Efficiency Of Discovery Research Articles

Exploring the amorphous phase formation and properties of W-Ta-(Cr, Fe, Ni) high-entropy alloy gradient films via a high-throughput technique

A high-throughput technique can realize fast and efficient material screening, and greatly improve the discovery efficiency of advanced materials. In the present work, the gradient composition films of the W-Ta-CrFeNi high-entropy alloy were prepared by the three-target magnetron co-sputtering technique. The gradient alloy films exhibited a body-center-cubic (BCC) structure near the W or Ta element target, and an amorphous structure near the CrFeNi target region. Considering the effect of the undercooling degree, the amorphous formation region (using Ω ~ δ ) was larger than that of the bulk alloys. These alloy films displayed ultra-high nano-indentation hardness, with the maximum hardness reaching ~ 20.6 GPa. The elastic modulus in the high-entropy composition region was lower than that in the low-entropy composition region. There is a nonlinear relationship between the nano-indentation hardness and mix-entropy of the alloys. This study provides a reference and alternative material library for the development of high-performance advanced materials. • The rapid screening with properties and phase structure of the W-Ta-(Cr, Fe, Ni) films via the high-throughput technique. • The W 15.39 Ta 38.81 Cr 14.58 Fe1 5.45 Ni 15.77 high-entropy alloy film possesses ultra-high hardness of ~ 20.6 GPa. • The amorphous phase formation ruler of high-entropy alloy films was investigated. • There is a nonlinear relationship with the mix-entropy and properties in these high-entropy alloy films. • The high mix-entropy is a reason for low elastic modulus in these high-entropy alloy films.

Would widening price limits improve the efficiency of price discovery?

Based on a quasi-natural experiment of widening price limits on GEM (Growth Enterprise Market) stocks of China, we examine how to price limit affects price discovery using data on GEM and Main Board stocks. Price discovery efficiency is measured in terms of both speed and accuracy. We find that widening the price limit enhances price discovery efficiency significantly, and the results are robust, using the DID (Differences-in-Differences) model.

SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective

Stateful network protocol fuzzing is one of the essential means for ensuring network communication security. However, the existing methods have problems, including frequent auxiliary message interaction, no in-depth state-space exploration, and high shares of invalid interaction time. To this end, we propose SATFuzz, a stateful network protocol fuzzing framework. SATFuzz first prioritizes the states identified by the status codes in response messages, then randomly selects a state to test among the high-priority states, and determines its corresponding optimal test sequence, which is composed of the minimum pre-lead sequence, the test case, and the fittest post-end sequence. Finally, SATFuzz uses a quasi-recurrent neural network (QRNN) to filter the test cases before performing interaction, and only the optimal test sequence, including the valid test case, can be fed to the protocol entity. To verify the proposed framework, we conduct extensive experiments with the state-of-the-art fuzzer on two popular protocols. The results show that the vulnerability discovery efficiency of the proposed approach increases by at least 1.48 times (at most by 3.06 times), making it superior to the rival methods. This not only confirms the effectiveness of SATFuzz in terms of improving the vulnerability discovery efficiency but also shows that SATFuzz has significant advantages.

PICF-LDA: a topic enhanced LDA with probability incremental correction factor for Web API service clustering

Web API is a popular way to organize network services in cloud computing environment. However, it is a challenge to find an appropriate service for the requestor from massive Web API services. Service clustering can improve the efficiency of service discovery for its ability of reducing search space. Latent Dirichlet Allocation (LDA) is the most frequently used topic model in service clustering. To further improve the topic representation ability of LDA, we propose a new variant model of LDA with probability incremental correction factor (PICF-LDA) to generate the high-quality service representation vectors (SRVs) for Web API services. We first compute the words’ topic contribution degree (TCD) in the service description text by its context weight and part-of-speech (POS) weight. Then the probability incremental correction factor (PICF) for a word is designed based on TCD and the word’s maximum topic probability value. PICF is used to correct the probability distributions in SRVs. Experiments show that PICF-LDA has a better performance than LDA, the variant LDA models and other state-of-the-art topic models in service clustering.

Rational Discovery of Antimicrobial Peptides by Means of Artificial Intelligence.

Antibiotic resistance is a worldwide public health problem due to the costs and mortality rates it generates. However, the large pharmaceutical industries have stopped searching for new antibiotics because of their low profitability, given the rapid replacement rates imposed by the increasingly observed resistance acquired by microorganisms. Alternatively, antimicrobial peptides (AMPs) have emerged as potent molecules with a much lower rate of resistance generation. The discovery of these peptides is carried out through extensive in vitro screenings of either rational or non-rational libraries. These processes are tedious and expensive and generate only a few AMP candidates, most of which fail to show the required activity and physicochemical properties for practical applications. This work proposes implementing an artificial intelligence algorithm to reduce the required experimentation and increase the efficiency of high-activity AMP discovery. Our deep learning (DL) model, called AMPs-Net, outperforms the state-of-the-art method by 8.8% in average precision. Furthermore, it is highly accurate to predict the antibacterial and antiviral capacity of a large number of AMPs. Our search led to identifying two unreported antimicrobial motifs and two novel antimicrobial peptides related to them. Moreover, by coupling DL with molecular dynamics (MD) simulations, we were able to find a multifunctional peptide with promising therapeutic effects. Our work validates our previously proposed pipeline for a more efficient rational discovery of novel AMPs.

Price discovery efficiency of China's crude oil futures: Evidence from the Shanghai crude oil futures market

This study investigates the pricing efficiency of the Shanghai Crude Oil Futures (SC) as a newly emerged crude oil future market from the perspective of co-integration and estimates its contribution to price discovery in China based on the vector error correction model (VECM), permanent–transitory (PT), information share (IS), and modified information share (MIS) models. All results show a long-run equilibrium relationship exists between crude oil futures and spot prices in China, but SC contributes just approximately 50% to price discovery, which is roughly equal to that of the spot market. Compared with West Texas Intermediate (WTI) and Brent crude oil markets, the pricing efficiency of SC lags significantly, which means that SC has a certain capability of price discovery, although it is now still in its initial development stage as the price taker of the international crude oil market.

ROLE OF COMPUTER-AIDED DRUG DESIGN IN THE DISCOVERY AND DEVELOPMENT OF NEW MEDICINAL AGENTS A REVIEW

Drug design and development is a time consuming and costly process. Nowadays, computer-aided drug design approaches are usually used to improve drug discovery and advancement efficiency. The role of Computer-Aided Drug Design (CADD) is a diverse discipline in which various versions of applied and basic analysis are interlinked. It is being implemented in various fields including biochemistry, molecular biology, nanotechnology etc. Various employed computational approaches includes ligand-based drug design, structure-based drug design, quantitative structure-property relationships and quantitative structure-activity. Computational techniques are commonly utilized in pharmaceutical industry and in research to improving the effectiveness of drug discovery and development. In this review, the authors have attempted to provide a broad overview of the function of CADD in modern medicine science.

Optimizing IoT Web Fuzzing by Firmware Infomation Mining

IoT web fuzzing is an effective way to detect security flaws in IoT devices. However, without enough information of the tested targets, IoT web fuzzing is often blind and inefficient. In this paper, we propose to use static analysis to assist IoT web fuzzing. Our insight is that plenty of useful information is hidden in firmwares, which can be mined by static analysis and used to guide the subsequent dynamic analysis—fuzzing. Hence, our approach contains two stages: pre-fuzzing stage and fuzzing stage. In the pre-fuzzing stage, we perform static analysis on the IoT firmwares to exploit helpful information, such as web page paths, interfaces, and shared keywords. These kinds of information are used to construct diverse seeds for covering more web paths and interfaces, and are also used to prioritize seeds according to their importance (related to shared keywords) in the fuzzing stage. Based on this approach, we implement a prototype IoT web fuzzing system—IoTParser. Experiments show that IoTParser increased the vulnerability discovery capability by 44% on average, while increasing the vulnerability discovery efficiency by 48.2% on average compared with state-of-the-art IoT web fuzzer. In addition, IoTParser has found 13 vulnerabilities, including 7 0-day.

An adaptive graph learning method for automated molecular interactions and properties predictions

Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work takes a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency.

Augmentative biological control of Halyomorpha halys using the native European parasitoid Anastatus bifasciatus: Efficacy and ecological impact

We report the first large-scale augmentative biological control project carried out in Europe against Halyomorpha halys (Stål) (Hemiptera: Pentatomidae) using the native egg parasitoid Anastatus bifasciatus (Geoffroy) (Hymenoptera: Eupelmidae). During summer 2020, a total of 325,000 adults of A. bifasciatus were released at a rate of 1000 individuals/ha of orchard in 11 sites in Trentino-Alto Adige (Northern Italy). Parasitism parameters were compared between release and control (no release) sites, in which at least three egg masses naturally laid by H. halys were collected (for a total of 262 egg masses). Anastatus bifasciatus and Trissolcus mitsukurii (Ashmead) (Hymenoptera: Scelionidae) were the dominant parasitoids, but parasitism by both species fluctuated widely among sites. At release sites, A. bifasciatus showed a significantly higher discovery efficiency (31.4%) and parasitism rate (16.7%) of H. halys egg masses than at control sites (1.7% and 1.2%, respectively). Parasitism by A. bifasciatus was not dependent on egg mass abundance at release sites, but at control sites a host density-dependent response was revealed by a positive relationship between parasitism and number of H. halys egg masses. On the other hand, parasitism by the adventive T. mitsukurii was not affected by either the releases of A. bifasciatus or by the abundance of H. halys egg masses per site. In conclusion, augmentative releases of A. bifasciatus contributed to increasing its parasitization of H. halys, without causing any negative effects on parasitization by naturally occurring species.

Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations.

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/.

A review of approaches in computer-aided drug design in drug discovery

The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to conduct virtual screening for the purpose of identifying and optimizing leads. In recent years, computational tools have become increasingly popular in the pharmaceutical industry and academic fields as a means of improving the efficiency and effectiveness of the drug discovery and development pipeline. In this post, we'll go over computational methods, which are a creative way of discovering new leads and assisting in drug discovery and development research.

Repurposing drugs to treat cardiovascular disease in the era of precision medicine.

Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine — computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities — support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities.

Association between the 24-hour movement guidelines and executive function among Chinese children

ObjectiveChildhood is a critical period for brain development. However, it remains unknown whether the behaviors in a typical 24-h day are related to children’s executive function (EF). This study aimed to investigate the relationship between the 24-h movement guidelines and children’s EF.MethodChildren aged 7–12 years (n = 376) were studied in 2017 in China. Physical activity (PA) was accelerometer-derived, while screen time (ST) and sleep duration were self-reported. Meeting the 24-h movement guidelines was defined as: 1) ≥ 60 min/day of moderate-to-vigorous PA; 2) ≤ 2 h/day of recreational ST; 3) 9–11 h/night of sleep. EF was assessed by the Wisconsin Card Sorting Test (WCST). Number of completed categories (CC), shifting efficiency (SE), non-perseverative errors (NPE), and failure to maintain set (FMS) were used to measure four processes of EF, respectively represented global performance, cognitive flexibility, efficiency in rule discovery, and sustained attention. Generalized linear mixed models (GLMM) were completed to explore the associations of meeting the PA, ST, and sleep duration recommendations with four processes of EF.ResultsStatistically significant positive associations were observed between the number of guidelines met, regarded as a continuous variable, with CC [β = 0.343 (95% confidence interval [CI]: 0.125, 0.561)] and SE [β = 4.028 (95% CI: 0.328, 7.727)], while number of guidelines met negatively related to NPE [β = − 4.377 (95% CI:-7.952,-0.802)]. Participants not meeting the two recommendations for PA and sleep duration had lower scores in CC [β = -0.636(95% CI:-1.125,-0.147)] and SE [β = -10.610 (95% CI:-18.794,-2.425)] compared with those meeting the two, suggesting inferior global performance and worse efficiency in rule discovery. However, ST recommendation had no significant association with any processes of EF.ConclusionMeeting more recommendations of the 24-h movement guidelines was associated with superior EF in children. Specifically, more PA and healthy sleep duration should be encouraged to promote children’s EF.

Prediction of High-Frequency Economic Data Based on Stochastic Fluctuation Model

In order to improve the effect of economic high-frequency data analysis, this paper combines the stochastic fluctuation model to carry out the forecast analysis of economic high-frequency data. Moreover, this paper uses the spider web model for data processing and makes a preliminary judgment on the extent to which futures/stock prices lead the spot through error correction model, impulse response, and variance decomposition. Furthermore, this paper studies the contribution of futures/stock price changes to the effective price through the common factor model so as to obtain its price discovery efficiency. In addition, this paper judges the efficiency of price discovery by studying the contribution of futures/stock price changes to the effective price. Finally, based on the research idea of futures/stock price discovery efficiency, an intelligent data analysis model is constructed. The experimental study shows that the economic high-frequency data prediction model based on the stochastic fluctuation model proposed in this paper has a good effect on the analysis and prediction of economic high-frequency data.

A Novel Literary Translation Text Classification Method Based on Distributed Incremental Sequence Data Mining Algorithm

With the rapid development of electronic information technology and Internet technology, people's ability to generate and collect data is also increasing. With the rapid development of international exchanges, the number of literary translation texts has also increased dramatically. The information contained in the huge data of literary translation texts is huge, but these data are disorganized at this stage. Therefore, the classification of literary translation texts has become the key to efficient management of translated text information. Literary translation text classification is the process of classifying a given text as one or several of several predetermined text categories according to the content of the text. As one of the key steps in processing huge amounts of text data, text classification is generally regarded as the orderly organization of text sets, that is, grouping similar and related texts together. In this way, the problem of information clutter can be solved to a greater extent, and the efficiency of users' discovery, filtering, and analysis of text information resources can be effectively improved. At present, the basis of text classification is mainly based on the characteristics of words in the article, to analyze the correlation between words and categories. This approach ignores information such as word order and collocations in literary translation texts. To solve this problem, this paper introduces the distributed incremental sequence data mining algorithm into the classification of literary translation texts. This method can fully mine the features of syntactic order and other features based on considering the words and phrasing characteristics of articles in different application fields. The text classification effect is strengthened by discovering more effective information. The experimental results show that the method can improve the classification performance of literary translation texts.

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.

HerbKG: Constructing a Herbal-Molecular Medicine Knowledge Graph Using a Two-Stage Framework Based on Deep Transfer Learning.

Recent advances have witnessed a growth of herbalism studies adopting a modern scientific approach in molecular medicine, offering valuable domain knowledge that can potentially boost the development of herbalism with evidence-supported efficacy and safety. However, these domain-specific scientific findings have not been systematically organized, affecting the efficiency of knowledge discovery and usage. Existing knowledge graphs in herbalism mainly focus on diagnosis and treatment with an absence of knowledge connection with molecular medicine. To fill this gap, we present HerbKG, a knowledge graph that bridges herbal and molecular medicine. The core bio-entities of HerbKG include herbs, chemicals extracted from the herbs, genes that are affected by the chemicals, and diseases treated by herbs due to the functions of genes. We have developed a learning framework to automate the process of HerbKG construction. The resulting HerbKG, after analyzing over 500K PubMed abstracts, is populated with 53K relations, providing extensive herbal-molecular domain knowledge in support of downstream applications. The code and an interactive tool are available at https://github.com/FeiYee/HerbKG.

Inverse Mixed-Solvent Molecular Dynamics for Visualization of the Residue Interaction Profile of Molecular Probes.

To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure.

Development Model of Enterprise Green Marketing Based on Cloud Computing

Internet-based cloud computing is currently an important core technology for computer development in China. It can be used not only in marketing but also in various industries. At the same time, people-oriented and green projects and products that focus on the development of the ecological environment and green consumption dominate the current market trend. The purpose of this paper is at studying the development model of enterprise green marketing based on cloud computing. This article compares and analyzes enterprise green marketing systems through big data algorithms and statistical methods. It starts with the basic characteristics of cloud computing, studies the opportunities and challenges that cloud computing brings to enterprises’ green marketing efforts, describes the green marketing processes and characteristics of enterprises, and analyzes cloud. The feasibility of the construction of the computing system, the basic architecture of the cloud computing system, and the construction of a complete cloud computing data processing flow are proposed. The research data found that the combination of the green marketing development model and cloud computing in the enterprise operating system is conducive to the development of the enterprise; it improves the discovery efficiency of the enterprise and reduces the pollution in the production of the enterprise; the cloud computing can greatly improve the work efficiency of the employees. Cloud computing can improve employees’ speed to complete tasks by about 20% and reduce the error rate by about 50%. The cloud computing enterprise green marketing development model has guiding significance for the long-term development of the enterprise.