of the recombinant P-domain. de: 4LLF ) as the V capsid , 18. Amiectively . The P-dectively . The modMrNV-CP was expressed as a soluble protein in E. coli. The recombinant P-domain formed dimers with a hydrodynamic diameter ~6 nm which resemble the full-length capsid protein. Our current data provide crucial biophysical parameters for downstream analyses of the P-domain of MrNV-CP such as structural analysis using X-ray crystallography or nuclear magnetic resonance. Availability of a high-resolution 3D structure of the P-domain is essential to elucidate the mechanisms involved in host cell recognition and penetration.The P-domain of S1 FigMrNV-CP.The model was generated by program Phyre 2 using the P-domain of cucumber necrosis virus  as a template. The confidence score of this model is 98% over 90% of the amino acid sequence of the P-domain of (TIFF)Click here for additional data file.