Browse Articles

Phosphorus acquisition capacity and size trait evolution in Achillea wilhelmsii reflect adaptation to environmental gradients

Segmentation detection method in tree-shaded environment for road cracks collected by inspection vehicle on WFU-Unet

Achieving high quality electron beam with ultralow energy spread from mismatched plasma channels

Warfarin use and vestibular dysfunction insights from NHANES data, network pharmacology, Mendelian randomization, and molecular docking

Abstract Despite numerous anticoagulants available, warfarin is widely used due to its efficacy and cost-effectiveness in treating thromboembolic diseases. However, its potential impact on vestibular function remains unexplored. This study investigates the association between warfarin use and vestibular dysfunction using data from the NHANES database and examines underlying mechanisms through network pharmacology, Mendelian randomization (MR), and molecular docking. We conducted a cross-sectional analysis of NHANES data (1999–2004) to evaluate the prevalence of vestibular dysfunction among warfarin users. Network pharmacology identified overlapping genes between warfarin targets and vestibular dysfunction-related genes. MR analysis assessed the causal relationship, and molecular docking examined interactions between warfarin and significant genes. The study included 1681 participants, revealing a higher prevalence of vestibular dysfunction in warfarin users. Multiple regression analysis confirmed a significant association between warfarin use and vestibular dysfunction. Network pharmacology identified 31 overlapping genes, with MAPK8 emerging as a key gene through MR analysis. Molecular docking showed a strong binding affinity between warfarin and MAPK8. Findings suggest that warfarin use is significantly associated with vestibular dysfunction, potentially through interactions with MAPK8. This highlights the importance of monitoring vestibular function in patients on warfarin therapy and considering genetic factors to personalize treatment. Future research should explore these mechanisms further and validate findings in broader populations.

Naringin attenuates angiotensin II induced cardiac hypertrophy by inhibiting carbonic anhydrase II

Blood nerve barrier permeability enables nerve targeting of circulating nanoparticles in experimental autoimmune neuritis

Abstract Guillain-Barré syndrome (GBS) is a devastating autoimmune disease of the peripheral nervous system (PNS) with limited treatment options. Several studies have shown attenuation of the well-characterized GBS preclinical experimental autoimmune neuritis (EAN) model with systemically administered therapeutic compounds via anti-inflammatory or immunomodulatory mechanisms. Despite this, clinical advancement of these findings is limited by dosing that is not translatable to humans or is associated with off-target and toxic effects. This is due, in part, to the blood-nerve barrier (BNB), which restricts access of the circulation to peripheral nerves. However, during acute neuroinflammation, the normally restrictive BNB exhibits increased vascular permeability and enables immune cell infiltration. This may offer a unique window to access the otherwise restricted peripheral nerve microenvironment for therapeutic delivery. Here, we assessed the degree to which BNB permeability and immune cell infiltration over the course of EAN enables accumulation of circulating nanoparticles. We found that at disease stages defined by distinct clinical scores and pathology (onset, effector phase, and peak of EAN severity), intravenously administered small molecules and nanoparticles ranging from 50 to 150 nm can permeate into the endoneurium from the endoneurial vasculature in a size- and stage-dependent manner. This permeation occurs uniformly in both sciatic nerves and in proximal and distal regions of the nerves. We propose that this nerve targeting enabled by pathology serves as a platform by which potential therapies for GBS can be reevaluated and investigated preclinically in nanoparticle delivery systems.

Analyzing the performance of biomedical time-series segmentation with electrophysiology data

Gas adsorption analysis of pore structure differences and influencing factors in coal with varying metamorphic grades

Neck dose-limiting rings reduce head and neck lymphedema in early nasopharyngeal carcinoma patients undergoing IMRT: a dosimetric-clinical validation

Application and evaluation of PCA-CA composite model for technical effectiveness in table tennis matches

Bromide-mediated membraneless electrosynthesis of ethylene carbonate from CO2 and ethylene

Complex-valued neural networks to speed-up MR thermometry during hyperthermia using Fourier PD and PDUNet

Abstract Hyperthermia (HT) in combination with radio- and/or chemotherapy has become an accepted cancer treatment for distinct solid tumour entities. In HT, tumour tissue is exogenously heated to temperatures between 39 and 43 °C for 60 min. Temperature monitoring can be performed non-invasively using dynamic magnetic resonance imaging (MRI). However, the slow nature of MRI leads to motion artefacts in the images due to the movements of patients during image acquisition. By discarding parts of the data, the speed of the acquisition can be increased - known as undersampling. However, due to the invalidation of the Nyquist criterion, the acquired images might be blurry and can also produce aliasing artefacts. The aim of this work was, therefore, to reconstruct highly undersampled MR thermometry acquisitions with better resolution and with fewer artefacts compared to conventional methods. The use of deep learning in the medical field has emerged in recent times, and various studies have shown that deep learning has the potential to solve inverse problems such as MR image reconstruction. However, most of the published work only focuses on the magnitude images, while the phase images are ignored, which are fundamental requirements for MR thermometry. This work, for the first time, presents deep learning-based solutions for reconstructing undersampled MR thermometry data. Two different deep learning models have been employed here, the Fourier Primal-Dual network and the Fourier Primal-Dual UNet, to reconstruct highly undersampled complex images of MR thermometry. MR images of 44 patients with different sarcoma types who received HT treatment in combination with radiotherapy and/or chemotherapy were used in this study. The method reduced the temperature difference between the undersampled MRIs and the fully sampled MRIs from 1.3 to 0.6 °C in full volume and 0.49 °C to 0.06 °C in the tumour region for a theoretical acceleration factor of 10.

Benchmarking large language models for biomedical natural language processing applications and recommendations

Abstract The rapid growth of biomedical literature poses challenges for manual knowledge curation and synthesis. Biomedical Natural Language Processing (BioNLP) automates the process. While Large Language Models (LLMs) have shown promise in general domains, their effectiveness in BioNLP tasks remains unclear due to limited benchmarks and practical guidelines. We perform a systematic evaluation of four LLMs—GPT and LLaMA representatives—on 12 BioNLP benchmarks across six applications. We compare their zero-shot, few-shot, and fine-tuning performance with the traditional fine-tuning of BERT or BART models. We examine inconsistencies, missing information, hallucinations, and perform cost analysis. Here, we show that traditional fine-tuning outperforms zero- or few-shot LLMs in most tasks. However, closed-source LLMs like GPT-4 excel in reasoning-related tasks such as medical question answering. Open-source LLMs still require fine-tuning to close performance gaps. We find issues like missing information and hallucinations in LLM outputs. These results offer practical insights for applying LLMs in BioNLP.

Research on the discrete element modeling method and tensile fracture behavior of the control unit stranded wire of Shearer cables

Life on the dry side: a roadmap to understanding desiccation tolerance and accelerating translational applications

Abstract To thrive in extreme conditions, organisms have evolved a diverse arsenal of adaptations that confer resilience. These species, their traits, and the mechanisms underlying them comprise a valuable resource that can be mined for numerous conceptual insights and applied objectives. One of the most dramatic adaptations to water limitation is desiccation tolerance. Understanding the mechanisms underlying desiccation tolerance has important potential implications for medicine, biotechnology, agriculture, and conservation. However, progress has been hindered by a lack of standardization across sub-disciplines, complicating the integration of data and slowing the translation of basic discoveries into practical applications. Here, we synthesize current knowledge on desiccation tolerance across evolutionary, ecological, physiological, and cellular scales to provide a roadmap for advancing desiccation tolerance research. We also address critical gaps and technical roadblocks, highlighting the need for standardized experimental practices, improved taxonomic sampling, and the development of new tools for studying biology in a dry state. We hope that this perspective can serve as a roadmap to accelerating research breakthroughs and unlocking the potential of desiccation tolerance to address global challenges related to climate change, food security, and health.

A novel PV power prediction method with TCN-Wpsformer model considering data repair and FCM cluster

Abstract Short-term day-ahead photovoltaic power prediction is of great significance for power system dispatch plan formulation. In this work, to improve the accuracy of photovoltaic power prediction, a TCN-Wpsformer (temporal convolutional network-window probability sparse Transformer) day-ahead photovoltaic power prediction model based on combining data restoration and FCM (fuzzy C means) cluster is proposed. The time code of the dataset obtained after data restoration and FCM clustering was spliced with the location code. A temporal convolutional neural network is introduced to extract temporal segment features and incorporate a self-attention mechanism. The short-term photovoltaic power prediction is outputted by the window probability sparse Transformer model in multiple steps. Compared with the original Transformer model, the window probability sparse Transformer model uses the window probability sparse self-attention mechanism. It captures the long-term dependencies while filtering out the time segment features with relatively high importance for computation, which improves the prediction accuracy and reduces the computational cost. The computing time is reduced to 68.83% and R squared is improved by 5.3% compared to Transformer. The comparison is made through 11 models, and the R squared of this model is above 99% while different data volume and different power station data. It proves that the model stability and cross scene generalisation ability is well. Meanwhile, it can also provide more accurate confidence intervals on the basis of point prediction, which has certain application value.

Selective dissolution as a tool for detecting spatial variations in the metastability within lamellar polymer crystals

Associations between happiness with social factors and opioid agonist therapy among people who inject drugs

Abstract The level of happiness is low among patients with chronic mental or physical disorders. However, happiness and its association to sociodemographic and clinical factors remain unknown among patients with opioid use disorder (OUD) and people who inject drugs (PWID). This prospective cohort study aimed to examine self-reported happiness levels in patients with OUD/PWID, changes over time, and its associations with sociodemographic factors, opioid agonist therapy (OAT), and substance use. From 2017 to 2023, 967 patients with OUD/PWID were examined at baseline and one-year follow-up assessments in eight OAT outpatient and municipality clinics in Norway. Happiness was assessed with an eleven-point Likert scale and presented as percentage (from 0% “completely unhappy” to 100% “completely happy”). A linear mixed model analysed associations between exposures and outcomes at baseline and over time. Participants were predominantly men (71%), with a mean age of 43 (SD: 11); 87% received OAT. Mean happiness was 45% of min-to-max (95% CI: 35;54) with an 11% improvement over time. High substance use (-24%, 95% CI: -32; -16) was associated with lower self-reported happiness at baseline, but self-reported happiness increased over time among those with more substance use. Living with someone was linked to higher happiness compared to living alone.

Atomic-scale observation of geometric reconstruction in a fluorine-intercalated infinite layer nickelate superlattice

Abstract Anion doping offers immense potential for tailoring material properties, but precise control over anion incorporation remains challenging due to complex synthesis and limitations in dopant detection. This study investigates F-ion intercalation within an infinite-layer NdNiO2+x /SrTiO3 superlattice using a two-step process. We employ advanced four-dimensional scanning transmission electron microscopy (4D-STEM) coupled with electron energy loss spectroscopy (EELS) to map the F distribution and its impact on the atomic and electronic structure. Our observations reveal a fluorination-induced geometric reconstruction of the infinite layer structure, resulting in a more distorted orthorhombic phase compared to the pristine perovskite. F-ion are primarily located at apical polyhedral sites, with some basal sites occupation in localized regions, leading to the formation of two distinct domains. These domains reflect a competition between polyhedral distortion and Nd displacement at domain interfaces. Interestingly, we observe an anomalous structural distortion where basal site anions are displaced in the same direction as Nd atoms, potentially linked to the partial basal site F-ion occupation. This coexistence of diverse structural distortions signifies a locally disordered F-ion distribution with distinct configurations. These findings provide crucial insights into the mechanisms of anion doping at the atomic level, contributing to the design of materials with tailored functionalities.

Efficient traffic management with adaptive SDN in vehicular networks