Sound and Precise Analysis of Multithreaded Programs through Schedule Specialization and Execution Filters

Multithreaded programs are known to be difficult to analyze. A key reason is that they typically have an enormous number of execution interleavings, or schedules. Static analysis with respect to all schedules requires over-approximation, resulting in poor precision; dynamic analysis rarely covers more than a tiny fraction of all schedules, so its result may not hold for schedules not covered. To address this challenge, we propose a novel approach called schedule specialization that restricts the schedules of a program to make it easier to analyze. Schedule specialization combines static and dynamic analysis. It first statically analyzes a multithreaded program with respect to a small set of schedules for precision, and then enforces these schedules at runtime for soundness of the static analysis results. To demonstrate that this approach works, we build three systems. The first system is a specialization framework that specializes a program into a simpler program based on a schedule for precision. It allows stock analyses to automatically gain precision with only little modification. The second system is Peregrine, a deterministic multithreading system that collects and enforces schedules on future inputs. Peregrine reuses a small set of schedules on many inputs, ensuring our static analysis results to be sound for a wide range of inputs. It also enforces these schedules efficiently, making schedule specialization suitable for production usage. Although schedule specialization can make static concurrency error detection more precise, some concurrency errors such as races may still slip detection and enter production systems. To mitigate this limitation, we build Loom, a live-workaround system that protects a live multithreaded program from races that slip detection. It allows developers to easily write execution filters to safely and efficiently work around deployed races in live multithreaded programs without restarting them

Oxazolidinones as versatile scaffolds in medicinal chemistry

Oxazolidinone is a five-member heterocyclic ring with several biological applications in medicinal chemistry. Among the three possible isomers, 2-oxazolidinone is the most investigated in drug discovery. Linezolid was pioneered as the first approved drug containing an oxazolidinone ring as the pharmacophore group. Numerous analogues have been developed since its arrival on the market in 2000. Some have succeeded in reaching the advanced stages of clinical studies. However, most oxazolidinone derivatives reported in recent decades have not reached the initial stages of drug development, despite their promising pharmacological applications in a variety of therapeutic areas, including antibacterial, antituberculosis, anticancer, anti-inflammatory, neurologic, and metabolic diseases, among other areas. Therefore, this review article aims to compile the efforts of medicinal chemists who have explored this scaffold over the past decades and highlight the potential of the class for medicinal chemistry

Research on neural network prediction method for upgrading scale of natural gas reserves

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields

Multi-omics approaches to studying gastrointestinal microbiome in the context of precision medicine and machine learning

The human gastrointestinal (gut) microbiome plays a critical role in maintaining host health and has been increasingly recognized as an important factor in precision medicine. High-throughput sequencing technologies have revolutionized -omics data generation, facilitating the characterization of the human gut microbiome with exceptional resolution. The analysis of various -omics data, including metatranscriptomics, metagenomics, glycomics, and metabolomics, holds potential for personalized therapies by revealing information about functional genes, microbial composition, glycans, and metabolites. This multi-omics approach has not only provided insights into the role of the gut microbiome in various diseases but has also facilitated the identification of microbial biomarkers for diagnosis, prognosis, and treatment. Machine learning algorithms have emerged as powerful tools for extracting meaningful insights from complex datasets, and more recently have been applied to metagenomics data via efficiently identifying microbial signatures, predicting disease states, and determining potential therapeutic targets. Despite these rapid advancements, several challenges remain, such as key knowledge gaps, algorithm selection, and bioinformatics software parametrization. In this mini-review, our primary focus is metagenomics, while recognizing that other -omics can enhance our understanding of the functional diversity of organisms and how they interact with the host. We aim to explore the current intersection of multi-omics, precision medicine, and machine learning in advancing our understanding of the gut microbiome. A multidisciplinary approach holds promise for improving patient outcomes in the era of precision medicine, as we unravel the intricate interactions between the microbiome and human health

Peste des Petits Ruminants Virus in Tibet, China

Serologic and molecular evidence indicates that peste des petits ruminants virus (PPRV) infection has emerged in goats and sheep in the Ngari region of southwestern Tibet, People’s Republic of China. Phylogenetic analysis confirms that the PPRV strain from Tibet is classified as lineage 4 and is closely related to viruses currently circulating in neighboring countries of southern Asia

gpucc: An Open-Source GPGPU Compiler

Abstract Graphics Processing Units have emerged as powerful accelerators for massively parallel, numerically intensive workloads. The two dominant software models for these devices are NVIDIA's CUDA and the cross-platform OpenCL standard. Until now, there has not been a fully open-source compiler targeting the CUDA environment, hampering general compiler and architecture research and making deployment difficult in datacenter or supercomputer environments. In this paper, we present gpucc, an LLVM-based, fully open-source, CUDA compatible compiler for high performance computing. It performs various general and CUDAspecific optimizations to generate high performance code. The Clang-based frontend supports modern language features such as those in C++11 and C++14. Compile time is 8% faster than NVIDIA's toolchain (nvcc) and it reduces compile time by up to 2.4x for pathological compilations (>100 secs), which tend to dominate build times in parallel build environments. Compared to nvcc, gpucc's runtime performance is on par for several open-source benchmarks, such as Rodinia (0.8% faster), SHOC (0.5% slower), or Tensor (3.7% faster). It outperforms nvcc on internal large-scale end-to-end benchmarks by up to 51.0%, with a geometric mean of 22.9%