learning science

OBJECTIVE: To construct a deep learning-based target detection method to help radiologists perform rapid diagnosis of lesions in the CT images of patients with novel coronavirus pneumonia (NCP) by restoring detailed information and mining local information. METHODS: We present a deep learning approach that integrates detail upsampling and attention guidance. A linear upsampling algorithm based on bicubic interpolation algorithm was adopted to improve the restoration of detailed information within feature maps during the upsampling phase...

Early and accurate diagnosis of osteosarcomas (OS) is of great clinical significance, and machine learning (ML) based methods are increasingly adopted. However, current ML-based methods for osteosarcoma diagnosis consider only X-ray images, usually fail to generalize to new cases, and lack explainability. In this paper, we seek to explore the capability of deep learning models in diagnosing primary OS, with higher accuracy, explainability, and generality. Concretely, we analyze the added value of integrating the biochemical data, i...

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere...

Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vivo datasets remains challenging. In this study, we trained a deep learning model, Scaden-CA, for deconvoluting tumor data into proportions of cancer-type-specific cell lines. Then, we developed a drug response prediction method using the deconvoluted proportions and the drug sensitivity data from cell lines...

This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties...

The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate two new prediction tasks-outcome-specific length-of-stay and early-mortality prediction for COVID-19 patients in intensive care-which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks while also evaluating 18 predictive models, including clinical scoring methods and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data...

The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean...

With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English courses. To solve this problem, this paper proposes a graph convolutional neural network model based on College English course texts, students' major, English foundation and network structure characteristics. First, by analyzing the relevant data of College English courses and combining with graph neural network, an English course recommendation algorithm model based on the College English learning strategy of proximity comparison is proposed...

This study delves into the intricate dynamics of internet addiction among university students, leveraging a comprehensive quantitative approach to unravel the myriad factors influencing this modern-day malaise. Utilizing logistic regression analysis, this research delineates the predictive significance of Daily Internet Usage Time (DIUT) and Communicative Internet Use Frequency (CIUF) on the propensity for internet addiction, with the analysis substantiating these variables as potent predictors. The model elucidates a significant variance in internet addiction, affirming the complexity of internet addiction as influenced by a constellation of behavioral patterns...

The development of programming skills and computational thinking in the formal educational context is one of the most recent horizons set by many educational systems worldwide. Although the first computational thinking initiatives are being applied from the earliest school ages, this research focuses on the secondary education level. Specifically, the objective is the following: to analyse the implementation of Arduino, as well as the benefits and opportunities it brings to secondary school students. For this purpose, documentary research has been undertaken applying a systematic review according to the PRISMA 2020 framework following the PiCoS strategy...

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...

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease...

The Covid-19 pandemic has forced the educational sector to quickly adapt to the crisis and shift to online learning environments (OLEs). Therefore, a critical area for research is assessing the readiness of teachers in terms of their familiarity and preference for OLEs. In this study, we aimed to improve the experience and preference of in-service teacher trainees with OLEs during the Covid-19 pandemic in Ethiopia. To achieve this goal, questionnaires were used to gather data from in-service teacher trainees...

RATIONALE AND OBJECTIVES: Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. MATERIALS AND METHODS: A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups...

Over the last few years, the inclusion of psychosocial factors in the teaching and learning processes has become increasingly important due to their proven influence on students' academic performance, especially at the university stage. In this regard, the aim of this study is to analyse the impact of emotional intelligence and academic self-concept on the students' academic achievement. The results obtained revealed some differences according to gender in all the variables considered. Specifically, women presented higher levels of emotional attention, academic self-concept and performance, while men stood out in emotional clarity and emotional repair...

This qualitative study was conducted with the aim of investigating the experience of mothers in Tehran in the field of virtual education during the COVID-19 pandemic. The participants in this study were 17 mothers of school children who experienced virtual education in Tehran during the COVID-19 pandemic. Data collection was done through semi-structured interviews, over the phone, and lasted for approximately 30-45 min. The interviews were audio recorded with the permission of the participants, transcribed verbatim, and analyzed using the method of conventional content analysis...

OBJECTIVE: During the first year of the COVID-19 pandemic, most of the Centers for Disease Control and Prevention (CDC)'s National Breast and Cervical Cancer Early Detection Program (NBCCEDP) funded programs (recipients) experienced significant declines in breast and cervical cancer screening volume. However, 6 recipients maintained breast and/or cervical cancer screening volume during July-December 2020 despite their states' high COVID-19 test percent positivity. We led a qualitative multi-case study to explore these recipients' actions that may have contributed to screening volume maintenance...

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84...

PURPOSE: Concrete epidemiological evidence has suggested the mutually-contributing effect respectively between nonalcoholic fatty liver disease (NAFLD), type 2 diabetes mellitus (T2DM), and periodontitis (PD); however, their shared crosstalk mechanism remains an open issue. METHOD: The NAFLD, PD, and T2DM-related datasets were obtained from the NCBI GEO repository. Their common differentially expressed genes (DEGs) were identified and the functional enrichment analysis performed by the DAVID platform determined relevant biological processes and pathways...

Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process...