Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Bacterial α-l-Fucosidases with an Ortho-Quinone Methide-Based Probe and Mapping of the Probe-Protein Adducts

Fucosidases are associated with several pathological conditions and play an important role in the health of the human gut. For example, fucosidases have been shown to be indicators and/or involved in hepatocellular carcinoma, breast cancer, and helicobacter pylori infections. A prerequisite for the detection and profiling of fucosidases is the formation of a specific covalent linkage between the enzyme of interest and the activity-based probe (ABP). The most commonly used fucosidase ABPs are limited to only one of the classes of fucosidases, the retaining fucosidases. New approaches are needed that allow for the detection of the second class of fucosidases, the inverting type. Here, we report an ortho-quinone methide-based probe with an azide mini-tag that selectively labels both retaining and inverting bacterial α-l-fucosidases. Mass spectrometry-based intact protein and sequence analysis of a probe-labeled bacterial fucosidase revealed almost exclusive single labeling at two specific tryptophan residues outside of the active site. Furthermore, the probe could detect and image extracellular fucosidase activity on the surface of live bacteria.
PECULIARITIES OF ONBOARDING IN MODERN IT PROJECTS

One of the most important factors in the successful development of modern IT companies is an effective and flexible personnel policy that can ensure the continuous recruiting of new employees and introducing them to professional activities. The article examines the issues of professional development of newcomers in modern IT projects, determines the factors of its success and analyzes the essence of the phenomenon of onboarding in the context of its relation to such constructs as "adaptation", "mentoring" and "tutoring". Onboarding is considered, on the one hand, as a phenomenon that reflects the systematic purposeful interaction between a social organization and its new employee, which aims to integrate a new team member with existent working conditions and ensure their productive performance in the shortest possible time, and on the other - as a partnership between an experienced employee (mentor) and a new specialist for the purpose of training, support, and professional development facilitation. The work contains an analysis of specialized software for successful onboarding in the conditions of modern IT projects. It also reveals the results of an empirical study of onboarding in IT projects with the participation of experienced mentors and young professionals, the results of which revealed the main factors of successful adaptation of newcomers to the new conditions of their professional activity: regulation of onboarding and mentoring processes in the project team and IT companies using internal regulatory documents, specialized software, etc.; productive cooperation between the mentor and the novice, which involves the analysis of project documentation, implemented through reflective technologies and aimed at understanding the new employee's expectations and requirements for their professional performance, communication, behavior in the project team; professional competence of the mentor, which is determined by their experience in project activities, deep understanding of the project and processes in it, as well as non-technical skills (communicative, emotional, regulatory, cognitive and metacognitive).
A Hidden Markov Model-Based Tagging Approach for Arabic Isnads of Hadiths

Hadith judgment implies checking the validity of Hadith to decide whether it is correct (trustworthy) or false (bogus). “Matn” and “Isnad” are the main constituents of Hadith; “Matn” is the sayings of the prophet, whereas “Isnad” represents the narrators’ series. The first step of Hadith judgment is the extraction of narrators’ names, after that, the rules of judgment, which were set out by Hadith’s scientists, could be implemented, three of these rules are particularly related to the narrators’ series, and these rules are continuity of the transmission chain, the trustworthiness of the narrators, and the preciseness of the narrators. Therefore, to check the authenticity of Hadiths, the three conditions must be satisfied, and to do so, the narrators’ names must be extracted first. Isnad contains many words and phrases called “Isnad-Phrases”; these phrases have many types or categories called part of Isnads (POIs) like Narrator-Name, Prophet-Name, and Received-Method. A lot of computational research studies suggest serving Hadith sciences by extracting the narrators’ names and other POIs using various approaches. This study presents a new hybrid approach founded on the hidden Markov model (HMM) and gazetteer lists to process “Isnad. ” The approach objective is to expect all POIs in the Isnad including narrators’ names. The experiments carried on 1,000 Hadiths from “Sahih Muslim”: 900 Hadiths as training dataset and 100 Hadiths as testing dataset, and the results show a noteworthy accuracy for the proposed hybrid approach.
Scale-aware network with scale equivariance

Convolutional neural network (CNN) has achieved good performance in object classification due to its inherent translation equivariance, but its ability of scale equivariance is poor. A Scale-Aware Network (SA Net) with scale equivariance is proposed, which can estimate scale, that is, the size of image, while classifying. In the training stage, only one scale pattern is learned. In the testing stage, firstly, the testing sample with unseen scale is zoomed-in and zoomed-out into a set of images with different scales, which form an image pyramid. The image zooming-in channels are up-sampled by bilinear interpolation. The image zooming-out channels are down-sampled, and the combination of dyadic discrete wavelet transform (DWT) and bilinear interpolation are used to avoid spectrum aliasing. Then, the image pyramid with different scales is sent to siamese CNNs with weight-sharing for inferencing. A two-dimensional classification score matrix is obtained. Through the position of the maximum of the classification score matrix, the classification and scale estimation can be carried out at the same time. Experiments are carried out on MNIST Large Scale testing set. In scale estimation experiments, the relative value of root mean square error (RMSE) can be obtained by scaling the testing sample images in a geometric series with common ration of 4√2 in the range of [1/2，2]. The classification experiments show that when the scale is greater than 1.0, the classification accuracy can surpass 90%. SA Net can estimate the scale while improving the classification accuracy, and mis-estimated samples are always near the ground-truths (GTs), so the correct scale of the unseen scale can always be obtained roughly.
A low ripple power supply for high current magnet load

Conventional magnet power supply systems in a particle accelerator environment have a problem of low efficiency due to the energy loss in the linear regulator which is used to reduce the ripple content of the magnet load current. The authors propose a high-current magnet power supply system configuration which replaces the linear regulator with a switch-mode ripple regulator. This replacement improves system efficiency significantly. In addition, the complete system is smaller in size and lower in cost. The basic system operation principle is explained. Theoretical analysis is performed on system steady-state operation. The harmonic reduction function of the switch-mode regulator is examined. An illustrative example is presented. <<ETX>>
Colour-weighted rank transform and improved dynamic programming for fast and accurate stereo matching

ABSTRACT Real-time stereo matching with high accuracy is a dynamic research topic; it is attractive in diverse computer vision applications. This paper presents a stereo-matching algorithm that produces high-quality disparity map while maintaining real-time performance. The proposed stereo-matching method is based on three per-pixel difference measurements with adjustment elements. The absolute differences and the gradient matching are combined with a colour-weighted extension of complete rank transform to reduce the effect of radiometric distortion. The disparity calculation is realized using improved dynamic programming that optimizes along and across all scanlines. It solves the inter-scanline inconsistency problem and increases the matching accuracy. The proposed algorithm is implemented on parallel high-performance graphic hardware using the Compute Unified Device Architecture to reach over 240 million disparity evaluations per second. The processing speed of our algorithm reaches 98 frames per second on 240 × 320-pixel images and 32 disparity levels. Our method ranks fourth in terms of accuracy and runtime for quarter-resolution images in the Middlebury stereo benchmark.
PEMBUATAN DC CHOPPER TIPE BOOST BERBASIS TRANSISTOR SC2555

Abstrak Di dalam kehidupan saat ini semua kegiatan yang kita lakukan pasti dibantu dengan peralatan elektronik. Untuk menjalankan peralatan elektronik ini dibutuhkan sumber daya searah atau DC (Direct Current) yang disearahkan dari sumber daya AC. Konverter DC – DC (DC Chopper) merupakan suatu sirkuit elektronikq daya yang dapat mengkonversi tegangan DC menjadi nilai tegangan DC yang berbeda. Pada tugas akhir kali ini bertujuan merancang konverter DC – DC tipe boost yang menggunakan transistor sebagai pensaklarannya. Untuk memicu pensaklaran pada transistor akan digunakan metode Pulse Width Modulation (PWM) dengan IC TL494. Konverter tipe boost ini akan megubah nilai tegangan keluarannya lebih besar atau sama dengan tegangan pada sumber, yang dapat diatur oleh lebar pulsa (duty cycle) pada PWM. Sumber tegangan yang digunakan adalah tegangan AC 220 V yang diturunkan menggunakan trafo CT (center tap) menjadi 15 V dan disearahkan menggunakan dioda bridge untuk mensuplai konverter. Frekuensi yang digunakan untuk pensaklaran sebesar 15 KHz. Pada pengujian didapatkan hasil pengujian DC Chopper Boost bekerja pada mode DCM dengan nilai duty cycle 10% - 50% dan mode CCM dengan nilai duty cycle 60% - 80% pada beban resistif. Sedangkan pada beban induktif DC Chopper bekerja pada mode DCM dengan nilai duty cycle 10% dan mode CCM pada nilai duty cycle 20% - 80%. Kata Kunci : DC chopper boost, transistor, IC TL494, CCM, DCM Abstract In this present life, all of the things we did definitely helped by electronics tools. For running this electronics tools need DC (Direct Current) source witch rectified from AC (Alternative Current) source. DC – DC converter (DC Chopper) are power electronic circuit that convert a DC voltage to a different DC voltage level. This final project is purposes to design a DC – DC converter boost type witch used transistor for the switching. For trigging a transistor switching will use Pulse Width Modulation (PWM) method with IC TL494. This boost converter type will change output voltage level higher or same as input voltage level, witch regulated by duty cycle from PWM. Voltage source used 220 AC voltage downgraded used center tap transformator (CT) become 15 V for converter supplies and rectified used diode bridge for converter supply. Frequency level witch used for trigging is 15 KHz. In examination obtained result DC Chopper Boost work in DCM mode with duty cycle range 10% - 50% and CCM mode with duty cycle range 60% - 80% for resistive load. While in inductive load DC Chopper Boost work in DCM mode with duty cycle range 10% and CCM mode with duty cycle range 20% - 80%. Keywords : DC Chopper boost, Transistor, IC TL494, CCM, DCM
Problem Based Learning: Its Advantages, Current Situations and Future Development

Since the traditional teaching method overemphasizes systematic learning knowledge and rote learning, students lack practical and knowledge exploration abilities. In addition, in traditional learning curriculum is taught in single or multiple disciplines. In this case, disciplines are isolated from each other, which is the reason why students may lack interdisciplinary ability. Judging from above, taught in traditional teaching mode, pupils perhaps are short of many higher-order abilities, and Problem Based Learning can overcome these shortcomings. Thus Problem Based Learning was implemented at McMaster University as an innovative teaching approach. Up to now, it has been applied in many schools. Using the mode of literature review, in this paper we review the concept, origin, and characteristics of Problem Based Learning. In addition, it also reflects the advantages compared to traditional teaching mode, teachers’ tasks, dilemmas, and future prospects. By comparing Problem Based Learning with the traditional learning method, Problem Based Learning is found to perform better in four dimensions: teaching objectives, teaching methods, discipline linkage modes, and teaching achievements. Owing to its merits, it is a wider tendency to implement Problem Based Learning in more schools and classes. However, it still has some weaknesses in practical application: reticent culture, educators’ preference to previous teaching custom, and lack of resource-intensive environment. More endeavors are needed to be taken in research to overcome these shortcomings. And in a future implementation, Problem Based Learning should be associated more closely with present practical problems, not confined to settled problems of textbooks.
Molecular Imaging and Theragnostics of Thyroid Cancers

Simple Summary According to the American Cancer Society, approximately 53,000 new cases of thyroid cancer were diagnosed and more than 2200 people died from the disease in 2020. New developments in molecular imaging are significantly improving thyroid cancer diagnostics and therapy. Continuous research in molecular imaging techniques additionally contributes to an understanding of a variety of diseases and enables more efficient care of thyroid cancer patients. Molecular imaging-based personalized therapy has been a fascinating concept for individualized therapeutic strategy, which is able to attain the highest efficacy and reduce adverse effects in certain patients. Theragnostics, which integrates diagnostic testing to detect molecular targets for particular therapeutic modalities, is one of the key technologies that contributes to the success of personalized medicine. This review details the inception of molecular imaging and theragnostic applications for thyroid cancer management. Abstract Molecular imaging plays an important role in the evaluation and management of different thyroid cancer histotypes. The existing risk stratification models can be refined, by incorporation of tumor-specific molecular markers that have theranostic power, to optimize patient-specific (individualized) treatment decisions. Molecular imaging with varying radioisotopes of iodine (i.e., 131I, 123I, 124I) is an indispensable component of dynamic and theragnostic risk stratification of differentiated carcinoma (DTC) while [18F]F-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) helps in addressing disease aggressiveness, detects distant metastases, and risk-stratifies patients with radioiodine-refractory DTC, poorly differentiated and anaplastic thyroid cancers. For medullary thyroid cancer (MTC), a neuroendocrine tumor derived from thyroid C-cells, [18F]F-dihydroxyphenylalanine (6-[18F]FDOPA) PET/CT and/or [18F]FDG PET/CT can be used dependent on serum markers levels and kinetics. In addition to radioiodine therapy for DTC, some theragnostic approaches are promising for metastatic MTC as well. Moreover, new redifferentiation strategies are now available to restore uptake in radioiodine-refractory DTC while new theragnostic approaches showed promising preliminary results for advanced and aggressive forms of follicular-cell derived thyroid cancers (i.e., peptide receptor radiotherapy). In order to help clinicians put the role of molecular imaging into perspective, the appropriate role and emerging opportunities for molecular imaging and theragnostics in thyroid cancer are discussed in our present review.
A Comprehensive Review of Pathological Examination in Forensic Medicine: Past, Present, and Future

Pathological examination (PE) encompasses a gross or macroscopy and histopathological or microscopic examination. It is prudent in finding the cause of death (COD) in clinical and medicolegal autopsies. There are various auxiliary techniques in the form of clinical history, communication, specialized training, and protocols for consolidation of the PE results. After a thorough search of the literature in PubMed with relevant keywords along with further analysis of the results, it emerged that even with the modernization of forensic medicine, a PE is unbeatable in detecting the COD. It has various useful aspects, apart from regular finding the COD, such as in student teaching, epidemiology of disease, audit tool, and quality assurance. There are also limitations of PE, which should be dealt with great caution. Hence, limitations must be understood by a forensic expert as well as a pathologist. In this review, all factors that are related to PE in any manner are discussed in detail, and the scope for improving the quality of PE to be relevant in the present scenario is reviewed. It is a comprehensive reassessment of the literature review that also casts light on the future along with a critical analysis of the facts that deal with PE.
Politics, Promotions, and Pandemics: Political Ideologies Shape Consumers’ Responses to Framed versus Unframed Brand Logos and COVID-19 Recommendations

This research examines how exposing conservative (vs. liberal) consumers to a framed logo improves their evaluation of the promoted brand relative to seeing an unframed version of that logo. A core effect reveals that framed, but not unframed, logos generally elicit more favorable product purchase intentions as conservativism increases. Such an effect is theorized to occur because framed stimuli are symbolically aligned with a need for structure that is typically associated with conservatism. Consistent with this possibility, liberals who are primed to think about structure exhibit responses similar to those made by conservatives (i.e., more favorable evaluations of framed logos). The effect observed among conservatives is eliminated, however, when frames are viewed as restrictions on freedom. The implications of these findings are also extended to examine whether framing messages that endorse governmental recommendations to adopt COVID-curbing behavior influences how conservatives respond to these advocacies.
Integrating services for HIV and multidrug-resistant tuberculosis: A global cross-sectional survey among ART clinics in low- and middle-income countries

Tuberculosis (TB) is the leading cause of death among PLHIV and multidrug-resistant-TB (MDR-TB) is associated with high mortality. We examined the management for adult PLHIV coinfected with MDR-TB at ART clinics in lower income countries. Between 2019 and 2020, we conducted a cross-sectional survey at 29 ART clinics in high TB burden countries within the global IeDEA network. We used structured questionnaires to collect clinic-level data on the TB and HIV services and the availability of diagnostic tools and treatment for MDR-TB. Of 29 ART clinics, 25 (86%) were in urban areas and 19 (66%) were tertiary care clinics. Integrated HIV-TB services were reported at 25 (86%) ART clinics for pan-susceptible TB, and 14 (48%) clinics reported full MDR-TB services on-site, i.e. drug susceptibility testing [DST] and MDR-TB treatment. Some form of DST was available on-site at 22 (76%) clinics, while the remainder referred testing off-site. On-site DST for second-line drugs was available at 9 (31%) clinics. MDR-TB treatment was delivered on-site at 15 (52%) clinics, with 10 individualizing treatment based on DST results and five using standardized regimens alone. Bedaquiline was routinely available at 5 (17%) clinics and delamanid at 3 (10%) clinics. Although most ART clinics reported having integrated HIV and TB services, few had fully integrated MDR-TB services. There is a continued need for increased access to diagnostic and treatment options for MDR-TB patients and better integration of MDR-TB services into the HIV care continuum.
Mental Health Analyzer for Depression Detection Based on Textual Analysis

The global coronavirus pandemic and lockdown has had negative impacts on individuals’ mental health and well-being. The crisis has generated symptoms of depression in many, which may last even after the lockdown is over. To provide support to individuals in terms of counseling and psychiatric treatment, it is necessary to identify such depressive symptoms in a timely fashion. To address this problem, an artificial intelligence-based system is proposed to assess the changes, if any, in the mental health of an individual as a function of time, starting from the pre-lockdown period (in India from 20 April 2020). A Mental Health Analyzer has been implemented to automatically detect whether an individual is trending toward a state of depression based on his or her tweets over time. The deep learning models of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM have been implemented and compared for the emotion classification task, specifically to detect the emotions of sadness, fear, anger, and joy present in a person’s tweets. The system identifies the emotion of sadness present in tweets to detect depression. An ensemble maximizing model using CNN, LSTM, and Bidirectional LSTM is proposed to maximize the recall metric to improve the performance for the task of depression detection. The implemented system was tested using the dataset provided for the SemEval-2018 semantic evaluation tasks and achieves better results than previous models for the task of emotion classification and, further, can detect depression when tested on real Twitter data.
Short-Term Air Pollution Exposure and Risk of Acute Exacerbation of Chronic Obstructive Pulmonary Disease in Korea: A National Time-Stratified Case-Crossover Study

We investigated the association between short-term exposure to air pollution and the risk of acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) in seven metropolitan cities in Korea. We used national health insurance claims data to identify AE-COPD cases in 2015. We estimated short-term exposure to particulate matter (PM) with a diameter of ≤2.5 μm (PM2.5), PM with diameters of ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) obtained from the Ministry of Environment. We conducted a time-stratified, case-crossover study to evaluate the effect of short-term exposure to air pollution on hospital visits for AE-COPD, using a conditional logistic regression model. The risk of hospital visits for AE-COPD was significantly associated with interquartile range increases in PM10 in a cumulative lag model (lag 0–2, 0.35%, 95% confidence interval (CI) 0.06–0.65%; lag 0–3, 0.39%, 95% CI 0.01–0.77%). The associations were higher among patients who were men, aged 40–64 years, with low household income, and with a history of asthma. However, other air pollutants were not significantly associated with the risk of hospital visits for AE-COPD. Short-term exposure to air pollution, especially PM10, increases the risk of hospital visits for AE-COPD.