Browse Articles
Discover research articles across all indexed journals
Stages prediction of Alzheimer’s disease with shallow 2D and 3D CNNs from intelligently selected neuroimaging data
The shortcomings of synthetic census microdata
U.S. Census Bureau officials recently reaffirmed the Bureau’s ongoing efforts to replace the American Community Survey (ACS) public use microdata sample with “fully synthetic” data to protect respondent confidentiality. With the growth of computing power and expansion of private sector data about the population, the Census Bureau has valid concerns about confidentiality threats to public data. The current plan for fully synthetic data, however, threatens a cornerstone of the nation’s scientific infrastructure. Census microdata samples are among the most frequently used sources in social science research, and they are an essential tool for policy formation and planning from the local to the national level. Synthetic census microdata are not suitable for most research and policy applications. There have been no recent attempts to quantify disclosure risk in the ACS microdata, and the sole existing study failed to establish a credible threat for positive identification of ACS respondents by external intruders. I argue that we need new empirical research to pinpoint specific vulnerabilities that could allow an intruder to determine a particular individual’s confidential census responses. If significant vulnerabilities are uncovered, the Census Bureau in partnership with the research community should develop targeted methods for disclosure risk reduction that minimize damage to data usability.
The analysis of rural revitalization serviceplatform in smart city under back propagation neural network
To achieve rural revitalization and enhance the development of rural tourism, this study employs a back propagation neural network (BPNN) to construct a rural revitalization development model. Additionally, the Grey Relation Analysis (GRA) algorithm is used to classify rural revitalization efforts across different cities. Consistency testing is applied to analyze rural revitalization indicators, and a tourism service evaluation model is established to assess rural revitalization tourism services from the perspective of smart cities. The research results indicate that: (1) the training results and expected values of the ten cities are relatively consistent, and the classification of rural revitalization development is good; (2) The five major indicators of tourism information services, tourism security services, tourism transportation services, tourism environment services, and tourism management services all meet the consistency test, and the consistency test results are all less than 0.1, confirming the reliability and effectiveness of the research data; (3) The tourism information and management services are mainly evaluated at level C, accounting for 62% and 62.5% respectively. The tourism transportation and safety services are mainly evaluated at level D, and the model can indicate the level of rural revitalization tourism service; (4) Compared with other algorithms, the GRA-BPNN algorithm performs the best in rural revitalization evaluation, with an accuracy of 92.3%, precision of 91.8%, recall rate of 93.7%, and F1 score of 92.7%. This study optimizes the rural revitalization tourism service platform, enhances the quality of rural tourism, promotes the development of the rural tourism industry, and contributes to the realization of rural revitalization.
Assembly and comparative analysis of the complete mitochondrial genome of Lactuca sativa var. ramosa Hort
Randomised, double-blind study to evaluate the efficacy of rituximab in the treatment of idiopathic membranous nephropathy: A clinical trial protocol
In Japan, corticosteroid monotherapy has traditionally been recommended as the first-line therapy for membranous nephropathy with nephrotic syndrome. In contrast, except for Japan, rituximab is recommended as the first-line therapy for membranous nephropathy with nephrotic syndrome. This clinical trial aimed to verify the efficacy and safety of the intravenous administration of rituximab without steroids or immunosuppressants as an induction therapy in Japanese patients with idiopathic membranous nephropathy and nephrotic syndrome. This was a multicentre (15 in Japan), placebo-controlled, randomized, double-blind, parallel-group comparative study. A total of 88 patients diagnosed with idiopathic membranous nephropathy and nephrotic syndrome were randomly allocated to rituximab and placebo groups in a 1:1 ratio; rituximab 1,000 mg or placebo IV infusion was administered every 2 weeks for two doses in a double-blinded manner. The primary endpoint was the percentage of patients achieving less than 1.0 g/g creatinine in urine protein/creatinine ratio in random urine at 26 weeks after the first administration of rituximab or placebo. This study was approved by the institutional review boards and conducted in accordance with the Good Clinical Practice guidelines. This study was registered at ClinicalTrials.gov, NCT05914155 and the Japan Registry of Clinical Trials, jRCT2041230037 on 13 June 2023.
Data driven prediction based reliability assessment of solar energy systems incorporating uncertainties for generation planning
Reply to Crabtree: Market research panels augmenting microtask platforms
Predicting treatment response to cognitive behavior therapy in social anxiety disorder on the basis of demographics, psychiatric history, and scales: A machine learning approach
Only about half of patients with social anxiety disorder (SAD) respond substantially to cognitive behavioral therapy (CBT). However, there has been little evidence available to clinicians or patients about whether any individual patient is more or less likely to have a positive response to CBT. Here, we used machine learning on data from 157 patients to examine whether individual patient responses to CBT can be predicted based on demographic information, psychiatric history, and self-reported or clinician-reported scales, subscales and questionnaires acquired prior to treatment. Machine learning models were able to explain about 26% of the variance in final treatment improvements. To assess generalizability, we evaluated multiple machine learning models using cross-validation and determined which input features were essential for prediction. While prediction accuracy was similar across models, the importance of specific features varied across models. In general, the combination of total scale score, subscale scores and responses to individual questions on a severity measure, the Liebowitz Social Anxiety Scale (LSAS), was the most informative in achieving the highest predictions that alone accounted for about 26% of the variance in treatment outcome. Demographic information, psychiatric history, personality measures, other self-reported or clinician-reported questionnaires, and clinical scales related to anxiety, depression, and quality of life provided no additional predictive power. These findings indicate that combining scaled and individual responses to LSAS questions are informative for predicting individual response to CBT in patients with SAD.
Centrobin serves as a safeguard to guide timely centriole maturation during the cell cycle
Parthenolide regulates microglial and astrocyte function in primary cultures from ALS mice and has neuroprotective effects on primary motor neurons
Over the last twenty years, the role of microgliosis and astrocytosis in the pathophysiology of neurodegenerative diseases has increasingly been recognized. Dysregulation of microglial and astrocyte properties and function has been described also in the fatal degenerative motor neuron disease amyotrophic lateral sclerosis (ALS). Microglia cells, the immune cells of the nervous system, can either have an immunonegative neurotoxic or immunopositive neuroprotective phenotype. The feverfew plant (Tanacetum parthenium) derived compound parthenolide has been found to be capable of interfering with microglial phenotype and properties. Positive treatment effects were shown in animal models of neurodegenerative diseases like Alzheimer’s disease and Parkinson’s disease. Now we were able to show that PTL has a modulating effect on primary mouse microglia cells, both wild type and SOD1, causing them to adopt a more neuroprotective potential. Furthermore, we were able to show that PTL, through its positive effect on microglia, also has an indirect positive impact on motor neurons, although PTL itself has no direct effect on these primary motor neurons. The results of our study give reason to consider PTL as a drug candidate for ALS.
A multi-scale small object detection algorithm SMA-YOLO for UAV remote sensing images
A corpus-based analysis of noun modifiers in L2 writing: The respective impact of L2 proficiency and L1 background
Complex noun phrases, as a distinctive feature of academic writing, pose an important learning task for L2 learners. Noun modifiers are the primary means of constructing complex noun phrases. Due to the development of natural language processing (NLP) technologies in recent years, noun phrase complexity, which is a micro-syntactic complexity indicator reflecting the complexity and diversity of clausal and phrasal structures, has emerged as an important research topic. This study applies Bayesian regression with informative priors to analyze the use of English noun modifiers by L2 learners of different proficiency levels and L1 backgrounds through the exploration of the EF Cambridge Open Language Database (EFCAMDAT) corpus. It finds that L2 proficiency has a significant impact on the development of noun phrase complexity in non-academic writing, while the influence of L1 background is observable but limited. It thus concludes that as second language proficiency increases, learners tend to converge towards a common grammatical competence that transcends their native linguistic frameworks.
A novel fractal fractional mathematical model for HIV/AIDS transmission stability and sensitivity with numerical analysis
Correction for Chen et al., Live-attenuated virus vaccine defective in RNAi suppression induces rapid protection in neonatal and adult mice lacking mature B and T cells
Types, method, and mode of implementation of pain/symptom maps in musculoskeletal pain rehabilitation: A scoping review protocol
Introduction Pain maps are tools used for assessing the extent, location, or distribution of pain or symptoms for clinical or research purposes. Pain mapping involves a transformational representation of patients’ experiences of pain into a graphical, numerical, or descriptive form that typically requires a patient to indicate the affected body regions and may include additional information such as qualitative description or intensity. In preparation for innovative technology-enabled development of quantifiable pain maps, this review will focus on the methodological aspects of recent pain maps in addition to the reported measurement properties of each mapping approach. This will identify current gaps in knowledge, consistencies in implementation, and inform directions for future development of more person-centric and meaningful pain maps. The objective of this scoping review is to explore the commonly used types of pain/symptom maps in musculoskeletal pain by classifying design (types) across five categorical features: scalability, region-specificity, aspect or orientation, segmentation, and sex identification, and investigate their methods and modes of implementation. Methods Key sources of evidence such as Medline, Embase, PsycINFO, CINAHL, Scopus, Web of Science, will be searched from inception to June 5, 2024, including grey literature from reference screening, library and organizational collections such as WorldCat, ProQuest Global Dissertation, Google Scholar, and Google to find descriptions or evaluations of pain/symptom maps in people with pain of a primarily musculoskeletal origin. Studies reporting standard patient-reported pain or body mapping interventions will be considered but studies that present X-ray or CT or MRI scans or artistic body maps will be excluded. Primary outcomes include ‘types’ of design: scale, segments, sex, orientation, region; pain mapping methods: marking, shading, checking; and mode of implementation: paper, digital, etc. Secondary outcomes include axis I: pain location, extent or distribution; and axis II: pain severity, intensity, and quality. Eligibility screening and data extraction will be conducted by two independent reviewers. The review is intended to initiate research that promotes the integration of data-friendly solutions and supports the application of machine learning in musculoskeletal pain evaluation.
MT1H inhibits the growth of gastric cancer by regulating SLC6A19/TTC39B/ADM2 and activating p53-dependent autophagy
Retraction: Relay protection system of transmission line based on AI
Multi-beam multi-slice X-ray ptychography
Abstract X-ray ptychography provides the highest resolution non-destructive imaging at synchrotron radiation facilities, and the efficiency of this method is crucial for coping with limited experimental time. Recent advancements in multi-beam ptychography have enabled larger fields of view, but spatial resolution for large 3D samples remains constrained by their thickness, requiring consideration of multiple scattering events. Although this challenge has been addressed using multi-slicing in conventional ptychography, the integration of multi-slicing with multi-beam ptychography has not yet been explored. Here we present the first successful combination of these two methods, enabling high-resolution imaging of nanofeatures at depths comparable to the lateral dimensions that can be addressed by state-of-the-art multi-beam ptychography. Our approach is robust, reproducible across different beamlines, and ready for broader application. It marks a significant advancement in the field, establishing a new foundation for high-resolution 3D imaging of larger, thicker samples.
Forecasting stock prices using long short-term memory involving attention approach: An application of stock exchange industry
The Stability of the economy is always a great challenge across the world, especially in under developed countries. Many researchers have contributed to forecasting the Stock Market and controlling the situation to ensure economic stability over the past several decades. For this purpose, many researchers have built various models and gained benefits. This journey continues to date and will persist for the betterment of the stock market. This study is also a part of this journey, where four learning-based models are tailored for stock price prediction. Daily business data from the Karachi Stock Exchange (100 Index), covering from February 22, 2008 to February 23, 2021, is used for training and testing these models. This paper presenting four deep learning models with different architectures, namely the Artificial Neural Network model, the Recurrent Neural Network with Attention model, the Long Short-Term Memory Network with Attention model, and the Gated Recurrent Unit with Attention model. The Long Short-Term Memory with attention model was found to be the top-performing technique for accurately predicting stock exchange prices. During the Training, Validation and Testing Sessions, we observed the R-Squared values of the proposed model to be 0.9996, 0.9980 and 0.9921, respectively, making it the best-performing model among those mentioned above.