Longitudinal evaluation of common and unique brain-networks in variants of primary progressive aphasia by Rajan Kashyap, Rose Dawn Bharath, Changsong Zhou, Kyrana Tsapkini, John E. Desmond, SH Annabel Chen, Kaviraja Udupa, PT Sivakumar, Sagarika Bhattacharjee

“…To validate the rsfMRI networks and the longitudinal changes, we evaluated the grey matter (GM) volume, GM atrophy and the rate-of-atrophy of the brain areas. To corroborate the findings, we applied persistent homology on the structural networks derived from diffusion tensor images. …”
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A mobile app to improve adherence to colorectal cancer screening and post polypectomy surveillance guidelines by María A. Casas, Francisco Schlottmann, Leandro Steinberg, Xavier Bessa, Anna Serradesanferm, Angels Pozo, Sonia Torres, Antoni Castells, Francesc Balaguer, Jaume Grau, Lisandro Pereyra, María Pellisé

“…Users’ characteristics, consultation types, and patient data were analyzed to assess app’s implementation, usage patterns, and impact on CRC screening and surveillance outcomes. …”
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Ultrafast complex-valued 4D fMRI reveals sleep-induced brain respiratory pulsation changes in both magnitude and phase signals by Saara Sofia Syväoja, Lauri Raitamaa, Heta Helakari, Jussi Kantola, Matti Järvelä, Janne Kananen, Ville Isokoski, Vesa Korhonen, Tommi Väyrynen, Timo Tuovinen, Jürgen Hennig, Vesa Kiviniemi

“…To study these pulsations more deeply, we employed ultrafast magnetic resonance encephalography (MREG) to capture complex-valued 4D fMRI brain data at a critical 10 Hz sampling rate in healthy volunteers during wakefulness and sleep. …”
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An interpretable XAI deep EEG model for schizophrenia diagnosis using feature selection and attention mechanisms by Ahmad Almadhor, Stephen Ojo, Thomas I. Nathaniel, Shtwai Alsubai, Abdullah Alharthi, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…The study proposes a novel automated technique for diagnosing Schizophrenia based on electroencephalogram (EEG) sensor data, aiming to enhance interpretability and prediction performance.MethodsThis research utilizes Deep Learning (DL) models, including the Deep Neural Network (DNN), Bi-Directional Long Short-Term Memory-Gated Recurrent Unit (BiLSTM- GRU), and BiLSTM with Attention, for the detection of Schizophrenia based on EEG data. …”
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The diagnostic utility of lung ultrasound in the assessment of interstitial lung disease associated with rheumatoid arthritis by Shaoyu Zheng, Zexuan Zhou, Guangzhou Du, Qingzi Chen, Shaoqi Chen, Jianqun Lin, Shijian Hu, Weijin Zhang, Kedi Zheng, Jinghua Zhuang, Meigan Huang, Barbara Ruaro, Cosimo Bruni, Anna-Maria Hoffmann-Vold, Marco Matucci-Cerinic, Daniel E. Furst, Yukai Wang

“…Sonographic ILD was detected in 76 patients who underwent LUS examination. The concordance rate between two modalities was 83.33% (Kappa value = 0.64, 95% CI 0.50–0.78). …”
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Gesture recognition for hearing impaired people using an ensemble of deep learning models with improving beluga whale optimization-based hyperparameter tuning by Mohammed Assiri, Mahmoud M. Selim

“…For the feature extraction process, the squeeze-and-excitation capsule network (SE-CapsNet) effectively captures spatial hierarchies and complex relationships within gesture patterns. In addition, an ensemble of classification processes, such as bidirectional gated recurrent unit (BiGRU), Variational Autoencoder (VAE), and bidirectional long short-term memory (BiLSTM) technique, is employed. …”
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An Overview of Deep Learning Applications in Groundwater Level Modeling: Bridging the Gap between Academic Research and Industry Applications by Ahmed Shakir Ali Ali, Farhad Jazaei, Peyman Babakhani, Muhammad Masood Ashiq, Alireza Bakhshaee, Brian Waldron

“…Later, this article delves specifically into the latest advancements in DL for modeling GWL, including recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and discusses their technical promising performance and advantages. …”
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Mediodorsal thalamic nucleus mediates resistance to ethanol through Cav3.1 T-type Ca2+ regulation of neural activity by Charles-francois V Latchoumane, Joon-Hyuk Lee, Seong-Wook Kim, Jinhyun Kim, Hee-Sup Shin

“…Thalamocortical activity is known to orchestrate sensory gating and consciousness switching. The precise thalamic regions involved, or the firing patterns related to the unconsciousness, remain unclear. …”
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Analysis of HBV infection characteristics and OBI prevalence in pregnant women: a focus on HBcAb + alone or in combination with HBeAb + subgroups in Northwest China by Yun Xie, Ru Kang, Xiaoyue Cheng, Dan Li, Qi Wang, Jiao Li, Xuan Guo, Wenhui Gao, Xin He, Liutong Jiang, Gaili Meng

“…Abstract Background Hepatitis B virus (HBV) infection is a major global health concern. Serological patterns such as “HBeAb + and HBcAb+” or “HBcAb + only” are typically markers, which may represent either resolved infection or potential occult HBV infection (OBI) with risk of vertical transmission. …”
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The effect of photodynamic therapy on the wound process dynamics in patients with purulent hand diseases by J. L. Chepurnaya, G. G. Melkonyan, N. T. Gul’muradova, A. A. Sorokin

“…The analysis of cytological and morphological patterns also revealed an accelerated switch from the inflammatory stage of the wound process to the reparative one - the reparative processes in the PDT group began earlier by 2 days. …”
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Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. by Felipe J Colón-González, Leonardo Soares Bastos, Barbara Hofmann, Alison Hopkin, Quillon Harpham, Tom Crocker, Rosanna Amato, Iacopo Ferrario, Francesca Moschini, Samuel James, Sajni Malde, Eleanor Ainscoe, Vu Sinh Nam, Dang Quang Tan, Nguyen Duc Khoa, Mark Harrison, Gina Tsarouchi, Darren Lumbroso, Oliver J Brady, Rachel Lowe

“…As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. …”
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Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits by F. Shokor, P. Croiseau, H. Gangloff, R. Saintilan, T. Tribout, T. Mary-Huard, B.C.D. Cuyabano

“…Deep learning (DL) offers a promising alternative for capturing nonlinear genetic relationships due to its ability to identify complex patterns without prior assumptions about the data structure. …”
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Population immunity to SARS-CoV-2 virus in residents of the Irkutsk Region in the dynamics of the epidemic by S. V. Balakhonov, V. I. Dubrovina, M. V. Chesnokova, D. D. Bryukhova, N. O. Kiseleva, A. B. Pyatidesyatnikova, K. M. Korytov, V. V. Voitkova, A. N. Perezhogin, T. A. Gavrilova, A. A. Seledtsov

“…In this regard, the study of the manifestations of the epidemic process of the new coronavirus infection COVID-19 and the patterns of its development are an urgent area of research. …”
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Groundwater rebound and seepage characteristics and water quality evolution in a sealed mining area in North China type coalfield by Huiyong YIN, Dehui SUN, Fangying DONG, Fanhua WANG, Lifeng ZHANG, Tao WU, Chao LIU, Yifan ZENG

“…After the goaf sealing, water-rock interactions persisted during the reinjection of the accumulated water, but the cation exchange rate slowed down. The mineral phases showed similar but weakened variation trends compared to the water inrush stage. …”
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Dynamics of changes in population immunity to the SARS-CоV-2 virus in residents the Irkutsk region in the context of the COVID-19 pandemic by S. V. Balakhonov, V. I. Dubrovina, A. B. Pyatidesyatnikova, D. D. Briukhova, N. O. Kiseleva, K. M. Korytov, V. V. Voitkova, A. N. Perezhogin, M. V. Chesnokova, T. A. Gavrilova, A. A. Seledtcov

“…A comprehensive study of the patterns of development and manifestations of the epidemic process of the new coronavirus infection COVID-19 is an urgent line of research. …”
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A first large study of whole-exome sequencing (WES) in 489 patients with suspected rare genetic disorders at a tertiary centre in Malaysia by Lip Hen Moey, Go Hun Seo, Boon Eu Cheah, Wee Teik Keng, Hane Lee, Gaik Siew Ch’ng

“…Introduction: Rare genetic disorders occur at a rate of 40–82 per 1000 live births worldwide. Identifying the molecular cause of rare genetic disorders is beneficial because it allows early diagnosis, facilitates treatment planning and provision of accurate genetic counselling. …”
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Effects of Dietary Schizochytrium Algae as ω-3 PUFA Source on the Egg-Laying Quail Performance, Serum Indexes, and Egg Yolk Fatty Acids Contents by Yifan Chang, Yue Xuan, Ruinan Zhang, Xuemei Ding, Qiufeng Zeng, Jianping Wang, Shiping Bai, Shanshan Li, Yan Liu, Yuchuan Chen, Keying Zhang

“…The objective of this experiment is to investigate the effects of SAP or SAO as ω-3 PUFA raw materials on production performance, egg quality, serum immunity, serum lipids, and fatty acid deposition patterns in the eggs of laying quails. Chinese yellow-feathered quails served as the experimental subjects. …”
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Identify suitable artificial groundwater recharge zones using hybrid deep learning models by Navaz Khalillollahi, Mohsen Isari, Hamed Faroqi, Kaywan Othman Ahmed, Kamran Nobakht Vakili, Miklas Scholz, Saad Sh. Sammeng

“…This study evaluated four deep learning models for delineating groundwater recharge zones: Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and hybrid deep learning Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). …”
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Conserved missense variant pathogenicity and correlated phenotypes across paralogous genes by Tobias Brünger, Alina Ivaniuk, Eduardo Pérez-Palma, Ludovica Montanucci, Stacey Cohen, Lacey Smith, Shridhar Parthasarathy, Ingo Helbig, Michael Nothnagel, Patrick May, Dennis Lal

“…For the gene family of voltage-gated sodium channels, where variants and expert-curated clinical phenotypes are available, we also assess whether phenotype patterns of multiple disorders for each gene are conserved across variant positions within the gene family. …”
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Insights on the effect of mega-carcass abundance on the population dynamics of a facultative scavenger predator and its prey by Sidous, Mellina, Cubaynes, Sarah, Gimenez, Olivier, Drouet-Hoguet, Nolwenn, Dray, Stéphane, Bollache, Loïc, Madhlamoto, Daphine, Ngwenya, Nobesuthu Adelaide, Fritz, Hervé, Valeix, Marion

“…Models that allow hyaena and their prey populations’ growth rate to vary depending on these four periods contributed significantly to explain variations in their density, which is consistent with an effect of management measures on the population dynamics of hyaena and its prey.  …”
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