Miracle-dz/UniFAR-FTUs huggingface dataset:
Postdischarge pain, fatigue severity and quality of life in COVID-19 survivors

Objectives: Fatigue and pain symptoms were common complaints among post-COVID-19 patients, and these lead to impaired quality of life (QoL). We aimed to evaluate severity of pain and fatigue 3 months after disease onset in discharged COVID-19 patients. Methods: Patients were contacted by phone at their third month following disease onset. Demographic data of the patients such as weight, height, body mass index (BMI), gender, smoking history, comorbidities, length of hospitalization, duration of stay in the intensive care unit, were recorded. The patients' pain and fatigue severities were evaluated by visual analog scale (VAS). QoL was questioned with the EuroQol Group Association five-domain, three-level questionnaire (EQ-5D-3L). Results: A total of 392 participants enrolled into the study. At admission, 94.6% of the participants had fatigue and 73.7% of them had pain. A high proportion of them still reported fatigue (55.1%) and pain (41.3%) at third month. The mean value of pain-VAS score was 5.37 ± 3.85, and it was 7.58 ± 2.82 for fatigue-VAS at admission. VAS scores of pain and fatigue at third month were 1.44 ± 2.11 and 2.04 ± 2.40 respectively. While 66.6% of the patients reported moderate-severe pain at disease onset, the rate was 18.1% at the third month. And also almost half had severe pain at admission (48%), it was 2.8% at third month. At disease onset 89.6% of the patients reported moderate-severe fatigue (severe: 48%). Aproximately one third of them had moderate-severe fatigue (27.9%) at third month (severe: 5.1%). The mean value of EQ-VAS score was 26.76 ± 20.26 at admission, and it was 78.84 ±16.15 at third month. Statistically significant differences were recorded between the disease onset and third month in terms of pain-VAS fatigue-VAS, and EQ-VAS scores (p < 0.001). Female gender, ICU admission, long duration of hospitalization, older age, higher BMI scores, multiple comorbidities, fatigue and pain severity were related to the decrease in QoL scores. Conclusions: Hospitalized COVID-19 survivors need ongoing support for pain, fatigue complaints and impaired QoL after discharge. The factors that cause poor QoL should be taken into account during post-COVID-19 follow up.
Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses

Electric vehicles (EVs) have been progressing rapidly in urban transport systems given their potential in reducing emissions and energy consumptions. The Shared Free-Floating Electric Scooter (SFFES) is an emerging EV publicized to address the first-/last-mile problem in travel. It also offers alternatives for short-distance journeys using cars or ride-hailing services. However, very few SFFES studies have been carried out in developing countries and for university populations. Currently, many universities are facing an increased number of short-distance private car travels on campus. The study is designed to explore the attitudes and perceptions of students and staff towards SFFES usage on campus and the corresponding influencing factors. Three machine learning models were used to predict SFFES usage. Eleven important factors for using SFFESs on campus were identified via the supervised and unsupervised feature selection techniques, with the top three factors being daily travel mode, road features (e.g., green spaces) and age. The random forest model showed the highest accuracy in predicting the usage frequency of SFFESs (93.5%) using the selected 11 variables. A simulation-based optimization analysis was further conducted to discover the characterization of SFFES users, barriers/benefits of using SFFESs and safety concerns.
Comparison of Selected Non-Coding RNAs and Gene Expression Profiles between Common Osteosarcoma Cell Lines

Simple Summary Osteosarcoma (OS) is a malignant tumour affecting mainly children and elderly people. Despite significant advances in cancer medicine, osteosarcoma patients’ survival is not improving. The primary treatment methods are established using in vitro models that rely upon the application of well-established cell lines, including U-2 OS, Saos-2 and MG-63. The molecular phenotype of these cell lines is still not fully outlined. Therefore, our study aimed to establish the expression profile of molecular markers related to osteosarcoma survival, progression and metastasis. Non-bone-related cells were used as a reference, i.e. HeLa cell line and human adipose-derived stromal cells (hASCs). Evaluated osteosarcoma cell lines showed characteristic phenotypes with unique patterns related to upregulation of MMP-7, MMP-14, BMP-7, miR-21-5p, miR-124-3p and downregulation of lncRNA MEG3. Our findings may facilitate the selection of the most reliable cellular model for pre-clinical investigations focused on developing new and satisfying methods of osteosarcoma therapy. Abstract Osteosarcoma (OS) is a bone tumour affecting adolescents and elderly people. Unfortunately, basic treatment methods are still underdeveloped, which has a high impact on the poor survivability of the patients. Studies designed to understand the underlying mechanisms of osteosarcoma development, as well as preclinical investigations aimed at establishing novel therapeutic strategies, rely significantly upon in vitro models, which apply well-established cell lines such as U-2 OS, Saos-2 and MG-63. In this study, the expression of chosen markers associated with tumour progression, metastasis and survival were identified using RT-qPCR. Levels of several onco-miRs (miR-21-5p, miR-124-3p, miR-223-3p and miR-320a-3p) and long non-coding RNA MEG3 were established. The mRNA expression of bone morphogenetic proteins (BMPs), including BMP-2, BMP-3, BMP-4, BMP-6, BMP-7, as well as their receptors: BMPR-IA, BMPR-IB and BMPR-II was also determined. Other tested markers included metalloproteinases, i.e., MMP-7 and MMP-14 and survivin (BIRC5), C-MYC, as well as CYCLIN D (CCND1). The analysis included comparing obtained profiles with transcript levels established for the osteogenic HeLa cell line and human adipose-derived stromal cells (hASCs). The tested OS cell lines were characterised by a cancer-related phenotype, such as increased expression of mRNA for BMP-7, as well as MMP-7 and MMP-14. Osteosarcoma cells differ considerably in miR-21-5p and miR-124-3p levels, which can be related to uncontrolled tumour growth. The comprehensive examination of osteosarcoma transcriptome profiles may facilitate the selection of appropriate cell models for preclinical investigations aimed at the development of new strategies for OS treatment.
Emerging low-dimensional materials for nanoelectromechanical systems resonators

Low-dimensional materials (LDMs), due to exotic mechanical, optical and electrical properties enhanced by ultrahigh surface-to-volume ratio and quantum confinements, have taken centerstage in nanoelectromechanical system (NEMS) resonators. The ability to survive from high strains and complex environments enables LDMs-based NEMS resonators innovative effectuations for mass-, gas- and bio-sensings and computings. In this review, we highlighted the fabrication, actuation, detection, figure of merit, influence factors and recent process of LDMs-based NEMS resonators. After overviewing representative applications and feasible directions, we summarized the confronted challenges in fabrications, tunings and utilizations of LDMs-based NEMS resonators. Finally, the prospects that may properly tackle these obstacles were concluded, attempting to offer useful guidelines for both theoretical and experimental experts to promote NEMS resonators one step closer toward industrializations. IMPACT STATEMENT This review delineates the principles and advances of nanoelectromechanical system resonators based on low-dimensional materials, followed by promising applications, such as sensings, nano-electronics and quantum detections.
Zero Sequence Voltage Modulation Strategy of Variable Flux Reluctance Machine Based on Single Leg Excitation Control

Conventionally, the variable flux reluctance machines (VFRMs) are controlled by armature current and field current, which can be generated through a separated H-bridge. However, high switching losses in the H-bridge are not avoidable. To reduce the switching losses and improve efficiency, a VFRM control system based on single-leg excitation control is proposed. The field winding of VFRM is connected to the neutral point of armature winding by using single-leg to utilize the zero-sequence current as the DC excitation. A novel zero sequence voltage modulation technique is developed to generate the equivalent DC field excitation, i.e., the DC bias in the current of the armature winding. Compared with the separated DC field excitation, lower copper losses and higher efficiency can be obtained. Finally, experimental results on a prototype VFRM are presented for verification.
A lossless image compression using context modeling prediction errors and error feedback

A lossless image compression algorithm using the integer wavelet transform is proposed in this paper. The S+P wavelet transform is compared with the various integer coefficient filter banks (ICFB), and the 9/7 ICFB is applied to the wavelet transform. The compression ratios of the proposed algorithm are compared with those of the S+P transform for several different types of images.
Effects of Oxathiapiprolin on the Structure, Diversity and Function of Soil Fungal Community

Pesticides can affect non-target microorganisms in the soil and are directly related to soil microecological health and environmental safety. Oxathiapiprolin is a piperidinyl thiazole isoxazoline fungicide that shows excellent control effect against oomycete fungal diseases, including late blight, downy mildew, root rot, stem rot, and blight. Though it can exist stably in the soil for a long time, its effects on soil microbial structure and diversity are not well investigated. In the present study, the effects of oxathiapiprolin on the abundance and diversity of soil fungal communities in typical farmland were studied. The results show that the abundance and diversity of soil fungi were increased by oxathiapiprolin treatment with differences not significant on the 30th day. Oxathiapiprolin was found to change the structure of soil fungal communities, among which Ascomycota and Mortierellomycota were the most affected. Undefined saprophytic fungi increased in the treatment groups, and the colonization of saprophytic fungi can act as a major contributor to the function of soil microbial communities. This study lays a solid foundation regarding environmental behavior with the use of oxathiapiprolin in soil and details its scientific and rational use.
IndoUKC: A Concept-Centered Indian Multilingual Lexical Resource

We introduce the IndoUKC, a new multilingual lexical database comprised of eighteen Indian languages, with a focus on formally capturing words and word meanings specific to Indian languages and cultures. The IndoUKC reuses content from the existing IndoWordNet resource while providing a new model for the cross-lingual mapping of lexical meanings that allows for a richer, diversity-aware representation. Accordingly, beyond a thorough syntactic and semantic cleaning, the IndoWordNet lexical content has been thoroughly remodeled in order to allow a more precise expression of language-specific meaning. The resulting database is made available both for browsing through a graphical web interface and for download through the LiveLanguage data catalogue.
Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach

Analysis of data with a censored survival response and high-dimensional omics measurements is now common. Most of the existing analyses are based on specific (semi)parametric models, in particular the Cox model. Such analyses may be limited by not having sufficient flexibility, for example, in accommodating nonlinearity. For categorical and continuous responses, neural networks (NNs) have provided a highly competitive alternative. Comparatively, NNs for censored survival data remain limited. Omics measurements are usually high-dimensional, and only a small subset is expected to be survival-associated. As such, regularized estimation and selection are needed. In the existing NN studies, this is usually achieved via penalization. In this article, we propose adopting the threshold gradient descent regularization (TGDR) technique, which has competitive performance (for example, when compared to penalization) and unique advantages in regression analysis, but has not been adopted with NNs. The TGDR-based NN has a highly sensible formulation and an architecture different from the unregularized and penalization-based ones. Simulations show its satisfactory performance. Its practical effectiveness is further established via the analysis of two cancer omics datasets. Overall, this study can provide a practical and useful new way in the NN paradigm for survival analysis with high-dimensional omics measurements.
Assessment of Bacterial Diversity of Industrial Poultry Wastewater by Denaturing Gradient Gel Electrophoresis (DGGE) and the Cultivation Method in Order to Inform Its Reuse in Agriculture

Effluents discharged by poultry meat industries are heavily polluted with raw materials, such as fat, blood residues, and proteins. Thus, untreated effluents directly discharged into the environment may constitute a public health threat. This study aims to evaluate the bacterial diversity of three water qualities: industrial poultry wastewater (PWW), tap water (TW), and PWW diluted with TW (50 : 50) (V/V) (TWPWW) by the combination of culture-independent and culture-dependent approaches. The total bacterial DNA was extracted using phenol/chloroform method. The hypervariable 16S rRNA region V3-V5 was amplified by PCR using universal primers. The amplicons were separated by vertical electrophoresis on a polyacrylamide gel of increasing denaturing gradient according to their richness in GC bases. Selected bands were reamplified and sequenced. Pure isolated bacteria from nutrient agar medium were characterized according to their morphological and biochemical characteristics. Genomic DNA from pure strains was extracted by boiling method, and a molecular amplification of the 16S–23S ITS region of the 16S rRNA gene was performed using the universal primers. Selected isolates were identified by sequencing. Results showed a high bacterial load and diversity in PWW in comparison with TW and TWPWW. A collection of 44 strains was obtained, and 25 of them were identified by sequencing. Proteobacteria represented 76% of isolated bacteria Gamma-Proteobacteria was the predominate isolate (68%). Other isolates were Firmicutes (8%), Bacteroidetes (12%), and Actinobacteria (8%). These isolates belong to different genera, namely, Pseudomonas, Acinetobacter, Proteus, Empedobacter, Corynebacterium, Enterobacter, Comamonas, Frondibacter, Leclercia, Staphylococcus, Atlantibacter, Klebsiella, and Microbacterium.
Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways

Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention.
Promising Uses of the iPad Pro Point Clouds: The Case of the Trunk Flare Diameter Estimation in the Urban Forest

The rule of thumb “the right tree in the right place” is a common idea in different countries to avoid damages caused by trees on sidewalks. Although many new planting techniques can be used, the estimation of the trunk flare diameter (TFD) could help the planning process to give tree roots more space to grow over the years. As such, we compared the applicability of point clouds based on iPad Pro 2020 image processing and a precise terrestrial laser scanner (TLS FARO) for the modeling of the TFD using different modeling procedures. For both scanning methods, 100 open-grown and mature trees of 10 different species were scanned in an urban park in Cracow, Poland. To generate models, we used the PBH (perimeter at breast height) and TFD variables and simple linear regression procedures. We also tested machine learning algorithms. In general, the TFD value corresponded to two times the size of a given DBH (diameter at breast height) for both methods of point cloud acquisition. Linearized models showed similar statistics to machine learning techniques. The random forest algorithm showed the best fit for the TFD estimation, R2 = 0.8780 (iPad Pro), 0.8961 (TLS FARO), RMSE (m) = 0.0872 (iPad Pro), 0.0702 (TLS FARO). Point clouds generated from iPad Pro imageries (matching approach) promoted similar results as TLS FARO for the TFD estimations.
A Method for Estimating Emotions Using HRV for Vital Data and Its Application to Self-mental care management system

In this paper, we present an emotion estimation method using heart rate variability parameters of vital data. Recently, as sensors have become more precise and smaller, it has been possible to obtain users' vital data in real-time quickly. In our method, ECG (electrocardiogram) data are measured beforehand while listening to a story with voice narration that evokes emotions and based on the trends obtained through the measurement, the emotions that have a high correlation with the newly acquired ECG data are estimated to be the emotions expressed in the ECG data. With the implementation of our method, it is possible to estimate the user's emotions based on ECG data. In this paper, we also represent the application of our method to chat icons that see users' emotions in real-time. By realizing this application, users will see the changes in their emotions and control their mental health.
Method for Detection of First Phase in Seismogram Based on Akaike Information Criterion (AIC) Function

A method has been developed to detect and pick-up the first phase arrival in seismogram from the vertical component by using two properties of the Akaike Information Criterion (AIC) function in wavelet domain i. e. 1) the phase arrival point belongs to global minima in the AIC function, and 2) the gradient of incremental after the phase arrival is larger than that before. Present method has been tested on earthquake waveform data with varying signal to noise ratio and different selection window. Consideration of the additional property of the AIC function in present method increase the performance and outperform the presently available methods based on AIC function, that uses property of AIC function having global minimum at the phase arrival in seismogram. KeywordsAkaike Information Criterion, particle motion analysis, seismic phase, objective function.
Effect of Temperature on Growth of Shoot Apices Excised from Onions in Rest1

Excised onion (Allium cepa L.) shoot apices exposed directly to temperatures of 10 to 15°C grew more than those excised from similarly treated half or whole bulbs. The temperature pretreatments which promoted the greatest growth of excised onion shoots at 20° ranged from 5 to 12.5°. Exposure to 30° following a 10° treatment negated the promotive effect of the lower temperature.
Pharmacology studying practices and theory presentation skills followed by under graduates

Background: We observed subpar performance in written assessments. We wanted to identify and address the modifiable contributing factors. Aim: study was undertaken to assess studying practices and preparation for assessments by students in Pharmacology. Methods: study was conducted using questionnaire among 147 fifth and 147 third semester medical students.Questions regarding timing of preparation, revision, sources of drug information, difficult topics, practical exercise preference, e-learning sources, time allotment for questions and diagrams usage were posed. Results: 56% third semesters started studying pharmacology only before monthly tests in contrast to fifth semesters who were trisected in their approach of serious studying.Textbooks were primary source. Higher number of fifth semesters (93%), than third semesters (80%), used internet for learning. Fifth semesters browsed internet for learning more than third semesters. Greater number of fifth semesters viewed online lecture videos and referred to online medical dictionary compared to newer batch.When asked to select practical exercises, fifth semesters favoured prescription writing whereas third semesters preferred clinical problems.Conclusion: Blended approach and peer group learning was more common in seniors. Junior batch students had superficial approach, were task driven towards exam preparation and presentation individualistically.
Using Reading Questioning Answering (RQA) Model to Improve Student Reading Skill During Online Learning

This research is motivated by the lack of reading interest in students at MTS Khazanah Kebajikan which affects online learning activeness in increasing reading interest, student understanding and students' critical thinking skills. As we already know, online learning makes learning less effective because students can easily answer all questions from the internet without reading in detail, but there are also students who are hampered in terms of facilities, technology and networks. This study uses a descriptive qualitative method based on information data collection, interviews and observations about the problems or difficulties of students during online learning and especially in increasing students' reading interest. The main target of this research is students at MTS Khazanah Kebajikan, where students lack reading interest during online learning. With the RQA model it can be seen how high the level of students 'reading interest is during online learning and can optimize students' understanding at school. Keyword: Education, Online Learning, Reading Interest, RQA