Intrinsically disordered regions (IDRs) are central to diverse cellular processes but present unique challenges for interpreting genetic variants implicated in human disease. Unlike structured protein domains, IDRs lack stable three-dimensional conformations and are often involved in regulation through transient interactions and post-translational modifications. These features can affect both the distribution of pathogenic variants and the performance of computational tools used to predict their effects. Here, we systematically assessed the distribution of pathogenic vs benign missense variants across disordered, intermediate, and structured protein regions in the human proteome. Pathogenic variants were notably depleted in IDRs yet were associated with distinct molecular mechanisms, particularly dominant gain- and loss-of-function effects. We evaluated 33 variant effect predictors (VEPs), revealing widespread reductions in sensitivity for pathogenic variants in IDRs, despite high AUROC scores driven by accurate benign variant predictions. We also observed substantial discordance among VEP classifications in disordered regions, underscoring the need for region-aware thresholds and disorder-informed prediction strategies. Incorporating features reflective of IDR biology, such as transient interaction motifs and modification sites, may enhance the accuracy and interpretability of future tools.

The recent development in computer processing and memory performance, and the growing availability of satellite imageries have made deep learning and convolutional neural networks more effective with wide range applications in urban research fields. The urban environment comprises several significant uses of remote sensing, such as feature extraction, building identification, transportation system management, and infrastructure monitoring. AI-based algorithms, especially convolution neural networks, are characterized by their ability to achieve higher performance for satellite image classification by analyzing multiple accessible data and utilizing all available features. In this paper, a Convolutional Neural Network (CNN) model, based on GoogLeNet, was applied for land cover classification in urban environment. The suggested model was designed, trained, validated, and implemented in a built-up study area with building, road, and vegetation classes. To enhance the classification capabilities required for accurate discrimination of land cover classes, WorldView-2 satellite image has been integrated with point cloud data which enable deriving digital surface model, intensity, normal vector, surface variation, and vertical layers. The model assessment process involved comparing the classification results to the Object Based Image Analysis (OBIA) method to verify and validate the approach. The proposed CNN model achieved an overall accuracy of 83.25% for the classification process and 87.00% for the post-classification refinement outperforming the OBIA classification results of 80.75% and refinement accuracy of 83.50%.

Machine learning sits at the core of many essential products and services at Facebook. This paper describes the hardware and software infrastructure that supports machine learning at global scale. Facebook's machine learning workloads are extremely diverse: services require many different types of models in practice. This diversity has implications at all layers in the system stack. In addition, a sizable fraction of all data stored at Facebook flows through machine learning pipelines, presenting significant challenges in delivering data to high-performance distributed training flows. Computational requirements are also intense, leveraging both GPU and CPU platforms for training and abundant CPU capacity for real-time inference. Addressing these and other emerging challenges continues to require diverse efforts that span machine learning algorithms, software, and hardware design.