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Long-Term Risk of Stroke After Transient Ischemic Attack or Minor Stroke A Systematic Review and Meta-Analysis
No full textImportance After a transient ischemic attack (TIA) or minor stroke, the long-term risk of stroke is not well-known. Objective To determine the annual incidence rates and cumulative incidences of stroke up to 10 years after TIA or minor stroke. Data Sources MEDLINE, Embase, and Web of Science were searched from inception through June 26, 2024. Study Selection Prospective or retrospective cohort studies reporting stroke risk during a minimum follow-up of 1 year in patients with TIA or minor stroke. Data Extraction and Synthesis Two reviewers independently performed data extraction and assessed study quality. Unpublished aggregate-level data on number of events and person-years during discrete follow-up intervals were obtained directly from the authors of the included studies to calculate incidence rates in individual studies. Data across studies were pooled using random-effects meta-analysis. Main Outcomes and Measures The primary outcome was any stroke. Study-level characteristics were investigated as potential sources of variability in stroke rates across studies. Results The analysis involved 171 068 patients (median age, 69 years [IQR, 65-71]; median proportion of male patients, 57% [IQR, 52%-60%]) from 38 included studies. The pooled rate of stroke per 100 person-years was 5.94 events (95% CI, 5.18-6.76; 38 studies; I-2 = 97%) in the first year, 1.80 events (95% CI, 1.58-2.04; 25 studies; I-2 = 90%) annually in the second through fifth years, and 1.72 events (95% CI, 1.31-2.18; 12 studies; I-2 = 84%) annually in the sixth through tenth years. The 5- and 10-year cumulative incidence of stroke was 12.5% (95% CI, 11.0%-14.1%) and 19.8% (95% CI, 16.7%-23.1%), respectively. Stroke rates were higher in studies conducted in North America (rate ratio [RR], 1.43 [95% CI, 1.36-1.50]) and Asia (RR, 1.62 [95% CI, 1.52-1.73]), compared with Europe, in cohorts recruited in or after 2007 (RR, 1.42 [95% CI, 1.23-1.64]), and in studies that used active vs passive outcome ascertainment methods (RR, 1.11 [95% CI, 1.07-1.17]). Studies focusing solely on patients with TIA (RR, 0.68 [95% CI, 0.65-0.71) or first-ever index events (RR, 0.45 [95% CI, 0.42-0.49]) had lower stroke rates than studies with an unselected patient population. Conclusions and Relevance Patients who have had a TIA or minor stroke are at a persistently high risk of subsequent stroke. Findings from this study underscore the need for improving long-term stroke prevention measures in this patient group.N
Relative size matters: eyespots on large insect prey deter small arthropod predators
No full textCircular chromatic patterns that appear to resemble vertebrate eyes ('eyespots') are commonplace in the animal kingdom and are widely believed to have evolved as an anti-predator defence. For example, experiments have shown that eyespots on caterpillar-like pastry baits can deter predation by birds. However, little is known about the extent to which eyespots deter (or promote) attack by arthropod predators. Here, we describe two separate experiments in which salticid spiders (Salticus scenicus) and Chinese mantids (Tenodera sinensis) were presented with a choice of mealworms (Tenibrio molitor) with or without eyespots. In a complementary experiment, we observed the time taken for adult Chinese mantids to attack hawkmoth (Manduca quinquemaculata) larvae of two different sizes, with and without eyespots. All three experiments indicate that eyespots on insect larvae can deter predation, so long as the larvae are sufficiently large compared with the size of the arthropod predator. However, when larvae are small relative to the arthropod predator, eyespots cease to be protective and may even promote attacks. Our results suggest that small arthropods can show an aversion to large prey with eyespots and help explain the presence of eyespots in medium-sized caterpillars, because these traits are unlikely to deter avian predators.N
Advanced Algorithm for Continuous Melt Onset Detection on Arctic Sea Ice
No full textExpansion of the Arctic melting season with an earlier melt onset date (MOD) is a well-known indicator of Arctic warming. Since 1979, the pan-Arctic MOD distributions usually have been estimated using passive satellite microwave radiometer observations. However, there is a poor agreement in MOD between previous MOD detection algorithms based on passive microwave measurements, raising doubts regarding the accuracy of their MOD products. Thus, this study developed a new MOD algorithm, namely TBmax algorithm, to improve the estimation accuracy of continuous melt onset. The TB(max )algorithm utilizes the microwave radiation characteristics of sea ice, and the daily brightness temperature time series shows their maximum brightness temperature on MOD. By using AMSR2 brightness temperature data, the pan-Arctic MOD distributions estimated from 2013 to 2021 using the TB(max )algorithm successfully reproduced a feature of sea ice melting that mainly during May or June over the Arctic, including the late melting tendency of ice at high latitudes and multiyear ice (MYI). Validation with independent dataset (ice mass balance (IMB) buoy data) suggested that the TBmax MODs showed superior performance compared to other previous algorithms (biases of 0.1 days versus -2.7 and 13.9 days). As MOD can provide information about surface emissivity and the energy budget of the sea ice, the improved MOD may contribute to a more precise analysis of Arctic environment change and enhanced estimation of sea ice parameters.N
Stoichiometric anion exchange by a low-dielectric-constant solvent for highly-doped conjugated polymers with enhanced environmental stability
No full textHigh-degree doping of conjugated polymers often employs a strong redox agent, which facilitates polymer ionization but results in poor environmental stability for the counter-ion. Here, we demonstrate an anion-exchange doping using a model study that systematically investigates the effect of the solvent dielectric constant on both doping and anion exchange. The dielectric constant significantly affects the initial doping of poly(2,5-bis(3-hexadecylthiophene-2-yl)thieno[3,2-b]thiophene) (PBTTT) films using FeCl3, as well as in the subsequent anion exchange of [FeCl4]- to dodecylbenzenesulfonate ([DBS]-). A solvent with a higher dielectric constant improves the FeCl3 doping efficiency but hinders the subsequent anion exchange. Such conflicting effects can be resolved by stepwise immersion in separate solutions of FeCl3 and dodecylbenzenesulfonic acid (DBSA). Stepwise anion-exchange doping achieves high electrical conductivity with improved environmental stability, while also allowing for the application of desired anions that require extended time for the direct doping method, such as in Br & oslash;nsted acid doping.N
Review of Data and User Interface in Online Apparel Size Recommendation Systems
No full textThis paper examines the effectiveness of online clothing size recommendation systems, focusing on data utilization and user interface (UI) design. The challenges involved in selecting appropriate clothing sizes are a major factor behind high return rates in fashion e-commerce. As such, size recommendation systems that utilize advanced algorithms and personalized data to improve accuracy have emerged as a potential solution. By analyzing existing size recommendation systems and their integration with personalized algorithms, this study investigates how data-driven approaches—such as incorporating user body measurements, fit preferences, and purchase history—can enhance the accuracy of size predictions. The role of the user interface in improving customer engagement and trust in these systems is also explored, emphasizing the impact of clear and intuitive designs on user satisfaction. Through these analyses, the paper aims to identify current limitations and provide actionable insights for refining both the data architecture and user experience in size recommendation systems. Ultimately, this research seeks to offer valuable information to companies aiming to develop sophisticated systems, while also contributing to a more accurate and reliable size recommendation experience for customers.N
Distribution analysis of the finless porpoises (Neophocaena sp.) and oceanic dolphins (Delphinidae) in the Korean Sea using environmental DNA
No full textEnvironmental DNA (eDNA) serves as a non-invasive tool for monitoring the presence of specific organisms in challenging or hard-to-access areas. We attempted non-invasive monitoring of Korean cetacean species by extracting eDNA from the western and southern seas of the Republic of Korea, as well as around Jeju Island. In the present study, we focused on two representative cetaceans of the Korean Sea: the narrow-ridged finless porpoise (Neophocaena asiaeorientalis sunameri) and oceanic dolphins (Family Delphinidae). When selecting polymerase chain reaction primers, mitochondrial DNA (mtDNA) of N. asiaeorientalis and microsatellite Slo4 of oceanic dolphins were identified as the most effective gene sequences in high abundance in low concentration eDNA samples, using tissue samples for eDNA detection of the target species. A total of 139 samples were collected, and eDNA was detected from finless porpoises (Neophocaena sp.) in 94 samples (68%) and from oceanic dolphins in 50 samples (36%). Significantly, eDNA revealed the considerable presence of finless porpoise around Jeju Island, despite a lack of visual confirmation. In the Yellow Sea, eDNA primarily detected the presence of common dolphin (Delphinus delphis), orca (Orcinus orca), and Indo-Pacific bottlenose dolphin (Tursiops aduncus). Indo-Pacific bottlenose dolphins were identified along the coast of Jeju Island. The value of this research lies in being the first attempt to explore cetacean eDNA across various species in Korea. Further cetacean eDNA research should focus on developing metabarcoding primers capable of detecting a greater variety of cetacean species and primers for detecting specific porpoise species. This study will serve as a valuable reference for future studies.N
Transforming Japans Cultural Heritage System and Local Public-Private Partnership Governance
No full textFacilitation of pyrite dissolution through enhanced electron transfer in pyrite-fuel cells
No full textThis study investigates a pyrite-fuel cell (PFC) approach to overcome challenges in pyrite oxidation for metal recovery and environmental mitigation, particularly under acidic conditions that inhibit Fe²⁺ oxidation and cause electron passivation. By introducing an external circuit, the PFC allows efficient electron transfer. This study compares three oxidation pathways: direct oxidation by dissolved oxygen, bio-mediated oxidation by Acidithiobacillus ferrooxidans, and electrochemical oxidation. Using 2.5 g of pyrite in 125 mL of pH 2 electrolyte, the PFC showed significantly higher Fe leaching (0.84 mM) over four weeks compared to 0.11 mM and 0.56 mM for dissolved oxygen and A. ferrooxidans, respectively. Importantly, Fe–O layer formation did not result in electron passivation within the electrochemical system, as verified by microscopy techniques. Enhanced electron transfer, facilitated by lower external resistance, accelerated pyrite dissolution, while a redox potential exceeding 0.66 V further boosted dissolution rates. This study highlights PFCs as a sustainable and efficient method for improving pyrite oxidation and metal recovery, offering clear advantages over conventional oxidation techniques.N
Recent advances in AI-driven protein-ligand interaction predictions
No full textStructure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligand binding site prediction, protein-ligand binding pose estimation, scoring function development, and virtual screening. In this review, we summarize the recent AI-driven advances in various protein-ligand interaction prediction tasks. Traditional docking methods based on empirical scoring functions often lack accuracy, whereas AI models, including graph neural networks, mixture density networks, transformers, and diffusion models, have enhanced predictive performance. Ligand binding site prediction has been refined using geometric deep learning and sequence-based embeddings, aiding in the identification of potential druggable target sites. Binding pose prediction has evolved with sampling-based and regression-based models, as well as protein-ligand co-generation frameworks. AI-powered scoring functions now integrate physical constraints and deep learning techniques to improve binding affinity estimation, leading to more robust virtual screening strategies. Despite these advances, generalization across diverse protein-ligand pairs remains a challenge. As AI technologies continue to evolve, they are expected to revolutionize molecular docking and affinity prediction, increasing both the accuracy and efficiency of structure-based drug discovery.Y