Miracle-dz/UniFAR-FTUs huggingface dataset:
Starch nanoparticles produced via ultrasonication as a sustainable stabilizer in Pickering emulsion polymerization

Pickering emulsion polymerizations using starch nanoparticles (SNPs) as the sole stabilizer and persulfate as initiator have been successfully accomplished. The SNPs were prepared in water via high-power ultrasonication without the addition of any chemical reagent. The polymer dispersion stabilized by SNPs proved to be stable for several months, when the SNP content was above 4 wt% relative to the monomer phase. As the SNP loading increased, the particle sizes of the polymer dispersion decreased steadily, confirming the key role of the SNPs in the stabilization process. On the basis of the zeta-potential measurement and change in polymer particle size during emulsion polymerization, a mechanistic aspect of the Pickering emulsion polymerization is proposed. The film-formation properties of the nanocomposite dispersion and the optical properties of the ensuing films are also discussed. It was shown that nanocomposites prepared via the Pickering emulsion route displayed better optical transparency than that obtained by an ex situ mixing route.
The Relationship Between PMS Symptoms and Menstrual Attitudes Among University Students in Turkey

Citation: Gul Pinar [2020) The Relationship Between PMS Symptoms and Menstrual Attitudes Among University Students in Turkey. Int J Nur Man Pat Car 1: 1-8. Received Date: November 25, 2020 Accepted Date: December 25, 2020 Published Date: December 27, 2020 *Corresponding author: Gul Pinar, Associate Professor, Faculty of Health Sciences, Department of Nursing, Yildirim Beyazit University, Ankara, Turkey, Tel: +9 [0) 312 324 15 55; Fax: +9 [0) 312 324 15 05, E-mail: gpinar_1@gmail.com
COVID-19 and Intrauterine Fetal Death (IUFD): Possible Immunological Causes and Pathologies COVID-19 and Intrauterine Fetal Death (IUFD): Possible Immunological Causes and Pathologies Archives of Health Science

1Department of Obstetrics & Gynecology, Faculty of Medicine, Assiut University, Egypt. 2 COvid-19 Research of Assiut UNiversity Association (CORAUNA) group. 3 General Practitioner and Postgraduate, Faculty of Medicine, Assiut University, Egypt. 4 Undergraduate student, Faculty of Medicine, Assiut University, Egypt 5 House-officer, Faculty of Medicine, Assiut University, Egypt. 6 Department of Pathology, Faculty of Medicine, Assiut University, Egypt.
Linking Work-Life Balance and Employee Well-Being: Do Supervisor Support and Family Support Moderate the Relationship?

This study used the Partial Least Squares (PLS) method to investigate the influence of work-life balance (WLB) on individual well-being (career satisfaction and psychological well-being) among an Open Distance Learning (ODL) university’s staff. Furthermore, the moderating effects of supervisor support and family support on the relationship between WLB and both components of individual well-being were also examined. Data were collected via an online survey, in which 94 academic, academic support, and operational staff working in one of Malaysia’s ODL universities took part. The findings confirmed that the WLB of this institution’s employees influenced their career satisfaction and psychological well-being. Besides that, supervisor support and family support were found not to moderate the relationship between WLB and individual well-being. In addition, the findings have contributed to an improved understanding of the WLB of an ODL university’s staff.
Forecasting of air quality in Delhi using principal component regression technique

Abstract Over the past decade, an increasing interest has evolved by the public in the day–to–day air quality conditions to which they are exposed. Driven by the increasing awareness of the health aspects of air pollution exposure, especially by most sensitive sub–populations such as children and the elderly, short–term air pollution forecasts are being provided more and more by local authorities. The Air Quality Index (AQI) is a number used by governmental agencies to characterize the quality of the air at a given location. AQI is used for local and regional air quality management in many metropolitan cities of the world. The main objective of the present study is to forecast short–term daily AQI through previous day’s AQI and meteorological variables using principal component regression (PCR) technique. This study has been made for four different seasons namely summer, monsoon, post monsoon and winter. AQI was estimated for the period of seven years from 2000–2006 at ITO (a busiest traffic intersection) for criteria pollutants such as respirable suspended particulate matter (RSPM), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ) and suspended particulate matter (SPM) using a method of US Environmental Protection Agency (USEPA), in which sub–index and breakpoint pollutant concentration depends on Indian National Ambient Air Quality Standard (NAAQS). The Principal components have been computed using covariance of input data matrix. Only those components, having eigenvalues ≥ 1, were used to predict the AQI using principal component regression technique. The performance of PCR model, used for forecasting of AQI, was better in winter than the other seasons as studied through statistical error analysis. The values of normalized mean square error (NMSE) were found as 0.0058, 0.0082, 0.0241 and 0.0418 for winter, summer, post monsoon and monsoon respectively. The other statistical parameters are also supporting the same result.
Central Bank Collateral Framework as an Unconventional Policy Tool∗

This paper investigates the macroeconomic effects of easing collateral standards in open market operations (OMOs), an unconventional policy tool that central banks in advanced economies (AE) implemented during the Great Recession to attenuate the effects of financial market disruptions on the real economy. Using a New-Keynesian general equilibrium model with an explicit banking sector and central bank collateralized lending, we study the central bank policy of broadening the range of assets that are accepted as collateral in OMOs, e.g. accepting a wider range of government bonds or taking corporate loans as eligible. Taking the analysis to the experience of central banks in AE during the Great Recession, when the policy rate endogenously hits the zero lower bound (ZLB) due to sudden rise in funding stress, active use of collateral policies helps mitigate the sharp drop in asset prices, credit, investment, and output. Our analysis also reveals that under severe financial market conditions, central banks do not need to wait until the policy rate hits the ZLB: active collateral policies reduce the need for looser conventional policy response and can help central banks avoid the ZLB.
Linking job insecurity to negative decision making:

Job insecurity has increased markedly in the developed economies of the world (Gray, 2002). The effects of job insecurity on individual employees and on organisational outcomes, however, are controversial. For instance, Greenhalgh and Rosenblatt (1984) point out that job insecurity can result in increased work effort, while Dekker and Schaufeli, (1995) argue that insecurity leads to stress and decreased performance. In this paper, we outline a study examining the indirect impact of job insecurity on decision-making, via job-related tension. Based on a web survey involving 217 participants, we found that job insecurity indirectly increased the adoption of negative decisionmaking strategies by increasing employees’ level of job-related tension. Limitations and implications for theory and managers are also discussed.
Knowledge of Technology Poultry Production and Entrepreneurship For Development of Indigenous Chicken Farms in Indonesia

The objective of this study is to analyze the knowledge of technology poultry production and entrepreneurship for the development of indigenous chicken farms. The research location Sidrap and Bone regencies, South Sulawesi Province, Indonesia. The data collected through direct observation and structured interviews with 96 indigenous chicken farmers. The data analyzed by descriptive statistics and Spearman's Rank correlation. The results showed that the development of indigenous chicken farms was decreasing and not increasing in the number of chickens, the knowledge of farmer respondents in technology poultry production and entrepreneurship was categorized as inadequate. The results show that in the technology poultry production, knowledge of suitable feed, and control and prevention of disease have a positive and significant relationship with the development of indigenous chicken farms. In entrepreneurial knowledge, knowledge of results-oriented and opportunity-seeking have a positive and significant relationship with the development of indigenous chicken farms. The conclusion, the technology poultry production, and entrepreneurship knowledge have an important role in the development of indigenous chicken farms
A Comparative Study on Dietary Pattern and their Associated Factors among the Rural and Urban School Going Adolescents

Adolescence is a critical period and one of the most challenging periods of life. This is the time when they learn to develop new relationships, inculcate social skills, become an independent person and have behavioral changes which seem to last for the rest of their lives. According to World Health Organization about 1.2 billion people are living in the adolescent age group, 10–19 years. During this transition from childhood to adulthood adolescents undergo a wide range of health risk starting from physical to mental health. [1] Adolescence is often considered to be neglected group in the continuum of care in health-care system. Also to achieve universal health coverage, a core component of 2030 agenda for sustainable development attention has to be given to this neglected group of adolescence. [2]
Characteristics of Machine Learning Research with Impact

In recent years there has been a huge surge in machine learning publication volume and industry investments [1, 2]. The number of publications grew far beyond what any researcher can read. ArXiv [3], one of the major preprint platforms for the field of machine learning, shows an exponential increase in papers uploaded to the platform as shown in Figure 1. A preprint platform allows papers to be uploaded prior to publication and without any peer-review process. Additionally, the field is becoming very competitive with research institutions and companies such as Google, Facebook, DeepMind, Apple, Microsoft, Uber, and many others involved. New research achievements are quickly outdated and surpassed in a matter of a few months or years. This poses the question, what kind of research outlasts the test of time? What characterizes the most impactful research and how can impact be measured? What kind of research areas and questions should the machine learning community focus on to maximize impact, and what kind of methodologies should be used?
Linear Matrix Inequalities for Normalizing Matrices

A real square matrix is normal if it can be diagonalized by an unitary matrix. In this paper novel convex conditions are derived to decide if a matrix is similar to a normal matrix and it is shown how to use these conditions to normalize matrices. The conditions are expressed in terms of linear matrix inequalities and hence they are efficiently solvable using semidefinite programming. Since a matrix is similar to a normal matrix if and only if the matrix is diagonalizable, the present results also provide novel convex conditions for deciding if a matrix is diagonalizable or not. Some applications of these results are presented.
Aqueous room temperature synthesis of zeolitic imidazole framework 8 (ZIF-8) with various concentrations of triethylamine

In this study, a zeolitic imidazole framework (ZIF-8) was prepared, and physico-chemical characterizations were performed to investigate the influences of triethylamine (TEA) on the crystallinity, particle size, morphology, and defects of ZIF-8 synthesized via an aqueous room temperature approach with a relatively low molar ratio of ligands to metal salts. Increasing the concentration of TEA by raising the TEA/total molar ratio from 0.004 to 0.007 prompted the formation of pure phase ZIF-8, whereas a TEA/total molar ratio below 0.004 did not result in a yield of ZIF-8. The particle size of ZIF-8 decreased from approximately 288 to 133 nm with increasing TEA/total molar ratio, except at a TEA/total molar ratio of 0.007. However, an excessive TEA/total molar ratio alters the 2-MeIM chemistry, and partially forms hydroxylated 2-MeIM. Thus, this study shows that at relatively low molar ratios of ligands to metal salts, up to a maximum TEA/total molar ratio of 0.006, the TEA concentration plays an important role in assisting crystal formation and in controlling the particle size of ZIF-8. The proposed procedure enables pure phase nanoscale ZIF-8 to be synthesized with a high product yield and with minimal chemical usage.
Covid-19 in end-stage renal disease patients with renal replacement therapies: A systematic review and meta-analysis

Introduction The novel coronavirus (COVID-19), caused by SARS-CoV-2, showed various prevalence and case-fatality rates (CFR) among patients with differentpre-existing chronic conditions. End-stage renal disease (ESRD) patients with renal replacement therapy (RRT) might have a higher prevalence and CFR due to reduced immune function from uremia and kidney tropism of SARS-CoV-2, but there was no systematic study on the infection and mortality of the SARS-CoV-2 infection in ESRD patients who are on RRT. Methods We searched five electronic databases and performed a systematic review and meta-analysis up to June 30, 2020, to evaluate the prevalence and case fatality rate (CFR) of the COVID-19 infection among ESRD patients with RRT. The global COVID-19 data were retrieved from the international database on June 30, 2020, for estimating the prevalence and CFR of the general population as referencing points. Results Of 3,272 potential studies, 34 were eligible studies consisted of 1,944 COVID-19 confirmed cases in 21,873 ESRD patients with RRT from 12 countries in four WHO regions. The overall pooled prevalence in ESRD patients with RRT was 3.10% [95% confidence interval (CI) 1.25-5.72] which was higher than referencing 0.14% global average prevalence. The overall estimated CFR of COVID-19 in ESRD patients with RRT was 18.06% (95% CI 14.09-22.32) which was higher than the global average at 4.98%. Conclusions This meta-analysis suggested high COVID-19 prevalence and CFR in ESRD patients with RRT. ESRD patients with RRT should have their specific protocol of COVID-19 prevention and treatment to mitigate excess cases and deaths.
Clinical Validation of a Proteomic Biomarker Threshold for Increased Risk of Spontaneous Preterm Birth and Associated Clinical Outcomes: A Replication Study

Preterm births are prevalent and a leading cause of neonatal death in the United States. Despite the availability of effective interventions, to date there is not a robust and widely applicable test to identify pregnancies at high risk for spontaneous preterm birth (sPTB). Previously, a sPTB predictor based on the ratio of two proteins, IBP4/SHBG, was validated as an accurate predictor of sPTB in the observational study Proteomic Assessment of Preterm Risk (PAPR). Here it is demonstrated that the same predictor threshold associated with 2-fold increased risk of sPTB, namely -1.4, is also statistically significant for predicting elevated risk of sPTB in the observational study Multicenter Assessment of a Spontaneous Preterm Birth Risk Predictor (TreeToP).
SARS‐CoV‐2 RNA screening in routine pathology specimens

Virus detection methods are important to cope with the SARS‐CoV‐2 pandemics. Apart from the lung, SARS‐CoV‐2 was detected in multiple organs in severe cases. Less is known on organ tropism in patients developing mild or no symptoms, and some of such patients might be missed in symptom‐indicated swab testing. Here, we tested and validated several approaches and selected the most reliable RT‐PCR protocol for the detection of SARS‐CoV‐2 RNA in patients’ routine diagnostic formalin‐fixed and paraffin‐embedded (FFPE) specimens available in pathology, to assess (i) organ tropism in samples from COVID‐19‐positive patients, (ii) unrecognized cases in selected tissues from negative or not‐tested patients during a pandemic peak, and (iii) retrospectively, pre‐pandemic lung samples. We identified SARS‐CoV‐2 RNA in seven samples from confirmed COVID‐19 patients, in two gastric biopsies, one small bowel and one colon resection, one lung biopsy, one pleural resection and one pleural effusion specimen, while all other specimens were negative. In the pandemic peak cohort, we identified one previously unrecognized COVID‐19 case in tonsillectomy samples. All pre‐pandemic lung samples were negative. In conclusion, SARS‐CoV‐2 RNA detection in FFPE pathology specimens can potentially improve surveillance of COVID‐19, allow retrospective studies, and advance our understanding of SARS‐CoV‐2 organ tropism and effects.
REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

Deep learning models are receiving increasing attention in clinical decision-making, however the lack of explainability impedes their deployment in day-to-day clinical practice. We propose REM, an explainable methodology for extracting rules from deep neural networks and combining them with rules from non-deep learning models. This allows integrating machine learning and reasoning for investigating basic and applied biological research questions. We evaluate the utility of REM in two case studies for the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision-making.
Myelin pathology in ataxia-telangiectasia is the cell-intrinsic consequence of ATM deficiency in the oligodendrocytes

Ataxia-telangiectasia (A-T) is a rare genetic disease caused by mutations in the gene encoding the ATM (Ataxia-telangiectasia mutated) protein. Although neuronal degeneration in the cerebellum remains the most prominent sign in A-T, neuroimaging studies reveal myelin abnormalities as early comorbidities. We hypothesize that these myelin defects are the direct consequence of ATM deficiencies in the oligodendrocytes (OL) lineage. We examined samples from ten A-T brains in which the ATM mutations had been mapped by targeted genomic sequencing as well as samples from Atm-/- mice. In healthy human and wild type mouse cerebellum we confirmed the presence of ATM in white matter OLs. In A-T but not age-matched controls, a significant reduction in OL density was coupled with a massive astrogliosis. We found that the extent of this OL pathology was particularly strong in cases with frameshifts or premature termination ATM mutations. Similar pathologies were also found in Atm-/- mice in an age- and gene dose-dependent fashion. In vitro, DNA damage induced by etoposide-induced DSBs or blockade of ATM activity with KU-60019 differentially jeopardized cell cycle control in OL progenitors and mature OLs. Turning to structural analysis in silico, we identified likely interactions between ATM and myelin basic protein as well as myelin regulatory factor and confirm this by immunoprecipitation. These novel OL-specific functions of the ATM protein affect all stages of the OL lineage. They thus provide a cell biological basis for a direct role for ATM in central nervous system myelination and illustrate how the myelin pathology found in A-T is at least in part independent of neuronal degeneration.
Characterization of Native and Modified Starches by Potentiometric Titration

The use of potentiometric titration for the analysis and characterization of native and modified starches is highlighted. The polyelectrolytic behavior of oxidized starches (thermal and thermal-chemical oxidation), a graft copolymer of itaconic acid (IA) onto starch, and starch esters (mono- and diester itaconate) was compared with the behavior of native starch, the homopolymer, and the acid employed as a graft monomer and substituent. Starch esters showed higher percentages of acidity, followed by graft copolymer of itaconic acid and finally oxidized starches. Analytical techniques and synthesis of modified starches were also described.
Psychiatric, Emotional, and Brain Volumetric Footprints of Childhood Conduct in Healthy Young Adults

Background: Conduct Disorder (CD) is defined as aggressive, antisocial, and rule-breaking behavior during childhood. Despite being the major risk factor for developing an antisocial personality disorder, nearly half the patients return to a nearly normal and healthy status. We aimed to identify psychiatric, emotional, and brain volumetric footprints of childhood CD in healthy young adults with a prior history of CD. Methods: We recruited 156 healthy young adults from the Human Connectome Project: 78 with a prior history of CD (CC) and 78 age-, gender-, and education-matched subjects with no history of CD (HC). Psychiatric, emotional, and personality assessments were based on the Adult Self Report, NIH toolbox emotion battery and Penn emotion recognition task, and NEO-Five Factor Inventory. We acquired MRI volumetric measures and hippocampal and amygdala segmentation values using FreeSurfer. Between-group differences and associations between the assessments and the hippocampal or amygdala nuclei were assessed statistically. Results: After Benjamini-Hochberg correction, we found higher anger aggression, antisocial personality problems, aggressive and rule-breaking behaviors, externalizing, and lower agreeableness in the CC group. No differences between volumetric measures were noted. Interestingly, only in the CC group, better angry face recognition was associated with larger volumes of several hippocampal nuclei. Discussion: Despite having no notable MRI volumetric differences, healthy young adults with a prior history of CD still exhibit some forms of antisocial-like behavior, without evidence of emotional recognition disturbances. Moreover, hippocampal-related learning may provide a compensatory mechanism for angry face recognition deficits in CD, playing a potential protective role.
Two original observations concerning bacterial infections in COVID-19 patients hospitalized in intensive care units during the first wave of the epidemic in France

Among 197 COVID-19 patients hospitalized in ICU, 88 (44.7%) experienced at least one bacterial infection, with pneumonia (39.1%) and bloodstream infections (15,7%) being the most frequent. Unusual findings include frequent suspicion of bacterial translocations originating from the digestive tract as well as bacterial persistence in the lungs despite adequate therapy