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Background: The rarity of primary central nervous system lymphoma (PCNSL) and treatment heterogeneity contributes to a lack of prognostic models for evaluating posttreatment remission. This study aimed to develop and validate radiomic‐based models to predict the durable response (DR) to high‐dose methotrexate (HD‐MTX)‐based chemotherapy in PCNSL patients. Methods: A total of 159 patients pathologically diagnosed with PCNSL between 2011 and 2021 across two institutions were enrolled. According to the NCCN guidelines, the DR was defined as the remission lasting ≥1 year after receiving HD‐MTX‐based chemotherapy. For each patient, a total of 1218 radiomic features were extracted from prebiopsy T1 contrast‐enhanced MR images. Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. The radiomic‐clinical integrated models were developed using the random forest method. Model performance was externally validated to verify its clinical utility. Results: A total of 105 PCNSL patients were enrolled after excluding 54 cases with ineligibility. The training and validation cohorts comprised 76 and 29 individuals, respectively. Among them, 65 patients achieved DR. The radiomic signature, consisting of 8 selected features, demonstrated strong predictive performance, with area under the curves of 0.994 in training cohort and 0.913 in validation cohort. This signature was independently associated with the DR in both cohorts. Both the radiomic signature and integrated models significantly outperformed the clinical models in two cohorts. Decision curve analysis underscored the clinical utility of the established models. Conclusions: This radiomic signature and integrated models have the potential to accurately predict the DR to HD‐MTX‐based chemotherapy in PCNSL patients, providing valuable therapeutic insights. [ABSTRACT FROM AUTHOR]

Under complex field conditions, robust and efficient boll detection at maturity is an important tool for pre-harvest strategy and yield prediction. To achieve automatic detection and counting of long-staple cotton in a natural environment, this paper proposes an improved algorithm incorporating deformable convolution and attention mechanism, called YOLO-C, based on YOLOv7: (1) To capture more detailed and localized features in the image, part of the 3 × 3 convolution in the ELAN layer of the backbone is replaced by deformable convolution to improve the expressiveness and accuracy of the model. (2) To suppress irrelevant information, three SENet modules are introduced after the backbone to improve the ability of feature maps to express information, and CBAM and CA are introduced for comparison experiments. (3) A WIoU loss function based on a dynamic non-monotonic focusing mechanism is established to reduce the harmful gradients generated by low-quality examples on the original loss function and improve the model performance. During the model evaluation, the model is compared with other YOLO series and mainstream detection algorithms, and the model mAP@0.5 achieves 97.19%, which is 1.6% better than the YOLOv7 algorithm. In the model testing session, the root mean square error and coefficient of determination ( R 2 ) of YOLO-C are 1.88 and 0.96, respectively, indicating that YOLO-C has higher robustness and reliability for boll target detection in complex environments and can provide an effective method for yield prediction of long-staple cotton at maturity. [ABSTRACT FROM AUTHOR]

ABSTRACT The present paper concerns the impact of climate on building heating energy consumption for different types of energy in Tianjin, a large city in northern China. The results show that heating energy consumption is dominantly related to dry bulb temperature ( DBT), but the impacts vary with energy types and time scales (day, month or year). DBT can explain 97.7% of the coal consumption at different time scales and 83.0-89.7% of the natural gas consumption. DBT can also account for 25% of the daily and monthly heating electricity consumption and 89% of the annual heating electricity consumption. These results suggest that different measures for building energy conservation should be taken because of the differing climate impacts for different energy types. Especially, likely increase in temperature in the future should be considered to improve the energy efficiency of buildings. In addition, different models should be used to predict building energy consumption at different time scales. This would be helpful for energy policy makers and energy providers for adjusting energy use strategies. [ABSTRACT FROM AUTHOR]

Recently, representation-based classifiers have gained increasing interest in hyperspectral image (HSI) classification. In this letter, based on our previously developed joint collaborative representation (JCR) classifier, an improved version, which is called weighted JCR (WJCR) classifier, is proposed. JCR adopts the same weights when extracting spatial and spectral features from surrounding pixels. Differing from JCR, WJCR attempts to utilize more appropriate weights by considering the similarity between the center pixel and its surroundings. Experimental results using two real HSIs demon strate that the proposed WJCR outperforms the original JCR and some other traditional classifiers, such as the support vector machine (SVM), the SVM with a composite kernel, and simultaneous orthogonal matching pursuit. [ABSTRACT FROM PUBLISHER]

In this letter, kernel collaborative representation with Tikhonov regularization (KCRT) is proposed for hyperspectral image classification. The original data is projected into a high-dimensional kernel space by using a nonlinear mapping function to improve the class separability. Moreover, spatial information at neighboring locations is incorporated in the kernel space. Experimental results on two hyperspectral data prove that our proposed technique outperforms the traditional support vector machines with composite kernels and other state-of-the-art classifiers, such as kernel sparse representation classifier and kernel collaborative representation classifier. [ABSTRACT FROM PUBLISHER]