Browse Articles

Structure of a pH-sensitive pentameric ligand-gated ion channel from the Sarcoptes scabies mite

Nitrogen-based cationic copolymers as corrosion inhibitors for P110 carbon steel in 20% sulfuric acid investigated through experimental and theoretical studies

Recycling senescent cell lipids for targeted senotherapy

Influential factors of residents’ online green behavior in China: the mediating role of social trust

Post-infection sequelae of COVID-19 and other infectious diseases—a nationwide Danish study with 40-month follow-up

Abstract Nationwide follow-up studies of long-term post-acute COVID-19 sequelae compared with sequelae of other infections have been lacking. Using nationwide registers, we analyzed all SARS-CoV-2 PCR test results, prescriptions for anti-infective agents, and hospitalizations with COVID-19 or other infections in Denmark from March 2020 to June 2023, including up to 40-month follow-up. We used Cox proportional hazards models with time-varying exposures to estimate the rates of first-time mental disorders ( n  = 5,306,132) and general medical conditions ( n  = 3,517,630). Here we show that positive SARS-CoV-2 PCR tests alone were not associated with clinically relevant increased rates of mental disorders or general medical conditions when compared with negative SARS-CoV-2 PCR tests, nor when compared to individuals with anti-infective prescriptions. Rates of general medical conditions after a positive compared with a negative SARS-CoV-2 PCR test were only elevated for virus types preceding Omicron and for individuals with less than 3 vaccination doses. Compared with the general population, the rates of mental disorders or general medical conditions were elevated among hospitalized COVID-19 patients, and particularly when ICU treatment was required. However, when comparing patients hospitalized with COVID-19 to patients hospitalized with non-COVID-19 pulmonary infections or other infections, the rates of mental disorders or general medical conditions were increased to the same extent. In conclusion, severe COVID-19 post-infection sequelae are comparable to sequelae observed after other infections of similar severity.

Detection of exercising ectopic atrial and ventricular beats using non-linear analysis of clinically normal racehorse electrocardiograms at rest or low-intensity exercise

Abstract Cardiac arrhythmias are common in healthy athletic horses and may lead to poor athletic performance or exercise-associated sudden death. Early detection of high-risk horses is an important goal of cardiovascular diagnostics. We hypothesised that non-linear analysis of electrocardiogram disorderliness can be used to identify horses exhibiting intermittent ectopic atrial and ventricular heart rhythm abnormalities at exercise using brief, artifact-free recordings of normal sinus rhythm electrocardiograms collected at submaximal heart rates. In a convenience prospective cross-sectional study, ambulatory electrocardiograms were recorded using the Televet 100 or II devices from 110 Thoroughbred or Standardbred racehorses during routine training. Acceptable quality 60 s electrocardiogram strips with stable heart rate (20–120 beats per minute) were identified automatically. Disorderliness of the electrocardiograms was estimated using Lempel–Ziv’76 and ’78, and Titchener complexity, and Shannon, sample, and approximate entropy algorithms. Numerical estimates obtained by these algorithms were corrected to the heart rate. For the optimal performance recordings of 60–100 beats per minute should be used, with Lempel–Ziv ’76 complexity, R peak and ends of S and T peaks as fiducial points. The receiver operating curve analysis has demonstrated the area under curve of 0.86 for this combination, indicating acceptable differentiation between cases and controls.

Enterocytes rely on purine biosynthesis/salvage pathway to facilitate dietary fat absorption

Abstract Dietary fat absorption is among the most energy-demanding processes of nutrient uptake. Fatty acid activation, triglyceride synthesis, and the trafficking of chylomicrons through the secretory pathway - all require ATP. How enterocytes accommodate the surge in ATP consumption following fat uptake is unclear. We show that the purine biosynthesis/salvage pathway supplies necessary ATP and that Ankyrin Repeat Domain 9 (ANKRD9) couples ATP synthesis and lipoprotein trafficking. Ankrd9 regulates enzymes within the purine biosynthesis pathway to increase ATP synthesis and facilitate Golgi dynamics. Intracellular localization of ANKRD9 is lipid and ATP-dependent. Inactivation of Ankrd9 in mice reduces intestinal ATP despite intact mitochondrial and glycolytic function, alters Golgi morphology, delays ApoB/chylomicron trafficking, and causes lipid accumulation in enterocytes, along with a lean body phenotype. Taken together, the results reveal a previously unrecognized mechanism that regulates lipid absorption in enterocytes and identify ANKRD9 as a central component of this mechanism.

Aerodynamic performance optimization of the archimedes spiral wind turbine: combined experimental and CFD analysis of step ratio and blade number effects

The COP1-ADA2b module mediates light regulation of DNA double-strand break repair in Arabidopsis

Prediction of ship bulkhead deflection under internal explosion

Abstract Internal explosions within ship compartments pose a severe threat to structural integrity. To investigate the plastic dynamic response of ship bulkheads under such loading, this study presents a comprehensive approach integrating experimental testing, numerical simulation, and theoretical derivation. First, a numerical model for shock wave propagation in a confined cabin was established to characterize the complex load distribution. Based on the simulation results, fitting formulas for reflected shock wave overpressure in different bulkhead regions were derived. Subsequently, a simplified theoretical model for predicting plastic deformation was developed based on the principle of energy conservation and the momentum theorem. By utilizing impulse equivalence and constructing a deflection shape function, the plastic dissipation energy—including the bending of plastic hinge lines and in-plane membrane stretching—was analytically solved. To validate the proposed theoretical model, cabin explosion tests using 80 g TNT charges were conducted. The results indicate that the theoretical predictions align well with the experimental measurements, with a relative error of approximately 13.8%. This demonstrates that the proposed method provides a rapid and effective tool for predicting the plastic deformation of bulkheads under internal blast loading, offering a practical alternative to time-consuming numerical simulations.

Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy

Abstract Nanofluidic scattering microscopy characterizes single molecules in subwavelength nanofluidic channels label-free, using the interference of visible light scattered by the molecule and nanochannel. It determines a molecule’s hydrodynamic radius by tracking its diffusion trajectory and its molecular weight by analyzing its scattering intensity along that trajectory. However, using standard analysis algorithms, it is limited to characterization of proteins larger than  ≈ 60 kDa. Here, we push this limit by one order of magnitude to below  ≈ 6 kDa molecular weight and  ≈ 1.5 nm hydrodynamic radius — as we exemplify on the peptide hormone insulin — by using ultrasmall nanofluidic channels and by analyzing the data with a hierarchical vision transformer. When we benchmark this approach against the theoretical limit set by the Cramér–Rao Lower Bound, we find that it can be approached with sufficiently long molecular trajectories. This enables quantitative label-free single-molecule microscopy for biologically relevant families of sub-10-kDa molecules, such as cytokines, chemokines and peptide hormones.

Cystatin C regulates cell division in primary central nervous system lymphoma

Desegregation of neuronal predictive processing

Abstract Neural circuits construct internal ‘world-models’ to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. To understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. Contrary to previous proposals of functionally specialized cell-types, stimulus and prediction-error representations are desegregated in networks performing robust predictive processing. We confirmed these model predictions by using a rich stimulus-set to violate animals’ learned expectations. We propose that predictive processing is optimal when excitation/inhibition balance is loose, and reveal distinct functional roles of excitatory and inhibitory neurons. Together, we demonstrate that neural representations of internal models are highly distributed, yet structured to support flexible readout of behaviorally-relevant information. Our results advance the understanding of how internal models are computed, by incorporating different computations into a unifying model.

Cattle lameness detection using depth image and deep learning

Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes

Compliance patterns in adopting sustainability practices: A cluster analysis of oil palm producers in Colombia

Abstract This study analyzes the adoption patterns of sustainability practices among oil palm producers in Colombia using cluster analysis of the Sustainability Index (SI). Employing advanced data mining algorithms, including K-means and Ward’s method, producers were grouped according to their compliance with sustainable practices at national and regional levels. The analysis revealed ten distinct producer typologies, ranging from “Advanced adopters” to “Lagging adopters” in economic, environmental, and social sustainability dimensions. Key factors influencing cluster formation included palm-growing area, producer scale, age, and gender. At the same time, the Palm Nucleus (organizational model for fruit commercialization) emerged as a significant variable at the regional level. Our findings challenge the conventional notion that adoption of sustainability practices is primarily scale-dependent, showing that contextual factors play a crucial role across all producer categories. This research underscores the importance of more nuanced, context-specific technological extension strategies. By providing a comprehensive understanding of adoption patterns, this study contributes to developing tailored interventions that can effectively promote sustainable practices in the Colombian oil palm sector, regardless of producer characteristics.

Operando insights on stable Cu2+ active sites for efficient electrochemical CO2-to-C2H4 conversion

Strength estimation of damaged wood by Xylotrechus arvicola Olivier in grapevine branches in relation to the density

Humans with function-disrupting variants in the myostatin gene (MSTN) have increased skeletal muscle mass and strength, and less adiposity

Internal variability in numerical morphodynamical experimentation

Abstract It has long been recognized that the morphodynamics of coastal bays are not fully deterministic but instead exhibit inherent uncertainty. This intrinsic variability is reminiscent of internal variability in climate systems, arising not only from dynamical instabilities but also from the integration of random disturbances. Traditionally, uncertainty in morphodynamic behavior has been investigated through stability analyses. However, traditional assessments of stability have largely focused on the stability property of low-dimensional analysis. In realistic dynamical numerical models, morphodynamic systems are rarely low-dimensional. In these cases, it is often more appropriate to regard the uncertainty as a manifestation of stochasticity. For real-world systems, this raises an important question: when does an observed development lie outside the range of intrinsic variability, implying the influence of an external driver? Answering this is considerably more complex than in controlled numerical experiments, where a single parameter or process can be isolated and modified. In this paper, we examine uncertainty in a relatively simple morphodynamic numerical model of a coastal bay. We show that minor changes in the initial conditions—such as the tidal phase at model initialization—can lead to differences among ensemble members, substantial in local features (e.g., the fine-scale structure of channels) and less so in overall properties, such as mean bay depth and channel number count. Consequently, a robust evaluation of numerical experiments, such as those testing the effects of altered parameterizations, requires explicit estimates of the system’s inherent uncertainty so that the “signal” generated by experimental changes can be distinguished from internal variability (“noise”).