Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Green Synthesized MMT/Ag Nanocomposite for Removal of Methylene Blue from Aqueous Solution

Textile industries are the largest consumer of synthetic dyestuff compounds and consequently, they are the prime contributor of colored organic contaminants to the environment. The dye compounds when released in soil or freshwater resources such as rivers, cause a potential hazard to living beings due to their toxic, allergic and carcinogenic nature. Current conventional treatment methods for removal or degradation of such dyestuff materials from water systems are not sufficient, and therefore, there is an immediate need to find efficient and eco-friendly approaches. In this regard, nanotechnology can offer an effective solution to this problem. In the present work, montmorillonite/silver nanocomposite (MMT/Ag nanocomposite) is developed through green synthesis methods using naturally occurring montmorillonite (MMT) clay and silver nanoparticles. The material was characterized by using a particle size analyzer (PSA), UV/Visible spectroscopy, Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscope (FE-SEM), energy dispersive X-ray (EDX) spectroscopy and a Brunner–Emmett–Teller (BET) surface area analyzer. The adsorption efficiency of the nanocomposite and per cent removal of methylene blue (MB) was investigated by using a batch system.
The Role of Animal Ownership for People with Severe Mental Illness during the COVID-19 Pandemic: A Mixed-Method Study Investigating Links with Health and Loneliness

Research has reported the benefits of companion animals for people with severe mental illness (SMI). However, this evidence base is fragmented and unclear. The COVID-19 pandemic presents an opportunity to explore the role of companion animals in the context of social distancing and isolation measures for people with SMI. Therefore, we aimed to investigate the links between mental and physical health and animal ownership in people with SMI and to explore animal owners’ perceptions related to human–animal interactions during the pandemic restrictions. A survey was conducted with a previously assembled cohort of individuals with SMI in the UK. The survey included previously validated and new bespoke items measuring demographics, and outcomes related to mental and physical health, and human–animal interactions. The survey also included a question inviting free-text responses, allowing participants to describe any experiences of their human–animal relationships during the pandemic. Of 315 participants who consented to participate, 249 (79%) completed the survey. Of these, 115 (46.2%) had at least one companion animal. Regression analyses indicated that animal ownership was not significantly associated with well-being and loneliness. However, animal ownership was associated with a self-reported decline in mental health (b = 0.640, 95% CI [0.102–1.231], p = 0.025), but no self-reported change in physical health. Thematic analysis identified two main themes relating to the positive and negative impact of animal ownership during pandemic restrictions. Animal ownership appeared to be linked to self-reported mental health decline in people with SMI during the second wave of the pandemic in the UK. However, the thematic analysis also highlighted the perceived benefit of animal ownership during this time. Further targeted investigation of the role of human–animal relationships and the perceived human–animal bond for human health is warranted.
Research Progress of Gliomas in Machine Learning

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.
Short Duration Repetitive Transcranial Electrical Stimulation During Sleep Enhances Declarative Memory of Facts

Transcranial electrical stimulation (tES) during sleep has been shown to successfully modulate memory consolidation. Here, we tested the effect of short duration repetitive tES (SDR-tES) during a daytime nap on the consolidation of declarative memory of facts in healthy individuals. We use a previously described approach to deliver the stimulation at regular intervals during non-rapid eye movement (NREM) sleep, specifically stage NREM2 and NREM3. Similar to previous studies using tES, we find enhanced memory performance compared to sham both after sleep and 48 h later. We also observed an increase in the proportion of time spent in NREM3 sleep and SDR-tES boosted the overall rate of slow oscillations (SOs) during NREM2/NREM3 sleep. Retrospective investigation of brain activity immediately preceding stimulation suggests that increases in the SO rate are more likely when stimulation is delivered during quiescent and asynchronous periods of activity in contrast to other closed-loop approaches which target phasic stimulation during ongoing SOs.
YIELD AND QUALITY OF TWO SUGARCANE VARIETIES AS AFFECTED BY BIO AND INORGANIC NITROGEN FERTILIZATION

The present investigation was carried out at Mattana Agricultural Research Station, Qena Governorate during 2003/2004 (plant cane) and 2004/2005 (1 ratoon) to investigate the effect of inorganic-N (120, 180 and 240 kg N/fed represented 50, 75 and 100% of the recommended rate, respectively), bio-N (without, Cerialin and Microbin) and their combinations on yield and quality of two sugarcane varieties (G.T.54-9 and Ph.8013). A split-split plot design with three replications was used in both seasons. Bio-N fertilizers were spread in the main plots; inorganic N levels were distributed in the sub plots, while the sub-sub plots were assigned for the two sugarcane varieties. The results showed significant differences in all studied traits between sugarcane inoculated with Cerioalin and/or Microbin bio-N fertilizers and that left without bio-N. Meanwhile, except for the number of millable cane/m where, Microbin gave higher of it, no statistical variance between the two bio-N fertilizers was found in stalk height, stalk diameter, sucrose%, sugar recovery%, cane and sugar yields. Increasing inorganic-N levels from 120 up to 240 kg N/fed resulted in a significant increase stalk height, stalk diameter, millable cane/m, cane and sugar yields in the plant cane and 1 ratoon canes. Applying 180 kg N/fed gave the highest sucrose% (in the plant cane), while 240 kg N/fed was required to obtain the highest value of this trait in the 1 ratoon. The highest sugar recovery% was recorded by applying 180 kg N/fed in both cane crops. Sugarcane variety G.T. 54-9 surpassed Ph.8013 significantly in all of the studied traits except for stalk diameter, where Ph.8013 variety had thicker stalks. Under conditions of the present work, supplying sugarcane G.T. 54-9 variety grown as a plant or 1 ratoon with 100% of the recommended N-rate (240 kg N/fed) integrated with Cerialin bio-N at the rate of 4 bags (1.6 kg)/fed resulted in the highest sugar yield/fed.
Espresso vs. EyeAutomate: An Experiment for the Comparison of Two Generations of Android GUI Testing

Context: Different approaches exist for automated GUI testing of Android applications, each with its peculiarities, advantages, and drawbacks. The most common are either based on the structure of the GUI or use visual recognition. Goal: In this paper, we present an empirical evaluation of two different GUI testing techniques with the use for each of a representative tool: (1) Visual GUI testing, with the use of EyeAutomate, and (2) Layout-based GUI testing, with the use of Espresso. Method: We conducted an experiment with a population of 78 graduate students. The participants of the study were asked to create the same test suite for a popular, open-source Android app (Omni-Notes) with both the tools, and to answer a survey about their preference to the one or the other, and the perceived difficulties when developing the test scripts. Results: By analyzing the outcomes of the delivered test suites (in terms of number of test scripts delivered and ratio of working ones) and the answers to the survey, we found that the participants showed similar productivity with both the tools, but the test suites developed with EyeAutomate were of higher quality (in terms of correctly working test scripts). The participants expressed a slight preference towards the EyeAutomate testing tool, reflecting a general complexity of Layout-based techniques -- represented by Espresso -- and some obstacles that may make the identification of components of the GUI quite a long and laborious task. Conclusions: The evidence we collected can provide useful hints for researchers aiming at making GUI testing techniques for mobile applications more usable and effective.
MRI brain imagery processing software in data analysis

Modern brain scanning techniques are one of the most consistent sources of medical information which included in most diagnostic protocols. These data possess a great potential for the machine learning (ML) analysis and clinical applications of the results. MRI imaging is prevalent in brain analysis, allowing acquisition of structural (3D image) and functional imaging (timeseries of 3D images). Relative to the MRI data volumes these research are considered to the big data and massive analysis, connected with numerous methods of signal processing, advanced ML approaches and feature extraction. In current paper we update the surveys of most software used for the feature generation, processing and analysis of MRI brain imagery. Systematizing the toolboxes, we describe common scanner characteristics, image modalities, corresponding features and their informational context, as well as common machine learning classification problems in working with the data. And as a result, we propose evaluation and comparison of conventional software tools with their detailed description.
ECG-Based Subject Identification Using Common Spatial Pattern and SVM

In this paper, a nonfiducial electrocardiogram (ECG, the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin) identification system based on the common spatial pattern (CSP) feature extraction technique is presented. The single- and multilead ECG signals of each subject are divided into nonoverlapping segments, and different segment lengths (1, 3, 5, 7, 10, or 15 seconds) are investigated. Features are extracted from each signal segment through projection on a CSP projection matrix. The extracted features are then used to train a radial basis function kernel-based Support Vector Machine (SVM) classifier, which is then employed in the identification phase. The proposed identification system was evaluated on 10, 20, …, 200 reference subjects of the Physikalisch-Technische Bundesanstalt (PTB) ECG database. Using a single limb-based lead (I) with 200 reference subjects, the system achieved an identification rate of 95.15% and equal error rate of 0.1. The use of a single chest-based lead (V3) for 200 reference subjects resulted in an identification rate of 98.92% and equal error rate of 0.08.
OncomiR miR-182-5p Enhances Radiosensitivity by Inhibiting the Radiation-Induced Antioxidant Effect through SESN2 in Head and Neck Cancer

Radiotherapy is routinely used for the treatment of head and neck squamous cell carcinoma (HNSCC). However, the therapeutic efficacy is usually reduced by acquired radioresistance and locoregional recurrence. In this study, The Cancer Genome Atlas (TCGA) analysis showed that radiotherapy upregulated the miR-182/96/183 cluster and that miR-182 was the most significantly upregulated. Overexpression of miR-182-5p enhanced the radiosensitivity of HNSCC cells by increasing intracellular reactive oxygen species (ROS) levels, suggesting that expression of the miR-182 family is beneficial for radiotherapy. By intersecting the gene targeting results from three microRNA target prediction databases, we noticed that sestrin2 (SESN2), a molecule resistant to oxidative stress, was involved in 91 genes predicted in all three databases to be directly recognized by miR-182-5p. Knockdown of SESN2 enhanced radiation-induced ROS and cytotoxicity in HNSCC cells. In addition, the radiation-induced expression of SESN2 was repressed by overexpression of miR-182-5p. Reciprocal expression of the miR-182-5p and SESN2 genes was also analyzed in the TCGA database, and a high expression of miR-182-5p combined with a low expression of SESN2 was associated with a better survival rate in patients receiving radiotherapy. Taken together, the current data suggest that miR-182-5p may regulate radiation-induced antioxidant effects and mediate the efficacy of radiotherapy.
How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature

Using extensive and comprehensive databases to select a subset of research papers, we aim to critically analyze previous empirical studies to identify certain patterns in determining the optimal number of stocks in well-diversified portfolios in different markets, and to compare how the optimal number of stocks has changed over different periods and how it has been affected by market turmoil such as the Global Financial Crisis (GFC) and the current COVID-19 pandemic. The main methods used are bibliometric analysis and systematic literature review. Evaluating the number of assets which lead to optimal diversification is not an easy task as it is impacted by a huge number of different factors: the way systematic risk is measured, the investment universe (size, asset classes and features of the asset classes), the investor’s characteristics, the change over time of the asset features, the model adopted to measure diversification (i.e., equally weighted versus optimal allocation), the frequency of the data that is being used, together with the time horizon, conditions in the market that the study refers to, etc. Our paper provides additional support for the fact that (1) a generalized optimal number of stocks that constitute a well-diversified portfolio does not exist for whichever market, period or investor. Recent studies further suggest that (2) the size of a well-diversified portfolio is larger today than in the past, (3) this number is lower in emerging markets compared to developed financial markets, (4) the higher the stock correlations with the market, the lower the number of stocks required for a well-diversified portfolio for individual investors, and (5) machine learning methods could potentially improve the investment decision process. Our results could be helpful to private and institutional investors in constructing and managing their portfolios and provide a framework for future research.
Features that Predict the Acceptability of Java and JavaScript Answers on Stack Overflow

Context: Stack Overflow is a popular community question and answer portal used by practitioners to solve problems during software development. Developers can focus their attention on answers that have been accepted or where members have recorded high votes in judging good answers when searching for help. However, the latter mechanism (votes) can be unreliable, and there is currently no way to differentiate between an answer that is likely to be accepted and those that will not be accepted by looking at the answer's characteristics. Objective: In potentially providing a mechanism to identify acceptable answers, this study examines the features that distinguish an accepted answer from an unaccepted answer. Methods: We studied the Stack Overflow dataset by analyzing questions and answers for the two most popular tags (Java and JavaScript). Our dataset comprised 249,588 posts drawn from 2014-2016. We use random forest and neural network models to predict accepted answers, and study the features with the highest predictive power in those two models. Results: Our findings reveal that the length of code in answers, reputation of users, similarity of the text between questions and answers, and the time lag between questions and answers have the highest predictive power for differentiating accepted and unaccepted answers. Conclusion: Tools may leverage these findings in supporting developers and reducing the effort they must dedicate to searching for suitable answers on Stack Overflow.
Comprehensive Kinetic Survey of Intestinal, Extra-Intestinal and Systemic Sequelae of Murine Ileitis Following Peroral Low-Dose Toxoplasma gondii Infection

We have recently shown that following peroral low-dose Toxoplasma gondii infection susceptible mice develop subacute ileitis within 10 days. Data regarding long-term intestinal and extra-intestinal sequelae of infection are scarce, however. We therefore challenged conventional C57BL/6 mice with one cyst of T. gondii ME49 strain by gavage and performed a comprehensive immunopathological survey 10, 36, and 57 days later. As early as 10 days post-infection, mice were suffering from subacute ileitis as indicated by mild-to-moderate histopathological changes of the ileal mucosa. Furthermore, numbers of apoptotic and proliferating/regenerating epithelial cells as well as of T and B lymphocytes in the mucosa and lamina propria of the ileum were highest at day 10 post-infection, but declined thereafter, and were accompanied by enhanced pro-inflammatory mediator secretion in ileum, colon and mesenteric lymph nodes that was most pronounced during the early phase of infection. In addition, subacute ileitis was accompanied by distinct shifts in the commensal gut microbiota composition in the small intestines. Remarkably, immunopathological sequelae of T. gondii infection were not restricted to the intestines, but could also be observed in extra-intestinal tissues including the liver, kidneys, lungs, heart and strikingly, in systemic compartments that were most prominent at day 10 post-infection. We conclude that the here provided long-term kinetic survey of immunopathological sequalae following peroral low-dose T. gondii infection provides valuable corner stones for a better understanding of the complex interactions within the triangle relationship of (parasitic) pathogens, the host immunity and the commensal gut microbiota during intestinal inflammation. The low-dose T. gondii infection model may be applied as valuable gut inflammation model in future pre-clinical studies in order to test potential treatment options for intestinal inflammatory conditions in humans.
Comparison of Whey Versus Almond Protein Powder on Nitrogen Balance in Female College Students; The California Almond Protein Powder Project (CAlmond-P3)

Plant-based diets have become increasingly popular in the past decade, with approximately 11% of Americans self-identifying as vegan or vegetarian and many others trying to reduce meat consumption. Due to increasing interest, the plant-based food market has significantly expanded, with several innovative products serving as alternatives to animal-based products. One such example is almond protein powder, a new protein supplement created as an alternative to whey protein. Due to the novelty of almond protein products, little is known regarding how well the protein supplement supports nitrogen metabolism. The effects of both an almond-based protein beverage and a whey-based protein beverage on nitrogen balance are investigated in the work presented herein. Twenty female college students aged 20–25 years were randomly assigned to consume either an almond- or whey-based protein drink twice daily for one week; 24-h urine collection was performed at the baseline and endpoint of the 7-day treatment period and nitrogen balance was assessed. Body composition and hydration status were also assessed. Both protein sources (almond and whey) were able to notably improve nitrogen balance, thus indicating that almond protein powder may be a functional plant-based alternative to whey protein powder and may be of interest in future research regarding muscle mass and body composition improvement.
Ontology-mediated query answering over temporal and inconsistent data

Stream-based reasoning systems process data stemming from different sources that are received over time. In this kind of applications, reasoning needs to cope with the temporal dimension and should be resilient against inconsistencies in the data. Motivated by such settings, this paper addresses the problem of handling inconsistent data in a temporal version of ontology-mediated query answering. We consider a recently proposed temporal query language that combines conjunctive queries with operators of propositional linear temporal logic (LTL), and consider these under three inconsistency-tolerant semantics that have been introduced for querying inconsistent description logic knowledge bases. We investigate their complexity for temporal EL ⊥ and DL-LiteR knowledge bases. In particular, we consider two different cases, depending on the presence of negations in the query. Furthermore, we complete the complexity picture for the consistent case. We also provide two approaches toward practical algorithms for inconsistency-tolerant temporal query answering.
Plantar Pressure Changes and Their Relationships with Low Back Pain during Pregnancy Using Instrumented Insoles

Low back pain affects around 50% of pregnant women and presents significant morbidity and persistence for years in 20% of postpartum women who report that pain. Numerous studies have documented gait alterations during pregnancy and postpartum. Therefore, an analysis of the relationship between certain gait parameters and low back pain was attempted using low-cost validated instrumented insoles. This work presents a longitudinal cohort study carried out during routine gynecological follow-up visits in the first, second, and third trimesters of pregnancy at an Obstetrics and Gynecology Service. Sample size was 62. Plantar pressure data were collected with specially designed instrumented insoles containing four force sensors to measure peak pressure, center of pressure, and stance phase time in each foot and in each pregnancy trimester. The analysis was carried out on a two-dimensional level, simultaneously considering the data from both feet using Hotelling’s T2 test. This longitudinal study detected relationships between certain gait parameter changes and low back pain during pregnancy. It revealed a cyclic tendency of low back pain prevalence with a maximum in the second trimester and a decrease in the third trimester, which was correlated with alterations of the pregnant gait: excessive foot pronation and rearfoot pressure increase.