bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk sequencing results

Motivation: Bulk sequencing experiments are essential for exploring a wide range of biological questions. To bring data analysis closer to its interpretation, and facilitate both interactive, exploratory tasks and the sharing of easily accessible information, we present bulkAnalyseR, an R package that offers a seamless, customisable solution for most bulk RNAseq datasets. Results: In bulkAnalyseR, we integrate state-of-the-art approaches, without relying on extensive computational support. We replace static summary images with interactive panels to further strengthen the usability and interpretability of data. The package enables standard analyses on bulk sequencing output, using an expression matrix as the starting point (with the added flexibility of choosing subsets of samples). In an interactive web-based interface, steps such as quality checking, noise detection, inference of differential expression and expression patterns, and biological interpretation (enrichment analyses and identification of regulatory interactions), can be customised, easing the exploration and testing of hypotheses. Availability: bulkAnalyseR is available on GitHub, along with extensive documentation and usage examples (https://github.com/Core-Bioinformatics/bulkAnalyseR).

“Because I'm Bad at the Game!” A Microanalytic Study of Self Regulated Learning in League of Legends

Self-regulated learning (SRL) is a form of learning guided by the student's own meta-cognition, motivation, and strategic action, often in the absence of an educator. The use of SRL processes and skills has been demonstrated across numerous academic and non-academic contexts including athletics. However, manifestation of these processes within esports has not been studied. Similar to traditional athletes, esports players' performance is likely correlated with their ability to engage SRL skills as they train. Thus, the study of SRL in the context of esports would be valuable in supporting players' learning and mastery of play through specialized training and computational support. Further, an understanding of how SRL manifests in esports would highlight new opportunities to use esports in education. Existing work on SRL in games, however, predominantly focuses on educational games. In this work, we aim to take a first step in the study of SRL in esports by replicating Kitsantas and Zimmerman's (2002) volleyball study in the context of League of Legends. We compared the self-regulatory processes of expert, non-expert, and novice League of Legends players, and found that there were significant differences for processes in the forethought phase. We discuss three implications of these findings: what they mean for the development of future computational tools for esports players, implications that esports may be able to teach SRL skills that transfer to academics, and what educational technology can learn from esports to create more effective tools.

Modeling of the processes of extracting rules from Neural Network components with Petri nets

The paper deals with the use of extended Petri nets in modeling the processes of extracting rules from neural network components. The mathematical model for extracting rules from neural network components based on a modified timed Petri net is constructed, followed by an analysis of its dynamic behavior based on a timed reachability graph, which is a set of all its states that can be reached when a finite number of transitions are fired. The proposed model allows us to move from the initial detailed structure to its simplified description, which preserves the possibility of obtaining information about the structure and dynamic behavior of the neural network system. The proposed approach can be used in the synthesis of cognitive systems with a neural network organization to provide computational support for the functions of forming, learning, and correcting cognitive networks that display neural network models.

A bi-directional approach to building-integrated PV systems configuration

The configuration of local building-integrated photovoltaic (PV) installations can benefit from computational support. Especially in cases where a high degree of energy self-sufficiency is desired, it is important to optimally match the temporal profiles of the building’s energy demand and the available solar radiation intensity. Typically, the building’s demand profile is taken as given, which is treated as the basis for the sizing and configuration of the PV installation. The computational approach framework introduced in this paper is intended to offer additional functionalities. Specifically, it is conceived to facilitate a bi-directional approach to supporting the design and configuration of PV installations. This approach not only informs the configuration of PV system based on the building’s demand profile, but also allows for the exploration of the consequences of the magnitude and temporal profile of the PV’s energy supply potential for the values of relevant building design variables (e.g., building orientation, fraction of glazing in the envelope). The paper presents this computational approach and its functionality in terms of an illustrative case study.

UniBind: maps of high-confidence direct TF-DNA interactions across nine species

Background Transcription factors (TFs) bind specifically to TF binding sites (TFBSs) at cis-regulatory regions to control transcription. It is critical to locate these TF-DNA interactions to understand transcriptional regulation. Efforts to predict bona fide TFBSs benefit from the availability of experimental data mapping DNA binding regions of TFs (chromatin immunoprecipitation followed by sequencing - ChIP-seq). Results In this study, we processed ~ 10,000 public ChIP-seq datasets from nine species to provide high-quality TFBS predictions. After quality control, it culminated with the prediction of ~ 56 million TFBSs with experimental and computational support for direct TF-DNA interactions for 644 TFs in > 1000 cell lines and tissues. These TFBSs were used to predict > 197,000 cis-regulatory modules representing clusters of binding events in the corresponding genomes. The high-quality of the TFBSs was reinforced by their evolutionary conservation, enrichment at active cis-regulatory regions, and capacity to predict combinatorial binding of TFs. Further, we confirmed that the cell type and tissue specificity of enhancer activity was correlated with the number of TFs with binding sites predicted in these regions. All the data is provided to the community through the UniBind database that can be accessed through its web-interface (https://unibind.uio.no/), a dedicated RESTful API, and as genomic tracks. Finally, we provide an enrichment tool, available as a web-service and an R package, for users to find TFs with enriched TFBSs in a set of provided genomic regions. Conclusions UniBind is the first resource of its kind, providing the largest collection of high-confidence direct TF-DNA interactions in nine species.

RGB-D Data-Based Action Recognition: A Review

Classification of human actions is an ongoing research problem in computer vision. This review is aimed to scope current literature on data fusion and action recognition techniques and to identify gaps and future research direction. Success in producing cost-effective and portable vision-based sensors has dramatically increased the number and size of datasets. The increase in the number of action recognition datasets intersects with advances in deep learning architectures and computational support, both of which offer significant research opportunities. Naturally, each action-data modality—such as RGB, depth, skeleton, and infrared (IR)—has distinct characteristics; therefore, it is important to exploit the value of each modality for better action recognition. In this paper, we focus solely on data fusion and recognition techniques in the context of vision with an RGB-D perspective. We conclude by discussing research challenges, emerging trends, and possible future research directions.

Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects

Anterior regions of the ventral visual stream have substantial information about object categories, prompting theories that category-level forces are critical for shaping visual representation. The strong correspondence between category-supervised deep neural networks and ventral stream representation supports this view, but does not provide a viable learning model, as these deepnets rely upon millions of labeled examples. Here we present a fully self-supervised model which instead learns to represent individual images, where views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find category information implicitly emerges in the feature space, and critically that these models achieve parity with category-supervised models in predicting the hierarchical structure of brain responses across the human ventral visual stream. These results provide computational support for learning instance-level representation as a viable goal of the ventral stream, offering an alternative to the category-based framework that has been dominant in visual cognitive neuroscience.

Syntheses and solid-state structures of two cofacial (bis)dipyrrin dichromium complexes in different charge states

The dichromium Pacman complex ( tBudmx)Cr2Cl2·C4H10O (1) [( tBudmx)H2 is a dimethylxanthene-bridged cofacial (bis)dipyrrin, C49H58N4O] was synthesized by salt metathesis using anhydrous CrCl2 and previously reported ( tBudmx)K2. Treatment of 1 with two equivalents of the reductant potassium graphite afforded K2( tBudmx)Cr2Cl2(thf)3·0.5C4H10O·0.5C4H8O (thf is tetrahydrofuran, C4H8O) (2), with both potassium ions intercalated between the pyrrolic subunits. Comparison of the solid-state structures for 1 and 2 reveals minimal changes in the primary coordination sphere of each Cr ion, with notable elongation of the dipyrrin C—C and C—N bonds upon reduction, consistent with computational support for a ligand-based reduction.

Feasible Bases for a Polytope Related to the Hamilton Cycle Problem

We study a certain polytope depending on a graph G and a parameter β ∈ (0,1) that arises from embedding the Hamiltonian cycle problem in a discounted Markov decision process. Literature suggests a conjecture a lower bound on the proportion of feasible bases corresponding to Hamiltonian cycles in the set of all feasible bases. We make progress toward a proof of the conjecture by proving results about the structure of feasible bases. In particular, we prove three main results: (1) the set of feasible bases is independent of the parameter β when the parameter is close to one, (2) the polytope can be interpreted as a generalized network flow polytope, and (3) we deduce a combinatorial interpretation of the feasible bases. We also provide a full characterization for a special class of feasible bases, and we apply this to provide some computational support for the conjecture.