Evolution and differentiation of the cybersecurity communities in three social question and answer sites: A mixed-methods analysis.

Cybersecurity affects us all in our daily lives. New knowledge on best practices, new vulnerabilities, and timely fixes for cybersecurity issues is growing super-linearly, and is spread across numerous, heterogeneous sources. Because of that, community contribution-based, question and answer sites have become clearinghouses for cybersecurity-related inquiries, as they have for many other topics. Historically, Stack Overflow has been the most popular platform for different kinds of technical questions, including for cybersecurity. That has been changing, however, with the advent of Security Stack Exchange, a site specifically designed for cybersecurity-related questions and answers. More recently, some cybersecurity-related subreddits of Reddit, have become hubs for cybersecurity-related questions and discussions. The availability of multiple overlapping communities has created a complex terrain to navigate for someone looking for an answer to a cybersecurity question. In this paper, we investigate how and why people choose among three prominent, overlapping, question and answer communities, for their cybersecurity knowledge needs. We aggregated data of several consecutive years of cybersecurity-related questions from Stack Overflow, Security Stack Exchange, and Reddit, and performed statistical, linguistic, and longitudinal analysis. To triangulate the results, we also conducted user surveys. We found that the user behavior across those three communities is different, in most cases. Likewise, cybersecurity-related questions asked on the three sites are different, more technical on Security Stack Exchange and Stack Overflow, and more subjective and personal on Reddit. Moreover, there appears to have been a differentiation of the communities along the same lines, accompanied by overall popularity trends suggestive of Stack Overflow's decline and Security Stack Exchange's rise within the cybersecurity community. Reddit is addressing the more subjective, discussion type needs of the lay community, and is growing rapidly.

Evolutionary network genomics of wood formation in a phylogenetic survey of angiosperm forest trees.

Wood formation was present in early angiosperms, but has been highly modified through evolution to generate the anatomical diversity seen in extant angiosperm lineages. In this project, we modeled changes in gene coexpression relationships associated with the evolution of wood formation in a phylogenetic survey of 13 angiosperm tree species. Gravitropic stimulation was used as an experimental treatment to alter wood formation and also perturb gene expression. Gene transcript abundances were determined using RNA sequencing of developing wood tissues from upright trees, and from the top (tension wood) and bottom (opposite wood) tissues of gravistimulated trees. A network-based approach was employed to align gene coexpression networks across species based on orthologous relationships. A large-scale, multilayer network was modeled that identified both lineage-specific gene coexpression modules and modules conserved across multiple species. Functional annotation and analysis of modules identified specific regulatory processes associated with conserved modules, including regulation of hormones, protein phosphorylation, meristem development and epigenetic processes. Our results provide novel insights into the evolution and development of wood formation, and demonstrate the ability to identify biological processes and genes important for the evolution of a foundational trait in nonmodel, undomesticated forest trees.

Status, identity, and language: A study of issue discussions in GitHub.

Successful open source software (OSS) projects comprise freely observable, task-oriented social networks with hundreds or thousands of participants and large amounts of (textual and technical) discussion. The sheer volume of interactions and participants makes it challenging for participants to find relevant tasks, discussions and people. Tagging (e.g., @AmySmith) is a socio-technical practice that enables more focused discussion. By tagging important and relevant people, discussions can be advanced more effectively. However, for all but a few insiders, it can be difficult to identify important and/or relevant people. In this paper we study tagging in OSS projects from a socio-linguistics perspective. First we argue that textual content per se reveals a great deal about the status and identity of who is speaking and who is being addressed. Next, we suggest that this phenomenon can be usefully modeled using modern deep-learning methods. Finally, we illustrate the value of these approaches with tools that could assist people to find the important and relevant people for a discussion.

Multiple feedback loops of the Arabidopsis circadian clock provide rhythmic robustness across environmental conditions.

Although circadian oscillators in diverse eukaryotes all depend on interlinked transcriptional feedback loops, specific components are not conserved across higher taxa. Moreover, the circadian network in the model plant Arabidopsis thaliana is notably more complex than those found in animals and fungi. Here, we combine mathematical modeling and experimental approaches to investigate the functions of two classes of Myb-like transcription factors that antagonistically regulate common target genes. Both CCA1/LHY- and RVE8-clade factors bind directly to the same cis-element, but the former proteins act primarily as repressors, while the latter act primarily as activators of gene expression. We find that simulation of either type of loss-of-function mutant recapitulates clock phenotypes previously reported in mutant plants, while simulated simultaneous loss of both type of factors largely rescues circadian phase at the expense of rhythmic amplitude. In accord with this prediction, we find that plants mutant for both activator- and repressor-type Mybs have near-normal circadian phase and period but reduced rhythmic amplitude. Although these mutants exhibit robust rhythms when grown at mild temperatures, they are largely arrhythmic at physiologically relevant but nonoptimal temperatures. LHY- and RVE8-type Mybs are found in separate clades across the land plant lineage and even in some unicellular green algae, suggesting that they both may have functioned in even the earliest arising plant circadian oscillators. Our data suggest that the complexity of the plant circadian network may have arisen to provide rhythmic robustness across the range of environmental extremes to which plants, as sessile organisms, are regularly subjected.

Determinants of quality, latency, and amount of Stack Overflow answers about recent Android APIs.

Stack Overflow is a popular crowdsourced question and answer website for programming-related issues. It is an invaluable resource for software developers; on average, questions posted there get answered in minutes to an hour. Questions about well established topics, e.g., the coercion operator in C++, or the difference between canonical and class names in Java, get asked often in one form or another, and answered very quickly. On the other hand, questions on previously unseen or niche topics take a while to get a good answer. This is particularly the case with questions about current updates to or the introduction of new application programming interfaces (APIs). In a hyper-competitive online market, getting good answers to current programming questions sooner could increase the chances of an app getting released and used. So, can developers anyhow, e.g., hasten the speed to good answers to questions about new APIs? Here, we empirically study Stack Overflow questions pertaining to new Android APIs and their associated answers. We contrast the interest in these questions, their answer quality, and timeliness of their answers to questions about old APIs. We find that Stack Overflow answerers in general prioritize with respect to currentness: questions about new APIs do get more answers, but good quality answers take longer. We also find that incentives in terms of question bounties, if used appropriately, can significantly shorten the time and increase answer quality. Interestingly, no operationalization of bounty amount shows significance in our models. In practice, our findings confirm the value of bounties in enhancing expert participation. In addition, they show that the Stack Overflow style of crowdsourcing, for all its glory in providing answers about established programming knowledge, is less effective with new API questions.

Transcriptional and temporal response of Populus stems to gravi-stimulation.

Plants modify development in response to external stimuli, to produce new growth that is appropriate for environmental conditions. For example, gravi-stimulation of leaning branches in angiosperm trees results in modifications of wood development, to produce tension wood that pulls leaning stems upright. Here, we use gravi-stimulation and tension wood response to dissect the temporal changes in gene expression underlying wood formation in Populus stems. Using time-series analysis of seven time points over a 14-d experiment, we identified 8,919 genes that were differentially expressed between tension wood (upper) and opposite wood (lower) sides of leaning stems. Clustering of differentially expressed genes showed four major transcriptional responses, including gene clusters whose transcript levels were associated with two types of tissue-specific impulse responses that peaked at about 24-48 h, and gene clusters with sustained changes in transcript levels that persisted until the end of the 14-d experiment. Functional enrichment analysis of those clusters suggests they reflect temporal changes in pathways associated with hormone regulation, protein localization, cell wall biosynthesis and epigenetic processes. Time-series analysis of gene expression is an underutilized approach for dissecting complex developmental responses in plants, and can reveal gene clusters and mechanisms influencing development.

Social Synchrony on Complex Networks.

Social synchrony (SS) is an emergent phenomenon in human society. People often mimic others which, over time, can result in large groups behaving similarly. Drawing from prior empirical studies of SS in online communities, here we propose a discrete network model of SS based on four attributes: 1) depth of action; 2) breadth of impact, i.e., a large number of actions are performed with a large group of people involved; 3) heterogeneity of role, i.e., people of higher degree play more important roles; and 4) lastly, emergence of phenomenon, i.e., it is far from random. We analyze our model both analytically and with simulations, and find good agreement between the two. We find this model can well explain the four characters of SS, and thus hope it can help researchers better understand human collective behavior.

Identifying gene coexpression networks underlying the dynamic regulation of wood-forming tissues in Populus under diverse environmental conditions.

Trees modify wood formation through integration of environmental and developmental signals in complex but poorly defined transcriptional networks, allowing trees to produce woody tissues appropriate to diverse environmental conditions. In order to identify relationships among genes expressed during wood formation, we integrated data from new and publically available datasets in Populus. These datasets were generated from woody tissue and include transcriptome profiling, transcription factor binding, DNA accessibility and genome-wide association mapping experiments. Coexpression modules were calculated, each of which contains genes showing similar expression patterns across experimental conditions, genotypes and treatments. Conserved gene coexpression modules (four modules totaling 8398 genes) were identified that were highly preserved across diverse environmental conditions and genetic backgrounds. Functional annotations as well as correlations with specific experimental treatments associated individual conserved modules with distinct biological processes underlying wood formation, such as cell-wall biosynthesis, meristem development and epigenetic pathways. Module genes were also enriched for DNase I hypersensitivity footprints and binding from four transcription factors associated with wood formation. The conserved modules are excellent candidates for modeling core developmental pathways common to wood formation in diverse environments and genotypes, and serve as testbeds for hypothesis generation and testing for future studies.

Converging Work-Talk Patterns in Online Task-Oriented Communities.

Much of what we do is accomplished by working collaboratively with others, and a large portion of our lives are spent working and talking; the patterns embodied in the alternation of working and talking can provide much useful insight into task-oriented social behaviors. The available electronic traces of the different kinds of human activities in online communities are an empirical goldmine that can enable the holistic study and understanding of these social systems. Open Source Software (OSS) projects are prototypical examples of collaborative, task-oriented communities, depending on volunteers for high-quality work. Here, we use sequence analysis methods to identify the work-talk patterns of software developers in online communities of Open Source Software projects. We find that software developers prefer to persist in same kinds of activities, i.e., a string of work activities followed by a string of talk activities and so forth, rather than switch them frequently; this tendency strengthens with time, suggesting that developers become more efficient, and can work longer with fewer interruptions. This process is accompanied by the formation of community culture: developers' patterns in the same communities get closer with time while different communities get relatively more different. The emergence of community culture is apparently driven by both "talk" and "work". Finally, we also find that workers with good balance between "work" and "talk" tend to produce just as much work as those that focus strongly on "work"; however, the former appear to be more likely to continue to be active contributors in the communities.

Transcriptional and Hormonal Regulation of Gravitropism of Woody Stems in Populus.

Angiosperm trees reorient their woody stems by asymmetrically producing a specialized xylem tissue, tension wood, which exerts a strong contractile force resulting in negative gravitropism of the stem. Here, we show, in Populus trees, that initial gravity perception and response occurs in specialized cells through sedimentation of starch-filled amyloplasts and relocalization of the auxin transport protein, PIN3. Gibberellic acid treatment stimulates the rate of tension wood formation and gravibending and enhances tissue-specific expression of an auxin-responsive reporter. Gravibending, maturation of contractile fibers, and gibberellic acid (GA) stimulation of tension wood formation are all sensitive to transcript levels of the Class I KNOX homeodomain transcription factor-encoding gene ARBORKNOX2 (ARK2). We generated genome-wide transcriptomes for trees in which gene expression was perturbed by gravistimulation, GA treatment, and modulation of ARK2 expression. These data were employed in computational analyses to model the transcriptional networks underlying wood formation, including identification and dissection of gene coexpression modules associated with wood phenotypes, GA response, and ARK2 binding to genes within modules. We propose a model for gravitropism in the woody stem in which the peripheral location of PIN3-expressing cells relative to the cambium results in auxin transport toward the cambium in the top of the stem, triggering tension wood formation, while transport away from the cambium in the bottom of the stem triggers opposite wood formation.

Temporal motifs reveal collaboration patterns in online task-oriented networks.

Real networks feature layers of interactions and complexity. In them, different types of nodes can interact with each other via a variety of events. Examples of this complexity are task-oriented social networks (TOSNs), where teams of people share tasks towards creating a quality artifact, such as academic research papers or software development in commercial or open source environments. Accomplishing those tasks involves both work, e.g., writing the papers or code, and communication, to discuss and coordinate. Taking into account the different types of activities and how they alternate over time can result in much more precise understanding of the TOSNs behaviors and outcomes. That calls for modeling techniques that can accommodate both node and link heterogeneity as well as temporal change. In this paper, we report on methodology for finding temporal motifs in TOSNs, limited to a system of two people and an artifact. We apply the methods to publicly available data of TOSNs from 31 Open Source Software projects. We find that these temporal motifs are enriched in the observed data. When applied to software development outcome, temporal motifs reveal a distinct dependency between collaboration and communication in the code writing process. Moreover, we show that models based on temporal motifs can be used to more precisely relate both individual developer centrality and team cohesion to programmer productivity than models based on aggregated TOSNs.

A resource for characterizing genome-wide binding and putative target genes of transcription factors expressed during secondary growth and wood formation in Populus.

Identifying transcription factor target genes is essential for modeling the transcriptional networks underlying developmental processes. Here we report a chromatin immunoprecipitation sequencing (ChIP-seq) resource consisting of genome-wide binding regions and associated putative target genes for four Populus homeodomain transcription factors expressed during secondary growth and wood formation. Software code (programs and scripts) for processing the Populus ChIP-seq data are provided within a publically available iPlant image, including tools for ChIP-seq data quality control and evaluation adapted from the human Encyclopedia of DNA Elements (ENCODE) project. Basic information for each transcription factor (including members of Class I KNOX, Class III HD ZIP, BEL1-like families) binding are summarized, including the number and location of binding regions, distribution of binding regions relative to gene features, associated putative target genes, and enriched functional categories of putative target genes. These ChIP-seq data have been integrated within the Populus Genome Integrative Explorer (PopGenIE) where they can be analyzed using a variety of web-based tools. We present an example analysis that shows preferential binding of transcription factor ARBORKNOX1 to the nearest neighbor genes in a pre-calculated co-expression network module, and enrichment for meristem-related genes within this module including multiple orthologs of Arabidopsis KNOTTED-like Arabidopsis 2/6.

The Populus ARBORKNOX1 homeodomain transcription factor regulates woody growth through binding to evolutionarily conserved target genes of diverse function.

The class I KNOX homeodomain transcription factor ARBORKNOX1 (ARK1) is a key regulator of vascular cambium maintenance and cell differentiation in Populus. Currently, basic information is lacking concerning the distribution, functional characteristics, and evolution of ARK1 binding in the Populus genome. Here, we used chromatin immunoprecipitation sequencing (ChIP-seq) technology to identify ARK1 binding loci genome-wide in Populus. Computational analyses evaluated the distribution of ARK1 binding loci, the function of genes associated with bound loci, the effect of ARK1 binding on transcript levels, and evolutionary conservation of ARK1 binding loci. ARK1 binds to thousands of loci which are highly enriched proximal to the transcriptional start sites of genes of diverse functions. ARK1 target genes are significantly enriched in paralogs derived from the whole-genome salicoid duplication event. Both ARK1 and a maize (Zea mays) homolog, KNOTTED1, preferentially target evolutionarily conserved genes. However, only a small portion of ARK1 target genes are significantly differentially expressed in an ARK1 over-expression mutant. This study describes the functional characteristics and evolution of DNA binding by a transcription factor in an undomesticated tree, revealing complexities similar to those shown for transcription factors in model animal species.

Evaluation of experimental design and computational parameter choices affecting analyses of ChIP-seq and RNA-seq data in undomesticated poplar trees.

BACKGROUND: One of the great advantages of next generation sequencing is the ability to generate large genomic datasets for virtually all species, including non-model organisms. It should be possible, in turn, to apply advanced computational approaches to these datasets to develop models of biological processes. In a practical sense, working with non-model organisms presents unique challenges. In this paper we discuss some of these challenges for ChIP-seq and RNA-seq experiments using the undomesticated tree species of the genus Populus. RESULTS: We describe specific challenges associated with experimental design in Populus, including selection of optimal genotypes for different technical approaches and development of antibodies against Populus transcription factors. Execution of the experimental design included the generation and analysis of Chromatin immunoprecipitation-sequencing (ChIP-seq) data for RNA polymerase II and transcription factors involved in wood formation. We discuss criteria for analyzing the resulting datasets, determination of appropriate control sequencing libraries, evaluation of sequencing coverage needs, and optimization of parameters. We also describe the evaluation of ChIP-seq data from Populus, and discuss the comparison between ChIP-seq and RNA-seq data and biological interpretations of these comparisons. CONCLUSIONS: These and other "lessons learned" highlight the challenges but also the potential insights to be gained from extending next generation sequencing-supported network analyses to undomesticated non-model species.

Modeling transcriptional networks regulating secondary growth and wood formation in forest trees.

The complex interactions among the genes that underlie a biological process can be modeled and presented as a transcriptional network, in which genes (nodes) and their interactions (edges) are shown in a graphical form similar to a wiring diagram. A large number of genes have been identified that are expressed during the radial woody growth of tree stems (secondary growth), but a comprehensive understanding of how these genes interact to influence woody growth is currently lacking. Modeling transcriptional networks has recently been made tractable by next-generation sequencing-based technologies that can comprehensively catalog gene expression and transcription factor-binding genome-wide, but has not yet been extensively applied to undomesticated tree species or woody growth. Here we discuss basic features of transcriptional networks, approaches for modeling biological networks, and examples of biological network models developed for forest trees to date. We discuss how transcriptional network research is being developed in the model forest tree genus, Populus, and how this research area can be further developed and applied. Transcriptional network models for forest tree secondary growth and wood formation could ultimately provide new predictive models to accelerate hypothesis-driven research and develop new breeding applications.

Janus: prediction and ranking of mutations required for functional interconversion of enzymes.

Identification of residues responsible for functional specificity in enzymes is a challenging and important problem in protein chemistry. Active-site residues are generally easy to identify, but residues outside the active site are also important to catalysis and their identities and roles are more difficult to determine. We report a method based on analysis of multiple sequence alignments, embodied in our program Janus, for predicting mutations required to interconvert structurally related but functionally distinct enzymes. Conversion of aspartate aminotransferase into tyrosine aminotransferase is demonstrated and compared to previous efforts. Incorporation of 35 predicted mutations resulted in an enzyme with the desired substrate specificity but low catalytic activity. A single round of DNA back-shuffling with wild-type aspartate aminotransferase on this variant generated mutants with tyrosine aminotransferase activities better than those previously realized from rational design or directed evolution. Methods such as this, coupled with computational modeling, may prove invaluable in furthering our understanding of enzyme catalysis and engineering.

Statistical mutation calling from sequenced overlapping DNA pools in TILLING experiments.

BACKGROUND: TILLING (Targeting induced local lesions IN genomes) is an efficient reverse genetics approach for detecting induced mutations in pools of individuals. Combined with the high-throughput of next-generation sequencing technologies, and the resolving power of overlapping pool design, TILLING provides an efficient and economical platform for functional genomics across thousands of organisms. RESULTS: We propose a probabilistic method for calling TILLING-induced mutations, and their carriers, from high throughput sequencing data of overlapping population pools, where each individual occurs in two pools. We assign a probability score to each sequence position by applying Bayes' Theorem to a simplified binomial model of sequencing error and expected mutations, taking into account the coverage level. We test the performance of our method on variable quality, high-throughput sequences from wheat and rice mutagenized populations. CONCLUSIONS: We show that our method effectively discovers mutations in large populations with sensitivity of 92.5% and specificity of 99.8%. It also outperforms existing SNP detection methods in detecting real mutations, especially at higher levels of coverage variability across sequenced pools, and in lower quality short reads sequence data. The implementation of our method is available from: http://www.cs.ucdavis.edu/filkov/CAMBa/.

Discovery of rare mutations in populations: TILLING by sequencing.

Discovery of rare mutations in populations requires methods, such as TILLING (for Targeting Induced Local Lesions in Genomes), for processing and analyzing many individuals in parallel. Previous TILLING protocols employed enzymatic or physical discrimination of heteroduplexed from homoduplexed target DNA. Using mutant populations of rice (Oryza sativa) and wheat (Triticum durum), we developed a method based on Illumina sequencing of target genes amplified from multidimensionally pooled templates representing 768 individuals per experiment. Parallel processing of sequencing libraries was aided by unique tracer sequences and barcodes allowing flexibility in the number and pooling arrangement of targeted genes, species, and pooling scheme. Sequencing reads were processed and aligned to the reference to identify possible single-nucleotide changes, which were then evaluated for frequency, sequencing quality, intersection pattern in pools, and statistical relevance to produce a Bayesian score with an associated confidence threshold. Discovery was robust both in rice and wheat using either bidimensional or tridimensional pooling schemes. The method compared favorably with other molecular and computational approaches, providing high sensitivity and specificity.

Strong associations between microbe phenotypes and their network architecture.

Understanding the dependence and interplay between architecture and function in biological networks has great relevance to disease progression, biological fabrication, and biological systems in general. We propose methods to assess the association of various microbe characteristics and phenotypes with the topology of their networks. We adopt an automated approach to characterize metabolic networks of 32 microbial species using 11 topological metrics from complex networks. Clustering allows us to extract the indispensable, independent, and informative metrics. Using hierarchical linear modeling, we identify relevant subgroups of these metrics and establish that they associate with microbial phenotypes surprisingly well. This work can serve as a stepping stone to cataloging biologically relevant topological properties of networks and toward better modeling of phenotypes. The methods we use can also be applied to networks from other disciplines.

A simple model of the modular structure of transcriptional regulation in yeast.

Resolving the general organizational principles that govern the interactions during transcriptional gene regulation has great relevance for understanding disease progression, biofabrication, and biological systems in general. The available genome-level monitoring technologies and the best understood biological work on gene regulation are together providing us with unprecedented amounts of data and universal modeling frameworks in which to reason about regulatory systems on a computational level. Gene regulatory systems exhibit modularity in their regulatory sequences as well as in the corresponding gene expression. This modularity has a nontrivial, general combinatorial structure that can be studied and generalized to model classes of regulatory systems. Here, we study computationally the combinatorial nature of transcriptional regulation by assuming a one-to-one relationship between shared patterns in genome-wide gene-expression and cis-region modules. In our combinatorial framework, the DNA binding events are complementary to their expression counterparts, and together let us approximate the underlying regulation structure. Our model maps regulatory systems onto hierarchical structures which can be approximated by conflating existing large scale gene expression and ChIP-chip data. We have developed methods for building regulatory hierarchies and identifying the basic functional units, or modules, of transcriptional regulation. We validate our model using yeast data by showing agreement of our predictions with experimental data, and using the hierarchies to resolve a finer structure of co-regulation.