Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational Statistics and Data Analysis

Gaussian graphical models are promising tools for analysing genetic networks. In many applications, biologists have some knowledge of the genetic network and may want to assess the quality of their model using gene expression data. This is why one introduces a novel procedure for testing the neighborhoods of a Gaussian graphical model. It is based on the connection between the localMarkov property and conditional regression of aGaussian random variable. Adapting recent results on tests for high-dimensional Gaussian linear models, one proves that the testing procedure inherits appealing theoretical properties. Besides, it applies and is computationally feasible in a high-dimensional setting: the number of nodes may be much larger than the number of observations. A large part of the study is devoted to illustrating and discussing applications to simulated data and to biological data. © 2008 Elsevier B.V. All rights reserved.
A study on the effects of climate change on viticulture on Santorini Island

The Mediterranean basin is regarded as one of the regions the most affected by climate change in the world. Traditionally, viticulture in this region copes with high temperatures, heat waves and drought. Such particularly extreme conditions, which induce severe abiotic stress on plants, are expected to intensify due to the predicted climate changes in the future. Our study focuses on the viticulture of Santorini Island, located in South Aegean (Greece). Vines have been cultivated on Santorini Island for thousands of years on their own roots owing to the phylloxera-free volcanic soil of the island. The vineyards of Mediterranean regions are already encountering difficulties because of ongoing climate change. The aim of this study was to analyse chronological climate data, evaluate the trends in the change of climate parameters and bioclimatic indices and correlate them with viticultural indices. In brief, the average annual temperature has increased in the last 45 years by almost 4 °C and a significant increase in the frequency of days with high temperatures has been recorded. Bioclimatic indicators correspond to warmer climates with warmer nights and longer periods of drought. Finally, it appears that the high temperatures occurring during the critical stages of the development and differentiation of the vine seem to affect production in the following growing season, while earlier harvest dates and higher sugar content than 45 years ago are observed.
The Role of Technology in English Language Learning in Online Classes at Tertiary Level

: The primary objective of the study is to investigate the relationship between the TAM (Technology Acceptance Model) factors and online applications usage for language learning. Additionally, the influence of gender and prior technology use practice have been investigated by the study. In order to comprehend the user's responses to the technology used for language learning, this study used a quantitative approach and a TAM questionnaire to collect data. The study's participants are one hundred undergraduate English as a second language students who took online courses during the COVID-19 pandemic. Students' behavioral intentions, attitudes, and app use were found to be significantly positively correlated in the research results. Besides, a positive affiliation exists between the factors; seen usability, perceived use, genuine use, and behavior. In addition, the experience has been found to be positively correlated with TAM factors, whereas gender does not appear to be associated with any TAM factors. The study suggested using technology in the classroom to make learning a second language more fun for students.
Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching

Introduction Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs. Methods We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient. Results Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2. Discussion This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs.
PHYSICAL, CHEMICAL AND OTHER PHENOMENA, AS THE BASES OF SENSORS

Within the gauge-invariant quantum electrodynamics perturbation theory it is presented a new relativistic approach to definition of the thermal photoionization characteristics for the Rydberg atoms, in particular, the Rydberg atoms, ionized by the Blackbody radiation. Theory of corresponding phenomena in the Rydberg systems is a basis for creation of new class of the atomic sensors.
Factors associated with excess all-cause mortality in the first wave of the COVID-19 pandemic in the UK: A time series analysis using the Clinical Practice Research Datalink

Background Excess mortality captures the total effect of the Coronavirus Disease 2019 (COVID-19) pandemic on mortality and is not affected by misspecification of cause of death. We aimed to describe how health and demographic factors were associated with excess mortality during, compared to before, the pandemic. Methods and findings We analysed a time series dataset including 9,635,613 adults (≥40 years old) registered at United Kingdom general practices contributing to the Clinical Practice Research Datalink. We extracted weekly numbers of deaths and numbers at risk between March 2015 and July 2020, stratified by individual-level factors. Excess mortality during Wave 1 of the UK pandemic (5 March to 27 May 2020) compared to the prepandemic period was estimated using seasonally adjusted negative binomial regression models. Relative rates (RRs) of death for a range of factors were estimated before and during Wave 1 by including interaction terms. We found that all-cause mortality increased by 43% (95% CI 40% to 47%) during Wave 1 compared with prepandemic. Changes to the RR of death associated with most sociodemographic and clinical characteristics were small during Wave 1 compared with prepandemic. However, the mortality RR associated with dementia markedly increased (RR for dementia versus no dementia prepandemic: 3.5, 95% CI 3.4 to 3.5; RR during Wave 1: 5.1, 4.9 to 5.3); a similar pattern was seen for learning disabilities (RR prepandemic: 3.6, 3.4 to 3.5; during Wave 1: 4.8, 4.4 to 5.3), for black or South Asian ethnicity compared to white, and for London compared to other regions. Relative risks for morbidities were stable in multiple sensitivity analyses. However, a limitation of the study is that we cannot assume that the risks observed during Wave 1 would apply to other waves due to changes in population behaviour, virus transmission, and risk perception. Conclusions The first wave of the UK COVID-19 pandemic appeared to amplify baseline mortality risk to approximately the same relative degree for most population subgroups. However, disproportionate increases in mortality were seen for those with dementia, learning disabilities, non-white ethnicity, or living in London.
A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty

The paper aims to present a multi-product, multi-stage, multi-period, and multi-objective, probabilistic mixed-integer linear programming model for a sustainable forward and reverse logistics network problem. It looks at original and return products to determine both flows in the supply chain -forward and reverse-simultaneously. Besides, to establish centres of forward and reverse logistics activities and make a decision for transportation strategy in a more close-to-real manner, the demand is considered uncertain. We attempt to represent all major dimensions in the objective functions: First objective function is minimizing the processing, transportation, fixed establishing cost and costs of CO 2 emission as environmental impacts. Furthermore, the processing time of reverse logistics activities is developed as the second objective function. Finally, in the third objective function, it is tried to maximize social responsibility. Indeed, a complete sustainable approach is developed in this paper. In addition, this model provides novel environmental constraint and social matters in the objective functions as its innovation and contribution. Another contribution of this paper is using probabilistic programming to manage uncertain parameters. Moreover, a non-dominated sorting genetic algorithm (NSGA-II) is configured to achieve Pareto front solutions. the performance of the NSGA-II is compared with a multi-objective particle swarm optimization (MOPSO) by proposing 10 appropriate test problems according to five comparison metrics using analysis of variance (ANOVA) to validate the modeling approach.Overall, according to the results of ANOVA and the comparison metrics, the performance of NSGA-II algorithm is more satisfying compared with that of MOPSO algorithm.
CODE-MM: Convex Deep Mangrove Mapping Algorithm Based on Optical Satellite Images

Mangrove mapping (MM) is a critical satellite remote sensing technology since mangrove forests have a large capacity for carbon storage among the blue carbon ecosystems. However, we surprisingly found that benchmark MM methods are all index-based ones, completely ignoring the spatially neighboring information on the one hand and quite sensitive to the threshold setting on the other hand. Deep learning has been proven to be an effective solution for incorporating the desired spatial information, but the induced big data collection of MM is difficult and time-consuming, especially for ground-truth labeling; this would be the reason why benchmark methods are all index-based ones. To solve the dilemma, we introduce convex analysis into deep learning, thereby achieving small-data learning. The proposed algorithm is hence termed convex deep MM (CODE-MM), mainly developed for the Sentinel-2 satellite, which is the mainstream satellite for the MM mission, as it involves those key green/infrared bands for characterizing mangrove multispectral signatures. We also generalize our CODE-MM to test the hyperspectral satellite data, which should be the trend for various classification missions in the future due to its strong material identifiability. Simply speaking, CODE-MM first infers a rough mangrove signature for designing a Siamese deep regularizer, which is then plugged into a convex criterion customized for the mapping task. We implement the convex criterion by deriving closed-form solutions for all the algorithmic steps, ensuring computational efficiency. Extensive experiments demonstrate that CODE-MM is insensitive to the threshold setting and yields state-of-the-art performance in accurate mangrove forest mapping.
CeO2 SiO2 Hyperbranched polyethyleneimine towards the development of homogeneous and highly porous CuO-CeO2-SiO2 catalytic materials

A novel templating strategy for the development of composite nanoporous CuO/CeO2/SiO2 materials with advanced properties and significant thermal stability is proposed in this work, for potential use in automotive catalysts. The synthetic process is based on the dual role of low cost hyperbranched polyethyleneimine (PEI) acting both as a reactive template to control the formation of a siliceous porous network and as a metal chelating agent to facilitate the incorporation of finely dispersed CuO/CeO2 nanoparticles into the silica matrix. Direct Ce(III)/Cu(II) introduction into the reaction mixture or post-synthetic impregnation of a preformed hybrid organic/inorganic xerogel was employed. Materials properties were assessed by means of XRD, SEM, TEM, N2 adsorption, TPR and XPS techniques. Final Cu loading was determined by a novel UV-Vis spectroscopic method. Activity tests were performed under stoichiometric, O2-deficient and O2-rich conditions simulating realistic exhaust environment. Catalytic performance was found to be strongly depended on the employed synthetic pathway. The CuO/CeO2/SiO2 composites prepared by direct introduction, exhibited enhanced structural characteristics (BET≈ 680 m 2 /g), high homogeneity, superior oxidation performance and notable thermal stability, with great potential for use in automotive aftertreatment systems.
Prediction of Acute Hypotensive Episode in ICU Using Chebyshev Neural Network

An Acute Hypotensive Episode (AHE) is a serious threat to the lives of Intensive Care Unit (ICU) patients. The proposed method of accurately predicting an AHE will allow doctors to make a timely and effective intervention; therefore, it has a high clinical value. In recent years, the Chebyshev neural network model has been favorably applied in other fields. In this study, we built a Chebyshev neural network based on pattern recognition to predict AHE in ICU patients. We preprocessed the arterial blood pressure (ABP) data of the ICU patients, and then, extracted time-domain signal features from the data to construct the feature vector. We trained the neural network using a classified predictive model to predict AHEs. We used the classic BP neural network and its improved versions for comparison. Our experimental results show that our Chebyshev neural network model performs better than other solutions in predicting AHEs; therefore, it can provide a reference for clinical applications.
Incidence of Hand-Foot Syndrome in Patients with Breast Cancer on Capecitabine Treatment

Majeed AAA1*, Sahib AS2, Abbas RF1 and Mohsin KK3 1Post-graduate Student, Department of Pharmacy, University of Kerbala, Kerbala-Iraq 2Professor of Pharmacology at the College of Pharmacy-University of Kerbala-Iraq, Dean of the College of Pharmacy, University of Karbala-Iraq, Iraq 3Subspecialty Endocrine Imam Hussein Hospital Kerbala, Iraq Incidence of Hand-Foot Syndrome in Patients with Breast Cancer on Capecitabine Treatment
Biochemistry of Tau in Alzheimer’s disease and related neurological disorders

Microtubule-associated Tau proteins belong to a family of factors that polymerize tubulin dimers and stabilize microtubules. Tau is strongly expressed in neurons, localized in the axon and is essential for neuronal plasticity and network. From the very beginning of Tau discovery, proteomics methods have been essential to the knowledge of Tau biochemistry and biology. In this review, we have summarized the main contributions of several proteomic methods in the understanding of Tau, including expression, post-translational modifications and structure, in both physiological and pathophysiological aspects. Finally, recent advances in proteomics technology are essential to develop further therapeutic targets and early predictive and discriminative diagnostic assays for Alzheimer’s disease and related disorders.
Elite neutralizers of human cytomegalovirus are characterized by high magnitude plasma IgG responses against multiple glycoprotein complexes

Background. Human cytomegalovirus (HCMV) is the most common infectious complication of organ transplantation and cause of birth defects worldwide. There are limited therapeutic options and no licensed vaccine to prevent HCMV infection or disease. To inform development of HCMV antibody-based interventions, a previous study identified individuals with potent and broad plasma HCMV-neutralizing activity, termed elite neutralizers (EN), from a cohort of HCMV-seropositive (SP) blood donors. Yet, the specificities and functions of plasma antibodies associated with EN status remained undefined. Methods. We sought to determine the plasma antibody specificities, breadth, and Fc-mediated antibody effector functions associated with the most potent HCMV-neutralizing responses in plasma from EN (n=25) relative to SP (n=19). We measured antibody binding against various HCMV strains and glycoprotein targets, and evaluated Fc-mediated effector functions, antibody dependent cellular cytotoxicity (ADCC) and antibody dependent cellular phagocytosis (ADCP). Results. We demonstrate that elite HCMV neutralizers have elevated IgG binding responses against multiple viral glycoproteins, relative to SP. Our study also revealed potent HCMV-specific ADCC and ADCP activity of EN plasma, Conclusions. We conclude that antibody responses against multiple glycoprotein specificities may be needed to achieve potent plasma neutralization and that potently HCMV elite-neutralizing plasma antibodies can also mediate polyfunctional responses.
Efficient Uncertainty Quantification of Deterministic Wireless Channel Models Using Polynomial Chaos Expansion

Uncertainty in the input speciﬁcation for deterministic channel models such as ray-tracing introduces variations in the predicted signal strength. This necessitates the quantiﬁ-cation and analysis of the impact of input uncertainties on the channel models, as a means of ensuring performance robustness. The polynomial chaos expansion (PCE) method has emerged as a promising uncertainty quantiﬁcation technique compared to the commonly used yet computationally inefﬁcient Monte Carlo methods. However, PCE-based methods generally suffer from a “curse of dimensionality”, where the computational cost increases rapidly with the number of random variables included in the analysis. This paper applies an orthogonal matching pursuit algorithm to mitigate the computational cost of PCE and facilitate the uncertainty analysis of ray-tracing based channel models. The performance is demonstrated in an indoor environment and validated against Monte Carlo simulations and experimental measurements.
Biometrics Security and Internet of Things (IoT)

The human-to-machine and human-to-human communications are transforming to machine-to-machine communications by which several decision-making systems can be built. When different Internet enabled smart devices interact with each other’s to achieve a goal (application depended), then a network is formed in which different sophisticated technologies will integrate to each other to form Internet of Things (IoT). It encompasses the vast amount of diverse smart devices, which collaborate with each other to achieve different smart applications like, smart cities, connected cars, automated agriculture and so on. Though Radio Frequency Identification (RFID), Wireless, mobile and sensor technologies make IoT feasible, but it suffers from many challenges like scalability, security, and heterogeneity problems. Out of many challenges, security is one of the primary concerns in IoT. Without proper security and privacy, the business model of IoT will not succeed. This chapter discusses the secure solutions for IoT using biometric features of users as well as end users. The chapter will demonstrate that biometric security is most feasible, reliable and efficient with respect to other existing security arrangements.
Differentiation Learning Training in Projects as an Implementation Strategy for the Merdeka Curriculum at Yas'a Sumenep Middle School

One way of student-centered learning is by implementing differentiated learning. Although this differentiated learning is not new, teaching and learning activities are still rarely implemented, including in the Yas'a Sumenep Middle School, so there is a need for community service in the form of differentiation learning training in projects as a strategy for implementing the Merdeka Curriculum at Yas'a Sumenep Middle School. The purpose of this activity is (1) to provide knowledge to educators and stakeholders at Yas'a Sumenep Middle School about differentiation learning and projects; (2) to train the skills of Yas'a Sumenep Middle School educators in developing differentiation learning and projects to improve the quality of learning. The method in this activity includes (a) expository; (b) question and answer; (c) discussion; (d) demonstrations; (e) FGD. The results of community service activities obtained that knowledge of differentiation learning and projects were in the high category of 57 people and as many as 3 people in the medium category, as well as increasing skills in developing differentiation learning and projects in the high category of 52 people and as many as 8 people. The results of these activities can be concluded that the differentiation learning training activities in the project as an implementation strategy for the Merdeka Curriculum at Yas'a Sumenep Middle School can provide knowledge to educators and stakeholders about differentiation learning and projects in the Merdeka Curriculum, as well as to train skills educators in developing differentiation learning and projects to improve the quality of learning
Streamlining xt e-ocessing and trics traction

Natural Language Processing involves reshaping and reﬁning data sets into data that can be used for analysis, ensuring that the data is well formatted. The efﬁciency gap of data scientists spending most of their time preparing data is an opportunity for the technology sector to work on solutions to the problem. For this reason, a web tool has been developed that is capable of, on the one hand, speeding up the text cleaning process and, on the other hand, facilitating the extraction of metrics by analyzing and processing the texts through customized dictionaries in LIWC format, uploaded by the users themselves, and through sentiment analysis. All this, from a single interface that allows the user to customize the whole pipeline offering different modules for pre-processing and metrics extraction in order to be a solution to facilitate, streamline and automate the whole process.
Simultaneous Transmit and Receive Self-Duplexing Antenna for Head Implants

A novel dual-port, dual-band self-duplexing implan- table antenna for head implants is presented in this article. It operates at 915 MHz Industrial, Scientific, and Medical (ISM) band (when Port 1 is active) and 1420 MHz Wireless Medical Telemetry Service (WMTS) band (when Port2 is active). The proposed antenna is kept inside a flat device and simulated in a human head model. The miniaturization of the proposed antenna is achieved using a high dielectric substrate, shorted pins, and multiple capacitive slots. Consequently, it occupies a compact volume of $7.13 \times 8.9 \times 0.13= 8.24$ mm 3. The coupling between both radiators is enhanced by printing it on a thin substrate and placing vias between them. As a result, an isolation level better than 31.4 dB is achieved. It has peak realized gains of −18.94 and −17.06 dBi at 915 and 1420 MHz, respectively. The link budget analysis and specific absorption rate (SAR) are performed, showing promising results. The proposed concept is practically validated by measuring its performance inside the minced pork meat. Furthermore, the simultaneous transmit and receive concept is practically verified with the aid of software-defined radio (SDR). The main advantages of this antenna are its compact size, low coupling level, independently controllable bands, and simultaneous transmission and reception of signals without using a multiplexer circuit.