The relationship between general self-efficacy and nursing practice competence for second-year nurses: Empirical quantitative research.

AIM: To examine the relationship between general self-efficacy and nursing practice competence for nurses in the second year of employment. DESIGN: A cross-sectional design was used. DATA SOURCES: The study included 596 nurses in their second year of employment at 75 medical facilities across Japan and used an online questionnaire survey for data collection. RESULTS: The covariance structure analysis showed the path from general self-efficacy (latent variable) to nursing practice competence. Positive correlations were found between all factors on both scales. Multiple regression analysis results showed that the general self-efficacy factors of 'positivity in behavior' and 'confidence in social competence' affect nursing practice competence. CONCLUSION: This study emphasizes the importance of enhancing the general self-efficacy of second-year nurses to improve their nursing practice competence. To achieve this, it suggests developing strategies from the perspective of the factors that comprise general self-efficacy. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: The findings suggest that improving general self-efficacy can enhance nursing practice competence, which could inform the development of interventions to support nurses in improving their competence. The study provides basic data for improving nurses' practice competence. IMPACT: This study is the first to establish a relationship between general self-efficacy and nursing practice competence among second-year nurses. It demonstrates the significance of general self-efficacy in enhancing nursing practice competence, particularly for second-year nurses worldwide who may be struggling with their nursing practice competence and considering leaving the profession. The findings offer practical implications for stakeholders involved in nursing education and training programs, with potential applications in professional development. REPORTING METHOD: This manuscript adheres to the STROBE guidelines for the reporting of cross-sectional studies. PATIENT OR PUBLIC CONTRIBUTION: There was no patient or public contribution.

Influence of chemical structures on reduction rates and defluorination of fluoroarenes during catalytic reduction using a rhodium-based catalyst.

Continuous growth in fluoroarene production has led to environmental pollution and health concerns owing to their persistence, which is attributed to the stable C-F bond in their structures. Herein, we investigated fluoroarene decomposition via hydrodefluorination using a rhodium-based catalyst, focusing on the effects of the chemical structure and functional group on the defluorination yield. Most compounds, except (pentafluoroethyl)benzene, exhibited full or partial reduction with pseudo-first-order rate constants in the range of 0.002-0.396 min-1 and defluorination yields of 0%-100%. Fluoroarenes with hydroxyl, methyl, and carboxylate groups were selected to elucidate how hydrocarbon and oxygen-containing functional groups influence the reaction rate and defluorination. Inhibition of the reaction rate and defluorination yield based on functional groups increased in the order of hydroxyl < methyl < carboxylate, which was identical to the order of the electron-withdrawing effect. Fluoroarenes with polyfluoro groups were also assessed; polyfluoro groups demonstrated a different influence on catalyst activity than non-fluorine functional groups because of fluorine atoms in the substituents undergoing defluorination. The reaction kinetics of (difluoromethyl)fluorobenzenes and their intermediates suggested that hydrogenation and defluorination occurred during degradation. Finally, the effects of the type and position of functional groups on the reaction rate and defluorination yield were investigated via multivariable linear regression analysis. Notably, the electron-withdrawing nature of functional groups appeared to have a greater impact on the defluorination yield of fluoroarenes than the calculated C-F bond dissociation energy.

Factors determining the short-term clinical outcomes of conservative treatment in patients with supraspinatus tear.

INTRODUCTION: To identify factors influencing the better and worse changes in the Western Ontario Rotator Cuff Index of patients undergoing conservative treatment for supraspinatus tendon tear. METHODS: The study included 30 patients with supraspinatus tendon tear who underwent conservative treatment. The average duration of intervention was 35.4 days. The Western Ontario Rotator Cuff Index, shoulder range of motion, isometric muscle strength, supraspinatus tendon thickness, thickness of the supraspinatus, infraspinatus, and teres minor muscles, and acromiohumeral interval were assessed before and after the intervention. In the statistical analyses, change in the Western Ontario Rotator Cuff Index was the dependent variable, and the amount of change in each measurement variable before and after the intervention was the independent variable. RESULTS: The average Western Ontario Rotator Cuff Index improved from 1067 at pre-treatment to 997 at post-treatment, but without a significant difference (p = 0.29). A multiple regression analysis revealed that supraspinatus tendon thickness and muscle strength in shoulder external rotation at 90° scaption had a significant effect on the change in the Western Ontario Rotator Cuff Index (R2 = 0.44, p < 0.01). CONCLUSION: Supraspinatus tendon thickness and muscle strength in shoulder external rotation at 90° scaption were important factors for the Western Ontario Rotator Cuff Index score in the early stages of conservative treatment for patients with supraspinatus tendon tear.

Understanding and alleviating informal caregiver burden through the development and validation of a caregiver strain index-based model in Taiwan.

BACKGROUND: Quantifying the informal caregiver burden is important for understanding the risk factors associated with caregiver overload and for evaluating the effectiveness of services provided in Long-term Care (LTC). OBJECTIVE: This study aimed to develop and validate a Caregiver Strain Index (CSI)-based score for quantifying the informal caregiver burden, while the original dataset did not fully cover evaluation items commonly included in international assessments. Subsequently, we utilized the CSI-based score to pinpoint key caregiver burden risk factors, examine the initial timing of LTC services adoption, and assess the impact of LTC services on reducing caregiver burden. METHODS: The study analyzed over 28,000 LTC cases in Southern Taiwan from August 2019 to December 2022. Through multiple regression analysis, we identified significant risk factors associated with caregiver burden and examined changes in this burden after utilizing various services. Survival analysis was employed to explore the relationship between adopting the first LTC services and varying levels of caregiver burden. RESULTS: We identified 126 significant risk factors for caregiver burden. The most critical factors included caregiving for other disabled family members or children under the age of three (ß = 0.74, p < 0.001), the employment status of the caregiver (ß = 0.30-0.53, p < 0.001), the frailty of the care recipient (ß = 0.28-0.31, p < 0.001), and the behavioral symptoms of dementia in care recipients (ß = 0.28-2.60, p < 0.05). Generally, caregivers facing higher burdens sought LTC services earlier, and providing home care services alleviated the caregiver's burden. CONCLUSION: This comprehensive study suggests policy refinements to recognize high-risk caregivers better early and provide timely support to improve the overall well-being of both informal caregivers and care recipients.

Effects of temperature and moisture fluctuations for suitable use of raw-crushed wind-turbine blade in concrete.

Raw-crushed wind-turbine blade (RCWTB), a waste from the recycling of wind-turbine blades, is used as a raw material in concrete in this research. It contains not only fiberglass-composite fibers that bridge the cementitious matrix but also polyurethane and balsa-wood particles. Therefore, concrete containing RCWTB can be notably affected by moisture and temperature fluctuations and by exposure to high temperatures. In this research, the performance of five concrete mixes with 0.0%, 1.5%, 3.0%, 4.5%, and 6.0% RCWTB, respectively, is studied under moist/dry, alternating-sign-temperature-shock, and high-temperature-shock tests. Two damage mechanisms of RCWTB within concrete were found through these tests: on the one hand, micro-cracking of the cementitious matrix, which was verified by microscopic analyses and was dependent on concrete porosity; on the other, damage and degradation of the RCWTB components, as the polyurethane melted, and the balsa-wood particles burned. Both phenomena led to larger remaining-strain levels and reduced concrete compressive strength by up to 25% under temperature and humidity variations, although the bridging effect of the fiberglass-composite fibers was effective when adding RCWTB amounts higher than 3.0%. The compressive-strength loss after the high-temperature-shock test increased with the RCWTB content, reaching maximum values of 8% after an exposure time of 7 days. Statistical analyses revealed that effect of the RCA amount in the concrete was conditioned by the exposure times in all the tests. The accurate definition of those times is therefore key to set an RCWTB content in concrete that ensures its suitable behavior under the environmental conditions analyzed.

A clinical risk score enables early prediction of dissatisfaction 1 year after total knee arthroplasty.

PURPOSE: Dissatisfaction after total knee arthroplasty (TKA) is a prevalent and clinically relevant problem that affects approximately 10%-20% of patients. The aim of this study is to identify factors associated with dissatisfaction 1 year after TKA. METHODS: A total of 236 patients undergoing TKA were included in this prospective cohort study. Demographic data, preoperative clinical parameters (e.g., axial alignment, osteoarthritis severity) and patient-reported outcome measures (PROMs) were collected preoperatively, at 1 month and 1 year after TKA, encompassing the Knee Society Score (KSS) and Knee injury and Osteoarthritis Outcome Score (KOOS). The primary outcome was dissatisfaction 1 year after TKA, defined as ≤20 points on the KSS satisfaction scale. A risk score based on multiple regression and area under the curve (AUC) analyses was calculated to predict dissatisfaction. RESULTS: One year after TKA, 16% of the patients were dissatisfied. Dissatisfied patients were significantly younger (p = 0.023) and had a higher body mass index (BMI) (p = 0.007). No differences were observed in preoperative objective (p = 0.903) and functional KSS (p = 0.346), pain (p = 0.306), osteoarthritis severity (p = 0.358), axial knee alignment (p = 0.984) or psychological distress (p = 0.138). The likelihood of dissatisfaction at 1 year was 3.0, 4.0, 7.4, 4.3 and 2.8 times higher amongst patients aged <63.5 years, with a BMI > 30.1 kg/m2, a KOOSPain < 50%, a KSSFunction < 42 points and a KSSExpectation < 9 points (all at 1 month), respectively. Using these variables, a risk score with a maximum of 7 points was developed, demonstrating a high predictive value for dissatisfaction (AUC: 0.792 [95% confidence interval: 0.700-0.884], p < 0.001). CONCLUSION: Dissatisfaction 1 year after TKA can be predicted by a weighted risk score that includes patient age, BMI, pain, subjective functionality and unmet expectation 1 month postoperatively. Using the risk score, early detection of dissatisfaction has the potential to enable targeted interventions and improve patients' quality of life. LEVEL OF EVIDENCE: Level II, Prognostic study.

Estimation of instantaneous peak flows in Canadian rivers: an evaluation of conventional, nonlinear regression, and machine learning methods.

Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.

Reappraisal of serum retinol-binding protein as a surrogate marker for retinol and discovery of a novel retinol estimation formula.

BACKGROUND & AIMS: Serum retinol (ROH) is commonly used for population level assessment of vitamin A status. High-performance liquid chromatography (HPLC) is considered most accurate method for measuring ROH. However, with the technical difficulty of using HPLC for routine assays, serum retinol-binding protein (RBP) measured by immunological assays is expected to be a surrogate marker for ROH, with reports of a close correlation between serum RBP and ROH. Nevertheless, RBP is not commonly tested to assess vitamin A status with concerns over RBP alterations under various physiopathological conditions. Thus, we reappraised the extent to which RBP could be used as a surrogate marker in representative disorders that alter serum RBP levels. As a related marker, diagnostic utility of transthyretin (TTR) was also evaluated. METHODS: To evaluate the reliability of ROH and RBP assays, specimen stability was assessed in terms of (1) storage at 25, 4, -20, and -80 °C for 1-28 days, (2) five-cycle freeze-thawing, and (3) fluorescent light exposure for 1-14 days. Sources of variation (sex, age, body mass index [BMI], and drinking habits) and reference intervals for ROH, RBP, and TTR were determined in 617 well-defined healthy individuals. To investigate the influence of disorders that affect serum RBP, patients with five diagnostic groups were enrolled: 26 with chronic kidney disease (CKD); 13 with various malignancies in advanced stages (AdM), 12 with acute bacterial infections (ABI), 6 with liver cirrhosis (LC), and 26 with simple obesity (BMI ≥ 27 kg/m2). RESULTS: The stability of RBP and ROH in serum was confirmed under all conditions. In healthy individuals, serum ROH, RBP, and TTR were appreciably high in males with a slight increase in proportion to age and BMI. The major-axis regression line between RBP (x) and ROH (y) in healthy individuals was y = x, with a correlation coefficient of 0.986. In the LC, AdM, and ABI groups, similar strong correlations were observed; however, the regression lines were shifted slightly rightward from the healthy group line, indicating a positive bias in estimating ROH. Interestingly, the same analyses between TTR and ROH revealed similar strong linear relationships in all groups; however, the regression line of each group showed a leftward (opposite) shift from the healthy group line. Based on these observations, we developed a novel regression model composed of RBP and TTR, which gave much improved accuracy in estimating ROH, even under these pathological conditions. CONCLUSIONS: The perfect RBP-ROH correlation in healthy individuals indicates the utility of RPB as a surrogate marker for ROH. Nevertheless, under RBP-altered conditions, a slight overestimation of ROH is inevitable. However, when the TTR was tested together, the bias can be corrected almost perfectly using the novel ROH estimation formula comprising RBP and TTR.

High-Resolution Estimates of N2O Emissions from Inland Waters and Wetlands in China.

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N2O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N2O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N2O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N2O emissions in China was established, employing a data-driven approach that upscaled from site-specific N2O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N2O emissions from inland waters and wetlands were 1.02 × 105 t N2O yr-1, with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N2O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis.

BACKGROUND: The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information. AIM: To identify key factors that may explain the variability in case lethality across countries. METHODS: We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship. RESULTS: The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR. CONCLUSION: The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Predicting neural activity of whole body cast shadow through object cast shadow in dynamic environments.

Shadows, as all other objects that surround us, are incorporated into the body and extend the body mediating perceptual information. The current study investigates the hypothesis according to which the perception of object shadows would predict the perception of body shadows. 38 participants (19 males and 19 females) aged 23 years on average were immersed into a virtual reality environment and instructed to perceive and indicate the coincidence or non coincidence between the movement of a ball shadow with regard to ball movement on the one hand, and between their body shadow and their body position in space on the other. Their brain activity was recording via a 32-channel EEG system, in which beta (13.5-30 Hz) oscillations were analyzed. A series of Multiple Regression Analysis (MRA) revealed that the beta dynamic oscillations patterns of the bilateral occipito-parieto-frontal pathway associated with the perception of ball shadow appeared to be a significant predictor of the increase in beta oscillations across frontal areas related to the body shadow perception and the decrease in beta oscillations across frontal areas connected to the decision making of the body shadow. Taken together, the findings suggest that inferential thinking ability relative to body shadow would be reliably predicted from object shadows and that the bilateral beta oscillatory modulations would be indicative of the formation of predictive neural frontal assemblies, which encode and infer body shadow neural representation, that is, a substitution of the physical body.

Escalation on Kihon Checklist Scores Preceding the Certification of Long-Term Care Need in the Older Population in Japan. A 9-Year Retrospective Study.

Objectives: The Kihon Checklist (KCL) is valuable for predicting long-term care (LTC) certification. However, the precise association between KCL scores and the temporal dynamics of LTC need certification remains unclear. This study clarified the characteristic trajectory of KCL scores in individuals certified for LTC need. Methods: The KCL scores spanning from 2011 to 2019 were obtained from 5630 older individuals, including those certified for LTC need in November 2020, in Iiyama City, Nagano, Japan. We analyzed the KCL score trajectories using a linear mixed model, both before and after propensity score matching. Results: Throughout the 9-year observation period, the KCL scores consistently remained higher in the certified group compared to the non-certified group. Notably, a significant score increase occurred within the 3 years preceding LTC certification. Discussion: Our findings highlight the effectiveness of continuous surveillance using the KCL in identifying individuals likely to require LTC within a few years.

A Confidence Interval for the Difference Between Standardized Regression Coefficients.

Researchers are often interested in comparing predictors, a practice commonly done via informal comparisons of standardized regression slopes. However, formal interval-based approaches offer advantages over informal comparison. Specifically, this article examines a delta-method-based confidence interval for the difference between two standardized regression coefficients, building upon previous work on confidence intervals for single coefficients. Using Monte Carlo simulation studies, the proposed approach is evaluated at finite sample sizes with respect to coverage rate, interval width, Type I error rate, and statistical power under a variety of conditions, and is shown to outperform an alternative approach that uses the standard covariance matrix found in regression textbooks. Additional simulations evaluate current software implementations, small sample performance, and multiple comparison procedures for simultaneously testing multiple differences of interest. Guidance on sample size planning for narrow confidence intervals, an R function to conduct the proposed method, and two empirical demonstrations are provided. The goal is to offer researchers a different tool in their toolbox for when comparisons among standardized coefficients are desired, as a supplement to, rather than a replacement for, other potentially useful analyses.

Estimating sectoral COVID-19 economic losses in the Philippines using nighttime light and electricity consumption data.

Introduction: Economic loss estimation is critical for policymakers to craft policies that balance economic and health concerns during pandemic emergencies. However, this task is time-consuming and resource-intensive, posing challenges during emergencies. Method: To address this, we proposed using electricity consumption (EC) and nighttime lights (NTL) datasets to estimate the total, commercial, and industrial economic losses from COVID-19 lockdowns in the Philippines. Regression models were employed to establish the relationship of GDP with EC and NTL. Then, models using basic statistics and weather data were developed to estimate the counterfactual EC and NTL, from which counterfactual GDP was derived. The difference between the actual and the counterfactual GDP from 2020 to 2021 yielded economic loss. Results: This paper highlights three findings. First, the regression model results established that models based on EC (adj-R2 ≥ 0.978) were better at explaining GDP than models using NTL (adj-R2 ≥ 0.663); however, combining both EC and NTL improved the prediction (adj-R2 ≥ 0.979). Second, counterfactual EC and NTL could be estimated using models based on statistics and weather data explaining more than 81% of the pre-pandemic values. Last, the estimated total loss amounted to 2.9 trillion PhP in 2020 and 3.2 trillion PhP in 2021. More than two-thirds of the losses were in the commercial sector as it responded to both policies and the COVID-19 case surge. In contrast, the industrial sector was affected primarily by the lockdown implementation. Discussion: This method allowed monitoring of economic losses resulting from long-term and large-scale hazards such as the COVID-19 pandemic. These findings can serve as empirical evidence for advocating targeted strategies that balance public health and the economy during pandemic scenarios.

Leveraging Statistical Theory In Sports Competitions: An Analysis Of Probabilistic Models And Multiple Regression Within The Framework Of Big Data

CBA is a sports event that allows fans to enjoy themselves and players to give full play, and traditional Chinese cultural values have a profound influence on it. This paper takes the 100 sets of historical rating data of the fourteen teams in CBA league as the basic basis, firstly, we simply deal with the 100 sets of historical rating data and use Excel function formula to find out the mean, extreme deviation and variance of each team, then we carry out SAS normal test, and we find that except for the very few data with large deviation, the historical rating data satisfy the normal distribution. Through the outlier algorithm to screen the values, compare the confidence intervals as well as carry out hypothesis testing, to objectively and scientifically explore the probability of each team winning the championship in the CBA league. Compare the probability of winning the championship of these fourteen teams and predict the top four teams in the CBA league to ensure that the prediction results are as reasonable as possible. With the help of hierarchical analysis to qualitatively analyze the level of each team, and then through cluster analysis to compare these data, and combined with the trend of the development of the world's basketball movement, the use of multiple regression and SPSS to analyze the level of the team's factors, in-depth thinking about the league, a more reasonable to give a more scientific to improve the probability of the team's winning the championship, and to promote better development of the basketball movement. (AU)

Postoperative C-reactive protein levels correlate with reduced spinal column mobility after median sternotomy: a prospective cohort study.

Background: The sternum is connected to the spinal column via the ribs, forming the thorax. Therefore, it is necessary to consider the effect of a midline sternotomy on the spinal column, but no in vivo studies have been conducted to date. We investigated the changes in the range of motion of the spinal column before and after midline sternotomy and the perioperative factors that have the greatest influence. Methods: The participants were patients who had undergone cardiac surgery through a standby midline sternotomy. Spinal range of motion in forward flexion was measured before and after surgery. The following perioperative factors were investigated: operating time, days to postoperative measurement, C-reactive protein (CRP) measurement on the third postoperative day, the day of the start of bed release, and the stage of bed release progression on the second postoperative day. Statistics were compared between the two groups before and after surgery for each factor. Multiple regression analysis (forced entry method) was then performed with the change in spinal range of motion, which showed statistical differences between the preoperative and postoperative groups, as the dependent variable and each perioperative factor as the independent variable. Results: The study included 93 patients. Postoperatively, there was a significant decrease in thoracic spine range of motion. Multiple regression analysis showed that an increase in CRP on the third postoperative day was responsible for the decrease in thoracic range of motion (ß=-0.30, P<0.01). Conclusions: After median sternotomy, thoracic spine range of motion was decreased and correlated with postoperative inflammation.

Non-traditional abiotic drivers explain variability of chlorophyll-a in a shallow estuarine embayment.

Understanding the factors influencing eutrophication, as represented by concentrations of chlorophyll-a (Chl-a), is needed to inform effective management and conservation strategies promoting ecological resilience. The objective of this study was to evaluate a unique combination of abiotic explanatory factors to describe Chl-a concentrations within the study estuary (North Biscayne Bay, Florida, USA). Multiple linear regression determined the strength and direction of influence of factors using data from 10 water quality monitoring stations. The analysis also considered time scales for evaluating cumulative effects of freshwater inflow and wind. Results show that dominant drivers of Chl-a were temperature, freshwater volume (whose cumulative effects were evaluated up to a 60-day time scale), and turbidity, which were statistically significant at 60, 60, and 70 % of the investigated stations, respectively. All drivers collectively accounted for 22 to 63 % of the variability of Chl-a measurements. Of the nine variables evaluated, nutrient concentrations (orthophosphate and ammonia) were not among the top three overall drivers. Despite nutrients historically being cited in the literature as the most significant factor, this study asserts that non-nutrient factors often govern Chl-a levels, necessitating a paradigm shift in management strategies to bolster estuarine resilience against climate change.

Removal characteristics of 53 micropollutants during ozonation, chlorination, and UV/H2O2 processes used in drinking water treatment plant.

The removal of 53 emerging micropollutants (MPs), including 10 per- and polyfluorinated substances (PFASs), 25 pharmaceuticals and personal care products (PPCPs), 7 pesticides, 5 endocrine disrupters (EDCs), 3 nitrosamines, and 3 taste and odor compounds (T&Os), by chlorination, ozonation, and UV/H2O2 treatment was examined in deionized water and surface waters used as the raw waters in drinking water treatment plants (DWTPs) in South Korea. The UV/H2O2 treatment was effective in the removal of most MPs, whereas chlorination was selectively effective for 19 MPs, including EDCs (>70 %). MPs containing aromatic ring with electron-donating functional group, or primary and secondary amines were effectively removed by chlorination immediately upon reaction initiation. The removal of MPs by ozonation was generally lower than that of the other two processes at a low ozone dose (1 mg L-1), but higher than chlorination at a high ozone dose (3 mg L-1), particularly for 16 MPs, including T&Os. Compared in deionized water, the removals of MPs in the raw water samples were lower in all three processes. The regression models predicting the rate constants (kobs) of 53 MPs showed good agreement between modeled and measured value for UV/H2O2 treatment (R2 = 0.948) and chlorination (R2 = 0.973), despite using only dissolved organic carbon (DOC) and oxidant concentration as variables, whereas the ozonation model showed a variation (R2 = 0.943). Our results can provide the resources for determining which oxidative process is suitable for treating specific MPs present in the raw waters of DWTPs.

Ecosystem-scale insights into the dynamics of dissolved organic matter in an Asia's largest brackish water lagoon: Sources, fluxes, and biogeochemical significance.

A twenty-four month long observational study conducted in an Asia's largest brackish water ecosystem, Chilika Lagoon, India, aimed to unravel dissolved organic matter (DOM) dynamics in this tropical brackish water ecosystem. The study assessed the interplay between allochthonous and autochthonous DOM sources during lean and active flow periods based on regional rainfall. Dissolved organic carbon (DOC), nitrogen (DON), and phosphorus (DOP) fluxes were analyzed, considering catchment runoff, phytoplankton production, benthic-pelagic interactions, and sea-lagoon exchanges as contributors. Contrary to conventional thinking, the study found autochthonous processes to be more significant than conservative mixing in shaping DOM dynamics. It introduced a novel conceptual model illustrating the multifaceted origins of DOM, encompassing catchment runoff, phytoplankton, benthic-pelagic interactions, bacterial activity, and sea-lagoon exchanges. These findings underscore the importance of holistic management strategies for Chilika Lagoon to preserve its ecological health, given its vital role in global carbon cycling, fisheries, and aquaculture.

Wnt dose escalation during the exit from pluripotency identifies tranilast as a regulator of cardiac mesoderm.

Wnt signaling is a critical determinant of cell lineage development. This study used Wnt dose-dependent induction programs to gain insights into molecular regulation of stem cell differentiation. We performed single-cell RNA sequencing of hiPSCs responding to a dose escalation protocol with Wnt agonist CHIR-99021 during the exit from pluripotency to identify cell types and genetic activity driven by Wnt stimulation. Results of activated gene sets and cell types were used to build a multiple regression model that predicts the efficiency of cardiomyocyte differentiation. Cross-referencing Wnt-associated gene expression profiles to the Connectivity Map database, we identified the small-molecule drug, tranilast. We found that tranilast synergistically activates Wnt signaling to promote cardiac lineage differentiation, which we validate by in vitro analysis of hiPSC differentiation and in vivo analysis of developing quail embryos. Our study provides an integrated workflow that links experimental datasets, prediction models, and small-molecule databases to identify drug-like compounds that control cell differentiation.