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A structural atlas of death domain fold proteins reveals their versatile roles in biology and function
Death domain fold (DDF) superfamily proteins are critically important players in pathways of cell death and inflammation. DDFs are often essential scaffolding domains in receptors, adaptors, or effectors of these pathways by mediating homo- and hetero-oligomerization including helical filament assembly. At the downstream ends of these pathways, effector oligomerization by DDFs brings the enzyme domains into proximity for their dimerization and activation. Hundreds of structures of these domains have been solved. However, a comprehensive understanding of DDFs is lacking. In this article, we report the curation of a DDF structural atlas as a public website (deathdomain.org) and deduce the common and distinct principles of DDF-mediated oligomerization among the four families (death domain or DD, death effector domain or DED, caspase recruitment domain or CARD, and pyrin domain or PYD). We further annotate DDFs genome-wide based on AlphaFold-predicted models and protein sequences. These studies reveal mechanistic rules for this widely distributed domain superfamily.
Neuroleptics used in critical COVID associated with moderate-severe dyspnea after hospital discharge
Characterization of jackfruit Artocarpus heterophyllus Lam. for economic traits in dry zones of Karnataka, India
<i>Enterobacter hormaechei</i> replaces virulence with carbapenem resistance via porin loss
Pathogenic Enterobacter species are of increasing clinical concern due to the multidrug-resistant nature of these bacteria, including resistance to carbapenem antibiotics. Our understanding of Enterobacter virulence is limited, hindering the development of new prophylactics and therapeutics targeting infections caused by Enterobacter species. In this study, we assessed the virulence of contemporary clinical Enterobacter hormaechei isolates in a mouse model of intraperitoneal infection and used comparative genomics to identify genes promoting virulence. Through mutagenesis and complementation studies, we found two porin-encoding genes, ompC and ompD , to be required for E. hormaechei virulence. These porins imported clinically relevant carbapenems into the bacteria, and thus loss of OmpC and OmpD desensitized E. hormaechei to the antibiotics. Our genomic analyses suggest porin-related genes are frequently mutated in E. hormaechei , perhaps due to the selective pressure of antibiotic therapy during infection. Despite the importance of OmpC and OmpD during infection of immunocompetent hosts, we found the two porins to be dispensable for virulence in a neutropenic mouse model. Moreover, porin loss provided a fitness advantage during carbapenem treatment in an ex vivo human whole blood model of bacteremia. Our data provide experimental evidence of pathogenic Enterobacter species gaining antibiotic resistance via loss of porins and argue antibiotic therapy during infection of immunocompromised patients is a conducive environment for the selection of porin mutations enhancing the multidrug-resistant profile of these pathogens.
Efficacy of Naringenin against aging and degeneration of nucleus pulposus cells through IGFBP3 inhibition
Temporal autocorrelation is predictive of age—An extensive MEG time-series analysis
Understanding the evolving dynamics of the brain throughout life is pivotal for anticipating and evaluating individual health. While previous research has described age effects on spectral properties of neural signals, it remains unclear which ones are most indicative of age-related processes. This study addresses this gap by analyzing resting-state data obtained from magnetoencephalography (MEG) in 350 adults (18 to 88 y). We employed advanced time-series analysis at the brain region level and machine learning to predict age. While traditional spectral features achieved low to moderate accuracy, over a hundred time-series features proved superior. Notably, temporal autocorrelation (AC) emerged as the most robust predictor of age. Distinct patterns of AC within the visual and temporal cortex were most informative, offering a versatile measure of age-related signal changes for comprehensive health assessments based on brain activity.
Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning
A novel quinone biosynthetic pathway illuminates the evolution of aerobic metabolism
The dominant organisms in modern oxic ecosystems rely on respiratory quinones with high redox potential (HPQs) for electron transport in aerobic respiration and photosynthesis. The diversification of quinones, from low redox potential (LPQ) in anaerobes to HPQs in aerobes, is assumed to have followed Earth’s surface oxygenation ~2.3 billion years ago. However, the evolutionary origins of HPQs remain unresolved. Here, we characterize the structure and biosynthetic pathway of an ancestral HPQ, methyl-plastoquinone (mPQ), that is unique to bacteria of the phylum Nitrospirota . mPQ is structurally related to the two previously known HPQs, plastoquinone from Cyanobacteriota /chloroplasts and ubiquinone from Pseudomonadota /mitochondria, respectively. We demonstrate a common origin of the three HPQ biosynthetic pathways that predates the emergence of Nitrospirota , Cyanobacteriota , and Pseudomonadota . An ancestral HPQ biosynthetic pathway evolved ≥ 3.4 billion years ago in an extinct lineage and was laterally transferred to these three phyla ~2.5 to 3.2 billion years ago. We show that Cyanobacteriota and Pseudomonadota were ancestrally aerobic and thus propose that aerobic metabolism using HPQs significantly predates Earth’s surface oxygenation. Two of the three HPQ pathways were later obtained by eukaryotes through endosymbiosis forming chloroplasts and mitochondria, enabling their rise to dominance in modern oxic ecosystems.
Posterior approach unilateral laminectomy, debridement, and preshaped titanium mesh bone grafting with internal fixation for treatment of lumbar tuberculosis
Poly(ADP-ribose) binding sites on collagen I fibrils for nucleating intrafibrillar bone mineral
Bone calcification is essential for vertebrate life. The mechanism by which mineral ions are transported into collagen fibrils to induce intrafibrillar mineral formation requires a calcium binding biopolymer that also has highly selective binding to the collagen fibril hole zones where intrafibrillar calcification begins, over other bone extracellular matrix components. Poly(ADP-ribose) (PAR) has been shown to be a candidate biopolymer for this process and we show here that PAR has high affinity, highly conserved binding sites in the collagen type I C-terminal telopeptides. The identification of these PAR–collagen binding sites gives insights into the chemical mechanisms underlying bone calcification and possible mechanisms behind pathologies where there is dysfunctional bone calcification.
The association of chronic pain, painkiller use, and potential mediators with liver fat content
The impacts of power transmission and transformation projects on ecological corridors and landscape connectivity: a case study of Shandong province, China
Learning-based inference of longitudinal image changes: Applications in embryo development, wound healing, and aging brain
Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on significant changes. In this work, we present a machine learning–based method that automatically ignores irrelevant changes and extracts the time-varying signal of interest. Our method, called Learning-based Inference of Longitudinal imAge Changes (LILAC), performs a pairwise comparison of longitudinal images in order to make a temporal difference prediction. LILAC employs a convolutional Siamese architecture to extract feature pairs, followed by subtraction and a bias-free fully connected layer to learn meaningful temporal image differences. We first showcase LILAC’s ability to capture key longitudinal changes by simply training it to predict the temporal ordering of images. In our experiments, temporal ordering accuracy exceeded 0.98, and predicted time differences were strongly correlated with actual changes in relevant variables (Pearson Correlation Coefficient r = 0.911 with embryo phase change, and r = 0.875 with time interval in wound healing). Next, we trained LILAC to explicitly predict specific targets, such as the change in clinical scores in patients with mild cognitive impairment. LILAC models achieved over a 40% reduction in root mean square error compared to baseline methods. Our empirical results demonstrate that LILAC effectively localizes and quantifies relevant individual-level changes in longitudinal imaging data, offering valuable insights for studying temporal mechanisms or guiding clinical decisions.
Dual-ribbon grating resonance modes: a survey based on diffraction orders
Publisher Correction: Multiplication rate variation of malaria parasites from hospital cases and community infections
Prevalence and genotyping of Enterocytozoon bieneusi in cattle from Shanxi and Inner Mongolia, China
The endocannabinoid 2-arachidonoylglycerol is released and transported on demand via extracellular microvesicles
While it is known that endocannabinoids (eCB) modulate multiple neuronal functions, the molecular mechanism governing their release and transport remains elusive. Here, we propose an “ on-demand release ” model, wherein the formation of microvesicles, a specific group of extracellular vesicles (EVs) containing the eCB, 2-arachidonoylglycerol (2-AG), is an important step. A coculture model system that combines a reporter cell line expressing the fluorescent eCB sensor, G protein-coupled receptor-based (GRAB) eCB2.0 , and neuronal cells revealed that neurons release EVs containing 2-AG, but not anandamide, in a stimulus-dependent process regulated by protein kinase C, Diacylglycerol lipase, Adenosinediphosphate (ADP) ribosylation factor 6 (Arf6), and which was sensitive to inhibitors of eCB facilitated diffusion. A vesicle contained approximately 2,000 2-AG molecules. Accordingly, hippocampal eCB-mediated synaptic plasticity was modulated by Arf6 and transport inhibitors. The “ on-demand release ” model, supported by mathematical analysis, offers a cohesive framework for understanding eCB trafficking at the molecular level and suggests that microvesicles carrying signaling lipids in their membrane regulate neuronal functions in parallel to canonical synaptic vesicles.
A feature explainability-based deep learning technique for diabetic foot ulcer identification
Resilience of the electric grid through trustable IoT-coordinated assets
The electricity grid has evolved from a physical system to a cyberphysical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity cosimulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.