States of India like Bihar, Uttar Pradesh, and West Bengal along the Ganga River, endure natural disasters periodically, resulting in repeated trends of economic loss and damages. Especially, most of the districts of Bihar, India, are prone to floods. Based on this background, this study aims to assess the flood vulnerabilities across districts of Bihar, India, employing data from the Central Water Commission from 1953 to 2020. Further, we explore trends and patterns of loss and damage due to flood risks in Bihar. Using the flood vulnerability integrated method and the principal component analysis, the index is constructed by incorporating the three major indicators: exposure, sensitivity, and adaptive capacity. This study is unique, and advances from previous studies in using a greater number of variables in exposure indicator. The proxy variable for each indicator is identified through both inductive and deductive approaches, and the composite index is constructed using all three indicators. Also, we identify the districts with high level of education and per capita income are less likely to expose flood vulnerability. The comparison of the districts reflects wide range of variation in terms of flood vulnerability as per their adaptive capacity and sensitivity. Specifically, these findings align with Target Sustainable Development Goal 11.5. This study addresses the policy for disaster prevention, risk reduction, and mitigation measures, as well as the enhancement of the capability of adaptation to floods by the affected community.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a reduced attention span, hyperactivity, and impulsive behaviors, which typically manifest during childhood. This study employs functional magnetic resonance imaging (fMRI) to use spontaneous brain activity for classifying individuals with ADHD, focusing on a 3D convolutional neural network (CNN) architecture to facilitate the design of decision support systems. We developed a novel deep learning model based on 3D CNNs using the ADHD-200 database, which comprises datasets from NeuroImage (NI), New York University (NYU), and Peking University (PU). We used fractional amplitude of low-frequency fluctuations (fALFF) and regional homogeneity (ReHo) data in three dimensions and performed a fivefold cross-validation to address the dataset imbalance. We aimed to verify the efficacy of our proposed 3D CNN by contrasting it with a fully connected neural network (FCNN) architecture. The 3D CNN achieved accuracy rates of 76.19% (NI), 69.92% (NYU), and 70.77% (PU) for fALFF data. The FCNN model yielded lower accuracy rates across all datasets. For generalizability, we trained on NI and NYU datasets and tested on PU. The 3D CNN achieved 69.48% accuracy on fALFF outperforming the FCNN. Our results demonstrate that using 3D CNNs for classifying fALFF data is an effective approach for diagnosing ADHD. Also, FCNN confirmed the efficiency of the designed model.

Stream macroinvertebrate assemblages are shaped by natural and human-related factors that operate through complex hierarchical pathways. Quantifying these relationships can provide additional insights into stream ecological assessment. We applied a structural equation modeling framework to evaluate hypothesized pathways by which watershed, riparian, and in-stream factors affect benthic macroinvertebrate condition in the Western Mountains (WMT) and Xeric (XER) ecoregions in the United States. We developed a conceptual model grounded in theory, empirical evidence, and expert opinion to evaluate the following hypotheses: (1) macroinvertebrate assemblages are primarily driven by proximal, in-stream factors (e.g., water quality and physical habitat); (2) anthropogenic land uses affect macroinvertebrates indirectly by altering in-stream characteristics; and (3) riparian vegetation cover attenuates land use effects. We tested our model separately on three measures of benthic macroinvertebrate assemblage condition: ratio of observed-to-expected taxonomic richness (O/E); a multimetric index (MMI); and richness of Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT). In the WMT, site-level riparian cover, in-stream physical habitat (relative bed stability), and water chemistry (total nitrogen) were the top three predictors of macroinvertebrate assemblages, each having over two times the magnitude of effect on macroinvertebrates compared with watershed-level predictors. In the arid XER, annual precipitation and stream flow characteristics were top predictors of macroinvertebrate assemblages and had similar magnitudes of effect as in-stream water chemistry. Path analyses revealed that land use activities in the watershed and at the stream site degraded macroinvertebrate assemblages indirectly by altering relative bed stability, water quality, and riparian cover/complexity. Increased riparian cover was associated with greater macroinvertebrate condition by reducing land use impacts on stream flow, streambed substrate, and water quality, but the pathways differed among ecoregions. In the WMT, site-level riparian cover affected macroinvertebrate assemblages partly through indirect pathways associated with greater streambed stability and reduced total nitrogen concentrations. In contrast, in the XER, watershed-level riparian cover affected macroinvertebrate assemblages through greater specific stream power. Identifying the relative effects of and pathways by which natural and anthropogenic factors affect macroinvertebrates can serve as a framework for prioritizing management and conservation efforts.

Tropical cyclones (TCs) and their associated intense rainfall are among the most significant natural disasters. Exploring the characteristics of tropical cyclone precipitation (TCP) has always been a challenging issue in TC research. This study utilized the TC track data from the International Best Track Archive for Climate Stewardship and precipitation data from the multi-source weighted-ensemble precipitation covering the years 1980-2019, to examine shifts in precipitation rates and peak precipitation levels before and after TC landfall. The results highlight several key findings: (1) Precipitation during the TC landfall process is relatively stable beforehand but tends to decrease slightly after landfall. Generally, the maximum precipitation occurs during the landfall. (2) From 1980 to 2019, the rate of precipitation changes before landfall has significantly increased. Conversely, after the year 2000, the rate of precipitation changes after landfall has significantly decreased. (3) Over the past 40 years, while peak precipitation levels of landfalling TCs have remained relatively constant, the total precipitation has shown an increasing trend, particularly in regions like the main island of Hainan, southern Zhejiang, and Shanghai, which are characterized by high peak precipitation. The results help clarify the TC processes and provide reference points for parameter selection in regional TCP modeling.

BACKGROUND: Patients with moderate-to-severe atopic dermatitis (AD) who are most likely to respond to the Janus kinase (JAK) 1/2 inhibitor baricitinib (BARI) are known to have an impacted body surface area (BSA) ≤ 40% and severe itch (numerical rating scale [NRS] ≥ 7], collectively termed \'BARI itch-dominant\' patients. Our objective is to build on our previous work by providing a body region-specific, clinical characterization of the BARI itch-dominant patient at baseline and their response to BARI 4 mg.
METHODS: BREEZE-AD7 was a phase 3 trial in adults with moderate-to-severe AD receiving placebo or 2 mg or 4 mg BARI in combination with topical corticosteroids. Assessing only data from BARI itch-dominant patients, we summarized the baseline characteristics and conducted body region-specific analyses on Eczema Area and Severity Index (EASI) data in order to report the response to placebo versus BARI 4 mg within this patient subtype.
RESULTS: BARI 4 mg was highly effective across all body regions; at week 16, 75% improvement was seen in EASI scores (EASI75), and response rates with BARI 4 mg (head/neck, 58.3%; trunk, 69.2%; upper extremities, 61.5%; lower extremities, 87.5%) all exceeded those with placebo (head/neck: 37.5%; trunk, 40.6%; upper extremities, 18.8%; lower extremities, 40.6%) as well as the overall EASI75 rates of the intent-to-treat (ITT) population (BARI, 48.0%; placebo, 23.0%). At baseline, most BARI itch-dominant patients presented with involvement of all regions (mean regional BSA 22.7%-40.3%), highest in the head and neck, mean EASI region scores of 15.7-24.0, and considerably severe sign ratings (mean EASI sub-scores: 1.4-2.3, out of 3), especially for erythema.
CONCLUSIONS: BARI itch-dominant patients exhibit AD involvement across all body regions and considerable sign severity, especially erythema. In response to BARI 4 mg, EASI quickly improved across regions, substantially more so in this subtype than in the ITT population.

BACKGROUND: Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity.
RESULTS: To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data.
METHODS: The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

OBJECTIVE: Atrial fibrillation (AF) is a widespread cardiac arrhythmia that significantly impacts heart function. AF disrupts atrial mechanical contraction, leading to irregular, uncoordinated, and slow blood flow inside the atria which favors the formation of clots, primarily within the left atrium (LA). A standardized region-based analysis of the LA is missing, and there is not even any consensus about how to define the LA regions. In this study we propose an automatic approach for regionalizing the LA into segments to provide a comprehensive 3D region-based LA contraction assessment. LA global and regional contraction were quantified in control subjects and in AF patients to describe mechanical abnormalities associated with AF.
METHODS: The proposed automatic approach for LA regionalization was tested in thirteen control subjects and seventeen AF patients. After dividing LA into standard regions, we evaluated the global and regional mechanical function by measuring LA contraction parameters, such as regional volume, global and regional strains, regional wall motion and regional shortening fraction.
RESULTS: LA regionalization was successful in all study subjects. In the AF group compared with control subjects, results showed: a global impairment of LA contraction which appeared more pronounced along radial and circumferential direction; a regional impairment of radial strain which was more pronounced in septal, inferior, and lateral regions suggesting a greater reduction in mechanical efficiency in these regions in comparison to the posterior and anterior ones.
CONCLUSIONS: An automatic approach for LA regionalization was proposed. The regionalization method was proved to be robust with several LA anatomical variations and able to characterize contraction changes associated with AF.

Evidence of climate change is widespread and severe across all parts of the world. This poses a threat to humanity, and the entire environment. Appropriate policies are therefore required to help reduce greenhouse gas emission levels, limit the rate of global warming and its impact on climate change while pursuing national growth targets. This study employs the Tapio decoupling method to analyse the decoupling relationship (DR) between economic growth and carbon dioxide (CO2) emissions from 1998 to 2018. We also apply the Logarithmic Mean Divisia Index (LMDI) decomposition method on an extended Kaya identity to analyse CO2 emissions drivers in 145 countries. Last, the study examined the relative impacts of trade intensity and trade efficiency on the DR between economic growth and CO2 emissions. The results revealed that regions with relatively many developing and emerging countries (i.e., SSA, EAP, LAC, MENA, and SA) generally performed Weak Decoupling (WD), Expansive Negative Decoupling (END) and Expansive Coupling (EC), and the decoupling process was largely unstable. The ECA and NA regions on the other hand, which are typically composed of developed economies performed stable WD and Strong Decoupling (SD) statuses throughout the study period. The evidence further revealed that while trade intensity, activity, population, output per carbon emission and Carbon Intensity (CI) effects promote CO2 emissions, trade efficiency and energy intensity (EI) hinder emissions. We recommend that developing countries should enforce laws and cooperate with the developed economies to gain access to green technology to promote environmental sustainability.

Purpose: Increased incidence of anterior cruciate ligament injuries has amplified the need for quantitative research in clinical and academic settings. We used a novel digital arthrometer to measure knee laxity in healthy people and patients with anterior cruciate ligament injuries. Changes in stiffness were also assessed to develop new indicators for detecting anterior cruciate ligament injury. The purpose of this study was to use arthrometer to measure the quantitative indicator of knee laxity, bringing clinicians a new perspective on how to identify injury to the ACL. Methods: In this cross-sectional study, anterior tibial displacement under continuous loading was measured using a novel digital arthrometer in 30 patients with unilateral anterior cruciate ligament injury and 30 healthy controls. Load-displacement curves were plotted, using real-time load and displacement changes. Stiffness was defined by the slope of the applied load to tibial displacement. Anterior tibial displacement and instantaneous stiffness values under different loads were compared. The restricting contribution of the anterior cruciate ligament transformed the displacement-stiffness curve from a sharp decrease to a stable increase, resulting in a minimum stiffness value. Using the minimum stiffness as the turning point, the load-displacement curve was divided into regions 1 and 2. The two regions\' stiffness changes were compared. Based on the findings, receiver operating characteristic curves were plotted and the area under the curve was calculated to estimate the diagnostic accuracy. Results: Anterior tibial displacement was significantly greater in the anterior cruciate ligament injury group than in the controls under each 10-N increase load (p < 0.05). In the anterior cruciate ligament injury group, instantaneous stiffness was significantly lower on the injured side than on the healthy side (p < 0.05). In the two regions of the load-displacement curve, stiffness was significantly lower in the anterior cruciate ligament injury group than in the control group (all, p < 0.05). Receiver operating characteristic curves were plotted, using changes in stiffness under the two regions in both groups. Stiffness in region 2 had the largest area under the curve (0.94; 95% CI, 0.88-0.99). Using the cut-off value of 9.62 N/mm to detect ACL injury, the sensitivity and specificity were 93% and 82%, respectively. Conclusion: Our investigation of ligament stiffness provides novel insights into the properties of knee laxity. Stiffness in the later stages of increased loading <9.62 N/mm could be a valid indicator for identifying knee laxity.

Medical waste (MW) is exploding due to the COVID-19 pandemic, posing a significant environmental threat, and leading to the urgent requirement for affordable and environmentally friendly MW disposal technologies. Prior research on individual MW disposal plants is region-specific, applying these results to other regions may introduce bias. In this study, major MW disposal technologies in China, i.e., incineration technologies (pyrolysis incineration and rotary kiln incineration), and sterilization technologies (steam sterilization, microwave sterilization, and chemical disinfection) with residue landfill or incineration were analyzed from an industry-level perspective via life cycle assessment (LCA), life cycle costing (LCC) and net present value (NPV) methods. Life cycle inventories and economic cost data for 4-5 typical companies were selected from 128 distinct enterprises and academic sources for each technology. LCA results show that microwave sterilization with residue incineration has the lowest environmental impact, emitting only 480 kg CO2 eq. LCC and NPV analyses indicate that steam sterilization with landfilling is the most economical, yielding revenues of 1,210 CNY/t and breaking even in the first year. Conversely, pyrolysis and rotary kiln incineration break even between the 4th and 5th years. Greenhouse gas emissions from the MW disposal in ten cities with the largest MW production in 2020 increased by 7% over 2019 to 43,800 tons and other pollutants increased by 6% to 12%. Economically, Shanghai exhibits the highest cost-effectiveness, while Nanjing delivers the lowest. It can be observed that the adoption of optimal environmental technologies has resulted in a diminution of greenhouse gas emissions by 279,000 tons and energy conservation of 1.76 billion MJ.