Parental migration and self-reported health status of adolescents in China: a cross-sectional study

Background: Over 100 million children are parented by migrant workers in China. The aim of this study was to investigate how self-reported adolescent physical and mental health are associated with parental migration. Methods: Based on cross-sectional data of 13996 students in 112 schools drawn from a nationally representative sample of middle school students in China, this study used self-reported measures for adolescent physical and mental health. Ordered logistic regression was used for the analysis of self-reported physical health, and linear regression was used for the analysis of self-reported mental health, both adjusting for socio-economic covariates and school fixed effects, to determine how adolescent health is associated with parental migration. Findings: In urban areas, migrant adolescents were physically healthier (OR=1.19, 95% CI: 1.03–1.36), and similarly mentally healthy (b=-0.07, 95% CI: -0.37–0.23), compared to urban adolescents from intact families; in rural areas, left-behind adolescents were less physically (OR=0.84, 95% CI: 0.76–0.94) and mentally (b=0.45, 95% CI: 0.24–0.66) healthy than rural-intact adolescents, holding other variables constant. Left-behind adolescents had less close parent-adolescent relationships than rural-intact adolescents with both father (OR=0.63, 95% CI: 0.56–0.71) and mother (OR=0.62, 95% CI: 0.54–0.70). Interpretation: Our study highlights a great need for health interventions aimed at left-behind adolescents in China and globally, and the important roles of parent-adolescent relationships in addressing the health needs of left-behind adolescents

LINGUISTICALLY AND CULTURALLY RESPONSIVE TEACHING: EMPOWERING INTERNATIONAL ENGLISH-SPEAKING TEACHERS AT INTERNATIONAL SCHOOLS

The research study presented in this dissertation examines the problem of what contributes to the discrepancy between schools’ aspirations for their students and their students’ performance and how schools can support English language learners (ELLs). Chapter one of this dissertation examined factors contributing to ELLs’ development using Bronfenbrenner’s (1979, 1994) ecological systems theory. Chapter two examines three factors that surfaced from the literature review in chapter one: culturally responsive teaching, inquiry science instruction, and instruction to support English language development. The needs assessment in chapter two revealed that international English-speaking teachers prioritized different strategies for teaching inquiry science to elementary ELLs despite their self-reported perceptions that they adhered to research-suggested practices. The assessment also foregrounded multiple areas to rectify the teachers’ priorities, including using culturally responsive teaching strategies to support ELLs’ social and emotional development. After delving into empirical studies about interventions that developed teachers’ culturally responsive teaching strategies and their intercultural teaching competence, the researcher followed Bandura’s (1978, 1986) social cognitive theory and Hargreaves and Fullan’s (2012) professional capital theory to guide a design of professional learning program. Furthermore, the researcher evaluated the process and outcome of the professional learning program with a quasi-experimental pretest-posttest exploratory mixed methods design. The 13-hour intervention significantly increased teachers’ self-efficacy about culturally responsive teaching. The study’s findings shed light on schools’ support for enhancing their teachers’ linguistic and cultural responsiveness in interacting with English language learners

Method to Annotate Arrhythmias by Deep Network

This study targets to automatically annotate on arrhythmia by deep network. The investigated types include sinus rhythm, asystole (Asys), supraventricular tachycardia (Tachy), ventricular flutter or fibrillation (VF/VFL), ventricular tachycardia (VT). Methods: 13s limb lead ECG chunks from MIT malignant ventricular arrhythmia database (VFDB) and MIT normal sinus rhythm database were partitioned into subsets for 5-fold cross validation. These signals were resampled to 200Hz, filtered to remove baseline wandering, projected to 2D gray spectrum and then fed into a deep network with brand-new structure. In this network, a feature vector for a single time point was retrieved by residual layers, from which latent representation was extracted by variational autoencoder (VAE). These front portions were trained to meet a certain threshold in loss function, then fixed while training procedure switched to remaining bidirectional recurrent neural network (RNN), the very portions to predict an arrhythmia category. Attention windows were polynomial lumped on RNN outputs for learning from details to outlines. And over sampling was employed for imbalanced data. The trained model was wrapped into docker image for deployment in edge or cloud. Conclusion: Promising sensitivities were achieved in four arrhythmias and good precision rates in two ventricular arrhythmias were also observed. Moreover, it was proven that latent representation by VAE, can significantly boost the speed of convergence and accuracy

Current Biological Strategies to Enhance Surgical Treatment for Rotator Cuff Repair

Rotator cuff tear is one of the most common shoulder problems encountered by orthopedic surgeons. Due to the slow healing process and high retear rate, rotator cuff tear has distressed millions of people all around the world every year, especially for the elderly and active athletes. This disease significantly impairs patients’ motor ability and reduces their quality of life. Besides conservative treatment, open and arthroscopic surgery contributes a lot to accelerate the healing process of rotator cuff tear. Currently, there are many emerging novel treatment methods to promote rotator cuff repair. A variety of biological stimulus has been utilized in clinical practice. Among them, platelet-rich plasma, growth factors, stem cells, and exosomes are the most popular biologics in laboratory research and clinical trials. This review will focus on the biologics of bioaugmentation methods for rotator cuff repair and tendon healing, including platelet-rich plasma, growth factors, exosomes and stem cells, etc. Relevant studies are summarized in this review and future research perspectives are introduced

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition

IntroductionIn the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain.MethodsTo address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest.Results and discussionExperimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models

Performance evaluation of an AI-based preoperative planning software application for automatic selection of pedicle screws based on computed tomography images

IntroductionRecent neurosurgical applications based on artificial intelligence (AI) have demonstrated its potential in surgical planning and anatomical measurement. We aimed to evaluate the performance of an AI planning software application on screw length/diameter selection and insertion accuracy in comparison with freehand surgery.MethodsA total of 45 patients with 208 pedicle screw placements on thoracolumbar segments were included in this analysis. The novel AI planning software was developed based on a deep learning model. AI-based pedicle screw placements were selected on the basis of preoperative computed tomography (CT) data, and freehand surgery screw placements were observed based on postoperative CT data. The performance of AI pedicle screw placements was evaluated on the components of screw length, diameter, and Gertzbein grade in comparison with the results achieved by freehand surgery.ResultsAmong 208 pedicle screw placements, the average screw length/diameters selected by the AI model and used in freehand surgery were 48.65 ± 5.99 mm/7.39 ± 0.42 mm and 44.78 ± 2.99 mm/6.1 ± 0.27 mm, respectively. Among AI screw placements, 85.1% were classified as Gertzbein Grade A (no cortical pedicle breach); among free-hand surgery placements, 64.9% were classified as Gertzbein Grade A.ConclusionThe novel AI planning software application could provide an accessible and safe pedicle screw placement strategy in comparison with traditional freehand pedicle screw placement strategies. The choices of pedicle screw dimensional parameters made by the model, including length and diameter, may provide potential inspiration for real clinical discretion

Similar operation template attack on RSA-CRT as a case study

A template attack, the most powerful side-channel attack methods, usually first builds the leakage profiles from a controlled profiling device, and then uses these profiles to recover the secret of the target device. It is based on the fact that the profiling device shares similar leakage characteristics with the target device. In this study, we focus on the similar operations in a single device and propose a new variant of the template attack, called the similar operation template attack (SOTA). SOTA builds the models on public variables (e.g., input/output) and recovers the values of the secret variables that leak similar to the public variables. SOTA’s advantage is that it can avoid the requirement of an additional profiling device. In this study, the proposed SOTA method is applied to a straightforward RSA-CRT implementation. Because the leakage is (almost) the same in similar operations, we reduce the security of RSA-CRT to a hidden multiplier problem (HMP) over GF(q), which can be solved byte-wise using our proposed heuristic algorithm. The effectiveness of our proposed method is verified as an entire prime recovery procedure in a practical leakage scenario

Stabilized COre Gene and Pathway Election Uncovers Pan-Cancer Shared Pathways and a Cancer-Specific Driver

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data