Browse Articles

Serum IL-6 and PTX3 predict severe outcome from COVID-19 in ambulatory subjects: Impact for future therapeutic decisions

SARS-CoV-2 infections lead to a wide-range of outcomes from mild or asymptomatic illness to serious complications and death. While many studies have characterized hospitalized SARS-CoV-2 patient immune responses, we were interested in whether serious complications of SARS-CoV-2 infection could be predicted early in ambulatory subjects. To that end, we used samples from SARS-CoV-2-infected individuals from the placebo arm of the BLAZE-1 clinical trial who progressed to hospitalization or death compared to individuals in the same study who did not require medical intervention and investigated whether baseline serum cytokines and chemokines could predict severe outcome. High-risk demographic factors at baseline, including age, nasal pharyngeal viral load, duration from symptom onset, and BMI provide significant predictive capacity for a hospitalization or death with an AUC of ROC = 0.77. The predictive performance of our outcome modeling increased when baseline serum protein markers were included. In fact, the one-marker model indicated that there were 51 individual proteins (including known markers of inflammation like IL-6, MCP-3, CXCL10, IL-1Ra, and PTX3) that significantly increased the AUC of ROC beyond high-risk patient demographics alone to range between 0.78 to 0.88. Moreover, a two-marker model incorporating levels of both IL-6 and PTX3 further improved the prediction over the addition of a single protein marker to an AUC of ROC = 0.91. While the analytes identified in this study have been well-documented to be altered in SARS-CoV-2 infection, this analysis demonstrates the potential value of their use in predicting hospitalization or death in ambulatory participants infected with SARS-CoV-2 and could guide early treatment decisions.

Do all women shy away from competition? Competitive preferences among Dutch and non-Western females in the Netherlands

This study explores the willingness to compete among native Dutch individuals and non-Western migrants in the Netherlands, with a particular focus on first-generation non-Western migrant women. Drawing upon existing literature on competitiveness and career preferences, this research suggests that non-Western migrants, especially the first generation of non-Western migrant women, may exhibit different levels of competitiveness compared to the native Dutch population or native Dutch women. This aspect is crucial for understanding the challenges non-Western immigrant women encounter in the job market and broader societal contexts. Utilizing data from two existing experiments conducted within the publicly accessible LISS framework, our research findings reveal that first generation of non-Western women demonstrate a higher propensity for competition than their Dutch counterparts do. Notably, this pattern is not replicated among non-Western men participants in all generations. The results align with research on gender equality and competitiveness in the countries of origin. In nations like the Netherlands, where gender equality is relatively high, notable differences in competitive preferences between native women and men are observed. However, this disparity is less pronounced among first-generation non-Western migrant women from countries with lower levels of gender equality. Additionally, the study uncovers variations in competitive preferences between native Dutch and non-Western migrant women within the same societal setting. This research contributes to a deeper understanding of the complex interactions among ethnicity, competitiveness, and gender dynamics in Dutch society.

HCK regulates NLRP12-mediated PANoptosis

NOD-like receptors (NLRs) are a highly conserved family of cytosolic pattern recognition receptors that drive innate immune responses against pathogens, pathogen-associated molecular patterns, damage-associated molecular patterns, and homeostatic disruptions. Within the NLR family, NLRP12 was recently identified as a key regulator of PANoptosis, which is an innate immune, lytic cell death pathway initiated by innate immune sensors and driven by caspases and RIPKs through PANoptosome complexes. While NLRP12 activation is critical for maintaining homeostasis, aberrant activation has been implicated in a broad range of disorders, including cancers and metabolic, infectious, autoinflammatory, and hemolytic diseases. However, the molecular mechanisms of NLRP12 activation remain poorly understood. Here, we identified hematopoietic cell kinase (HCK) as a regulator of NLRP12-mediated PANoptosis. HCK expression was significantly upregulated in response to NLRP12-PANoptosome triggers. Moreover, Hck knockdown inhibited NLRP12-mediated PANoptosis. Computational analyses identified residues in the putative interaction interface between NLRP12 and HCK, suggesting that HCK likely binds NLRP12 in the region between its NACHT domain and pyrin domain (PYD); removal of the NLRP12 PYD abrogated this interaction in vitro. Overall, our work identifies HCK as a regulator of NLRP12-mediated PANoptosis, suggesting that it may serve as a potential therapeutic target for mitigating inflammation and pathology.

Estimating multiplicity of infection, haplotype frequencies, and linkage disequilibria from multi-allelic markers for molecular disease surveillance

Background Molecular/genetic methods are becoming increasingly important for surveillance of diseases like malaria. Such methods allow monitoring routes of disease transmission or the origin and spread of variants associated with drug resistance. A confounding factor in molecular disease surveillance is the presence of multiple distinct variants in the same infection (multiplicity of infection – MOI), which leads to ambiguity when reconstructing which pathogenic variants are present in an infection. Heuristic approaches often ignore ambiguous infections, which leads to biased results. Methods To avoid bias, we introduce a statistical framework to estimate haplotype frequencies alongside MOI from a pair of multi-allelic molecular markers. Estimates are based on maximum likelihood using the expectation-maximization (EM)-algorithm. The estimates can be used as plug-ins to construct pairwise linkage disequilibrium (LD) maps. The finite-sample properties of the proposed method are studied by systematic numerical simulations. These reveal that the EM-algorithm is a numerically stable method in our case and that the proposed method is accurate (little bias) and precise (small variance) for a reasonable sample size. In fact, the results suggest that the estimator is asymptotically unbiased. Furthermore, the method is appropriate to estimate LD (by D ′ , r 2 , Q * , or conditional asymmetric LD). Furthermore, as an illustration, we apply the new method to a previously published dataset from Cameroon concerning sulfadoxine-pyrimethamine (SP) resistance. The results are in accordance with the SP drug pressure at the time and the observed spread of resistance in the country, yielding further evidence for the adequacy of the proposed method. Conclusion The proposed method can be readily applied in practice for malaria disease surveillance as a replacement for heuristic methods. The first benefit is its ability to estimate MOI, which scales with transmission intensities, and, in a temporal context, can be used to evaluate the effectiveness of disease control measures. MOI is best estimated from molecular markers that are not under selection (neutral markers) and exhibit sufficient genetic variation. The second advantage is that it can estimate pairwise LD without deflating sample size as in heuristic methods, thereby limiting uncertainty in the estimates. This is particularly useful when deriving LD maps from data with many ambiguous observations due to MOI. Importantly, the method per se is not restricted to malaria, but applicable to any disease with a similar transmission pattern. The method and several extensions are implemented in an easy-to-use R script.

Retraction: Digital inclusive finance, agricultural green technology innovation and agricultural carbon emissions: Impact mechanism and empirical test

Cnot4 heterozygosity attenuates high fat diet-induced obesity in mice and impairs PPARγ-mediated adipocyte differentiation

Adipocyte differentiation is crucial for formation and expansion of white adipose tissue and is also associated with the pathologies of obesity. CNOT4 is an E3 ubiquitin ligase and also contains RNA binding domain. In mammals CNOT4 has been suggested to interact with CCR4-NOT complex, a major executor of mRNA poly(A) shortening. While several subunits within the CCR4-NOT complex were shown to be involved in obesity and energy metabolism, the roles of CNOT4 in obesity remain unexplored. In this study, we generated and analyzed Cnot4 knockout mice and found that Cnot4 heterozygous (Cnot4 Het) mice exhibit resistance to high fat diet-induced obesity, including significant reduction in adipose tissue mass and hepatic lipid depots. However, Cnot4 Het did not affect mRNA expression of metabolic genes as well as serum lipid levels or glucose tolerance. On the other hand, Cnot4 Het fibroblasts significantly reduced the capability of differentiation into adipocytes and down-regulated adipogenic gene expression compared to wild type fibroblasts. Mechanistically, heterozygous deletion of Cnot4 down-regulated the transcriptional activity through decreased binding of PPARγ to promoter region of the target gene, thereby suppressing up-regulation of adipocyte marker gene expression in response to rosiglitazone, a PPARγ agonist. These results suggest that CNOT4 mediates adipocyte differentiation during formation and growth of adipose tissue partly through positively regulating transcriptional activity of PPARγ.

An optimal integrated control strategy of urban expressway and adjacent signalized intersection with rolling horizon framework in MPC method

Urban expressway congestion around on-ramp bottlenecks is associated with traffic conditions on the main road, on-ramp and adjacent signalized intersection. Existing coordinated control strategies have rarely considered these components as a unified system. To enhance traffic system operation, this paper proposes an optimal integrated control strategy based on the model predictive control (MPC) method. Within the rolling horizon control framework, this strategy integrates ramp metering and intersection signal timing. To validate its effectiveness, simulation scenarios were developed in VISSIM software, based on a section of the South Ring Road in Ningbo, China. In contrast, the proposed strategy overall outperforms the other two referring strategies, as it improves mainline and on-ramp traffic. Additionally, traffic operation of movements at the intersection is slightly better. Therefore, the integrated control strategy ensures mainline traffic efficiency while balancing on-ramp and intersection traffic. Comparatively, referring strategy 2 exhibits poorer performance at the on-ramp and intersection, which optimally coordinates mainline speed limit, ramp metering and intersection signal timing. Traffic fluctuations on the main road due to dynamic speed limits have a negative impact on overall traffic performance.

Neuron–astrocyte associative memory

Astrocytes, the most abundant type of glial cell, play a fundamental role in memory. Despite most hippocampal synapses being contacted by an astrocyte, there are no current theories that explain how neurons, synapses, and astrocytes might collectively contribute to memory function. We demonstrate that fundamental aspects of astrocyte morphology and physiology naturally lead to a dynamic, high-capacity associative memory system. The neuron–astrocyte networks generated by our framework are closely related to popular machine learning architectures known as Dense Associative Memories. Adjusting the connectivity pattern, the model developed here leads to a family of associative memory networks that includes a Dense Associative Memory and a Transformer as two limiting cases. In the known biological implementations of Dense Associative Memories, the ratio of stored memories to the number of neurons remains constant, despite the growth of the network size. Our work demonstrates that neuron–astrocyte networks follow a superior memory scaling law, outperforming known biological implementations of Dense Associative Memory. Our model suggests an exciting and previously unnoticed possibility that memories could be stored, at least in part, within the network of astrocyte processes rather than solely in the synaptic weights between neurons.

Assessment of cross-cultural adaptations and patient-reported outcome measures relevant to shoulder disorders in Turkish: A systematic review using the COSMIN methodology

Background There are many shoulder assessment outcome measures in the literature that have been studied for validity and reliability. However, there is no study examining the Turkish-adapted patient outcome measures (PROMs) on the shoulder according to the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) checklist. In addition, there is a small number of studies that carry out this examination on the shoulder internationally. Determining the most appropriate questionnaires for clinical use will also be an important guide in patient evaluation by filling the gap in both literature and clinical aspects. Objective Our aim in this study is to identify the valid and reliable Turkish scales used to evaluate shoulder pain and disability, to reveal how compatible these scales are with the aspects of study quality and psychometric quality according to COSMIN criteria list. Methods A systematic search was performed in the following electronic databases: MED-LINE, Web of Science (WOS), EMBASE, SCOPUS, and ULAKBIM were searched from inception to September 2024. Results A total of 2890 articles were found in the databases. Four articles were included additionally without a search. Then, after eliminating the duplicates, 2231 articles were included based on the title and abstract search. 63 articles were included in full-text review. After the full-text review, according to our criteria, 29 articles were included in the systematic review. According to the assessment by the COSMIN checklist, 28 articles received the final classification of “inadequate”. Only 1 article (UCLA-Tr) received the final classification of “adequate”. Conclusion There is a wide range of Turkish PROMs available for the shoulder, with only one fully meeting COSMIN methodology criteria. However, this does not imply that other PROMs lack clinical utility. Future studies should focus on refining PROMs by incorporating MCID and PASS scores and prioritizing their responsiveness to enhance their clinical relevance. Additionally, variations in PROM performance across different patient populations highlight the need for context-specific evaluations to ensure their applicability in diverse clinical settings. Trial registration PROSPERO registration number: CRD42022298425.

Autophagy-mediated downregulation of AXL and TIM-1 promotes sustained Zika virus infection

Zika virus (ZIKV) infection can lead to a variety of clinical outcomes, including severe congenital abnormalities. The phosphatidylserine receptors AXL and TIM-1 are recognized as critical entry factors for ZIKV in vitro. However, it remains unclear whether and how ZIKV regulates these receptors during infection. In this study, we investigated AXL and TIM-1 expression in human lung adenocarcinoma epithelial A549 cells, glioblastoma U87 cells, and embryonic stem cell-derived trophoblasts following ZIKV infection. We found that both the Asian strain FSS13025 and the African strain MR766 of ZIKV downregulate AXL, with a milder effect on TIM-1. We identified several ZIKV proteins, notably envelope (E), NS2A, NS3, and NS4B, that contribute to this downregulation. Notably, treatment with lysosomal inhibitor NH 4 Cl or the autophagy inhibitor 3-methyladenine mitigated the AXL/TIM-1 downregulation, indicating autophagy’s involvement in the process. Importantly, this downregulation facilitates sustained viral replication and promotes viral spread by preventing superinfection and limiting cell death, which is also associated with impaired innate immune signaling. Our findings uncover a mechanism by which ZIKV downregulates entry factors to enhance prolonged viral replication and spread.

Does competitive position matter: Investigating the impact of information risk on COE and corporate investment

Purpose The purpose of this study is to find out how firm’s competitive position plays the moderating role between the relation of information risk and COE. The study considers the effect of two different types of information risk, i.e., lack of information quality and transparent information. Methodology The data of the study is collected from all the non-financial firms listed on PSX from 2007 to 2022. Two-step system GMM dynamic panel estimators are applied to test the dynamic nature of the proposed model. Findings/Results The findings show that firms having better competitive position signal their strength through improved information disclosure in order to gain the confidence of shareholders. This competitive environment poses a governance effect by imposing discipline on manager’s behavior, reducing information asymmetry and improving the quality of information disclosure, resulting in reduction of the COE. Further, a more competitive environment improves the readability of the annual report and reduces information asymmetry. In addition, by reducing financing frictions, this research provides new and unique insights pertaining to the importance of competitive position in the sensitivity of investment to information risk. Originality This research extends to the corpus of literature by investigating the unexplored strategic determinants, such as a firm’s competitive position, to mitigate the impact of two distinct types of information risk: lower quality and reduced transparency. Additionally, it explores how these risks influence both the cost of equity and corporate investment.

Learning to estimate sample-specific transcriptional networks for 7,000 tumors

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intracluster heterogeneity. We propose to move beyond coarse analyses of predefined subgroups by using contextualized learning, a multitask learning paradigm that uses multiview contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, and Neighborhood Selection) and estimate context-specific GRNs for 7,997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized .

Exploring the level of metabolic reprogramming and the role of prognostic factor SF3A3 in hepatocellular carcinoma through integrated single-cell landscape analysis

This study aims to investigate metabolic reprogramming heterogeneity in hepatocellular carcinoma (HCC) cells and identify novel therapeutic targets for HCC treatment. Single-cell RNA sequencing data from public databases were used to analyze the TME of HCC and reveal the characteristics of different cell subsets, including mononuclear phagocytes, epithelial cells, endothelial cells, NK/T cells, B cells, and unknown cells. The analysis revealed that these cell subsets play their own unique roles in tumor progression and immune escape. Analysis of copy number variations (CNVs) was performed on tumor-derived epithelial cells, with the epithelial cells in Cluster 3 subgroup showing the highest CNV levels. Gene Ontology (GO) enrichment analysis revealed that these cell subsets were involved in a variety of biological processes such as immune response, cell communication, and metabolic pathways, which were consistent with their functional roles. Pseudotemporal analysis further delineated the malignant trajectory of HCC cells, with Cluster 3 exhibiting enhanced phosphatidylinositol metabolism, suggesting a critical role for metabolic reprogramming in tumor invasion and proliferation. Furthermore, a diagnostic model incorporating metabolic reprogramming-associated gene signatures was established, which effectively distinguished HCC from normal tissues. Among these signatures, splicing factor 3a subunit 3 (SF3A3) was identified as both diagnostic and independent prognostic biomarker. Mechanistically, SF3A3 knockdown in HCC cell lines significantly suppressed proliferation, migration, PI3K/AKT signaling, and EMT marker expression, thereby demonstrating its role in driving HCC aggressiveness. In conclusion, these findings elucidate novel molecular characteristics of HCC based on metabolic reprogramming, while establishing SF3A3 as a promising multi-faceted target for HCC diagnosis, prognostic assessment, and therapeutic intervention.

The cortical critical power law balances energy and information in an optimal fashion

A recent study has suggested that the stimulus responses of cortical neural populations follow a critical power law. More precisely, the power spectrum of the covariance matrix of neural responses follows a power law with an exponent indicating that the neural manifold lies on the edge of differentiability. This criticality is hypothesized to balance expressivity and robustness in neural encoding, as population responses on a nondifferential fractal manifold are thought to be overly sensitive to perturbations. However, contrary to this hypothesis, we prove that neural coding is far more robust than previously assumed. We develop a theoretical framework that provides an analytical expression for the Fisher information of population coding under the small noise assumption. Our results reveal that, due to its intrinsic high dimensionality, population coding maintains reliability even on a nondifferentiable fractal manifold, despite its sensitivity to perturbations. Furthermore, the theory reveals that the trade-off between energetic cost and information makes the critical power-law coding the optimal neural encoding of sensory information for a wide range of conditions. In this derivation, we highlight the essential role of a neural correlation, known as differential correlation, in power-law population coding. By uncovering the nontrivial nature of high-dimensional information coding, this work deepens our understanding of criticality and power laws in both biological and artificial neural computation.

Antiviral potential of proton-type zeolite

This study reports the potent antiviral ability of proton-type zeolites without the aid of any metal cations. Antiviral activities of zeolites with different topologies and chemical compositions are investigated using M13 phage and influenza virus. Proton-type zeolites exhibit excellent antiviral activity, equivalent to 99% inactivated. Antiviral tests using a centricon device suggest that direct contact of viruses on the external surface of zeolites is required to inactivate the viruses. This discovery is of importance in an interdisciplinary research field covering both zeolite science and virological science and shed light on the possibility of the development of low-cost and environmentally friendly antiviral zeolites.

Correction for Fernandez Velasco et al., Expert navigators deploy rational complexity–based decision precaching for large-scale real-world planning

The distribution of highly deleterious variants across human ancestry groups

A major focus of human genetics is to map severe disease mutations. Increasingly, that goal is understood as requiring huge numbers of people to be sequenced from every broadly defined genetic ancestry group, so as not to miss “ancestry-specific variants.” Here, we consider whether this focus is warranted. We start from first principles considerations, based on models of mutation–drift-selection balance, which suggest that since severe disease mutations tend to be strongly deleterious, and thus evolutionarily young, they will be kept at relatively constant frequency through recurrent mutation. Therefore, highly pathogenic alleles should be shared identically by descent within extended families, not broad ancestry groups, and sequencing more people should yield similar numbers regardless of ancestry. We test the model predictions using gnomAD genetic ancestry groupings and show that they provide a good fit to the classes of variants most likely to be highly pathogenic, notably sets of loss of function alleles at strongly constrained genes. These findings clarify that strongly deleterious alleles will be found at comparable rates in people of all ancestries, and the information they provide about human biology is shared across ancestries.

BRD9 functions as an HIV-1 latency regulatory factor

A major challenge for HIV type 1 (HIV-1) cure is the presence of viral latent reservoirs. The “Shock & Kill” strategy involves the combined use of latency reversal agents (LRA) and antiretroviral treatment (ART) to reactivate HIV-1 latent reservoirs, followed by elimination of infected cells. However, current LRAs are insufficient in fully reactivating the latent reservoirs. Therefore, investigation on novel HIV-1 latency regulators will be crucial to the success of HIV-1 cure research. Here, we identify bromodomain-containing protein 9 (BRD9) as an HIV-1 latency regulator. BRD9 inhibition induces HIV-1 latency reactivation in T cell lines, human resting memory CD4 + T cells, and PBMCs derived from people living with HIV-1 (PWH) on ART. BRD9 inhibition, gene depletion, and protein degradation consistently reactivate HIV-1 latency. Moreover, BRD9 inhibition synergizes with BRD4 inhibition in inducing HIV-1 production. Mechanistically, BRD9 binds to HIV-1 LTR promoter and competes with HIV-1 Tat protein for binding to the HIV-1 genome. Additionally, our integrated CUT&RUN DNA sequencing, transcriptomics, and pharmacological analysis revealed downstream host targets of BRD9, including ATAD2 and MTHFD2, that modulate HIV-1 latency.

Dinucleotide preferences underlie apparent codon preference reversals in the <i>Drosophila melanogaster</i> lineage

We employ fine-scale population genetic analyses to reveal dynamics among interacting forces that act at synonymous sites and introns among closely related Drosophila species. Synonymous codon usage bias has proven to be well suited for population genetic inference. Under major codon preference (MCP), translationally superior “major” codons confer fitness benefits relative to their less efficiently and/or accurately decoded synonymous counterparts. Our codon family and lineage-specific analyses expand on previous findings in the Drosophila simulans lineage; patterns in naturally occurring polymorphism demonstrate fixation biases toward GC-ending codons that are consistent in direction, but heterogeneous in magnitude, among synonymous families. These forces are generally stronger than fixation biases in intron sequences. In contrast, population genetic analyses reveal unexpected evidence of codon preference reversals in the Drosophila melanogaster lineage. Codon family-specific polymorphism patterns support reduced efficacy of natural selection in most synonymous families but indicate reversals of favored states in the four codon families encoded by NAY. Accelerated synonymous fixations in favor of NAT and greater differences for both allele frequencies and fixation rates among X-linked, relative to autosomal, loci bolster support for fitness effect reversals. The specificity of preference reversals to codons whose cognate tRNAs undergo wobble position queuosine modification is intriguing. However, our analyses reveal prevalent dinucleotide preferences for ApT over ApC that act in opposition to GC-favoring forces in both coding and intron regions. We present evidence that changes in the relative efficacy of translational selection and dinucleotide preference underlie apparent codon preference reversals.

Horizontal transmission of functionally diverse transposons is a major source of new introns

Since the discovery of spliceosomal introns in eukaryotic genomes, the proximate molecular and evolutionary processes that generate new introns have remained a critical mystery. Specialized transposable elements (TEs), introners, are thought to be one of the major drivers of intron gain in diverse eukaryotes. However, the molecular mechanism(s) and evolutionary processes driving introner propagation within and between lineages remain elusive. Here, we analyze 8,716 genomes, revealing 1,093 introner families in 201 species spanning 1.7 billion years of evolution. Introners are derived from functionally diverse TEs including families of terminal-inverted-repeat DNA TEs, retrotransposons, cryptons, and helitrons as well as mobile elements with unknown molecular mechanisms. We identify eight cases where introners recently transferred between divergent host species and show that giant viruses that integrate into genomes may facilitate introner transfer across lineages. We propose that ongoing intron gain is primarily a consequence of TE activity in eukaryotes, thereby resolving a key mystery of genome structure evolution.