Binary code similarity analysis based on naming function and common vector space.

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match .

High-frequency rTMS over the left DLPFC improves the response inhibition control of young healthy participants: an ERP combined 1H-MRS study.

Introduction: Unlike the effect of repetitive transcranial magnetic stimulation (rTMS) in treating neuropsychiatric diseases, little is known about how personal factors might account for the disparity of results from studies of cognition and rTMS. In this study, we investigated the effects of high-frequency rTMS on response inhibition control and explored the time course changes in cognitive processing and brain metabolic mechanisms after rTMS using event-related potentials (ERPs) and magnetic resonance spectroscopy (1H-MRS). Methods: Participants were all right-handed and were naive to rTMS and the Go/NoGo task. Twenty-five healthy young participants underwent one 10 Hz rTMS session per day in which stimulation was applied over the left dorsolateral prefrontal cortex (DLPFC), and a homogeneous participant group of 25 individuals received a sham rTMS treatment for 1 week. A Go/NoGo task was performed, an electroencephalogram (EEG) was recorded, and 1H-MRS was performed. Results: The results revealed that there was a strong trend of decreasing commission errors of NoGo stimuli by high frequency rTMS over the left DLPFC, whereas there was no significant difference between before and after rTMS treatment with respect to these parameters in the sham rTMS group. High-frequency rTMS significantly increased the amplitude of NoGo-N2 but not Go-N2, Go-P3, or NoGo-P3. The myo-inositol /creatine complex (MI/Cr) ratio, indexing cerebral metabolism, in the left DLPFC was decreased in the rTMS treated group. Discussion: This observation supports the view that high-frequency rTMS over the left DLPFC has the strong tendency of reducing commission errors behaviorally, increase the amplitude of NoGo-N2 and improve the response inhibition control of healthy young participants. The results are consistent with the excitatory properties of high frequency rTMS. We suggest that the increase in the NoGo-N2 amplitude may be related to the increased excitability of the DLPFC-anterior cingulate cortex (ACC) neural loop. Metabolic changes in the DLPFC may be a possible mechanism for the improvement of the response inhibition control of rTMS.

Knockdown of PDCD4 ameliorates neural cell apoptosis and mitochondrial injury through activating the PI3K/AKT/mTOR signal in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a complex neurodegenerative disorder and hampers normal living. It has been reported that programmed cell death 4 (PDCD4) is associated with tumor suppression, inflammatory response, and apoptosis. OBJECTIVE: The aim of this study was to investigate the role of PDCD4 in PD. METHODS: The in vivo and in vitro PD models were established by MPTP-induced mice and MMP+ stimulated MN9D cells, respectively. The expression of PDCD4 was detected by western blot. The MN9D cell viability and apoptosis were determined by MTT and flow cytometry assay. Moreover, the MN9D cell mitochondrial injury was evaluated by JC-1 staining. RESULTS: In this study, PDCD4 was highly expressed in brain tissue of MPTP-induced PD mouse model. In a loss-function experiments, knockdown of PDCD4 promoted MN9D cell viability and allayed MPP+-triggered MN9D cell apoptosis. Furthermore, knockdown of PDCD4 ameliorated MPP+-evoked MN9D cell mitochondrial injury. Mechanically, knockdown of PDCD4 abolished the effect of MMP+ stimulation via activating phosphoinositide 3-kinase(PI3K)/AKT/mammalian target of rapamycin (mTOR) signal. Notably, the protective effects of shPDCD4 on cell apoptosis and mitochondrial injury were suppressed by PI3K inhibitor LY294002. CONCLUSION: In summary,knockdown of PDCD4 ameliorates neural cell apoptosis and mitochondrial injury through activating the PI3K/AKT/mTOR signal, providing a novel target for PD treatment. AVAILABILITY OF DATA AND MATERIALS: All data generated or analyzed during this study are included in this published article.

Influence of Management of Intensive Weight, Blood Pressure, and Lipids on Disease Severity in Patients with Carotid Atherosclerosis.

Context: Cardiovascular diseases (CVDs caused by atherosclerosis, such as coronary heart disease and stroke, have become major causes of death and disability worldwide. Atherosclerosis is the primary pathological factor causing CVDs. Managing weight, blood pressure, and lipids is one of the tenets of chronic-disease management, including atherosclerosis. Objective: The study intended to investigate the effects of managing weight, blood pressure, and lipids on disease severity in patients with carotid atherosclerosis. Design: The research team designed a randomized, controlled trial. Setting: The study took place in the pediatric department at the First Hospital of Hebei Medical University in Shijiazhuang, Hebei Province, China. Participants: Participants were 380 patients with carotid atherosclerosis who entered the hospital between March 2018 and June 2020. Intervention: Participants were randomly assigned, using the random-number-table method, to an intervention or a control group, with 190 participants in each group. Both groups received anti-atherosclerotic treatments, and the intervention group also took part in a program for combined management of weight, blood pressure, and blood lipids. Outcome Measures: All measurements occurred at baseline and postintervention. Using a questionnaire, the study measured the changes in the two groups related to alcohol consumption, smoking, high-fat diet, high-salt diet, and lack of exercise. A physical examination provided participants' weights, blood pressures, and lipid levels, and the Self-Care Ability Assessment Scale (ESCA) provided the changes in their self-management ability. A carotid-artery examination measured parameters related to carotid atherosclerosis, including intima-media thickness (IMT), Crouse scores, plaque-class scores, and plaque-grade scores. Results: At baseline, no statistically significant differences existed between the groups. Postintervention, the intervention group had significantly greater decreases than the control group for alcohol consumption, smoking, high-fat diet, high-salt diet, lack of exercise, weight, blood pressure, lipid levels, intima-media thickness (IMT) scores, Crouse scores, and plaque-grade scores. Postintervention, the intervention group had significantly greater increases than the control group for self-responsibility, health knowledge, self-concept, and self-care-skills scores. Conclusions: A program for management of body weight, blood pressure, and blood lipids can effectively control the severity of carotid atherosclerosis, can prevent the disease's progression, and can be promoted as a clinical application.

A new method of software vulnerability detection based on a quantum neural network.

In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information.

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.

Record Atomistic Simulation of Crystalline Silicon: Bridging Microscale Structures and Macroscale Properties.

Based on the molecular dynamics software package CovalentMD 2.0, the fastest molecular dynamics simulation for covalent crystalline silicon with bond-order potentials has been implemented on the third highest performance supercomputer "Sunway TaihuLight" in the world (before June 2019), and already obtained 16.0 Pflops (1015 floating point operation per second) in double precision for the simulation of crystalline silicon, which is recordly high for rigorous atomistic simulation of covalent materials. The simulations used up to 160,768 64-core processors, totally nearly 10.3 million cores, to simulate more than 137 billion silicon atoms, where the parallel efficiency is over 80% on the whole machine. The running performance on a single processor reached 15.1% of its theoretical peak at highest. The longitudinal dimension of the simulated system is far beyond the range with scale-dependent properties, while the lateral dimension significantly exceeds the experimentally measurable range. Our simulation enables virtual experiments on real-world nanostructured materials and devices for predicting macroscale properties and behaviors from microscale structures directly, bringing about many exciting new possibilities in nanotechnology, information technology, electronics and renewable energies, etc. © 2019 Wiley Periodicals, Inc.

Serum long noncoding RNA LRB1 as a potential biomarker for predicting the diagnosis and prognosis of human hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a type of malignant tumor with a high mortality rate. Long non-coding RNAs (lncRNAs) serve important roles in cellular processes and gene regulation. Identifying novel prognostic biomarkers is important for the monitoring and treatment of HCC. However, only a limited number of biomarkers with high sensitivity and specificity have been determined and are used in clinical practice. The aim of the present study was to investigate the use of serum lncRNA uc007biz.1 (LRB1) expression levels as a novel non-invasive biomarker for the monitoring and diagnosis of HCC. The expression levels of LRB1 were detected in 326 patients with HCC and 73 healthy volunteers by using lncRNA expression microarrays and reverse transcription quantitative polymerase chain reaction analysis, and the associations between LRB1 expression and clinical parameters were analyzed. The results indicated that the serum LRB1 levels in patients with HCC were significantly increased compared with healthy volunteers. The serum LRB1 levels were positively associated with α-fetoprotein (AFP) expression, large tumor sizes, tumor stage (tumor-node metastasis or Barcelona Clinic Liver Cancer stage) and venous invasion, and were negatively associated with overall survival. Additionally, the use of a combination of LRB1, AFP and des-γ-carboxy prothrombin (DCP) markers for the diagnosis of HCC, the diagnostic accuracy was increased compared with using LRB1 alone. LRB1 may act as an important regulator in the progression of HCC, and LRB1 may be considered as a novel biomarker for diagnosis and prediction of prognosis of HCC, additionally complementing the accuracy of AFP and DCP.