state innovation models

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder regulation to human-computer interaction. In this paper, we address two fundamental aspects of EEG emotion recognition: continuous regression of emotional states and discrete classification of emotions. While classification methods have garnered significant attention, regression methods remain relatively under-explored. To bridge this gap, we introduce MASA-TCN, a novel unified model that leverages the spatial learning capabilities of Temporal Convolutional Networks (TCNs) for EEG emotion regression and classification tasks...

As the cornerstone of medicine, the development of anatomy is related to many disciplines and fields and has received extensive attention from researchers. How to integrate and grasp the cutting-edge information in this field quickly is a challenge for researchers, so the aim of this study is to analyze research in anatomy using CiteSpace and VOSviewer in order to identify research hotspots and future directions. To offer a fresh viewpoint for assessing the academic influences of researchers, nations, or institutions on anatomy, and to examine the development of hotspots in anatomical study and to forecast future trends...

BACKGROUND: Parkinson's disease (PD) is a progressive, neurodegenerative illness marked by the loss of dopaminergic neurons, causing motor symptoms. Oral levodopa replacement therapy remains the gold standard in the treatment of PD. It is, nevertheless, a symptomatic treatment. There is currently no effective treatment for PD. Therefore, new therapies for PD are highly desirable. Low-intensity pulsed ultrasound (LIPUS) has been shown to improve behavioral functions in PD animal models...

The rapid advance of nanotechnology has led to the development and widespread application of nanomaterials, raising concerns regarding their potential adverse effects on human health and the environment. Traditional (experimental) methods for assessing the nanoparticles (NPs) safety are time-consuming, expensive, and resource-intensive, and raise ethical concerns due to their reliance on animals. To address these challenges, we propose an in silico workflow that serves as an alternative or complementary approach to conventional hazard and risk assessment strategies, which incorporates state-of-the-art computational methodologies...

INTRODUCTION: Comprehensive sexuality education (CSE) is associated with positive sexual and reproductive health (SRH) outcomes, including increased contraceptive use, lower rates of unintended pregnancy and prevention of sexual violence. However, implementation of and requirements for CSE vary across the United States which can negatively impact students, both during and beyond high school, including among college students. METHODS: and Analysis: This paper describes the research protocol for a multi-staged approach for designing, implementing and evaluating an SRH course for up to 60 undergraduate students at a public university in California...

Accurately delineating and categorizing individual hand bones in 3D ultrasound (US) is a promising technology for precise digital diagnostic analysis. However, this is a challenging task due to the inherent imaging limitations of the US and the insignificant feature differences among numerous bones. In this study, we have proposed a novel deep learning-based solution for pediatric hand bone segmentation in the US. Our method is unique in that it allows for effective detailed feature mining through an adaptive multi-dimensional weighting attention mechanism...

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., classifying environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management (G × E × M)...

Deep neural networks (DNNs) have been widely applied in medical image classification and achieve remarkable classification performance. These achievements heavily depend on large-scale accurately annotated training data. However, label noise is inevitably introduced in the medical image annotation, as the labeling process heavily relies on the expertise and experience of annotators. Meanwhile, DNNs suffer from overfitting noisy labels, degrading the performance of models. Therefore, in this work, we innovatively devise noise-robust training approach to mitigate the adverse effects of noisy labels in medical image classification...

Improving corporate green technology innovation is a key link in achieving green transformation and development. Compared with formal environmental regulations that force companies to carry out green innovation passively, ESG ratings under soft environmental regulations can better stimulate the internal motivation of companies. This study uses the ESG ratings of listed companies published for the first time by SynTao Green Finance as an exogenous impact. Taking China's A-share listed companies from 2011 to 2022 as a research sample, the multi-period differences-in-differences model was used to empirically test the impact of ESG rating soft supervision on corporate green technology innovation...

BACKGROUND: Effective financing mechanisms are essential to ensuring that people can access and utilize effective treatments and services. Financing mechanisms are needed not only to pay for the delivery of those treatments and services, but also ancillary costs, while also keeping care affordable. AIMS: This article highlights key areas of the interest of the National Institute of Mental Health (NIMH) and the National Institute on Drug Abuse (NIDA) in supporting applied health economics and health care financing research...

Automatic and precise polyp segmentation in colonoscopy images is highly valuable for diagnosis at an early stage and surgery of colorectal cancer. Nevertheless, it still posed a major challenge due to variations in the size and intricate morphological characteristics of polyps coupled with the indistinct demarcation between polyps and mucosas. To alleviate these challenges, we proposed an improved dual-aggregation polyp segmentation network, dubbed Dua-PSNet, for automatic and accurate full-size polyp prediction by combining both the transformer branch and a fully convolutional network (FCN) branch in a parallel style...

Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits...

BACKGROUND: Social media is essential in the lives of adolescents, with 97% of US teenagers engaging daily. While it facilitates communication, learning, and identity development, it also poses risks like harmful content exposure and psychological distress, particularly for adolescents in their critical developmental stage. Teaching digital life skills innovatively counters these risks, adapting traditional competencies such as decision-making, problem-solving, creative and critical thinking, communication, interpersonal skills, self-awareness, empathy, and emotional and stress management to digital challenges...

OBJECTIVE: Using data from 5 academic-practice sites across the United States, researchers developed and validated a scale to measure conditions that enable healthcare innovations. BACKGROUND: Academic-practice partnerships are a catalyst for innovation and healthcare development. However, limited theoretically grounded evidence exists to provide strategic direction for healthcare innovation across practice and academia. METHODS: Phase 1 of the analytical strategy involved scale development using 16 subject matter experts...

BACKGROUND: Neuro-ophthalmology, bridging neurology and ophthalmology, highlights the nervous system's crucial role in vision, encompassing afferent and efferent pathways. The evolution of this field has emphasized the importance of neuroanatomy for precise surgical interventions, presenting educational challenges in blending complex anatomical knowledge with surgical skills. This review examines the interplay between neuroanatomy and surgical practices in neuro-ophthalmology, aiming to identify educational gaps and suggest improvements...

This comprehensive review delves into the pivotal role of the tumor microenvironment (TME) in cancer metastasis and therapeutic response, offering fresh insights into the intricate interplay between cancer cells and their surrounding milieu. The TME, a dynamic ecosystem comprising diverse cellular and acellular elements, not only fosters tumor progression but also profoundly affects the efficacy of conventional and emerging cancer therapies. Through nuanced exploration, this review illuminates the multifaceted nature of the TME, elucidating its capacity to engender drug resistance via mechanisms such as hypoxia, immune evasion, and the establishment of physical barriers to drug delivery...

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data...

This study focuses on the objective assessment of sport development in socio-economic environments, considering the challenges faced by the industry. These challenges include disparities in regional investments, limited market participation, slow progress towards sports professionalization, and insufficient technological innovations. To tackle these challenges, we suggest implementing an integrated evaluation model that follows the DPSIR (Drivers, Pressures, States, Impacts, Responses) framework and incorporates comprehensive socioeconomic indicators...

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions...

Global declines in donor funding present a substantial threat to development financing in low- and middle-income countries. In Nigeria, the resources required to achieve states' health goals surpass existing government budgets and available donor funding, a shortfall that incentivizes efforts to expand nondonor sources of financing, including public-driven cofinancing models. The Challenge Initiative (TCI) in Nigeria implements a demand-led model wherein 13 state governments requested technical support from TCI to adapt and scale up high-impact family planning and reproductive health (FP/RH) interventions...