Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Questionnaire for the Assessment of Awareness about Heart Attack among Adults in Quetta Pakistan

Objectives: Awareness of heart attack is very important to reduce the mortality and morbidity associated with a heart attack. The current study aims to develop a questionnaire to assess heart attack awareness among adults in Quetta, Pakistan, and also focused on translating into the Urdu language and validating the translated questionnaire. Methods: The questionnaire was developed in English after the review of relevant literature and translated into Urdu. Content validity was performed by eight experts in medicine and pharmacy practice and a content validity index was calculated. Internal consistency and reliability were assessed by Cronbach alpha and corrected item-total correlation. Test re-test reliability was determined using the Spearman rank correlation coefficient. The cross-sectional pilot study was conducted among 100 adults and a chi-square test was done to find a correlation between score groups and demographics. Results: A content validity index was 0.804 while the Cronbach alpha was . 935 for the entire scale, indicating internal consistency. Spearman's rank correlation coefficient value of 0.78 (p = 0.006) was obtained during the evaluation of test-retest reliability. Significant relationships were observed between knowledge scores and gender, education level, and smoking during the pilot study. Conclusion: The current study showed that the developed questionnaire is reliable and valid for accessing adults’ awareness about heart attack.
Interrogating and Quantifying In Vitro Cancer Drug Pharmacodynamics via Agent-Based and Bayesian Monte Carlo Modelling

The effectiveness of chemotherapy in cancer cell regression is often limited by drug resistance, toxicity, and neoplasia heterogeneity. However, due to the significant complexities entailed by the many cancer growth processes, predicting the impact of interference and symmetry-breaking mechanisms is a difficult problem. To quantify and understand more about cancer drug pharmacodynamics, we combine in vitro with in silico cancer models. The anti-proliferative action of selected cytostatics is interrogated on human colorectal and breast adenocarcinoma cells, while an agent-based computational model is employed to reproduce experiments and shed light on the main therapeutic mechanisms of each chemotherapeutic agent. Multiple drug administration scenarios on each cancer cell line are simulated by varying the drug concentration, while a Bayesian-based method for model parameter optimisation is employed. Our proposed procedure of combining in vitro cancer drug screening with an in silico agent-based model successfully reproduces the impact of chemotherapeutic drugs in cancer growth behaviour, while the mechanisms of action of each drug are characterised through model-derived probabilities of cell apoptosis and division. We suggest that our approach could form the basis for the prospective generation of experimentally-derived and model-optimised pharmacological variables towards personalised cancer therapy.
Factors Affecting Cost and Time Control in Construction Projects

The quest of construction project stakeholders is effective control of cost and time. Accomplishing this quest however, remains a difficult task. This study examines factors affecting cost and time control of construction projects with a view to proposing recommendations that could assist stakeholders to achieve enhanced cost and time performance of construction projects. A survey of professionals managing construction projects in Lagos, Nigeria was conducted. Structured questionnaire was developed and administered to eighty (80) managers of construction projects who were randomly selected from a sample frame of One-hundred (100) construction project contractors. A total of fifty-two (52) questionnaires were retrieved representing 62% response rate. Using Statistical Package for Social Sciences, version 20.0, descriptive and inferential statistical tools including, bar chart, mean, minimum and maximum values, frequency tables, T-test and Analysis of Variance (ANOVA) were employed to analyse collected data. The results of the analysis revealed that the top three significant factors affecting cost and time control of construction projects are; design and documentation issues, poor labour productivity and financial resource management. This study recommends the avoidance of poor work quality in construction activities. Also, project and construction managers should focus on project tripod constraints of cost, quality and time while workers hired and deployed on construction projects should be adequately skilled in order to achieve desired cost and time performance.
Microencapsulation and Bioaccessibility of Phenolic Compounds of Vaccinium Leaf Extracts

In recent years, Vaccinium spp. (bilberry-VMT, lingonberry-VVIT, and blueberry-VCS) have sparked particular interest for their prospective health benefits. The latest investigations have place them as important alternative sources of nutraceuticals as their leaves are the main by-products of berry harvesting. The present study is aimed at investigating the bioaccessibility of phenolic compounds from leaves of the Vaccinium species, both as microencapsulated powder and aqueous extracts, following exposure to in vitro simulated digestion. Moreover, the impact of maltodextrin and glucose microencapsulation carriers on the extracts’ phenolic content was assessed. Prior to encapsulation, the viscosity of the emulsions was shown at a shear stress of 50 s−1 dilatant and a Newtonian behaviour above this value with a final viscosity between 1.024 and 1.049 mPa·s. The final microencapsulation yield for the samples ranged between 79 and 81%. Although the microencapsulated forms presented a targeted release at the intestinal level, the phenolic content decreased after gastrointestinal digestion. The bioaccessibility of the microencapsulated extracts showed higher values than their non-encapsulated counterparts, with the highest value of 45.43% in the VVIT sample, followed by VCS with 41.07%. However, the non-encapsulated VCS sample presented high bioaccessibility after in vitro digestion (38.65%). As concluded, further in vivo research should be conducted on the leaves of the Vaccinium species.
Trapped Under Ice: Spatial and Seasonal Dynamics of Dissolved Organic Matter Composition in Tundra Lakes

Arctic lakes store, modify, and transport large quantities of carbon from terrestrial environments to the atmosphere; however, the spatial and temporal relationships between quantity and composition of dissolved organic matter (DOM) have not been well characterized across broad arctic regions. Moreover, most arctic lake DOM compositions have been examined during the ice‐free summer, whereas DOM cycling between the ice‐covered winter months and summer have not been addressed. To resolve these spatial and seasonal uncertainties in DOM cycling, we sampled a series of arctic lakes from the North Slope of Alaska across a latitudinal gradient in the winter and summer over 3 years. Samples were analyzed for dissolved organic carbon concentration and DOM composition was characterized using optical and fluorescence properties combined with molecular‐level analysis using Fourier transform‐ion cyclotron resonance mass spectrometry. Tundra lake DOM properties including aromaticity and molecular stoichiometries were similar to other northern high‐latitude lakes, but optical parameters related to aromaticity and molecular weight were greater in major arctic rivers and in coastal lakes in the North Slope region. DOM composition was highly seasonal, with ice exclusion concentrating microbially processed DOM in the winter water columns, potentially influencing DOM cycling the following summer. However, the greatest variations in DOM composition were related to lake depth and likely other physical features including morphology and bathymetry. As the Arctic warms, we expect changes in hydrology and ice cover to enhance under‐ice microbial DOM processing, early summer photodegradation, and ultimately carbon fluxes to the atmosphere after ice‐out.
Assessing Nordihydroguaiaretic Acid Therapeutic Effect for Glioblastoma Multiforme

In this study, we demonstrate that Raman microscopy combined with computational analysis is a useful approach to discriminating accurately between brain tumor bio-specimens and to identifying structural changes in glioblastoma (GBM) bio-signatures after nordihydroguaiaretic acid (NDGA) administration. NDGA phenolic lignan was selected as a potential therapeutic agent because of its reported beneficial effects in alleviating and inhibiting the formation of multi-organ malignant tumors. The current analysis of NDGA’s impact on GBM human cells demonstrates a reduction in the quantity of altered protein content and of reactive oxygen species (ROS)-damaged phenylalanine; results that correlate with the ROS scavenger and anti-oxidant properties of NDGA. A novel outcome presented here is the use of phenylalanine as a biomarker for differentiating between samples and assessing drug efficacy. Treatment with a low NDGA dose shows a decline in abnormal lipid-protein metabolism, which is inferred by the formation of lipid droplets and a decrease in altered protein content. A very high dose results in cell structural and membrane damage that favors transformed protein overexpression. The information gained through this work is of substantial value for understanding NDGA’s beneficial as well as detrimental bio-effects as a potential therapeutic drug for brain cancer.
Perception of the Impact of Freshmen Academic Involvement Activities, and Use of Academic Support Services on Academic Performance - (A Case Study of Virginia Tech Second Year Engineering Students): Implications for Counseling

This study identifies and discusses the academic activities and support services that second-year-engineering students perceived as having impacted their freshman year academic performance. Guided by Astin’s (1984) student involvement theory, this investigation involved a total of 34 participants, and was conducted at a large land-grant university in the southeastern United States during the spring semester of the 1998/99 academic year. The following questions were addressed by this study: (1) Which academic activities do students consider as important to their academic performance? (2) Which academic support services do students consider as important to their academic performance? (3) Are there academic performance level-related differences in students’ perceptions of the impact of various academic activities in which they are involved, relative to their academic performance? (4) Are there academic performance level-related differences in students’ perceptions of impact of the academic support services they use, relative to their academic performance? (5) Are there gender-related differences in students’ perceptions of the impact of various academic activities in
Neutrophil-to-Lymphocyte Ratio as a Predictor of Disease Severity and Mortality in Coronavirus Disease 2019: Prospective Study From Central India

Background: Clinical presentation of coronavirus disease 2019 (COVID-19) varies from an asymptomatic state to severe disease characterized by acute respiratory distress syndrome, respiratory failure, thrombosis, and multi-organ dysfunction syndrome. The neutrophil-to-lymphocyte ratio (NLR) has been reviewed as one of the laboratory factors that have been proposed to predict the severity of disease and mortality in COVID-19 pandemic. Aim and objectives: To evaluate the association between NLR and the disease severity and mortality in COVID-19. Materials and methods: After approval from Institutional Ethics Committee, this prospective cohort study was carried out in a tertiary-care teaching medical institute of Central India. COVID-19 patients of the age group 18 years and above admitted during the study period were included. Cases were categorized into four groups as asymptomatic (Group A), mild (Group B), moderate (Group C), and severe (Group D) based on clinical symptoms, respiratory rate, oxygen saturation, and chest imaging. NLR was calculated by doing a complete blood count at the time of hospitalization by the Mindray BC-6000 auto hematology analyzer. The outcome of the disease was classified as recovery and death during hospitalization. Receiver operating characteristic (ROC) curve analysis was used to assess the ability of NLR at admission to predict severe COVID-19 or mortality. Ordinal regression analysis was used to assess the impact of NLR on disease severity and mortality. Results: Mean NLR was significantly higher in the severe COVID-19 group as compared to the mild/moderate group and in deceased as compared to discharged cases. ROC curve analysis revealed NLR to be an excellent predictor of disease severity as well as a prognostic parameter for risk of death. NLR was found to be a significant independent positive predictor for contracting the severe disease (Odd’s ratio 1.396, 95% CI=1.112-1.753, p=0.004) and mortality (Odd’s ratio 1.276, 95% CI=1.085-1.499, p=0.003). Conclusion: High NLR was significantly associated with the disease severity and mortality in COVID-19.
The Correlation between Vowel Production and Vowel Perception as Elicited in the Performance of Adult Iraqi Learners of English: Acoustic Perspective

This paper reports the results of an acoustic study to the correlation between vowel production and vowel perception as performed by adult Iraqi learners (AILs) of English. The subjects representing the sample group of this work are 24 ( 12 males and 12 females) first- year Iraqi students who pursue their B.A. degree in English language and literature at the Department of English, College of Arts, University of Basra (Academic Year 2011-2012). They live in the city centre of Basra and the nearby districts. They are chosen randomly to represent the population of the study with no speaking and hearing defects. They perform a production and a perception tests where a list of 20English tokens is used. For the production experiment, a list of 5 dummy words (randomly chosen to decrease the subjects' tension and hesitation) as well as 20 monosyllabic and disyllabic words containing B.B.C. English pure vowels are randomly arranged to test the subjects' production performance . A perception-based test is designed to examine the participants' perceptibility of the vowels they have already produced. The perception stimuli are recorded on a computer model Toshiba NB505 with a CPU speed (1.7) GHZ, RAM (1.00) GB, and hard disk (222) GB. The recorded material is encoded into a computerized software input (PRAAT) ( version 4.0). This software allows recording speech materials at a speech rate ( 16000 HZ) to create wide-band spectrograms accompanied by waveform graphs required for acoustic measurements. The acoustic data are subjected to t-test analysis where two levels of significance are deployed, pvalue equals or less than 0.01(for considerable significance), and p-value equals or less than 0.05 ( when the difference is only significant). Mean values and standard deviations for the responses of the vowels groups are also calculated. The main findings of this work are the following: 1- Statistical results clearly reveal that there is irregular connection between short/long vowel production and perception.
Openness to Experience Moderates the Association of Warmth Profiles and Subjective Well-Being in Left-Behind and Non-Left-Behind Youth

Crouched in the socioecological framework, the present research compared the subjective well-being of left-behind youth with their non-left-behind peers. Furthermore, this research investigated the association of parental warmth and teacher warmth using a person-centered approach with adolescents’ subjective well-being on the whole sample, and examined its conditional processes by ascertaining the moderating role of openness to experience and left-behind status in this association. A total of 246 left-behind youth (53.6% girls; Mage = 15.77; SD = 1.50) and 492 socio-demographically matched, non-left-behind peers (55.1% girls; Mage = 15.91; SD = 1.43) was involved in this study. During school hours, these adolescents were uniformly instructed to complete a set of self-report questionnaires. The results from ANCOVA exhibited no significant differences in subjective well-being between these two groups of youth. Moreover, four warmth profiles were revealed: congruent low, congruent highest, congruent lowest, and incongruent moderate, and youth within the congruent highest profile were more likely than the other three profiles to report higher subjective well-being. Additionally, moderation analyses demonstrated that high openness was one protective factor for subjective well-being, when left-behind youth perceived the lowest levels of parental warmth and teacher warmth congruently. These findings indicate that left-behind youth may not be psychologically disadvantaged in terms of positive psychosocial outcomes, such as subjective well-being, and school activities or social initiatives emphasizing openness to experience would be essential for them to facilitate positive adaptive patterns after parental migration.
Scheduling and Optimization of Wireless Virtual Network Resources Based on Reinforcement Learning

With the booming and proliferation of 5G wireless network services in the future, a large number of wireless virtual network resources will emerge, and the densification and heterogeneity of the wireless communication networks that provide services for them will become the trend of development. To this end, a wireless virtual network resource scheduling model based on user satisfaction is constructed, and a reinforcement learning-based wireless virtual network resource scheduling mechanism, IRSUP, is proposed. IRSUP is designed with an intelligent optimization module for user service preferences to address the personalized needs of user service customization, and a reinforcement learning-based intelligent scheduling module is designed to address the challenge of joint optimization of multistar resources. Simulation results show that IRSUP can effectively improve resource scheduling rationality and link resource utilization and user satisfaction, among which service capacity is improved by 30% to 60% and user satisfaction is more than doubled.
Endogenous Opioids and Their Role in Stem Cell Biology and Tissue Rescue

Opioids are considered the oldest drugs known by humans and have been used for sedation and pain relief for several centuries. Nowadays, endogenous opioid peptides are divided into four families: enkephalins, dynorphins, endorphins, and nociceptin/orphanin FQ. They exert their action through the opioid receptors (ORs), transmembrane proteins belonging to the super-family of G-protein-coupled receptors, and are expressed throughout the body; the receptors are the δ opioid receptor (DOR), μ opioid receptor (MOR), κ opioid receptor (KOR), and nociceptin/orphanin FQ receptor (NOP). Endogenous opioids are mainly studied in the central nervous system (CNS), but their role has been investigated in other organs, both in physiological and in pathological conditions. Here, we revise their role in stem cell (SC) biology, since these cells are a subject of great scientific interest due to their peculiar features and their involvement in cell-based therapies in regenerative medicine. In particular, we focus on endogenous opioids’ ability to modulate SC proliferation, stress response (to oxidative stress, starvation, or damage following ischemia–reperfusion), and differentiation towards different lineages, such as neurogenesis, vasculogenesis, and cardiogenesis.
Learning Stress Patterns with a Sequence-to-Sequence Neural Network

We present the ﬁrst application of modern neural networks to the well studied task of learning word stress systems. We tested our adaptation of a sequence-to-sequence network on the Tesar and Smolensky test set of 124 “lan-guages”, showing that it acquires generalizable representations of stress patterns in a very high proportion of runs. We also show that it learns restricted lexically conditioned patterns, known as stress windows. The ability of this model to acquire lexical idiosyncracies, which are very common in natural language systems, sets it apart from past, non-neural models tested on the Tesar and Smolensky data set.
Value-Based Reinforcement Learning for Sequence-to-Sequence Models

This paper demonstrates the theoretical possibility of applying advanced value-based reinforcement learning methods on sequence-to-sequence models for the first time. This approach avoids major issues that have emerged with supervised sequence-to-sequence models such as loss-evaluation mismatch, exposure bias and search error. At the same time, when compared to policy gradient meth-ods, it does not rely on well-trained fully supervised models and is not restricted to fine-tuning. Specifically, a sequence-to-sequence model is introduced, which is trained in a Rainbow-like setup. While such a model is practically still limited by its scalability, the work contributes towards a more generally applicable approach to reinforcement learning in natural language processing which is beyond the scope of fine-tuning. For this, the paper provides a theoretical and practical framework, a first baseline, and valuable insights by studying ablated models and different approaches for utilizing demonstration data.
Product Aesthetic Design: A Machine Learning Augmentation

Aesthetics are critically important to market acceptance. In the automotive industry, an improved aesthetic design can boost sales by 30% or more. Firms invest heavily in designing and testing aesthetics. A single automotive “theme clinic” can cost more than $100,000, and hundreds are conducted annually. We propose a model to augment the commonly used aesthetic design process by predicting aesthetic scores and automatically generating innovative and appealing product designs. The model combines a probabilistic variational autoencoder (VAE) with adversarial components from generative adversarial networks (GAN) and a supervised learning component. We train and evaluate the model with data from an automotive partner—images of 203 SUVs evaluated by targeted consumers and 180,000 high-quality unrated images. Our model predicts well the appeal of new aesthetic designs—43.5% improvement relative to a uniform baseline and substantial improvement over conventional machine learning models and pretrained deep neural networks. New automotive designs are generated in a controllable manner for use by design teams. We empirically verify that automatically generated designs are (1) appealing to consumers and (2) resemble designs that were introduced to the market five years after our data were collected. We provide an additional proof-of-concept application using open-source images of dining room chairs. History: Puneet Manchanda served as the senior editor. Funding: A. Burnap received support from General Motors to partially fund a postdoctoral research position for the research conducted in this work. He certifies that none of the research or its results were censored or obfuscated in its publication. J. Hauser and A. Timoshenko certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Supplemental Material: The data files are available at https://doi.org/10.1287/mksc.2022. 1429 .