Browse Articles

Predictive value of FST for renal replacement therapy in patients with acute kidney injury: a meta-analysis

Neuronal Activity in Orbitofrontal Cortex during Trinary Choices under Risk

Economic choice entails computing and comparing the subjective values of different goods. Orbitofrontal cortex (OFC) is thought to contribute to both operations. However, previous work focused almost exclusively on binary choices, raising the question of whether current notions hold for multinary choices. Here we recorded from male rhesus monkeys making trinary choices. Offers varied on three dimensions: juice flavor, quantity, and probability. In these experiments, quantity and probability varied continuously within a preset range. Animal choices were generally risk seeking and satisfied independence of irrelevant alternatives (IIA)—a fundamental assumption in standard economic theory. Different neurons encoded the values of individual offers, the choice outcome, and the chosen value—i.e., the same variables previously identified under binary choices. In addition, other cell groups encoded the chosen probability and the chosen hemifield. The activity of offer value cells reflected the risk attitude and fluctuated from session to session in ways that matched fluctuations observed behaviorally. In other words, the activity of these neurons reflected the subjective nature of value. Importantly, the representation of decision variables in OFC was invariant to changes in menu size—a property that effectively implies IIA.

Which Catalyst Exhibits Superior C–C Bond Cleavage Ability in Ethanol Electrooxidation: An In-Situ ATR-SEIRAS Investigation

Optimizing biometric system selection via complex spherical fuzzy einstein aggregation operators

Side-Chain Free Semiconducting Polymer for High-Performance n-Type Organic Electrochemical Transistors

Preparation of motion sensor using AgNWs material and performance analysis in sports activities

Desymmetrization of <i>meso</i> -Pyrrolidines via Oxoammonium-Catalyzed Enantioselective Hydride Transfer

EffectorFisher: association of disease phenotype with pangenomic protein-isoform profiles for improved prediction of fungal pathogenicity effectors

Abstract Plant-pathogenic fungi cause crop disease via a range of secreted effector proteins that interact with specific receptors of host plant cells. Effector identification can enable the diagnosis of disease outcomes and enable selection or breeding of disease-resistant crop cultivars. Bioinformatic methods have been developed to predict proteins with ‘effector-like’ properties, but the resulting number of candidates tends to be larger than can be feasibly validated and may contain numerous false positives. Challenges to effector discovery include the obfuscating effects of genome-wide mutations common to Fungi, such as Repeat-Induced Point (RIP) mutations. Refining effector predictions by incorporating disease phenotyping into genome-wide association studies (GWAS) have had mixed success for a handful of pathogen species. But the utility of GWAS approaches may be limited by low ‘signal-to-noise’ caused by widespread RIP-like SNP mutations across the genomes of most fungal pathogens. This study presents an alternative method for effector candidate refinement called ‘EffectorFisher’. EffectorFisher extends the output of Predector – a tool that automates and combines results of several bioinformatic tools commonly used in effector discovery – to apply pangenome-derived protein-isoform profiling to remove candidate effector protein isoforms with weak association with virulent phenotypes. This method was benchmarked using corresponding pangenome and phenotype data for two model wheat pathogens, each with multiple known effectors: the necrotroph Parastagonospora nodorum and the hemibiotroph Zymoseptoria tritici . Compared to prior methods based on effector-like protein properties, EffectorFisher improved predicted rankings of known effectors and reduced the total number of effector candidates. We present EffectorFisher ( https://github.com/ccdmb/EffectorFisher-core ) as a useful tool for refining effector predictions with phenotype data, which is broadly applicable to many fungal pathogen species, and is capable of predicting effectors involved in both gene-for-gene and inverse gene-for-gene effector-receptor interactions.

Copper-Catalyzed Asymmetric Vinylation of α-Cyano Substituted Carbonyl Compounds

Cold atmospheric plasma degrades methylene blue and shifts bacterial inactivation during photodynamic therapy

Multinuclear Ruthenium Sites Confined in Metal–Organic Frameworks with Bio-Inspired Water Networks for Efficient Water Oxidation

1,2-propanediol ameliorated radiation-induced intestinal injury in mice

Covalent-Coordination-Dual-Driven <i>In Situ</i> Modular Assembly of 12-Connected Nickel-Based Metal–Organic Frameworks

GP perspectives on a computer-assisted strategy to support PPI deprescribing: a qualitative study

Abstract Proton pump inhibitors (PPIs) are widely prescribed in primary care but often continued longer than clinically necessary, exposing patients to avoidable risks. Digital decision-support tools have been proposed to assist clinicians in identifying and managing potentially inappropriate medications. The arriba-PPI tool was developed to facilitate conversations on PPI deprescribing between general practitioners (GPs) and patients, supporting evidence-based and shared decision-making. To explore GPs’ experiences with the arriba-PPI tool in clinical practice and understand factors influencing its use and impact on PPI prescribing and discontinuation. A qualitative exploratory study embedded within a multicentre cluster-randomised controlled trial conducted in German general practices. Semi-structured interviews were conducted with 26 GPs from the intervention arm who had used the tool. Interviews were analysed following Braun and Clarke’s six-step thematic analysis methodology, applying a combined deductive–inductive approach. Six main themes emerged: perceived usefulness and acceptance of the tool; tool functionality and areas for improvement; patient perspectives and characteristics; doctor–patient interaction and consultation dynamics; PPI prescribing and discontinuation practices; and implementation context and long-term use. GPs valued the tool’s structured format and visual aids for enhancing communication, particularly with patients reluctant to stop PPIs. Some described an educational benefit, reporting greater awareness and reflection on their own prescribing behaviour. Barriers to sustained use included technical issues, workflow integration challenges, and the absence of non-pharmacological alternatives. Trust and established doctor–patient relationships were seen as critical for successful deprescribing, often surpassing the tool’s direct influence. While the arriba-PPI tool supports deprescribing conversations, its effectiveness depends on seamless integration, technical optimisation, and complementary non-drug strategies. Future digital interventions should follow established frameworks for complex intervention development, adopt a more holistic approach, and combine technological support with broader patient-centred care to achieve sustained deprescribing success in primary care.

A Movement-Independent Signature of Urgency during Human Perceptual Decision-Making

How does the brain adjust its decision processes to ensure timely decision completion? Computational modeling and electrophysiological investigations have pointed to dynamic “urgency” processes that serve to progressively reduce the quantity of evidence required to reach choice commitment as time elapses. In humans, such urgency dynamics have been observed exclusively in neural signals that accumulate evidence for a specific motor plan. Across three complementary experiments in humans (male and female), we characterize an electrophysiological signal that traces dynamic urgency and exhibits unique properties not observed in effector-selective signals. Firstly, it provides a representation of urgency alone, growing only as a function of time and not evidence strength. Secondly, when choice reports must be withheld until a response cue, this signal peaks and decays long before response execution, mirroring the early termination dynamics of a motor-independent evidence accumulation signal. These properties suggest that the brain may use urgency signals not only to expedite motor planning but also to hasten cognitive deliberation. These data demonstrate that urgency processes operate in a variety of perceptual choice scenarios and that they can be monitored in a model-independent manner via noninvasive brain signals.

Green space suitability assessment for sustainable urban development using geospatial technology in Eka Tafo, Ethiopia

A pharmacovigilance data–driven approach to reveal high fatal adverse events following checkpoint immunotherapy

Immune checkpoint inhibitors (ICIs) have significantly improved the outcome of cancer treatment, but they also expose most patients to a variety of treatment-related adverse events (AEs). This study introduces a modified pharmacovigilance approach to identify “ICI-related high-mortality AEs”, a subset of treatment-related complications that are disproportionately reported with elevated fatality during ICI therapy. Utilizing large-scale pharmacovigilance data from 148,972 ICI-treated cases in the FDA AEs Reporting System and 142,645 ICI-treated cases in the WHO global VigiBase, we found 63 types of ICI-related high-mortality AE, such as interstitial lung disease, myositis, and hepatic failure, which necessitate heightened clinical vigilance. Patients who experienced these events had significantly higher fatality reporting rates compared to other cases (36.25% vs. 10.66%). These findings were corroborated in the additional clinical datasets to ensure their generalizability. In summary, identifying these ICI-related high-mortality AEs provides crucial insights for developing proactive monitoring strategies and prioritizes specific events for prevention and intervention to mitigate fatality risk.

Effect of COVID-19 on mortality due to diabetes mellitus in Brazil: A time series analysis from 2010 to 2023

Diabetes mellitus (DM) is a major global public health concern, particularly in Brazil. This study aimed to analyze DM-related mortality trends before and during the pandemic to assess the possible influence of the COVID-19 pandemic on DM mortality in Brazil. Mortality was assessed using standardized mortality rates stratified by age group (20–29 years, 30–49 years, 50–69 years, 70 years or older), sex (male, female), and geographic region (North, Northeast, Southeast, South, and Midwest). Mortality rates were calculated using the Brazilian Mortality Information System and official national population estimates. An analytical ecological time series analysis was performed using the Prais–Winsten regression model to assess the DM-related mortality trends in two scenarios: before (2010–2019) and including the pandemic period (2010–2023). The annual percentage change (APC) and 95% confidence intervals (CIs) were calculated to synthesize the trends as decreasing, increasing, or stationary. DM-related mortality rates varied from 42.3 in 2010 to 35.9 per 100,000 deaths in 2023, with the highest values in the Northeastern region. An increase in mortality was observed from 2020 onwards. Regression analysis shows a decreasing trend in the pre-pandemic period (2010–2019) in women (−1.87%), 50–69 years (−1.24%), and ≥70 years (−1.21%). A decline was also observed in the Northeast (−1.06%), Southeast (−1.73%), and nationwide (−0.96%). However, after incorporating data from the pandemic period (2010–2023), the trend became stationary for individuals aged 50–69 years and ≥70 years, the Southeastern region, and Brazil. This change makes the expected mortality uncertain and highlights the need for effective coping strategies after the pandemic.

Selecting AI-enabled music learning technologies in higher education using AHP and TOPSIS

Elites moved toward democrats more than nonelites moved away: Income, education, and occupational class in US presidential elections, 1980–2020

Recent discussion of voting in US elections claims a strong movement of White working-class voters away from voting for Democrats, with much discussion focusing only on elections between 2012 and the present. We examine longer-term trends from 1980 to 2020 in how more and less privileged White voters—measured by household income, education, and occupational class—moved toward or away from voting Democratic. We also explore how these movements changed the shape of the relationships between these three socioeconomic indicators and voting Democratic. We find little evidence of a long-term movement away from Democrats among voters with lower income, less education, or working-class jobs, although there is some evidence of this after 2012. The clearest long-term trend is that voters in the highest decile of income, college graduates, and white-collar workers moved steadily toward voting Democratic across the 40 y. Thus, the change from negative to flat for income’s relationship to voting Democratic, and from negative to positive for education’s relationship to voting Democratic comes less from a movement of less privileged voters away from Democratic voting and more from a long-term movement of those in the top decile of income, college graduates, and white-collar workers toward voting Democratic. Whether the post-2012 movement away from voting Democratic among voters without a high school degree and in working-class jobs becomes an enduring trend or is idiosyncratic to Trump’s candidacy is an important question for future research.