AI for predicting chemical-effect associations at the chemical universe level - deepFPlearn Many chemicals are out there in our environment, and all living species are exposed. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known. Chemical risk assessment and subsequent regulation of use require efficient and systematic strategies. Lab-based methods - even if high throughput - are too slow to keep up with the pace of chemical innovation. Existing computational approaches are designed for specific chemical classes or sub-problems but not usable on a large scale. Further, the application range of these approaches is limited by the low amount of available labeled training data.

We present the ready-to-use and stand-alone program deepFPlearn that predicts the association between chemical structures and effects on the gene/pathway level using a combined deep learning approach. deepFPlearn uses a deep autoencoder for feature reduction before training a deep feedforward neural network to predict the target association. We received good prediction qualities and showed that our feature compression preserves relevant chemical structural information. Using a vast chemical inventory (unlabeled data) as input for the autoencoder did not reduce our prediction quality but allowed capturing a much more comprehensive range of chemical structures. We predict meaningful - experimentally verified-associations of chemicals and effects on unseen data. deepFPlearn classifies hundreds of thousands of chemicals in seconds.

We provide deepFPlearn as an open-source and flexible tool that can be easily retrained and customized to different application settings at https://github.com/yigbt/deepFPlearn.

Supplementary informationSupplementary data are available at bioRxiv online.

Contactjana.schor@ufz.de
A metric and its derived protein network for evaluation of ortholog database inconsistency Orthology prediction is challenging yet rewarding. Orthologs are the cornerstone of almost all comparative genomics studies. Dozens of ortholog resources have been available and broadly used over the past decades. However, the inconsistency between these resources has drawn growing concerns, especially when more proteomes are available and ortholog databases expand. It is no longer easy to decide which ortholog database to use, and it becomes necessary to compare conclusions based on different resources. We are presenting here a metric to assess ortholog functional consistency. Using this metric, we built a network connecting proteins based on their functional similarity. We then detected network communities as ortholog groups, and each protein in our ortholog group inherited the network degree centrality. By benchmarking Quest for Orthologs (QfO) and some representative ortholog resources, we concluded the degree centrality could serve as the index for the reliability of functional consistency. The numerical nature of degree centrality could also open a door for quantitative study in pan-genome and other comparative genomics studies.
Force profile of the two-handed hardstyle kettlebell swing in novice older adults: an exploratory profile BackgroundUnderstanding the force profile of an exercise increases clinical confidence when assessing the benefits and potential risks of a prescribed exercise. This exploratory study presents the force profile of the hardstyle kettlebell swing in novice older adults and compares peak force with kettlebell deadlifts. These data will help inform healthcare providers and coaches who are considering prescribing kettlebell exercises for older adults.

MethodsThirty-five community-dwelling males and females (59-79 years) were recruited, from applicants to participate in the BELL trial. Two-handed hardstyle swings were performed with 8-16 kg. Deadlifts were performed with 8-24 kg and 8-32 kg for females and males, respectively. Ground reaction force was obtained from a floor-mounted force platform. Pairwise comparisons of peak force, forward force, rate of force development, swing cadence, sex, and kettlebell mass, were investigated for the kettlebell swing, with representative force-time curves described. Pairwise comparisons of peak force, sex and kettlebell mass were investigated for the deadlift, with comparisons of peak force, kettlebell mass, and sex, between swings and deadlifts.

ResultsFor kettlebells up to 16 kg, paired samples T-tests show a large exercise effect ({delta} > 1.4) with peak force higher for swings than deadlifts. Data shows: (i) higher peak force during swings than deadlifts ({delta} = 1.77), reaching 4.5 (1.0) N.kg-1, (ii) peak force during an 8 kg swing was greater than a 32kg deadlift, (iii) negligible difference in normalised peak force between males and females performing kettlebell swings, but a moderately large effect size during deadlifts (males > females, {delta} = 0.69), (iv) mean rate of force development of 19.9 (4.7) N.s-1.kg-1 with a very weak, positive correlation with kettlebell mass (y = 14.4 + 0.32x), and trivial effect of sex, (v) mean forward force equal to 5.5% of vertical force during swings, increasing from 3.8 (1.6) % with 8 kg to 7.1 (2.6) % with 16 kg.

ConclusionDuring kettlebell swings, there is negligible difference in normalised net peak force between novice males and females using the same absolute loads. Where ground reaction force is a therapeutic target, kettlebell swings with an 8 kg kettlebell could have similar effects to much heavier deadlifts (>24 kg). Kettlebell swings performed with lighter loads, could provider similar therapeutic value to much heavier deadlifts, and may be a more appealing, affordable, and convenient option for older adults. Flight-induced compass representation in the monarch butterfly heading network For navigation, animals use a robust internal compass. Compass navigation is especially crucial for long-distance migrating animals like monarch butterflies, which use a sun compass to navigate every fall over 4,000 km to their overwintering sites. The central complex, a brain region equipped with sun-compass neurons, is proposed to control the butterflys heading. Although the activity of central-complex neurons exhibits a locomotor-dependent modulation in many insects, the consequences of such modulations for the coding of heading remain unexplored. Here, we developed tetrode recordings from tethered flying monarch butterflies to reveal how flight modulates heading representation. We found that during flight, heading-direction neurons change their tuning, transforming the central-complex network to function as a global compass. This compass coding is characterized by the dominance of steering feedback and allows for robust heading representation even under unreliable visual scenarios, an ideal strategy for maintaining a migratory heading over enormous distances. The Achilles' heel of coronaviruses: targeting the 5' Polyuridines tract of the antigenome to inhibit Mouse coronavirus-induced cell death The current coronavirus pandemic situation is worsened by the rapidly-spreading SARS-CoV-2 virus variants. Identification of viral targets that are indispensable for the virus can be targeted to inhibit mutation-based new escape variant development. The 5-polyU tract of the antigenome offers such a target. Host cells do not harbor 5-polyU tracts on any of their transcripts, making the tract an attractive, virus-specific target. Inhibiting the 5-polyU can limit the use of the tract as template to generate 3 polyA tails of +RNAs of coronaviruses. Here, a modified DNA oligo with 3 polyAs is used to target the 5-polyU tract in mouse coronavirus (MHV-A59). The oligo treatment in mouse 17CL-1 cells infected with MHV-A59 significantly prevented virus-induced cell deaths. This proof-of-concept result shows a unique mode of action against mouse coronavirus without affecting host cells, and can be used for the development of novel classes of drugs that inhibit coronavirus infection in host cells, specifically by the COVID-19-causing virus SARS-CoV-2. In addition, as the 5-polyU tract is immediately generated upon infection, the tag can also be targeted for reliable early detection of viral infection. 'rbioacc': an R-package to analyse toxicokinetic data O_LI rbioacc is an  R-package dedicated to the analysis of experimental data collected from bioaccumulation tests during which organisms are exposed to a chemical (exposure phase) and then put into a clean media (depuration phase). Internal concentrations are regularly measured over time all along the experiment.
C_LIO_LI rbioacc provides ready-to-use functions to visualize and fully analyze such data. Under a Bayesian framework, this package fits a generic one-compartment toxicokinetic (TK) model automatically built from the data. It provides TK parameter estimates (appropriate uptake and elimination rates) and bioaccumulation metrics (e.g., BCF, BSAF, BMF). All parameter estimates, bioaccumulation metrics as well as predictions of internal concentrations into organisms are delivered with their uncertainty.
C_LIO_LIBioaccumulation metrics are primarily provided in support of environmental risk assessment, in full compliance with regulatory requirements required to approve marketing applications of chemical substances.
C_LIO_LIThis paper gives brief worked examples of the use of  rbioacc from data collected through standard bioaccumulation tests, and publicly available within the scientific literature. These examples constitute step-by-step user-guides to analyze any new data set, uploaded in the right format.
C_LI Simultaneous suppression of ribosome biogenesis and Tor activation by TRIM-NHL proteins promotes terminal differentiation Proper tissue development and homeostasis depends on the balance between growth and terminal differentiation, but the mechanisms coordinating these processes remain elusive. Accumulating evidence indicates that ribosome biogenesis (RiBi) and protein synthesis, two of the most energy-consuming cellular processes sustaining growth, are tightly regulated and yet can be uncoupled during stem cell fate transitions. Here, using the Drosophila adult female germline stem cell (GSC) and larval neuroblast (NB) systems, we show that Mei-P26 and Brat, two Drosophila TRIM-NHL paralogs of the mammalian TRIM32 protein family, are responsible for uncoupling RiBi and protein synthesis during GSC and NB differentiation, respectively. Mei-P26 and Brat modify the metabolism of differentiating cells by activating the Target of rapamycin (Tor) kinase to promote translation, while concomitantly repressing RiBi. Depletion of Mei-P26 or Brat results in excessive cellular growth and defective terminal differentiation, which can be rescued by ectopic activation of Tor together with suppression of RiBi. Our results indicate that the anabolic reprogramming established by TRIM-NHL activity by uncoupling RiBi and translation activities creates the conditions required for terminal differentiation. Designing single-cell experiments to harvest fluctuation information while rejecting measurement noise Measurement error is a complicating factor that reduces or distorts the information contained in any experiment. This problem becomes even more serious in the context of experiments that are specifically designed to quantify single-cell gene expression heterogeneity, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. It is not clear how measurement noise should be managed, in addition to other experiment design variables such as sampling size and frequency, to ensure that collected data will provide useful insights on gene expression mechanisms of interest. To address these fundamental single-cell experiment design challenges, we propose a computational framework that makes explicit use of measurement error modeling and Fisher Information Matrix-based criteria to decide between experimental methods. This unified approach not only quantifies how different noise characteristics affect uncertainty in parameter estimation, but also enables a systematic approach to design hybrid experiments that combine different measurement modalities. Identifying and correcting multiple sources of misspecification in GWAS summary statistics for polygenic scores Publicly available genome-wide association studies (GWAS) summary statistics exhibit uneven quality, which can impact the validity of follow-up analyses. First, we present an overview of possible misspecifications that come with GWAS summary statistics. Then, in both simulations and real data analyses, we show that additional information such as imputation INFO scores, allele frequencies, and per-variant sample sizes in GWAS summary statistics can be used to detect possible issues and correct for misspecifications in the GWAS summary statistics. One important motivation for us is to improve the predictive performance of polygenic scores built from these summary statistics. Unfortunately, due to the lack of reporting standards for GWAS summary statistics, this additional information is not systematically reported. We also show that using well-matched LD references can improve model fit and translate into more accurate prediction. Finally, we discuss how to make polygenic score methods such as lassosum and LDpred2 more robust to these misspecifications to improve their predictive power. If Research is Not the Evidence, What is it? The Lack of Research Citations in Health Vlogs Doctor-influencers have the knowledge needed to understand scientific research, and they have the social media popularity to share it with large audiences. In this article, I explore the treatment of evidence in vlogs by Egyptian doctor-influencers: how they present it and what they use instead of it. I answer the following questions: 1) How do doctor-influencers present evidence on social media? 2) If evidence is not the research, then what is it? The data was collected through in-depth interviews with 12 Egyptian doctor-influencers from different specializations; two focus groups; and a critical discourse analysis of 48 of the influencers most popular and most engaging videos. I found that the doctor-influencers cited academic research only in the videos about medical controversies, new treatments, misinformation, or common medical mistakes. In most of their videos, the Egyptian doctor-influencers scarcely referred to medical research. This is because they believe that their audiences would not understand evidence due to their low educational attainment, weak research skills, and the language barrier (as most medical research is published in English rather than Arabic). These findings highlight the challenges of communicating science in societies with a low level of education. In these settings, communicators may need to move beyond mediating research studies on social media and instead focus on simplifying, translating, and connecting evidence-based information to the cultural context. Resurrected Rubisco suggests uniform carbon isotope signatures over geologic time The earliest geochemical indicators of microbes--and the enzymes that powered them--extend back almost 3.8 billion years on our planet. Paleobiologists often attempt to understand these indicators by assuming that the behaviors of modern microbes and enzymes are consistent (uniform) with those of their predecessors. A uniformitarian assumption (i.e., the idea that fundamental geobiological processes have occurred in much the same manner over Earth history) seems at odds with our understanding of the inherent variability of living systems. Here, we examine whether a uniformitarian assumption for an enzyme thought to generate carbon isotope indicators of biological activity, RuBisCO, can be corroborated by independently studying the history of changes recorded within RuBisCOs genetic sequences. Specifically, we resurrected a Precambrian-age, ancient RuBisCO by engineering its ancient DNA inside a modern cyanobacterium genome and measured the engineered organisms fitness and carbon-isotope-discrimination profile. The envelope of ancestral RuBisCO isotopic fractionation observed here indicates that uniformitarian assumptions may be warranted, but with important caveats. Our results suggest that further inquiries that link molecule-level evolutionary changes with planet-level geochemical conditions are needed to discern whether enzyme-affected isotope fractionation trends extend deeper into the early Precambrian. Experimental studies illuminating lifes early molecular innovations are crucial to explore the foundations of Precambrian uniformitarian assumptions. SMC protein RecN drives RecA filament translocation and remodelling for in vivo homology search While the molecular repertoire of the homologous recombination pathway is well-studied, the search mechanism that enables recombination between distant homologous regions is poorly understood. Earlier work suggests that the recombinase RecA, an essential component for homology search, forms an elongated filament, nucleating at the break site. How this RecA structure carries out long distance search remains unclear. Here, we follow the dynamics of RecA after induction of a single double-strand break on the Caulobacter chromosome. We find that the RecA-nucleoprotein filament, once formed, rapidly translocates in a directional manner in the cell, undergoing several pole-to-pole traversals, until homology search is complete. Concomitant with translocation, we observe dynamic remodelling of the filament. Importantly in vivo, the RecA filament alone is incapable of such long distance movement; both translocation and dynamic remodelling are contingent on action of SMC-like protein RecN, via its ATPase cycle. We provide a stochastic description of RecN-regulated changes in filament length during translocation via modulation of RecA assembly-disassembly. In summary, we have uncovered the three key elements of homology search driven by RecN: mobility of a finite segment of RecA, filament remodelling and ability to conduct multiple pole-to-pole traversals, which together point to a novel optimal search strategy. Diverse stem-chondrichthyan oral structures and evidence for an independently acquired acanthodid dentition The teeth of sharks famously form a series of transversely-organised files with conveyor-belt replacement that are borne directly on the jaw cartilages, in contrast to the dermal plate-borne dentition of bony fishes that undergoes site-specific replacement. A major obstacle in understanding how this system evolved is the poorly understood relationships of the earliest chondrichthyans and the profusion of morphologically and terminologically diverse bones, cartilages, splints and whorls that they possess. Here we use tomographic methods to investigate mandibular structures in several early branching  acanthodian-grade stem-chondrichthyans. We show that the dentigerous jaw bones of disparate genera of ischnacanthids are united by a common construction, being growing bones with non-shedding dentition. Mandibular splints, which support the ventro-lateral edge of the Meckels cartilage in some taxa, are formed from dermal bone and may be an acanthodid synapomorphy. We demonstrate that the teeth of Acanthodopsis are borne directly on the mandibular cartilage and that this taxon is deeply nested within an edentulous radiation, representing an unexpected independent origin of teeth. Many or even all of the range of unusual oral structures may be apomorphic, but they should nonetheless be considered when building hypotheses of tooth and jaw evolution, both in chondrichthyans and more broadly. Membrane contact probability: an essential and predictive character for the structural and functional studies of membrane proteins One of the unique traits of membrane proteins is that a significant fraction of their hydrophobic amino acids is exposed to the hydrophobic core of lipid bilayers rather than being embedded in the protein interior, which is often not explicitly considered in the protein structure and function predictions. Here, we propose a characteristic and predictive quantity, the membrane contact probability (MCP), to describe the likelihood of the amino acids of a given sequence being in direct contact with the acyl chains of lipid molecules. We show that MCP is complementary to solvent accessibility in characterizing the outer surface of membrane proteins, and it can be predicted for any given sequence with a machine learning-based method by utilizing a training dataset extracted from MemProtMD, a database generated from molecular dynamics simulations for the membrane proteins with a known structure. As the first of many potential applications, we demonstrate that MCP can be used to systematically improve the prediction precision of the protein contact maps and structures. Allopurinol Blocks The Formation And Progression Of Aortic Aneurysm In A Mouse Model Of Marfan Syndrome BackgroundThe pathogenesis and progression of aortic aneurysm in Marfan syndrome (MFS) involves dysregulated TGF-{beta} and nitric oxide signaling, altered hemodynamics, and biomechanical forces. Increasing evidence indicates that redox stress participates in MFS aortopathy, though its contribution is not well established. We reported elevated reactive oxygen species (ROS) formation and NADPH oxidase NOX4 upregulation in MFS patients and mice aortic samples. Here we address the contribution of xanthine oxidoreductase (XOR), which is transcribed as dehydrogenase (XDH) and then post-translationally modified to oxidase (XO), catabolizing mainly purines into uric acid and ROS.

Methods and ResultsWe report that in MFS patients, XOR protein levels increased in aortic samples. In MFS mice (Fbn1C1041G/+), XOR mRNA transcripts augmented in the aorta of young but not older MFS mice. Moreover, the enzymatic activity of XO form increased with respect to XDH. The administration of the XOR inhibitor allopurinol (ALO) blocked the progression of aortic root aneurysm in MFS mice. ALO was also protective when administrated before the appearance of aneurysm. MFS-induced elastic fiber fragmentation, fibrotic remodeling, nuclear translocation of pNRF2, and increased 3-nitrotyrosine levels in the aortic tunica media as well as endothelial dysfunction were all prevented by allopurinol. The large overproduction of H2O2 in MFS aorta did not occur in allopurinol-treated mice whose uric acid plasma levels remained unaltered.

ConclusionsThis study strengthens the concept that redox stress is an important determinant of aortic aneurysm formation and progression in MFS and supports a clinical trial for ALO in the pharmacological treatment of MFS aortopathy.

GRAPHICAL ABSTRACT

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org.highwire.dtl.DTLVardef@eab02aorg.highwire.dtl.DTLVardef@1d3b39eorg.highwire.dtl.DTLVardef@1b54d0forg.highwire.dtl.DTLVardef@1d61ad0_HPS_FORMAT_FIGEXP  M_FIG C_FIG New Segmentation and Feature Extraction Algorithm for Classification of White Blood Cells in Peripheral Smear Images This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate. Population differences in the length and early-life dynamics of telomeres among European pied flycatchers Telomere length and shortening rate are increasingly used as biomarkers for long-term costs in ecological and evolutionary studies because of their relationships with survival and fitness. Both early-life conditions and growth, and later-life stressors can create variation in telomere shortening rate. Studies on between- population telomere length and dynamics are scarce, despite the expectation that populations exposed to varying environmental constraints would present divergent telomere length patterns. The pied flycatcher (Ficedula hypoleuca) is a passerine bird breeding across Eurasia (from Spain to western Siberia) and migrating through the Iberian Peninsula to spend the non-breeding period in sub-Saharan Africa. Thus, different populations show marked differences in migration distance. We studied the large-scale variation of telomere length and early-life dynamics in the pied flycatcher by comparing six European populations across a north- south gradient (Finland, Estonia, England, and Spain) predicting negative effect of migration distance on adult telomere length, and of nestling growth on nestling telomere dynamics. There were clear population differences in telomere length, with English birds from mid-latitudes having the longest telomeres. Telomere length did not thus show consistent latitudinal variation and was not linearly linked to differences in migration distance. Early-life telomere shortening rate tended to vary between populations. Fast growth was associated with shorter telomeres in the early life, but faster nestling growth affected telomeres more negatively in northern than southern populations. While the sources of between-population differences in telomere-related biology remain to be more intensively studied, our study illustrates the need to expand telomere studies at the between-population level. Discoidin Domain Receptor 2 regulates AT1R expression in Angiotensin II-stimulated cardiac fibroblasts via fibronectin-dependent Integrin-β1 signaling Recent reports on the cardioprotective effects of fibronectin inhibition following myocardial injury suggest a largely unexplored role for the extracellular matrix (ECM) glycoprotein in cardiac fibroblast function. We probed the molecular regulation and functional implications of fibronectin gene expression in cardiac fibroblasts exposed to Angiotensin II, a major pro-fibrotic factor in the myocardium. Using gene knockdown and over-expression approaches, western blotting and promoter pull-down assay, we show that collagen type I-activated Discoidin Domain Receptor 2 (DDR2) mediates Angiotensin II-stimulated transcriptional up-regulation of fibronectin by Yes-activated Protein in cardiac fibroblasts. Further, siRNA-mediated fibronectin knockdown attenuated Angiotensin II-stimulated expression of collagen type I and anti-apoptotic cIAP2, and enhanced susceptibility to apoptosis. Importantly, an obligate role for fibronectin was observed in Angiotensin II-stimulated expression of AT1R, the Angiotensin II receptor, which would link ECM signaling and Angiotensin II signaling in cardiac fibroblasts. Moreover, conditioned medium from DDR2- or fibronectin-silenced cardiac fibroblasts reduced AT1R expression in H9c2 cardiomyoblasts. The regulatory role of fibronectin in Angiotensin II-stimulated cIAP2, collagen type I and AT1R expression was mediated by Integrin-{beta}1-integrin-linked kinase signaling. In vivo, we observed modestly reduced basal levels of AT1R in DDR2-null mouse myocardium, associated with the previously reported reduction in myocardial Integrin-{beta}1 levels. The role of fibronectin, downstream of DDR2, could be a critical determinant of cardiac fibroblast-mediated wound healing following myocardial injury. In summary, our findings suggest a complex mechanism of regulation of cardiac fibroblast function involving two major extracellular matrix proteins, collagen type I and fibronectin, and their receptors, DDR2 and Integrin-{beta}1. Antibacterial potency of Type VI amidase effector toxins is dependent on substrate topology and cellular context Members of the bacterial T6SS amidase effector (Tae) superfamily of toxins are delivered between competing bacteria to degrade cell wall peptidoglycan. Although Taes share a common substrate, they exhibit distinct antimicrobial potency across different competitor species. To investigate the molecular basis governing these differences, we quantitatively defined the functional determinants of Tae1 from Pseudomonas aeruginosa PAO1 using a combination of nuclear magnetic resonance (NMR) and a high-throughput in vivo genetic approach called deep mutational scanning (DMS). As expected, combined analyses confirmed the role of critical residues near the Tae1 catalytic center. Unexpectedly, DMS revealed substantial contributions to enzymatic activity from a much larger, ring-like functional hot spot extending around the entire circumference of the enzyme. Comparative DMS across distinct growth conditions highlighted how functional contribution of different surfaces is highly context-dependent, varying alongside composition of targeted cell walls. These observations suggest that Tae1 engages with the intact cell wall network through a more distributed three-dimensional interaction interface than previously appreciated, providing an explanation for observed differences in antimicrobial potency across divergent Gram-negative competitors. Further binding studies of several Tae1 variants with their cognate immunity protein demonstrate that requirements to maintain protection from Tae activity may be a significant constraint on the mutational landscape of tae1 toxicity in the wild. In total, our work reveals that Tae diversification has likely been shaped by multiple independent pressures to maintain interactions with binding partners that vary across bacterial species and conditions. Population-based structure modeling reveals key roles of nuclear microenvironment in gene functions The nuclear folding of chromosomes relative to nuclear bodies is an integral part of gene function. Here, we demonstrate that population-based modeling--from ensemble Hi-C data--can provide a detailed description of the nuclear microenvironment of genes and its role on gene function. We define the microenvironment by the subnuclear positions of genomic regions with respect to nuclear bodies, local chromatin compaction, and preferences in chromatin compartmentalization. These structural descriptors are determined in single cell models on a genome-wide scale, thereby revealing the structural variability between cells. We demonstrate that the structural microenvironment of a genomic region is linked to its functional potential in gene transcription, replication and chromatin compartmentalization. Some chromatin regions are distinguished by their strong preferences to a single microenvironment, due to associations to specific nuclear bodies in most cells. Other chromatin shows high structural variability, which is a strong indicator of functional heterogeneity. Moreover, we identify specialized nuclear microenvironments, which distinguish chromatin in different functional states and reveal a key role of nuclear speckles in chromosome organization. We demonstrate that our method produces highly predictive 3-dimensional genome structures, which accurately reproduce data from TSA-seq, DamID, GPSeq and super-resolution imaging. Thus, our method considerably expands the range of Hi-C data analysis and is widely applicable. Failure to account for behavioral variability significantly compromises accuracy in indirect population monitoring Indirect wildlife population surveying largely depends upon counts of artefacts of behavior (e.g., nests or dung). Likelihood to encounter these artefacts is derived from both artefact production and decay, and variability in artefact production behavior is considered to contribute minimally to inaccuracy in wildlife estimation. Here we demonstrate how ignoring behavioral variability contributes to significant population misestimation, using an example of an endangered ape, the bonobo (Pan paniscus). Until now, a single estimate of (i.e., nest construction) has been used to extrapolate bonobo densities, assumed to be broadly representative of bonobo sign production behavior. We estimated nest construction rate across seasons and social groups at the Kokolopori Bonobo Reserve, DRC and find that nest construction rates in bonobos to be highly variable across populations as well as seasonal. Failure to account for this variability led to potentially severe degradation in accuracy of bonobo population estimates of abundance, accounting for a likely overestimation of bonobo numbers by 34%, and at worst as high as 80%. With this example, we demonstrate that failure to account for inter- and intra-population behavioral variation compromises the ability to estimate both relative and absolute wildlife abundances. We argue that variation in sign production is but one of several potential ways that behavioral variability can affect conservation monitoring, should be measured across contexts whenever possible, and must be considered in population estimation confidence intervals. With increasing attention to behavioral variability as a potential tool for conservation, conservationists must also account for the impact that behavioral variability can play upon wildlife population estimation. Our results underline the importance of observational research to wildlife monitoring schemes as a critical but overlooked component of conservation management. We extensively discuss the various avenues through which behavioral variability is likely to impact wildlife monitoring accuracy and precision and propose potential avenues for accounting for behavioral variability in wildlife monitoring. The linkage of methionine addiction, overmethylation of histone H3 lysines and malignancy demonstrated when cancer cells revert to methionine-independence Methionine addiction is a fundamental and general hallmark of cancer and is an area of current intense interest. Methionine addiction results from the overuse of methionine by cancer cells for excess transmethylation reactions. In order to identify excess transmethylation reactions in cancer and further understand the basis of methionine addiction, we compared the histone H3 lysine-methylation status and malignancy between methionine-addicted cancer cells and their methionine-independent revertants which have regained the ability to grow on low levels of methionine or independently of exogenous methionine. The levels of trimethylated histone H3 lysine marks were reduced in methionine-independent revertants compared to parental cancer cells in vitro. Tumorigenicity and experimental metastatic potential in nude mice were also highly reduced in the methionine-independent revertants compared to the parental cells. Our present results demonstrate that overmethylation of histone H3 lysines is linked with methionine addiction of cancer and to malignancy which suggests a possible causal relationship. The Functional Nanopore Screen: A Versatile High-throughput Assay to Study and Engineer Protein Nanopores in Escherichia coli Nanopores comprise a versatile class of membrane proteins that carry out a range of key physiological functions and are increasingly developed for different biotechnological applications. Yet, a capacity to study and engineer protein nanopores by combinatorial means has so far been hampered by a lack of suitable assays that combine sufficient experimental resolution with throughput. Addressing this technological gap, the Functional Nanopore (FuN) screen now provides a quantitative and dynamic read-out of nanopore assembly and function in E. coli. The assay is based on genetically-encoded fluorescent protein sensors that resolve the nanopore-dependent influx of Ca2+ across the inner membrane of E. coli. Illustrating its versatile capacity, the FuN screen is first applied to dissect the molecular features that underlie the assembly and stability of nanopores formed by the S2168 holin. In a subsequent step, nanopores are engineered by recombining the transmembrane module of S2168 with different ring-shaped oligomeric protein structures that feature defined hexa-, hepta- and octameric geometries. Library screening highlights substantial plasticity in the ability of the S2168 transmembrane module to oligomerize in alternative geometries while the functional properties of the resultant nanopores can be fine-tuned through the identity of the connecting linkers. Overall, the FuN screen is anticipated to facilitate both fundamental studies and complex nanopore engineering endeavors with many potential applications in biomedicine, biotechnology and synthetic biology. How do stomata evolve on the road to C4 photosynthesis? C4 photosynthesis optimizes plants carbon and water relations, allowing high photosynthetic rate with low stomatal conductance. Stomata have long been believed as a part of C4 syndrome. However, it remains unclear how stomata traits evolved along the path from C3 to C4. Stomatal patterning was examined in Flaveria genus, a model for studying C4 evolution. Comparative, transgenic and semi-in-vitro experiments were used to study molecular basis that underlies stomatal traits along C4 evolution. Novel results: the evolution from C3 to C4 species through intermediate species is accompanied by a stepwise rather than an abrupt change in the stomatal traits. The initial change occurs near Type II and dramatic change occurs at the C4-like species. On the road to C4, stomata become less in number but bigger in size and changes in stomatal density dominates changes in maximum stomatal conductance (gsmax). The reduction of FSTOMAGEN expression underlies altered gsmax between Flaveria species with different photosynthetic pathways and likely occurs in other C4 lineages. Our study provides insight into the pattern, mechanism and role of stomatal evolution along the road towards C4. This work highlights the stomatal traits in the current C4 evolutionary model and the co-evolution of photosynthetic pathway and stomata. Leaf Aqueous Extract of Manilkara hexandra influenced Glucose Metabolism in fish: An observation with Labeo rohita Fingerlings as model organism Manilkara hexandra (Roxb.) Dubard (common name Khirni, family: Sapotaceae) is an evergreen traditionally used medicinal plant. The present study aimed to see the effects of leaf aqueous extract of M. hexandra (LAEMH) on digestive and glucose metabolic enzyme action and serum glucose level in rohu, Labeo rohita, fingerlings. Experimental fish were fed a basal diet (Group I), and diets supplemented with LAEMH at 300 mg (Group II) and 600 mg (Group III) kg-1body weight. A significant reduction in serum glucose was noticed in the treated groups when compared with the control group. The reduced amylase activity was noticed in the treated fingerlings in a dose-dependent manner. However, lipase and protease activities didnt differ significantly among the experimental groups. Reduced serum glucose level might be correlated with the decline in the activity of digestive amylase in fish. Group II (3.35{+/-}0.19 U) and Group III (3.49{+/-}0.13 U) were recorded with reduced glucose-6-phosphatase activities than the control group (4.4{+/-}0.39 U). Moreover, the study revealed a decline in fructose-1,6-phosphatase activities in the treated groups in comparison to the control group. The decline in the activities of the metabolic enzymes might be associated with the non-availability of glucose owing to reduced activity of the digestive amylase in the treated groups. In conclusion, the present study established the hypoglycaemic effect of the leaf aqueous extract of M. hexandra in a fish model. CellKb Immune: a manually curated database of hematopoietic marker gene sets from 7 species for rapid cell type identification Using single-cell RNA sequencing, it is now possible to get the detailed gene expression pattern in each cell for millions of cells in a single experiment under varying conditions. Annotating cells by assigning a type or state to them is important for correct interpretation of results. In spite of current advances in technology, assigning cell types in single-cell datasets remains a bottleneck due to the lack of a comprehensive reference database and an accurate search method in a single tool. CellKb Immune is a knowledgebase of manually collected, curated and annotated marker gene sets representing cell types in the immune response in mouse. It allows users to find matching cell types in literature given a list of genes. We evaluated the contents and search methodology of CellKb Immune using a leave-one-out approach. We further used CellKb Immune to annotate previously defined marker gene sets from a bulk RNA-seq dataset to confirm its accuracy and coverage. CellKb Immune provides an easy-to-use online tool with a reliable method to find matching cell types and annotate cells in single-cell experiments. It is available at https://www.cellkb.com/immune.