Mortality prediction on unsupervised and semi-supervised clusters of medical intensive care unit patients based on MIMIC-II database

Introduction: The study aimed to propose a framework for identifying patient clusters in medical intensive care units (MICUs) based on the Medical Information Mart for Intensive Care II (MIMIC-II) database. The suggested framework makes use of the survival outcomes and physiological information available in the dataset and is hence called a semi-supervised approach. Five neural networks were trained on the clusters identified using the proposed approach to determine whether the proposed framework could improve the predictive accuracy of the deep learning models. Methods: This study utilized data from the MIMIC-II database, which is a publicly available database that contains information on patients admitted to intensive care units. The clusters underlying the MICU patient population were identified using unsupervised and semi-supervised K-means clustering. Mortality in the resulting clusters was predicted using five deep learning-based survival models and the performance of these models was compared using two metrics. Results: Three clusters (cluster 1, n = 1304; cluster 2, n = 474; cluster 3, n = 1079) were identified using unsupervised K-means, and another three clusters (cluster 1, n = 479; cluster 2, n = 1492; cluster 3, n = 886) were identified using semi-supervised K-means clustering. Experimental results demonstrate that, in general, the performance of deep learning models was better on semi-supervised clusters obtained by combining Cox proportional hazards (Cox-PH) model-based feature selection and K-means compared to unsupervised clusters. Conclusions: In the present study, it was observed that deep learning-based survival models tend to perform better on clusters that are identified in a semi-supervised fashion. This approach helps to extract more meaningful patterns and associations between different clinical features and patient outcomes

Sequencing analysis of genetic loci for resistance for late leaf spot and rust in peanut (Arachis hypogaea L.)

The aim of this study was to identify candidate resistance genes for late leaf spot (LLS) and rust diseases in peanut (Arachis hypogaea L.). We used a double-digest restriction-site associated DNA sequencing (ddRAD-Seq) technique based on next-generation sequencing (NGS) for genotyping analysis across the recombinant inbred lines (RILs) derived from a cross between a susceptible line, TAG 24, and a resistant line, GPBD 4. A total of 171 SNPs from the ddRAD-Seq together with 282 markers published in the previous studies were mapped on a genetic map covering 1510.1 cM. Subsequent quantitative trait locus (QTL) analysis revealed major genetic loci for LLS and rust resistance on chromosomes A02 and A03, respectively. Heterogeneous inbred family-derived near isogenic lines and the pedigree of the resistant gene donor, A. cardenasii Krapov. & W.C. Greg., including the resistant derivatives of ICGV 86855 and VG 9514 as well as GPBD 4, were employed for whole-genome resequencing analysis. The results indicated the QTL candidates for LLS and rust resistance were located in 1.4- and 2.7-Mb genome regions on A02 and A03, respectively. In these regions, four and six resistance-related genes with deleterious mutations were selected as candidates for LLS and rust resistance, respectively. These delimited genomic regions may be beneficial in breeding programs aimed at improving disease resistance and enhancing peanut productivity

Clinicians’ perspectives of therapeutic alliance in face-to-face and telepractice speech–language pathology sessions

Purpose: To investigate the face validity of a measure of therapeutic alliance for paediatric speech–language pathology and to determine whether a difference exists in therapeutic alliance reported by speech–language pathologists (SLPs) conducting face-to-face sessions, compared with telepractice SLPs or in their ratings of confidence with technology. Method: SLPs conducting telepractice (n = 14) or face-to-face therapy (n = 18) completed an online survey which included the Therapeutic Alliance Scales for Children–Revised (TASC-r) (Therapist Form) to rate clinicians’ perceptions of rapport with up to three clients. Participants also reported their overall perception of rapport with each client and their comfort with technology. Result: There was a strong correlation between TASC-r total scores and overall ratings of rapport, providing preliminary evidence of TASC-r face validity. There was no significant difference between TASC-r scores for telepractice and face-to-face therapy (p = 0.961), nor face-to-face and telepractice SLPs’ confidence with familiar (p = 0.414) or unfamiliar technology (p = 0.780). Conclusion: The TASC-r may be a promising tool for measuring therapeutic alliance in speech–language pathology. Telepractice does not appear to have a negative effect on rapport between SLPs and paediatric clients. Future research is required to identify how SLPs develop rapport in telepractice.</p