The interaction between temperature and precipitation on the potential distribution range of Betula ermanii in the alpine treeline ecotone on the Changbai Mountain by Yu Cong, Yongfeng Gu, Wen J. Wang, Lei Wang, Zhenshan Xue, Yingyi Chen, Yinghua Jin, Jiawei Xu, Mai-He Li, Hong S. He, Ming Jiang

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AQP5 trafficking is regulated by its C-terminal tail and interaction with prolactin-inducible protein by Claudia D’Agostino, Egor Zindy, Louise Conrard, Amel Takkal, Françoise Gregoire, Nargis Bolaky, Susanna Törnroth-Horsefield, Jason Perret, Christine Delporte

“…The innovative methodology to assess AQP5 translocation to the plasma membrane sets the stage for future investigations to identify the role of individual amino acids and phosphorylation sites within the distal AQP5 C-terminus in the trafficking mechanism and protein-protein interaction, and to explore the dynamic of the process by high resolution live cell imaging. Further research in this area is warranted to uncover critical insights into the regulation of AQP5, offering opportunities for the development of innovative therapeutic strategies.…”
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A Convolutional Neural Network Model for Classifying Resting Tremor Amplitude in Parkinson’s Disease by Augusto Ielo, Serena Dattola, Lilla Bonanno, Paolo De Pasquale, Alberto Cacciola, Angelo Quartarone, Maria Cristina De Cola

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Modern methods for adhesive bowel obstruction diagnosis by K. D. Rybakov, G. S. Sednev, E. M. Askerov, A. M. Morozov, A. N. Pichugova, M. A. Belyak

“…Currently, new promising methods for diagnosing this disease, including biomarkers and high-tech methods for visualizing the pathological process, such as computed tomography and magnetic resonance imaging, are acquiring high importance. …”
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A METHOD OF CONTACTLESS MEASUREMENT OF SURFACE TEMPERATURE OF RADIO ELECTRONIC OBJECTS by V. K. Bityukov, A. N. Zhukov, D. S. Simachkov

“…A procedure for the pyrometry of objects (pyrometers, thermal imagers), using OESs, is proposed, which includes planar arrangement within the view of OES of the object under investigation, the normal spectral emissivity of the surface of which is known, and a reference transmitter, the normal spectral emissivity of which is also known, and its temperature is controlled and measured. …”
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Spectral purification improves monitoring accuracy of the comprehensive growth evaluation index for film-mulched winter wheat by Zhikai Cheng, Xiaobo Gu, Yadan Du, Zhihui Zhou, Wenlong Li, Xiaobo Zheng, Wenjing Cai, Tian Chang

“…Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV. …”
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Observational study of sudden cardiac arrest risk (OSCAR): Rationale and design of an electronic health records cohort by Kyndaron Reinier, Harpriya S. Chugh, Audrey Uy-Evanado, Elizabeth Heckard, Marco Mathias, Nichole Bosson, Vinicius F. Calsavara, Piotr J. Slomka, David A. Elashoff, Alex A.T. Bui, Sumeet S Chugh

“…We will use conventional approaches (diagnosis code algorithms) and artificial intelligence (natural language processing, deep learning) to define patient phenotypes and biostatistical and machine learning approaches for analysis. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. …”
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Discrimination between sea ice and clouds over the Chukchi Sea via China’s ultraviolet–visible-infrared observations by Ziyi Suo, Yingcheng Lu, Lijian Shi, Bin Zou, Qing Wang, Ling Li, Jun Tang, Weimin Ju, Manchun Li

“…To address these limitations, this study proposes a novel methodological framework for discriminating between sea ice and different cloud types (cirrus and cumulus) via the ultraviolet–visible-infrared observations from China’s Haiyang-1C/D (HY-1C/D) satellites, and the ultraviolet (UV) data from the onboard Ultraviolet Imager (UVI) are used to study sea ice and clouds over the Chukchi Sea for the first time. …”
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Optical Characterization of Coastal Waters with Atmospheric Correction Errors: Insights from SGLI and AERONET-OC by Hiroto Higa, Masataka Muto, Salem Ibrahim Salem, Hiroshi Kobayashi, Joji Ishizaka, Kazunori Ogata, Mitsuhiro Toratani, Kuniaki Takahashi, Fabrice Maupin, Stephane Victori

“…This study identifies the characteristics of water regions with negative normalized water-leaving radiance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><msub><mrow><mi>L</mi></mrow><mrow><mi>w</mi></mrow></msub><mo>(</mo><mi>λ</mi><mo>)</mo></mrow></semantics></math></inline-formula>) values in the satellite observations of the Second-generation Global Imager (SGLI) sensor aboard the Global Change Observation Mission–Climate (GCOM-C) satellite. …”
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kMetha-Mamba: K-means clustering mamba for methane plumes segmentation by Yuquan Liu, Hailiang Shi, Ke Cao, Shichao Wu, Hanhan Ye, Xianhua Wang, Erchang Sun, Yunfei Han, Wei Xiong

“…Existing quantitative segmentation methods based on convolutional neural networks (CNNs) and Transformers are limited in their ability to process large-scale remote sensing images. They are also susceptible to interference from species with similar spectral features, resulting in high false positive rates. …”
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Mixed nontuberculous mycobacteria in an immunocompromised patient with progressive multifocal leukoencephalopathy by Dr Christoffel Opperman, Dr Janet Scott, Dr Aliasgar Esmail, Dr Sarishna Singh, Mr Yonas Ghebrekristos, Prof Rob warren, Dr Wynand Goosen

“…In a previous admission computed tomography brain image showed a right superior cerebellar peduncle infarct. …”
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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

“…To reveal the physical properties of pores and sunspots varying with solar cycle, we carried out a statistical comparison among pores, transitional sunspots, and mature sunspots using Solar Dynamics Observatory/Helioseismic and Magnetic Imager from 2010 April to 2023 July. 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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Modeling the cooling effect of urban green infrastructures with an ecosystem services approach ( case study: Tehran metropolis) by S. Malekzadeh, H.R. Jafari, R. Nazari, T. Blaschke, A. Hof, M. Karimi

“…Land use/land cover maps of Tehran were generated using Landsat imagery from Thematic Mapper (2002), Enhanced Thematic Mapper Plus (2012), and Operational Land Imager (2022), processed with geometric and radiometric corrections in ENVI 5.3. …”
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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Although porescale imaging and network modeling techniques can compute capillary pressure from micro-CT rock images (Øren and Bakke, 2003; Valvatne and Blunt, 2004), these approaches are time-consuming, limited to small sample volumes, and not yet practical for routine reservoir evaluation. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…Following acid-fast staining, whole slide images (WSI) were digitized, and AFB organisms were manually annotated. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Before pathological biopsy, all patients underwent biparametric MRI, including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient scans. …”
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Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology by Yinhu Gao, Peizhen Wen, Yuan Liu, Yahuang Sun, Hui Qian, Xin Zhang, Huan Peng, Yanli Gao, Cuiyu Li, Zhangyuan Gu, Huajin Zeng, Zhijun Hong, Weijun Wang, Ronglin Yan, Zunqi Hu, Hongbing Fu

“…With the development of artificial intelligence (AI) technologies such as deep learning, real-time lesion detection with endoscopic assistance and automated pathological image analysis have shown potential in improving diagnostic accuracy and efficiency. …”
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Compatibility Between OLCI Marine Remote-Sensing Reflectance from Sentinel-3A and -3B in European Waters by Frédéric Mélin, Ilaria Cazzaniga, Pietro Sciuto

“…For the atmospheric correction <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>l</mi><mn>2</mn><mi>g</mi><mi>e</mi><mi>n</mi></mrow></semantics></math></inline-formula>, validation results obtained with field data from the ocean-color component of the Aerosol Robotic Network (AERONET-OC) and uncertainty estimates appear consistent between S-3A and S-3B as well as with other missions processed with the same algorithm. Estimates of the error correlation between S-3A and S-3B <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>R</mi><mrow><mi>R</mi><mi>S</mi></mrow></msub></semantics></math></inline-formula>, required to evaluate their compatibility, are computed based on common matchups and indicate varying levels of correlation for the various bands and sites in the interval 0.33–0.60 between 412 and 665 nm considering matchups of all sites put together. …”
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Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma by Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Fan Yang, Fan Yang, Fan Yang, Fan Yang, Wenjing Hou, Wenjing Hou, Wenjing Hou, Wenjing Hou, Ying Wang, Ying Wang, Ying Wang, Ying Wang, Jiali Mu, Jiali Mu, Jiali Mu, Jiali Mu, Hailing Wang, Hailing Wang, Hailing Wang, Hailing Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei

“…We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively.ResultsThe study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. …”
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