Discriminant Variables Research Articles

Religiously flexible: Acculturation of second-generation Muslims in Europe

Second-generation Muslims who follow their faith and feel discriminated against tend to maintain their heritage culture and distance themselves from the culture of the country where they grew up, setting the conditions for psychosocial maladjustment. Yet some second generation do find ways to adopt the mainstream culture while remaining attached to their heritage culture. To explain these contradictory observations, we investigated how second-generation Muslims manage to be part of both mainstream and heritage culture although their religion is commonly regarded as incompatible with Western values. To do this, we examined the role of flexibility in existential quest (EQ) in the acculturation of second-generation Muslims. Our hypothesis was that second generation integration is fostered by their ability to be flexible on EQ, which allows them to reflect on cultural and religious issues and to create a safe psychological place where they can practice their faith without feeling they need to withdraw from mainstream society. Two samples of second-generation Muslims, one from Italy (N = 240) and one from Belgium (N = 209), completed an online questionnaire. A multi-group structural equation model was tested. Religiosity, perceived discrimination, and sociodemographic variables were also considered. We noted a positive association between EQ and mainstream culture only for the Italian sample. Our findings suggest that flexibility on EQ is one aspect of the acculturation of second-generation Muslims and that it can provide a resource for coping with the challenge of growing up under dual cultural pressures.

Pattern Recognition: Age-Specific Features of Fundamental Movement Skills Formation in Elementary School Students

The purpose of the study was to determine the age-specific features of fundamental movement skills formation in elementary school students.
 Materials and methods. The study participants were boys aged 7 to 10 (24 boys aged 7; 28 boys aged 8; 35 boys aged 9; 36 boys aged 10). The children and their parents or legal guardians were fully informed about all the special aspects of the study and all the parents or legal guardians gave their consent thereto. The study protocol was approved by the Ethics Committee of the University. 
 The features of teaching 7–10-year-old boys to throw a ball at a vertical target were investigated. The proficiency level in throwing exercises in class was assessed using an alternative method (“performed”, “failed”), the probability of the exercise performance was calculated (p = n/m, where n is the number of successful attempts, m is the total number of attempts). A method of algorithmic instructions was used in teaching boys aged 7 to 10. The study materials were processed by IBM SPSS 20 statistical analysis software. A discriminant analysis was conducted. 
 Results. It was established that the first canonical function explains 64.5% of the variation in results, while the second one does 34.4%, which indicates their high informativity (r1 = 0.762; r2 = 0.652). The materials of the canonical function analysis show the statistical significance of the first and second canonical functions (λ1 = 0.236; р1 = 0.001; λ2 = 0.5633; р2 = 0.001). The first and second functions have a high discriminative ability and value in interpretation with respect to the general population. 
 Conclusions. The discriminant analysis made it possible to determine the age-specific features of throwing skills formation in boys aged 7 to 10; answer the questions to which extent the differences in the effectiveness of skills formation in boys aged 7 to 10 are significant; which motor tasks are the most specific to boys aged 7, 8, 9, 10; which class an object belongs to based on the values of discriminant variables. 
 It was established that the level of proficiency in exercise 4 “Throwing a ball forward-upward standing with the left side to the throwing direction” has the greatest effect on the process of throwing movement skills formation in boys aged 7 to 10. For boys aged 8 to 10, such an exercise is “Throwing a ball forward-upward standing feet apart”, and for boys aged 9 to 10, such an exercise is “Throwing a ball at a target 3 m away”.

Testing the Reserve Capacity Model: Does Race Moderate the Relationship Between Negative Emotions and Neurocognition?

To test whether race moderates the relationship between negative emotions and neurocognition by applying the reserve capacity model within a large sample that spans adulthood. The study sample (N = 1,020) consisted of community-dwelling adults between 18 and 84years of age who were drawn from the Virginia Cognitive Aging Project. Demographic variables were used to match a sample of Black participants to a sample of White participants. Race was examined as a moderator of the relationship between negative emotions (i.e., depressive symptoms, trait anxiety, and the negative affect subscale from the Positive and Negative Affect Schedule) and neurocognitive variables (episodic memory, reasoning, spatial visualization, and processing speed) with multiple-group structural equation modeling. After accounting for sociodemographic variables, depressive symptoms were negatively associated with processing speed in both groups, and with worse reasoning in the White subsample. Negative affect was associated with lower reasoning performance in both groups and with lower spatial visualization in the White subsample. Trait anxiety was not significantly associated with the neurocognitive constructs in either group. Multigroup structural equation models showed that the magnitudes of the associations were not different between the Black and White subsamples. Thus, race did not moderate the relationships between depressive symptoms, trait anxiety, and negative affect with neurocognition. Negative emotions are associated with lower performance on different neurocognitive tasks, but race does not moderate these relationships. Future research should examine perceived discrimination or other psychosocial variables when examining the relationships among negative emotions and neurocognition.

Are topsoil spectra or soil-environmental factors better indicators for discrimination of soil classes?

Visible and near-infrared (Vis-NIR) spectroscopy (350–2500 nm) is often used to discriminate different soil classes. Discrimination variables included generally reflectance, topographic factors, soil texture, and climate. In the present study, more soil properties, soil organic matter, soil pH, bulk density and thickness, were considered to build soil classes model, and the most important discrimination variables were identified. Reasons for misclassification and challenges in elaborating the soil class map were analyzed. The topsoil spectra of 1432 soil samples of nine soil classes in northeast China were measured and converted into continuum removal (CR) curves, and principal component analysis (PCA) was applied to extract spectral information for reducing data redundancy. Other discrimination variables, soil-environmental factors, were acquired, including topographic factors, soil texture, climate, and four soil properties of each soil sample. The importance of soil–environmental factors and CR spectra was calculated for building models. Random forest (RF) models were trained to discriminate nine soil classes based on five kinds of variables, and they were the principal components (PCs) of reflectance, PCs of CR spectra, soil–environmental factors, PCs of CR spectra + soil–environmental factors, and optimum combinations between CR spectra and soil–environmental factors according to the importance. Optimum discrimination variables of each soil class were selected to build random forest models, and then the sum accuracy was calculated. Our study showed that: (1) it was challenging to acquire a high discrimination accuracy using reflectance and CR spectra for large-scale soil data; (2) soil–environmental factors were more suitable to discriminate between soil classes than reflectance and CR spectra in northeast China; (3) the order of importance of discrimination variables was soil texture > climate > pH > thickness > other soil–environmental factors; and (4) the RF model with optimum combinations between CR spectra and soil–environmental factors led to an accuracy of 81.5 % and a kappa coefficient of 0.77. We validated soil-environmental factors were better than topsoil spectra for discrimination of soil classes, and we found soil constituents in soil-environmental factors had high importance, and we successfully discriminated nine soil classes with 1432 soil samples by optimum combinations between topsoil spectra and soil–environmental factors at a large scale.

Predictors of Empirically Derived Substance Use Patterns Among Sexual and Gender Minority Populations of a Rural Midwestern State.

Purpose: The study purpose was to (1) identify latent classes of substance use behaviors among a sample of sexual and gender minority (SGM) adults living in a predominantly rural midwestern state and (2) determine the association between SGM-related discrimination and the empirically derived substance use classes. Methods: We conducted a latent class analysis on 494 responses to a state-wide survey, followed by a multinomial logistic regression to test predictors of class membership, including distal experiences of discrimination and sociodemographic variables. Results: A three-class model fit best and included (1) polysubstance use, (2) binge drinking, and (3) no/low use classes. In the adjusted model, polysubstance class membership was positively associated with cisgender male identity and negatively associated with being 60 years of age or older and college educated. Binge drinking class membership was negatively associated with bisexual/pansexual identity and non-White race/ethnicity. In contrast to hypothesized outcomes guided by the Minority Stress Model, experiences of discrimination were not associated with membership in substance-using classes. Likewise, bisexual/pansexual individuals were not more likely to be members of polysubstance use or binge drinking classes, despite published reports of greater risk of substance use. Conclusion: These contradictions warrant intersectional approaches to advance substance use research, which may provide important evidence for targeted prevention/treatment interventions, particularly among polysubstance users.

Health Inequalities by Sexual Orientation: Results from the 2016-2017 Barcelona Health Survey.

Purpose: The aim of this study was to assess health inequalities by sexual attraction in the 2016-2017 Barcelona population, stratifying by sex. Methods: Data came from the 2016-2017 Barcelona Health Survey, where 3362 adults answered among other instruments the EuroQol-5 dimensions-5 levels (EQ-5D-5L), which measures five dimensions and summarizes health-related quality of life into a single utility index score. To assess health differences by sexual attraction, we constructed Tobit models for the EQ-5D index score and Poisson regression models for the EQ-5D dimensions. Nested models were constructed to examine the mediating role of discrimination and health-related variables. Results: After adjusting for sociodemographic variables, women feeling attraction to more than one sex showed a lower EQ-5D index score (worse health) than those with only other sex attraction (-0.042, p = 0.012), and higher prevalence of problems with mobility, usual activities, and anxiety/depression with the following adjusted prevalence ratios (aPR) and confidence intervals (CIs): 1.79 (95% CI 1.05-3.05), 1.84 (95% CI 1.05-3.21), and 1.76 (95% CI 1.27-2.43). Women feeling attraction only to their same sex also presented higher prevalence of anxiety/depression (aPR = 1.46, CI 95% 1.10-1.92). In contrast, differences were not observed for men. Conclusion: Women, but not men, feeling attraction to more than one sex and only same-sex attraction in Barcelona in 2016-2017 presented worse health than those feeling only other sex attraction, with discrimination playing a mediating role in explaining such inequalities. These results among women indicate the need to develop public health strategies in Barcelona addressed to lesbian and bisexual women, considering the intersection of gender and sexual orientation.

Urinary proteome of dogs with renal disease secondary to leishmaniosis

Canine leishmaniosis is frequently associated with the development of renal disease. Its pathogenesis is complex and not fully understood. For this reason, this study aimed to describe the urinary proteome, and identify possible new biomarkers in dogs with kidney disease secondary to leishmaniosis. Urine samples were collected from 20 dogs, 5 from healthy dogs, and 15 from stages Leishvet III and IV. Urine samples were analyzed by UHPLC–MS/MS. The data are available via ProteomeXchange with identifier PXD029165. A total of 951 proteins were obtained. After bioinformatic analysis, 93 urinary proteins were altered in the study group. Enrichment analysis performed on these proteins showed an overrepresentation of the complement activation pathway, among others. Finally, 12 discriminant variables were found in dogs with renal disease secondary to leishmaniosis, highlighting C4a anaphylatoxin, apolipoprotein A-I, haptoglobin, leucine-rich alpha-2-glycoprotein 1, and beta-2-microglobulin. This study is the first to describe the urinary proteomics of dogs with renal disease caused by leishmaniosis, and it provides new possible biomarkers for the diagnosis and monitoring of this disease.

Abstract 785: Multi-omics profiling identifies genetic and epigenetic regulation underlying alcohol induced liver fibrosis in a porcine hepatocellular carcinoma model

Abstract Background: Cirrhosis is a form of end-stage liver disease which manifests with a host of metabolic and hematologic derangements, and is commonly the result of long-term ethanol exposure. Large animal models of cirrhosis, and of one of its common long-term sequelae, hepatocellular carcinoma (HCC), are needed to study novel and emerging therapeutic interventions. Methods: Liver fibrosis was induced in the Oncopig cancer model, a large animal model of HCC, via intrahepatic, intra-arterial ethanol infusion. Post-mortem liver sections from five fibrosis induction and five age-matched controls were harvested for RNA-seq (mRNA and lncRNA), small RNA-seq (miRNA), and reduced representation bisulfite sequencing (RRBS; DNA methylation). Supervised, multi-omic analysis of the 5,000 genes and methylated regions demonstrating the largest inter-sample variance with non-zero counts along with all sequenced miRNA were analyzed using the DIABLO method within the mixOmics 6.8.5 R library. Results: Single component DIABLO latent variable analysis of mRNAs, lncRNAs, miRNAs, and methylated regions was performed to identify the genetic elements (variables) which most reliably discriminated between experimental arms. Analysis of the top 100 discriminatory variables from each ‘omics data set was performed to provide sufficient data for pathway analysis while maintaining separation of the experimental arms within the latent space. IPA pathway analysis of these genes revealed differential regulation of pathways related to hepatic fibrosis, adipogenesis, ketogenesis as well as several other liver-specific metabolic pathways. Identification of the five most discriminatory variables was also performed. Single component, five variable analysis revealed ELOVL2, LIPK, and LAMA3 most reliably differentiated control and fibrosis groups, along with two lncRNA transcripts, XLOC035153 and XLOC036409 which were predicted to target S1PR1 and UHRF1BP1L, respectively. LAMA3, XLOC036409, and its target UHRF1BP1L were overexpressed in Oncopig fibrotic liver samples. UHRF1, a potential binding target for UHRF1BP1L, has previously been implicated as an oncogene for hepatocellular carcinoma. S1PR1 is involved in vasculogenesis and cancer cell motility via the RAC-CDC42 and ERK pathways. Conclusions The differentially regulated genetic elements identified by the multi-omic analyses were largely concordant with known alterations in human alcoholic liver disease. This agreement lends validity to the Oncopig model as a model organism for human alcoholic liver disease. In addition, novel genes, UHRF1BP1L and S1PR1, were associated with porcine liver fibrosis and further work to characterize their precise mechanistic roles in the natural history of this disease is required. Citation Format: Mark Hieromnimon, Lawrence Schook, Ron Gaba, Kyle Schachtschneider. Multi-omics profiling identifies genetic and epigenetic regulation underlying alcohol induced liver fibrosis in a porcine hepatocellular carcinoma model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 785.

Racial Discrimination to Bullying Behavior among White and Black Adolescents in the USA: From Parents' Perspectives.

The present study proposes and tests pathways by which racial discrimination might be positively related to bullying victimization among Black and White adolescents. Data were derived from the 2016 National Survey of Children’s Health, a national survey that provides data on children’s physical and mental health and their families. Data were collected from households with one or more children between June 2016 to February 2017. A letter was sent to randomly selected households, who were invited to participate in the survey. The caregivers consisted of 66.9% females and 33.1% males for the White sample, whose mean age was 47.51 (SD = 7.26), and 76.8% females and 23.2% males for the Black sample, whose mean age was 47.61 (SD = 9.71). In terms of the adolescents, 49.0% were females among the White sample, whose mean age was 14.73 (SD = 1.69). For Black adolescents, 47.9% were females and the mean age was 14.67(SD = 1.66). Measures for the study included bullying perpetration, racial discrimination, academic disengagement, and socio-demographic variables of the parent and child. Analyses included descriptive statistics, bivariate correlations, and structural path analyses. For adolescents in both racial groups, racial discrimination appears to be positively associated with depression, which was positively associated with bullying perpetration. For White adolescents, racial discrimination was positively associated with academic disengagement, which was also positively associated with bullying perpetration. For Black adolescents, although racial discrimination was not significantly associated with academic disengagement, academic disengagement was positively associated with bullying perpetration.

Discrimination, Bias, Fairness, and Trustworthy AI

In this study, we analyze “Discrimination”, ”Bias”, “Fairness”, and “Trustworthiness” as working variables in the context of the social impact of AI. It has been identified that there exists a set of specialized variables, such as security, privacy, responsibility, etc., that are used to operationalize the principles in the Principled AI International Framework. These variables are defined in such a way that they contribute to others of more general scope, for example, the ones studied in this study, in what appears to be a generalization–specialization relationship. Our aim in this study is to comprehend how we can use available notions of bias, discrimination, fairness, and other related variables that will be assured during the software project’s lifecycle (security, privacy, responsibility, etc.) when developing trustworthy algorithmic decision-making systems (ADMS). Bias, discrimination, and fairness are mainly approached with an operational interest by the Principled AI International Framework, so we included sources from outside the framework to complement (from a conceptual standpoint) their study and their relationship with each other.

Spectroscopic based partial least-squares models to estimate soil features

Spectroscopic data of Dystrudepts and Haplodux soils with variable selection are scarcely found but they are currently investigated as alternative tools to minimize the deleterious impacts of classical analytical techniques on environment. In this study, Fourier transform mid-infrared spectroscopy, energy dispersive X-ray fluorescence and photoacoustic spectroscopy were applied to the analysis of these soils and data were used to build models by interval partial least-squares regression and partial least-squares discriminant analysis to estimate soil properties (type, geographic origin, management and sand, silt and clay contents). The combination of spectroscopy techniques and statistical tools allows the classification of soil by region and all samples (whole soil and their fractions, topsoil, and subsoil) were appropriate to this aim. The most accurate technique for the distinction of soil types and classification of fractions by content was energy dispersive X-ray fluorescence-based interval partial least-squares-discriminant analysis model for whole soil and fractions, the model exhibited above 97.5% (for validation) of correct classification of samples. On the other hand, to differentiate soil management, Fourier transform mid-infrared spectroscopy-based interval partial least-squares-discriminant (iPLS-DA) analysis model using whole soil presented the best results, with 100% of correct classification samples. The soil management iPLS-DA models of energy dispersive X-ray fluorescence and photoacoustic spectroscopy did not have adequate prediction capability and are not useful to differentiate soil management. The most representative discriminant variables were discussed. Energy dispersive X-ray fluorescence (silt and sand content, RPDCV > 2.0), Fourier transform mid-infrared spectroscopy (clay and sand content, RPDCV > 2.0) and photoacoustic spectroscopy (RPDCV > 1.4) combined with interval partial least-squares-regression and cross validation can be a useful approach to estimate the texture in Dystrudepts and Haplodux soils, and the one of the three textual classification calculated for difference.

Discrimination of Geographical Origin of Agricultural Products From Small-Scale Districts by Widely Targeted Metabolomics With a Case Study on Pinggu Peach.

Geographical indications of agricultural products are characterized by high quality and regional attributes, while they are more likely to be counterfeited by similar products from nearby regions. Accurate discrimination of origin on small geographical scales is extremely important for geographical indications of agricultural products to avoid food fraud. In this study, a widely targeted metabolomics based on ultra-high-performance liquid chromatography–tandem mass spectrometry combined with multivariate statistical analysis was used to distinguish the geographical origin of Pinggu Peach of Beijing and its two surrounding areas in Heibei province (China). Orthogonal partial least squares-discriminant analysis (OPLS-DA) based on 159 identified metabolites showed significant separation from Pinggu and the other adjacent regions. The number of the most important discriminant variables (VIP value >1) was up to 62, which contributed to the differentiation model. The results demonstrated that the metabolic fingerprinting combined with OPLS-DA could be successfully implemented to differentiate the geographical origin of peach from small-scale origins, thus providing technical support to further ensure the authenticity of geographical indication products. The greenness of the developed method was assessed using the Analytical GREEnness Metric Approach and Software (ARGEE) tool. It was a relatively green analytical method with room for improvement.

Determinants of Women's Education Level in West Java: Impact of Economic Conditions or Discriminative Policies?

Various studies have shown that poor households with economic disruption have to choose their priorities. In such circumstances, expenses on girls’ education tend to be sacrificed. On the other hand, the emergence of public policies which discriminate against women and minority groups threatens to reduce women’s educational participation due to the psychological pressure and stigma they experience in educational institutions. This phenomenon is part of the reason why women’s educational participation in Indonesia is lower than men. This research applied a mix of quantitative and qualitative methods to investigate and compare the impact of discriminative policies and economic conditions on women’s education. Quantitative methods were conducted using a panel data regression model, while qualitative methods were conducted through content analysis. This study’s results showed that: a) discriminative policies were barriers to women’s rights for education; b) discriminative policies and economic variables had a significant influence on women at primary education level (elementary-junior high school) and at secondary education level (senior high school); c) regional income and expenditure had a significant effect on women’s education at primary and secondary education levels, but had no effect at post-secondary (university) education level; and d) economic and discriminative policy variables had no effect on women’s education at post-secondary (university) level.
 Keywords: discriminatory policy, economy, women’s education level

Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.

Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels inMéxico.

Species distribution models (SDMs) are an increasingly important tool for conservation particularly for difficult‐to‐study locations and with understudied fauna. Our aims were to (1) use SDMs and ensemble SDMs to predict the distribution of freshwater mussels in the Pánuco River Basin in Central México; (2) determine habitat factors shaping freshwater mussel occurrence; and (3) use predicted occupancy across a range of taxa to identify freshwater mussel biodiversity hotspots to guide conservation and management. In the Pánuco River Basin, we modeled the distributions of 11 freshwater mussel species using an ensemble approach, wherein multiple SDM methodologies were combined to create a single ensemble map of predicted occupancy. A total of 621 species‐specific observations at 87 sites were used to create species‐specific ensembles. These predictive species ensembles were then combined to create local diversity hotspot maps. Precipitation during the warmest quarter, elevation, and mean temperature were consistently the most important discriminatory environmental variables among species, whereas land use had limited influence across all taxa. To the best of our knowledge, our study is the first freshwater mussel‐focused research to use an ensemble approach to determine species distribution and predict biodiversity hotspots. Our study can be used to guide not only current conservation efforts but also prioritize areas for future conservation and study.

Are bottled mineral waters and groundwater for human supply different?

Increasingly, bottled natural mineral water (NMW) is proposed as a healthy and safe alternative to supply water. However, tap supply water often comes from aquifers (TGW), even from the same aquifers as NMW, sharing the exact formation mechanisms and mineralization processes. Therefore, it is hypothesized that NMW and TGW cannot be distinguished. The chemical composition of TGW and NMW samples in Spain has been compared using five criteria: expert judgment, hydrochemistry, legal regulations, statistical analysis, and machine learning (ML). Hydrochemical criteria included all the NMW samples in the TGW group, as did the legal criterion, whereas classical statistical analysis could not find significant differences between the two groups. Although experts could correctly differentiate a small subsample of both types of water with an accuracy of 0.67, ML-based classification with Extreme Gradient Boosting yielded a balanced accuracy of 0.92 on an extremely imbalanced data set. Shapley Additive Explanations (SHAP) analysis identified pH, SiO2, E, K+, Ca2+, K+/Na+ and NO3− as the most relevant variables for water type discrimination. The overall consistency and generalization ability of the ML classifier has been proven by the spatial distribution of hits and misses, where the few cases of indistinguishable waters seem to be related to proximity to nature reserves (i.e., land use) more than to geological characteristics. Therefore, it can be concluded that NMW and TGW are indeed different and that only ML could find the hidden structure in the chemical data that determines the differences. This structure originates in how the market and consumers decide which water is ultimately bottled. The results can help on future choices of TGW and NMW in a context of water scarcity.

Minimal subphenotyping model for acute heart failure with preserved ejection fraction.

AimsApplication of the latent class analysis to acute heart failure with preserved ejection fraction (HFpEF) showed that the heterogeneous acute HFpEF patients can be classified into four distinct phenotypes with different clinical outcomes. This model‐based clustering required a total of 32 variables to be included. However, this large number of variables will impair the clinical application of this classification algorithm. This study aimed to identify the minimal number of variables for the development of optimal subphenotyping model.Methods and resultsThis study is a post hoc analysis of the PURSUIT‐HFpEF study (N = 1095), a prospective, multi‐referral centre, observational study of acute HFpEF [UMIN000021831]. We previously applied the latent class analysis to the PURSUIT‐HFpEF dataset and established the full 32‐variable model for subphenotyping. In this study, we used the Cohen's kappa statistic to investigate the minimal number of discriminatory variables needed to accurately classify the phenogroups in comparison with the full 32‐variable model. Cohen's kappa statistic of the top‐X number of discriminatory variables compared with the full 32‐variable derivation model showed that the models with ≥16 discriminatory variables showed kappa value of >0.8, suggesting that the minimal number of discriminatory variables for the optimal phenotyping model was 16. The 16‐variable model consists of C‐reactive protein, creatinine, gamma‐glutamyl transferase, brain natriuretic peptide, white blood cells, systolic blood pressure, fasting blood sugar, triglyceride, clinical scenario classification, infection‐triggered acute decompensated HF, estimated glomerular filtration rate, platelets, neutrophils, GWTG‐HF (Get With The Guidelines‐Heart Failure) risk score, chronic kidney disease, and CONUT (Controlling Nutritional Status) score. Characteristics and clinical outcomes of the four phenotypes subclassified by the minimal 16‐variable model were consistent with those by the full 32‐variable model. The four phenotypes were labelled based on their characteristics as ‘rhythm trouble’, ‘ventricular‐arterial uncoupling’, ‘low output and systemic congestion’, and ‘systemic failure’, respectively.ConclusionsThe phenotyping model with top 16 variables showed almost perfect agreement with the full 32‐variable model. The minimal model may enhance the future clinical application of this clustering algorithm.

The Role of von Willebrand Factor Antigen in Predicting Survival of Patients with HBV-Related Cirrhosis

Objective The model for end-stage liver disease (MELD) scoring system cannot be used to assess the deterioration of patients with liver cirrhosis caused by infection and portal hypertension. Elevated von Willebrand factor antigen (vWF-Ag) in plasma is associated with portal pressure and complications in patients with liver cirrhosis. We aimed to evaluate whether the addition of vWF-Ag can improve the risk prediction ability of the MELD scoring system. Methods A total of 228 patients with hepatitis B virus (HBV)-related liver cirrhosis were eligible for inclusion in this retrospective study. The vWF-Ag level was assessed by enzyme-linked immunosorbent assay (ELISA). The endpoint of this study was defined as the time to liver transplantation or death. Univariate and multivariate analyses were performed to assess the risk factors associated with transplant-free mortality. Receiver operating characteristic (ROC) curve analysis was used to assess potential discriminatory variables for transplant-free mortality. Results During a median follow-up interval of 30.23 months, 124 patients (54.4%) reached the endpoint of this study. Patients who died or underwent liver transplantation had elevated levels of MELD and vWF-Ag. Moreover, vWF-Ag and MELD showed comparable predictive potential for transplant-free survival (area under the curve [AUC], vWF-Ag = 0.71; AUC, MELD = 0.73). Ultimately, vWF-Ag can significantly improve the predictive potential of MELD in determining transplant-free mortality (AUC, MELD-vWF-Ag = 0.79, P = 0.006). Conclusion An elevated vWF-Ag level was independently associated with transplant-free mortality in patients with liver cirrhosis. The inclusion of vWF-Ag in the MELD scoring system can improve mortality predictions in patients with liver cirrhosis.

Release of Ropinirole in Acrylate Transdermal Patches: Mutual Interactions Between Formulation Variables

The aim of this study is to evaluate the cooperative interactions between formulation variables of ropinirole transdermal patches and characterize the effects of drug loading and crystallinity, degree of ionization and drug-polymer solubilization, functionalization of acrylate polymeric basis, and the addition of permeation enhancers over the release profiles. Several series of transdermal films based on carboxylic or hydroxylic acrylates (DuroTak®) and containing 1 to 10% ropinirole hydrochloride were laminated by mold-casting and evaporation. Formulations were characterized for crystallinity, drug particle size, drug assay, and residual solvents. Release profiles were obtained at different drug ionization state using paddle over disk apparatus. Mechanisms were elucidated with nonlinear data fitting of relevant release equations. Fickian and erosion processes were evaluated with the Peppas–Sahlin equation, and burst release risks were estimated as an independent term added to Higuchi kinetics. X-ray diffraction and microscopy evidenced differences in drug-polymer solubilization and density of drug crystals. Concerning drug release, area under the curve of dissolved quantities and release percentage were discriminant variables in mutual influence. Peppas–Shalin equation was the majority descriptor of release suggesting a combination of Fickian and erosion processes, revealing a decrease in the Fickian component as drug loading increased. Major burst release risks were evidenced mostly with Higuchi kinetics with vinylacetate acrylates. The carboxylic polymer without vinylacetate provided the best release extent, being more highly efficient as lower the drug loading was. Permeation enhancers with carboxylic or aliphatic radicals have, additionally, modified the release properties of ropinirole. Chemical interactions between the drug and acrylic polymers have been demonstrated. Only the effect with carboxylic polymer is pH dependent. The vinyl acetate comonomer reduces the drug release rate most effectively in formulations with low drug loads. The acrylic polymers without vinylacetate achieved the highest drug solubilization and thus the highest degree of release, providing a release of approximately 15% of the drug load.

Mapping Restoration Activities on Dirk Hartog Island Using Remotely Piloted Aircraft Imagery

Conservation practitioners require cost-effective and repeatable remotely sensed data for assistive monitoring. This paper tests the ability of standard remotely piloted aircraft (DJI Phantom 4 Pro) imagery to discriminate between plant species in a rangeland environment. Flights were performed over two 0.3–0.4 ha exclusion plot sites, established as controls to protect vegetation from translocated animal disturbance on Dirk Hartog Island, Western Australia. Comparisons of discriminatory variables, classification potential, and optimal flight height were made between plot sites with different plant species diversity. We found reflectance bands and height variables to have high differentiation potential, whilst measures of texture were less useful for multisegmented plant canopies. Discrimination between species varied with omission errors ranging from 13 to 93%. Purposely resampling c. 5 mm imagery as captured at 20–25 m above terrain identified that a flight height of 120 m would improve capture efficiency in future surveys without hindering accuracy. Overall accuracy at a site with low species diversity (n = 4) was 70%, which is an encouraging result given the imagery is limited to visible spectral bands. With higher species diversity (n = 10), the accuracy reduced to 53%, although it is expected to improve with additional bands or grouping like species. Findings suggest that in rangeland environments with low species diversity, monitoring using a standard RPA is viable.