Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitors of riboflavin biosynthetic pathway enzymes as potential antibacterial drugs

Multiple drug resistance is the main obstacle in the treatment of bacterial diseases. Resistance against antibiotics demands the exploration of new antimicrobial drug targets. A variety of in silico and genetic approaches show that the enzymes of the riboflavin biosynthetic pathway are crucial for the survival of bacteria. This pathway is absent in humans thus enzymes of the riboflavin biosynthetic pathway are emerging drug targets for resistant pathogenic bacterial strains. Exploring the structural details, their mechanism of action, intermediate elucidation, and interaction analysis would help in designing suitable inhibitors of these enzymes. The riboflavin biosynthetic pathway consists of seven distinct enzymes, namely, 3,4-dihydroxy-2-butanone 4-phosphate synthase, GTP cyclohydrolase II, pyrimidine deaminase/reductase, phosphatase, lumazine synthase, and riboflavin synthase. The present review summarizes the research work that has been carried out on these enzymes in terms of their structures, active site architectures, and molecular mechanism of catalysis. This review also walks through small molecule inhibitors that have been developed against several of these enzymes.
Radio Frequency Interference Detection in Passive Microwave Remote Sensing Using One-Class Support Vector Machines

Radio frequency interference (RFI) is a serious threat to the accurate estimation of critical geophysical parameters via passive microwave remote sensing and the presence of RFI in microwave radiometer measurements is increasing over time. On the other hand, the nature and the occurrence of RFI captured by radiometers are usually unknown making their detection and mitigation difficult. To overcome this challenge, this article presents a novel RFI detection algorithm that relies only on the information extracted from the RFI-free radiometer measurements which can be collected over oceans and rural areas with limited human activity, i.e., a one-class algorithm, to be implemented in future remote sensing radiometers. The algorithm transforms raw time-series radiometer measurements into a heterogeneous feature-based representation. Then, a feature selection algorithm identifies the most discriminant features to detect interference based on the probabilities of misdetections and false alarms. Finally, the optimal decision boundaries that discriminate the RFI-contaminated radiometer measurements from the RFI-free ones are computed via support vector machines (SVM) using only the RFI-free radiometer measurements. Regardless of the characteristics of RFI contamination, the algorithm, therefore, outputs a generalized decision boundary for RFI-free measurements. A performance evaluation of the proposed algorithm against the traditional RFI detection algorithms has been performed using simulated radiometer data, and the results have shown that the novel algorithm, unlike the traditional methods, can successfully detect RFI, even when the interference-to-noise ratio (INR) of the radiometer measurements is as low as $-18$ dB.
Experimental Assessment of an Active Flap Device

A project called RPA (Rotor à Pales Actives) was launched three years ago to study the possible benefits of implementing active trailing edge flaps on a helicopter main rotor. The main objectives of this project are to decrease BVI noise in descent flight and improve the dynamic behavior of the rotor throughout the largest possible flight domain. After a first phase dedicated to the design of the best flap configuration at scale 1, the second phase of the project deals with the design of a wind-tunnel scale model of a rotor equipped with active flaps. An offthe-shelf piezo-electric actuator is used together with a specific patented flap-driving mechanism. Such an active device was tested under centrifugal loads as well as under aerodynamic loads in order to prepare future wind-tunnel tests. The results obtained under centrifugal loads allowed to clear the active device but the aerodynamic testing showed that some improvements were needed. Corresponding modifications are under way to fully clear the active device to be used on a complete rotor model.
Distance based String Search using IR-Tree

A spatial info directs two-dimensional things, (for instance, centers, rectangles, et cetera.), and offers speedy access to those articles in perspective of explicit determination criteria. The importance of spatial databases is mirrored by the solace of exhibiting substances of reality in a very geometric manner for instance, zones of restaurants, motels, mending focuses thence on habitually self-addressed as concentrates in AN aide, whereas larger degrees, as an example, stops, lakes, and scenes routinely as a mix of rectangles. During this paper, have many deficiencies that really impact its adequacy. Motivated by this, we tend to boost another access methodology known as the spatial adjusted record that extends the routine miracle document to regulate to two-dimensional knowledge, and goes hand in hand with figuring’s which will answer nearest neighbor queries with catchphrases unceasingly. As Affirmed by examinations, the planned systems beat the IR2 -tree in request latent period through and thru, each currently and once more by a section of solicitations of enormity.
Constructing a User-Centered Fake News Detection Model by Using Classification Algorithms in Machine Learning Techniques

As fake news spreads rapidly in social media, attempts to develop detection technology to automatically identify fake news are actively being developed, recently. However, most of them focus only on the linguistic and compositional characteristics of fake news (e.g., source or authors indication, length of a message, frequency of negative words). Compared to them, this study proposes a fake news detection model based on machine learning that reflects the characteristics of users, news content, and social networks based on social capital. To comprehensively reflect the characteristics related to the spread of fake news, this study applied the XGBoost model to estimate the feature importance of each variable to derive the priority factors that preferentially affect fake news detection. Based on the derived variables, we established SVM, RF, LR, CART, and NNET, which are representative classification models of machine learning, and compared the performance rate of fake news detection. To generalize the established models (i.e., to avoid overfitting or underfitting), this study performed a cross-validation step, and to compare the predictive accuracy of the established models. As a result, the RF model indicated the highest prediction rate at about 94%, while the NNET had the lowest performance rate at about 92.1%. The results of this study are expected to contribute to improve the fake news detection system in preparation for the more sophisticated generation and spread of fake news.
CRSS-LDNN: Long-duration naturalistic noise corpus containing multi-layer noise recordings for robust speech processing

Multi-layer noise refers to scenarios where multiple distinct noise sources are simultaneously active in an audio stream. We collected a corpus named the CRSS long-duration naturalistic noise (CRSS-LDNN) corpus. It contains noise captured from complex daily-life activities using wearable LENA units. The diversity in noise-sources include construction noise, noise like multi-speaker babble, large-crowd noise, vehicle/bus noise on the road, home environment noise, etc. Corpus contains recordings of complex mixtures of these noise types. The babble noise and bus-engine noise present along with occasional impulsive-noise over long-duration is an example of such scenario. The data were collected at 16 kHz sampling rate with 16-bit precision in . wav format. This data would be released to speech community (http://crss.utdallas.edu). During the summer semester, a CRSS student wore a LENA device that was switched ON when multiple noise-sources were present. This corpus was recorded in naturalistic scenarios with uncontrolled mixing of various noise-sources. It provides naturalistic multi-layer noise recordings for evaluation of robust speech algorithms such as speech recognition, speaker diarization and verification, sentiment analysis. It consists of approximately 19 hours noise recordings. The CRSS-LDNN noise is more challenging as compared to existing noise corpora such as NOISEX that contains only single noise. Multi-layer noise refers to scenarios where multiple distinct noise sources are simultaneously active in an audio stream. We collected a corpus named the CRSS long-duration naturalistic noise (CRSS-LDNN) corpus. It contains noise captured from complex daily-life activities using wearable LENA units. The diversity in noise-sources include construction noise, noise like multi-speaker babble, large-crowd noise, vehicle/bus noise on the road, home environment noise, etc. Corpus contains recordings of complex mixtures of these noise types. The babble noise and bus-engine noise present along with occasional impulsive-noise over long-duration is an example of such scenario. The data were collected at 16 kHz sampling rate with 16-bit precision in . wav format. This data would be released to speech community (http://crss.utdallas.edu). During the summer semester, a CRSS student wore a LENA device that was switched ON when multiple noise-sources were present. This corpus was recorded in naturalistic scenarios with u.
Evaluation of lncRNA Expression Pattern and Potential Role in Heart Failure Pathology

During the last few decades, the morbidity and mortality of heart failure (HF) have remained on an upward trend. Despite the advances in therapeutic and diagnostic measures, there are still many aspects requiring further research. This study is aimed at finding potential long noncoding RNAs (lncRNAs) that could aid with the diagnosis and treatment of HF. We performed RNA sequencing on the peripheral blood of healthy controls as well as HF patients. The expression of lncRNAs was validated by RT-qPCR. Bioinformatic analysis was performed to investigate the possible mechanism of differentially expressed lncRNAs and mRNAs. The diagnostic value of lncRNAs was analysed by ROC analysis. Finally, a total of 207 mRNAs and 422 lncRNAs were identified. GO and KEGG pathway analyses revealed that biological pathways such as immune response, regulation of cell membrane, and transcriptional regulatory process were associated with the pathological progress of HF. The lncRNA-mRNA coexpression network was conducted, and several mRNAs were identified as key potential pathological targets, while lncRNA CHST11, MIR29B2CHG, CR381653.1, and FP236383.2 presented a potential diagnostic value for HF. These findings provide novel insights for the underlying mechanisms and possible therapeutic targets for HF.
A PROGRAMMING APPROACH TO ESTIMATE PRODUCTION FUNCTIONS USING BOUNDS ON THE TRUE PRODUCTION SET

Varian (1984) developed procedures to establish bounds on a true convex negative monotonic production set that p-rationalizes a set of data consistent with the weak axiom of profit maximization. These bounds can provide additional information for estimating parametric production functions. A mathematical programming procedure is developed to maximize various measures of goodness of fit of alternative parametric specifications relative to the true production set.
Nonlinearities in Artificial Neural Systems Interpreted as an Application of Ising Physics

In this review, the nonlinearities in different processes s uch as spin glasses, finite field models, Hamiltonian functions, le arning and storing capabilities, mean field systems and others in the area of phy sics related to the artificial neural networks namely the main brain structure i nterpreted as Ising spin systems are discussed. It is shown that nonlinearities serve as exclusive role in the applied physics field.
Anisotropic mesh generation

In this thesis, we study the generation of anisotropic meshes using the concepts of Delaunay triangulations and Voronoi diagrams. We first consider the framework of locally uniform anisotropic meshes introduced by Boissonnat, Wormser and Yvinec. Despite known theoretical guarantees, the practicality of this approach has only been hardly studied. An exhaustive empirical study is presented and reveals the strengths but also the overall impracticality of the method. In a second part, we investigate the anisotropic Voronoi diagram introduced by Labelle and Shewchuk and give conditions on a set of seeds such that the corresponding diagram has a dual that is an embedded triangulation in any dimension; an algorithm to generate such sets is devised. Using the same diagram, we propose an algorithm to generate efficiently anisotropic triangulations of low-dimensional manifolds embedded in high-dimensional spaces. Our algorithm is provable, but produces disappointing results. Finally, we study Riemannian Voronoi diagrams and introduce discrete Riemannian Voronoi diagrams, which employ recent developments in the numerical computation of geodesic distances and whose computation is accelerated through the use of an underlying anisotropic graph structure. We give conditions that guarantee that our discrete structure is combinatorially equivalent to the Riemannian Voronoi diagram and that its dual is an embedded triangulation, using both straight and curved simplices. We obtain significantly better results than with our other methods, but the overall utility of
Diagnostic accuracy analysis for ordinal competing risk outcomes using ROC surface

Many medical conditions are marked by a sequence of events or statuses that are associated with continuous changes in some biomarkers. However, few works have evaluated the overall accuracy of a biomarker in separating various competing events. Existing methods usually focus on a single cause and compare it with the event-free controls at each time. In our study, we extend the concept of ROC surface and the associated volume under the ROC surface (VUS) from multi-category outcomes to ordinal competing risks outcomes. We propose two methods to estimate the VUS. One views VUS as a numerical metric of correct classiﬁcation probabilities representing the distributions of the diagnostic marker given the subjects who have experienced diﬀerent cause-speciﬁc events. The other measures the concordance between the marker and the sequential competing outcomes. Since data are often subject to lost of follow up, inverse probability of censoring weight is introduced to handle the missing disease status due to independent censoring. Asymptotic results are derived using counting process techniques and U-statistics theory. Practical performances of the proposed estimators in ﬁnite samples are evaluated through simulation studies and the procedure of the methods are illustrated in two real data examples. Public Health Signiﬁcance: ROC curve has long been treated as a gold standard in evaluating the accuracy of continuous predictors in separating binary outcomes in various ﬁelds including biomedical, ﬁnancial, and geographical areas. Our proposed methods extend its utilization in multi-category events outcomes to competing risks censoring. Our methods aim to assess a global accuracy of a biomarker’s predictive power to each simultaneously, especially, to which stages of disease progression that patients would land by a speciﬁc time in followup. Our work provides much-needed global assessment for the predictive power of a biomarker for disease progression.
5G Radiofrequency Radiation Caused the Microwave Syndrome in a Family Living Close to the Base Stations

The fifth generation, 5G, for wireless communication has been implemented in various countries since 2019. The deployment in Sweden started in 2020 where the used frequencies for 5G in cities are around 3.5 GHz. Recently, we published three case reports on persons that developed the microwave syndrome within short after the installation of 5G base stations close to their dwellings. The health symptoms were attributed to high levels of radiofrequency (RF) radiation measured in their apartments. In this article we examine a family of three persons living at distances of about 50 and 70 meters to two 5G base stations. The base stations are located on the top of two 6-floor buildings and the antennas are directed towards the family’s apartment on the 4 th floor on the opposite side of the street. Measurements in the apartment were made 10 times at every place, each measurement during 1 minute. Highest levels were measured close to the two windows in the master bed room varying from 320 000 to 1 200 000 μW/m 2 . High levels were also found at the window of the son’s room, 121 000 to 490 000 μW/m 2 , and the daughter’s room 34 800 to 166 000 μW/m 2 . Somewhat lower levels were found in the beds at the place of the pillow for all family members. Lowest levels were measured in the kitchen on the opposite side of the apartment, 710 to 3 260 μW/m 2 . Health problems were assessed by using a structured questionnaire similar to our previous studies. The family members reported symptoms included in the microwave syndrome to varying self-estimated degrees. The daughter had the most severe health issues, for example sleeping problems, headache, concentration and memory problems, skin disorders, irregular heartbeat, light sensitivity, anxiety and panic attacks These results are in line with previous case studies showing that deployment of 5G cannot be made without risks to human health, especially for those living or working in the proximity of the base stations.
Functional Characterization of Novel Lunatic Fringe Variants in Spondylocostal Dysostosis Type-III with Scoliosis

Scoliosis affects over four million Americans, with most cases having an idiopathic cause. Pathogenic variants in the LUNATIC FRINGE (LFNG) gene can cause spondylocostal dysostosis type-III (SCD3), which is a rare skeletal dysplasia characterized by the absence, fusion, or partial development of vertebrae and ribs. Acute restrictive lung disease and scoliosis may also be present in some cases. The variability in symptoms suggests that there may be other underlying pathological mechanisms that are yet to be discovered. We conducted an analysis of two novel LFNG variants, c.766G>A (p.G256S) and c.521G>A (p.R174H), that were observed in a patient with SCD3 phenotype and scoliosis. Characterizing these variants can help us better understand the relationship between genotype and phenotype. We assessed both variants for impaired glycosyltransferase activity, subcellular mislocalization, and aberrant pre-proprotein processing. Our results indicate that the p.G256S variant is enzymatically nonfunctional, while the p.R174H variant is functionally less effective. Both variants were correctly localized and processed. Our findings suggest that the hypomorphic variant (p.R174H) may have partially improved the patient’s stature, as evidenced by a lower arm span-to-height ratio, increased height, and more vertebrae. However, this variant did not appear to have any effect on the severity of vertebral malformations, including scoliosis. Further research is necessary to determine the extent to which variations in LFNG activity affect the presentation of SCD3.
Exploring the Students’ Perceived Effectiveness of Online Education during the COVID-19 Pandemic: Empirical Analysis Using Structural Equation Modeling (SEM)

The world faced COVID-19, which was a threat to public health and disturbed the educational system and economic stability. Educational institutes were closed for a longer period, and students faced difficulty to complete their syllabus. The government adopted a policy of “suspending classes without stopping learning” to continue education activities. However, student satisfaction with online education is a growing concern. Satisfaction of students is an important indicator of academic quality. Therefore, this study attempts to investigate the influencing factors behind learning satisfaction using information from 335 students from various institutes in Pakistan. This research examined the impact of computer and internet knowledge, instructor and course material, and Learning Management Systems (LMS) on learning satisfaction. The path coefficients were obtained via Partial Least Square-Structural Equation Modeling (PLS-SEM). The LMS is a tool that facilitates the learning process with the provision of all types of educational material. The path coefficient was more in the case of LMS (0.489), which indicates its positive and significant role to attain learning satisfaction. The instructor and course material ordered second (0.261), which shows that the quality of an instructor and course material also plays a positive role to attain learning satisfaction. The computer and internet are essential ingredients of online education, showing a significant and positive path coefficient (0.123), implying that computer and internet knowledge could enhance learning satisfaction. The universities should develop their LMS to implement online education with quality course materials. It is also vital that the instructor should be up to date with modern learning techniques while ensuring internet connectivity, especially in rural areas. The government should provide an internet connection to students at discounted rates.
Implementasi Metode Similarity Untuk Pendukung Keputusan Diagnosis Kanker Serviks

Similarity Merupakan Salah Satu Metode Yang Digunakan Untuk Menghitung Kemiripan Dua Objek. Similarity Banyak Digunakan Dalam Perhitungan Klasifikasi, Pengelompokan, Dll. Untuk Menghindari Kesalahan Diagnosis, Metode Similarity Jaccard Dan Similarity Cosine Digunakan Untuk Mendukung Diagnosis Kanker Serviks. Data Yang Digunakan Berupa Data Matriks Gejala Penyakit Kanker, Data Matriks Gejala Non Kanker, Dan Data Matriks Gejala Pasien. Perhitungan Similarity Dilakukan Dengan Cara Membandingkan Data Matriks Gejala Pasien Dengan Data Matriks Gejala Penyakit Kanker. Data Matriks Gejala Pasien Dengan Data Matriks Gejala Penyakit Non Kanker. Kemudian Dihitung Nilai Similaritynya. Nilai Similarity Terbesar Menjadi Kesimpulan Hasil Diagnosis. Hasil Diagnosis Dibandingkan Dengan Data Sekunder Kemudian Dihitung Akurasinya. Similarity Jaccard Memiliki Jumlah Rekomendasi Paling Banyak Yaitu 4 Buah Rekomendasi Sedangkan Similarity Cosine Memiliki Jumlah Rekomendasi Paling Banyak Yaitu 3 Buah Rekomendasi. Akurasi Hasil Pengujian Sistem Pada 54 Data Uji Untuk Similarity Jaccard Di Urutan Pertama Adalah 96,69%, Dan Di Urutan Kedua Adalah 3,17%. Akurasi Hasil Pengujian Untuk Similarity Cosine Di Urutan Pertama Adalah 96,69%, Dan Di Urutan Kedua Adalah 3,17%.
Statistics as unbiased estimators: exploring the teaching of standard deviation

ABSTRACT This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator – not just a descriptive statistic for the variation (spread) in data – is related to teachers’ instruction regarding standard deviation, particularly around the issue of division by n-1. In this regard, the study contributes to our understanding about how knowledge of mathematics beyond the current instructional level, what we refer to as nonlocal mathematics, becomes important for teaching. The findings indicate that acquired knowledge of nonlocal mathematics can play a role in altering teachers’ planned instructional approaches in terms of student activity and cognitive demand in their instruction.