Lung cancer is one of the major causes of cancer-related deaths due to its aggressive nature and delayed detections at advanced stages. Early detection of lung cancer is very important for the survival of an individual, and is a significant challenging problem. Generally, chest radiographs (X-ray) and computed tomography (CT) scans are used initially for the diagnosis of the malignant nodules; however, the possible existence of benign nodules leads to erroneous decisions. At early stages, the benign and the malignant nodules show very close resemblance to each other. In this paper, a novel deep learning-based model with multiple strategies is proposed for the precise diagnosis of the malignant nodules. Due to the recent achievements of deep convolutional neural networks (CNN) in image analysis, we have used two deep three-dimensional (3D) customized mixed link network (CMixNet) architectures for lung nodule detection and classification, respectively. Nodule detections were performed through faster R-CNN on efficiently-learned features from CMixNet and U-Net like encoder–decoder architecture. Classification of the nodules was performed through a gradient boosting machine (GBM) on the learned features from the designed 3D CMixNet structure. To reduce false positives and misdiagnosis results due to different types of errors, the final decision was performed in connection with physiological symptoms and clinical biomarkers. With the advent of the internet of things (IoT) and electro-medical technology, wireless body area networks (WBANs) provide continuous monitoring of patients, which helps in diagnosis of chronic diseases—especially metastatic cancers. The deep learning model for nodules’ detection and classification, combined with clinical factors, helps in the reduction of misdiagnosis and false positive (FP) results in early-stage lung cancer diagnosis. The proposed system was evaluated on LIDC-IDRI datasets in the form of sensitivity (94%) and specificity (91%), and better results were obatined compared to the existing methods.

Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2_Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the “vehicle face” is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability.

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In this retrospective cohort study, we found BiPAP, compared with CPAP, reduced the need for intubation within the first 72 h of age.

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Constitutive activation of FGFR1, as a result of diverse chromosome translocations, is the hallmark of stem cell leukemia/lymphoma syndrome. The BCR‐FGFR1 variant is unique in that the BCR component contributes a serine–threonine kinase (STK) to the N‐terminal end of the chimeric FGFR1 kinase. We have deleted the STK domain and mutated the critical Y177 residue and demonstrate that the transforming activity of these mutated genes is reduced compared to the BCR‐FGFR1 parental kinase. In addition, we demonstrate that deletion of the FGFR1 tyrosine kinase domain abrogates transforming ability, which is not compensated for by BCR STK activity. Unbiased screening for proteins that are inactivated as a result of loss of the BCR STK identified activated S6 kinase and SHP2 kinase. Genetic and pharmacological inhibition of SHP2 function in SCLL cells expressing BCR‐FGFR1 in vitro leads to reduced viability and increased apoptosis. In vivo treatment of SCLL in mice with SHP099 leads to suppression of leukemogenesis, supporting an important role for SHP2 in FGFR1‐driven leukemogenesis. In combination with the BGJ398 FGFR1 inhibitor, cell viability in vitro is further suppressed and acts synergistically with SHP099 in vivo suggesting a potential combined targeted therapy option in this subtype of SCLL disease.