Matrix Plots Research Articles

Meadow degradation reduces microbial β diversity and network complexity while enhancing network stability

Continuing meadow degradation under the dual impacts of climate change and human activities has altered microbially-mediated biogeochemical cycles. However, microbial diversity and network stability in response to meadow degradation and their relationships to environmental variables remain insufficiently understood. This study focused on alpine meadows with varying degrees of degradation in the hinterland of the Qinghai-Tibetan Plateau. It analyzed the soil microbial diversity (α and β diversity) and ecological network topology characteristics under the degradation of alpine meadows. Furthermore, the extent to which these factors elucidate the environmental changes observed during degradation was explored. The results demonstrated that: (1) the α diversity of soil microbial under degradation of alpine meadows exhibited an increasing trend, although not significantly. The β diversity (community dissimilarity) exhibited a significant decreasing trend. These indicated that meadow degradation resulted in microbial homogenization intra- and inter-community. In the light degradation stage, the β diversity of bacteria and fungi significantly increased with geographic and environmental distance rise. Only bacterial β diversity significantly increased with geographical distance in the moderate degradation stage and environmental distance in the heavy degradation stage, respectively. This indicated that meadow degradation possibly altered microbial assembly, especially for fungi. (2) The degree of soil bacterial nodes exhibited a significant decreasing trend with degradation and fungal eigencentrality demonstrated a significant decreasing trend. After removing the identical nodes, the natural connectivity of the fungal network exhibited a significant decreasing trend with degradation. This suggested that the stability of the soil microbial network increased while the network complexity decreased under meadow degradation. These changes possibly marked the irreversible course of alpine meadow degradation. (3) The Mantel test indicated that bacterial diversity and network topological features were more strongly associated with environmental factors than those of fungi. Akaike information criterion (AIC) values and the upset matrix plots of variance partitioning based on Redundancy analysis (RDA) indicated that higher rates of soil factors, such as soil pH, organic carbon (SOC) content, and total potassium (TK), elucidated changes in microbial community diversity and network topological features. This study highlights the importance of soil microorganisms for ecological stability and the role of microorganisms should be emphasized in future efforts to restore alpine grasslands.

Features selection in a predictive model for cardiac surgery-associated acute kidney injury.

Cardiac surgery-associated acute kidney injury (CSA-AKI) is related to increased morbidity and mortality. However, limited studies have explored the influence of different feature selection (FS) methods on the predictive performance of CSA-AKI. Therefore, we aimed to compare the impact of different FS methods for CSA-AKI. CSA-AKI is defined according to the kidney disease: Improving Global Outcomes (KDIGO) criteria. Both traditional logistic regression and machine learning methods were used to select the potential risk factors for CSA-AKI. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. In addition, the importance matrix plot by random forest was used to rank the features' importance. A total of 1977 patients undergoing cardiac surgery at Fuwai hospital from December 2018 to April 2021 were enrolled. The incidence of CSA-AKI during the first postoperative week was 27.8%. We concluded that different enrolled numbers of features impact the final selected feature number. The more you input, the more likely its output with all FS methods. In terms of performance, all selected features by various FS methods demonstrated excellent AUCs. Meanwhile, the embedded method demonstrated the highest accuracy compared with the LR method, while the filter method showed the lowest accuracy. Furthermore, NT-proBNP was found to be strongly associated with AKI. Our results confirmed some features that previous studies have reported and found some novel clinical parameters. In our study, FS was as suitable as LR for predicting CSA-AKI. For FS, the embedded method demonstrated better efficacy than the other methods. Furthermore, NT-proBNP was confirmed to be strongly associated with AKI.

Theoretical investigation of benzodithiophene-based donor molecules in organic solar cells: from structural optimization to performance metrics.

Achieving high power conversion efficiency (PCE) remains a significant challenge in the advancement of organic solar cells (OSCs). In the field of organic photovoltaics (OPVs), considerable progress has been made in optimizing molecular structures to improve the PCE. However, innovative material design strategies specifically aimed at enhancing PCE are still needed. Here, we have designed BDTS-2DPP-based molecules and propose a molecular design approach to develop donor materials that can significantly improve the PCE of OSCs. Density functional theory (DFT) and time-dependent DFT (TD-DFT) methods have been adopted in both gas and solvent phases. Our newly designed molecule M1 shows the highest absorption value (λ max = 846 nm), highest electron reorganization energy (λ e = 0.18 eV), and the lowest energy gap (E g = 1.81 eV) among all the designed molecules. M1 molecule also exhibits the highest dipole moment in both gas (10.62 D) and solvent phase (13.62 D), and their ground and excited state dipole moment difference is also higher (μ e - μ g = 2.99 D), which enhances its separation to make it a suitable candidate for charge transfer between HOMO-LUMO (97%). Newly designed molecule M3 is observed to have the highest voltage when the current is zero (V oc = 1.15 V) highest PCE value (21.90%) and highest fill factor (FF) value (89.42%). The lowest excitation binding energy is estimated by newly designed molecule M2 (E b = 0.30 eV), which indicates a higher rate of dissociation during the excitation as observed in transition density matrix (TDM) plots. Utilizing electron density difference maps, the newly designed molecules in dichloromethane solvent exhibited consistent intramolecular charge transfer (ICT). The designed molecules were evaluated against reference molecule R to determine if they exhibit superior optoelectronic capabilities. It is found that all designed molecules (M1-M5) exhibit reduced band gaps, are red-shifted in wavelength in comparison to a reference molecule R, and have remarkable charge motilities in terms of reorganisation energies.

Rocky outcrops form islands of high and unique tree biodiversity within an ocean of grass in Serengeti National Park

AbstractAfrican savannahs are characterised by a high plant diversity, partly resulting from a high turnover in community compositions across space. However, it is poorly understood what is driving this spatial turnover in plant communities. Here, we investigate to which extent the presence of rocky outcrops (also called kopjes) explains the community composition of trees in an African savannah, and how we can understand the responses of tree species to rocky outcrops by their functional traits. Along a precipitation gradient, we visited 24 sites in Serengeti National Park (Tanzania). At each site, we characterised tree communities, as well as their functional traits, in both a kopje and an adjacent open savannah plot (matrix plot). We found that kopjes harboured elevated tree abundances and species richness. Their dominant trees were more often evergreen, had a higher specific leaf area, a lower leaf nitrogen content and a lower spine density, than dominant trees in the savannah matrix. Differences in tree communities between kopjes and savannah matrix plots were generally the largest at sites with low precipitation. Our results indicate that kopjes are strong drivers of tree biodiversity, possibly due to locally increased soil moisture and low fire frequencies. The uniqueness of kopje tree communities may have important implications for higher trophic levels and ecosystem functioning.

A need analysis of English proficiency in engineering graduates: perspectives of the world’s leading companies in Thailand

ABSTRACT This study examines the crucial role of English proficiency in engineering careers, specifically within multinational corporations, addressing disparities between industry expectations and graduates’ language skills in non-native English-speaking countries. It aims to bridge this gap by aligning engineering English courses with international job market demands. Focusing on leading Thai companies with global headquarters, the investigation employs various techniques, including interviews, the index of item-objective congruence, the analytic hierarchy process, the analysis of variance, the Wilcoxon signed-ranks test, and a matrix plot. The comprehensive gap analysis highlights verbal communication skills as highly valued by engineering firms, emphasising adjustments in English curricula towards listening and speaking skills, with reading and writing skills constituting a smaller portion. Cross-regional consistency in skill importance is confirmed, facilitating generalisable conclusions. The research underlines the necessity for universities to improve students’ abilities in technical conversations, presentations, and negotiations through tailored lesson plans aligned with industry requirements.

Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia

AimTo develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.MethodsA dataset of 284 BPD neonates on mechanical ventilation was used to develop predictive models via machine-learning algorithms, including extreme gradient boosting (XGBoost), random forest, support vector machine, naïve Bayes, logistic regression, and k-nearest neighbor. The top three models were assessed by the area under the receiver operating characteristic curve (AUC), and their performance was tested by decision curve analysis (DCA). Confusion matrix was used to show the high performance of the best model. The importance matrix plot and SHapley Additive exPlanations values were calculated to evaluate the feature importance and visualize the results. The nomogram and clinical impact curves were used to validate the final model.ResultsAccording to the AUC values and DCA results, the XGboost model performed best (AUC = 0.873, sensitivity = 0.896, specificity = 0.838). The nomogram and clinical impact curve verified that the XGBoost model possessed a significant predictive value. The following were predictive factors for EF: pO2, hemoglobin, mechanical ventilation (MV) rate, pH, Apgar score at 5 min, FiO2, C-reactive protein, Apgar score at 1 min, red blood cell count, PIP, gestational age, highest FiO2 at the first 24 h, heart rate, birth weight, pCO2. Further, pO2, hemoglobin, and MV rate were the three most important factors for predicting EF.ConclusionsThe present study indicated that the XGBoost model was significant in predicting EF in BPD neonates with mechanical ventilation, which is helpful in determining the right extubation time among neonates with BPD to reduce the occurrence of complications.

OTHR-12. GENERATING A COMPREHENSIVE INTERACTIVE PEDIATRIC BRAIN TUMOR VISUALIZATION PORTAL FROM MOLECULAR AND CLINICAL DATA DERIVED FROM TWO MULTI-INSTITUTIONAL PROTOCOLS (SJMB03, SJYC07) AND NON-PROTOCOL PATIENTS TREATED AT ST JUDE CHILDREN’S RESEARCH HOSPITAL

Abstract BACKGROUND Pediatric central nervous system (CNS) tumors represent a diverse group of neoplasms with heterogeneous molecular and clinical characteristics. The need for well-organized, searchable datasets is paramount to enhance our understanding and improve the treatment approaches for these devastating diseases. To illustrate this, we have organized multifaceted data from patients into an interactive portal equipped with cutting-edge visualization tools. METHODS Clinical and molecular data from the SJMB03 and SJYC07 protocols and select published non-protocol St. Jude patients were collated and harmonized. A data dictionary was constructed to record demographic information, treatment details, histology, methylation classification, mutations, chromosomal aberrations, and outcomes. We implemented five visualization techniques - summary plots, sample view, scatter plots, sample matrix, and survival plots. These techniques are interconnected, enabling interaction and “crosstalk”. RESULTS Data from 710 patients with CNS tumors were incorporated: 386 Medulloblastomas (MB); 88 Atypical teratoid Rhabdoid Tumors (ATRT); 54 Ependymomas (EPN); 49 Pineoblastomas (PB); 28 Embryonal Tumors with Multilayered Rosettes (ETMR), 22 Infant-type hemispheric glioma (IHG), 16 High grade glioma (HGG), 13 Choroid Plexus Tumors (CPT); 11 CNS Sarcoma; 8 CNS tumor with BCOR internal tandem duplication (CNS BCOR); 6 CNS neuroblastoma FOXR2 activated (CNS NB FOXR2); 5 Low grade glioma (LGG); and 24 other entities or unclassified tumors. The portal features enabled the creation of custom cohorts. For example, patients with TP53 mutations (n=32; 15 MB, 7 CPT, 6 HGG, 1 CNS Sarcoma, 1 CNS NB FOXR2, 1 PB, 1 unclassified tumor) or DICER1 mutations (n=10; 7 PB, 2 CNS Sarcoma, and 1 ETMR) could be identified, cross-compared and analyzed adjusting for variables at users’ choice. CONCLUSION The integration of molecular and clinical data from multiple sources into an interactive visualization portal presents a comprehensive resource for research and clinical practice. Future work will focus on expanding the portal to integrate more cases/cohorts.

MDB-105. CONSTRUCTION OF AN INTERACTIVE MEDULLOBLASTOMA VISUALIZATION PORTAL FROM MOLECULAR AND CLINICAL DATA DERIVED FROM THREE MULTI-INSTITUTIONAL PROTOCOLS (ACNS0331, SJMB03, ACNS0332) TO COMPARE, CONTRAST, AND INVESTIGATE COHORTS AS A MEANS TO REFINE THERAPY

Abstract BACKGROUND Medulloblastoma (MB) patients display a heterogeneous mix of molecular and clinical characteristics that influence tumor behavior and survival. We hypothesized that if these multiple characteristics could be incorporated into an organized, searchable, and interactive dataset, this would advance our understanding and improve treatment of this disease. Consequently, we organized multifaceted datasets into an interactive portal equipped with cutting-edge visualization tools. METHODS Clinical and molecular data from the ACNS0331, SJMB03, and ACNS0332 protocols were collated and harmonized. A data dictionary was built to capture demographics, treatment, histology, methylation classification, mutations, chromosomal copy number variations and outcomes. To display the data, a software framework was applied. Five visualization methods were implemented (summary plots, sample view, scatter plots, sample matrix, and survival plots) in an interconnected manner such that these “cross-talk” with each other. RESULTS Data from 898 patients with MB were incorporated: 131 WNT, 151 SHH, 220 G3, 396 G4. The portal features enabled the creation of custom cohorts that could be instantly queried by any of the dictionary terms. For example, cohorts could be customized to include only cases with a mutation of the user’s choice and analyzed by molecular group, gender, age at diagnosis, or treatment given. Survival of patients with SHH-MB harboring TP53 mutations could be compared to patients with WNT-MB harboring TP53 mutations. Moreover, the influence of MB subtype (e.g. G34_I-VIII), focal amplifications (MYC, MYCN) and whole chromosome aberration phenotype on survival could be queried across treatment regimens accounting for metastatic status and/or additional variables of the user’s choice. CONCLUSION The integration of molecular and clinical data from multiple trials into an interactive visualization data portal presents an unprecedented resource for research and clinical practice. This portal facilitates answering highly specific questions about mutational frequency and patient outcome by molecular and clinical risk factors.

Clustering of Wind Speed Time Series as a Tool for Wind Farm Diagnosis

In several industrial fields, environmental and operational data are acquired with numerous purposes, potentially generating a huge quantity of data containing valuable information for management actions. This work proposes a methodology for clustering time series based on the K-medoids algorithm using a convex combination of different time series correlation metrics, the COMB distance. The multidimensional scaling procedure is used to enhance the visualization of the clustering results, and a matrix plot display is proposed as an efficient visualization tool to interpret the COMB distance components. This is a general-purpose methodology that is intended to ease time series interpretation; however, due to the relevance of the field, this study explores the clustering of time series judiciously collected from data of a wind farm located on a complex terrain. Using the COMB distance for wind speed time bands, clustering exposes operational similarities and dissimilarities among neighboring turbines which are influenced by the turbines’ relative positions and terrain features and regarding the direction of oncoming wind. In a significant number of cases, clustering does not coincide with the natural geographic grouping of the turbines. A novel representation of the contributing distances—the COMB distance matrix plot—provides a quick way to compare pairs of time bands (turbines) regarding various features.

Predictive model for non-malignant portal vein thrombosis associated with cirrhosis based on inflammatory biomarkers.

Portal vein thrombosis (PVT), a complication of liver cirrhosis, is a major public health concern. PVT prediction is the most effective method for PVT diagnosis and treatment. To develop and validate a nomogram and network calculator based on clinical indicators to predict PVT in patients with cirrhosis. Patients with cirrhosis hospitalized between January 2016 and December 2021 at the First Hospital of Lanzhou University were screened and 643 patients with cirrhosis who met the eligibility criteria were retrieved. Following a 1:1 propensity score matching 572 patients with cirrhosis were screened, and relevant clinical data were collected. PVT risk factors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. Variance inflation factors and correlation matrix plots were used to analyze multicollinearity among the variables. A nomogram was constructed to predict the probability of PVT based on independent risk factors for PVT, and its predictive performance was verified using a receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). Finally, a network calculator was constructed based on the nomograms. This study enrolled 286 cirrhosis patients with PVT and 286 without PVT. LASSO analysis revealed 13 variables as strongly associated with PVT occurrence. Multivariate logistic regression analysis revealed nine indicators as independent PVT risk factors, including etiology, ascites, gastroesophageal varices, platelet count, D-dimer, portal vein diameter, portal vein velocity, aspartate transaminase to neutrophil ratio index, and platelet-to-lymphocyte ratio. LASSO and correlation matrix plot results revealed no significant multicollinearity or correlation among the variables. A nomogram was constructed based on the screened independent risk factors. The nomogram had excellent predictive performance, with an area under the ROC curve of 0.821 and 0.829 in the training and testing groups, respectively. Calibration curves and DCA revealed its good clinical performance. Finally, the optimal cutoff value for the total nomogram score was 0.513. The sensitivity and specificity of the optimal cutoff values were 0.822 and 0.706, respectively. A nomogram for predicting PVT occurrence was successfully developed and validated, and a network calculator was constructed. This can enable clinicians to rapidly and easily identify high PVT risk groups.

Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center study.

This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19). A total of 519 children with COVID-19 were assessed to develop predictive models using machine learning algorithms, including extreme gradient boosting (XGBoost), random forest (RF) and logistic regression (LR). The performance of the models was assessed using area under the receiver operating characteristic curve (AUC) values. Importance matrix plot and SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and to show the visualization results. The nomogram and clinical impact curve were used to validate the final model. Two hundred and seventeen children with COVID-19 had seizures. According to the AUC, the RF model performed the best. Based on the SHAP values, the top three most important variables in the RF model were neutrophil percentage, cough and fever duration. The nomogram and clinical impact curve also verified that the RF model possessed significant predictive value. Our research indicates that the RF model demonstrates excellent performance in predicting seizures, and our novel nomogram can facilitate clinical decision-making and potentially offer benefit for clinicians to prevent and treat seizures in children with COVID-19.

Variability in Crithmum maritimum L. Essential Oils’ Chemical Composition: PCA Analysis, Food Safety, and Sustainability

In this study, four accessions of Crithmum maritimum L., not previously studied, collected in Isola delle Femmine (Italy) (S43), Croatia (S44), Montenegro (S45), and Israel (S46) were investigated. The volatile profile of essential oils was evaluated using GC–MS and 38 compounds were identified. All the analyzed samples show a composition characterized essentially by monoterpene hydrocarbons (94.0–97.6%), with limonene, γ-terpinene, β-phellandrene, α-pinene, and p-cymene as the principal compounds. In addition, a comprehensive review of the composition of C. maritimum essential oils that have been studied thus far was conducted. To evaluate the similarity between samples, principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) were utilized. To evaluate the possibility of addressing food value to natural species that can strengthen sustainable food policies, it appears necessary to consider the previous safety of the dietary intake of C. maritimum. A matrix plot analysis of the content of dillapiole, a toxic constituent, in the samples was performed. The results of the statistical analysis show the presence of six clusters indicating some differences between C. maritimum accessions from different locations. Regarding dillapiole content, the four accessions discussed in this paper showed dillapiole values of less than 2%, suggesting the healthiness of sea fennel from these locations, while the highest values were found in samples from France, Portugal, and Tunisia.

Prediction of intraoperative red blood cell transfusion in valve replacement surgery: machine learning algorithm development based on non-anemic cohort.

Our study aimed to develop machine learning algorithms capable of predicting red blood cell (RBC) transfusion during valve replacement surgery based on a preoperative dataset of the non-anemic cohort. A total of 423 patients who underwent valvular replacement surgery from January 2015 to December 2020 were enrolled. A comprehensive database that incorporated demographic characteristics, clinical conditions, and results of preoperative biochemistry tests was used for establishing the models. A range of machine learning algorithms were employed, including decision tree, random forest, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), support vector classifier and logistic regression (LR). Subsequently, the area under the receiver operating characteristic curve (AUC), accuracy, recall, precision, and F1 score were used to determine the predictive capability of the algorithms. Furthermore, we utilized SHapley Additive exPlanation (SHAP) values to explain the optimal prediction model. The enrolled patients were randomly divided into training set and testing set according to the 8:2 ratio. There were 16 important features identified by Sequential Backward Selection for model establishment. The top 5 most influential features in the RF importance matrix plot were hematocrit, hemoglobin, ALT, fibrinogen, and ferritin. The optimal prediction model was CatBoost algorithm, exhibiting the highest AUC (0.752, 95% CI: 0.662-0.780), which also got relatively high F1 score (0.695). The CatBoost algorithm also showed superior performance over the LR model with the AUC (0.666, 95% CI: 0.534-0.697). The SHAP summary plot and the SHAP dependence plot were used to visually illustrate the positive or negative effects of the selected features attributed to the CatBoost model. This study established a series of prediction models to enhance risk assessment of intraoperative RBC transfusion during valve replacement in no-anemic patients. The identified important predictors may provide effective preoperative interventions.

CURTAIN—A unique web-based tool for exploration and sharing of MS-based proteomics data

To facilitate analysis and sharing of mass spectrometry (MS)-based proteomics data, we created online tools called CURTAIN (https://curtain.proteo.info) and CURTAIN-PTM (https://curtainptm.proteo.info) with an accompanying series of video tutorials (https://www.youtube.com/@CURTAIN-me6hl). These are designed to enable non-MS experts to interactively peruse volcano plots and deconvolute primary experimental data so that replicates can be visualized in bar charts or violin plots and exported in publication-ready format. They also allow assessment of overall experimental quality by correlation matrix and profile plot analysis. After making a selection of protein "hits", the user can analyze known domain structure, AlphaFold predicted structure, reported interactors, relative expression as well as disease links. CURTAIN-PTM permits analysis of all identified PTM sites on protein(s) of interest with selected databases. CURTAIN-PTM also links with the Kinase Library to predict upstream kinases that may phosphorylate sites of interest. We provide examples of the utility of CURTAIN and CURTAIN-PTM in analyzing how targeted degradation of the PPM1H Rab phosphatase that counteracts the Parkinson's LRRK2 kinase impacts cellular protein levels and phosphorylation sites. We also reanalyzed a ubiquitylation dataset, characterizing the PINK1-Parkin pathway activation in primary neurons, revealing data of interest not highlighted previously. CURTAIN and CURTAIN-PTM are free to use and open source, enabling researchers to share and maximize the impact of their proteomics data. We advocate that MS data published in volcano plot format be reported containing a shareable CURTAIN weblink, thereby allowing readers to better analyze and exploit the data.

A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records

BackgroundPrevious models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients.MethodsThe electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively.ResultThe Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated.ConclusionsWe established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.

Use of decision trees for water quality assessment: Analysis of key parameters

The paper investigates the application of the decision tree method for analyzing and predicting water quality. The main objective of the study is to identify the key physical and chemical parameters that affect the potability of water. The Decisive Tree Method is used to create a model capable of classifying water as suitable or unsuitable for drinking. The results of the study showed that the decisive trees model achieved an accuracy of 62.03% and F1 Score of 0.5292. The most important parameters affecting the model predictions include sulfate content, pH and water hardness. The error matrix and feature importance plot provided valuable information to further improve the model and understand the effect of different parameters on water quality.

Scenario Planning of Digital Transformation in Indonesia’s Fast-Moving Consumer Goods Sales Organization: Uncertainties and Possibilities

Since uncertainty in the business domain has increased the urgency for scenario development as a strategic tool, the research examined a relationships between a leadership model, organizational citizenship behavior, and sales management control variables using scenario planning. The questionnaire’s scaling using a Likert scale was done toward results from discussions and in-depth interviews with several experts in Fast-Moving Consumer Goods (FMCG) organizations, FMCG organizational leaders (Chief Executive Officers/CEOs), leadership experts, organizational behavior consultants, and organizational performance practitioners. The research finds that external and internal forces determine the success of digital transformation. External forces consist of digital technology availability, consumer expectations,business competition maps, and digital talents. Internal forces are budget allocation, competency trap versus growth mindset, organizational structure, and organizational culture. The scenario matrix plot forms four scenarios of the digital transformation in the sales organization of Indonesia’s FMCG companies (realistic, pessimistic, optimistic, and superoptimistic), where realistic (iteration improvement scenario) is largely predicted to occur for companies carrying out digital transformation. Such scenario requires sales organizations to have a growth mindset to deal with the uncertainty of customer/consumer expectations properly. The strategy appearing as an FMCG digital transformation compass is expected to advise FMCG companies in Indonesia when executing and overseeing the digital transformation within theircompanies.

Conformational stability, quantum computational (DFT), vibrational, electronic and non-covalent interactions (QTAIM, RDG and IGM) of antibacterial compound N-(1-naphthyl)ethylenediamine dihydrochloride

Naphthalene derivatives are heterocyclic compounds with significant applications in the pharmaceutical and biomedical industries. Several spectroscopy methods, including FT-IR, FT-Raman, and UV–Vis, were used in this study to examine the molecular structure of N-(1-Naphthyl) ethylenediamine dihydrochloride (NEDD). By employing DFT through the WB97XD with the level of 6–311++G(d, p) as the basis set, the optimized geometrical characteristics and full vibrational allocations of frequencies by the potential energy distribution were generated and matched to the experimental data. The delocalization of charge caused by intermolecular connections becomes apparent in the natural bond orbital (NBO) study. Additionally, frontier molecular orbital bandgap energy and electron excitation analyses were acquired to provide a deeper understanding of the structural characteristics of NEDD. Frontier molecular orbital energies revealed an energy band gap of 5.505 eV, demonstrating the existence of charge transfer within the molecule. Electronic transitions seen in the experimentally recorded UV–visible spectra were assigned using the TD-DFT method whereas transition density matrix (TDM) plots showed the ability to transmit charges. Considering wave function-derived parameters like the Laplacian of electron density (LED), local information entropy (LIE), the average localized ionization energy (ALIE) as well as non-covalent interactions, provides essential insights into the reactivity and stability traits of molecular systems. Local reactivity descriptions aided in understanding the interactions of physiologically relevant molecular systems, thereby facilitating the discovery of potential pharmaceutical compounds. Drug-likeness and ADMET assessment were achieved to assess biological properties of NEDD. Antibacterial assay confirmed its effectiveness against bacterial strains, while molecular docking studies. The stability of the best docked protein has been investigated using 70 ns molecular dynamics simulation followed by effective free energy calculation using MMPBSA.

Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit

BackgroundAcute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods. The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance.Four hundred nineteen consecutive patients out of 1115 hospital admissions were enrolled in a single-center retrospective study. Patients were younger than 18 years and admitted from August 2018 to February 2020 in a pediatric cardiac intensive care unit (PCICU) undergoing cardiac surgery, invasive procedure (hemodynamic studies), and medical conditions with complete EHR records and discharged after 48 h or more.ResultsThirty-six variables were selected to build the algorithm according to commonly described cardiac surgery-associated AKI clinical predictors. We evaluated different models for different outcomes: binary AKI (no AKI vs. AKI), severe AKI (no-mild vs severe AKI), and multiclass classification (maximum AKI and the most frequent level of AKI, mode AKI). The algorithm performance was assessed with the area under the curve receiver operating characteristics (AUC ROC) for binary classification, with accuracy and K for multiclass classification. AUC ROC for binary AKI was 0.93 (95% CI 0.92–0.94), and for severe AKI was 0.99 (95% CI 0.98–1). Mode AKI accuracy was 0.95, and K was 0.80 (95% CI 0.94–0.96); maximum AKI accuracy was 0.92, and K was 0.71 (95% CI 0.91–0.93). The importance matrix plot demonstrated creatinine, basal creatinine, platelets count, adrenaline support, and lactate dehydrogenase for binary AKI with the addition of cardiopulmonary bypass duration for severe AKI as the most relevant variables of the model.ConclusionsWe validated a ML model to detect AKI occurring after 48 h in a retrospective observational study that could help clinicians in individuating patients at risk of AKI, in which a preventive strategy can be determinant to improve the occurrence of renal dysfunction.

Equivalent Circuits Application of Pin-Diodes and MEMS-Keys in Electrodynamic Modeling Problems

This article studies the possibility of using circuit formats to describe radio components in electrodynamic modeling problems. The study was carried out on the example of a controlled metamaterial in the form of an electromagnetic crystal with SPICE-models of equivalent circuits of pin-diodes and MEMS-keys. Also, the possibility of using Touchstone files as an alternative format for describing radio-electronic components was considered. The obtained results are illustrated by scatter matrix plots, as well as equivalent circuits and SPICE code. Obtained results can be used for the design of structures that combine active nonlinear radio components and microwave devices.