32 research outputs found
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
Predicting molecular properties (e.g., atomization energy) is an essential
issue in quantum chemistry, which could speed up much research progress, such
as drug designing and substance discovery. Traditional studies based on density
functional theory (DFT) in physics are proved to be time-consuming for
predicting large number of molecules. Recently, the machine learning methods,
which consider much rule-based information, have also shown potentials for this
issue. However, the complex inherent quantum interactions of molecules are
still largely underexplored by existing solutions. In this paper, we propose a
generalizable and transferable Multilevel Graph Convolutional neural Network
(MGCN) for molecular property prediction. Specifically, we represent each
molecule as a graph to preserve its internal structure. Moreover, the
well-designed hierarchical graph neural network directly extracts features from
the conformation and spatial information followed by the multilevel
interactions. As a consequence, the multilevel overall representations can be
utilized to make the prediction. Extensive experiments on both datasets of
equilibrium and off-equilibrium molecules demonstrate the effectiveness of our
model. Furthermore, the detailed results also prove that MGCN is generalizable
and transferable for the prediction.Comment: The 33rd AAAI Conference on Artificial Intelligence (AAAI'2019),
Honolulu, USA, 201
Transcriptional and Translational Relationship in Environmental Stress: RNAseq and ITRAQ Proteomic Analysis Between Sexually Reproducing and Parthenogenetic Females in Moina micrura
Moina micrura is a kind of small-bodied water flea within the family Moinidae. Similar to Daphnia, M. micrura could also switch its reproduction mode from parthenogenetic female (PF) to sexual female (SF) to adapt to the external environment. To uncover the mechanisms of reproductive switching in M. micrura, we used both RNA-Seq and iTRAQ analyses to investigate the differentially expressed genes (DEGs) and their protein products between SF and PF in M. micrura. A total of 1665 DEGs (702 up-regulated, 963 down-regulated) and 600 differentially expressed proteins (DEPs) (102 up-regulated, 498 down-regulated) were detected in SF. Correlation analyses indicated that 31 genes were expressed significantly differentially at both transcriptomic and proteomic levels, including 15 up-regulated genes and 16 down-regulated genes in SF. Meanwhile, our data also showed that 528 DEPs have discordant expression at transcript level, implying post-transcriptional (including translational) regulation. These top up-regulated genes and their protein products in SF were mainly grouped into the globin-related family, vitellogenin-related family, cuticle-related family, Hsp-related family and methyltransferases-related family, which were all involved in the reproductive switching in Daphnia. In contrast, a cluster of orthologous groups revealed that up-regulated genes and their protein products in PF were strongly associated with the metabolic process, which may be responsible for rapid population proliferation in M. micrura
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial
intelligence, enabling natural language processing tasks that were previously
thought to be exclusive to humans. In this work, we introduce Qwen, the first
installment of our large language model series. Qwen is a comprehensive
language model series that encompasses distinct models with varying parameter
counts. It includes Qwen, the base pretrained language models, and Qwen-Chat,
the chat models finetuned with human alignment techniques. The base language
models consistently demonstrate superior performance across a multitude of
downstream tasks, and the chat models, particularly those trained using
Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The
chat models possess advanced tool-use and planning capabilities for creating
agent applications, showcasing impressive performance even when compared to
bigger models on complex tasks like utilizing a code interpreter. Furthermore,
we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as
well as mathematics-focused models, Math-Qwen-Chat, which are built upon base
language models. These models demonstrate significantly improved performance in
comparison with open-source models, and slightly fall behind the proprietary
models.Comment: 59 pages, 5 figure
Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease
BACKGROUND:
Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes.
METHODS:
We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization.
RESULTS:
During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events.
CONCLUSIONS:
Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)
A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory
Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysis performance. To solve this problem, an author name disambiguation task is designed to divide documents related to an author name reference into several parts and each part is associated with a real-life person. Existing methods usually use either attributes of documents or relationships between documents and co-authors. However, methods of feature extraction using attributes cause inflexibility of models while solutions based on relationship graph network ignore the information contained in the features. In this paper, we propose a novel name disambiguation model based on representation learning which incorporates attributes and relationships. Experiments on a public real dataset demonstrate the effectiveness of our model and experimental results demonstrate that our solution is superior to several state-of-the-art graph-based methods. We also increase the interpretability of our method through information theory and show that the analysis could be helpful for model selection and training progress
Investigations on Transverse-Mode Competition and Beam Quality Modeling in End-Pumped Lasers
ProtGNN: Towards Self-Explaining Graph Neural Networks
Despite the recent progress in Graph Neural Networks (GNNs), it remains challenging to explain the predictions
made by GNNs. Existing explanation methods mainly focus on post-hoc explanations where another explanatory model is employed to provide explanations for a trained GNN. The fact that post-hoc methods fail to reveal the original reasoning process of GNNs raises the need of building GNNs with built-in interpretability. In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs. In ProtGNN, the explanations are naturally derived from the case-based reasoning process and are actually used during classification. The prediction of ProtGNN is obtained by comparing the inputs to a few learned prototypes in the latent space.
Furthermore, for better interpretability and higher efficiency, a novel conditional subgraph sampling module is incorporated to indicate which part of the input graph is most similar to each prototype in ProtGNN+. Finally, we evaluate our method on a wide range of datasets and perform concrete case studies. Extensive results show that ProtGNN and ProtGNN+ can provide inherent interpretability while achieving accuracy on par with the non-interpretable counterparts
Complication rate of different wound closures after primary hip arthroplasty - A survey of 373 patients
Background: Wound closure is highly associated with wound complications and the best wound closure method was controversial in total hip arthroplasty.
Methods: We performed a retrospective study of primary hip arthroplasty and compared three types of closure method.
Results: 155 cases were closed using continuous subcuticular sutures then with staples, 111 using staples, 141 using interrupted sutures. 28 cases of wound complications occurred. Wound complication rates in subcuticular suture group, staple group and interrupted suture group were 1.9%, 11.7% and 8.5%, respectively (p < 0.01).
Conclusion: Wound complication rate was significantly lower when wound was closed with continuous subcuticular suturue