"A quantitative analysis on the adoption of soil, water, and forest conservation technologies in the upper Gelana watershed, Northeast Ethiopian highlands".

Globally, the degradation of soil, water, and forests has had a significant impact on both livelihoods and the environment. This issue is particularly severe in developing countries, including Ethiopia. Despite extensive efforts to implement conservation measures for soil, water, and forests in the highlands of Ethiopia, there has been a lack of thorough evaluation and documentation regarding the adoption of these practices by rural households. It is crucial to have scientific and up-to-date information at various spatial scales in order to effectively monitor existing practices, scale up successful initiatives, and promote sustainable regional development. Therefore, this paper focuses on analyzing the adoption of soil, water, and forest conservation activities by households in the upper Gelana watershed, South Wollo zone, Amhara Regional State of Ethiopia. The field data collection for this study took place from January to March 2022, from 150 rural household heads. Data analysis was carried out using SPSS software version 23. Descriptive statistics, Pearson bivariate correlation, and multinomial logistic regression were used. The survey findings revealed that 69 % of the respondents had implemented various soil, water, and forest conservation measures at different stages. The Pearson correlation results indicated a positive relationship between the adoption of soil, water and forest conservation practices. The multinomial logistic regression analysis has revealed that age, gender, access to credit, and access to extension services, significantly influenced the households' decision behaviour to adopt soil conservation practices. Age, access to extension service, and access to water resource were significant predictors of adoption of water conservation practices; whereas age, educational status, and access to extension service were significant predictors of adoption of forest conservation practices. This study underscores the significance of institutional factors in driving the adoption of technology in the research area. It further recommends policies that prioritize the dissemination of information on effective strategies, improvement of access to extension services, water resources, and credit facilities to promote sustainable watershed management. This study is exceptional in its innovative approach, which explores the convergence of these vital conservation domains within the distinct setting of the upper Gelana watershed. Studying the adoption of these technologies is crucial for informing policy-making and designing effective interventions that promote sustainable watershed practices. In this case, the Ministry of Agriculture, and development agents should scale up the adoption of these practices and take remedial actions for those not yet adopted.

Molecular insights into the hedgehog signaling pathway correlated non-coding RNAs in acute lymphoblastic leukemia, a bioinformatics study.

BACKGROUND: Acute lymphoblastic leukemia (ALL) is a common hematologic cancer with unique incidence and prognosis patterns in people of all ages. Recent molecular biology advances have illuminated ALL's complex molecular pathways, notably the Hedgehog (Hh) signaling system and non-coding RNAs (ncRNAs). This work aimed to unravel the molecular complexities of the link between Hh signaling and ALL by concentrating on long non-coding RNAs (lncRNAs) and their interactions with significant Hh pathway genes. METHODS: To analyze differentially expressed lncRNAs and genes in ALL, microarray data from the Gene Expression Omnibus (GEO) was reanalyzed using a systems biology approach. Hh signaling pathway-related genes were identified and their relationship with differentially expressed long non-coding RNAs (DElncRNAs) was analyzed using Pearson's correlation analysis. A regulatory network was built by identifying miRNAs that target Hh signaling pathway-related mRNAs. RESULTS: 193 DEGs and 226 DElncRNAs were found between ALL and normal bone marrow samples. Notably, DEGs associated with the Hh signaling pathway were correlated to 26 DElncRNAs. Later studies showed interesting links between DElncRNAs and biological processes and pathways, including drug resistance, immune system control, and carcinogenic characteristics. DEGs associated with the Hh signaling pathway have miRNAs in common with miRNAs already known to be involved in ALL, including miR-155-5p, and miR-211, highlighting the complexity of the regulatory landscape in this disease. CONCLUSION: The complex connections between Hh signaling, lncRNAs, and miRNAs in ALL have been unveiled in this study, indicating that DElncRNAs linked to Hh signaling pathway genes could potentially serve as therapeutic targets and diagnostic biomarkers for ALL.

Development of a Predictive Model for Carbon Dioxide Corrosion Rate and Severity Based on Machine Learning Algorithms.

Carbon dioxide corrosion is a pervasive issue in pipelines and the petroleum industry, posing substantial risks to equipment safety and longevity. Accurate prediction of corrosion rates and severity is essential for effective material selection and equipment maintenance. This paper begins by addressing the limitations of traditional corrosion prediction methods and explores the application of machine learning algorithms in CO2 corrosion prediction. Conventional models often fail to capture the complex interactions among multiple factors, resulting in suboptimal prediction accuracy, limited adaptability, and poor generalization. To overcome these limitations, this study systematically organized and analyzed the data, performed a correlation analysis of the data features, and examined the factors influencing corrosion. Subsequently, prediction models were developed using six algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), XGBoost, and LightGBM. The results revealed that SVM exhibited the lowest performance on both training and test sets, while RF achieved the best results with R2 values of 0.92 for the training set and 0.88 for the test set. In the classification of corrosion severity, RF, LightGBM, SVM, and KNN were utilized, with RF demonstrating superior performance, achieving an accuracy of 99% and an F1-score of 0.99. This study highlights that machine learning algorithms, particularly Random Forest, offer substantial potential for predicting and classifying CO2 corrosion. These algorithms provide innovative approaches and valuable insights for practical applications, enhancing predictive accuracy and operational efficiency in corrosion management.

Combined transcriptome and microbiome analysis reveals the thyrotoxic effects of PM2.5 in female rats.

Pervasive environmental pollutants, specifically particulate matter (PM2.5), possess the potential to disrupt homeostasis of female thyroid hormone (TH). However, the precise mechanism underlying this effect remains unclear. In this study, we established a model of PM2.5-induced thyroid damage in female rats through intratracheal instillation and employed histopathological and molecular biological methods to observe the toxic effects of PM2.5 on the thyroid gland. Transcriptome gene analysis and 16S rRNA sequencing were utilized to investigate the impact of PM2.5 exposure on the female rat thyroid gland. Furthermore, based on the PM2.5-induced toxic model in female rats, we evaluated its effects on intestinal microbiota, TH levels, and indicators of thyroid function. The findings revealed that PM2.5 exposure induced histopathological damage to thyroid tissue by disrupting thyroid hormone levels (total T3 [TT3], (P < 0.05); total T4 [TT4], (P < 0.05); and thyrotropin hormone [TSH], (P < 0.05)) and functional indices (urine iodine [UI], P > 0.05), thus further inducing histopathological injuries. Transcriptome analysis identified differentially expressed genes (DEGs), primarily concentrated in interleukin 17 (IL-17), forkhead box O (FOXO), and other signaling pathways. Furthermore, exposure to PM2.5 altered the composition and abundance of intestinal microbes. Transcriptome and microbiome analyses demonstrated a correlation between the DEGs within these pathways and the flora present in the intestines. Moreover, 16 S rRNA gene sequencing analysis or DEGs combined with thyroid function analysis revealed that exposure to PM2.5 significantly induced thyroid hormone imbalance. We further identified key DEGs involved in thyroid function-relevant pathways, which were validated using molecular biology methods for clinical applications. In conclusion, the homeostasis of the "gut-thyroid" axis may serve as the underlying mechanism for PM2.5-induced thyrotoxicity in female rats.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis.

BACKGROUND: The assessment of the effectiveness of teaching interventions in enhancing students' understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students' understanding of the PCNE Classification System in pharmaceutical education. METHODS: Undergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires. RESULTS: The paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire's validity. CONCLUSION: The implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students' comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Evolution and health risk of indicator microorganisms in landscape water replenished by reclaimed water.

As an important means to solve water shortage, reclaimed water has been widely used for landscape water supply. However, with the emergence of large-scale epidemic diseases such as SARS, avian influenza and COVID-19 in recent years, people are increasingly concerned about the public health safety of reclaimed water discharged into landscape water, especially the pathogenic microorganisms in it. In this study, the water quality and microorganisms of the Old Summer Palace, a landscape water body with reclaimed water as the only replenishment water source, were tracked through long-term dynamic monitoring. And the health risks of indicator microorganisms were analyzed using Quantitative Microbial Risk Assessment (QMRA). It was found that the concentration of indicator microorganisms Enterococcus (ENT), Escherichia coli (EC) and Fecal coliform (FC) generally showed an upward trend along the direction of water flow and increased by more than 0.6 log at the end of the flow. The concentrations of indicator microorganisms were higher in summer and autumn than those in spring. And there was a positive correlation between the concentration of indicator microorganisms and COD. Further research suggested that increased concentration of indicator microorganisms also led to increased health risks, which were more than 30% higher in other areas of the park than the water inlet area and required special attention. In addition, (water) surface operation exposure pathway had much higher health risks than other pathways and people in related occupations were advised to take precautions to reduce the risks.

Ecological and human health risk associated with heavy metals (HMs) contaminant sourced from petroleum refinery oily sludge.

The environmental and human health risk of heavy metals (HMs) in petroleum based oily sludge (OS) varies depending upon the source of origin of the crude oil and treatment processes practiced at the refineries. Consequently, the present study explores the potential risk associated with HMs of OS obtained from different refinery sites to the environment and human health. The results showed that HMs (Cu, Ni, Zn, Mn) present in OS surpasses the permissible limit of WHO guidelines except for Cr. Additionally, the Igeo value (grade 3-6), Ef (2.48-121.4), PLI (5.12-22.65), Cd (32.48-204.76) and PERI (grade 1-5) confirmed the high level of HMs contamination into the OS and its risk to the environment. Besides, the hazard index (HI) and the total carcinogenic risk (TCR) for HMs show substantial risk to both adult and children health. Likewise, the G-mean enzyme index and potential soil enzyme risk index (PSERI) of the OS showed a high risk to soil biological properties. Furthermore, statistical analysis confirmed the heterogeneity in properties of the OS and its potential impact on the soil ecosystem arising from different sites. Finally, the study unveils a novel perspective on the environmental and human health consequences associated with the OS.

GIS based geostatistical modelling and trends analysis of groundwater quality for suitable uses in Dhaka division.

Preserving the quality of groundwater has become Bangladesh's primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend's variation and slope. In addition, the spatial-temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42-, Na+ and Cl- that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3-) ions across all sampling stations, with (HCO3-) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region.

Mitigation of ammonia volatilization from organic and inorganic nitrogen sources applied to soil using water hyacinth biochars.

The recent global population explosion has increased people's food demand. To meet this demand, huge amounts of nitrogen (N) fertilizer have been applied in the worldwide. However, ammonia (NH3) volatilization is one of the primary factors of N loss from soil after N application causing decrease crop N utilization efficiency and productivity. Incubation experiments were conducted on an acidic clayey soil with two different N sources (urea and anaerobic digestion effluent; ADE), two differently-produced biochars, and three biochar application rates (0%, 0.25%, and 1.0% w/w). Ammonia volatilization was lower from urea (14.0-23.5 mg N kg-1) and ADE (11.3-21.0 mg N kg-1) with biochar application than those without biochar (40.1 and 26.2 mg N kg-1 from urea and ADE alone, respectively). Biochar application significantly mitigated volatilization and reduction percentages for urea and ADE were 40%-64% and 18%-55%, respectively. 1.0% biochar application mitigated volatilization significantly compared to 0.25% application regardless of N source and biochar types. Possible mechanism for volatilization mitigation for urea and ADE were increased N immobilization by soil microorganisms and accelerated net nitrification rate due to increased soil nitrifying bacteria, respectively. Overall, our results clarified different mechanisms for N volatilization mitigation from different (inorganic vs. organic) N sources with biochar application.

Addressing the Gap: Racial Disparities and Public Health Strategies in the Epidemiology of Gastrointestinal Stromal Tumors.

Background Gastrointestinal stromal tumors (GISTs) represent the most common mesenchymal neoplasms of the gastrointestinal tract, arising from the interstitial cells of Cajal. These tumors bridge the nervous system and muscular layers of the gastrointestinal tract, playing a crucial role in the digestive process. The incidence of GISTs demonstrates notable variations across different racial and ethnic groups, underscoring the need for in-depth analysis to understand the interplay of genetic, environmental, and socioeconomic factors behind these disparities. Linear regression analysis is a pivotal statistical tool in such epidemiological studies, offering insights into the temporal dynamics of disease incidence and the impact of public health interventions. Methodology This investigation employed a detailed dataset from 2009 to 2020, documenting GIST incidences across Asian, African American, Hispanic, and White populations. A meticulous preprocessing routine prepared the dataset for analysis, which involved data cleaning, normalization of racial terminologies, and aggregation by year and race. Linear regression models and Pearson correlation coefficients were applied to analyze trends and correlations in GIST incidences across the different racial groups, emphasizing an understanding of temporal patterns and racial disparities in disease incidence. Results The study analyzed GIST cases among four racial groups, revealing a male predominance (53.19%) and an even distribution of cases across racial categories: Whites (27.66%), Hispanics (25.53%), African Americans (24.47%), and Asians (22.34%). Hypertension was the most common comorbidity (32.98%), followed by heart failure (28.72%). The linear regression analysis for Asians showed a decreasing trend in GIST incidences with a slope of -0.576, an R-squared value of 0.717, and a non-significant p-value of 0.153. A significant increasing trend was observed for Whites, with a slope of 0.581, an R-squared value of 0.971, and a p-value of 0.002. African Americans exhibited a moderate positive slope of 0.277 with an R-squared value of 0.470 and a p-value of 0.201, indicating a non-significant increase. Hispanics showed negligible change over time with a slope of -0.095, an R-squared value of 0.009, and a p-value of 0.879, suggesting no significant trend. Conclusions This study examines GIST incidences across racial groups, revealing significant disparities. Whites show an increasing trend (p = 0.002), while Asians display a decreasing trend (p = 0.153), with stable rates in African Americans and Hispanics. Such disparities suggest a complex interplay of genetics, environment, and socioeconomic factors, highlighting the need for targeted research and interventions that address these differences and the systemic inequalities influencing GIST outcomes.

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Genome-wide identification of the SAUR gene family and screening for SmSAURs involved in root development in Salvia miltiorrhiza.

KEY MESSAGE: SmSAUR4, SmSAUR18, SmSAUR28, SmSAUR37, and SmSAUR38 were probably involved in the auxin-mediated root development in Salvia miltiorrhiza. Salvia miltiorrhiza is a widely utilized medicinal plant in China. Its roots and rhizomes are the main medicinal portions and are closely related to the quality of this herb. Previous studies have revealed that auxin plays pivotal roles in S. miltiorrhiza root development. Whether small auxin-up RNA genes (SAURs), which are crucial early auxin response genes, are involved in auxin-mediated root development in S. miltiorrhiza is worthy of investigation. In this study, 55 SmSAUR genes in S. miltiorrhiza were identified, and their physical and chemical properties, gene structure, cis-acting elements, and evolutionary relationships were analyzed. The expression levels of SmSAUR genes in different organs of S. miltiorrhiza were detected using RNA-seq combined with qRT‒PCR. The root development of S. miltiorrhiza seedlings was altered by the application of indole-3-acetic acid (IAA), and Pearson correlation coefficient analysis was conducted to screen SmSAURs that potentially participate in this physiological process. The diameter of primary lateral roots was positively correlated with SmSAUR4. The secondary lateral root number was positively correlated with SmSAUR18 and negatively correlated with SmSAUR4. The root length showed a positive correlation with SmSAUR28 and SmSAUR37 and a negative correlation with SmSAUR38. The fresh root biomass exhibited a positive correlation with SmSAUR38 and a negative correlation with SmSAUR28. The aforementioned SmSAURs were likely involved in auxin-mediated root development in S. miltiorrhiza. Our study provides a comprehensive overview of SmSAURs and provides the groundwork for elucidating the molecular mechanism underlying root morphogenesis in this species.

The feedstock anatomical properties determine biochar adsorption capacities: A study using woody bamboos (Bambuseae) and methylene blue as a model molecule.

Feedstock characteristics impact biochar physicochemical properties, and reproducible biochar properties are essential for any potential application. However, in most articles, feedstock aspects (i.e., taxonomic name of the species, part of the plant, and phenological phase) are scarcely reported. This research aimed at studying the effect of species and phenological stage of the feedstock on the properties of the derived biochars and, thus, adsorption capacities in water treatment. In this study, we analysed the anatomical characteristics of three different woody bamboo species [Guadua chacoensis (GC), Phyllostachys aurea (PA), and Bambusa tuldoides (BT)] in culms harvested at two different phenological phases (young and mature), and statistically correlated them with the characteristics of the six derived biochars, including their adsorption performance in aqueous media. Sclerenchyma fibres and parenchyma cells diameter and cell-wall width significantly differed among species. Additionally, sclerenchyma fibres and parenchyma cell-wall width as well as sclerenchyma fibre cell diameters are dependent on the phenological phase of the culms. Consequently, differences in biochar characteristics (i.e., yield and average pore diameter) were also observed, leading to differential methylene blue (MB) adsorption capacities between individuals at different phenological phases. MB adsorption capacities were higher for biochar produced from young culms compared to those obtained from matures ones (i.e., GC: 628.66 vs. 507.79; BT: 537.45 vs. 477.53; PA: 477.52 vs. 462.82 mg/g), which had smaller cell wall widths leading to a lower percentage of biochar yield. The feedstock anatomical properties determined biochar characteristics which modulated adsorption capacities.

Phytochemical profile, enzyme inhibition, antioxidant, and antibacterial activity of Rosa pimpinellifolia L.: A comprehensive study to investigate the bioactivity of different parts (whole fruit, pulp, and seed part) of the fruit.

The pharmaceutical and nutraceutical potentials of whole fruit, pulp and seeds of Rosa pimpinellifolia L. were evaluated. Forty-two phenolic compounds and two triterpenoids were identified in extracts by LC-MS/MS and GC-MS, respectively. The most prominent compounds were ellagic acid, catechin, epicatechin, tannic acid, quercetin, oleanolic acid, and ursolic acid. The highest enzyme inhibitory activities of the extracts (94.83%) were obtained against angiotensin-converting enzyme and were almost equal to those of the commercial standard (lisinopril, 98.99%). Whole fruit and pulp extracts (IC50:2.47 and 1.52 µg DW/mL) exhibited higher antioxidant capacity than the standards (α-tocopherol, IC50:9.89 µg DW/mL). The highest antibacterial activity was obtained against Bacillus cereus (MIC: 256 µg/mL) for the whole fruit extract. Correlation analyses were conducted to find the correlation between individual phenolics and enzyme inhibitory activities. The results showed the remarkable future of not only the edible part but also the seeds of black rose hips in phytochemical and functional aspects.

Analysis of the Steelmaking Process via Data Mining and Pearson Correlation.

The continuous improvement of the steelmaking process is a critical issue for steelmakers. In the production of Ca-treated Al-killed steel, the Ca and S contents are controlled for successful inclusion modification treatment. In this study, a machine learning technique was used to build a decision tree classifier and thus identify the process variables that most influence the desired Ca and S contents at the end of ladle furnace refining. The attribute of the root node of the decision tree was correlated with process variables via the Pearson formalism. Thus, the attribute of the root node corresponded to the sulfur distribution coefficient at the end of the refining process, and its value allowed for the discrimination of satisfactory heats from unsatisfactory heats. The variables with higher correlation with the sulfur distribution coefficient were the content of sulfur in both steel and slag at the end of the refining process, as well as the Si content at that stage of the process. As secondary variables, the Si content and the basicity of the slag at the end of the refining process were correlated with the S content in the steel and slag, respectively, at that stage. The analysis showed that the conditions of steel and slag at the beginning of the refining process and the efficient S removal during the refining process are crucial for reaching desired Ca and S contents.

Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting.

Recognizing the evolution pattern of traffic condition and making accurate prediction play a vital role in intelligent transportation systems (ITS). With the massive increase of available traffic data, deep learning-based models have attracted considerable attention for their impressive performance in traffic forecasting. However, the majority of existing approaches neglect to model of asynchronously dynamic spatio-temporal correlation and fail to consider the impact of historical traffic data on future condition. Additionally, the attribute of deep learning method presents challenges in interpreting the explicit spatiotemporal relationships. In order to enhance the accuracy of traffic prediction as well as extract comprehensive and explainable spatial-temporal relevance in traffic networks, we propose a novel attention-based local spatial and temporal relation discovery (ALSTRD) model. Our model firstly implements feature representation learning to effectively express latent input traffic information. Then, a local attention mechanism structure is established to model asynchronous dependencies of historical input data. Finally, another attention network and the Pearson Correlation Coefficient method are introduced to extract the elaborate influence of the historical traffic condition of neighboring roads on the future condition of the target road. The experiment results on several datasets demonstrate that our model achieves significant improvements in prediction accuracy compared to other baseline methods, which can be attributed to its ability to extract the fine-grained correlation among historical traffic data and capture the dynamic association between past and future data. In addition, the incorporation of attention mechanism and Pearson Correlation Coefficient promotes the model's ability to elucidate spatiotemporal correlations among traffic data, thereby providing a more robust explanation.

Can USMLE and COMLEX-USA Scores Predict At-Risk Emergency Medicine Residents' Performance on In-Training Examinations?

PURPOSE: The United States Medical Licensing Examination (USMLE) and Comprehensive Osteopathic Medical Licensing Examination (COMLEX) scores are standard methods used to determine residency candidates' medical knowledge. The authors were interested in using the USMLE and COMLEX part 2 scores in our emergency medicine (EM) residency program to identify at-risk residents who may have difficulty on the in-training exam (ITE) and to determine the cutoff values under which an intern could be given an individualized study plan to ensure medical knowledge competency. METHODS: The authors abstracted the USMLE and COMLEX part 2 scores and the American Board of Emergency Medicine (ABEM) ITE scores for a cohort of first-year EM residents graduating years 2010-2022, converting raw scores to percentiles, and compared part 2 and ABEM ITE scores with Pearson's correlation, a Bland-Altman analysis of bias and 95% limits of agreement, and ROC analysis to determine optimal the cut-off values for predicting ABEM ITE < 50th percentile and the estimated test characteristics. RESULTS: Scores were available for 152 residents, including 93 USMLE and 88 COMLEX exams. The correlations between part 2 scores and ABEM ITE were r = 0.36 (95%CI: 0.17, 0.52; p < 0.001) for USMLE and r = 0.50 (95%CI: 0.33, 0.64; p < 0.001) for COMLEX. Bias and limits of agreement for both part 2 scores were -14 ± 63% for USMLE and 13 ± 50% for COMLEX in predicting the ABEM ITE scores. USMLE < 37th percentile and COMLEX < 53rd percentile identified 42% (N = 39) and 27% (N = 24) of EM residents, respectively, as at risk, with a sensitivity of 61% and 49% and specificity of 71% and 92%, respectively. CONCLUSION: USMLE and COMLEX part 2 scores have a very limited role in identifying those at risk of low ITE performance, suggesting that other factors should be considered to identify interns in need of medical knowledge remediation.

Data analysis of the influence of microstructure, composition, and loading conditions on stress corrosion cracking behavior of Mg alloys.

Stress corrosion cracking (SCC) can be a crucial problem in applying rare earth (RE) Magnesium alloys in environments where mechanical loads and electrochemical driven degradation processes interact. It has been proven already that the SCC behavior is associated with microstructural features, compositions, loading conditions, and corrosive media, especially in-vivo. However, it is still unclear when and how mechanisms acting on multiple scales and respective system descriptors predictable contribute to SCC for the wide set of existing Mg alloys. In the present work, suitable literature data along SCC of Mg alloys has been analyzed to enable the development of a reliable SCC model for MgGd binary alloys. Pearson correlation coefficient and linear fitting are utilized to describe the contribution of selected parameters to corrosion and mechanical properties. Based on our data analysis, a parameter ranking is obtained, providing information on the SCC impact with regard to ultimate tensile strength (UTS) and fracture elongation of respective materials. According to the analyzed data, SCC susceptibility can be grouped and mapped onto Ashby type diagrams for UTS and elongation of respective base materials tested in air and in corrosive media. The analysis reveals the effect of secondary phase content as a crucial materials descriptor for our analyzed materials and enables better understanding towards SCC model development for Mg-5Gd alloy based implant.