Browse Articles

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

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Abstract Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on four real-world omics datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

Analyzing the impacts of employing demand response and creating optimal coalition on optimal scheduling of multi-microgrid

Peptide-based inflammation-responsive implant coating sequentially regulates bone regeneration to enhance interfacial osseointegration

Abstract Aseptic loosening is the primary cause of bone prosthesis failure, commonly attributed to inadequate osseointegration due to coatings misaligned with bone regeneration. Here, we modify the titanium surface with a mussel-inspired peptide to form a 3,4-dihydroxyphenylalanine (DOPA)-rich coating, then graft N 3 -K15-PVGLIG-K23 (P1) and N 3 -Y5-PVGLIG-K23 (P2), which are composed of anti-inflammatory (K23), angiogenic (K15), osteogenic (Y5), and inflammation-responsive (PVGLIG) sequences, onto the surface via click chemistry, forming the DOPA-P1@P2 coating. DOPA-P1@P2 promotes bone regeneration through sequential regulation. In the initial stage, the outermost K23 induces M2 macrophage polarization, establishing a pro-regenerative immune microenvironment. Subsequently, K15 and Y5, exposed by the release of K23, enhance angiogenesis and osteogenesis. In the final stage, DOPA-P1@P2 outperforms the TiO₂ control, showing a 161% increase in maximal push-out force, a 207% increase in bone volume fraction, and a 1409% increase in bone-to-implant contact. These findings show that DOPA-P1@P2 efficiently enhances interfacial osseointegration by sequentially regulating bone regeneration, providing viable insights into coating design.

Multi objective elk herd optimization for efficient structural design

Multi-proton dynamics near membrane-water interface

Abstract Protons are crucial for biological energy transduction between membrane proteins. While experiments suggest rapid proton motion over large distances at the membrane-water interface, computational studies employing a single excess proton found the proton immobilized near the lipid headgroup. To address this discrepancy, we conduct DFTB3 simulations by incrementally adding protons up to three. We show that a single proton moves rapidly toward the nearest headgroup, where it is either repelled by a choline group or binds covalently to phosphatic oxygen. With multiple protons, while some are trapped by the lipid headgroups, the remaining proton diffuses laterally faster than in bulk water. Driven by excess energy, this proton initially jumps to the center of the water slab before relaxing into the third- and second-hydration shells. Lateral diffusion rates increase as the proton stabilizes in the second hydration shell. These results provide insights into proton dynamics near membranes and explain experimental observations.

EBG-backed ultrawideband circularly polarized Archimedean spiral antenna scheme for IoT applications

Observation of a non-Hermitian supersonic mode on a trapped-ion quantum computer

Virtual reality enhanced mindfulness and yoga intervention for postpartum depression and anxiety in the post COVID era

Primordial neon and the deep mantle origin of kimberlites

Abstract The genesis of kimberlites is unclear despite the economic and scientific interest surrounding these diamond-bearing magmas. One critical question is whether they tap ancient, deep mantle domains or the shallow convecting mantle with partial melting triggered by plumes or plate tectonics. To address this question, we report the He-Ne-Ar isotopic compositions of magmatic fluids trapped in olivine from kimberlites worldwide. The kimberlites which have been least affected by addition of deeply subducted or metasomatic components have Ne isotopes less nucleogenic than the upper mantle, hence requiring a deep-mantle origin. This is corroborated by previous evidence of small negative W isotope anomalies and kimberlite location along age-progressive hot-spot tracks. The lack of strong primordial He isotope signatures indicates overprinting by lithospheric and crustal components, which suggests that Ne isotopes are more robust tracers of deep-mantle contributions in intraplate continental magmas. The most geochemically depleted kimberlites may preserve deep remnants of early-Earth heterogeneities.

Elucidating molecular lipid perturbations in trigeminal neuralgia using cerebrospinal fluid lipidomics