Experiences of family caregivers and nursing home staff interactions during the adaptation process of elderly individuals moving to nursing home: a qualitative study.

Objective: To explore the interaction between family members and nursing home staff during the adjustment period of newly admitted elderly individuals in a nursing home.

Design: A qualitative descriptive study based on semistructured interviews; data were analysed using a thematic topic analysis approach.

Setting: Interviews were conducted face-to-face.

A Virtual-Label-Based Hierarchical Domain Adaptation Method for Time-Series Classification.

Unsupervised domain adaptation (UDA) is becoming a prominent solution for the domain-shift problem in many time-series classification tasks. With sequence properties, time-series data contain both local and sequential features, and the domain shift exists in both features. However, conventional UDA methods usually cannot distinguish those two features but mix them into one variable for direct alignment, which harms the performance.

SEA++: Multi-Graph-Based Higher-Order Sensor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation.

Unsupervised Domain Adaptation (UDA) methods have been successful in reducing label dependency by minimizing the domain discrepancy between labeled source domains and unlabeled target domains. However, these methods face challenges when dealing with Multivariate Time-Series (MTS) data. MTS data typically originates from multiple sensors, each with its unique distribution.

Human placental mesenchymal stromal cells promote the formation of CD8CD122PD-1Tregs via CD73/Foxo1 to alleviate liver injury in graft-versus-host disease mice.

Background: Human placental mesenchymal stromal cells (hPMSCs) are known to limit graft-versus-host disease (GVHD). CD8CD122PD-1Tregs have been shown to improve the survival of GVHD mice. However, the regulatory roles of hPMSCs in this subgroup remain unclear.

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks.

Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory, energy, and computation. To address these challenges, researchers have developed various model compression techniques such as model quantization and model pruning.

Local-Global Correlation Fusion-Based Graph Neural Network for Remaining Useful Life Prediction.

Remaining useful life (RUL) prediction is an essential component for prognostics and health management of a system. Due to the powerful ability of nonlinear modeling, deep learning (DL) models have emerged as leading solutions by capturing temporal dependencies within time series sensory data. However, in RUL prediction tasks, data are typically collected from multiple sensors, introducing spatial dependencies in the form of sensor correlations.

New insights into the on-site monitoring of probiotics eDNA using biosensing technology for heat-stress relieving in coral reefs.

Coral probiotics can improve the tolerance of corals to heat stress, thus mitigating the process of coral thermal bleaching. Sensitive and specific detection of coral probiotics at low abundances is highly desirable but remains challenging, especially for rapid and on-site detection of coral probiotics. Since the electrochemical biosensor has been recently used in the field of environmental DNA (eDNA) detection, herein, an efficient electrochemical biosensor was developed based on CoS/CoSe-NC HNCs electrode material with a specific DNA probe for the C.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning.

The value of Electrocardiogram (ECG) monitoring in early cardiovascular disease (CVD) detection is undeniable, especially with the aid of intelligent wearable devices. Despite this, the requirement for expert interpretation significantly limits public accessibility, underscoring the need for advanced diagnosis algorithms. Deep learning-based methods represent a leap beyond traditional rule-based algorithms, but they are not without challenges such as small databases, inefficient use of local and global ECG information, high memory requirements for deploying multiple models, and the absence of task-to-task knowledge transfer.

Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification.

Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled data via contrasting different augmented views of data. In this work, we propose a novel Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC) that learns representations from unlabeled data with contrastive learning.

A systematic review of image-guided, surgical robot-assisted percutaneous puncture: Challenges and benefits.

Percutaneous puncture is a common medical procedure that involves accessing an internal organ or tissue through the skin. Image guidance and surgical robots have been increasingly used to assist with percutaneous procedures, but the challenges and benefits of these technologies have not been thoroughly explored. The aims of this systematic review are to furnish an overview of the challenges and benefits of image-guided, surgical robot-assisted percutaneous puncture and to provide evidence on this approach.

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation.

The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling can be expensive and time-consuming.

SSA-ICL: Multi-domain adaptive attention with intra-dataset continual learning for Facial expression recognition.

Facial expression recognition (FER) is a kind of affective computing that identifies the emotional state represented in facial photographs. Various methods have been developed for completing this critical task. In spite of this progress, three significant obstacles, the interaction between spatial action units, the inadequacy of semantic information about spectral expressions and the unbalanced data distribution, are not well addressed.

Aligning Correlation Information for Domain Adaptation in Action Recognition.

Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research toward video DA. This is partly due to the complexity in adapting the different modalities of features in videos, which includes the correlation features extracted as long-range dependencies of pixels across spatiotemporal dimensions.

Bioinformatics analysis of BIRC5 in human cancers.

Background: An inhibitor of apoptosis (IAP) family member, baculoviral IAP repeat containing five (BIRC5) plays an important role in the occurrence and development of tumors. However, the underlying mechanism in human cancers remains unclear.

Methods: In this study, we investigated BIRC5 expression and explored the prognostic value of BIRC5 in different human cancers via bioinformatics analysis, including the databases of Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, GEPIA, DriverDBv3, GeneMANIA, WEB-based Gene Set Analysis Tool (WebGestalt) and TIMER.

Diagnostic Significance of Combined Calcitoninogen, Platelet, and D-Dimer Assay in Severe Heatstroke: with Clinical Data Analysis of 70 Patients with Severe Heatstroke.

The significance of calcitoninogen detection among inpatients was discussed by analyzing the clinical characteristics of severe heatstroke (HS). HS patients who were admitted to the Second Hospital of Nantong University, Jiangsu Province, China, between July 1, 2015, and October 30, 2020, were reviewed. Patients' clinical characteristics and laboratory data were recorded, and they were divided into three groups, that is, a control group (heat cramps and heat exhaustion), an exertional HS (EHS) group, and a classical HS (CHS) group to compare the differences among them.

Organochlorine pesticides (OCPs) in corals and plankton from a coastal coral reef ecosystem, south China sea.

Recent studies have indicated that coral mucus plays an important role in the bioaccumulation of a few organic pollutants by corals, but no relevant studies have been conducted on organochlorine pesticides (OCPs). Previous studies have also indicated that OCPs widely occur in a few coral reef ecosystems and have a negative effect on coral health. Therefore, this study focused on the occurrence and bioaccumulation of a few OCPs, such as dichlorodiphenyltrichloroethanes (DDTs), hexachlorobenzene (HCB) and p,p'-methoxychlor (MXC), in the coral tissues and mucus as well as in plankton and seawater from a coastal reef ecosystem (Weizhou Island) in the South China Sea.

Mechanosensitive Channel PIEZO1 Senses Shear Force to Induce KLF2/4 Expression via CaMKII/MEKK3/ERK5 Axis in Endothelial Cells.

Shear stress exerted by the blood stream modulates endothelial functions through altering gene expression. KLF2 and KLF4, the mechanosensitive transcription factors, are promoted by laminar flow to maintain endothelial homeostasis. However, how the expression of KLF2/4 is regulated by shear stress is poorly understood.

Self-Supervised Autoregressive Domain Adaptation for Time Series Data.

Unsupervised domain adaptation (UDA) has successfully addressed the domain shift problem for visual applications. Yet, these approaches may have limited performance for time series data due to the following reasons. First, they mainly rely on the large-scale dataset (i.

MAPK/ERK-CBP-RFPL-3 Mediates Adipose-Derived Stem Cell-Induced Tumor Growth in Breast Cancer Cells by Activating Telomerase Reverse Transcriptase Expression.

Adipose-derived stem cells (ASCs) improve the self-renewal and survival of fat grafts in breast reconstruction after oncology surgery. However, ASCs have also been found to enhance breast cancer growth, and its role in tumor proliferation remains largely elusive. Here, we explored a novel mechanism that mediates hTERT reactivation during ASC-induced tumor growth in breast cancer cells.

Circ_0039960 regulates growth and Warburg effect of breast cancer cells via modulating miR-1178/PRMT7 axis.

Background: Breast cancer (BC) is a serious threat to women's life and healthy. Increasing evidence indicated that blocking Warburg effect could attenuate the development of BC. Circular RNAs (circRNAs) has been found to be dysregulated in various carcinomas, including BC.

Privacy-Preserving Cross-Environment Human Activity Recognition.

Recent studies have demonstrated the success of using the channel state information (CSI) from the WiFi signal to analyze human activities in a fixed and well-controlled environment. Those systems usually degrade when being deployed in new environments. A straightforward solution to solve this limitation is to collect and annotate data samples from different environments with advanced learning strategies.

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG.

Automatic sleep stage mymargin classification is of great importance to measure sleep quality. In this paper, we propose a novel attention-based deep learning architecture called AttnSleep to classify sleep stages using single channel EEG signals. This architecture starts with the feature extraction module based on multi-resolution convolutional neural network (MRCNN) and adaptive feature recalibration (AFR).

Over-fitting suppression training strategies for deep learning-based atrial fibrillation detection.

Nowadays, deep learning-based models have been widely developed for atrial fibrillation (AF) detection in electrocardiogram (ECG) signals. However, owing to the inevitable over-fitting problem, classification accuracy of the developed models severely differed when applying on the independent test datasets. This situation is more significant for AF detection from dynamic ECGs.

Deep learning with long short-term memory networks for classification of dementia related travel patterns.

Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementia-related wandering patterns. In particular, we propose to use deep learning (DL) with long short-term memory networks (LSTM) as classifiers for detecting travel patterns including direct, pacing, lapping and random.