Plant-based diet and risk of all-cause mortality: a systematic review and meta-analysis.

Objective: A systematic analysis was conducted to determine the relationship between a plant-based diet and all-cause mortality.

Methods: The PubMed, Embase and Web of Science databases were searched. Two authors selected English documents from the database.

Plasma proteins and inflammatory dermatoses: proteome-wide Mendelian randomization and colocalization analyses.

Current genome-wide association studies (GWAS) of plasma proteomes provide additional possibilities for finding new drug targets for inflammatory dermatoses. We performed proteome-wide Mendelian randomization (MR) and colocalization analyses to identify novel potential drug targets for inflammatory dermatoses. We performed MR and colocalization analysis using genetic variation as instrumental variables to determine the causal relationship between circulating plasma proteins and inflammatory dermatoses.

Research on Convolutional Neural Network Inference Acceleration and Performance Optimization for Edge Intelligence.

In recent years, edge intelligence (EI) has emerged, combining edge computing with AI, and specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI.

Association between air pollution and the prevalence of allergic rhinitis in Chinese children: A systematic review and meta-analysis.

Allergic rhinitis (AR) is a common chronic inflammatory disease with bothersome symptoms. However, the effect of air pollution on the prevalence of AR in children is controversial. This study aimed to investigate the association between air pollution and the prevalence of AR in Chinese children.

Multiscale Local Enhancement Deep Convolutional Networks for the Automated 3D Segmentation of Gross Tumor Volumes in Nasopharyngeal Carcinoma: A Multi-Institutional Dataset Study.

Purpose: Accurate segmentation of gross target volume (GTV) from computed tomography (CT) images is a prerequisite in radiotherapy for nasopharyngeal carcinoma (NPC). However, this task is very challenging due to the low contrast at the boundary of the tumor and the great variety of sizes and morphologies of tumors between different stages. Meanwhile, the data source also seriously affect the results of segmentation.

Highly Expressed Soluble Recombinant Anti-GFP VHHs in Optimized Signal Peptides, Strains, and Inducers.

Antigen-binding variable domains of the H chain of heavy-chain antibodies (VHHs), also known as nanobodies (Nbs), are of great interest in imaging technique, disease prevention, diagnosis, and therapy. High-level expression of soluble Nbs is very important for its industrial production. In this study, we optimized the expression system of anti-green fluorescent protein (GFP) VHHs with three different signal peptides (SPs), outer-membrane protein A (OmpA), pectate lyase B (PelB), and L-asparaginase II SP (L-AsPsII), in different strains isopropyl β-D-thiogalactoside (IPTG) induction and auto-induction, respectively.

Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study.

Purpose: We developed a deep learning model to achieve automatic multitarget delineation on planning CT (pCT) and synthetic CT (sCT) images generated from cone-beam CT (CBCT) images. The geometric and dosimetric impact of the model was evaluated for breast cancer adaptive radiation therapy.

Methods: We retrospectively analyzed 1,127 patients treated with radiotherapy after breast-conserving surgery from two medical institutions.

Multifunctional Bulk Hybrid Foam for Infrared Stealth, Thermal Insulation, and Microwave Absorption.

Taking serious microwave pollution issues and the complex application environment into consideration, it is quite urgent to integrate several functions into one material. Electromagnetic (EM) absorbing materials with multiple functions are highly attractive to next-generation wireless techniques and portable electronic devices. Herein, melamine foam provides a decent platform for the uniform growth of Co-based metal-organic frameworks (MOFs), which bring the as-obtained hybrid foam with three-dimensional porous network structure and combination of dielectric along with magnetic attenuation abilities as advanced materials in multifunctional fields.

Affective Computing and the Impact of Gender and Age.

Affective computing aims at the detection of users' mental states, in particular, emotions and dispositions during human-computer interactions. Detection can be achieved by measuring multimodal signals, namely, speech, facial expressions and/or psychobiology. Over the past years, one major approach was to identify the best features for each signal using different classification methods.

Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults.

Background: Research suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling machines to access to human affective states.

Similarities and differences of emotions in human-machine and human-human interactions: what kind of emotions are relevant for future companion systems?

Cognitive-technical intelligence is envisioned to be constantly available and capable of adapting to the user's emotions. However, the question is: what specific emotions should be reliably recognised by intelligent systems? Hence, in this study, we have attempted to identify similarities and differences of emotions between human-human (HHI) and human-machine interactions (HMI). We focused on what emotions in the experienced scenarios of HMI are retroactively reflected as compared with HHI.