Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximizing ecological and evolutionary insight from bisulfite sequencing data sets

The role of DNA methylation in development, divergence, and the response to environmental stimuli is of substantial interest in ecology and evolutionary biology. Measuring genome-wide DNA methylation is increasingly feasible using sodium bisulfite sequencing. Here, we analyze simulated and published data sets to demonstrate how effect size, kinship/population structure, taxonomic differences, and cell type heterogeneity influence the power to detect differential methylation in bisulfite sequencing data sets. Our results reveal that the effect sizes typical of evolutionary and ecological studies are modest, and will thus require data sets larger than those currently in common use. Additionally, our findings emphasize that statistical approaches that ignore the properties of bisulfite sequencing data (e.g., its count-based nature) or key sources of variance in natural populations (e.g., population structure or cell type heterogeneity) often produce false negatives or false positives, thus leading to incorrect biological conclusions. Finally, we provide recommendations for handling common issues that arise in bisulfite sequencing analyses and a freely available R Shiny application for simulating and performing power analyses on bisulfite sequencing data. This app, available at www.tung-lab.org/protocols-and-software.html, allows users to explore the effects of sequencing depth, sample size, population structure, and expected effect size, tailored to their own system.
An RNA binding polymer specifies nematode sperm fate

Metazoan germ cells develop as sperm or oocytes, depending on chromosomal sex, extrinsic signaling from somatic tissue and intrinsic factors within the germ cells. Gamete fate regulatory networks have been analyzed in nematodes, flies and mammals, but only in C. elegans have terminal intrinsic regulators been identified, which include a Tob/BTG protein family member, FOG-3. Canonical Tob/BTG proteins function as monomeric adaptor proteins that link RNA binding proteins to deadenylases. To ask if FOG-3 functions similarly, we first determined its crystal structure. FOG-3 harbors a classical Tob/BTG fold, but unlike other Tob/BTG proteins, FOG-3 dimerizes and these FOG-3 dimers assemble into polymers. The importance of FOG-3 polymers to sperm fate specification was confirmed in vivo using CRISPR/Cas9 gene editing to create mutations designed to disrupt the polymer interface. The FOG-3 surface potential is highly basic, suggesting binding to nucleic acid. We find that FOG-3 binds RNA directly with a strong preference for 3’UTRs of oogenic mRNAs. Our results reveal a divergent but striking molecular assembly for proteins with a Tob/BTG fold, make key advances in understanding the mechanism of sperm fate specification and highlight the potential for undiscovered protein polymers in biology.
Review: Population Structure in Genetic Studies: Confounding Factors and Mixed Models

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to effectively test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.
Prevalence and risk factors for bovine tuberculosis in the State of São Paulo, Brazil

A cross sectional study was carried out between May and November 2011 to investigate the epidemiological situation of bovine tuberculosis (bTB) in the state of Sao Paulo, Brazil. The state was divided into seven regions. Three hundred farms from each region, with reproductive activity, were randomly chosen and included as primary sample units. A fixed number of bovine females, older than 2 years of age, were randomly selected and tested, using the comparative cervical tuberculin test. An epidemiological questionnaire based survey was conducted in the selected farms. Our results show that in the state of Sao Paulo, the apparent prevalence of positive farms was 9% (95% confidence interval, 95% CI = 7.8 – 10.5%). The prevalence in the individual regions varied between 3.5% (95% CI = 1.7 ? 6.8%) and 13.9% (95% CI = 10.2 – 18.8%). The apparent prevalence of positive animals in the state was 1.3% (95% CI = 0.9 – 1.7%) and varied from 0.3% (95% CI = 0.2 – 0.6%) to 2.5% (95% CI = 1.4 – 4.5%) in the regions. The risk factors associated with tuberculosis in the state were (i) number of adult females in a herd is ? 24 (Odds ratio, OR = 1.91, 95% CI = 1.32 – 2.75), (ii) type of farm enterprise (dairy: OR = 2.70, 95% CI = 1.40 – 5.21; mixed: OR = 2.03, 95% CI = 1.08 – 3.82), (iii) milking process (milking parlor: OR = 4.12, 95% CI = 1.46 – 11.64; portable milking machine: OR = 2.94, 95% CI = 1.42 – 6.09), and (iv) pasture sharing (OR = 1.58, 95% CI = 1.07 – 2.33). The state of Sao Paulo should implement a structured surveillance system to detect and mitigate the disease. Further, an efficient animal health education program, which encourages the farmers to test replacement animals for bTB prior to introduction in their herds and to avoid pasture sharing with farms of unknown sanitary conditions should also be implemented.
Major host transitions are modulated through transcriptome-wide reprograming events in Schistocephalus solidus, a threespine stickleback parasite

Parasites with complex life cycles have developed numerous phenotypic strategies, closely associated with developmental events, to enable the exploitation of different ecological niches and facilitate transmission between hosts. How these environmental shifts are regulated from a metabolic and physiological standpoint, however, still remain to be fully elucidated. We examined the transcriptomic response of Schistocephalus solidus, a trophically-transmitted parasite with a complex life cycle, over the course of its development in an intermediate host, the threespine stickleback, and the final avian host. Results from our differential gene expression analysis show major reprogramming events among developmental stages. The final host stage is characterized by a strong activation of reproductive pathways and redox homeostasis. The attainment of infectivity in the fish intermediate host – which precedes sexual maturation in the final host and is associated with host behaviour changes – is marked by transcription of genes involved in neural pathways and sensory perception. Our results suggest that un-annotated and S. solidus-specific genes could play a determinant role in host-parasite molecular interactions required to complete the parasite’s life cycle. Our results permit future comparative analyses to help disentangle species-specific patterns of infection from conserved mechanisms, ultimately leading to a better understanding of the molecular control and evolution of complex life cycles.
How ecosystems recover from pulse perturbations: A theory of short- to long-term responses

Quantifying stability properties of ecosystems is an important problem in ecology. A common approach is based on the recovery from pulse perturbations, and posits that the faster ecosystems return to their pre-perturbation state, the more stable they are. In theoretical studies the recovery dynamics are often collapsed into a single quantity: the long-term rate of return, called asymptotic resilience. However, empirical studies typically measure the recovery dynamics at much shorter time scales. In this paper we explain why asymptotic resilience is rarely representative of the short-term recovery. First, we show that, in contrast to asymptotic resilience, short-term return rates depend on features of the perturbation, in particular on the way its intensity is distributed over species. We argue that empirically relevant predictions can be obtained by considering the median response over a set of perturbations, for which we provide explicit formulas. Next, we show that the recovery dynamics are controlled through time by different species: abundant species tend to govern the short-term recovery, while rare species often dominate the long-term recovery. This shift from abundant to rare species typically causes short-term return rates to be unrelated to asymptotic resilience. Finally, we discuss how these findings might help to better connect empirical observations and theoretical predictions.
Hosts, parasites, and their interactions respond to climatic variables

Aim: Although there is a vast body of literature on the causes of variation in species composition in ecological communities, less effort has been invested in understanding how interactions between these species vary. Since interactions are crucial to the structure and functioning of ecological communities, we need to develop a better understanding of their spatial distribution. Here, we investigate whether species interactions vary more in response to different climate variables, than individual species do. Location: Eurasia. Time period: 2000s. Major taxa: Animalia. Methods: We used a measure of Local Contribution to Beta-Diversity to evaluate the compositional uniqueness of 51 host–parasite communities of rodents and their ectoparasitic fleas across Eurasia, using publicly available data. We measured uniqueness based on the species composition, and based on potential and realized biotic interactions (here, host-parasite interactions). Results: We show that species interactions vary more, across space, than species do. In particular, we show that species interactions respond to some climatic variables that have no effect on species distributions or dissimilarity. Main conclusions: Species interactions capture some degree of variation which is not apparent when looking at species occurrences only. In this system, this appeared as hosts and parasites interacting in different ways as a reponse to different environments, especially the temperature and dryness. We discuss the implications of this finding for the amount of information that should be considered when measuring community dissimilarity.
Calibrating Speed–Density Functions for Mesoscopic Traffic Simulation

Mesoscopic traffic simulation models combine macroscopic supply (e.g., link performance functions and capacities) with microscopic demand (e.g., individual drivers and disaggregate behavior models) to capture the time-varying evolution of congestion patterns, queues, and spillbacks on traffic networks. Such systems are being applied to solve a variety of off-line and online traffic management problems. Often, the link performance function takes the form of a speed–density relationship for each link or segment in the study network. These functions are based on the fundamental diagram and model the variation of average vehicle speed with traffic density. Since each network link or segment can have a separate speed–density function (each with a few parameters), the total number of parameters to be calibrated is potentially very large. This paper presents some experiences with calibrating speed–density functions on a variety of real networks. Flexible off-line and on-line calibration frameworks are discussed. The calibrated functions are embedded within the DynaMIT dynamic traffic assignment model, whose accuracy is evaluated against real data. The expected benefits from recalibrating speed–density functions during on-line operations using real-time sensor data are also illustrated.
Selection Corrected Statistical Inference for Region Detection with High-throughput Assays

Scientists use high-dimensional measurement assays to detect and prioritize regions of strong signal in a spatially organized domain. Examples include finding methylation enriched genomic regions using microarrays and identifying active cortical areas using brain-imaging. The most common procedure for detecting potential regions is to group together neighboring sites where the signal passed a threshold. However, one needs to account for the selection bias induced by this opportunistic procedure to avoid diminishing effects when generalizing to a population. In this paper, we present a model and a method that permit population inference for these detected regions. In particular, we provide non-asymptotic point and confidence interval estimates for mean effect in the region, which account for the local selection bias and the non-stationary covariance that is typical of these data. Such summaries allow researchers to better compare regions of different sizes and different correlation structures. Inference is provided within a conditional one-parameter exponential family for each region, with truncations that match the constraints of selection. A secondary screening-and-adjustment step allows pruning the set of detected regions, while controlling the false-coverage rate for the set of regions that are reported. We illustrate the benefits of the method by applying it to detected genomic regions with differing DNA-methylation rates across tissue types. Our method is shown to provide superior power compared to non-parametric approaches.
America's Private Forests: Challenges for Conservation

A survey of the literature and experts, including recently released government data, indicates thatthe long-term viability of privately owned forests in the United States is increasingly threatened by conversion, fragmentation, parcelization, and diminished ecological function. In the face of these threats, those concerned about the future of private forests need to join in a new initiative to more effectively conserve and steward America's private forests. A framework for this initiative is described, as well as key tools to expand forest conservation.
Improved genome assembly of American alligator genome reveals conserved architecture of estrogen signaling

The American alligator, Alligator mississippiensis, like all crocodilians, has temperature-dependent sex determination, in which the sex of an embryo is determined by the incubation temperature of the egg during a critical period of development. The lack of genetic differences between male and female alligators leaves open the question of how the genes responsible for sex determination and differentiation are regulated. One insight into this question comes from the fact that exposing an embryo incubated at male-producing temperature to estrogen causes it to develop ovaries. Because estrogen response elements are known to regulate genes over long distances, a contiguous genome assembly is crucial for predicting and understanding its impact. We present an improved assembly of the American alligator genome, scaffolded with in vitro proximity ligation (Chicago) data. We use this assembly to scaffold two other crocodilian genomes based on synteny. We perform RNA sequencing of tissues from American alligator embryos to find genes that are differentially expressed between embryos incubated at male-versus female-producing temperature. Finally, we use the improved contiguity of our assembly along with the current model of CTCF-mediated chromatin looping to predict regions of the genome likely to contain estrogen-responsive genes. We find that these regions are significantly enriched for genes with female-biased expression in developing gonads after the critical period during which sex is determined by incubation temperature. We thus conclude that estrogen signaling is a major driver of female-biased gene expression in the post-temperature sensitive period gonads.
Advances in Monoclonal Antibodies Production and Cancer Therapy

Monoclonal antibodies are highly specific class of antibodies which are produced by identical hybridoma cells. These antibodies are developed by various advanced and powerful technologies being used in the areas of cell biology, biochemistry, biotechnology, immunology and medical sciences. The techniques involved hybridoma technology, phage display technology, single B-cell amplification by polymerase chain reaction (PCR) and many other modified methods. The advancement in antibody therapeutics is an essential area of growth in the pharmaceutical manufacturing and more than 30 monoclonal antibodies have been approved on the US and Europe markets. Regardless of these advancements, certain significant target antigens remain intractable to therapeutic antibodies due to complexity of the target molecules. The present review describes recent advances in monoclonal antibody production and cancer therapy for better understanding the therapeutic efficacy.
Automatic test packet generation

Networks are getting larger and more complex; yet administrators rely on rudimentary tools such as ping and traceroute to debug problems. We propose an automated and systematic approach for testing and debugging networks called "Automatic Test Packet Generation" (ATPG). ATPG reads router configurations and generates a device-independent model. The model is used to generate a minimum set of test packets to (minimally) exercise every link in the network or (maximally) exercise every rule in the network. Test packets are sent periodically, and detected failures trigger a separate mechanism to localize the fault. ATPG can detect both functional (e.g., incorrect firewall rule) and performance problems (e.g., congested queue). ATPG complements but goes beyond earlier work in static checking (which cannot detect liveness or performance faults) or fault localization (which only localize faults given liveness results). We describe our prototype ATPG implementation and results on two real-world data sets: Stanford University's backbone network and Internet2. We find that a small number of test packets suffices to test all rules in these networks: For example 4000 packets can cover all rules in Stanford backbone network while 54 is enough to cover all links. Sending 4000 test packets 10 times per second consumes less than 1% of link capacity. ATPG code and the data sets are publicly available1[1].
Physical activity phenotyping with activity bigrams, and their association with BMI

Background Analysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute, and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person’s activity levels change across (brief) time points. These variables can, for instance, differentiate between two people with the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not. Methods We use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar. Results We found several associations between changes in bigram frequencies and BMI. For instance, a 1 standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m2 lower BMI [95% CI: -3.47, -1.26], after accounting for the time spent at sedentary, low, moderate and vigorous activity. Conclusions Activity bigrams are novel variables that capture how a person’s activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits. Key Messages Epidemiologists typically use only a small number of variables to analyse the association of physical activity with other traits, such as the average counts per minute and the proportion of time spent in moderate-vigorous physical activity or being sedentary. We demonstrate how activity bigrams can be used as a set of interpretable variables describing how a person’s activity levels change from one moment to the next. Testing the association of activity bigrams with exposures or outcomes can help us gain further understanding of how physical activity is associated with other traits; with further research they might provide evidence for more refined public health advice.
TAGGINGSENSE: Method Based On Sensemaking For Object-Oriented Source Code Comprehension.

All software requires maintenance, either for error correction or for implementing updates. However, maintenance is often complex and expensive, and one of the main problems in the high cost of maintenance is the difficulty of understanding the source code of other authors. Thus, this research presents TaggingSense, a method based on sensemaking that aims to reduce object-oriented source code comprehension time on systems maintenance. Through experimentation, it was possible to observe knowledge extracted from the source code, processing, and sharing, to be positively assisted in the source code maintenance and comprehension process, thus bringing benefits such as reduction time spent, quality, and greater security in the changes made. Keywords-knowledge;sensemaking;source code maintenance;
A PRACTICAL METHOD FOR INTERPRETATION OF THREADING SCORES: AN APPLICATION OF NEURAL NETWORK

Protein threading has become a popular technique for protein fold recog- nition and structure prediction. However it remains a challenging and unsolved problem to assess the significance or reliability of a threading prediction result. The lack of an effective mechanism for such an assessment has greatly limited fur- ther applications of threading on a genome-scale. We have developed a practical method for assessing the reliability of a threading result, using a neural network approach. As a key goal of threading is to separate true sequence-fold pairs (a pair of proteins that share the same structural fold) from false sequence-fold pairs, we have examined the distribution of true pairs against the many times more false pairs in the parameter space, and discovered that the vast majority of the true pairs fall into a continuous region without any false ones, providing the basis for pattern recognition using a neural network. We have trained a neural network trying to capture the shape of this "true" region. Our preli minary results are quite encouraging and show that our approach is more effective in assessing the prediction reliability than another neural network-based approach employed in the popular threading program GenThreader. This preliminary study also indicates that our current neural network is too simple to accurately capture the overall shape of the true region, and points to directions for further investigation on this highly important and challenging problem. This neural network-based assessment capability has been implemented in our threading program PROSPECT and used during the CASP4 predictions. Our successful performance in CASP4 suggests that even though this trained neural network is far from being perfect, it is fairly effective.