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Atherosclerosis cardiovascular disease (ASCVD) has become one of the leading death causes in humans. Low-density lipoprotein (LDL) is an important biomarker for assessing ASCVD risk level. Thus, monitoring LDL levels can be an important means for early diagnosis of ASCVD. Herein, a novel electrochemical aptasensor for determination LDL was designed based on nitrogen-doped reduced graphene oxide-hemin-manganese oxide nanoparticles (NrGO-H-Mn3O4 NPs) integrated with clustered regularly interspaced short palindromic repeats and associated proteins (CRISPR/Cas12a) system. NrGO-H-Mn3O4 NPs not only have a large surface area and remarkable enhanced electrical conductivity but also the interconversion of different valence states of iron in hemin can provide an electrical signal. Nonspecific single-stranded DNA (ssDNA) was bound to NrGO-H-Mn3O4 NPs to form a signaling probe and was immobilized on the electrode surface. The CRISPR/Cas12a system has excellent trans-cleavage activity, which can be used to cleave ssDNA, thus detaching the NrGO-H-Mn3O4 NPs from the sensing interface and attenuating the electrical signal. Significant signal change triggered by the target was ultimately obtained, thus achieving sensitive detection of the LDL in range from 0.005 to 1000.0 nM with the detection limit of 0.005 nM. The proposed sensor exhibited good stability, selectivity, and stability and achieved reliable detection of LDL in serum samples, demonstrating its promising application prospects for the diagnostic application of LDL. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence painting technology is in an era of prosperity and development. The “transformation” of artificial intelligence painting works has brought challenges to the traditional fair use system, and characterization as fair use may lead to an imbalance of interests. It is necessary to re-examine the rationality of qualifying the use of artificial intelligence paintings as fair use. At the same time, analyze the infringement risk in the painting process in combination with the painting principles of artificial intelligence, and put forward some regulatory suggestions.

The new economic model and the development of the Internet and media technology have bred the network mixed clip video. This new video type not only enriches people’s spiritual and cultural life, but also brings about copyright infringement problems. This article focuses on the copyright issue of Network remixes short video, and also focuses on the issues of whether the Network remixes short video belong to the “Works” stipulated in the Copyright Law of The People’s Republic of China and whether the use of other people’s works in the Network remixes short video can be regarded as reasonable, and hopes to put forward some suggestions for the prevention, solution or regulation of related problems.

The core of an Internet enterprise is the data of the network platform, which usually includes a large amount of personal data and personal information. Big data refers to the massive, high growth rate and diversified information assets that require new processing models to have stronger decision-making power, insight, and process optimization capabilities. It is characterized by a large number, high speed, variety, value density, and authenticity. There are ways and legal risks of leaking personal information everywhere on the network information platform. To effectively prevent and completely eliminate the leakage of personal privacy, the government should be taken as the leading factor and rely on all levels of society to deal with this issue from multiple angles.

The rapid development of network technology has brought profound changes to all sectors of society. The relaxation of national policies and the surge of sports demand have made the sports industry undergoing profound changes. The broadcast of sports events in the network environment has strong immediacy and interactive advantages, which not only brings new experience to the audience, but also causes frequent infringement. As the domestic and foreign regulations on the nature of sports broadcast rights are not clear, the judicial judgment of sports infringement is not uniform. This paper introduces the background of sports event broadcast under the network environment, and further explores the problems in the process of sports event broadcast by analyzing the development of its business model, and then puts forward some suggestions for sports event broadcast in China.

"Internet +" is a disruptive technological innovation that brings unlimited possibilities for business model innovation. However, the lack of the patent protection system of the business model has hindered the innovation and development of the business model to a certain extent. From the perspective of the Internet + business model, this article briefly describes the current status and shortcomings of the protection of business model patents in China, and puts forward some thoughts and suggestions on establishing an Internet + business model to form a business model patent protection system.

1,5-Anhydroglucitol (1,5-AG) is a sensitive biomarker for real-time detection of diabetes mellitus. In this study, an electrochemical biosensor to specifically detect 1,5-AG levels based on persimmon-tannin-reduced graphene oxide-PtPd nanocomposites (PT-rGO-PtPd NCs), which were modified onto the surface of a screen-printed carbon electrode (SPCE), was designed. The PT-rGO-PtPd NCs were prepared by using PT as the film-forming material and ascorbic acid as the reducing agent. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), ultraviolet–visible spectroscopy (UV–vis), and X-ray diffraction (XRD) spectroscopy analysis were used to characterise the newly synthesised materials. PT-rGO-PtPd NCs present a synergistic effect not only to increase the active surface area to bio-capture more targets, but also to exhibit electrocatalytic efficiency to catalyze the decomposition of hydrogen peroxide (H2O2). A sensitive layer is formed by pyranose oxidase (PROD) attached to the surface of PT-rGO-PtPd NC/SPCE. In the presence of 1,5-AG, PROD catalyzes the oxidization of 1,5-AG to generate 1,5-anhydrofuctose (1,5-AF) and H2O2 which can be decomposed into H2O under the synergistic catalysis of PT-rGO-PtPd NCs. The redox reaction between PT and its oxidative product (quinones, PTox) can be enhanced simultaneously by PT-rGO-PtPd NCs, and the current signal was recorded by the differential pulse voltammetry (DPV) method. Under optimal conditions, our biosensor shows a wide range (0.1–2.0 mg/mL) for 1,5-AG detection with a detection limit of 30 μg/mL (S/N = 3). Moreover, our electrochemical biosensor exhibits acceptable applicability with recoveries from 99.80 to 106.80%. In summary, our study provides an electrochemical method for the determination of 1,5-AG with simple procedures, lower costs, good reproducibility, and acceptable stability. [ABSTRACT FROM AUTHOR]

Glypican-3 (GPC3), as an emerging biomarker, has been shown to be beneficial for the early diagnosis and treatment of hepatocellular carcinoma (HCC). In this study, an ultrasensitive electrochemical biosensor for GPC3 detection has been constructed based on the hemin-reduced graphene oxide-palladium nanoparticles (H-rGO-Pd NPs) nanozyme-enhanced silver deposition signal amplification strategy. When GPC3 specifically interacted with GPC3 antibody (GPC3Ab) and GPC3 aptamer (GPC3Apt), an "H-rGO-Pd NPs-GPC3Apt/GPC3/GPC3Ab" sandwich complex was formed with peroxidase-like properties which enhanced H2O2 to reduce the silver (Ag) ions in solution to metallic Ag, resulting in the deposition of silver nanoparticles (Ag NPs) on the surface of the biosensor. The amount of deposited Ag, which was derived from the amount of GPC3, was quantified by the differential pulse voltammetry (DPV) method. Under ideal circumstances, the response value was linearly correlated with GPC3 concentration at 10.0–100.0 μg/mL with R2 of 0.9715. When the GPC3 concentration was in the range from 0.01 to 10.0 μg/mL, the response value was logarithmically linear with the GPC3 concentration with R2 of 0.9941. The limit of detection was 3.30 ng/mL at a signal-to-noise ratio of three and the sensitivity was 1.535 μAμM−1cm−2. Furthermore, the electrochemical biosensor detected the GPC3 level in actual serum samples with good recoveries (103.78–106.52%) and satisfactory relative standard deviations (RSDs) (1.89–8.81%), which confirmed the applicability of the sensor in practical applications. This study provides a new analytical method for measuring the level of GPC3 in the early diagnosis of HCC. [ABSTRACT FROM AUTHOR]

Compiler bugs may be triggered when programs are optimized with optimization sequences. However, diagnosing compiler optimization sequence bugs is difficult due to limited debugging information. Although some techniques (e.g., DiWi and RecBi) have been proposed to automatically localize compiler bugs, no systematic work has been conducted to automatically localize compiler optimization sequence bugs. In this article, we propose LocSeq, a novel technique to automatically localize compiler optimization sequence bugs of LLVM. The core insight of LocSeq is based on the fact that the behaviors of optimizations may be influenced by each other, and thus, the innocent files may be excluded by constructing bug-free optimization sequences. First, given a buggy optimization sequence that triggers a compiler bug, in LocSeq, we transform the problem of the localization for a compiler optimization sequence bug to the problem of the construction for bug-free optimization sequences, which are helpful to localize buggy compiler files. Then, a constrained genetic algorithm is presented in LocSeq to generate a set of bug-free optimization sequences that share similar compiler execution traces with the buggy optimization sequence. Finally, LocSeq leverages a spectrum-based bug localization technique to localize the compiler optimization sequence bug by comparing the execution traces between bug-free optimization sequences and the buggy optimization sequence. To evaluate the effectiveness of LocSeq, we build a benchmark, including 60 optimization sequence bugs of LLVM, and compare LocSeq with the state-of-the-art techniques DiWi and RecBi. The experimental results show that LocSeq significantly outperforms DiWi and RecBi by up to 366.66%/72.27% and 250.00%/56.00% for localizing optimization sequence bugs within Top-1/5 files, respectively. [ABSTRACT FROM AUTHOR]

Compiler testing is the most widely used way to assure compiler quality. However, since compilers require a large number of sophisticated test programs as inputs, the existing approaches in compiler testing still have a limited capability in generating both syntactically valid and diverse test programs. In this paper, we propose DeepGen, a deep learning-based approach to support compiler testing through the inference of a generative model for compiler inputs. First, DeepGen trains a Transformer-XL model based on a large corpus of seed programs, and uses the trained model to generate syntactically valid programs. Then, DeepGen adopts a sampling strategy in the inference phase to generate diverse test programs. Finally, DeepGen leverages differential testing on the generated programs to discover compiler bugs. We have evaluated DeepGen over two popular C++ compilers GCC and LLVM, and the results confirm the effectiveness of our approach. DeepGen detects 35.29%, 53.33%, and 187.50% more bugs than three existing approaches, i.e. DeepSmith, DeepFuzz, and Csmith, respectively. In addition, 30.43% bugs detected by DeepGen are not detected by other approaches. Furthermore, DeepGen has successfully detected 38 bugs in the latest development versions of GCC and LLVM; 21 of them have been confirmed/fixed by the developers. [ABSTRACT FROM AUTHOR]

Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. Evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality; and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model. [ABSTRACT FROM AUTHOR]