Rank Analysis Research Articles

Comparison of antifungal drugs in the treatment of invasive pulmonary aspergillosis: a systematic review and network meta-analysis

BackgroundVoriconazole, isavuconazole, and amphotericin (AmB) formulations are currently recommended to treat invasive pulmonary aspergillosis (IPA). We aimed to estimate the efficacy of different antifungal drugs in the initial treatment of IPA.MethodsWe included all available randomized controlled trials (RCTs) evaluating first-line treatments for IPA by searching PubMed, Medline, EMBASE, the Cochrane Library, and the ClinicalTrials.gov database. We performed a network meta-analysis to compare the relative efficacy of different drugs in treating IPA. The primary outcomes were the overall response and all-cause mortality (ACM).ResultsEight studies were identified that compared different drugs including voriconazole, isavuconazole, posaconazole, anidulafungin, liposomal AmB (L-AmB) at standard, high and low doses (3-5 mg/kg/d; 10 mg/kg/d; 1 mg/kg/d), AmB deoxycholate (dAmB) and amphotericin B colloidal dispersion (ABCD). We found that second-generation triazole antifungal drugs containing voriconazole, isavuconazole, and posaconazole exhibited significantly superior overall response to dAmB and ABCD. Voriconazole was ranked as the best drug on network rank analysis. We found no difference in efficacy between triazole antifungals and L-AmB. A combination of voriconazole with anidulafungin, isavuconazole and voriconazole showed significantly better safety than dAmB.ConclusionThe efficacy of second-generation triazole antifungal drugs for the first-line treatment of IPA is comparable with L-AmB and is better than both dAmB and ABCD. Isavuconazole may show better safety than voriconazole and posaconazole. Combination therapy with voriconazole and anidulafungin may serve as an alternative option for IPA patients with limited drug tolerance.Systematic review registrationhttps://inplasy.com/.

Patterns of Care in Adjuvant Radiation Therapy for Stage II Endometrioid Endometrial Adenocarcinoma: a National Cancer Database Analysis

PurposeTreating stage II endometrial cancer involves total hysterectomy, bilateral salpingo-oophorectomy (TH/BSO), and risk-adapted adjuvant therapy. Professional guidelines support various adjuvant treatments, but high-level data supporting specific options are conflicting. We sought to evaluate adjuvant radiation (RT) trends for these patients, hypothesizing increased utilization of external beam pelvic radiation (EBRT) over time. Methods and MaterialsPatients diagnosed in 2004-2019 with stage II endometrioid endometrial cancer who underwent TH/BSO and surgical staging were identified in the National Cancer Database. Patient characteristics per adjuvant RT received were compared using Wilcoxon Rank Sum and analysis of variance testing. Multivariable regression analysis (MVA) identified variables associated with EBRT, vaginal brachytherapy (VBT), or RT omission. P-value <0.05 was significant, except in MVA where Bonferoni correction was employed (p-value <0.017). ResultsPatients meeting criteria totaled 18,798; 19% received adjuvant EBRT alone, 25% VBT alone, 24% EBRT+VBT, and 32% no RT. Adjuvant RT use increased from 2004-2019, particularly EBRT+VBT (p<0.05). On MVA, community hospital treatment (OR 1.8, p<0.001) Midwest location (OR 1.2, p=0.02), single-agent chemotherapy receipt (OR 6.9, p<0.001), lympho-vascular space invasion (LVSI) (OR 1.4, p<0.001), and positive surgical margins (+SM) (OR 1.8, p<0.001) were positively associated with EBRT. No variables were positively associated with VBT. Black race (OR 1.2, p=0.03), community hospital treatment (OR 1.4, p=0.04), South (OR 2.2, p<0.001) or West (OR 2.1, p<0.001) location, distance >50 miles from treatment center (OR 1.5, p<0.001), and grade 2 (OR 1.2, p<0.001) or 3 (OR 1.3, p=0.01) disease were associated with RT omission. ConclusionAdjuvant RT for stage II endometrial cancer increased over time, particularly EBRT+VBT. Patient-related factors such as race, region, and distance from treatment center were associated with RT omission, suggesting sociodemographic barriers to care. Tumor-related factors such as +SM and LVSI were associated with EBRT receipt, suggesting consideration of high-risk factors for locoregional recurrence in adjuvant RT approaches.

Clinical Value of Circ-PNPT1 on Adverse Pregnancy Outcomes of Patients with Gestational Diabetes Mellitus.

Several circular RNAs are associated with important pathophysiological characteristics of gestational diabetes mellitus (GDM). This study intended to measure the expression of circ-PNPT1 in sera of GDM patients and to expound on its values on pregnancy outcomes. Totally 104 GDM patients and 71 healthy controls were recruited. The expression pattern of serum circ-PNPT1 was measured by reverse transcription-quantitative polymerase chain reaction. The diagnostic efficacy of circ-PNPT1 and fasting blood glucose (FBG) on GDM was evaluated by receiver operating characteristic (ROC) analysis. Parameters of glycolipid metabolism were determined using automatic biochemical analyzers. The correlation between circ-PNPT1 and glycolipid metabolism parameters was analyzed using Pearson analysis. GDM patients were divided into a high expression group and a low expression group based on the median value of circ-PNPT1 expression. Curves of adverse neonatal outcomes were drawn by Log Rank analysis. GDM patients exhibited higher circ-PNPT1 expression than healthy controls. The area under the ROC curve of circ-PNPT1 diagnosing GDM was 0.9184 and the cut-off value was 1.435 (90.38% sensitivity, 85.92% specificity). Serum circ-PNPT1 expression was positively correlated with FBG, total cholesterol, and triglyceride in GDM patients. Neonates born to GDM patients with high circ- PNPT1 expression were prone to adverse outcomes. Circ-PNPT1 was highly-expressed in the sera of GDM patients. Circ-PNPT1 affected glycolipid metabolism and its expression had certain reference values on adverse pregnancy outcomes.

Effectiveness of non-pharmacological interventions for pain reduction following chest tube removal: A systematic review and network meta-analysis

ObjectiveThis study aimed to determine the most effective non-pharmacological intervention for pain relief following chest tube removal in adult patients. MethodsWe performed a systematic review with network meta-analysis, searching electronic databases for randomized controlled trials up to January 20, 2024. Compared to control or placebo groups, the trials included adult patients receiving non-pharmacological interventions, such as cold therapy, relaxation exercises, music therapy, transcutaneous electrical nerve stimulation, or aromatherapy. Data analysis utilized a random-effects model, reporting standardized mean differences (SMD) with 95% confidence intervals (CI), and treatment ranking was determined through surface under the cumulative ranking analysis and forest plots. ResultsSixteen studies enrolling 1,342 patients were included, with moderate to high heterogeneity. Cold therapy and relaxation exercises effectively reduced pain after chest tube removal, with SMD of −1.84 (95 % CI: −2.81 to −0.87) and −2.04 (95 % CI: −3.43 to −0.65), respectively, from baseline to immediately post-removal. From baseline to ten to twenty minutes after removal, both therapies continued to show significant pain reduction, with SMD of −1.96 (95 % CI: −3.14 to −0.77) for cold therapy and −2.01 (95 % CI: −3.75 to −0.28) for relaxation exercises. Sensitivity analysis supported these findings, and cold therapy’s efficacy appeared unaffected by obesity. However, publication bias, possibly due to small study samples, was noted. ConclusionsCold therapy and relaxation exercises proved most effective for reducing pain immediately and up to ten to twenty minutes after chest tube removal. Given their cost-effectiveness, lack of side effects, and ease of use, we recommend relaxation exercises as a practical alternative to cold therapy. Nonetheless, further studies are needed to comprehensively assess non-pharmacological options for managing chest tube removal pain. Implications for clinical practiceIncorporate cold therapy and relaxation exercises into post-chest tube removal pain management to improve patient comfort and minimize reliance on pharmacological interventions.

Ranking Analysis of Tribological, Mechanical, and Thermal Properties of Nano Hydroxyapatite Filled Dental Restorative Composite Materials Using the R‐Method

ABSTRACTR‐method as multi criteria decision‐making method was proposed to determine the ranking of alternatives and performance criteria or attributes of dental composite materials. Five alternatives (DHM0, DHM2, DHM4, DHM6, and DHM8: nomenclatures of micro‐nano ceramic particulate filled dental restoration composites) and 14 attributes (performance criteria) were considered in this research. The decision‐maker determined the relevance of each rank and then attributed it to the performance criterion. In a similar manner, the options were ranked in relation to each performance criterion according to the associated performance criteria. Therefore, the order of the ranks of dental composites is DHM8 (1st rank) &gt; DHM4 (2nd rank) &gt; DHM6 (3rd rank) &gt; DHM0 (4th rank) &gt; DHM2 (5th rank). The suggested technique is showed simpler and advance compared to the other generally employed decision‐making techniques.

Outcomes of Liver Transplantation in Infants: A Retrospective Cohort Study

ObjectiveLiver transplantation (LT) is a crucial treatment for infants with end-stage liver disease, yet specific data on LT outcomes in infants under 12 months old remain limited. This study aims to present the clinical course and outcomes of infants who underwent LT, assessing the impact of demographic and etiological differences on mortality and survival. MethodsA retrospective analysis was conducted on 64 infants (<12 months) who underwent LT between January 2019 and March 2024. Demographic, clinical, and laboratory data were collected from medical records. LT-specific details, postoperative complications, and survival data were analyzed. ResultsThe median age of the infants was 157 days, with 37 males (57.8%) and 27 females (42.2%). Biliary atresia was the most common diagnosis (85.9%), and seven infants had undergone a previous Kasai procedure. The median PELD score was 15.5. Left lateral segmentectomy was the predominant graft type (60.9%). The median PICU and hospital stays were 5 and 21 days, respectively. Complications occurred in 20 infants (31.3%), with vascular and biliary complication rates both at 12.5%. The overall mortality rate was 17.2%, with early (30-day) and late mortality rates of 6.3% and 10.9%, respectively. The median OS was 204.5 days, and the one-year survival rate was 32.8%. Kaplan-Meier and Log Rank analyses showed no significant impact of sex, age, diagnostic groups, graft type, or surgical complications on OS (p>0.05). ConclusionLT in infants can be performed with acceptable morbidity and mortality rates, particularly with increased experience and standardized protocols.

Machine learning-based identification of critical factors for cadmium accumulation in rice grains.

The aggregation of Cadmium (Cd) in rice grains is a significant threat to human healthy. The complexity of the soil-rice system, with its numerous influencing parameters, highlights the need to identify the crucial factors responsible for Cd aggregation. This study uses machine learning (ML) modeling to predict Cd aggregation in rice grains and identify the influencing factors. Data from 474 data points from 77 published works were analyzed, and eight ML models were established using different algorithms. The input variables were total soil Cd concentration (TS Cd) and extractable Cd concentration (Ex-Cd), while rice Cd concentration (Cdrice) was the output variable. Among the models, the Extremely Randomized Trees (ERT) model performed the best (TS Cd: R2 = 0.825; Ex-Cd: R2 = 0.792), followed by Random Forest (TS Cd: R2 = 0.721; Ex-Cd: R2 = 0.719). The ERT feature importance ranking analysis revealed that the essential factors responsible for Cd aggregation are cation exchange capacity (CEC), TS Cd, Water Management Model (WMM), and pH for total soil Cd as input variables. For extractable Cd as an input variable, the vital factors are CEC, Ex-Cd, pH, and WMM. The study highlights the importance of the Water Management Model and its impact on Cd concentration in rice grains, which has been overlooked in previous research.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The authors and their respective affiliations are correct.Author details: Kindly check and confirm whether the corresponding author is correctly identified.It is correct.

Research and Optimization of Audit Risk Assessment Model Based on Regression Algorithm

Recently, auditors have used audit risk models’ conceptual tools to assess and control the many risks that could occur during an audit. The tool guides the auditor in determining the necessary evidence for each relevant allegation and the required categories of evidence. The importance of refining audit risk assessment models depends on the crucial role that these models perform in allocating resources and reducing financial disparities. The challenging characteristic of such an audit risk assessment model is that inaccurate information can lead to incorrect risk assessments, legal frameworks and failure to detect material misstatements in financial statements. Hence, in this research, BP neural network-enabled machine learning (BPNN-ML) technologies have been improved for the audit risk assessment model. In that financial disparity, the regression algorithm was used to establish the audit risk assessment for data processing and entirely monitor it. The suggested method provides a flexible framework that may be utilized by a wide range of organizations, including global enterprises and financial institutions, to optimize audit processes and ensure regulatory compliance. This research adds to advancing auditing procedures and regulatory compliance efforts in contemporary company contexts by addressing the issues inherent in traditional methods and proposing a practical approach to these concerns. The experimental analysis of BPNN-ML outperforms physical monitoring in terms of feature importance ranking analysis, contextual adaptability analysis, sensitivity analysis, performance analysis and optimized risk assessment analysis.

Examining genotype-phenotype associations of GRAM domain proteins using GWAS, PheWAS and literature review in cattle, human, pig, mouse and chicken

The GRAMD genes are involved in maintaining cholesterol homeostasis, apoptosis, cancer and production traits in livestock. A lipid-binding GRAM domain is implicated in lipid transport and metabolism. The functions of GRAMD proteins remain incompletely understood. The aim of the present study was therefore to investigate the associations between six GRAMD genes in cattle using data from the international genomic evaluation of the Interbull InterGenomics Centre and to evaluate genotype-phenotype associations in human, cattle, pig, mouse and, chicken. Genotyping of 55,013 bulls was performed using DNA microarrays and 11 SNPs were mapped to the five GRAMD genes. A phenome-wide association study (PheWAS) tested associations between the 11 SNPs and 36 traits. The integrated analysis of SNP effects, rankings, and clustering patterns revealed their potential for improving cattle productivity, health, and robustness, and established a baseline for the targeted improvement of cattle traits. This study lays the groundwork for functional experiments aimed at uncovering the mechanism of action of GRAMD genes and to evaluate the potential of using GRAMD sequence variants for selection programs in dairy cattle. The study presents an example of how the combination of GWAS and the PheWAS offers a promising toolbox for the systematic functional annotation of vertebrate genomes.

“Learning to ‘waltz’ rather than ‘wrestle’ … ” : a novel, ultra-brief intervention supporting early child–caregiver relationships

ABSTRACT Introduction The quality of the early child–caregiver relationship plays a crucial role in shaping a child’s development. In response to the lack of early intervention provisions for 2–5 year olds, the Leeds Infant Mental Health Service increased their offer to support children up to their fifth birthday (and their caregivers), where relational difficulties impact upon the child’s emotional wellbeing. Aims This paper aims to evaluate the effectiveness of the direct therapeutic work (named ‘Understanding Your Toddler’; UYT), in promoting the child–caregiver relationship. Method Nineteen families were accepted for UYT, where there was motivation and emotional availability to consider change within the relationship. Thirteen families engaged in the work. The UYT offer adopted a three session, home-visiting model, drawing upon several therapeutic approaches. A two-phased mixed methods design was adopted. Phase I analysed quantitative changes within the relationship. Phase II qualitatively evaluated families and professionals’ experiences of UYT. Results Paired t-tests and Wilcoxon signed rank analyses illustrated significant differences pre and post UYT in caregiver: goals, confidence, perceptions and feelings of irritation. No significant differences were found in caregiver feelings of warmth. Themes from interviews suggested that the strengths-based approach was valued, and that video work supported caregivers to ‘see the world from their (toddlers’) point of view’. Conclusion This evaluation contributes to the limited evidence evaluating the efficacy of child–caregiver interventions in practice. Despite the small sample, the UYT model provides a helpful framework (applicable to clinical contexts) to foster early relationships and emotional development of young children.

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

Social protection comparison across the world: a study of fiscal, structural and institutional performance discrepancies

PurposeSocial protection states the public-mandated (strategies and programmes) to address the vulnerability and risk among poor and near-poor households. Social protection must not only help people meet their basic needs but also contribute to the long-term well-being and broader societal goals of equity, social justice and empowerment. The role of social protection in achieving these goals has not been adequately documented. This paper examines the performance of the social protection mechanism from the perspective of the regional structural and institutional performance indicators. Social protection outlay dynamics are synthesized in the structural and institutional conducts.Design/methodology/approachA world regional comparability is made by considering the panel dataset for the time interval 1995–2020. In empirics, the unconditional and conditional ranks are constructed, and regression analyses are made subject to (1) the fiscal constraint of social protection, (2) structural performance indicators and (3) the institutional performance indicators of the targeted regions across the globe. The fully modified-ordinary least square (FM-OLS) method is used to construct the ranks.FindingsThe rank analysis demonstrates that the developed regions (i.e. Europe, Central Asia and North America) have relatively good welfare standards and fiscal capacity for social protection drives compared to the developing/underdeveloped regions (i.e. Sub-Saharan Africa and South Asia). The structural/institutional performance indicators have shown their significance in determining/utilizing their budget for social protection and maintaining welfare standards. Interestingly, most developed regions are relatively reluctant to maintain the structural/institutional performance for the determination/execution of the funds for social protection as compared to the developing and underdeveloped regions.Practical implicationsIn policy discourse, this research suggests that governments must make some market operations to enhance their regions’ structural and institutional performance to get better outcomes of social protection spending in the form of attractive welfare standards.Originality/valueMultiple studies have been done considering the social protection dynamics at nano, micro and macro levels, while this study considered the mega dataset across the globe to analyse the social protection dynamics in consideration of structural and institutional performances.

Abstract 4137913: Role of serial evaluation of myocardial inflammatory activity and oxidative stress in determining the therapeutic efficacy of immunosuppression and clinical outcome in patients with cardiac sarcoidosis

Background: Cardiac sarcoidosis (CS) was characterized by formation of granulomas in the heart. Enhancement in myocardial inflammatory activity and oxidative stress is a crucial cause of major cardiovascular adverse events (MACE). Although immunosuppressive therapy is recommended for active CS, there is no established markers for treatment and prognosis. Hypothesis: We hypothesized that the inflammation and oxidative stress in heart were associated with MACE after steroid therapy. Aims: We identified prognostic markers for MACE in patients with CS after steroid therapy. Methods: This study was a prospective observational cohort study. Patients with CS diagnosed according to Japanese criteria were enrolled in this study. Patients with abnormal accumulation of 18 F-FDG in heart were treated with steroids with standard guideline-recommended protocol. 18 F-FDG PET were performed after more than 6 months of induction. Urinary 8-hydroxy-2'-deoxyguanosine (U-8-OHdG), a marker of oxidative DNA damage, and indices of cardiac and renal function were measured. The major outcome was a composite of the first sustained ventricular tachycardia (sVT) /sudden cardiac death (SCD), hospitalization for heart failure, and radiological relapse with exacerbation of clinical manifestation. Results: Fifty consecutive patients with CS underwent steroid therapy and followed up (median follow-up period of 58 months). 39 of 50 patients underwent 18F-FDG PET/CT more than 6 months after steroid therapy. During the follow-up period, 17 of 39 patients showed MACE, consisting of sVT/SCD (N= 11), hospitalization (N= 2) and radiological relapse (N= 4). A Cox proportional-hazard model showed that the U-8-OHdG and SUV max value of FDG-PET were independent predictors of MACE. ROC analysis revealed that the cut-off values of U-8-OHdG and SUV max for predicting the MACE were 11.6 ng/mg Cr (AUC 0.913, sensitivity 86.7%, specificity 90.0%) and 4.64 (AUC 0.878, sensitivity 76.5%, specificity 91.0%), respectively (Fig.1). Patients with a U-8-OHdG ≧11.6 ng/mg Cr or SUV max ≧4.64 had a significantly higher MACE risk (P values for U-8-OHdG and SUV max were both 0.001 by Log Rank analysis) (Fig.2). It is noted that U-8-OHdG &lt;11.6 ng/mg Cr and SUV max &lt;4.64 after steroid therapy indicate the good responses to the immunosuppressive therapy and better prognosis (Fig.3). Conclusion: U-8-OHdG and SUV max may be useful markers in determining the therapeutic efficacy and clinical outcome in patients with CS.

Caspase-9 and p53 Protein Levels in Cancer Patients after Different Anesthesia Techniques

The aim of this study was to investigate the changes in caspase-9 and p53 levels as biomarkers of pro- and anti-apoptotic pathways in the early postoperative period in patients who underwent lung surgery for malignant tumors under different types of multimodal or inhalation-intravenous anesthesia.Material and Methods. A single-center prospective study of 22 patients aged 45-64 years was conducted at the Omsk Clinical Oncology Early Treatment and Prevention Center from January to April 2020. The participants were divided into two groups. Group 1 patients received multimodal anesthesia, which included sympathetic nerve block and prolonged epidural analgesia in the postoperative period. Group 2 patients received inhalational and intravenous anesthesia followed by systemic morphine analgesia. Serum caspase-9 and p53 protein levels were measured at four time points: before anesthesia, one, twelve, and twenty-four hours after surgery. Statistical hypotheses were tested using nonparametric (rank) analysis methods. Friedman's ANOVA was used to compare multiple time points, while the Wilcoxon test was used to compare variables between two time points in dependent samples. The Mann-Whitney test was used to assess differences between groups in independent samples. P-values &lt; 0.05 were considered statistically significant. Results are expressed as median ± half interquartile range (Me ± (LQ-UQ)/2).Results. At time point 2, caspase-9 levels were significantly higher in group 2 patients than in group 1 (P = 0.045). There were no significant differences between the groups at any other time points.Conclusion. The lack of a significant difference in serum levels of caspase-9 and p53 protein at most time points between the groups demonstrates the efficacy of the anesthesia and analgesia methods used. Meanwhile, a significantly higher level of caspase-9 one hour after surgery demonstrates a greater susceptibility of patients without sympathetic blockade to activation of the apoptotic cell death program.

Faktor yang Berhubungan dengan Perilaku Petani Padi dalam Penggunaan Pestisida di Desa Muara, Cilamaya Wetan Kabupaten Karawang

Unwise use of pesticides can cause residues that are harmful to human health and have a negative impact on the environment. Factors related to farmers' behavior can be one of the key determinants of how far they understand and implement safe, sustainable pesticide use practices. The study aims to analyze the factors associated with the behavior of peasants in Muara village, Cilamaya Wetan district, Karawang district. Behavior includes knowledge, attitudes, and actions consisting of aspects of pesticide type, dosage, time, spray frequency, self-protection, pesticides application techniques, and compliance with instructions for use. The research was conducted from September to October 2023. Sampling proportionate stratified random sampling obtained 85 respondents. Data collection techniques include observations, tests, questionnaires, and interviews. The study used a survey method using Spearman Rank analysis. Data were analyzed using validity tests, reliability tests, quantitative descriptive analysis, and Spearman Rank correlation tests. The results of the research showed that the factors associated with the behavior of wildfish farmers in the use of pesticides were land area (X3) with a correlation of 0.239, farming experience (X4) with the correlations of 0.258, and perception of pests and plant diseases (X7) with a correlation of 0,233. Whereas education (X1), income (X2), social involvement (X5), as well as the role of the farmer (X6) had no significant correlation with farmers' behaviour in pesticide use.

Machine learning approaches for predicting impact sensitivity and detonation performances of energetic materials

Excellent detonation performances and low sensitivity are prerequisites for the deployment of energetic materials. Exploring the underlying factors that affect impact sensitivity and detonation performances as well as exploring how to obtain materials with desired properties remains a long-term challenge. Machine learning with its ability to solve complex tasks and perform robust data processing can reveal the relationship between performance and descriptive indicators, potentially accelerating the development process of energetic materials. In this background, impact sensitivity, detonation performances, and 28 physicochemical parameters for 222 energetic materials from density functional theory calculations and published literature were sorted out. Four machine learning algorithms were employed to predict various properties of energetic materials, including impact sensitivity, detonation velocity, detonation pressure, and Gurney energy. Analysis of Pearson coefficients and feature importance showed that the heat of explosion, oxygen balance, decomposition products, and HOMO energy levels have a strong correlation with the impact sensitivity of energetic materials. Oxygen balance, decomposition products, and density have a strong correlation with detonation performances. Utilizing impact sensitivity of 2,3,4-trinitrotoluene and the detonation performances of 2,4,6-trinitrobenzene-1,3,5-triamine as the benchmark, the analysis of feature importance rankings and statistical data revealed the optimal range of key features balancing impact sensitivity and detonation performances: oxygen balance values should be between −40% and −30%, density should range from 1.66 to 1.72 g/cm3, HOMO energy levels should be between −6.34 and −6.31 eV, and lipophilicity should be between −1.0 and 0.1, 4.49 and 5.59. These findings not only offer important insights into the impact sensitivity and detonation performances of energetic materials, but also provide a theoretical guidance paradigm for the design and development of new energetic materials with optimal detonation performances and reduced sensitivity.

High-resolution sedimentary cyclostratigraphy and astronomical signals in the Upper Ordovician of Southwest China

During the Late Ordovician, a series of significant biotic and geological events occurred globally and more specifically in South China. The development of a high-resolution orbital cyclostratigraphic framework and a precise radioisotope dating has greatly enhanced our understanding of the factors controlling these events. In this study, quantitative proxies for palaeo-water depths, derived from sedimentological analyses, serve as indicators for orbital cyclostratigraphic assessment.Relatively shallow-marine carbonates with well-developed shallowing-upward cycles were deposited in the western Yangtze Platform during the Late Ordovician. High-resolution geochronological data were obtained from high-precision isotope dilution thermal ionization mass spectrometry (ID-TIMS) zircon dating of K-bentonite beds at the studied section. This facilitated sedimentary facies and environmental analyses, as well as the recognition of sedimentary cycles. Based on mud content, bioclastic content, and laminations, four sedimentary facies, twelve sedimentary microfacies, and forty-eight shallowing-upward sedimentary cycles were identified in the Daduhe Formation.Through comparative time series analysis of depth rank, lime mudstone-calcareous mudstone binary data, non-carbonate content, and anhysteretic remanent magnetization (ARM), astronomical cycles and trends in sedimentation rates have been identified. This study confirms the reliability of radioisotope dating, establishing a 0.46 Myr duration for the Dicellograptus complexus graptolite biozone and a 0.96 Myr duration for the Paraorthograptus pacificus graptolite biozone, both with an empirical uncertainty of 0.1 Myr. This research proposes a reliable method for identifying astronomical cycles through high-precision sedimentary microfacies and cyclostratigraphy, in comparison with rock magnetic cyclostratigraphy studies.

FungiFun3: systemic gene set enrichment analysis for fungal species.

The ever-growing amount of genome-wide omics data paved the way for solving life science problems in a data-driven manner. Among others, enrichment analysis is part of the standard analysis arsenal to determine systemic signals in any given transcriptomic or proteomic data. Only a part of the members of the fungal kingdom, however, can be analyzed via public web applications, despite the global rise of fungal pathogens and their increasing resistance to antimycotics. We present FungiFun3, a major update of our user-friendly gene set enrichment web application dedicated to fungi. FungiFun3 was rebuilt from scratch to support a modern and easy-to-use web interface and supports >4-fold more fungal strains (n = 1287 in total) than its predecessor. In addition, it also allows ranked gene set enrichment analysis at the genomic scale. FungiFun3 thus serves as a starting hub for identifying molecular signals in omics datasets related to a vast amount of available fungal strains including human fungal pathogens of the WHO's priority list and far beyond. FungiFun3, including sample data and FAQ, is freely available at https://fungifun3.hki-jena.de/.

Farmers&amp;#039; perceived constraints to Extension &amp;amp; Advisory Services (EAS): A garret ranking analysis

Farmers&amp;#039; perceived constraints to Extension &amp;amp; Advisory Services (EAS): A garret ranking analysis

Monthly Prediction on Summer Extreme Precipitation With a Deep Learning Approach: Experiments Over the Mid‐To‐Lower Reaches of the Yangtze River

AbstractAccurate predictions of monthly extremes assume paramount importance in enabling proactive decision‐making, which however are lacked in skills even for state‐of‐the‐art dynamical models. Taking the extreme precipitation prediction over the mid‐to‐lower reaches of the Yangtze River, China, as an instance, a multi‐predictor U‐Net deep learning approach is designed to enhance the prediction over the European Center for Medium‐Range Weather Forecasts (ECMWF) model, with the single‐predictor U‐Net parallelly examined as the benchmark. Focusing on the precipitation extremes, an extreme associated component is incorporated into the model loss function for optimization. Besides, predictions composed by daily outputs with multiple lead times are imported as a comprehensive set in the training phase to augment the deep learning sample size and to emphasize enhancements in predictions at the monthly timescale as a whole. Results indicate that the multi‐predictor U‐Net effectively improves predictions of extreme summer precipitation frequency, showing distinct superiority to the raw ECMWF and the single‐predictor U‐Net. Multiple evaluation metrics indicate that the model shows a significant positive improvement ratio ranging from 65.1% to 80.0% across all grids compared to the raw ECMWF prediction, which has also been validated through applications in the two extreme summer precipitation cases in 2016 and 2020. Besides, a ranking analysis of feature importance reveals that factors such as humidity and temperature play even more crucial roles than precipitation itself in the multi‐predictor extreme precipitation prediction model at the monthly timescale. That is, in such a deep learning approach, the monthly prediction on extreme precipitation benefits significantly from the inclusion of multiple associated predictors.