Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Putative Vaccine and Drug Targets against the Methicillin-Resistant Staphylococcus aureus by Reverse Vaccinology and Subtractive Genomics Approaches

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen and responsible for causing life-threatening infections. The emergence of hypervirulent and multidrug-resistant (MDR) S. aureus strains led to challenging issues in antibiotic therapy. Consequently, the morbidity and mortality rates caused by S. aureus infections have a substantial impact on health concerns. The current worldwide prevalence of MRSA infections highlights the need for long-lasting preventive measures and strategies. Unfortunately, effective measures are limited. In this study, we focus on the identification of vaccine candidates and drug target proteins against the 16 strains of MRSA using reverse vaccinology and subtractive genomics approaches. Using the reverse vaccinology approach, 4 putative antigenic proteins were identified; among these, PrsA and EssA proteins were found to be more promising vaccine candidates. We applied a molecular docking approach of selected 8 drug target proteins with the drug-like molecules, revealing that the ZINC4235426 as potential drug molecule with favorable interactions with the target active site residues of 5 drug target proteins viz., biotin protein ligase, HPr kinase/phosphorylase, thymidylate kinase, UDP-N-acetylmuramoyl-L-alanyl-D-glutamate-L-lysine ligase, and pantothenate synthetase. Thus, the identified proteins can be used for further rational drug or vaccine design to identify novel therapeutic agents for the treatment of multidrug-resistant staphylococcal infection.
Review—Surface Coatings for Cathodes in Lithium Ion Batteries: From Crystal Structures to Electrochemical Performance

Lithium ion batteries (LIBs) have dominated the energy industry due to their unmatchable properties, which include high energy density, compact design, and an ability to meet a number of required performance characteristics in comparison to other rechargeable systems. Both government agencies and industries are performing intensive research on Li-ion batteries for building an energy-sustainable economy. LIBs are single entities that consist of both organic and inorganic materials with features covering multiple length scales. Critical insights should be made for understanding the structure to property relationships and the behavior of components under the working condition of LIBs. Cathodes tend to react with the electrolytes and, hence, to undergo surface modifications accompanied by degradation. These side-reactions result in an erosion of battery performance, thereby causing a reduced battery life and power capacity. Recently, techniques for preparing surface coatings on cathode materials have been widely implemented as a measure to improve their stability, to enhance their electrochemical performance. This review will cover different types of surface coatings for cathode materials, as well as a comparison of the changes in electrochemical performance between those materials with and without an applied coating.
Classification and speed estimation of vehicles via tire detection using single‐element piezoelectric sensor

Summary This paper presents novel vehicle classification technology by utilizing a single-element piezoelectric sensor placed diagonally on a traffic lane to accurately identify vehicles. Novelty of this technique originates from using diagonally placed piezoelectric strip sensor and machine learning technology to provide a highly accurate and cost-effective alternative to current vehicle classification systems. Diagonal placements of the piezoelectric strip sensor ensure detection of passing vehicle tires by facilitating vehicle classification process. Presented technology is capable of accurately classifying vehicles into a relatively large number of classifications, including motorcycle, which has proven to be a challenging category in present-day commercial vehicle classifiers. Vehicle classification is a vital intelligent transportation systems application. Accurate data reporting aids suitable roadway design for safety and capacity and can also support other purposes, such as reporting highway congestion to the general public or providing area denseness data to interested businesses. To make a classification decision, a vehicle's signal is acquired from diagonal piezoelectric strip sensor, processed, and then applied to a machine learning algorithm. A speed estimation technique using the same single-element piezoelectric sensor was also developed, tested, and compared with an embedded vehicle classifier currently used by the Oklahoma Department of Transportation. Testing on several highway sites indicated up to 97% classification accuracy. This paper presents a complete description of the developed system, including sensor installation, data acquisition and processing, and classification algorithm. Overall, the system offers a high-performance cost-effective solution for vehicle classification that minimizes roadwork typically required for loop and sensor installations of current systems. Copyright © 2016 John Wiley & Sons, Ltd.
Clinical Efficacy of a Combination of Thymopentin and Antituberculosis Drugs in Treating Drug-Resistant Pulmonary Tuberculosis: Meta Analysis

Objective To make a systematic evaluation of the clinical efficacy of thymopentin combined with antituberculous drugs in treating drug-resistant pulmonary TB (PTB). Methods Relevant studies were retrieved from PubMed, Embase, Cochrane Library, Chinese Biomedical Literature Database, CNKI, and Wanfang Database. STATA software was used to evaluate the differences in focal absorption rate, the time to cough symptom remission, sputum culture-negative rate, CD3+ T, CD4+ T, and CD8+ T cell levels after treatment. Results A total of 23 randomized controlled trials literature involving 2031 cases were included. Meta-analysis revealed that compared with conventional therapy, the sputum culture-negative rate was significantly increased after 2–3 months and 6–9 months of treatment and the whole course of combined thymopentin treatment. The risk ratio (RR, 95% CI) was 1.44 (1.26–1.64), 1.47 (1.21–1.78), and 1.27 (1.18–1.36), respectively. In the combined thymopentin treatment group, the focal absorption rate was higher, with RR (95% CI) = 1.36 (1.25–1.47), the time of cough remission was shortened, with WMD (95% CI) =−9.46d (−10.36,−8.57) and the differences were all statistically significant. Combined thymopentin therapy could effectively improve the levels of CD3+ T and CD4+ T lymphocytes in patients with drug-resistant PTB after 2–3 months, 6–9 months of treatment. The WMD (95% CI) were 9.96% (7.84, 12.08), 4.68% (2.90, 6.47) and 10.26% (7.81, 12.71), 7.21% (6.28, 8.15), respectively, and could also reduce the level of CD8+ T lymphocytes after 2–3 months and 6–9 months of treatment. The WMD (95% CI) were −4.06% (−4.96, −3.13), −3.52%, (−4.07,−2.98), respectively, and the differences were all statistically significant. Conclusion Thymopentin adjuvant treatment for drug-resistant PTB can promote the therapeutic effect and improve the immune indexes in patients with drug-resistant PTB.
Fuzzy logic control for stand-alone photovoltaic energy conversion system, and innovation in renewable energy

In this dissertation, simulation and hardware emulation was implemented to experiment the operation of a power regulation system for stand-alone PV system with DC loads using Fuzzy Logic Control (FLC). The system encompasses the functions of Maximum Power Point Tracking (MPPT) to bring the power to the maximum value, load power regulation and control of battery operation. An algorithm that tracks the maximum power, and the corresponding fuzzy logic controller were developed. An improved method for the battery operation regulation including a fuzzy logic controller was applied. Load voltage regulation was achieved by a modified cascaded PI controller. The power regulation system managed to stabilize the load power, proved fast MPPT tracking and regulation of the battery operation in the presence of fluctuations and fast input variations. The work included a study of the importance of university-industry collaboration to innovation in renewable energy. The current collaboration channels and the contribution of the current work was analysed through a questionnaire directed to the supervisor.
Tissue engineering approaches for neurovascular structures

Tissue is frequently damaged or lost in injury and disease. There has been an increasing interest in stem cell applications and tissue engineering approaches in surgical practice to deal with damaged or lost tissue. Tissue engineering is an exciting strategy being explored to deal with damaged or lost tissue. It is the science of generating tissue using molecular and cellular techniques, combined with material engineering principles, to replace tissue. This could be in the form of cells with or without matrices. There have been developments in almost all surgical disciplines, including plastic surgery. Unfortunately significant hurdles remain to be overcome in many areas before tissue engineering becomes more routinely used in clinical practice. In this paper tissue engineering approaches relevant to neurovascular structures are discussed. Tissue engineering has been defined as the application of scientific principles to the design, construction, modification and growth of living tissue using biomaterials, cells and factors alone and in combination (Langer and Vacanti, 1993). In essence, three elements are central in tissue engineering stem or precursor cells, an appropriate biological scaffold and growth factors. It generally involves the use of cells with a matrix or scaffold that guides the cells during tissue repair or regeneration. The use of more undifferentiated cell types, such as stem cells or early mesenchymal progenitors that retain multi-lineage and self renewal potential is preferable to the use of terminally differentiated cells. The scaffold could be natural or biosynthetic. Cells harvested from donor tissues, including adult stem cells, can be expanded in culture and associated with resorbable biomaterials forming the scaffold. The cells can be stimulated by specific bioactive molecules such as growth factors. The cells can also be genetically modified via genomic insertion of a new healthy copy of a gene before expansion and subsequent tissue reconstitution. To date, the use of gene technology has not been applied to humans. The cells could be stimulated in-vitro to form tissue for future re-implantation in-vivo. Below, applications of tissue engineering principles are discussed relevant to neurovascular structures. © 2013 by Nova Science Publishers, Inc. All rights reserved.
Endocan in Acute Leukemia: Current Knowledge and Future Perspectives

Endocan is a soluble dermatan sulfate proteoglycan expressed by endothelial cells and detected in serum/plasma. Its expression is increased in tumors/tumor vessels in several human malignancies, and high expression (high serum/plasma levels or tumor levels) has an adverse prognostic impact in several malignancies. The p14 endocan degradation product can also be detected in serum/plasma, but previous clinical studies as well as previously unpublished results presented in this review suggest that endocan and p14 endocan fragment levels reflect different biological characteristics, and the endocan levels seem to reflect the disease heterogeneity in acute leukemia better than the p14 fragment levels. Furthermore, decreased systemic endocan levels in previously immunocompetent sepsis patients are associated with later severe respiratory complications, but it is not known whether this is true also for immunocompromised acute leukemia patients. Finally, endocan is associated with increased early nonrelapse mortality in (acute leukemia) patients receiving allogeneic stem cell transplantation, and this adverse prognostic impact seems to be independent of the adverse impact of excessive fluid overload. Systemic endocan levels may also become important to predict cytokine release syndrome after immunotherapy/haploidentical transplantation, and in the long-term follow-up of acute leukemia survivors with regard to cardiovascular risk. Therapeutic targeting of endocan is now possible, and the possible role of endocan in acute leukemia should be further investigated to clarify whether the therapeutic strategy should also be considered.
A new simple score to predict mortality of COVID-19 in the emergency department

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is one of the greatest challenges facing global medical research. The availability of a clinical score that can predict mortality risk at the time of diagnosis could be a valuable tool in the hands of emergency physicians to make clinical decisions. Our study is designed to evaluate clinical and laboratory endpoints associated with mortality and to determine a prognostic score based on clinical and laboratory variables. We retrospectively enrolled 367 patients diagnosed with coronavirus disease 19 (COVID-19) in our emergency department (ED). We evaluated their mortality 60 days after diagnosis. Symptoms, demographic data, concomitant diseases, and various laboratory parameters were obtained from all patients. Variables related to death were assessed using multiple logistic regression analysis. From these, we created a score called ANCOC (Age, blood urea Nitrogen, C-reactive protein, Oxygen saturation, Comorbidities). The area under the receiver operating characteristic (ROC) curve was calculated for the ANCOC and for the 4C score. The 4C score has been described and validated in previous works and can predict mortality in COVID-19 patients. We compared the 2 scores and analysed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for 60-day mortality for the ANCOC score. The ANCOC and 4C scores accurately predicted death from COVID-19. There were no differences in accuracy between the scores. An ANCOC score <–1 identified patients who will recover with a PPV and sensitivity of 100%, whereas a score ＞3 identified patients at high risk of death. The ANCOC score has very high diagnostic accuracy in predicting the risk of death in patients with COVID-19 diagnosed at ED. The ANCOC score has similar accuracy to the 4C score but is easier to calculate. If validated by external cohorts, this score could be an additional tool in the hands of ED physicians to identify COVID-19 patients at high risk of death.
A Convolution Location Method for Multi-Node Scheduling in Wireless Sensor Networks

The localization of continuous objects and the scheduling of resources are challenging issues in wireless sensor networks (WSNs). Due to the irregular shape of the continuous target area and the sensor deployment in WSNs, the sensor data are always discrete and sparse, and most network resources are also limited by the node energy. To achieve faster detection and tracking of continuous objects, we propose a convolution-based continuous object localization algorithm (named CCOL). Moreover, we implement the idea of greedy and dynamic programming to design an energy-saving and efficient strategy model (named MSSM) to respond to emergencies caused by multiple continuous targets in most specific WSNs. The simulation experiments demonstrate that CCOL is superior to other localization algorithms in terms of time complexity and execution performance. Furthermore, the feasibility of the multinode scheduling strategy is verified by setting different mobile nodes to respond to the target area in certain green WSNs.
Competitive strategies adopted by independent oil lubricant marketers in Kenya

Competitive strategy concerns what a firm is doing in order to gain sustainable competitive advantage. This study was to establish the competitive strategies adopted by independent Lubricant marketers in Kenya to meet the competition in the sector. The study was done through a cross-sectional census survey . Primary data was used in this study and was collected through questionnaires administered through a combination of drop and pick, face to face interviews or electronically transmitted to all independent Lubricants marketers who are currently active in the Kenyan Market as listed in Petroleum institute of East Africa insight publication of 1st Quarter 2012. A response rate of 63.6% was achieved. The study explored characteristics of marketers/factors that influence the competitiveness and hence adoption of the various competitive strategies. In this study the use of the term independent Lubricant marketers is used to differentiate from the Multinational Lubricant marketers. The results show that 78% of the independent Lubricants marketers have less than 20 retail outlets and majority were also engaged in other petroleum products business line and were either in the process of entering the lubricants marketing or strengthening their presence. Economic factors and intense rivalry within existing marketers was found to have the greatest influence on the organizations competitive strategies. The study established that the exiting of multinationals has opened up more opportunities for the independent Lubricant marketers. However the factors influencing competition in the lubricants business to the greatest extent were brand loyalty and Original equipment manufacturers (OEM) recommendations. Some independent Lubricants marketers have responded to this by getting into distributorship agreements with global partners, enhancing their service levels and improving their overall technical capabilities.
Direct Torque Control of a Small Wind Turbine with a Sliding-Mode Speed Controller

In this paper. the method of direct torque control in the presence of a sliding-mode speed controller is proposed for a small wind turbine being used in water heating applications. This concept and control system design can be expanded to grid connected or off-grid applications. Direct torque control of electrical machines has shown several advantages including very fast dynamics torque control over field-oriented control. Moreover. the torque and flux controllers in the direct torque control algorithms are based on hvsteretic controllers which are nonlinear. In the presence of a sliding-mode speed control. a nonlinear control system can be constructed which is matched for AC/DC conversion of the converter that gives fast responses with low overshoots. The main control objectives of the proposed small wind turbine can be maximum power point tracking and soft-stall power control. This small wind turbine consists of permanent magnet synchronous generator and external wind speed. and rotor speed measurements are not required for the system. However. a sensor is needed to detect the rated wind speed overpass events to activate proper speed references for the wind turbine. Based on the low-cost design requirement of small wind turbines. an available wind speed sensor can be modified. or a new sensor can be designed to get the required measurement. The simulation results will be provided to illustrate the excellent performance of the closed-loop control system in entire wind speed range (4-25 m/s).
Establishment of multi-stakeholder and functional interdisciplinary technical network for sustainable access to weather and climate information services by agricultural users in Zambia

Access to and the utility of weather and climate services is a crucial aspect of climate-resilient agriculture. Climate information, in particular, can impact decision-making by Zambian stakeholders – most importantly decision-making related to agricultural production, value addition, and marketing. The provision of weather and climate services involves the generation and contextualization of information and knowledge derived from climate research, for decision-making at all levels of society (Vaughan and Dessai, 2014). The aim of weather and climate services is to provide people and organizations with timely and tailored climate-related information and knowledge that can be used to enhance production and productivity, protect lives, livelihoods, and property and reduce climate-related losses.
Bladder Cancer in Young Patients: Management and Outcomes

Background and Objective: Bladder cancer frequently occurs between the ages of 50 years and 70 years and rarely occurs before the age of 40 years. It accounts for 1% - 4% of all cases of cancer. This study focuses on the clinical, etiological, histological, therapeutic, and evolutive profiles of patients with bladder cancer aged less than 45 years. Materials and Methods: This retrospective study was carried out over seven years (from January 2014 to December 2020) on 15 patients below 45 years of age who were being treated for bladder cancer. The diagnosis was made in all patients via cystoscopy and abdominal computerized tomography, while the nature, grade, and degree of infiltration were determined via endoscopic resection and pathology. Results: The mean age of the 15 patients in this study was 34.4 ± 5.19 years. In 86.6% of cases, the patients had macroscopic hematuria as the main presenting complaint. Twelve patients (80%) had bladder tumors that did not infiltrate the muscle. These patients were followed up clinically. In 86.6% of cases, there was a single tumor with a mean diameter of 2.53 cm. In four patients (26.6%), mitomycin was started early. The overall rates of tumor recurrence and progression were 26.6% and 20%, respectively. Conclusion: Bladder cancer also occurs in young people, with similar clinical and epidemiological profiles as exists in elderly patients. The prognosis of bladder cancer in young people depends on the treatment method chosen by the physician.
Short Text Classification for Faults Information of Secondary Equipment Based on Convolutional Neural Networks

As the construction of smart grids is in full swing, the number of secondary equipment is also increasing, resulting in an explosive growth of power big data, which is related to the safe and stable operation of power systems. During the operation of the secondary equipment, a large amount of short text data of faults and defects are accumulated, and they are often manually recorded by transportation inspection personnel to complete the classification of defects. Therefore, an automatic text classification based on convolutional neural networks (CNN) is proposed in this paper. Firstly, the topic model is used to mine the global features. At the same time, the word2vec word vector model is used to mine the contextual semantic features of words. Then, the improved LDA topic word vector and word2vec word vector are combined to absorb their respective advantages and utilizations. Finally, the validity and accuracy of the model is verified using actual operational data from the northwest power grid as case study.