Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Communication Technology Integration in School Systems and Management of Secondary Schools in Nakawa Division, Uganda

The purpose of the study was to examine the effect of information communication technology integration in school systems on the management of secondary schools in Nakawa Division. The study was guided by three specific objectives aimed at examining the effect of ICT integration in school systems on 1) managerial communication 2) financial management and 3) supervision and monitoring in secondary schools in Nakawa Division. These were followed by consistent research questions. The study adopted a cross-sectional survey design with a quantitative approach. The target population of the study was composed of 220 teachers spread across five (5) government aided secondary schools with a final sample of 195 teachers selected using a simple random sampling realized through a fish-bowl technique. A self-designed closed-ended questionnaire was administered to collect data. The CVI obtained for the instrument was 0.87 and a reliability index of . 818 Cronbach alpha coefficient premised on 20 items. Data was analysed using frequencies, percentages and tables. Meanwhile, a simple regression analysis was adopted to establish the effect of ICT integration in school systems on management of secondary schools. Findings indicate that ICT usage in school systems had a very strong significant effect on managerial communication (β = 0.380, p< 0.05), financial management (β = 0.560, p< 0.05) and supervision and monitoring (β = 0.411, p< 0.05). In view of the findings, the study concludes that ICT offers a flexible and more simplified approach to task accomplishment in management of secondary schools. The study thus recommends that schools ought to improve on the timing of communication of urgent school matters to the community and school heads need to find ways of installing security and alert systems in schools as well as utilizing them efficiently. Further research is proposed on ICT integration and other management functions, focus on other school personnel as well as a purely qualitative
mTOR inhibition represses transcription of cholesterol biosynthesis genes and alters cholesterol levels in primary neurons and in the developing brain

Dysregulated mammalian target of rapamycin (mTOR) activity is associated with various neurodevelopmental disorders ranging from idiopathic autism spectrum disorders to syndromes caused by single gene defects. This suggests that maintaining mTOR activity levels in a physiological range is essential for brain development and functioning. Upon activation, mTOR regulates a variety of cellular processes such as cell growth, autophagy and metabolism. On a molecular level, however, the consequences of mTOR activation in the brain are not well understood. We here describe numerous genes of the sterol/cholesterol biosynthesis pathway to be transcriptionally downregulated by mTOR complex 1 (mTORC1) inhibition in vitro in primary neurons and in vivo in the developing cerebral cortex of the mouse. These genes were shared targets of the transcription factors SREBP, SP1 and NF-Y. Reduced expression of these genes directly translated into reduced cholesterol levels pointing towards a substantial metabolic function of the mTORC1 signaling cascade. Altogether, our results indicate that mTORC1 is an essential transcriptional regulator of the expression of sterol/cholesterol biosynthesis genes in the developing brain. Altered expression of these genes may be an important factor contributing to the pathogenesis of neurodevelopmental disorders associated with dysregulated mTOR signaling.
The Human Ski Complex Prevents DNA-RNA Hybrid-Associated Telomere Instability

Super killer (SKI) complex is a well-known cytoplasmic 3′ to 5′ mRNA decay complex that functions with the exosome to degrade excessive and aberrant mRNAs. Recently, SKIV2L, the 3′ to 5′ RNA helicase of the human SKI (hSKI) complex has been implicated in the degradation of nuclear non-coding RNAs escaping to the cytoplasm. Here, we show that hSKI is present in the nucleus, on chromatin and in particular at telomeres during the G2 cell cycle phase. In cells, SKIV2L prevents telomeric loss, and DNA damage response activation, and its absence leads to DNA-RNA hybrid-mediated telomere fragility. Moreover, we demonstrate that purified hSKI complex preferentially unwinds telomeric DNA-RNA hybrids in vitro. Taken together, our results provide a nuclear function of the hSKI complex in overcoming replication stress caused by aberrant processing of telomeric DNA-RNA hybrids and thus maintaining telomere stability.
STUDIES ON PRESERVATION OF SUGARCANE JUICE

ABSTRACT The variety CoP 92226 was selected for preparing sugarcane juice beverage on the basis of yield and sensory attributes from eight promising varieties of sugarcane. Sugarcane juice beverage samples were prepared by pasteurizing the sugarcane juice at 70°C for 10 minutes and adding citric acid (40 mg/100 ml), ascorbic acid (40 mg/100 ml) and potassium metabisulphite (150 ppm). Samples of sugarcane juice beverage were stored at room (30±5°C) and refrigeration (4±2°C) temperature in pre-sterilized glass bottles and analyzed for physico-chemical, microbiological and sensory attributes at every 15 days interval for 90 days. The pH, total soluble solids and total sugars decreased, whereas, titratable acidity and reducing sugars increased significantly (P<0.01) during storage. An appreciable increase in total plate counts and yeast and mold counts were observed, however, no coliforms, were detected in sugarcane juice beverage during storage. The changes in different attributes were significantly (P<0.01) higher at room temperature as compared to refrigeration temperature. The sugarcane juice beverage having citric acid and potassium metabisulphite showed minimum changes in sensory qualities during storage, both at room and refrigeration temperature. An acceptable quality beverage of sugarcane juice with satisfactory storage stability for 90 days at room as well as refrigeration temperature could be prepared.
Injury Severity Score (ISS) versus Revised Trauma Score (RTS) as a predictive value and outcome in polytrauma patients

Back ground: Trauma score are used to obtain a numerical description of the severity of an individual's injuries and clinical condition, which in turn is associated with prognosis. Trauma score that used in predicting mortality among polytraumatized patients could be anatomical or physiological or combined. The Injury severity score (ISS) is the most commonly used measure of injury severity. It is an anatomical scoring system that provides an overall score for patients with multiple injuries. Each injury is assigned an Abbreviated Injury Scale (AIS) score. The Revised Trauma score (RTS) is a physiological scoring system with high inter-rater reliability and demonstrated accuracy in predicting death. The present study aimed to study outcome value of RTS in comparison to ISS and try to find a relation between initial scores and their outcome. Fifty patients randomly collected presented to the Critical Care Departments. The Injury Severity Score (ISS) and Revised Trauma Score (RTS) was calculated ,the patients had a mean ± SD age of 37.04 ± 13.42 years. They comprised 42 males (84%) and 8 females (16%). The major mechanism of injury in polytrauma patient was road traffic accidents (76.0%). The majority of cases in the age group 20 to 49 (72%). Males were 3 times more affected than females. The most common cause of death was respiratory. RTS was better than ISS in predicting mortality among polytraumatized patients.
Risk factors for postpartum depression among Egyptian women

Background: Postpartum depression (PPD) is a severe health issue and is considered to have a negative effect on the wellbeing of mothers and on the physical and cognitive growth of children.Literatures on postpartum depression were confined in Egypt.The goals of this study are to establish the prevalence of postpartum depression as well as the underlying risk factors. Objective: to assess the prevalence of postpartum depression and the risk factors that contributed to PPD. Subjects and methods: From February 2019 to July2019 a cross-sectional study was conducted involving 120 women whose youngestchild(ren) was less than 1 year old from obstetric outpatient clinic in Al-Zahraa University Hospital, Cairo, Egypt.The PPD symptoms were measured using an Arabic version of the Edinburgh Postnatal Depression Scale (EPDS). Results: Postpartum depressionprevalence among 120 mothers (above the age of 18) was 27.5% in the first year after birth. Women who had suffered of domestic violence [OR: 6.4, 95% CI: 2.5, 15.3], previously diagnosed with postpartum depression [OR: 5.5, 95% CI: 1.6, 17.9], experienced stressful life events [OR: 3.6, 95% CI: 1.4, 8.1] and difficulty in interaction with others when struggling [OR: 4.1, 95% CI: 1.7, 9.1] had greater odds of reporting PPD. Conclusion: Postpartum depression (PPD) is a widespread, postnatal mental illness.Many factors may cause PPD to occur.Early detection of such factors can help in predicting PPD development.
Impact of Electron-Phonon Interaction on Thermal Transport: A Review

ABSTRACT A thorough understanding of the microscopic picture of heat conduction in solids is critical to a broad range of applications, from thermal management of microelectronics to more efficient thermoelectric materials. The transport properties of phonons, the major microscopic heat carriers in semiconductors and insulators, particularly their scattering mechanisms, have been a central theme in microscale heat conduction research. In the past two decades, significant advancements have been made in computational and experimental efforts to probe phonon-phonon, phonon-impurity, and phonon-boundary scattering channels in detail. In contrast, electron-phonon scatterings were long thought to have negligible effects on thermal transport in most materials under ambient conditions. This article reviews the recent progress in first-principles computations and experimental methods that show clear evidence for a strong impact of electron-phonon interaction on phonon transport in a wide variety of technologically relevant solid-state materials. Under thermal equilibrium conditions, electron-phonon interactions can modify the total phonon scattering rates and renormalize the phonon frequency, as determined by the imaginary part and the real part of the phonon self-energy, respectively. Under nonequilibrium transport conditions, electron-phonon interactions can affect the coupled transport of electrons and phonons in the bulk through the “phonon/electron drag” mechanism as well as the interfacial thermal transport. Based on these recent results, we evaluate the potential use of electron-phonon interactions to control thermal transport in solids. We also provide an outlook on future directions of computational and experimental developments.
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data

Many methods now exist for conditioning models on task instructions and user-provided explanations for individual data points. These methods show great promise for improving task performance of language models beyond what can be achieved by learning from individual (x,y) pairs. In this paper, we (1) provide a formal framework for characterizing approaches to learning from explanation data, and (2) we propose a synthetic task for studying how models learn from explanation data. In the first direction, we give graphical models for the available modeling approaches, in which explanation data can be used as model inputs, as targets, or as a prior. In the second direction, we introduce a carefully designed synthetic task with several properties making it useful for studying a model’s ability to learn from explanation data. Each data point in this binary classification task is accompanied by a string that is essentially an answer to the why question: “why does data point x have label y? ” We aim to encourage research into this area by identifying key considerations for the modeling problem and providing an empirical testbed for theories of how models can best learn from explanation data.
Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations (including shapes and intensities) of different unlabeled MRI images with respect to the atlas via 3D variational autoencoders (VAEs). In this manner, our method is able to exploit the learned distributions of image deformations to generate new authentic brain MRI images, and the number of generated samples will be sufficient to train a deep segmentation network. Furthermore, we introduce a new standard segmentation benchmark to evaluate the generalization performance of a segmentation network through a cross-dataset setting (collected from different sources). Extensive experiments demonstrate that our method outperforms the state-of-the-art one-shot medical segmentation methods. Our code has been released at https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.
Introduction to Neural Transfer Learning with Transformers for Social Science Text Analysis

Transformer-based models for transfer learning have the potential to achieve high prediction accuracies on text-based supervised learning tasks with relatively few training data instances. These models are thus likely to benefit social scientists that seek to have as accurate as possible text-based measures but only have limited resources for annotating training data. To enable social scientists to leverage these potential benefits for their research, this paper explains how these methods work, why they might be advantageous, and what their limitations are. Additionally, three Transformer-based models for transfer learning, BERT (Devlin et al. 2019), RoBERTa (Liu et al. 2019), and the Longformer (Beltagy et al. 2020), are compared to conventional machine learning algorithms on three applications. Across all evaluated tasks, textual styles, and training data set sizes, the conventional models are consistently outperformed by transfer learning with Transformers, thereby demonstrating the benefits these models can bring to textbased social science research.
Structure of the correlation coefficients 
S(Ee)
 and 
U(Ee)
 of the neutron 
β
 decay

A. N. Ivanov, ∗ R. Höllwieser, 2, † N. I. Troitskaya, ‡ M. Wellenzohn, 3, § and Ya. A. Berdnikov ¶ Atominstitut, Technische Universität Wien, Stadionallee 2, A-1020 Wien, Austria Department of Physics, New Mexico State University, Las Cruces, New Mexico 88003, USA FH Campus Wien, University of Applied Sciences, Favoritenstraße 226, 1100 Wien, Austria 4 Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya 29, 195251, Russian Federation (Dated: March 24, 2021)
Inference of the Selective Auditory Attention Using Sequential LMMSE Estimation

Attentive listening in a multispeaker environment such as a cocktail party requires suppression of the interfering speakers and the noise around. People with normal hearing perform remarkably well in such situations. Analysis of the cortical signals using electroencephalography (EEG) has revealed that the EEG signals track the envelope of the attended speech stronger than that of the interfering speech. This has enabled the development of algorithms that can decode the selective attention of a listener in controlled experimental settings. However, often these algorithms require longer trial duration and computationally expensive calibration to obtain a reliable inference of attention. In this paper, we present a novel framework to decode the attention of a listener within trial durations of the order of two seconds. It comprises of three modules: 1) Dynamic estimation of the temporal response functions (TRF) in every trial using a sequential linear minimum mean squared error (LMMSE) estimator, 2) Extract the N1$-$P2 peak of the estimated TRF that serves as a marker related to the attentional state, and 3) Obtain a probabilistic measure of the attentional state using a support vector machine followed by a logistic regression. The efficacy of the proposed decoding framework was evaluated using EEG data collected from 27 subjects. The total number of electrodes required to infer the attention was four: One for the signal estimation, one for the noise estimation and the other two being the reference and the ground electrodes. Our results make further progress towards the realization of neuro-steered hearing aids.
Delayed winter spur‐pruning in New Zealand can alter yield components of Merlot grapevines

The effects of delayed winter spur-pruning on yield, yield components and fruit composition of Merlot vines was evaluated within a commercial vineyard in Marlborough, New Zealand. Vines were spur-pruned, trained using vertical shoot positioning, trickle irrigated and grafted to Kober 5BB rootstock. Delaying pruning from July (usual winter pruning time in New Zealand) until up to October (when apical shoots on canes were 5 cm long) resulted in yield increases of up to 93%, 63%, and 82% over the three seasons of experimentation. These yield increases reflected higher average berry weight and in turn bunch weights. The increase in average berry weight was associated with a change in the relative abundance of different berry types. Later pruning increased the proportion of large seeded berries while the proportion of smaller seedless berries and live green ovaries (shot berries) decreased. The delayed pruning may have enhanced fertilisation of flowers and development of seeds by postponing flowering to a time when climatic conditions are more favourable; measurements of shoot lengths support this proposal. By delaying winter pruning until after budburst the development of basal nodes was inhibited, so that budburst on the resulting spurs was also delayed.
The limit empirical spectral distribution of complex matrix polynomials

We study the empirical spectral distribution (ESD) for complex [Formula: see text] matrix polynomials of degree [Formula: see text] under relatively mild assumptions on the underlying distributions, thus highlighting universality phenomena. In particular, we assume that the entries of each matrix coefficient of the matrix polynomial have mean zero and finite variance, potentially allowing for distinct distributions for entries of distinct coefficients. We derive the almost sure limit of the ESD in two distinct scenarios: (1) [Formula: see text] with [Formula: see text] constant and (2) [Formula: see text] with [Formula: see text] bounded by [Formula: see text] for some [Formula: see text]; the second result additionally requires that the underlying distributions are continuous and uniformly bounded. Our results are universal in the sense that they depend on the choice of the variances and possibly on [Formula: see text] (if it is kept constant), but not on the underlying distributions. The results can be specialized to specific models by fixing the variances, thus obtaining matrix polynomial analogues of results known for special classes of scalar polynomials, such as Kac, Weyl, elliptic and hyperbolic polynomials.
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users

In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bias: the tendency of a recommender system to provide better recommendations to users who have a mainstream taste, as opposed to non-mainstream users. We propose NAECF, a conceptually simple but effective idea to address this bias. The idea consists of adding an autoencoder (AE) layer when learning user and item representations with text-based Convolutional Neural Networks. The AEs, one for the users and one for the items, serve as adversaries to the process of minimizing the rating prediction error when learning how to recommend. They enforce that the specific unique properties of all users and items are sufficiently well incorporated and preserved in the learned representations. These representations, extracted as the bottlenecks of the corresponding AEs, are expected to be less biased towards mainstream users, and to provide more balanced recommendation utility across all users. Our experimental results confirm these expectations, significantly improving the recommendations for non-mainstream users while maintaining the recommendation quality for mainstream users. Our results emphasize the importance of deploying extensive content-based features, such as online reviews, in order to better represent users and items to maximize the de-biasing effect.
METAL MOVEMENT IN SLUDGE‐TREATED SOILS AFTER SIX YEARS OF SLUDGE ADDITION: 2. NICKEL, COBALT, IRON, MANGANESE, CHROMIUM, AND MERCURY

Movement of the metals Ni, Co, Fe, Mn, Cr, and Hg in Dublin loam soil was studied after 6 yr of sludge addition. Sludges from two sources were added annually at rates ranging from 0 to 225 metric tons per hectare (t/ha) in increments of 45 t. Increasing rates of sludge addition resulted in almost no change in the concentration of metals in the surface soil, with the exception of Cr and Hg, where the metal concentrations in the added sludge were markedly greater than those found in the untreated soil. In these cases metal increased linearly with rate of sludge addition. Surface concentrations of HNO3-extracted Mn were less than those obtained at lower depths. The low values appeared to be due to the failure of HNO3 to extract all the Mn from sludge-treated soils. Only Fe of all elements increased significantly in the profile with time. No element moved more than 5 cm below the zone of sludge incorporation in either the highly acid (pH 4.5) Oakland or the near-neutral (pH 7.3) Pacheco sludge-treated soils. Relative metal availability followed the same order for both sludges, i.e., Mn > Co = Ni ≫ Fe > Cr. Percentage of available metal in Oakland sludge-treated plots was nearly double that in Pacheco sludge-treated plots. Percentage availability of Fe and Cr was extremely low.
TEyeD: Over 20 Million Real-World Eye Images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types

We present TEyeD, the world’s largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoor activities. The data set includes 2D&3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images. Landmarks and semantic segmentation are provided for the pupil, iris and eyelids. Video lengths vary from a few minutes to several hours. With more than 20 million carefully annotated images, TEyeD provides a unique, coherent resource and a valuable foundation for advancing research in the field of computer vision, eye tracking and gaze estimation in modern VR and AR applications. Data and code at DOWNLOAD LINK.