Article
Neurosciences
Trung Quang Pham, Shota Nishiyama, Norihiro Sadato, Junichi Chikazoe
Summary: Recent studies have shown that by using region-to-region decoding technique, it is possible to explore information transfer between different brain regions. In visual perception tasks, eliminating signals from different directions enhances different visual features, indicating that the information flow across the visual cortices is dynamically altered.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Neurosciences
Sreenivasan Meyyappan, Abhijit Rajan, George R. Mangun, Mingzhou Ding
Summary: Feature-based visual attention relies on the inferior frontal junction (IFJ) for control, while the debate continues regarding its role in spatial attention control. Functional connectivity between the right IFJ and visual cortex (V4) is associated with subsequent attentional selection of targets and behavioral performance during feature attention, but not during spatial attention.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Neurosciences
Jussi Alho, Athanasios Gotsopoulos, Juha Silvanto
Summary: Conscious experiences are usually caused by external input into our sensory systems, but we can also create conscious percepts independently of sensory stimulation, known as mental imagery. This study used fMRI to investigate the interaction between internal and external visual information during visual imagery. The results demonstrated that the representations of internal and external visual information interact in brain areas associated with visual object and shape encoding.
Article
Biology
Ya-Bian Luo, Yan-Yao Hou, Zhen Wang, Xin-Man Hu, Wei Li, Yan Li, Yong Liu, Tong-Jiang Li, Chun-Zhi Ai
Summary: This study developed machine learning models to predict the metabolic properties of UGT1A1 substrates. The models demonstrated good accuracy and robustness, and were validated with in vitro assays. This strategy is important for optimizing drug metabolism and avoiding drug-drug interactions in clinical practice.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Neurosciences
Eva Berlot, Nicola J. Popp, Scott T. Grafton, Jorn Diedrichsen
Summary: In the context of motor sequence learning, fMRI studies revealed differences in neuronal representations between premotor and parietal regions compared to the primary motor cortex (M1). While M1 showed specific representation of the first finger of each sequence, parietal areas represented the identity of the entire sequence and remained relatively stable during different executions. This suggests that the RS effect in M1 reflects a preparatory signal for movement initiation rather than a trained sequence representation.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Business, Finance
Yongkil Ahn
Summary: This study examines the relative importance of various factors associated with the disposition effect using trading records and survey responses of 76,172 retail investors. The findings reveal that gender, loss aversion, and investor sophistication are crucial in understanding the disposition effect.
FINANCE RESEARCH LETTERS
(2022)
Article
Agriculture, Multidisciplinary
Amaury Dubois, Fabien Teytaud, Sebastien Verel
Summary: Agricultural decision-making is crucial for future yields. The integration of sensors and agronomic models in smart farming helps growers better understand their crops and improve irrigation management efficiency. This paper demonstrates the importance of machine learning in the agricultural field through experiments.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Nayna Birla, Manoj Kumar Jain, Avinash Panwar
Summary: This paper presents a qualitatively enhanced methodology for automated score prediction of subjective assignments by analyzing multiple linguistic features. The study observed the effect of appropriate feature selection using Mutual Information Regression and investigated four ML algorithms. The results showed that a 3 Layer Neural Network with feature selection performed best among the chosen ML algorithms. Additionally, a new hybrid model was proposed by combining features with a higher level deep neural network to further improve accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Biochemical Research Methods
Adrienne Kline, Nils D. Forkert, Banafshe Felfeliyan, Daniel Pittman, Bradley Goodyear, Janet Ronsky
Summary: This study developed a computational approach to accurately map spatial brain activity in relation to imagined lower limb movement, achieving 66.5% accuracy in classifying left versus right lower limb movement. The use of a spatiotemporal feature selection method can improve classification accuracy.
JOURNAL OF NEUROSCIENCE METHODS
(2021)
Article
Biochemical Research Methods
Mingming Jiang, Bowen Zhao, Shenggan Luo, Qiankun Wang, Yanyi Chu, Tianhang Chen, Xueying Mao, Yatong Liu, Yanjing Wang, Xue Jiang, Dong-Qing Wei, Yi Xiong
Summary: This study developed an interpretable stacking model, NeuroPpred-Fuse, for the prediction of neuropeptides through fusing sequence-derived features and feature selection methods. The model achieved 90.6% accuracy and 95.8% AUC on the independent test set, outperforming current state-of-the-art models, demonstrating strong generalization ability.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemistry & Molecular Biology
Wei Wei, Yixue Li, Tao Huang
Summary: This study used RNA-seq data and gene chip data to identify potential biomarkers for colorectal cancer. The SMOTE method was used to address class imbalance, and four feature selection algorithms were used to select genes. Four machine learning algorithms were employed for optimal gene selection and model construction. Interpretable machine learning was used to uncover relationships among the selected genes, and survival analysis revealed significant correlations with prognosis. The study also investigated immune cell proportions and gene mutation rates for the selected biomarkers. The identified biomarkers have implications for personalized therapies and improved clinical outcomes.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2023)
Article
Computer Science, Information Systems
Sydney Mambwe Kasongo
Summary: In recent years, advances in technologies such as cloud computing, vehicular networks systems, and the Internet of Things (IoT) have led to a spike in the amount of information transmitted through communication infrastructures. Consequently, attackers have increased their efforts to exploit vulnerabilities in network systems. Therefore, it is crucial to enhance the security of these network systems. This study implements an IDS framework using Machine Learning techniques and evaluates its performance using benchmark datasets.
COMPUTER COMMUNICATIONS
(2023)
Article
Neurosciences
Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashok Samal, Prahalada K. Rao, Matthew R. Johnson
Summary: Multivariate pattern analysis (MVPA) and deep learning have had significant impacts on cognitive neuroscience and machine learning. While traditional MVPA uses simpler linear calculation techniques, the potential of deep learning in analyzing neuroimaging data is still largely unexplored.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Neurosciences
Roberto Guidotti, Cosimo Del Gratta, Mauro Gianni Perrucci, Gian Luca Romani, Antonino Raffone
Summary: Neuroscientific studies have explored the effects of intensive mental training based on meditation on the organization of the human brain, but the impact of long-term practice of main forms of meditation on connectivity patterns in large-scale brain networks remains to be fully understood. Functional Magnetic Resonance Imaging (fMRI) and multivariate pattern analysis have been used to investigate the impact of meditation expertise and age on functional connectivity patterns, revealing that expertise-predictive patterns are differently affected by Focused Attention (FA) and Open Monitoring (OM), while age-predictive patterns are not influenced by the meditation form. The study suggests that meditation expertise is associated with specific neuroplastic changes in connectivity patterns within and between multiple brain networks.
Article
Computer Science, Artificial Intelligence
Sunil Kumar Prabhakar, Dong-Ok Won
Summary: The diagnosis of cardiovascular diseases is crucial in medicine. Heart sound, which is influenced by blood turbulence and cardiac structures, plays a significant role in the early detection of heart diseases. Phonocardiogram (PCG) is a non-invasive technique used for heart sound analysis. This paper proposes efficient models for PCG signal classification, utilizing techniques like semi-supervised Non-negative Matrix Factorization (NMF), Brain Storming (BS) algorithm, Genetic Programming (GP), dimensionality reduction, and deep learning techniques. The experimental results demonstrate a high classification accuracy of 95.39% using the semi-supervised NMF concept with ABS-GP technique and Support Vector Machine (SVM) classifier.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)