Genome-wide identification of sweet potato U-Box E3 ubiquitin ligases and roles of IbPUB52 in negative regulation of drought stress.

The plant U-box (PUB) proteins, a family of ubiquitin ligases (E3) enzymes, are pivotal in orchestrating many biological processes and facilitating plant responses to environmental stressors. Despite their critical roles, exploring the PUB gene family's characteristics and functional diversity in sweet potato (Ipomoea batatas (L.) Lam.) has been notably limited. There were 81 IbPUB genes identified within the sweet potato genome, and they were categorized into eight distinct groups based on domain architecture, revealing a non-uniform distribution across the 15 chromosomes of I. batatas. The investigation of cis-acting elements has shed light on the potential of PUBs to participate in a wide array of biological processes, particularly emphasizing their role in mediating responses to abiotic stresses. Transcriptome profiles revealed that IbPUB genes displayed a wide range of expression levels among different tissues and were regulated by salt or drought stress. IbPUB52 has emerged as a gene of significant interest due to its induction by salt and drought stresses. Localization studies have confirmed the presence of IbPUB52 in both the nucleus and the cytoplasm, and its ubiquitination activity has been validated through rigorous in vitro and in vivo assays. Intriguingly, the heterogeneous expression of IbPUB52 in Arabidopsis resulted in decreased drought tolerance. The virus-induced gene silencing (VIGS) of IbPUB52 in sweet potatoes led to enhanced resistance to drought. This evidence suggests that IbPUB52 negatively regulates the drought tolerance of plants. The findings of this study are instrumental in advancing our comprehension of the functional dynamics of PUB E3 ubiquitin ligases in sweet potatoes.

Poly(G)7 box: a functional element of mammalian 18S rRNA involved in translation.

In eukaryotes, the ribosomal small subunit (40S) is composed of 18S rRNA and 33 ribosomal proteins. 18S rRNA has a special secondary structure and is an indispensable part of the translation process. Herein, a special sequence located in mammalian 18S rRNA named Poly(G)7box, which is composed of seven guanines, was found. Poly(G)7 can form a special and stable secondary structure by binding to the translation elongation factor subunit eEF1D and the ribosomal protein RPL32. Poly(G)7box was transfected into cells, and the translation efficiency of cells was inhibited. We believe that Poly(G)7box is an important translation-related functional element located on mammalian 18S rRNA, meanwhile the Poly(G)7 located on mRNA 5' and 3' box does not affect mRNA translation.

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients.

Objective: Pain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions. Methods: We enrolled critically ill patients during 2020-2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score. Results: A total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high. Conclusions: The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.

Dependency structure matrix, genetic algorithms, and effective recombination.

Abstract In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions between components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions-modularity, hierarchy, and overlap, facet-wise models are developed to dissect and inspect problem decomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix representation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure.