Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that allows users to specify local domain knowledge to enable fine-grained search. In Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint local searching strategies to different parts of the target program. To further optimize local searching strategies, we design an interval-based algorithm that automatically isolates the context of variables for different local searching strategies, avoiding conflicts between local searching strategies for the same variable. We implement Eunomia as a symbolic execution platform targeting WebAssembly, which enables us to analyze applications written in various languages (like C and Go) but can be compiled into WebAssembly. To the best of our knowledge, Eunomia is the first symbolic execution engine that supports the full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated microbenchmark suite for symbolic execution and six real-world applications. Our evaluation shows that Eunomia accelerates bug detection in real-world applications by up to three orders of magnitude. According to the results of a comprehensive user study, users can significantly improve the efficiency and effectiveness of symbolic execution by writing a simple and intuitive Aes script. Besides verifying six known real-world bugs, Eunomia also detected two new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 202

Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC

Background:SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure.Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database.Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database.Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment

Line identification of extreme ultraviolet spectra from aluminum ions in EAST Tokamak plasmas

Extreme ultraviolet (EUV) spectra emitted from aluminum in the 5-340 A wavelength range were observed in Experimental Advanced Superconducting Tokamak (EAST) discharges. Several spectral lines from aluminum ions with different degrees of ionization were successfully observed with sufficient spectral intensities and resolutions using three fast-time-response EUV spectrometers. The line identification uses three independent state-of-art computational codes for the atomic structure calculations, which provide the wavelengths and radiative transition probabilities rate coefficients. These programs are HULLAC (Hebrew University - Lawrence Livermore Atomic Code), AUTOSTRUCTURE, and FAC (Flexible Atomic Code). Using three different codes allows us to resolve some ambiguities in identifying certain spectral lines and assess the validity of the theoretical predictions

NeuroSeg-II: A deep learning approach for generalized neuron segmentation in two-photon Ca2+ imaging

The development of two-photon microscopy and Ca2+ indicators has enabled the recording of multiscale neuronal activities in vivo and thus advanced the understanding of brain functions. However, it is challenging to perform automatic, accurate, and generalized neuron segmentation when processing a large amount of imaging data. Here, we propose a novel deep-learning-based neural network, termed as NeuroSeg-II, to conduct automatic neuron segmentation for in vivo two-photon Ca2+ imaging data. This network architecture is based on Mask region-based convolutional neural network (R-CNN) but has enhancements of an attention mechanism and modified feature hierarchy modules. We added an attention mechanism module to focus the computation on neuron regions in imaging data. We also enhanced the feature hierarchy to extract feature information at diverse levels. To incorporate both spatial and temporal information in our data processing, we fused the images from average projection and correlation map extracting the temporal information of active neurons, and the integrated information was expressed as two-dimensional (2D) images. To achieve a generalized neuron segmentation, we conducted a hybrid learning strategy by training our model with imaging data from different labs, including multiscale data with different Ca2+ indicators. The results showed that our approach achieved promising segmentation performance across different imaging scales and Ca2+ indicators, even including the challenging data of large field-of-view mesoscopic images. By comparing state-of-the-art neuron segmentation methods for two-photon Ca2+ imaging data, we showed that our approach achieved the highest accuracy with a publicly available dataset. Thus, NeuroSeg-II enables good segmentation accuracy and a convenient training and testing process

Enhanced metabolic flux of methylerythritol phosphate (MEP) pathway by overexpression of Ginkgo biloba 1-Hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate Reductase 1 (GbHDR1) gene in poplar

Terpenoids are of great interests in a broad range of health-beneficial biological activities and various industrial applications. In plants, terpenoids are synthesized by two distinct pathways, methylerythritol phosphate (MEP) and mevalonate pathways in a separate location. MEP pathway supplies isoprene precursors isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP) of terpenoid biosynthesis in plant plastids. The MEP pathway has been an engineering target to increase the metabolic flux towards higher terpenoid production in plants. 1-Hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductase (HDR) is the terminal step of the MEP pathway to regulate the terpenoid biosynthesis and is encoded by three paralogous genes in Ginkgo biloba. In this study, we assessed the effect of overexpression of GbHDR1 on terpenoid metabolism in poplar plants. Overexpression of GbHDR1 in poplar plants accelerated growth and delayed winter-bud formation. Transcript levels of gibberellin, chlorophylls, and carotenoid biosynthetic genes in GbHDR1-overexpressing (GbHDR1ox) poplars were up-regulated, suggesting metabolic flux enhancement. Moreover, enhanced contents of chlorophylls and carotenoids in the leaves of the GbHDR1ox plants resulted in a higher photosynthetic rate as a consequence. Therefore, we expect the GbHDR1 overexpression will be a desirable engineering point of the MEP pathway for enhancing terpenoid metabolic flux and production in plants.This work was supported by the National Research Foundation of Korea (NRF) grant (2021R1A5A8029490); the Korea Foundation for Women In Science, Engineering and Technology (WISET) Returners into R&D Program grant; the intramural grant (2Z06670) from the Korea Institute of Science and Technology (KIST), Republic of Korea

Result of a year-long animal survey in a state-owned forest farm in Beijing, China

BackgroundArtificial forest can have great potential in serving as habitat to wildlife, depending on different management methods. As the state-owned forest farms now play a new role in ecological conservation in China, the biological richness of this kind of land-use type is understudied. Once owned by a mining company, a largest state-owned forest farm, Jingxi Forest Farm, has been reformed to be a state-owned forest farm with the purpose of conservation since 2017. Although this 116.4 km2 forest farm holds a near-healthy montaine ecosystem very representative in North China, a large proportion of artificial coniferous forest in the forest farm has been proven to hold less biodiversity than natural vegetation. This situation, however, provides a great opportunity for ecological restoration and biodiversity conservation. Therefore, from November 2019 to December 2020, we conducted a set of biodiversity surveys, whose results will serve as a baseline for further restoration and conservation.New informationHere, we report the result of a multi-taxa fauna diversity survey conducted in Jingxi Forest Farm mainly in year 2020 with explicit spatial information. It is the first survey of its kind conducted in this area, revealing a total of 19 species of mammals, 86 birds, four reptiles, two amphibians and one fish species, as well as 101 species of insects. Four species of mammals are identified as data-poor species as they have less than 100 occurrence records with coordination in the GBIF database. One species of insect, representing one new provincial record genus of Beijing, is reported

RESOURCE SHARING TECHNOLOGY OF CLOUD COMPUTING

Development of computer science and technology, application of network education has become more mature. The technology of network learning resource sharing has been promoted by computers. It is significant promote the development of cloud computing education. Aiming at the need of education resource sharing, combined with the cloud computing service model, infrastructure and key technology. This thesis set up the educational resources sharing system to provide high quality sharing resources for users. Cloud computing is an emerging shared infrastructure through virtualization technology in a large number of available network resources to form a virtual resource pool, automatic software implementation by management. Their cross-regional, cross-database resource integration capabilities break the scattered data resources to bring the information is not balanced, effective flow of resources and improve utilization; For cloud nodes can be easily added and removed and increase the size of the expansion resources to solve problems. Meanwhile, the data in the cloud uses distributed storage, capable of storing and accessing to share pressures, thereby improving system performance. Cloud resources take a pay model. In this way, the user can customize the resources of independent interest and promote personalized learning

Advanced Magnetic Resonance Vascular Imaging: Technical Development and Application

Vascular imaging plays a crucial role in the assessment of a variety of vascular diseases. In the past few decades, efforts have been made to establish various imaging modalities for the evaluation of vasculatures. For example, CT, PET, ultrasound, and MR, have all been shown to have the capability to visualize or quantify different vasculatures. MR vascular imaging, compared to other imaging modalities, has several unique advantages, and has evolved as an ever-attractive choice both in research and clinical studies.Based on the scale of the target vasculature, MR vascular imaging can be further classified into two subcategories: MR macrovascular imaging and MR microvascular imaging. Macrovascular imaging is developed to assess diseases of, for example, the aorta, the coronary, the sizable arteries in the brain, and etc. Classically, macrovascular imaging can be categorized into bright-blood and dark-blood techniques. As a bright-blood technique, MR angiography has evolved as an important tool in the evaluation of macro vessels. Well dark-blood imaging, also called vessel wall imaging, has shown great potentials because of its ability to directly visualize pathological changes within the vessel wall. In contrast, MR microvascular imaging concentrates upon “micro” blood vessels, including small arteries, arterioles, and capillaries, with some biomarkers quantified to assess vessel changes at the microscopic level. Permeability and perfusion are two widely adopted biomarkers in this scenario that can be generated by dynamic contrast-enhanced MR, which is DCE-MR and dynamic susceptibility contrast-enhanced MR, which is DSC-MR, respectively. In recent years, with the advancements in imaging technologies, both MR macrovascular imaging and microvascular imaging have been applied to real clinical workflows to help with diagnosis and prognosis. For example, MR macrovascular imaging, especially MRA, is nearly always adopted as an adjunct in stroke diagnosis and etiology evaluation, while MR microvascular imaging has been shown to provide insights into many different aspects of brain tumors, like surgical planning and treatment response assessment. However, the vascular imaging techniques adopted in current paradigm for both applications are suboptimal and have several limitations. The primary goal of the work in the dissertation is to address the limitations by developing advanced MR macroscopic and microscopic vascular imaging techniques that can assist in stroke diagnosis and etiology evaluation and brain cancer evaluation, respectively. Specifically, for stroke etiology evaluation, a comprehensive two-station stroke etiology evaluation technique covering both the head-neck vasculature and the heart was developed. Our goal is to develop a technique that can be easily incorporated into clinical workup, and hopefully improves the diagnostics and patient outcomes. The technical development was divided into two specific aims, where Aim 1 is to develop a novel motion-compensated, data-driven accelerated 3D MR vessel wall imaging for the evaluation of the head-neck vessels, and Aim 2 is to develop a novel ECG- and navigator-free multi-dimensional assessment of cardiovascular system technique for the evaluation of the thoracic aorta and the cardiac structures. As for brain cancer evaluation, we developed a novel dynamic imaging for cerebrovascular evaluation technique based on MR multitasking framework for simultaneous 3D permeability and leakage-insensitive perfusion quantification based on single-dose of contrast injection. Feasibility of each developed technique is evaluated on both healthy subjects and patients