A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining

Molecule pretraining has quickly become the go-to schema to boost the performance of AI-based drug discovery. Naturally, molecules can be represented as 2D topological graphs or 3D geometric point clouds. Although most existing pertaining methods focus on merely the single modality, recent research has shown that maximizing the mutual information (MI) between such two modalities enhances the molecule representation ability. Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules. To address this issue, we propose MoleculeSDE. MoleculeSDE leverages group symmetric (e.g., SE(3)-equivariant and reflection-antisymmetric) stochastic differential equation models to generate the 3D geometries from 2D topologies, and vice versa, directly in the input space. It not only obtains tighter MI bound but also enables prosperous downstream tasks than the previous work. By comparing with 17 pretraining baselines, we empirically verify that MoleculeSDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks

UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View

In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods have system complexity while the accuracy of single-modal ones is relatively low, how to make a tradeoff between them is difficult. In this work, we propose a universal cross-modality knowledge distillation framework (UniDistill) to improve the performance of single-modality detectors. Specifically, during training, UniDistill projects the features of both the teacher and the student detector into Bird's-Eye-View (BEV), which is a friendly representation for different modalities. Then, three distillation losses are calculated to sparsely align the foreground features, helping the student learn from the teacher without introducing additional cost during inference. Taking advantage of the similar detection paradigm of different detectors in BEV, UniDistill easily supports LiDAR-to-camera, camera-to-LiDAR, fusion-to-LiDAR and fusion-to-camera distillation paths. Furthermore, the three distillation losses can filter the effect of misaligned background information and balance between objects of different sizes, improving the distillation effectiveness. Extensive experiments on nuScenes demonstrate that UniDistill effectively improves the mAP and NDS of student detectors by 2.0%~3.2%

GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning

Molecule property prediction has gained significant attention in recent years. The main bottleneck is the label insufficiency caused by expensive lab experiments. In order to alleviate this issue and to better leverage textual knowledge for tasks, this study investigates the feasibility of employing natural language instructions to accomplish molecule-related tasks in a zero-shot setting. We discover that existing molecule-text models perform poorly in this setting due to inadequate treatment of instructions and limited capacity for graphs. To overcome these issues, we propose GIMLET, which unifies language models for both graph and text data. By adopting generalized position embedding, our model is extended to encode both graph structures and instruction text without additional graph encoding modules. GIMLET also decouples encoding of the graph from tasks instructions in the attention mechanism, enhancing the generalization of graph features across novel tasks. We construct a dataset consisting of more than two thousand molecule tasks with corresponding instructions derived from task descriptions. We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks. Experimental results demonstrate that GIMLET significantly outperforms molecule-text baselines in instruction-based zero-shot learning, even achieving closed results to supervised GNN models on tasks such as toxcast and muv

What do patients complain about online: A systematic review and taxonomy framework based on patient centeredness

© 2019 Journal of Medical Internet Research. All rights reserved. Background: Complaints made online by patients about their health care experiences are becoming prevalent because of widespread worldwide internet connectivity. An a priori framework, based on patient centeredness, may be useful in identifying the types of issues patients complain about online across multiple settings. It may also assist in examining whether the determinants of patient-centered care (PCC) mirror the determinants of patient experiences. Objective: The objective of our study was to develop a taxonomy framework for patient complaints online based on patient centeredness and to examine whether the determinants of PCC mirror the determinants of patient experiences. Methods: First, the best fit framework synthesis technique was applied to develop the proposed a priori framework. Second, electronic databases, including Web of Science, Scopus, and PubMed, were searched for articles published between 2000 and June 2018. Studies were only included if they collected primary quantitative data on patients' online complaints. Third, a deductive and inductive thematic analysis approach was adopted to code the themes of recognized complaints into the framework. Results: In total, 17 studies from 5 countries were included in this study. Patient complaint online taxonomies and theme terms varied. According to our framework, patients expressed most dissatisfaction with patient-centered processes (101,586/204,363, 49.71%), followed by prerequisites (appropriate skills and knowledge of physicians; 50,563, 24.74%) and the care environment (48,563/204,363, 23.76%). The least dissatisfied theme was expected outcomes (3651/204,363, 1.79%). People expressed little dissatisfaction with expanded PCC dimensions, such as involvement of family and friends (591/204,363, 0.29%). Variation in the concerns across different countries' patients were also observed. Conclusions: Online complaints made by patients are of major value to health care providers, regulatory bodies, and patients themselves. Our PCC framework can be applied to analyze them under a wide range of conditions, treatments, and countries. This review has shown significant heterogeneity of patients' online complaints across different countries

Spermidine endows macrophages anti-inflammatory properties by inducing mitochondrial superoxide-dependent AMPK activation, Hif-1α upregulation and autophagy.

Distinct metabolic programs, either energy-consuming anabolism or energy-generating catabolism, were required for different biological functions. Macrophages can adopt different immune phenotypes in response to various cues and exhibit anti- or pro-inflammatory properties relying on catabolic pathways associated with oxidative phosphorylation (OXPHOS) or glycolysis. Spermidine, a natural polyamine, has been reported to regulate inflammation through inducing anti-inflammatory (M2) macrophages. However, the underlying mechanisms remain elusive. We show here that the M2-polarization induced by spermidine is mediated by mitochondrial reactive oxygen species (mtROS). The levels of mitochondrial superoxide and H2O2 were markedly elevated by spermidine. Mechanistically, mtROS were found to activate AMP-activated protein kinase (AMPK), which in turn enhanced mitochondrial function. Furthermore, hypoxia-inducible factor-1α (Hif-1α) was upregulated by the AMPK activation and mtROS and was required for the expression of anti-inflammatory genes and induction of autophagy. Consistent with previous report that autophagy is required for the M2 polarization, we found that the M2 polarization induced by spermidine was also mediated by increased autophagy. The macrophages treated with spermidine in vitro were found to ameliorate Dextran Sulfate Sodium (DSS)-induced inflammatory bowel disease (IBD) in mice. Thus, spermidine can elicit an anti-inflammatory program driven by mtROS-dependent AMPK activation, Hif-1α stabilization and autophagy induction in macrophages. Our studies revealed a critical role of mtROS in shaping macrophages into M2-like phenotype and provided novel information for management of inflammatory disease by spermidine

Recipes and mechanisms of cellular reprogramming: a case study on budding yeast Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Generation of induced pluripotent stem cells (iPSCs) and converting one cell type to another (transdifferentiation) by manipulating the expression of a small number of genes highlight the progress of cellular reprogramming, which holds great promise for regenerative medicine. A key challenge is to find the recipes of perturbing genes to achieve successful reprogramming such that the reprogrammed cells function in the same way as the natural cells.</p> <p>Results</p> <p>We present here a systems biology approach that allows systematic search for effective reprogramming recipes and monitoring the reprogramming progress to uncover the underlying mechanisms. Using budding yeast as a model system, we have curated a genetic network regulating cell cycle and sporulation. Phenotypic consequences of perturbations can be predicted from the network without any prior knowledge, which makes it possible to computationally reprogram cell fate. As the heterogeneity of natural cells is important in many biological processes, we find that the extent of this heterogeneity restored by the reprogrammed cells varies significantly upon reprogramming recipes. The heterogeneity difference between the reprogrammed and natural cells may have functional consequences.</p> <p>Conclusions</p> <p>Our study reveals that cellular reprogramming can be achieved by many different perturbations and the reprogrammability of a cell depends on the heterogeneity of the original cell state. We provide a general framework that can help discover new recipes for cellular reprogramming in human.</p

Genome-Wide Association Study of Lung Adenocarcinoma in East Asia and Comparison With a European Population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P interaction  = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications