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Asymmetric spillover connectedness between clean energy markets and industrial stock markets: How uncertainties affect it
As the global climate crisis intensifies, clean energy is becoming increasingly important, and the intrinsic link between industry and energy highlights the connectedness between the industrial stock market and the clean energy market, and examining this connectedness can reveal risk spillovers between these markets. We categorise the clean energy market into hydro, wind and solar markets, and the industrial stock market into low-carbon portfolios, high-carbon portfolios and ordinary portfolios, and use the network connectedness methodology to investigate the connectedness of returns between the clean energy submarkets and the industrial stock submarkets in the time and frequency domains. The returns are categorised into positive and negative returns in order to investigate the asymmetry in the connectedness of the markets. Finally, we explore the effects of EPU, GPU, and CPU in terms of network connectedness. It is revealed that clean energy submarkets are net receivers of risk, industrial stock submarkets are risk transmitters. The hydropower market is the main risk receiver, while the low-carbon portfolio is the main risk transmitter. Risk spillovers are mainly driven by short-term spillovers and do not have persistent spillover transmission. Bad news has a greater impact on network connectedness, leading to higher levels of connectedness between markets. EPU and CPU have significant effects on network connectedness. Our findings are informative for both investors and policymakers.
Hybrid weighted fuzzy production rule extraction utilizing modified harmony search and BPNN
Dynamic imaging of myelin pathology in physiologically preserved human brain tissue using third harmonic generation microscopy
Myelin pathology is known to play a central role in disorders such as multiple sclerosis (MS) among others. Despite this, the pathological mechanisms underlying these conditions are often difficult to unravel. Conventional techniques like immunohistochemistry or dye-based approaches, do not provide a temporal characterization of the pathophysiological aberrations responsible for myelin changes in human specimens. Here, to circumvent this curb, we present a label-free, live-cell imaging approach of myelin using recent advancements in nonlinear harmonic generation microscopy applied to physiologically viable human brain tissue from post-mortem donors. Gray and white matter brain tissue from epilepsy surgery and post-mortem donors was excised. To sustain viability of the specimens for several hours, they were subjected to either acute or organotypic slice culture protocols in artificial cerebral spinal fluid. Imaging was performed using a femtosecond pulsed 1050 nm laser to generate second harmonic generation (SHG) and third harmonic generation (THG) signals directly from myelin and axon-like structures without the need to add any labels. Experiments on acute human brain slices and post-mortem human slice cultures reveal that myelin, along with lipid bodies, are the prime sources of THG signal. We show that tissue viability is maintained over extended periods during THG microscopy, and that prolonged THG imaging is able to detect experimentally induced subtle alterations in myelin morphology. Finally, we provide practical evidence that live-cell imaging of myelin with THG microscopy is a sensitive tool to investigate subtle changes in white matter of neurological donors. Overall, our findings support that nonlinear live-cell imaging is a suitable setup for researching myelin morphology in neurological conditions like MS.
Micronutrients and contaminants in the grazing and agricultural soils of Kashmir Valley, India
Health diagnosis associated with COVID-19 death in the United States: A retrospective cohort study using electronic health records
Background The United States has experienced high surge in COVID-19 cases since the dawn of 2020. Identifying the types of diagnoses that pose a risk in leading COVID-19 death casualties will enable our community to obtain a better perspective in identifying the most vulnerable populations and enable these populations to implement better precautionary measures. Objective To identify demographic factors and health diagnosis codes that pose a high or a low risk to COVID-19 death from individual health record data sourced from the United States. Methods We used logistic regression models to analyze the top 500 health diagnosis codes and demographics that have been identified as being associated with COVID-19 death. Results Among 223,286 patients tested positive at least once, 218,831 (98%) patients were alive and 4,455 (2%) patients died during the duration of the study period. Through our logistic regression analysis, four demographic characteristics of patients; age, gender, race and region, were deemed to be associated with COVID-19 mortality. Patients from the West region of the United States: Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming had the highest odds ratio of COVID-19 mortality across the United States. In terms of diagnoses, Complications mainly related to pregnancy (Adjusted Odds Ratio, OR:2.95; 95% Confidence Interval, CI:1.4 - 6.23) hold the highest odds ratio in influencing COVID-19 death followed by Other diseases of the respiratory system (OR:2.0; CI:1.84 – 2.18), Renal failure (OR:1.76; CI:1.61 – 1.93), Influenza and pneumonia (OR:1.53; CI:1.41 – 1.67), Other bacterial diseases (OR:1.45; CI:1.31 – 1.61), Coagulation defects, purpura and other hemorrhagic conditions(OR:1.37; CI:1.22 – 1.54), Injuries to the head (OR:1.27; CI:1.1 - 1.46), Mood [affective] disorders (OR:1.24; CI:1.12 – 1.36), Aplastic and other anemias (OR:1.22; CI:1.12 – 1.34), Chronic obstructive pulmonary disease and allied conditions (OR:1.18; CI:1.06 – 1.32), Other forms of heart disease (OR:1.18; CI:1.09 – 1.28), Infections of the skin and subcutaneous tissue (OR: 1.15; CI:1.04 – 1.27), Diabetes mellitus (OR:1.14; CI:1.03 – 1.26), and Other diseases of the urinary system (OR:1.12; CI:1.03 – 1.21). Conclusion We found demographic factors and medical conditions, including some novel ones which are associated with COVID-19 death. These findings can be used for clinical and public awareness and for future research purposes.
High dose methylprednisolone mediates YAP/TAZ-TEAD in vocal fold fibroblasts with macrophages
Effect of perfusion index on oxygen reserve index accuracy in estimating arterial oxygen tension in anesthetized dogs: Data reanalysis
Multi-wave CO-oximetry, utilizing the oxygen reserve index (ORi), estimates arterial partial pressure of oxygen (PaO2) in mild hyperoxemia, between 100 and 200 mmHg, and concurrently quantifies local perfusion at the measurement site using the perfusion index (PI). This study explores how variations in PI influence the accuracy of ORi in estimating PaO2 in anesthetized dogs. Data from 37 mechanically ventilated dogs were retrospectively reanalyzed using a different approach. ORi and PI values were collected using a CO-oximeter. The data were categorized into four groups based on PI quartiles. In each group, the relationship between ORi and PaO2 was assessed using linear regression analysis, and the area under the receiver operating characteristic curve (AUROC) investigated the diagnostic performance of ORi in detecting PaO2 > 150 mmHg. Strong relationships between ORi and PaO2 were observed in groups with PI values < 2 (r2 ≥ 0.63). The AUROC of ORi for identifying PaO2 > 150 mmHg decreased with PI > 2 compared to lower values (0.76 vs > 0.88). In this study, PI values > 2 negatively impacted ORi’s ability to estimate PaO2, likely due to fluctuations in blood flow perfusing the measurement site. The results of this study suggests that consideration of the PI value is essential when titrating oxygen therapy using ORi in anesthetized dogs.
Well log data generation and imputation using sequence based generative adversarial networks
Correction: Effectiveness of a virtual reality rehabilitation in stroke patients with sensory-motor and proprioception upper limb deficit: A study protocol
Integrated bioinformatics and clinical data identify three novel biomarkers for osteoarthritis diagnosis and synovial immune
Abstract Osteoarthritis (OA) is a degenerative joint disease that can be aggravated by synovitis and synovial immune disorders (SID). However, the role of synovial SID-related genes in OA synovium remains poorly understood. OA synovial and peripheral blood datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/). Immune-related genes (https://reactome.org/) showing differential expression in peripheral blood were identified as immune disorder genes. Subsequently, differentially expressed immune disorder genes in OA synovium were further identified as SID genes. The Venn diagram, random forest, SVM-RFE algorithm, and multivariate analysis were employed to determine SID-related hub genes in OA synovium. Using the identified hub genes, we constructed and validated a diagnostic model for predicting OA occurrence. The correlation between hub gene expression and immune-related modules was explored using CIBERSORT and MCP-counter analyses. We identified three SID-related hub genes (ACAT1, SPHK1, and ACACB) in OA synovium. The diagnostic model incorporating these hub genes demonstrated reliable predictive accuracy (AUC = 0.939). Through qPCR analysis, we quantitated the expression levels of the hub genes and confirmed that three hub genes could serve as novel biomarkers for OA patients (AUC = 0.960). Furthermore, we observed a significant correlation between the expression of these hub genes and immune cell infiltration, as well as inflammatory cytokine levels in OA synovium. Our findings suggest that three SID-related hub genes have the potential to serve as diagnostic biomarkers for OA patients. These genes are associated with immune disorder and contribute to immune alterations within the OA synovium.
Prognostic factors for long-term mortality after surgery of left-sided infective endocarditis
Background Infective endocarditis has low prevalence but a high mortality rate. Left-sided infective endocarditis (LSIE) has a higher mortality rate than right-sided infective endocarditis. Surgical treatment is occasionally considered for LSIE; however, few data are available on the long-term prognostic factors for LSIE after surgical treatment. This study investigated the risk factors for long-term mortality in LSIE patients who underwent surgical treatment. Methods This retrospective study enrolled adult patients with LSIE who were admitted to Severance Hospital in South Korea and underwent surgical treatment from November 2005 to August 2017. The primary outcome was risk factors for overall all-cause mortality. Multivariable Cox regression analysis was performed to identify risk factors for long-term mortality of patients with LSIE who received surgical treatment. Results This study enrolled 239 with LSIE who underwent surgery. The median follow-up period was 75.9 months, and there were 34 deaths (14.2%) during the study period. Multivariable Cox analysis showed that central nervous system complications (hazard ratio [HR]: 3.55, 95% confidence interval [CI]: 1.76–7.17, P < 0.001), chronic liver disease (CLD) (HR: 4.33, 95% CI: 1.57–11.91, P = 0.005), and age ≥ 65 years (HR: 2.65, 95% CI: 1.28–5.51, P = 0.009) were risk factors for overall mortality. Kaplan–Meier analysis indicated a significant difference in survival between patients with and without CNS complications (P < 0.001, log-rank). Conclusion Central nervous system complications, CLD, and older age were associated with long-term mortality in surgically treated patients with LSIE. Preventive strategies for CNS complications would improve the treatment of LSIE.
Human activity recognition algorithms for manual material handling activities
Study on creep hardening-damage constitutive model of cemented tailings backfill based on time-harden theory
The wide application of the fill mining method is necessary for practicing the development concept of “green mountains are golden mountains” and protecting the surface landscape. In the underground quarry, the cemented tailings backfill (CTB) is mainly subjected to the gravity of the overlying rock layer in the mining area, which will cause creep problems in the long term. In order to protect the long-term stability of the mine surface, to study the creep hardening-damage characteristics of the CTB under the uniaxial action, for the maintenance age of 28d, cement-tailings ratios of 1:6 CTB to carry out uniaxial grading loading creep test, combined with the theory of time hardening Explore the creep process of the CTB in the hardening-damage law and the construction of the creep isomorphic model. The results show that the differences in the creep process of 1:6 cement-tailings ratios filler are caused by the joint existence of hardening and damage effects. In the stage of decelerated creep, creep hardening plays a major role; in the stage of stabilized creep, creep hardening and damage play a joint role; in the stage of accelerated creep, the creep damage effect is dominant; the strength of the CTB is strong and then weakened, and ultimately, the cumulative damage is too large to produce a “Y” type damage. The intrinsic model constructed based on the time-hardening theory better characterizes the creep of the 1:6 cement-tailings ratios of CTB under different stress levels. K and r are the material constants of the CTB, and the parameter K affects the time of decelerated creep during the decelerated creep stage, and the parameter r affects the creep rate of isokinetic creep.
Unveiling the PEDOT-polypyrrole hybrid electrode for the electrochemical sensing of dopamine
Trump team guts AIDS-eradication programme and slashes HIV research grants
The impact of fine particulate matter on depression: Evidence from social media in China
Depression is a significant public health issue in China that imposes a heavy economic burden on society and families. Using a dataset of 8.54 million Weibo posts from 284 prefecture-level cities across China between 2016 and 2019, we calculate the depression tendency index for residents in each city. Using the weighting of pollutants in nearby cities as an instrumental variable, we apply the two-stage least squares method to estimate the impact of PM2.5 on depression. The findings reveal that (1) air pollution markedly influences residents’ susceptibility to depression, and every 1 μg/m3 increase in the PM2.5 concentration results in a 0.0559% increase in the depression tendency value. (2) The influence of air pollution on residents’ depression exhibits a distinct weekly pattern, with individuals in heating cities, on weekdays, and in lower-income brackets being more impacted. (3) Our analysis of healthcare expenditures affirms that China’s environmental governance policies have yielded significant economic advantages. As mitigation strategies, we propose the adoption of air pollution evasion measures, persistent refinement and enforcement of air pollution regulatory policies to reduce environmental pollution-related damage, paying attention to groups at risk of depression and fostering a healthy society.