Accurate Prediction of Inhibitor Binding to HIV-1 Protease Using CANDOCK

The human immunodeficiency virus 1 (HIV-1) protease is an important target for treating HIV infection. Our goal was to benchmark a novel molecular docking protocol and determine its effectiveness as a therapeutic repurposing tool by predicting inhibitor potency to this target. To accomplish this, we predicted the relative binding scores of various inhibitors of the protease using CANDOCK, a hierarchical fragment-based docking protocol with a knowledge-based scoring function. We first used a set of 30 HIV-1 protease complexes as an initial benchmark to optimize the parameters for CANDOCK. We then compared the results from CANDOCK to two other popular molecular docking protocols Autodock Vina and Smina. Our results showed that CANDOCK is superior to both of these protocols in terms of correlating predicted binding scores to experimental binding affinities with a Pearson coefficient of 0.62 compared to 0.48 and 0.49 for Vina and Smina, respectively. We further leveraged the Database of Useful Decoys: Enhanced (DUD-E) HIV protease set to ascertain the effectiveness of each protocol in discriminating active versus decoy ligands for proteases. CANDOCK again displayed better efficacy over the other commonly used molecular docking protocols with area under the receiver operating characteristic curve (AUROC) of 0.94 compared to 0.71 and 0.74 for Vina and Smina. These findings support the utility of CANDOCK to help discover novel therapeutics that effectively inhibit HIV-1 and possibly other retroviral proteases.

Accuracy and predictability of novel MS Excel-based surgically induced astigmatism calculator

Study of steepening, flattening, clockwise, and counter-clockwise torque effect is indispensable to understand and design surgical induced astigmatism calculator. Hence, in this study by constructing a novel Microsoft Office Excel 2007 based astigmatic calculator following cataract surgery, analysis on the accuracy and predictability evaluated for the performance. Post-cataract surgery patients from May 2019 to January 2020 at a tertiary medical institution recruited for this present study. Based on Pythagoras principle, MS Excel calculator designed and the law of cousins for calculating the vector magnitude and axis respectively. Manual keratometry measurements for pre and postoperative horizontal (Kh) and vertical (Kv) curvatures established, and statistical analysis for the resultant SIA magnitude and axis deduced with Medcalc software comparing with the existing SIA 2.1 version calculator. A total of 29 eyes of 25 patients studied with a mean age of 62.55 (±8.08) years, males contributing to 14 (56%), and right laterally in 17 (58%) eyes. MS Excel and SIA 2.1 versions calculated a mean SIA magnitude of 0.66 (±0.47) D and 0.64 (±0.55) D respectively. Pearson coefficient correlation (r=-0.16, p=0.40), paired-two sample test (t value= 0.11, p= 0.91) and ROC curve analysis (AUC = 0.75, p= 0.34, 95% CI= 0.25 to 0.99) calculated. Regression equation (y = 0.75 + -0.14 x) and limits of agreements (95% CI -0.29 to 0.31) analyzed, and, 95% of data points distributed within ±1.96 SD of the line of equality on Bland-Altman difference plots.The present calculator proclaimed an acceptable accuracy and agreement with a prediction of 0.61 Diopter for every unit change in the magnitude of SIA 2.1 software in addition to consideration of interchangeability.

On the correlation and sensitivity of so far statistical randomness tests based on runs

Abstract—Random numbers play a very important role in cryptography. More precisely, almost cryptographic primitives are ensured their security based on random values such as random key, nonces, salts... Therefore, the assessment of randomness according to statistical tests is really essential for measuring the security of cryptographic algorithms. In this paper, we focus on so far randomness tests based on runs in the literature. First, we have proved in detail that the expected number of gaps (or blocks) of length in a random sequence of length is . Secondly, we have evaluated correlation of some tests based on runs so far using Pearson coefficient method [5, 6] and Fail-Fail ratio one [7, 8]. Surprisingly, the Pearson coefficient method do not show any strong linear correlation of these runs-based tests but the Fail-Fail ratio do. Then, we have considered the sensitivity of these runs tests with some basic transformations. Finally, we have proposed some new runs tests based on the sensitivity results and applied evaluations to some random sources. Tóm tắt—Số ngẫu nhiên đóng một vai trò quan trọng trong mật mã. Cụ thể, độ an toàn của hầu hết các nguyên thủy mật mã đều được đảm bảo dựa trên các giá trị ngẫu nhiên như khóa, nonce, salt… Do đó, việc đánh giá tính ngẫu nhiên dựa trên các kiểm tra thống kê là thực sự cần thiết để đo độ an toàn cho các thuật toán mật mã. Trong bài báo này, chúng tôi tập trung vào các kiểm tra ngẫu nhiên dựa vào run trong các tài liệu. Đầu tiên, chúng tôi chứng minh chi tiết rằng kỳ vọng số các gap (khối) độ dài trong một chuỗi ngẫu nhiên độ dài là . Sau đó, chúng tôi đánh giá mối tương quan của một số kiểm tra dựa vào run bằng phương pháp hệ số Pearson [5, 6] và tỷ số Fail-Fail [7, 8]. Đáng ngạc nhiên là phương pháp hệ số Pearson không cho thấy bất kỳ mối tương quan tuyến tính mạnh nào của các kiểm tra dựa vào run, trong khi đó tỷ số Fail-Fail lại chỉ ra. Tiếp theo, chúng tôi xem xét độ nhạy của các kiểm tra run này với một số phép biến đổi cơ bản. Cuối cùng, chúng tôi đề xuất một số kiểm tra run mới dựa trên các kết quả độ nhạy và đánh giá áp dụng chúng cho một số nguồn ngẫu nhiên.

Application of Artificial Intelligence in an Unsupervised Algorithm for Trajectory Segmentation Based on Multiple Motion Features

With the development of the wireless network, location-based services (e.g., the place of interest recommendation) play a crucial role in daily life. However, the data acquired is noisy, massive, it is difficult to mine it by artificial intelligence algorithm. One of the fundamental problems of trajectory knowledge discovery is trajectory segmentation. Reasonable segmentation can reduce computing resources and improvement of storage effectiveness. In this work, we propose an unsupervised algorithm for trajectory segmentation based on multiple motion features (TS-MF). The proposed algorithm consists of two steps: segmentation and mergence. The segmentation part uses the Pearson coefficient to measure the similarity of adjacent trajectory points and extract the segmentation points from a global perspective. The merging part optimizes the minimum description length (MDL) value by merging local sub-trajectories, which can avoid excessive segmentation and improve the accuracy of trajectory segmentation. To demonstrate the effectiveness of the proposed algorithm, experiments are conducted on two real datasets. Evaluations of the algorithm’s performance in comparison with the state-of-the-art indicate the proposed method achieves the highest harmonic average of purity and coverage.

AYUSHMAN BHARAT - PMJAY

A pre-experimental study to assess effectiveness of folk media on the knowledge and perception regarding Ayushman Bharat-PMJAY among selected population of U.P. was conducted with the objective to assess the effectiveness of folk media on the knowledge and perception regarding Ayushman Bharat-PMJAY among selected population of U.P. Pre experimental research design was adopted and non-probability convenient sampling technique was used to select the sample. A structured questionnaire of 25 questions and perception likert scale of 10 items was prepared to assess the knowledge and perception of population regarding Ayushman Bharat-PMJAY. A structured knowledge questionnaire and structured perception scale on Ayushman Bharat -PMJAY (pre-test) was administered to the group, followed by administration of folk media on the Day-1. Then on Day-7, post-test was conducted. Data obtained were analyzed and interpreted by using both descriptive and inferential statistics in terms of frequency, percentages, mean, and S.D. score. Findings showed that: majority of population i.e. 47 (78.33%) were having good knowledge whereas 12 of these samples (20%) were having excellent knowledge and 1 of these samples were having poor knowledge. It was evident from the results that the mean post-test knowledge and practice score were more than mean pre-test scores. Findings showed that folk media was effective to gain knowledge and favorable perception regarding Ayushman Bharat -PMJAY. majority of samples i.e. 48 (40%) were having favorable perception and only 12(60%) samples were having neutral perception. The calculated Z value (2.10) for knowledge and the calculated Z value (4.06) for perception is greater than the tabulated Z value (1.645) at 0.05 level of significance at (df =59) which concluded that the Folk Media was effective in improving the knowledge and perception towards Ayushman Bharat -PMJAY among selected population of U.P.Chi square test was done showed that there was significant association between the mean post-test knowledge score with selected demographic variables. It was also showed that there was a significant association between the mean post-test perception score with selected demographic variables. The Karl Pearson coefficient of correlation was computed (r = 0.19) which indicate no relationship between the knowledge post-test scores and perception post-test scores regarding Ayushman Bharat-PMJAY among selected population of U.P.

A Fault Warning Method for Electric Vehicle Charging Process Based on Adaptive Deep Belief Network

If an accident occurs during charging of an electric vehicle (EV), it will cause serious damage to the car, the person and the charging facility. Therefore, this paper proposes a fault warning method for an EV charging process based on an adaptive deep belief network (ADBN). The method uses Nesterov-accelerated adaptive moment estimation (NAdam) to optimize the training process of a deep belief network (DBN), and uses the historical data of EV charging to construct the ADBN of the normal charging process of an EV model. The real-time data of EV charging is obtained and input into the constructed ADBN model to predict the output, calculate the Pearson coefficient between the predicted output and the actual measured value, and judge whether there is a fault according to the size of the Pearson coefficient to realize the fault warning of the EV charging process. The experimental results show that the method is not only able to accurately warn of a fault in the EV charging process, but also has higher warning accuracy compared with the back propagation neural network (BPNN) and conventional DBN methods.

HAZOP Ontology Semantic Similarity Algorithm Based on ACO-GRNN

Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse.

Comparison of the Methods for Determining Pyrogenically Modified Carbon Compounds

The soil organic matter (SOM) is searched for the biomarkers and specific features associated with the effect of wildfires by the case study of peat soil, Rheic Hemic Histosol (Lignic), in the south of the middle taiga of the Komi Republic. It is shown that fires considerably influence the peat organic matter. Pyrogenic activity is assessed according to the content of charcoal particles. SOM is examined using solid-state 13C-NMR spectroscopy to determine the concentrations of polycyclic aromatic hydrocarbons (PAHs) and benzene polycarboxylic acids (BPCAs). The used methods allow for diagnosing the effects of wildfires on the SOM composition. In the horizons with the signs of pyrogenesis, the share of carbon represented by aromatic fragments increases as well as the PAH concentration, mainly at the expense of naphthalene, phenanthrene, and chrysene. The carbon stock of pyrogenically modified compounds, amounting to 4.4 kg/m2, is for the first time assessed in the European north based on the BPCA content. The characteristics of pyrogenically changed organic compounds and their fragments obtained by different methods correlate well: the Pearson coefficient for the correlation of the carbon content in aromatic compounds (Caryl) with total BPCA content is R = 0.84 (p < 0.05) and with individual BPCAs, R = 0.81–0.90 (p < 0.05).

Exploring the Relationship Between Chinese EFL Students' Grit, Well-Being, and Classroom Enjoyment

It has been documented that grit plays an indispensable role in the process of language learning and teaching. It is postulated that gritty people are more able to become involved in classroom practice and remain motivated even in light of challenges; however, what remains vague is the interplay of grit, well-being, and classroom enjoyment. To this end, 335 male and female Chinese EFL (English as a Foreign Language) learners who were studying English in 28 universities took part in this study. They completed three questionnaires including the grit scale questionnaire (Grit-S), foreign language enjoyment scale, and PERMA well-being scale. The Pearson coefficient of correlation was run to investigate the first research question of the study while, after checking the preliminary assumptions, for the second research question a multiple regression analysis was used. The findings of the study demonstrated that there is a positive relationship between learners' grit and enjoyment, and high degrees of enjoyment were interrelated to high degrees of grit. The findings of the study also signified that grit significantly predicted students' well-being and was also a predictor of classroom enjoyment. Finally, some implications and recommendations have been offered for language teaching stakeholders in educational settings.

THE RELATIONSHIP BETWEEN STUDENTS' READING ATTITUDE WITH THE RESULT OF READING COMPREHENSION

Reading is a very important activity to get abundant information and add new knowledge. As students, reading activities have a very dominant role because they are required to be more scientific and rational in giving opinions or providing information both in oral and written. Besides, students' attitudes towards reading also have an important role. This study focused on their English reading comprehension and attitudes towards reading in English. This study aims to determine whether there is a significant relationship between students' attitudes with the result of reading comprehension. Data was collected by distributing questionnaires and reading comprehension tests for students of STKIP PGRI Jombang in the English Education Study Program. The Pearson Coefficient Product Moment is used to determine whether there is a significant relationship between students' attitudes with the result of reading comprehension. From the results found from the SPSS 16 application with a sample of 63 students in the 2019 class, there is a correlation of 0.478. These result indicates that students' reading attitudes are positively related to their reading comprehension. Thus, students must continue to read to improve their English reading skill.