Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

Grasslands are recognized as important reservoirs of soil biodiversity. Livestock grazing is implemented as a grassland management strategy to improve soil quality and enhance plant diversity. Soil microbial communities play a pivotal role in grassland ecosystems, so it is important to examine whether grazing practices affect the soil microbiome. Previous studies on grazing have primarily focused on bacteria and fungi, overlooking an important group-protists. Protists are vital in soil microbiomes as they drive nutrient availability and trophic interactions. Determining the impact of grazing on protists and their relationships with bacterial and fungal communities is important for understanding soil microbiome dynamics in grazed ecosystems. In this study, we investigated soil bacterial, fungal, and protist communities under four grazing levels: no grazing, moderate-use grazing, full-use grazing, and heavy-use grazing. Our results showed that heavy grazing led to a greater diversity of protists with specific groups, such as Discoba and Conosa, increasing in abundance. We also found strong associations between protist and bacterial/fungal members, indicating their intricate relationships within the soil microbiome. For example, the abundance of predatory protists increased under grazing while arbuscular mycorrhizal fungi decreased. Notably, arbuscular mycorrhizae were negatively associated with predatory groups. Furthermore, we observed that microbial network complexity increased with grazing intensity, with fungal members playing an important role in the network. Overall, our study reports the impact of temporal grazing intensity on soil microbial dynamics and highlights the importance of considering protist ecology when evaluating the effects of grazing on belowground communities in grassland ecosystems.
OBJECTIVE: The significance of this study lies in its exploration of the effects of temporal grazing intensity on the dynamics of the soil microbiome, specifically focusing on the often-neglected role of protists. Our findings provide insights into the complex relationships between protists, bacteria, and fungi, emphasizing their impact on trophic interactions in the soil. Gaining a better understanding of these dynamics is essential for developing effective strategies for grassland management and conservation, underscoring the importance of incorporating protist ecology into microbiome studies in grasslands.

To enumerate people experiencing homelessness in the U.S., the federal Department of Housing and Urban Development (HUD) mandates its designated local jurisdictions regularly conduct a crude census of this population. This Point-in-Time (PIT) body count, typically conducted on a January night by volunteers with flashlights and clipboards, is often followed by interviews with a separate convenience sample. Here, we propose employing a network-based (peer-referral) respondent-driven sampling (RDS) method to generate a representative sample of unsheltered people, accompanied by a novel method to generate a statistical estimate of the number of unsheltered people in the jurisdiction. First, we develop a power analysis for the sample size of our RDS survey to count unsheltered people experiencing homelessness. Then, we conducted three large-scale population-representative samples in King County, WA (Seattle metro) in 2022, 2023, and 2024. We describe the data collection and the application of our new method, comparing the 2020 PIT count (the last visual PIT count performed in King County) to the new method 2022 and 2024 PIT counts. We conclude with a discussion and future directions.

Coiled-coil \'bundlemer\' peptides were selectively modified with allyloxycarbonyl (alloc)-protected lysine, a non-natural amino acid containing an alkene on its side chain. The specific display of this alkene from the coiled-coil surface with protein-like specificity enabled this residue to be used as a covalent linkage for creating peptide networks with controllable properties or as a physical linkage for the self-assembly of bundlemers into unexpected, intricate lattices driven by the hydrophobic nature of the side chain. For network formation, peptides were modified with both alloc-protected lysine and cysteine amino acids for solution assembly into solvent-swollen films and subsequent covalent cross-linking via thiol-ene photo click reactions. The degree of network cross-linking, as determined by rheometry, was finely tuned by varying the specific spatial display of reactive groups on the bundlemer building block particles, transitioning between intrabundle and interbundle cross-linking. The designed display of alloc groups from the center of the bundlemer building block also prompted particle self-assembly into an unexpected intricate lattice with a porous morphology. The lattices were studied in a variety of solution conditions using transmission electron microscopy, cryotransmission electron microscopy, and small-angle X-ray scattering. The approximate particle arrangement in the lattice was determined by using coarse-grained modeling and machine learning optimization techniques along with experimental methods. The proposed truss-like face-centered cubic packing of the alloc-functionalized bundlemers agrees well with the experimental results.

We consider a problem of inferring contact network from nodal states observed during an epidemiological process. In a black-box Bayesian optimisation framework this problem reduces to a discrete likelihood optimisation over the set of possible networks. The cardinality of this set grows combinatorially with the number of network nodes, which makes this optimisation computationally challenging. For each network, its likelihood is the probability for the observed data to appear during the evolution of the epidemiological process on this network. This probability can be very small, particularly if the network is significantly different from the ground truth network, from which the observed data actually appear. A commonly used stochastic simulation algorithm struggles to recover rare events and hence to estimate small probabilities and likelihoods. In this paper we replace the stochastic simulation with solving the chemical master equation for the probabilities of all network states. Since this equation also suffers from the curse of dimensionality, we apply tensor train approximations to overcome it and enable fast and accurate computations. Numerical simulations demonstrate efficient black-box Bayesian inference of the network.

The representation of complex systems as networks has become a critical tool across many fields of science. In the context of physical networks, such as biological neural networks, vascular networks, or network liquids where the nodes and edges occupy volume in three-dimensional space, the question of how they become densely packed is of special importance. Here, we investigate a model network liquid, which is known to densify via two successive liquid-liquid phase transitions (LLPTs). We elucidate the importance of rings-cyclic paths formed by bonded particles in the networks-and their spatial disposition in understanding the structural changes that underpin the increase in density across the LLPTs. Our analyses demonstrate that the densification of these networks is primarily driven by the formation of linked rings, and the LLPTs correspond to a hierarchy of topological transitions where rings form the fundamental building blocks. We envisage entanglement to emerge as a general mechanism for densification, with wide implications for the embedding of physical networks, especially in confined spaces.

OBJECTIVE: The aim of this study was to examine the temporal dynamics of multiple health behaviours (physical activity, alcohol consumption, healthy eating, cigarette consumption, recreational drug use, vaping), and pandemic-related health behaviours (e.g., hand washing, physical distancing) using network psychometrics.
METHODS: The International COVID-19 Awareness and Responses Evaluation (iCARE) study is an international multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. A sub-sample of longitudinal data from Canadians (n = 254) was analysed across four waves (February-July 2020).
METHODS: We used temporal network models to fit temporal networks, contemporaneous networks, and between-subject networks from items within the iCARE survey.
RESULTS: Positive temporal associations were observed between physical activity and healthy eating, and a bidirectional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed positive associations between consumption behaviours (vaping, cigarette use, alcohol use, and recreational drug use), and negative associations between physical activity and drug use, and healthy eating and cigarette use.
CONCLUSIONS: Health behaviours are interconnected and can be modelled as networks or behavioural systems. The application of temporal network analysis to the study of multiple health behaviours is well suited to address key research questions in the field such as \'how do multiple health behaviours co-vary with one another over time\'. Future research using time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis-generating insights.

Entropy measures are increasingly being used to analyze the structure of neural activity observed by functional magnetic resonance imaging (fMRI), with resting-state networks (RSNs) being of interest for their reproducible descriptions of the brain\'s functional architecture. Temporal correlations have shown a dichotomy among these networks: those that engage with the environment, known as extrinsic, which include the visual and sensorimotor networks; and those associated with executive control and self-referencing, known as intrinsic, which include the default mode network and the frontoparietal control network. While these inter-voxel temporal correlations enable the assessment of synchrony among the components of individual networks, entropic measures introduce an intra-voxel assessment that quantifies signal features encoded within each blood oxygen level-dependent (BOLD) time series. As a result, this framework offers insights into comprehending the representation and processing of information within fMRI signals. Multiscale entropy (MSE) has been proposed as a useful measure for characterizing the entropy of neural activity across different temporal scales. This measure of temporal entropy in BOLD data is dependent on the length of the time series; thus, high-quality data with fine-grained temporal resolution and a sufficient number of time frames is needed to improve entropy precision. We apply MSE to the Midnight Scan Club, a highly sampled and well-characterized publicly available dataset, to analyze the entropy distribution of RSNs and evaluate its ability to distinguish between different functional networks. Entropy profiles are compared across temporal scales and RSNs. Our results have shown that the spatial distribution of entropy at infra-slow frequencies (0.005-0.1 Hz) reproduces known parcellations of RSNs. We found a complexity hierarchy between intrinsic and extrinsic RSNs, with intrinsic networks robustly exhibiting higher entropy than extrinsic networks. Finally, we found new evidence that the topography of entropy in the posterior cerebellum exhibits high levels of entropy comparable to that of intrinsic RSNs.

Dementia is a global public health concern. This study focuses on the genetic factors underlying dementia. We analyzed electronic medical records (EMR) from Taichung Veterans General Hospital, Taiwan, to confirm differences between dementia and non-dementia patients. This work was supported by Taipei Medical University [TMU111-AE1-B45].

The honeybee (Apis mellifera) is a key pollinator critical to global agriculture, facing threats from various stressors, including the ectoparasitic Varroa mite (Varroa destructor). Previous studies have identified shared bacteria between Varroa mites and honeybees, yet it remains unclear if these bacteria assemble similarly in both species. This study builds on existing knowledge by investigating co-occurrence patterns in the microbiomes of both Varroa mites and honeybees, shedding light on potential interactions. Leveraging 16S rRNA datasets, we conducted co-occurrence network analyses, explored Core Association Networks (CAN) and assess network robustness. Comparative network analyses revealed structural differences between honeybee and mite microbiomes, along with shared core features and microbial motifs. The mite network exhibited lower robustness, suggesting less resistance to taxa extension compared to honeybees. Furthermore, analyses of predicted functional profiling and taxa contribution revealed that common central pathways in the metabolic networks have different taxa contributing to Varroa mites and honeybee microbiomes. The results show that while both microbial systems exhibit functional redundancy, in which different taxa contribute to the functional stability and resilience of the ecosystem, there is evidence for niche specialization resulting in unique contributions to specific pathways in each part of this host-parasite system. The specificity of taxa contribution to key pathways offers targeted approaches to Varroa microbiome management and preserving honeybee microbiome. Our findings provide valuable insights into microbial interactions, aiding farmers and beekeepers in maintaining healthy and resilient bee colonies amid increasing Varroa mite infestations.