Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied comp... by HaDi MaBouDi, Hideaki Shimazaki, Martin Giurfa, Lars Chittka

“…The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). …”
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IoT-MFaceNet: Internet-of-Things-Based Face Recognition Using MobileNetV2 and FaceNet Deep-Learning Implementations on a Raspberry Pi-400 by Ahmad Saeed Mohammad, Thoalfeqar G. Jarullah, Musab T. S. Al-Kaltakchi, Jabir Alshehabi Al-Ani, Somdip Dey

“…The investigation proposes a framework called IoT-MFaceNet (Internet-of-Things-based face recognition using MobileNetV2 and FaceNet deep-learning) utilizing pre-existing deep-learning methods, employing the MobileNetV2 and FaceNet algorithms on both ImageNet and FaceNet databases. …”
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A protocol of a randomized control trial to test the feasibility and efficacy of the EMPOWER social-emotional learning curriculum for youth aged 11-14 years in after-school setting... by Alice-Simone Balter, Madison Moloney, Clement Ma, Alina Lee, Sandra Pierre, Sheldon Parkes, Doga Pulat, Nicole Racine, Brendan F Andrade

“…Program staff and youth across both intervention and no-intervention control groups will be asked to participate in baseline and post-intervention data collection where they may complete questionnaires about youth's social-emotional learning skills, global quality of functioning, and resilience skills. …”
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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Met... by A. S. Albahri, Z. T. Al-qaysi, Laith Alzubaidi, Alhamzah Alnoor, O. S. Albahri, A. H. Alamoodi, Anizah Abu Bakar

“…The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. …”
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Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…We subsequently conducted functional experiments to elucidate the mechanism of A2ML1 in promoting PC progression. …”
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Wide & deep learning for predicting relative mineral compositions of sediment cores solely based on XRF scans, a case study from Pleistocene Paleolake Olduvai, Tanzania by Gayantha R.L. Kodikara, Lindsay J. McHenry, Ian G. Stanistreet, Harald Stollhofen, Jackson K. Njau, Nicholas Toth, Kathy Schick

“…This study develops a method to use deep learning models to predict the mineral assemblages and their relative abundances in paleolake cores using high-resolution XRF core scan elemental data and X-ray diffraction (XRD) mineralogical results from the same core taken at coarser resolution. …”
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Three Decades of Land Cover Dynamics in a Boreal Coastal Basin: A Multisensor Spectral Index and Machine Learning Approach Using Landsat Data and GB-SAR Data by Jinsong Zhang, Bochi Zou, Yifei Yuan, Asad Khan, Muhammad Bilawal Junaid, Qaiser Abbas, Rana Muhammad Zulqarnain, Nazih Y. Rebouh, Olga D. Kucher, Hassan Alzahrani

“…Boreal coastal habitats, with their complex hydrological, ecological, and geological functions, play a pivotal role in regulating regional climate, supporting biodiversity, and sustaining local communities. …”
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A Self-Adaptive Serious Game to Improve Motor Learning Among Older Adults in Immersive Virtual Reality: Short-Term Longitudinal Pre-Post Study on Retention and Transfer by Gauthier Everard, Louise Declerck, Thierry Lejeune, Martin Gareth Edwards, Justine Bogacki, Cléo Reiprich, Kelly Delvigne, Nicolas Legrain, Charles Sebiyo Batcho

“…ConclusionsThese findings provide perspectives for the use of iVR to improve motor learning in older adults through delivering self-adaptive serious games targeting motor and cognitive functions. …”
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Big data for imaging assessment in glaucoma by Douglas R. da Costa, Felipe A. Medeiros

“…With the rapid development of computational technologies, artificial intelligence (AI) and deep learning (DL) algorithms are emerging as potential tools for screening, diagnosing, and monitoring glaucoma progression. …”
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THE DEVELOPMENT OF INFORMATION COMPETENCES FOR MEDICAL STUDENTS by S. I. Karas, O. I. Ostrikova, M. B. Arzhanik, I. O. Korneva

“…There are additional functions besides usual for paper medical record, for example: learning about specialized software, electronic re­ference books, and medical standard documents. …”
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Bridging prediction and decision: Advances and challenges in data-driven optimization by Yanzhi Wang, Jianxiao Wang, Haoran Zhang, Jie Song

“…Key methodologies such as ''sequential optimization,'' ''end-to-end learning,'' and ''direct learning'' are analyzed, offering both theoretical insights and practical implications. …”
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Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context by Matthias Jung, Vineet K. Raghu, Marco Reisert, Hanna Rieder, Susanne Rospleszcz, Tobias Pischon, Thoralf Niendorf, Hans-Ulrich Kauczor, Henry Völzke, Robin Bülow, Maximilian F. Russe, Christopher L. Schlett, Michael T. Lu, Fabian Bamberg, Jakob Weiss

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Multimodal approaches and AI-driven innovations in dementia diagnosis: a systematic review by Revati M. Wahul, Sarita Ambadekar, Deepesh M. Dhanvijay, Mrinai M. Dhanvijay, Manisha A. Dudhedia, Varsha Gaikwad, Bhavana Kanawade, J. R. Pansare, Balaji Bodkhe, S. H. Gawande

“…By causing progressive cognitive and functional decline, Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) subtypes are an essential area for urgently needed work. …”
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Motor imagery decoding network with multisubject dynamic transfer by Zhi Li, Mingai Li, Yufei Yang

“…Abstract Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient’s movement intentions accurately through decoding motor imagery EEG (MI-EEG) . …”
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A detection algorithm for small surface floating objects based on improved YOLOv5s by Xusheng YUE, Jun LI, Yaohong WANG, Penghao ZHU, Zhexing WANG, Xuanhao XU

“…Finally, the Normalized Wasserstein Distance (NWD) regression loss function was introduced and combined with the IoU loss function in a weighted manner to construct a comprehensive regression loss function, further enhancing detection accuracy for floating bottles on the water surface.ResultsExperimental results show that the proposed algorithm achieves a mAP@0.5 of 95.7% in detecting floating bottles on the water surface. …”
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A statistical framework for analysis of trial-level temporal dynamics in fiber photometry experiments by Gabriel Loewinger, Erjia Cui, David Lovinger, Francisco Pereira

“…We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. …”
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Analyzing the design of montessori-based media to introduce letters and numbers by Dandi Yunidar, Nurul Fitriana Bahri, Ica Ramawisari, Sheila Andita Putri

“…Sandpaper letters and numbers offer moderate functionality and safety, emphasizing tactile learning, while moving alphabet presents higher functionality and interactive engagement. …”
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Evaluating the representational power of pre-trained DNA language models for regulatory genomics by Ziqi Tang, Nirali Somia, Yiyang Yu, Peter K. Koo

“…Abstract Background The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experiments. …”
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Automated seizure detection in epilepsy using a novel dynamic temporal-spatial graph attention network by Kunxian Yan, Xiangyu Luo, Lei Ye, Wenping Geng, Jian He, Jiliang Mu, Xiaojuan Hou, Xiang Zan, Jiuhong Ma, Fei Li, Le Zhang, Xiujian Chou

“…By integrating graph signal processing with a hybrid deep learning framework, DTS-GAN collaboratively extracts spatiotemporal features through two key modules: an LSTM-based temporal encoder to model long-term dependencies in EEG sequences, and a dynamic graph attention network with probabilistic Gaussian connectivity, enabling adaptive learning of transient functional interactions across electrode nodes. …”
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Development of graphical user interface for design of experiments via Gaussian process regression and its case study by Yoshiki Hasukawa, Mikael Kuwahara, Lauren Takahashi, Keisuke Takahashi

“…Bayesian optimization, coupled with Gaussian process regression and acquisition functions, has proven to be a powerful tool in the field of experimental design. …”
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