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The compartmental organization of mammalian genomes and its changes play important roles in distinct biological processes. Here, we introduce dcHiC, which utilizes a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on four collections of bulk and single-cell contact maps from in vitro mouse neural differentiation ( n  = 3), mouse hematopoiesis ( n  = 10), human LCLs ( n  = 20) and post-natal mouse brain development ( n  = 3 stages), we show its effectiveness and sensitivity in detecting biologically relevant changes, including those orthogonally validated. dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, subcompartments, replication timing and lamin association. With its efficient implementation, dcHiC enables high-resolution compartment analysis as well as standalone browser visualization, differential interaction identification and time-series clustering. dcHiC is an essential addition to the Hi-C analysis toolbox for the ever-growing number of bulk and single-cell contact maps. Available at: https://github.com/ay-lab/dcHiC . The organisation of mammalian genomes plays a role in many biological processes. Here the authors report dcHiC, a tool which uses a multivariate distance measure to identify changes in compartmentalisation among multiple genome-wide chromatin contact maps, and apply this to different human and mouse datasets.

In this paper, we study the topological properties of the global supply chain network in terms of its degree distribution, clustering coefficient, degree-degree correlation, bow-tie structure, and community structure to test the efficient supply chain propositions proposed by E. J.S. Hearnshaw et al. The global supply chain data in the year 2017 are constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform. The in- and out-degree distributions are characterized by a power law of the form of [gamma].sub.in = 2.42 and [gamma].sub.out = 2.11. The clustering coefficient decays ( C ( k) ) ~ k - [beta] k with an exponent [beta].sub.k = 0.46. The nodal degree-degree correlations (k.sub.nn (k)) indicates the absence of assortativity. The bow-tie structure of giant weakly connected component (GWCC) reveals that the OUT component is the largest and consists 41.1% of all firms. The giant strong connected component (GSCC) is comprised of 16.4% of all firms. We observe that upstream or downstream firms are located a few steps away from the GSCC. Furthermore, we uncover the community structures of the network and characterize them according to their location and industry classification. We observe that the largest community consists of the consumer discretionary sector based mainly in the United States (US). These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity of Hearnshaw et al.'s efficient supply chain propositions, namely Proposition S1 (short path length), Proposition S2 (power-law degree distribution), Proposition S3 (high clustering coefficient), Proposition S4 (\"fit-gets-richer\" growth mechanism), Proposition S5 (truncation of power-law degree distribution), and Proposition S7 (community structure with overlapping boundaries) regarding the global supply chain network. While the original propositions S1 just mentioned a short path length, we found the short path from the GSCC to IN and OUT by analyzing the bow-tie structure. Therefore, the short path length in the bow-tie structure is a conceptual addition to the original propositions of Hearnshaw.

Every nation prioritizes the inclusive economic growth and development of all regions. However, we observe that economic activities are clustered in space, which results in a disparity in per-capita income among different regions. A complexity-based method was proposed by Hidalgo and Hausmann [PNAS 106, 10570-10575 (2009)] to explain the large gaps in per-capita income across countries. Although there have been extensive studies on countries' economic complexity using international export data, studies on economic complexity at the regional level are relatively less studied. Here, we study the industrial sector complexity of prefectures in Japan based on the basic information of more than one million firms. We aggregate the data as a bipartite network of prefectures and industrial sectors. We decompose the bipartite network as a prefecture-prefecture network and sector-sector network, which reveals the relationships among them. Similarities among the prefectures and among the sectors are measured using a metric. From these similarity matrices, we cluster the prefectures and sectors using the minimal spanning tree technique. The computed economic complexity index from the structure of the bipartite network shows a high correlation with macroeconomic indicators, such as per-capita gross prefectural product and prefectural income per person. We argue that this index reflects the present economic performance and hidden potential of the prefectures for future growth.

Objective: Handwashing is now considered as one of the best safety measures to prevent COVID-19 infection. The effect of excessive handwashing for health on OCD patients who are already having washing compulsion is not known. Furthermore, the fear of contamination of COVID-19 in patients who already have obsession of contamination is not known. This study aims to evaluate the effect of COVID-19 on OCD patients. Method: Phone interviews were done with 84 patients previously diagnosed with obsession of contamination and compulsive washing. Yale Brown Obsessive Compulsive Scale was used and the scores of the participants were compared to their prepandemic scores. Results: Only 5 patients (6%) had exacerbation of symptoms after the COVID-19 pandemic. Most of the patients did not report any deterioration of symptoms due to the pandemic. Conclusion: Handwashing protocol does not aggravate the washing compulsion of patients. Similarly, the fear of infection with COVID-19 does not increase their fear of contamination.

For centuries, national economies have been engaging in international trade and production. The resulting international supply networks not only increase wealth for countries, but also allow for economic shocks to propagate across borders. Using global, firm-level supply network data, we estimate a country’s exposure to direct and indirect economic losses caused by the failure of a company in another country. We show the network of international systemic risk-flows. We find that rich countries expose poor countries stronger to systemic risk than vice-versa. The risk is highly concentrated, however, higher risk levels are not compensated with a risk premium in GDP levels, nor higher GDP growth. Our findings put the often praised benefits for developing countries from globalized production in a new light, by relating them to risks involved in the production processes. Exposure risks present a new dimension of global inequality that most affects the poor in supply shock crises. Thurner and colleagues explore how economic shocks spread risk through the globalized economy. They find that rich countries expose poor countries stronger to systemic risk than vice-versa. The risk is highly concentrated, however higher risk levels are not compensated with a risk premium in GDP levels, nor higher GDP growth. The findings put the often-praised benefits for developing countries from globalized production in a new light, by relating them to risks involved in the production processes

Diabetic atherosclerosis results from the interplay between metabolic dysfunction and immune dysregulation and remains the major cause of mortality in patients with diabetes mellitus (DM) worldwide. Emerging evidence indicates that impaired immune checkpoint signaling, particularly through the PD-1/PD-L1 and CTLA-4 pathways, contributes to the chronic vascular inflammation characteristic of diabetic cardiovascular disease. These checkpoints normally help maintain vascular homeostasis by limiting proatherogenic immune responses. In type 2 diabetes (T2D), which accounts for 90-95% of cases, chronic hyperglycemia downregulates checkpoint expression in both immune effector cells and the vascular endothelium. In type 1 diabetes (T1D), autoimmune-mediated checkpoint failure within the pancreatic islets extends to the vascular tissues, creating an early cardiovascular risk through overlapping but distinct mechanisms. The loss of checkpoint regulation amplifies Th1 and Th17 responses while impairing regulatory T cell function and accelerating plaque formation and destabilization. Observations from cancer patients receiving checkpoint inhibitors, who exhibit increased cardiovascular events, further highlight the importance of these pathways in vascular integrity. Restoring checkpoint signaling through targeted interventions, combined with biomarker-driven stratification and personalized immune profiling, may provide new strategies for preventing or slowing atherosclerotic progression in patients with diabetes.

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor’s biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Economies are frequently affected by natural disasters and both domestic and overseas financial crises. These events disrupt production and cause multiple other types of economic losses, including negative impacts on the banking system. Understanding the transmission mechanism that causes various negative second-order post-catastrophe effects is crucial if policymakers are to develop more efficient recovery strategies. In this work, we introduce a credit-based adaptive regional input-output (ARIO) model to analyse the effects of disasters and crises on the supply chain and bank-firm credit networks. Using real Japanese networks and the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper aims to depict how these negative shocks propagate through the supply chain and affect the banking system. The credit-based ARIO model is calibrated using Latin hypercube sampling and the design of experiments procedure to reproduce the short-term (one-year) dynamics of the Japanese industrial production index after the 2008 Lehman Brothers bankruptcy and the 2011 Great East Japan earthquake. Then, through simulation experiments, we identify the chemical and petroleum manufacturing and transport sectors as the most vulnerable Japanese industrial sectors. Finally, the case of the 2011 Great East Japan Earthquake is simulated for Japanese prefectures to understand differences among regions in terms of globally engendered indirect economic losses. Tokyo and Osaka prefectures are the most vulnerable locations because they hold greater concentrations of the above-mentioned vulnerable industrial sectors.

Nanotechnology has revolutionized cancer therapy by introducing advanced drug delivery systems that enhance therapeutic efficacy while reducing adverse effects. By leveraging various nanoparticle platforms—including liposomes, polymeric nanoparticles, and inorganic nanoparticles—researchers have improved drug solubility, stability, and bioavailability. Additionally, new nanodevices are being engineered to respond to specific physiological conditions like temperature and pH variations, enabling controlled drug release and optimizing therapeutic outcomes. Beyond drug delivery, nanotechnology plays a crucial role in the theranostic field due to the functionalization of specific materials that combine tumor detection and targeted treatment features. This review analyzes the clinical impact of nanotechnology, spanning from early-phase trials to pivotal phase 3 studies that have obtained regulatory approval, while also offering a critical perspective on the preclinical domain and its translational potential for future human applications. Despite significant progress, greater attention must be placed on key challenges, such as biocompatibility barriers and the lack of regulatory standardization, to ensure the successful translation of nanomedicine into routine clinical practice.

Background Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. Results Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments. Conclusions In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.