Phosphate transporter gene families in rye (Secale cereale L.) – genome-wide identification, characterization and sequence diversity assessment via DArTreseq by David Chan-Rodriguez, Brian Wakimwayi Koboyi, Sirine Werghi, Bradley J. Till, Julia Maksymiuk, Fatemeh Shoormij, Abuya Hilderlith, Anna Hawliczek, Maksymilian Królik, Hanna Bolibok-Brągoszewska

“…The aim of this study was to: (i) identify and characterize putative members of different phosphate transporter families in rye, (ii) assess their sequence diversity in a collection of 94 diverse rye accessions via low-coverage resequencing (DArTreseq), and (iii) evaluate the expression of putative rye Pht genes under phosphate-deficient conditions. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…AGB was retrieved with significantly lower accuracy than SV, and S2-GEDI models outperformed EMIT-GEDI ones, likely because of the higher S2 spatial resolution better capturing AGB variability associated to different tree species. GEDI LiDAR proved to be a necessary input for accurate SV and AGB retrieval, and GPR was the best-performing ML algorithm. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…What we did We did five detailed analyses to evaluate how well different heart disease prevention methods work and if they are worth the cost: Big picture review: we summarised findings from 95 reviews on medications and lifestyle changes. …”
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Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma by Gang Wang, Jiaxing Zhang, Yirong Li, Yuyu Zhang, Weiwei Dong, Hengquan Wu, Jinglan Wang, Peiqing Liao, Ziqiang Yuan, Tao Liu, Wenting He

“…Three machine-learning algorithms were used to identify shared diagnostic genes. …”
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Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study by Juan Xie, Run-wei Ma, Yu-jing Feng, Yuan Qiao, Hong-yan Zhu, Xing-ping Tao, Wen-juan Chen, Cong-yun Liu, Tan Li, Kai Liu, Li-ming Cheng

“…Objective The aim of this study was to develop a pertussis risk prediction model that is both efficient and has good generalization ability, applicable to different datasets. The model was constructed using machine learning techniques based on multicenter data and screened for key features. …”
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Effectiveness of myocarditis therapy depending on the diagnosis approach (with or without myocardial biopsy) by O. V. Blagova, A. V. Nedostup, V. P. Sedov, A. Yu. Zaitsev, V. M. Novosadov, E. A. Kogan

“…Aim. To evaluate the effectiveness of myocarditis therapy depending on the diagnosis approach (with or without myocardial biopsy).Material and methods. …”
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The effect of Pressurised Intraperitoneal Aerosol Chemotherapy on the quality of life of patients with primary diagnosed ovarian cancer with peritoneal carcinomatosis during combin... by A. S. Dzasokhov, A. A. Kostin, V. L. Astashov, A. V. Turiev, A. D. Uskov

“…To identify a significant difference between the indicators at different stages of the study, the Page rank criterion for an ordered alternative and an algorithm based on the Friedman rank sum test were used. …”
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Analysis of influencing factors and equity in hospitalization expense reimbursement for mobile populations based on random forest model: a cross-sectional study from China by Lisheng Shen, Xinan Lu, Yanyun Zhang, Lin Fei, Bo Dong

“…Secondly, the random forest algorithm was applied to evaluate the importance of the factors influencing hospitalization expense reimbursement. …”
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A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…Furthermore, the performance of each hybrid model and the majority voting ensemble of the three competing ML models were evaluated using trained and refined hybrid CNN models combined with Support Vector Machine (SVM) algorithms, with the best-performing model selected as the benchmark. …”
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Cotton Fiber Micronaire and Relations to Fiber HVI and AFIS Qualities Between Deltapine^® and PhytoGen Upland Varieties by Yongliang Liu, Doug J. Hinchliffe

“…There were noticeable differences among HVI and AFIS qualities from Deltapine^® fiber samples and PhytoGen samples, with significant differences om HVI strength and elongation. …”
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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Furthermore, the lack of standardized evaluation benchmarks makes it difficult to compare the performance of different DL models in hydrogeological contexts. …”
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Fuzzy Model Predictive Control for Unmanned Helicopter by Łukasz Kiciński, Sebastian Topczewski

“…The proposed solution involves the use of the Model Predictive Control framework enhanced with the Takagi–Sugeno inference algorithm. The designed system uses a Parallel Distributed Compensation architecture and utilizes multiple linear dynamics models to precisely model the helicopter’s response in transitioning from hovering to forward flight. …”
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Ground truth clustering is not the optimum clustering by Lucia Absalom Bautista, Timotej Hrga, Janez Povh, Shudian Zhao

“…The results reveal that the optimum clusterings often differ significantly from the ground truth clusterings. …”
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Time synchronization for long-chain-type wireless sensor networks by Zhenping Chen, Yourui Huang, Zheng Wang, Feng Tao

“…Moreover, the sink node evaluates the network periodically and adjusts the synchronizing cycle based on the difference between the network synchronization error and the given synchronization accuracy. …”
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Regionalization of water quality parameters based on the landscape characteristics of small ungauged basins by Danilo Garofalo, Marcos Ferreira

“…The non-parametric k-nearest neighbour regression (K-NNR) algorithm was used to estimate the WQP values for the small sub-basins that lacked data. …”
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Ensuring information security in the production network of an industrial enterprise by A. R. Aydynan, D. G. Kirsanov

“…The analysis presented in the article identifies differences between automated process control systems and classical information systems, based on the NIST SP 800-82 standard, and evaluates their unique requirements and vulnerabilities. …”
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MLST reveals a clonal population structure for Cryptococcus neoformans molecular type VNI isolates from clinical sources in Amazonas, Northern-Brazil. by Diego Fernando Silva Rocha, Katia Santana Cruz, Carla Silvana da Silva Santos, Lizandra Stephanny Fernandes Menescal, João Ricardo da Silva Neto, Silviane Bezerra Pinheiro, Lucyane Mendes Silva, Luciana Trilles, João Vicente Braga de Souza

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THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection by Ju Liu, Haoqian Wang, Xiangai Cheng, Zhongyang Xing, Zhongjie Xu

“…Extensive experiments on five datasets verify the effectiveness and superiority of the proposed THANTD across various evaluation metrics.…”
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Spatiotemporal inhomogeneity of accuracy degradation in AI weather forecast foundation models: A GNSS perspective by Junsheng Ding, Wu Chen, Junping Chen, Jungang Wang, Yize Zhang, Lei Bai, Yuyan Wang, Xiaolong Mi, Tong Liu, Duojie Weng

“…Using these metrics, we analyzed the spatiotemporal inhomogeneity in the accuracy degradation of foundation models, represented by Huawei Cloud Pangu-Weather, Google DeepMind GraphCast, and Shanghai AI Lab FengWu. We evaluated how this inhomogeneity changes with forecast time and identified the best-performing models across different regions and forecast durations. …”
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