Violin Plot Research Articles

Quantification of river total phosphorus using integrative artificial intelligence models

Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utilizing machine learning based (support vector machines (SVMs), stochastic gradient boosting (SGB)), and deep learning based multilayer perceptron (DMLP) models. Besides, double-platform synthetic techniques were developed by integrating a prior data-processing (Discrete-wavelet) algorithm with the singular techniques. Six input scenarios conditional on different water quantity and quality parameters acquired from two stations (Hwangji and Toilchun), South Korea, were employed for appraising singular and double-platform synthetic techniques. The various promoted models were evaluated using four statistical standards viz., mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and Pearson correlation coefficient (CCp), and four visual references viz., scatter diagram, box-and-whisker plot, violin plot, and Taylor diagram. It can be indicated from the outcomes that the double-platform synthetic techniques did not always lead to more accurate predictions than the singular techniques. Further, results also supplied that the SGB with the 6th input (MAE = 0.012 mg/L, RMSE = 0.014 mg/L, and CCp = 0.650 for Hwangji; MAE = 0.011 mg/L, RMSE = 0.017 mg/L, and CCp = 0.963 for Toilchun) scenario model demonstrated the best accuracy for predicting river T-P concentration by the singular techniques for both stations, whereas the discrete-wavelet SVMs with the 4th input (MAE = 0.007 mg/L, RMSE = 0.011 mg/L, and CCp = 0.765 for Hwangji) and discrete-wavelet SGB with the 5th input (MAE = 0.012 mg/L, RMSE = 0.017 mg/L, and CCp = 0.953 for Toilchun) scenario models provided the best predictive accuracy among double-platform synthetic techniques, respectively.

Application of vine copulas to estimate dew point temperature

In this study, the accuracy of the copula-based model in the simulation of the dew point temperature in various climates of Iran was investigated, using simulations based on vine copulas such as C-, D-, and R-vine copulas. By examining the various vine copulas and their tree sequences, the best copula and best tree sequence based on AIC, BIC, and log-likelihood were selected. The results show that based on the complete similarity in our case between C-, D- and R-vine copulas, the selected best C-vine copulas fit well the dependence between the minimum and maximum air temperatures and dew point temperature. The simulation results were analyzed using root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE) coefficient, and violin plots. The results show that the copula-based model has high accuracy at all stations. The min (max) RMSE is related to Kerman (Ahvaz) station with RMSE = 0.396 oC (0.617 oC). Also, the min (max) NSE is related to Ahvaz (Urmia) station with NSE = 0.925 (0.955). Also, according to the violin plot, it is possible to appreciate the acceptable certainty of the copula-based model. Due to the diversity of the tree sequences of vine copulas and the use of the rotated states of the internal vine copulas, as well as the possibility of interfering with the effective parameters in high dimensions, the simulation results are reliable and have no restrictions. This model can be used as the best model to estimate dew point temperature due to the full coverage of the range of changes in data.

(304) Relevance of Self-masturbation-related vs. Coital Erectile Function in the Real-life Management of Patients with Erectile Dysfunction

Abstract Introduction The Masturbation Erection Index (MEI) has been recently validated in the setting of the investigation of men presenting with erectile dysfunction (ED). Objective We sought to analyze the correlation between MEI and the findings of the widely used International Index for Erectile Function (IIEF) in a cross-sectional study in men seeking medical help for ED. Methods Data from the last 87 consecutive men presenting for ED at a tertiary referral centre were analyzed. All patients were asked to complete both IIEF and MEI at the same time. Complete sociodemographic and clinical data were collected. ED severity was defined according to Cappelleri’s criteria. Linear regression model tested the association between MEI, IIEF total and IIEF domains scores. Violin plot was used to display distribution of MEI scores into IIEF-EF categories. Loess curve was applied to investigate the probability of MEI to predict ED as a function of age. Results Median (interquartile range [IQR]) age at presentation was 44 (3.5-53.5) years. According to IIEF-EF scores, ED severity was: no ED in 9 (10%), mild ED in 14 (16%), mild-to-moderate ED in 19 (22%), moderate ED 12 (14%) and severe ED in 33 (38%) patients, respectively. The more severe the ED, the higher the MEI scores (Figure 1); indeed, median MEI scored 7 (3-8) in men with no ED and 19 (10-26) in men with severe ED (p<0.05). Of note, 16 patients (18%) depicted low MEI scores (i.e., under the median), but moderate or severe ED as for their IIEF scores. Finally, a univariable negative association between MEI scores and IIEF-overall satisfaction and IIEF-intercourse satisfaction scores was found [i.e. the higher the MEI scores, the lower the intercourse and overall sexual satisfaction (p<0.001)]. Although MEI decreased throughout aging, it was not significantly different in younger vs. older men with ED. Conclusions As expected, there is a strong association between IIEF and MEI scores, which does not lower with aging. Of all, one out of 5 patients presented with lower overall and intercourse satisfaction notwithstanding high MEI scores, thus outlining the clinical relevance of both investigations in the real-life setting. Disclosure No

Performance comparison of IHACRES, random forest and copula-based models in rainfall-runoff simulation

In this study, two models of Random Forest and copula-based simulation were used to evaluate the accuracy and efficiency of the IHACRES rainfall-runoff model in simulating the daily discharge of the Siminehroud River in the south of Lake Urmia basin, Iran. A trivariate copula-based model was created using discharge, rainfall and temperature data on a daily scale in the period 1992–2018. Vine family models and their conditional densities were used to implement the copula-based model. By calibrating the IHACRES model and also selecting the tree sequence in accordance with the data, rainfall-runoff simulations were performed in the study area. The accuracy and efficiency of the studied models were evaluated using RMSE and NSE criteria, and also violin plot and Taylor diagram. The results of comparing the error rate of rainfall-runoff simulation in the study area showed that the vine-based model reduces the RMSE statistics by about 14.5 and 16.5%, respectively, compared to the IHACRES and Random Forest models. According to the presented diagrams, the efficiency and certainty of IHACRES and copula-based simulation models are acceptable. While the Random Forest model does not have acceptable accuracy and efficiency in the study area. The copula-based simulation model has a good performance due to the unique tree sequence as well as involving the marginal distributions fitted to the data. Although the copula-based simulation model has increased the efficiency of the model in simulating the daily discharge by about 5% compared to the IHACRES model, it is not significant compared to the mathematical complexity of the copula-based model.

Identification of the hub genes associated with prostate cancer tumorigenesis.

Prostate cancer (PCa) is one of the most common malignant tumors of the male urogenital system; however, the underlying mechanisms remain largely unclear. This study integrated two cohort profile datasets to elucidate the potential hub genes and mechanisms in PCa. Gene expression profiles GSE55945 and GSE6919 were filtered from the Gene Expression Omnibus (GEO) database to obtain 134 differentially expressed genes (DEGs) (14 upregulated and 120 downregulated) in PCa. Gene Ontology and pathway enrichment were performed using the Database for Annotation, Visualization, and Integrated Discovery, showing that these DEGs were mainly involved in biological functions such as cell adhesion, extracellular matrix, migration, focal adhesion, and vascular smooth muscle contraction. The STRING database and Cytoscape tools were used to analyze protein-protein interactions and identify 15 hub candidate genes. Violin plot, boxplot, and prognostic curve analyses were performed using Gene Expression Profiling Interactive Analysis, which identified seven hub genes, including upregulated expressed SPP1 and downregulated expressed MYLK, MYL9, MYH11, CALD1, ACTA2, and CNN1 in PCa compared with normal tissue. Correlation analysis was performed using the OmicStudio tools, which showed that these hub genes were moderately to strongly correlated with each other. Finally, quantitative reverse transcription PCR and western blotting were performed to validate the hub genes, showing that the abnormal expression of the seven hub genes in PCa was consistent with the analysis results of the GEO database. Taken together, MYLK, MYL9, MYH11, CALD1, ACTA2, SPP1, and CNN1 are hub genes significantly associated with PCa occurrence. These genes are abnormally expressed, leading to the formation, proliferation, invasion, and migration of PCa cells and promoting tumor neovascularization. These genes may serve as potential biomarkers and therapeutic targets in patients with PCa.

Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity.

The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.

Five decades of freshwater salinization in the Amu Darya River basin

Study regionThe Amu Darya River (ADR) basin in Central Asia. Study focusTo understand the spatiotemporal patterns and underlying driving mechanisms of river salinization in arid environments, this study gathered 50 years (1970–2019) of water chemistry data from 12 locations along the ADR. The variations in discharge and salinity were assessed by a linear regression model and violin plot. The salinity-discharge relationships were evaluated by a general hyperbolic model and Spearman’s rank correlation coefficient. Random forest models were also constructed to identify the predominant drivers of river water salinization. Finally, a conceptual model of river water salinization was constructed. New hydrological insights for the regionThe water salinity (S) in the upper stream of the ADR was 541–635 mg/L. Salinity showed an increasing trend along the river course, reaching 751–1560 mg/L downstream. In the downstream, the river salinity before the 1990 s (751–1128 mg/L) was slightly lower than that after the 1990 s (983–1560 mg/L). Generally, water salinity was notably correlated with river discharge (Q) in upstream, exhibiting a relationship of S= 17,497Q−0.62, p < 0.05, before the 1990 s. Interannual variation in river salinity is mainly controlled by secondary salinization, and intra-annual variation is controlled by river flow. From upstream to downstream, the controlling salinization process changes from primary salinization to secondary salinization. Specifically, secondary salinization has accelerated due to intensified agricultural activities in recent years.

MP21-04 GENDER BIAS IN UROLOGY: HOW DO PATIENTS REALLY CHOOSE THEIR UROLOGIST?

You have accessJournal of UrologyCME1 Apr 2023MP21-04 GENDER BIAS IN UROLOGY: HOW DO PATIENTS REALLY CHOOSE THEIR UROLOGIST? Alexander Tamalunas, Philipp Lenau, Leo Stadelmeier, Jan-Niclas Mumm, Theresa Vilsmaier, Henrik Höhn, Maja-Lena Mumm, Raphaela Waidelich, Jozefina Casuscelli, Giuseppe Magistro, Alexander Buchner, Thomas Kolben, Christian Stief, and Martin Hennenberg Alexander TamalunasAlexander Tamalunas More articles by this author , Philipp LenauPhilipp Lenau More articles by this author , Leo StadelmeierLeo Stadelmeier More articles by this author , Jan-Niclas MummJan-Niclas Mumm More articles by this author , Theresa VilsmaierTheresa Vilsmaier More articles by this author , Henrik HöhnHenrik Höhn More articles by this author , Maja-Lena MummMaja-Lena Mumm More articles by this author , Raphaela WaidelichRaphaela Waidelich More articles by this author , Jozefina CasuscelliJozefina Casuscelli More articles by this author , Giuseppe MagistroGiuseppe Magistro More articles by this author , Alexander BuchnerAlexander Buchner More articles by this author , Thomas KolbenThomas Kolben More articles by this author , Christian StiefChristian Stief More articles by this author , and Martin HennenbergMartin Hennenberg More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003246.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The present study aimed to investigate the influence of patients’ and urologists’ gender when choosing a urologist. With rising population diversity through immigration and generational differences, patient-centered healthcare has recently moved to the focus of European healthcare systems. As healthcare in urology often concentrates on sensitive topics, and often involves gender-specific diseases, research on the influence of gender on decision-making processes is of high importance. Understanding influence of gender on patients’ choices in real life would provide patients, and physicians alike, with the means to provide better resources to achieve greater satisfaction from visits to a urologist. METHODS: A questionnaire was prepared, and patients at our tertiary referral center were given the opportunity to voluntarily participate in our survey. We collected questionnaires from 1,012 patients during their visits from June 2021 to October 2021. RESULTS: Patients were divided into groups according to their gender: male (n=763), female (n=246), and non-binary (n=3). Our patient cohort consisted of more men than women (75% vs. 24%), with only three patients identifying as non-binary. Irrespective of the patients’ own gender, patients preferred a male urologist when problems were considered embarrassing, limiting daily activities, or when worrisome. When problems were considered painful, all patients preferred a female urologist. When patients had had a previous positive experience with a female or male urologist, they preferred to be treated by a female or male urologist, respectively. Overall, 65% of patients stated a gender preference for at least one given situation, or consultation scenario (Figure 1: Violin plot of gender preference, separated by patients’ gender). CONCLUSIONS: As the majority of our patients stated a gender preference, urological departments should be considerate of potential patients’ preferences for urologist gender that may be based on the individual patient's history, taking a comprehensive approach to fulfill the patients’ need for same gender urologists in educational hospitals and health care services. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e285 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alexander Tamalunas More articles by this author Philipp Lenau More articles by this author Leo Stadelmeier More articles by this author Jan-Niclas Mumm More articles by this author Theresa Vilsmaier More articles by this author Henrik Höhn More articles by this author Maja-Lena Mumm More articles by this author Raphaela Waidelich More articles by this author Jozefina Casuscelli More articles by this author Giuseppe Magistro More articles by this author Alexander Buchner More articles by this author Thomas Kolben More articles by this author Christian Stief More articles by this author Martin Hennenberg More articles by this author Expand All Advertisement PDF downloadLoading ...

Hydrocarbon Generation from Low-Mature Saline Lacustrine Sediments Studied Using Machine Learning and Chemometric Methods: The Succession of the Sikeshu Sag, Junggar Basin, NW China.

Significant attention has been given to the extensive development of saline environments in petroliferous basins. Further exploration and studies have discovered that saline environments, such as those for the deposition of source rocks in the Paleogene Anjihaihe (E2-3 a) Formation of the Sikeshu Sag, are ubiquitous in terrestrial lake basins. Previous studies have suggested that the oil reservoirs in the Sikeshu Sag and its peripheral regions are predominantly derived from the black mudstone and coal measures of the Lower Jurassic Badaowan (J1 b) Formation. However, with deeper exploration of the study area, a growing number of reservoirs with geochemical characteristics different from the J1 b oil source have been discovered, indicating that there are oil sources other than the J1 b source rocks. In this study, various machine learning algorithms were used (random forest, RF; convolutional neural networks, CNN; extreme gradient boosting, XGBoost; ElasticNetCV; Bayesian Ridge; and particle swarm optimization-support vector regression) to select the most suitable algorithm for predicting and comparing the quality of potential source rocks. A violin plot and Taylor diagram were applied to visually compare the reliability and application effectiveness of the models. The results demonstrated that XGBoost and RF can become essential tools for predicting the quality of potential source rocks. Moreover, the measured and predicted values of total organic carbon (TOC), hydrocarbon potential (S1 + S2), and hydrogen index indicate that there are three main source rocks: the E2-3 a, Lower Jurassic Sangonghe (J1 s), and J1 b formations. The thermal maturity of the E2-3 a source rocks is still early mature because of the saline-brackish water nature of these rocks, although large-scale hydrocarbon generation and expulsion can be achieved in the early mature stage. Based on their geochemical characteristics and stepwise discriminant analysis, the oils in the Sikeshu Sag and its peripheral regions can be categorized into two types: groups A and B. Comprehensive organic geochemical evidence suggests that genetically, group A oils are originated from E2-3 a less-mature saline lacustrine sedimentary rocks, while group B oils indicate similar affinity to the Jurassic source. Fluid inclusion microthermometry and one-dimensional basin modeling showed that the oil charging periods of group A and B oils were Middle-Late Miocene (13-8 Ma) and Late Oligocene (23-20 Ma), respectively. Quantitative grain fluorescence (QGF) analysis further propose that the hydrocarbon supply region of the E2-3 a sources is mainly located east of the Western Chepaizi Uplift and the interior area of the Sikeshu Sag, which breaks through the previous understanding that the Jurassic coal-derived oil source is the only main contributor in this study area. The research results can be widely applied to assess the petroleum resources of source rocks in similar areas worldwide.

Machine learning intelligence to assess the shear capacity of corroded reinforced concrete beams

The ability of machine learning (ML) techniques to forecast the shear strength of corroded reinforced concrete beams (CRCBs) is examined in the present study. These ML techniques include artificial neural networks (ANN), adaptive-neuro fuzzy inference systems (ANFIS), decision tree (DT) and extreme gradient boosting (XGBoost). A thorough databank with 140 data points about the shear capacity of CRCBs with various degrees of corrosion was compiled after a review of the literature. The inputs parameters of the implemented models are the width of the beam, the effective depth of the beam, concrete compressive strength (CS), yield strength of reinforcement, percentage of longitudinal reinforcement, percentage of transversal reinforcement (stirrups), yield strength of stirrups, stirrups spacing, shear span-to-depth ratio (a/d), corrosion degree of main reinforcement, and corrosion degree of stirrups. The coefficient of determination of the ANN, ANFIS, DT, and XGBoost models are 0.9811, 0.9866, 0.9799, and 0.9998, respectively. The MAPE of the XGBoost model is 99.39%, 99.16%, and 99.28% lower than ANN, ANFIS, and DT models. According to the results of the sensitivity examination, the shear strength of the CRCBs is most affected by the depth of the beam, stirrups spacing, and the a/d. The graphical displays of the Taylor graph, violin plot, and multi-histogram plot additionally support the XGBoost model's dependability and precision. In addition, this model demonstrated good experimental data fit when compared to other analytical and ML models. Accurate prediction of shear strength using the XGBoost approach confirmed that this approach is capable of handling a wide range of data and can be used as a model to predict shear strength with higher accuracy. The effectiveness of the developed XGBoost model is higher than the existing models in terms of precision, economic considerations, and safety, as indicated by the comparative study.

MHA, an interactive website for scRNA-seq data of male genitourinary development and disease.

The development of single-cell sequencing technology has expanded the understanding of cell heterogeneity and disease progression in the male genitourinary system. However, complex processing and unprofessional analytical annotations limit the daily use and widely sharing of published datasets. Single-cell sequencing data of male-specific tissues and organs. The data were downloaded from published studies and were processed based on the Seurat R package, including quality control, cell clustering, reduction and graph generation, and cell type annotation were differentiated by referring to the related paper or recognized cell markers. Input and visual results output through the Shiny package, which was loaded into the remote server. The current version of the Male Health Atlas database includes two species (human and mouse), five male-specific tissues and organs (testis, epididymis, vas deferens, corpus cavernosum, and prostate), and eight major cell types, with a total of 57 samples and 258,428 single-cell profiles. The results were divided into two main parts: Cell Clustering and Gene Display. In CellClusteringsection, visitors are free to change cell dimensionality reduction (t-distributed stochastic neighbor embedding, or uniform manifold approximation and projection), color palette, and annotation (cell type or sample type). The Gene Display section includes a reduced dimension scatter plot, violin plot, and bubble plot. Visitors can easily view the expression characteristics of single or multiple genes, and compare the expression differences between different cell types or groups. Male Health Atlas is the first single-cell database website in the field of andrology and male reproduction, providing researchers with single-cell sequencing resources and an accessible tool. Male Health Atlas is freely available at http://malehealthatlas.cn/.

Visualizing Uncertainty to Promote Clinicians' Understanding of Measurement Error.

Measurement error is an inherent part of any test score. This uncertainty is generally communicated in ways that can be difficult to understand for clinical practitioners. In this empirical study, we evaluate the impact of several communication formats on the interpretation of measurement accuracy and its influence on the decision-making process in clinical practice. We provided 230 clinical practitioners with score reports in five formats: textual, error bar, violin plot, diamond plot, and quantile dot plot. We found that quantile dot plots significantly increased accuracy in the assessment of measurement uncertainty compared with other formats. However, a direct relation between visualization format and decision quality could not be found. Although traditional confidence intervals and error bars were favored by many participants due to their familiarity, responses revealed several misconceptions that make the suitability of these formats for communicating uncertainty questionable. Our results indicate that new visualization formats can successfully reduce errors in interpretation.

Abstract 127: White Matter Hyperintensity And Atherosclerotic Cardiovascular Disease Risk

Introduction: White matter hyperintensity (WMH) is a neuroimaging manifestation of cerebrovascular disease that is associated with vascular risk factors and the development of cognitive impairment and stroke. Estimating atherosclerotic cardiovascular disease (ASCVD) risk is a method of measuring the extent of vascular risk for a given individual and, thus, there exists a potential link between ASCVD risk and WMH volume. Methods: We included participants in the SPRINT MIND study who underwent a research MRI with volumetric quantification of the burden of WMH in mL. We report the WMH burden by categories of 10-year ASCVD risk using the Pooled Cohort Equations CV Risk (2014 edition). The ASCVD categories include low (0-5%), borderline (5-7.49%), intermediate (7.5-19.9%), and high (&gt;20%). We used a violin plot to show the results graphically and also report the medians and IQR. As a secondary outcome we report WMH progression, which is the difference in WMH burden between the baseline MRI and a follow-up MRI performed 48 months later. Results: We included 727 individuals, of which 22 (3.0%) were low, 46 (6.3%) were borderline, 370 (50.9%) were intermediate, and 289 (39.8%) were high ASCVD risk. The median (IQR) WMH burden was 1.6 mL (0.9-2.9) in the low, 1.7 mL (1.1-3.0) in the borderline, 2.7 mL (1.4-5.1) in the intermediate, and 4.7 mL (2.5-10.7) in the high ASCVD risk group (p&lt;0.001) (Figure 1). The progression of WMH was 0.2 mL (-0.1-0.7) in the low, 0.4 mL (-.2-1.5) in the borderline, 0.6 mL (0.01-1.7) in the intermediate, and 1.1 mL (0.2-3.0) in the high ASCVD risk group (p=0.001). Conclusions: There is a positive association between ASCVD risk and WMH volume and progression. ASCVD risk, which can be assessed through demographic and clinical variables, may be a useful predictor of the volume of WMH, which is traditionally obtained through an MRI scan.

Next generation phenotyping with quantitative narration for DEGCAGS syndrome.

The diagnosis of rare Mendelian disorders usually relies upon the interpretation of prose and is complicated by a lack of objective, reproducible phenotypic data. To address this limitation, we developed a next generation phenotyping workflow to phenotypically characterize developmental delay with gastrointestinal, cardiovascular, genitourinary, and skeletal abnormalities (DEGCAGS). We identified 15 people affected with DEGCAGS, including one novel patient identified at our hospital and 14 patients previously reported in the literature. Human Phenotype Ontology (HPO) terms were extracted from the patient chart and literature review. The HPO terms were sorted by count according to HPO hierarchy of terms. Phenotypes that cosegregate were identified utilizing a co-occurrence matrix. A quantitative narrative illustrated by violin plots was created for our patient from phenotypic data per each day of hospital admission. A total of 252 unique HPO terms were extracted from the patient record and literature review. The highest count of systemically sorted and unsorted terms and the most commonly co-occurring terms were described. A violin plot of phenotype occurrences demonstrated a progression of phenotypes over time. NGP offers a quantitative approach to phenotyping to generate phenotypic data in an objective and reproducible manner akin to NGS.

Time-Frequency Complexity Maps for EEG-Based Diagnosis of Alzheimer’s Disease Using a Lightweight Deep Neural Network

Alzheimer’s disease (AD) is a neurodegenerative disorder with an unknown etiology and a significant prevalence. Rapid and accurate detection of AD is crucial to assist in a more effective and tailored treatment plan to delay the progression of the disease. This paper introduces a novel approach based on a time-frequency complexity map (complextrogram) for the automated AD diagnosis. The complextrogram is the topographic complexity level of an EEG signal, plotted as a function of time and frequency. The complextrogram representations were fed into a well-known lightweight deep neural network called MobileNet for robust performance on resource and accuracy tradeoffs. The experiments were performed using a five-fold cross-validation technique on a publicly available database containing clinical EEG recordings from 24 patients with AD and 24 healthy, age-matched controls. The proposed pipeline provided competitive performance with just 2.2 M parameters and achieved the best overall accuracy for some locations in the frontal lobes (Fp2 and F8 channels). For both channels, the classification accuracy was 100%. Also, the violin plot was used to get further details of the distribution of complexity values for specific frequency rhythms. After statistical evaluation, it was observed that neurodegenerative conditions caused changes in chaotic behaviors, including increased delta complexity and decreased alpha complexity. Results demonstrated that the complextrogram representation proved its potency for the input quality required by the deep learning architectures. Furthermore, the complextrogram method is a promising pathway to discriminate and reflect the fundamental characteristics of AD abnormalities.

Application of augmented bat algorithm with artificial neural network in forecasting river inflow in Malaysia

Hydrologists rely extensively on anticipating river streamflow (SF) to monitor and regulate flood management and water demand for people. Only a few simulation systems, where previous techniques failed to anticipate SF data quickly, let alone cost-effectively, and took a long time to execute. The bat algorithm (BA), a meta-heuristic approach, was used in this study to optimize the weights and biases of the artificial neural network (ANN) model. The proposed hybrid work was validated in five different study areas in Malaysia. The statistical tests analysis of the preliminary results revealed that hybrid BA-ANN was superior to forecasting the SF at all five selected study areas, with average RMSE values of 0.103 m3/s for training and 0.143 m3/s for testing as compared to ANN standalone training and testing yielding 0.091 m3/s and 0.116 m3/s, respectively. This finding signifies that the implementation of BA into the ANN model resulted in a 20% improvement. In addition, with an R2 score of 0.951, the proposed model showed a better correlation than the 0.937 value of R2 of standard ANN. Nonetheless, while the proposed work outperformed the conventional ANN, the Taylor diagram, violin plot, relative error, and scatter plot findings confirmed the disparities in the proposed work’s performance throughout the research regions. The findings of these evaluations highlighted that the adaptability of the proposed works would need detailed investigation because its performance differed from case to case.

Pollen viability-based heat susceptibility index (HSIpv): A useful selection criterion for heat-tolerant genotypes in wheat.

Terminal heat stress during reproductive stage in wheat (Triticum aestivum L.) causes pollen grain sterility and has a drastic impact on wheat crop production. Finding genotypes with high pollen viability under heat stress is crucial to cope with the impact of climate change through developing heat-tolerant cultivars. To assess the effect of terminal heat stress on pollen viability in a panel of spring wheat genotypes (N = 200), RCBD (randomized complete block design) field trials were conducted under normal and heat stress conditions for two consecutive years (2020-2021 and 2021-2022). Analysis of variance showed significant variation in genotypes, treatments, and genotype×treatment interaction. Fifty and 46 genotypes were categorized as heat tolerant (HSI pv < 0.5) in the first and second year, respectively. Twelve genotypes, namely, Chenab-70, Pari-73, Pak-81, MH-21, Punjab-76, NIFA-Aman, NUWYT-63, Swabi-1, Nisnan-21, Frontana, Amin-2000, and Pirsabak-2004, were found to be heat tolerant across the years. The violin plot displayed a trend of improvement in heat tolerance (HSI pv < 0.5) over the period of time in many modern wheat varieties. However, some modern wheat varieties released after 2001 such as Janbaz-09 (57%), Ghazi-2019 (57%), and Sindhu-16 (43%) had very low pollen viability under heat stress conditions. The results of phenotypic coefficient of variance (PCV%), genotypic coefficient of variance (GCV%), broad sense heritability (h2 bs), and genetic advance (GA) suggested the major contribution of genetic factors in controlling pollen viability trait. Higher values of h2 bs and GA under heat stress conditions suggested pollen viability as a heat tolerance trait controlled by additive genetic effects. Taken together, these results suggested pollen viability as a useful trait for selection in early generations under elevated temperatures. The genotypes identified as heat tolerant in both years can be used as genetic resources for breeding cultivars with higher pollen viability under elevated temperature conditions.

Predicting Diabetic Retinopathy Using Machine Learning

Over the last 3 decades, advances in laser surgery and intraocular drug delivery have decreased the risk of diabetic retinopathy. However, the use of artificial intelligence and other forms of data analysis in retinopathy screening has not had the same advances. This project aimed to analyze the National Health and Nutrition Examination Survey and to develop the best machine learning model to predict the disease. Violin plots were created to compare the distribution of diabetic retinopathy diagnosis with gender. The plot showed that females who had said they were not taking insulin were less likely to be diagnosed with diabetic retinopathy. Another violin plot showed that those with hypertension who were taking insulin were less likely to have diabetic retinopathy. Histograms were also created to show the distributions of the variables, color-coded by retinopathy diagnosis. Specifically, it was found that the bigger the time gaps since the diabetes diagnosis, the more likely a person suffers from diabetic retinopathy. Finally, multiple machine learning models were tested and these were the most accurate in predicting diabetic retinopathy with an 80% accuracy, LinearSVC, CalibratedClassifierCV, and Logistic Regression.

High Serum IL-17A Is Associated with Bone Destruction in Newly Diagnosed Multiple Myeloma Patients

Background Multiple myeloma (MM) is a malignant proliferative disease of the blood system, characterized by the abnormal growth of clonal plasma cells in the bone marrow. The bone marrow microenvironment (BMM) is highly critical in the pathological process of MM. Many studies have shown that serum Interleukin-17A (IL-17A) plays a key role in various infectious diseases, autoimmune diseases, and cancers. However, more clinical researches need to be done to further proof the influence of serum IL-17A level on multiple myeloma patients. Methods Among a total of 357 participants in our institution's MM cohort, 175 were eligible for the retrospective study. Multivariate regression models adjusted by potential confounding factors, the violin plots, the generalized additive model and smooth curve fittings, receiver operating characteristic (ROC) curve and K-M curve analysis were applied to the research. Results A total of 175 patients with newly diagnosed MM were enrolled in this study. The multivariate linear regression analysis showed that serum IL-17A level in MM patients correlated with the degree of bone lesions and fracture incidence (Fully adjusted model, Pbone lesion<0.0001, Pfracture<0.0001). Violin plot showed that MM patients with higher serum IL-17A level had more sever bone lesions and higher fracture incidence than that with lower serum IL-17A level. 171 patients were included in the study of the relationship between serum IL-17A and best overall effect (BOE). We found that serum IL-17A level were independently related to the best inductive therapeutic efficacy (Fully adjusted model, P =0.037), and the relationship was especially obvious in the light chain group (Fully adjusted model, P=0.009) and IgA (Fully adjusted model, P=0.0456). It could be deduced from the smooth curve that the higher the serum IL-17A level, the worse the BOE (P=0.0163). The ROC prediction curve suggested that serum IL-17A could predict the BOE to a certain extent (AUC=0.717, P=0.0327). A total of 148 MM patients were observed in the longitudinal study of relationship between serum IL-17A and PFS/OS. The K-M curve analysis indicated that serum IL-17A levels in MM patients were not significantly correlated with PFS and OS. However, in the light chain subgroup, MM patients with high serum IL-17A had worse PFS (P=0.015) and OS (P=0.0076) compared to that with low serum IL-17A. In IgA type subgroup, the higher IL-17A level was related to worse OS (P=0.0061). Conclusion This retrospective study found that higher levels of serum IL-17A was independently correlated with higher severity of bone disease and fractures incidence in newly diagnosed MM patients. High serum IL-17A level was related with poor best overall efficacy in light chain type. High serum IL-17A was also associated with poor PFS and OS in light chain type, and OS in IgA type subgroup.

A Single Cell Atlas and Interactive Web-Resource of Pediatric Cancers and Healthy Bone Marrow

Single-cell profiling allows for in-depth characterization of the tumor microenvironment and revealing the heterogeneity of pediatric cancers. Ease of access and analysis of the primary tumor single-cell data from studies on solid and blood pediatric tumors is essential for improving diagnostics and therapies. We have developed the Pediatric Single Cell Cancer Atlas (PedSCAtlas), with the goal of highlighting the heterogeneity of malignant and microenvironment cells across pediatric cancers. The atlas allows for quick exploration, comparative analysis, visualization, and prognostic assessment of genes/pathways across pediatric cancers without requiring bioinformatics analysis or computational support. The PedSCAtlas currently contains expression data of >1.2 million single cell (sc) and single nuclei (sn) from 134 pediatric hematological and solid-organ cancer samples and healthy bone marrow (BM). The hematological cancer samples are dominated by acute leukemias containing scRNA-seq data of acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL (T-ALL), and mixed phenotype acute leukemia (MPAL). The scRNA-seq data are generated from the BM samples collected during the clinical diagnosis of the disease. The data for the atlas was either generated at the Bhasin lab or collected from public resources including the GEO database, ScPCA (https://scpca.alexslemonade.org/), and HCA (https://data.humancellatlas.org/). Additionally, the atlas also contains bulk RNA-sequencing data and clinical information retrieved from the TARGET Initiative (https://ocg.cancer.gov/programs/target). The single cell/nuclei datasets are processed using a uniform workflow that includes quality control and filtering, normalization, integration with batch correction (if needed), dimensionality reduction, clustering, and cellular annotations. The data normalization and integration are performed using sctransform (Hao and Hao et al, Cell 2021) and harmony (Korsunsky et al, Nature Methods 2019) algorithms respectively. The visualization of integrated pediatric cancers single cell was achieved using Uniform Manifold Approximations and Projection (UMAP) method (Becht et al, Nature Biotech 2018). The clusters were annotated based on the expression of cell-specific markers. Differential expression analysis was used to identify potential biomarker sets for malignant cells of disease subtypes. To further ascertain that these biomarkers were malignant cell (blast) specific in the acute leukemia subtypes, the genes were filtered to exclude those with consistent expression in healthy BM cell types. To compare the expression profile of genes between single cells and bulk data, the raw counts of bulk RNA-seq datasets were filtered and normalized using the Voom algorithm (Ritchie et al, Nucleic Acids Research 2015). The resource has modules for biomarker analysis, bulk RNA deconvolution, and survival analysis to facilitate easy analysis of single-cell data. Survival analysis is available for the bulkRNA-seq dataset; the survival is calculated using the overall survival values and gene expression/enrichment input by the user. The SingleCell module allows users to visualize the pediatric cancer dataset on a UMAP by selecting the aspect (i.e., cell type) to group the dataset by; it also allows users to enter a gene name or select a pathway of their interest and view its expression or enrichment in a feature or violin plot. The ImmuneCell module allows users to interact with the adult healthy BM dataset by inputting a gene and viewing its expression on a feature plot. The Biomarkers module contains pre-defined biomarker sets for each disease subtype in the selected cancer. The final module, BulkExpression, contains the bulk RNA-seq data for the selected cancer type and allows users to view gene expression or analyze the survival associations of an input gene or gene set. The web resource source code is written in R programming language and the interactive web server has been implemented using the R Shiny and ShinyDashboard packages. The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of pediatric cancer subtype heterogeneity, and transcriptome profile of the tumor microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal