Browse Articles

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.

The MiR-139-5p and CXCR4 axis may play a role in high glucose-induced inflammation by regulating monocyte migration

Mussel-inspired cross-linking mechanisms enhance gelation and adhesion of multifunctional mucin-derived hydrogels

Mucus supports human health by hydrating, lubricating, and preventing infection of wet epithelial surfaces. The beneficial material properties and bioactivity of mucus stem from glycoproteins called mucins, motivating the development of mucin-derived hydrogels for wound dressings and antifouling coatings. However, these applications require robust gelation and adhesion to a wide range of substrates. Inspired by the chemical cross-linking and water-tolerant adhesion of marine mussel adhesive structures, we use catechol–thiol bonding to drive gelation of native mucin proteins and synthetic mucin-inspired polymers, forming soft, adhesive hydrogels that can be coated onto diverse surfaces. The gelation dynamics and adhesive properties can be systematically tuned by varying the hydrogel composition, polymer architecture, and thiol availability, with gelation timescales adjustable from seconds to hours, and values of elastic modulus, failure stress, and debonding work spanning orders of magnitude. We demonstrate the functionality of these gels in two applications: as tissue adhesives, using porcine skin as a proxy for human skin, and as bioactive surface coatings to prevent bacterial colonization. The results highlight the potential of catechol–thiol cross-linking as a versatile platform for engineering multifunctional glycoprotein hydrogels with applications in wound repair and antimicrobial surface engineering.

Prediction models show differences in highly pathogenic avian influenza outbreaks in Japan and South Korea compared to Europe

Druggable genome screens identify SPP as an antiviral host target for multiple flaviviruses

Mosquito-borne flaviviruses, such as dengue virus (DENV), Zika virus (ZIKV), West Nile virus, and yellow fever virus, pose significant public health threats globally. Extensive efforts have led to the development of promising highly active compounds against DENV targeting viral non-structural protein 4B (NS4B) protein. However, due to the cocirculation of flaviviruses and to prepare for emerging flaviviruses, there is a need for more broadly acting antivirals. Host-directed therapy where one targets a host factor required for viral replication may be active against multiple viruses that use similar replication strategies. Here, we used a CRISPR-Cas9 library that we designed to target the druggable genome and identified signal peptide peptidase (SPP, encoded by Histocompatibility Minor 13, HM13), as a critical host factor in DENV infection. Genetic knockout or introducing mutations that disrupt the proteolytic activity of SPP markedly reduced the replication of multiple flaviviruses. Although their substrates differ, SPP has structural homology with γ-secretase, which has been pursued as a pharmacological target for Alzheimer’s disease. Notably, SPP-targeting compounds exhibited potent anti-DENV activity at low nanomolar concentrations across multiple primary and disease-relevant cell types, acting specifically through SPP inhibition rather than γ-secretase inhibition. Importantly, SPP inhibitors were active at low nanomolar concentrations against flaviviruses other than DENV including ZIKV while DENV NS4B inhibitors lost activity. This study emphasizes the strong potential of SPP as a pan-flaviviral target and provides a framework for identifying host druggable targets to screen for broad-spectrum antivirals.

Retraction Note: Pre-activation of mesenchymal stem cells with TNF-α, IL-1β and nitric oxide enhances its paracrine effects on radiation-induced intestinal injury

Biophysical modeling of membrane curvature generation and curvature sensing by the glycocalyx

Generation of membrane curvature is fundamental to cellular function. Recent studies have established that the glycocalyx, a sugar-rich polymer layer at the cell surface, can generate membrane curvature. While there have been some theoretical efforts to understand the interplay between the glycocalyx and membrane bending, there remain open questions about how the properties of the glycocalyx affect membrane bending. For example, the relationship between membrane curvature and the density of glycosylated proteins on its surface remains unclear. In this work, we use polymer brush theory to develop a detailed biophysical model of the energetic interactions of the glycocalyx with the membrane. Using this model, we identify the conditions under which the glycocalyx can both generate and sense curvature. Our model predicts that the extent of membrane curvature generated depends on the grafting density of the glycocalyx and the backbone length of the polymers constituting the glycocalyx. Furthermore, when coupled with the intrinsic membrane properties such as spontaneous curvature and a line tension along the membrane, the curvature generation properties of the glycocalyx are enhanced. These predictions were tested experimentally by examining the propensity of glycosylated transmembrane proteins to drive the assembly of highly curved filopodial protrusions at the plasma membrane of adherent mammalian cells. Our model also predicts that the glycocalyx has curvature-sensing capabilities, in agreement with the results of our experiments. Thus, our study develops a quantitative framework for mapping the properties of the glycocalyx to the curvature generation capability of the membrane.

Predicting 90-day risk of urinary tract infections following urostomy in bladder cancer patients using machine learning and explainability

Cryo-EM of native membranes reveals an intimate connection between the Krebs cycle and aerobic respiration in mycobacteria

To investigate the structure of the mycobacterial oxidative phosphorylation machinery, we prepared inverted membrane vesicles from Mycobacterium smegmatis , enriched for vesicles containing complexes of interest, and imaged the vesicles with electron cryomicroscopy. We show that this analysis allows determination of the structure of both mycobacterial ATP synthase and the supercomplex of respiratory complexes III and IV in their native membrane. The latter structure reveals that the enzyme malate:quinone oxidoreductase (Mqo) physically associates with the respiratory supercomplex, an interaction that is lost on extraction of the proteins from the lipid bilayer. Mqo catalyzes an essential reaction in the Krebs cycle, and in vivo survival of mycobacterial pathogens is compromised when its activity is absent. We show with high-speed spectroscopy that the Mqo:supercomplex interaction enables rapid electron transfer from malate to the supercomplex. Further, the respiratory supercomplex is necessary for malate-driven, but not NADH-driven, electron transport chain activity and oxygen consumption. Together, these findings indicate a connection between the Krebs cycle and aerobic respiration that directs electrons along a single branch of the mycobacterial electron transport chain.

Characterization of a mock up nuclear waste package using energy resolved MeV neutron analysis

Abstract Reliable radiographic methods for characterizing nuclear waste packages non-destructively (without the need to open containers) have the potential to significantly contribute to safe handling and future disposal options, particularly for legacy waste of unknown content. Due to required shielding of waste containers and the need to characterize materials consisting of light elements, X-ray methods are not suitable. Here, energy-resolved MeV neutron radiography is demonstrated as a first-of-its-kind application for non-destructive and remote examination of mock up nuclear waste packages from a safe position using time-of-flight techniques enabled by a novel event-mode imaging detector system. Energy-resolved neutron transmission spectra were measured spatially, permitting the detection of analogue materials to actual nuclear waste such as water, melamine, and ion exchange resin within a 2.54 cm wall thickness steel pipe. The results demonstrate the capability to locate the materials through this wall thickness by radiography and tomographic reconstruction, revealing detailed 3D distributions and structural anomalies. The method effectively detects residual water in ion exchange resin, highlighting its sensitivity to moisture content, a crucial parameter for nuclear waste characterization. Monte Carlo simulations are in agreement with the experimental findings, providing a pathway to simulate waste forms more difficult to tackle experimentally. This work paves the way to apply sub-nanosecond intense MeV neutron sources, such as laser-driven neutron sources under development, to nuclear waste characterization.

Profile of Mark Kirkpatrick