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Abstract The cleavage and functionalization of C–S bonds have become a rapidly growing field for the design or discovery of new transformations. However, it is usually difficult to achieve in a direct and selective fashion due to the intrinsic inertness and catalyst-poisonous character. Herein, for the first time, we report a novel and efficient protocol that enables direct oxidative cleavage and cyanation of organosulfur compounds by heterogeneous nonprecious-metal Co-N-C catalyst comprising graphene encapsulated Co nanoparticles and Co-Nx sites using oxygen as environmentally benign oxidant and ammonia as nitrogen source. A wide variety of thiols, sulfides, sulfoxides, sulfones, sulfonamides, and sulfonyl chlorides are viable in this reaction, enabling access to diverse nitriles under cyanide-free conditions. Moreover, modifying the reaction conditions also allows for the cleavage and amidation of organosulfur compounds to deliver amides. This protocol features excellent functional group tolerance, facile scalability, cost-effective and recyclable catalyst, and broad substrate scope. Characterization and mechanistic studies reveal that the remarkable effectiveness of the synergistic catalysis of Co nanoparticles and Co-Nx sites is crucial for achieving outstanding catalytic performance.

BackgroundThe relationship between frailty and the long-term clinical outcome of gastric cancer (GC) patients has not yet been established, although frailty is associated with a poor short-term outcome. The impact of frailty on long-term survival of GC patients was investigated through a systematic review and meta-analysis.MethodsObservational studies with longitudinal follow-ups for a minimum of one year were identified through a search of the PubMed, Embase, Cochrane Library, and Web of Science databases, in accordance with the objective of the meta-analysis. Combining the findings was achieved using a random-effects model, which accounted for inter-study heterogeneity.ResultsTen datasets from nine cohort studies were included, which involved 7613 patients with GC. A total of 2074 patients (27.2%) were with frailty at baseline, and the mean follow-up duration was 48.1 months. A pooled analysis of the results showed that frailty was linked to a poor long-term overall survival in GC patients (risk ratio [RR]: 1.65, 95% confidence interval [CI]: 1.27 to 2.13, p < 0.001; I2 = 80%). Sensitivity analysis showed consistent results in older patients (≥ 65 years, RR: 1.51, p = 0.002) and the oldest old (≥ 80 years, RR: 1.41, p = 0.01). In addition, frailty was also associated with poor long-term progression-free survival (RR: 1.65, 95% CI: 1.39 to 1.96, p < 0.001; I2 = 0%) and disease-specific survival (RR: 1.71, 95% CI: 1.23 to 2.37, p = 0.001; I2 = 4%).ConclusionFrailty is associated with poor long-term survival of patients with GC.

Summary: The cleavage and functionalization of carbon-carbon (C–C) bonds has emerged as a powerful tool for preparing value-added chemicals. In this protocol, we describe the preparation of amorphous manganese oxide and its application as a heterogeneous catalyst in the direct synthesis of amides via successive cleavage and amidation of C–C bonds in alcohols. Furthermore, we describe how a slight modification of reaction conditions allows for the cleavage and cyanation of alcohols to access sterically hindered nitriles.For complete details on the use and execution of this protocol, please refer to He et al. (2022). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The aim of this study was to investigate the relationship between apoA-I, chemerin, and Procalcitonin (PCT) and the severity of hyperlipidaemia-induced acute pancreatitis (HLAP), as well as the importance of their application in the diagnosis of severe HLAP. This study was conducted at the 363 Hospital, Chengdu City, China, from January 2016 to August 2020. There were significant differences in the levels of serum apoA-I, chemerin, and PCT among the three—mild, moderate and severe—groups (all p

Software defect prediction can assist developers in finding potential bugs and reducing maintenance cost. Traditional approaches usually utilize software metrics (Lines of Code, Cyclomatic Complexity, etc.) as features to build classifiers and identify defective software modules. However, software metrics often fail to capture programs' syntax and semantic information. In this paper, we propose Seml, a novel framework that combines word embedding and deep learning methods for defect prediction. Specifically, for each program source file, we first extract a token sequence from its abstract syntax tree. Then, we map each token in the sequence to a real-valued vector using a mapping table, which is trained with an unsupervised word embedding model. Finally, we use the vector sequences and their labels (defective or non-defective) to build a Long Short Term Memory (LSTM) network. The LSTM model can automatically learn the semantic information of programs and perform defect prediction. The evaluation results on eight open source projects show that Seml outperforms three state-of-the-art defect prediction approaches on most of the datasets for both within-project defect prediction and cross-project defect prediction.

Given a bug report, bug localization technique can help developers automatically locate potential buggy files. Information retrieval and deep learning approaches have been applied in bug localization by extracting lexical features in bug reports and syntactic features in source code files, though they fail to utilize the structural and semantic information of source code files. In this paper, we present a bug localization system CAST, which exploits deep learning and customized abstract syntax trees of programs to locate potential buggy source files automatically and effectively. Specifically, CAST extracts both lexical semantics in bug reports (e.g., words) and source files (e.g., method names) and program semantics in source files (e.g., abstract syntax tree, AST). Moreover, CAST enhances the tree-based convolutional neural network (TBCNN) model with customized ASTs, which distinguish between user-defined methods and system-provided ones to reflect their contributions leading to defects. Furthermore, customized ASTs group the syntactic entities with similar semantics and prune the ones with little or redundant semantics in order to facilitate the learning performance. Experimental results on four widely-used software projects show that CAST significantly outperforms the state-of-the-art methods in locating the buggy source files.

With an increasing number of applications appearing, smartphones with powerful processors and a variety of sensors are ideal mobile devices at hand. On one hand, various applications which are able to provide personalization functions that provide the service of interest to users rely on gathering and analyzing the sensor data and other sensitive information. On the other hand, attackers can accurately classify activities of mobile users from these data. As a result, the risk of users compromising their privacy has risen exponentially. Mobile users currently cannot control how various applications handle the privacy of their sensor data. To address the issue, we present P3Android whose architecture can support applications' personalization functions in Android according to users' personae and use the concept of profile to prevent sensitive information from unauthorized access. Furthermore, we provide a privacy protection service for legacy apps. which can enforce different protection policies according to the risk level of apps. All of these cooperate to provide both personalization awareness and privacy protection in Android. Experiments show that P3Android is feasible and effective.

Program bugs may result in unexpected software error, crash or serious security attack. Static program analysis is one of the most common methods to find program bugs. In this paper we present MLSA -- a static analysis tool based on LLVM Intermediate Representation (IR), which can analyze programs written in multiple programming languages. MLSA combines symbolic execution with Z3 SMT solver to find bugs. At present, MLSA can detect some kinds of bugs, such as divide zero error, pointer overflow and dead code. Moreover, as a framework, MLSA follows the scalability and extensibility principles, which can help detect other types of bugs. Experiments show that MLSA is effective in finding bugs in real world software.

Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT.

Let (X,d) be a metric space and a sequence of continuous maps fn:X→X that converges uniformly to a map f. We investigate the transitive subsets of fn whether they can be inherited by f or not. We give sufficient conditions such that the limit map f has a transitive subset. In particular, we show the transitive subsets of fn that can be inherited by f if fn converges uniformly strongly to f.