Reveromycin A-Induced Apoptosis in Osteoclasts Is Not Accompanied by Necrosis.

Reveromycin A (RM-A), a small natural product isolated from Streptomyces bacteria, is a potential osteoporosis therapeutic in that it specifically induces apoptosis in osteoclasts but not osteoblasts. The purpose of the study presented here was to further elucidate the intracellular mechanisms of RM-A death effects in mature osteoclasts. A specific clone of RAW264.7 murine macrophages that was previously characterized for its ability to acquire an osteoclast nature on differentiation was differentiated in the presence of receptor activator of nuclear factor kappa B ligand (RANKL). Subsequent staining was performed for tartrate-resistant acid phosphatase to confirm their osteoclast character. These osteoclasts were treated with ten micromolar RM-A for 2, 4, 6, 24, and 48 h at a pH of 5.5. Peak apoptosis induction occurred at 4-6 h as measured by caspase 3 activity. Lactate dehydrogenase release assay revealed no significant RM-A-induced necrosis. Western blot analysis of cytoplasmic extracts demonstrated activation of caspase 9 (2.3-fold at 2 h and 2.6-fold at 4 h, each P < 0.05) and no significant changes in Bcl-XL . In nuclear extracts, NFκB levels significantly increased on differentiation with RANKL but then remained constant through RM-A treatment. Over the extended time course studied, RM-A-induced apoptosis in osteoclasts was not accompanied by necrosis, suggesting that RM-A would likely have limited effects on immediate, neighboring bone cell types. This specific cell death profile is promising for potential clinical investigations of RM-A as a bone antiresorptive.

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics.

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling. Yet, despite their promise, the transposition of these techniques to the neuroimaging domain has been challenging due to the expansive number of potential preprocessing pipelines and the large parameter search space for graph-based dataset construction. In this paper, we introduce NeuroGraph, a collection of graph-based neuroimaging datasets, and demonstrated its utility for predicting multiple categories of behavioral and cognitive traits. We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking. Additionally, we provide generic frameworks for learning on both static and dynamic graphs. Our extensive experiments lead to several key observations. Notably, using correlation vectors as node features, incorporating larger number of regions of interest, and employing sparser graphs lead to improved performance. To foster further advancements in graph-based data driven neuroimaging analysis, we offer a comprehensive open-source Python package that includes the benchmark datasets, baseline implementations, model training, and standard evaluation.

ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering.

Advancements in spatial transcriptomics (ST) have enabled an in-depth understanding of complex tissues by quantifying gene expression at spatially localized spots. Several notable clustering methods have been introduced to utilize both spatial and transcriptional information in the analysis of ST datasets. However, data quality across different ST sequencing techniques and types of datasets influence the performance of different methods and benchmarks. To harness spatial context and transcriptional profile in ST data, we developed a graph-based, multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on a graph autoencoder backbone and performs an iterative clustering on imputed, differentially expressed genes-based matrices to minimize the variance of clustering results. ADEPT outperformed other popular methods on ST data generated by different platforms across analyses such as spatial domain identification, visualization, spatial trajectory inference, and data denoising.

Integrating Expert Knowledge with Deep Learning Improves QSAR Models for CADD Modeling.

In recent years several applications of graph neural networks (GNNs) to molecular tasks have emerged. Whether GNNs outperform the traditional descriptor-based methods in the quantitative structure activity relationship (QSAR) modeling in early computer-aided drug discovery (CADD) remains an open question. This paper introduces a simple yet effective strategy to boost the predictive power of QSAR deep learning models. The strategy proposes to train GNNs together with traditional descriptors, combining the strengths of both methods. The enhanced model consistently outperforms vanilla descriptors or GNN methods on nine well-curated high throughput screening datasets over diverse therapeutic targets.

Inferring EHR Utilization Workflows through Audit Logs.

Electronic health records (EHRs) usage and clinical workflows are intrinsically linked. To accommodate the complex care settings (e.g., emergency departments), EHR utilization workflows dynamically change in clinical practice, which in turn shapes the clinical workflows. Learning EHR workflows would provide an opportunity for healthcare organizations to enhance clinical workflows in the context of EHRs. However, very few studies investigated HER utilization workflows executed in clinical practice. We develop a network analysis framework and apply it to EHR audit logs to infer EHR workflows. We then measure the differences in the workflows between patient subgroups divided by races via differential network analysis. We apply our framework to trauma patients admitted to the emergency department, which is one of the clinical settings that need timely support from EHR utilizations. Our results show five core EHR workflows related to Narrator, Navigator, SmartTools, Chart Review, and ED workup activities in the ED. We find EHR workflows involving Narrator, SmartTools, and BPA are different when comparing patient subgroups.

Interpretable Visual Understanding with Cognitive Attention Network.

While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge. In this paper, we propose a novel Cognitive Attention Network (CAN) for visual commonsense reasoning to achieve interpretable visual understanding. Specifically, we first introduce an image-text fusion module to fuse information from images and text collectively. Second, a novel inference module is designed to encode commonsense among image, query and response. Extensive experiments on large-scale Visual Commonsense Reasoning (VCR) benchmark dataset demonstrate the effectiveness of our approach. The implementation is publicly available at https://github.com/tanjatang/CAN.