Wandering around: a bioinspired approach to visual attention through object motion sensitivity by Giulia D’Angelo, Victoria Clerico, Chiara Bartolozzi, Matej Hoffmann, P Michael Furlong, Alexander Hadjiivanov

“…Integrating event-based sensors with neuromorphic algorithms represents a paradigm shift, using spiking neural networks (SNNs) to parallelise computation and adapt to dynamic environments. …”
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Helicobacter pylori eradication treatment for gastric carcinoma prevention in asymptomatic or dyspeptic adults: systematic review and Bayesian meta-analysis of randomised controlle... by Teruhiko Terasawa, Isao Miyashiro, Katsuaki Kato, Takaki Yoshikawa, Reo Takaku, Hiroshi Nishida

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Subtidal seagrass and blue carbon mapping at the regional scale: a cloud-native multi-temporal Earth Observation approach by Mar Roca, Chengfa Benjamin Lee, Avi Putri Pertiwi, Alina Blume, Isabel Caballero, Gabriel Navarro, Dimosthenis Traganos

“…Our synoptic tool uses Sentinel-2 A/B satellite imagery at 10 m spatial resolution to generate a multi-temporal composite (2016–2022) of the Balearic Islands’ coastal waters within the Google Earth Engine cloud computing platform, optimizing image processing and highlighting the importance of a high-resolution bathymetric dataset to increase seagrass mapping accuracies. Machine learning algorithms have been applied to perform seagrass detection, obtaining a seagrass cartography up to 30 m of depth, estimating 505.6 km2 of seagrass habitat extent. …”
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SPYDER: QoS-Aware Radio Resource Allocation in Multiuser ISAC-Capable C-V2X Networks by Syed Najaf Haider Shah, Aamir Ullah Khan, Christian Schneider, Joerg Robert

“…Since SPYDER adopts a non-uniform TF interleaved OFDM grid, it may experience multiuser resource overlapping that could degrade radar detection performance. To counteract this, we employ sparse reconstruction algorithms within the compressed sensing framework, enhancing flexibility in TF resource allocation and providing high-resolution radar sensing despite uncoordinated resource selection. …”
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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Abstract Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. However, no comprehensive review or meta-analysis has assessed the performance of these models across different sociocultural contexts. …”
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Explainable Artificial Intelligence Driven Segmentation for Cervical Cancer Screening by Niruthikka Sritharan, Nishaanthini Gnanavel, Prathushan Inparaj, Dulani Meedeniya, Pratheepan Yogarajah

“…This innovative approach to segmentation is formally introduced through two algorithms detailed in this paper. The weakly supervised segmentation framework achieved a Dice Similarity Coefficient (DSC) of 62.05% and an Intersection over Union (IoU) of 61.89%. …”
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Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. …”
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Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study by Jiani Liu, Xin Zhang, Wei Li, Francis Manyori Bigambo, Dandan Wang, Xu Wang, Beibei Teng

“…Abstract Background Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. …”
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Limitations of using artificial intelligence services to analyze chest x-ray imaging by Yuriy A. Vasilev, Anton V. Vladzymyrskyy, Kirill M. Arzamasov, Igor M. Shulkin, Elena V. Astapenko, Lev D. Pestrenin

“…All false-positive cases were associated with erroneous detection of clinically significant pathology: pneumothorax, lung nodules, and pulmonary consolidation. …”
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AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images by Csaba Csikos, Sándor Barna, Ákos Kovács, Péter Czina, Ádám Budai, Melinda Szoliková, Iván Gábor Nagy, Borbála Husztik, Gábor Kiszler, Ildikó Garai

“…For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. …”
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Commensal and virulent Escherichia coli strains of vaginal origin are reservoirs of resistance cassettes in class 1 integrons by Manalli Itzahaya Blancarte–Lagunas, Graciela Castro-Escarpulli, Armando Navarro-Ocaña, Gabriela Ibáñez-Cervantes, Laura Margarita Marquez-Valdelamar, José Misael Hernández-Carrillo, Juana Salazar-Salinas, Omar Fernando Mendoza-Vásquez, Gabriel Damazo-Hernández, Oscar Sosa-Hernández, Gregorio León-García, Monica Alethia Cureño-Díaz, Juan Manuel Bello-López

“…Finally, genotyping by Clermont algorithm and serotyping were performed. Seventeen strains showed integrons located in plasmids. …”
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Prospects of artificial intelligence for the sustainability of sugarcane production in the modern era of climate change: An overview of related global findings by Rajan Bhatt, Akbar Hossain, Debjyoti Majumder, Mandapelli Sharath Chandra, Rajiv Ghimire, Muhammad Faisal Shahzad, Krishan K. Verma, Amarinder Singh Riar, Vishnu D. Rajput, Mauro Wagner Oliveira, Adel Nisi, Riyadh S. Almalki, Viliam Bárek, Marian Brestic, Sagar Maitra

“…Artificial intelligence (AI) tools, such as machine learning algorithms, can optimize irrigation, increase yields, save water, and properly estimate sugarcane production. …”
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Statistical Comparison between Pores and Sunspots during the Time Interval 2010–2023 by Yang Peng, Yu Fei, Nan-bin Xiang, Lin-hua Deng, Ting-ting Xu, Sheng Zheng, Shu-guang Zeng, Hai-yang Zhang, Shi-hu Liu

“…The OTSU method and region-growing algorithm were combined to detect umbrae of 11,876 sunspots covering solar cycles 24 and 25. …”
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Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning by Kaiqi Cheng, Kaiqi Cheng, Jingzhe Xiao, Jingyuan He, Rongguang Yang, Jinjin Pei, Jinjin Pei, Wengang Jin, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…A total of 243 volatile metabolites were detected, with sulfur compounds accounting for 51-64%, followed by organic acids and their derivatives (4-19%). …”
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Multicenter exploration of microbial communities in hospital toilets reveals: antibiotic exposure in nosocomial settings selects for Enterococcus over commensal taxa by Claudio Neidhöfer, Marcel Neuenhoff, Esther Sib, Anna Rehm, Lúcia Ribeiro Dias, Bernd Neumann, Jörg Steinmann, Manuel Döhla, Yousra Kherabi, Ana Budimir, Crystel Hajjar, Rouba Khatib, Katharina Axtmann, Katjana Schwab, Peter Brossart, Steffen Engelhart, Nico T. Mutters, Gabriele Bierbaum, Stefan Janssen, Marijo Parčina

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Deep Learning−Driven Exophthalmometry through Facial Photographs in Thyroid Eye Disease by Joonhyeon Park, PhD, Jin Sook Yoon, MD, PhD, Namju Kim, MD, PhD, Kyubo Shin, PhD, Hyun Young Park, MD, PhD, Jongchan Kim, MS, Jaemin Park, MS, Jae Hoon Moon, MD, PhD, JaeSang Ko, MD, PhD

“…The receiver operating characteristic curve analysis showed area under the curves of 0.91 for SH and 0.88 for SNUBH in detecting proptosis. The system detected significant proptosis changes (≥ 2 mm) with 74.6% accuracy. …”
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Five abdominal computer tomography characteristics facilitate diagnosis of spontaneous perforation of pyometra in women with acute abdomen: a case control study by Yingying He, Chaoran Li, Fang Long, Zhigang Sun, Yangzong Cidan, Jing Li, Penglong Cao, Rui Ma, Weiwei Wang, Xiaoguang Xu, Zhen Xiao

“…Conclusions: The above five CT characteristics and the developed dignostic algorithm could be used as a tool to facilitate the preoperative diagnosis of spontaneous perforation of pyometra, although more tests are needed in large samples of cases.…”
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Mapping Invasive <italic>Spartina alterniflora</italic> Using Phenological Information and Red-Edge Bands of Sentinel-2 Time-Series Data by Yiwei Ma, Li Zhuo, Jingjing Cao

“…Results indicated that red-edge information at different phenological periods is essential for detecting <italic>S. alterniflora</italic>. <italic>S. alterniflora</italic> extraction achieved the highest accuracy of 96.57&#x0025; by using the eXtreme gradient boost algorithm when compared with other machine-learning algorithms. …”
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National-scale mapping topsoil organic carbon of cropland in China using multitemporal Sentinel-2 images by Jie Xue, Xianglin Zhang, Songchao Chen, Zhongxing Chen, Rui Lu, Feng Liu, Bas van Wesemael, Zhou Shi

“…First, a new time-series bare soil extraction method, the Two-Dimensional Bare Soil Separation Algorithm, was applied, utilizing Sentinel-2 images from 2018 to 2022 to generate a continuous spectral reflectance composite. …”
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An autonomous navigation method for field phenotyping robot based on ground-air collaboration by Zikang Zhang, Zhengda Li, Meng Yang, Jiale Cui, Yang Shao, Youchun Ding, Wanneng Yang, Wen Qiao, Peng Song

“…It utilizes SegFormor-B0 semantic segmentation models to detect crop rows, and extracts key coordinate points of these rows, and generates navigation paths for the phenotyping robots by mapping these points to actual geographic coordinates. …”
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