Soft X-ray tomography reveals variations in B. subtilis biofilm structure upon tasA deletion by Anthoula Chatzimpinou, Anne Diehl, A. Tobias Harhoff, Kristina Driller, Bieke Vanslembrouck, Jian-Hua Chen, Kristaps Kairišs, Valentina Loconte, Mark A. Le Gros, Carolyn Larabell, Kürşad Turgay, Hartmut Oschkinat, Venera Weinhardt

“…This approach offers 50 nm isotropic spatial resolution, rapid imaging, and quantitative native analysis of biofilm structure. …”
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Enhancing dysarthria severity classification: efficient audio based deep learning models by Varsha J Varma, Aniruddha Jana, Arpan Kumar Samal, Aurobindo S, Rani Chinnappa Naidu, Somaya Al-Maadeed, Moutaz Saleh, Rajesh Kumar M, Prakash Ramachandran, Zachariah C Alex, Rajeev Khoodeeram

“…Abstract A complex motor speech disorder, dysarthria makes diagnosis and its severity classification extremely challenging, thereby affecting suitable therapy and intervention strategies. …”
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The role of ultrasound combined with water bath in the establishment of animal models of rat urethral stricture by Xiaojian Ye, Xianjing Lin, Yehui Chen, Rongquan Xu, Jiawei Zhou

“…Urethral stricture presents a complex challenge in urology, often requiring invasive treatment modalities. …”
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Deep learning for video-based assessment of endotracheal intubation skills by Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De

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Inertial sensor-based heel strike and energy expenditure prediction using a hybrid machine learning approach by Kethohalli R Vidyarani, Viswanath Talasila, Raafay Umar, Venkatesan Prem, Ravi Prasad K Jagannath, Gurusiddappa R Prashanth

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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…Coastal boundaries, a critical factor in coastal erosion monitoring, are typically extracted from remote sensing images. Due to the extensive scale of coastal areas and the complexity of the data, manually extracting coastal boundaries is challenging. …”
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Multi scale supervised entropy weighted binary pattern for texture classification by Xiaochun Xu, Bin Li

“…Abstract Texture is a crucial visual and sensory attribute in understanding the world. The complexity of imaging environments, variations in acquisition angles and distances, and differences in resolution make representing multi-scale texture features a core challenge in texture analysis. …”
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Symbiotic probiotic communities with multiple targets successfully combat obesity in high-fat-diet-fed mice by Dingming Guo, Yun Deng, Qianqian Yang, Min Li, Xiangfeng Wang, Xuchun Wan, Junqing He, Ying Xu, Wenxin Huang, Guohua Lin, Ya Xu, Yi Sun, Ruilin Zhang, Wei-Hua Chen, Zhi Liu

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Giant renal angiomyolipoma unmasks underlying lymphangioleiomyomatosis: a case report and literature review by Xuan Zhou, Xuan Zhou, Qiaoling Chen, Qiaoling Chen, Zhiyong Xiong, Zhiyong Xiong, Miao Wang, Miao Wang, Xiaoping Zhang, Xiaoping Zhang, Xiaoping Zhang

“…This case highlights the need to consider LAM in young women with renal AML and emphasizes the value of multidisciplinary management in addressing such complex clinical scenarios.…”
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Non-cadaveric spine surgery simulator training in neurosurgical residency by Paul Pöser, MD, Robert Schenk, MD, Hannah Miller, MD, Ahmad Alghamdi, MD, Adrien Lavalley, MD, Katharina Tielking, MD, Nitzan Nissimov, MD, Anton Früh, MD, Denny Chakkalakal, MD, Victor Patsouris, MD, Tarik Alp Sargut, MD, Robert Mertens, MD, Ran Xu, MD, Peter Truckenmüller, MD, Kiarash Ferdowssian, MD, Judith Rösler, MD, David Wasilewski, MD, Claudius Jelgersma, MD, Anna Roethe, MD, Aminaa Sanchin, MD, Peter Vajkoczy, MD, Thomas Picht, MD, Julia Sophie Onken, PD, MD

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AI Innovations in Liver Transplantation: From Big Data to Better Outcomes by Eleni Avramidou, Dominik Todorov, Georgios Katsanos, Nikolaos Antoniadis, Athanasios Kofinas, Stella Vasileiadou, Konstantina-Eleni Karakasi, Georgios Tsoulfas

“…Artificial intelligence (AI) has emerged as a transformative field in computational research with diverse applications in medicine, particularly in the field of liver transplantation (LT) given its ability to analyze and build upon complex and multidimensional data. This literature review investigates the application of AI in LT, focusing on its role in pre-implantation biopsy evaluation, development of recipient prognosis algorithms, imaging analysis, and decision-making support systems, with the findings revealing that AI can be applied across a variety of fields within LT, including diagnosis, organ allocation, and surgery planning. …”
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A ‘torn bag mechanism’ of small extracellular vesicle release via limiting membrane rupture of en bloc released amphisomes (amphiectosomes) by Tamás Visnovitz, Dorina Lenzinger, Anna Koncz, Péter M Vizi, Tünde Bárkai, Krisztina V Vukman, Alicia Galinsoga, Krisztina Németh, Kelsey Fletcher, Zsolt I Komlósi, Csaba Cserép, Ádám Dénes, Péter Lőrincz, Gábor Valcz, Edit I Buzas

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68Ga-NOTA-RM26 PET/CT in the evaluation of glioma: a pilot prospective study by Yilin Li, Rongxi Wang, Jingci Chen, Zhaohui Zhu, Yu Wang, Wenbin Ma

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Direct DLP-based 3D-printing of metallopolymers featuring shape-memory abilities by Michael Klein, Michael F. Agyemang, Oswald Müschke, Michael Schmitt, Jürgen Popp, Martin D. Hager, Stefan Zechel, Ulrich S. Schubert

“…Additionally, an imaging across the 3D-printed structure could be performed indicating the equal distribution of the complexes in the structure. …”
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Application of deep learning for coherent pixel selection in time series InSAR for urban area and transport infrastructure monitoring by S. Azadnejad, A. Kandiri, A. Hrysiewicz, F. O’Loughlin, E.P. Holohan, S. Dev, S. Donohue

“…In this study, a novel framework is proposed for selecting coherent pixels within the Stanford Method for Persistent Scatterers Small Baseline (StaMPS-SB) approach based on the temporal coherence predicted by deep learning. Unlike previous complex deep learning approaches to this problem, a multi-layer perceptron (MLP) model is trained on time-domain and frequency-domain features extracted from a time series of SAR amplitude images to predict the temporal coherence value of each individual pixel, and thus to select coherent pixels. …”
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Decomposing door-to-needle time: insights into workflow delays and quality improvement in acute stroke care by Lianyan Wei, Hongfei Li, Zhuangzhuang Jiang, Dongjuan Xu, Xiaolan Wu

“…Semiannual trends showed a gradual improvement in DTN performance until T3 (May–October 2024), followed by a plateau in T4 (November 2024–April 2025), possibly due to increased use of imaging-guided thrombolysis and more complex referrals.ConclusionPhysician seniority, thrombolysis strategy, and MRI-negative status significantly influence DTN time. …”
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Selecting a Superior Landing Area in the Region Far From Procellarum KREEP Terrane by Wenqing Chang, Zhiguo Meng, Xuegang Dong, Meng Zou, Xiaoping Zhang, Yuanzhi Zhang

“…Utilizing high-resolution images, topographic data, surface compositions, and Chang'e-2 microwave radiometer (MRM) data, a systematic analysis and comparison have been done between Maria Crisium and Smythii in terms of geomorphological feature, regolith properties, lunar crust and mantle properties, and microwave thermal emission characteristics. …”
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Is Ultrasonography an Effective Method for Diagnosing Degenerative Changes in the Temporomandibular Joint? by Barbara Wojciechowska, Arkadiusz Szarmach, Adam Michcik, Maciej Sikora, Barbara Drogoszewska

“…The first table presents a comparative analysis of radiological outcomes in patients with temporomandibular joint disorders using different imaging techniques. …”
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Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation by Dong Q, Wu J, Zhang H, Chen X, Xu X, Chen J, Shi C, Luo L, Zhang D

“…Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). …”
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A Comprehensive Review of Deep Learning Applications in Cotton Industry: From Field Monitoring to Smart Processing by Zhi-Yu Yang, Wan-Ke Xia, Hao-Qi Chu, Wen-Hao Su, Rui-Feng Wang, Haihua Wang

“…Traditional technologies such as spectral imaging and machine learning improved cotton cultivation and processing, yet their performance often falls short in complex agricultural environments. …”
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