Miracle-dz/UniFAR-FTUs huggingface dataset:
Autoimmune Manifestations Associated with COVID 19 Infection in Children: A Series of Three Cases From a Single Centre

Various immune-related manifestations are increasingly recognized in association with COVID-19. We describe a series of three pediatric cases, who presented with diverse autoimmune manifestations Immune Thrombocytopenic Purpura, Nephrotic Syndrome and Autoimmune Encephalitis, which occurred 2-3 weeks after testing positive for SARS-COV-2 infection. During this pandemic, it might be appropriate to consider SARS-COV-2 testing (including antibodies) in children presenting with diseases of presumed autoimmune etiology.
RELATIONSHIP BETWEEN THE NON-PERFORMING ASSETS AND MACROECONOMIC VARIABLES-A PANEL DATA ANALYSIS OF INDIAN BANKING SECTOR

This paper empirically evaluates the relationship between accumulation of NPA in banking sector and economic growth of Indian economy. The relationships between nonperforming assets of different bank groups and annual GDP, interest rate, inflation rate, and bank wise gross advances and bank wise gross assets are analysed using panel data framework. The study finds that GDP have significant impact on accumulation of NPA and interest rate also plays a significant role in accumulation of NPA in Indian banking sector. Bank specific both factors, gross assets and gross advances, are also significant for accumulation of nonperforming assets in Indian banking industry.
DOES POLITICAL CORRUPTION IN THE FOURTH REPUBLIC SHIFT NIGERIA TO THE WORLD`S POVERTY HEADQUARTERS? AN ERROR CORRECTION MODEL APPROACH

The aim of this study is to provide an empirical answer to the question whether political corruption in the fourth republic has shifted Nigeria to the World`s Poverty headquarters. The answer to this question has been controversial among the scholars and policymakers. In view of this, the study utilized secondary data from 2000 to 2019 with the application of an error correction model. The findings that originated from the study are summarized as follows; 59.9% of the error due to shocks in the previous period was corrected in the current year. Political corruption has an insignificant positive relationship with income inequality in the short run, but this relationship becomes significant in the long run. However, political corruption and poverty level in Nigeria have a significant positive relationship in the short run. In the long run, the relationship becomes negative, though insignificant. This implies that political corruption brings about a rise in poverty level in Nigeria. In other words, political corruption during the fourth republic has led Nigeria to the world`s poverty headquarters. Due to this important finding, recommendations were made for the policy makers in Nigeria as follows; tackling poverty in Nigeria requires policies that will address political corruption holistically. Therefore, the Nigerian government should strengthen antigraft institutions like ICPC and EFCC to curb corruption so that poverty menace could be addressed in the country.
ASSESSMENT OF NURSES KNOWLEDGE AND PRACTICE FOR NEPHROTIC CHILDREN IN A MILITARY HOSPITAL

509 ASSESSMENT OF NURSES KNOWLEDGE AND PRACTICE FOR NEPHROTIC CHILDREN IN A MILITARY HOSPITAL By RAGAEY MOHAMMADY and SUZAN ATTIA ELSHAHAT Consultant of Pathology and Nursing Pediatrician, Military Medical Academy, Cairo, 11291, Egypt (*Correspondence: ragapath@yahoo.com or majorsuzanattya@gmail.com) Abstract Nephrotic syndrome is a condition that causes the kidneys to leak large amounts of protein into the urine. This can lead to a range of problems, including swelling of body tissues and a greater chance of catching infections. This descriptive study assessed of nurses' knowledge and practice caring for nephrotic children. A convenience sample was 57 pediatric nurses caring children with nephrotic syndrome. Tools of data collection: Two tools were utilized for data collection; Tool I: Selfadministered questionnaire sheet: Tool II: An Observational checklists: adopted by the researcher for evaluation of nursing practices of children with nephrotic syndrome. The results showed that majority of the pediatric nurses had inadequate practice about nephrotic syndrome, but there were a positive correlation between practice, age, of pediatric experience and general experience.
Bacterial Hydrophobicity , an Overall Parameter for the Measurement of Adhesion Potential to Soil Particles

The adhesion of Salmonella typhimurium to the mineral particles quartz, albite, feldspar, and magnetite was shown to correlate with the hydrophobicity of the cell surface as measured by hydrophobic interaction chromatography. The same effects were also seen for seven other selected test strains, including Streptococcus faecalis, Streptococcus faecium, Escherichia coli, Citrobacter freundii, Shigella sonnei, and Shigella boydii. When the test strain of Salmonella typhimurium, was repeatedly cultivated in Luria broth, thus selecting for different degrees of fimbriation and roughness of the cell surface, varied cell hydrophobicity but constant negative and positive charge values were obtained. High hydrophobicity values always coincided with enhanced adhesion to the mineral particles. The negative charge of the bacterial surface as measured by electrostatic interaction chromatography appeared to play no role in the adhesion event. However, the positive charges on the cell surface contributed to the adhesion process. This was especially evident for cells exhibiting a high degree of hydrophobicity. Alteration of the pH between 4 and 9 did not significantly affect the adhesion process.
Residual Structural Equation Models

Abstract The residual variables in a structural equation model can be used to create a secondary structural model which we call the residual structural equation model (RSEM). We describe the maximum-likelihood, weighted least squares and Bayesian estimations for RSEM. The methodology is illustrated with several examples and simulation studies. We discuss the implementation of RSEM in the Mplus software package and provide scripts for the simulation studies. The RSEM framework is utilized to estimate and simplify popular models such as the random intercept cross-lagged panel model (RI-CLPM) and the latent curve model with structured residuals (LCM-SR). We discuss in details RSEM models with categorical observed variables as well as categorical latent variables in the context of mixture modeling.
NOTE: This is a pre-publication manuscript version of a published book chapter. It is not the copy of record and may not exactly replicate the contents of the chapter as it appears in the book. Human Evolution and Social Cognition

The massive and complex information-crunching capacities of the human brain were designed to help our ancestors make functional decisions in an environment that included other people as a prominent feature. Some of those people were relatives; some were strangers. Some were socially dominant; some were meek. Some were potential allies; others were potential enemies. Some were potential mates; others were potential competitors for those mates. Many aspects of human cognition – especially the processes that define the conceptual territory of social cognition – are adapted to the recurrent problems and opportunities posed by these other members of ancestral human populations. So, if we are to understand social cognition fully and deeply, it is useful – perhaps even essential – to employ the following scientific strategy: First, identify the set of fitness-relevant "problems" recurrently posed by human social environments (what opportunities and dangers have other people traditionally posed? ). Second, employ an evolutionary cost–benefit analysis to deduce plausible cognitive adaptations that would have helped "solve" those problems. Third, deduce the specific implications of these adaptations for human cognition in contemporary social environments. And, fourth, test those hypothesized implications rigorously with empirical data. This evolutionarily informed approach to the study of human social cognition produces at least two substantial scientific benefits. First, this approach can yield a deeper understanding of many well-documented social cognitive phenomena – an appreciation not only for the proximate triggers of those phenomena in the contemporary workings of the human mind, but also for the ultimate causes of these phenomena within the history of the species. When considered in an evolutionary light, human social cognition is not merely one domain of inquiry within the small scientific province of social psychology; it is instead a topic of relevance to any scientist who cares about the evolution and behavioral ecology of mammalian species in general. Beyond connecting social cognition to these broader questions, the other benefit is its powerful heuristic potential. To those whose primary goal is simply to predict human social cognition and behavior, the evolutionary approach to social cognition yields novel and important discoveries about the contemporary workings of the human mind.
When Do Optimistic CEOs Enhance Firm Value?

We derive and test predictions about how the effect of CEO optimism on firm value varies across different types of firms. We also model the effect of industry competition on the relation between CEO optimism and firm value. Using measures of optimism based on option-exercise behavior, we find that CEO optimism results in an additional value of about 17-23%, implying that the benefits of optimism outweigh the costs for an average firm. Consistent with theoretical predictions, we find that CEO optimism is more likely to be a value-enhancing trait in firms that are risky, that operate in competitive industries and in those with a larger fraction of optimistic CEOs, that engage in greater innovation and investment, and that have more internal resources. We perform various endogeneity checks including the use of an instrumental variable and the results provide support for a causal effect of CEO optimism on firm value. JEL Classification Codes: G30, G32, G40, G41 ∗Sanjay Deshmukh is from the Department of Finance at DePaul University, Keith Howe (Emeritus) is from DePaul University, and Anand Goel is from Stevens Institute of Technology. Sanjay Deshmukh: (312) 362-8472, sdeshmuk@depaul.edu; Anand Goel: goelanand@gmail.com; Keith Howe: khowe@depaul.edu When Do Optimistic CEOs Enhance Firm Value?
Network Constraints on Longitudinal Grey Matter Changes in First Episode Psychosis

Background: Different regions of the brain's grey matter are connected by a complex structural network of white matter fibres which are responsible for the propagation of action potentials and the transport of trophic and other molecules. In neurodegenerative disease, these connections constrain the way in which grey matter volume loss progresses. Here, we investigated whether connectome architecture also shapes the spatial pattern of longitudinal grey matter volume changes attributable to illness and antipsychotic medication in first episode psychosis (FEP). Methods: We conducted a triple-blind randomised placebo-control MRI study where 62 young adults with first episode psychosis received either an atypical antipsychotic or placebo over 6-months. A healthy control group was also recruited. Anatomical MRI scans were acquired at baseline, 3-months and 12-months. Deformation-based morphometry was used to estimate illness-related and antipsychotic-related grey matter volume changes over time. Representative functional and structural brain connectivity patterns were derived from an independent healthy control group using resting-state functional MRI and diffusion-weighted imaging. We used neighbourhood deformation models to predict the extent of brain change in a given area by the changes observed in areas to which it is either structurally connected or functionally coupled. Results: At baseline, we found that empirical illness-related regional volume differences were strongly correlated with predicted differences using a model constrained by structural connectivity weights ({rho} = .541; p < .001). At 3-months and 12-months, we also found a strong correlation between longitudinal regional illness-related ({rho} > .516; p < .001) and antipsychotic-related volume change ({rho} > .591; p < .001) with volumetric changes in structurally connected areas. These correlations were significantly greater than those observed across various null models accounting for lower-order spatial and network properties of the data. Associations between empirical and predicted volume change estimates were much lower for models that only considered binary structural connectivity (all {rho} < .376), or which were constrained by inter-regional functional coupling (all {rho} < .436). Finally, we found that potential epicentres of volume change emerged posteriorly early in the illness and shifted to the prefrontal cortex by later illness stages. Conclusion: Psychosis- and antipsychotic-related grey matter volume changes are strongly shaped by anatomical brain connectivity. This result is consistent with findings in other neurological disorders and implies that such connections may constrain pathological processes causing brain dysfunction in FEP.
Global 
Λ
 polarization in heavy-ion collisions at energies 2.4–7.7 GeV: Effect of meson-field interaction

Yu. B. Ivanov 2, 3, ∗ and A. A. Soldatov Bogoliubov Laboratory for Theoretical Physics, Joint Institute for Nuclear Research, Dubna 141980, Russia National Research Nuclear University ”MEPhI”, Moscow 115409, Russia National Research Centre ”Kurchatov Institute”, Moscow 123182, Russia Based on the three-fluid model, the global Λ polarization in Au+Au collisions at 2.4 ≤ sNN ≤ 7.7 GeV is calculated, including its rapidity and centrality dependence. Contributions from the thermal vorticity and meson-field term (proposed by Csernai, Kapusta and Welle) to the global polarization are considered. The results are compared with data from recent and ongoing STAR and HADES experiments. It is predicted that the polarization maximum is reached at √ sNN ≈ 3 GeV, if the measurements are performed with the same acceptance. The value of the polarization is very sensitive to interplay of the aforementioned contributions. In particular, the thermal vorticity results in quite strong increase of the polarization from the midrapidity to forward/backward rapidities, while the meson-field contribution considerably flattens the rapidity dependence. The polarization turns out to be very sensitive to details of the equation of state. While collision dynamics become less equilibrium with decreasing collision energy, the present approach to polarization is based on the assumption of thermal equilibrium. It is found that equilibrium is achieved at the freeze-out stage, but this equilibration takes longer at moderately relativistic energies.
Differentiating geographic movement described in text documents

Understanding movement described in text documents is important, since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more. Our research makes several contributions to improve our understanding of movement descriptions in text. First, we show how interpreting geographic movement described in text is challenging because of general spatial terms, linguistic constructions that make the thing(s) moving unclear, and many types of temporal references and groupings, among others. Next, as a step to overcome these challenges, we report on an experiment with human subjects through which we identify multiple important characteristics of movement descriptions (found in text) that humans use to differentiate one movement description from another. Based on our empirical results, we provide recommendations for computational analysis using movement described in text documents. Our findings contribute toward an improved understanding of the important characteristics of the underused information about geographic movement that is in the form of text descriptions.
Scalable production of single 2D van der Waals layers through atomic layer deposition: bilayer silica on metal foils and films

The self-limiting nature of atomic layer deposition (ALD) makes it an appealing option for growing single layers of two-dimensional van der Waals (2D-VDW) materials. In this paper it is demonstrated that a single layer of a 2D-VDW form of SiO2 can be grown by ALD on Au and Pd polycrystalline foils and epitaxial films. The silica was deposited by two cycles of bis(diethylamino) silane and oxygen plasma exposure at 525 K. Initial deposition produced a three-dimensionally disordered silica layer; however, subsequent annealing above 950 K drove a structural rearrangement resulting in 2D-VDW. The annealing could be performed at ambient pressure. Surface spectra recorded after annealing indicated that the two ALD cycles yielded close to the silica coverage obtained for 2D-VDW silica prepared by precision SiO deposition in ultra-high vacuum (UHV). Analysis of ALD-grown 2D-VDW silica on a Pd(111) film revealed the co-existence of amorphous and incommensurate crystalline 2D phases. In contrast, ALD growth on Au(111) films produced predominantly the amorphous phase while SiO deposition in UHV led to only the crystalline phase, suggesting that the choice of Si source can enable phase control.
Transmission Scheme, Detection and Power Allocation for Uplink User Cooperation With NOMA and RSMA

In this paper, we propose two novel cooperative-non-orthogonal-multiple-access (C-NOMA) and cooperative-rate-splitting-multiple-access (C-RSMA) schemes for uplink user cooperation. At the first mini-slot of these schemes, each user transmits its signal and receives the transmitted signal of the other user in full-duplex mode, and at the second mini-slot, each user relays the other user’s message with amplify-and-forward (AF) protocol. At both schemes, to achieve better spectral efficiency, users transmit signals in the non-orthogonal mode in both mini-slots. In C-RSMA, we also apply the rate-splitting method in which the message of each user is divided into two streams. In the proposed detection schemes for C-NOMA and C-RSMA, we apply a combination of maximum-ratio-combining (MRC) and successive-interference-cancellation (SIC). Then, we derive the achievable rates for C-NOMA and C-RSMA, and formulate two optimization problems to maximize the minimum rate of two users by considering the proportional fairness coefficient. We propose two power allocation algorithms based on successive-convex-approximation (SCA) and geometric-programming (GP) to solve these non-convex problems. Next, we derive the asymptotic outage probability of the proposed C-NOMA and C-RSMA schemes, and prove that they achieve diversity order of two. Finally, the above-mentioned performance is confirmed by simulations.
Cytokine Elevation in Severe COVID-19 From Longitudinal Proteomics Analysis: Comparison With Sepsis

Introduction Coronavirus disease 2019 (COVID-19) is a new viral disease. Uncontrolled inflammation called “cytokine storm” is reported to contribute to disease pathogenesis as well as sepsis. We aimed to identify cytokines related to the pathogenesis of COVID-19 through a proteomics analysis of 1463 plasma proteins, validate these cytokines, and compare them with sepsis. Materials and Methods In a derivation cohort of 306 patients with COVID-19, 1463 unique plasma proteins were measured on days 1, 4, and 8. Cytokines associated with disease severity and prognosis were derived. In a validation cohort of 62 COVID-19 patients and 38 sepsis patients treated in the intensive care unit [ICU], these derived cytokines were measured on days 1 (day of ICU admission), 2-3, and 6-8 (maximum: 3 time points/patient). Derived cytokines were compared with healthy controls and between COVID-19 and sepsis patients, and the associations with prognosis were evaluated. The time to wean off mechanical ventilation (MV) was evaluated only for COVID-19. Results IL-6, amphiregulin, and growth differentiation factor (GDF)-15 were associated with disease severity and prognosis in the derivation cohort. In the validation cohort, IL-6 and GDF-15 were elevated in COVID-19 and sepsis on day 1, and the levels of these cytokines were higher in sepsis than in COVID-19. IL-6 and GDF-15 were associated with prognosis in sepsis. Cox proportional hazards model with time as a dependent covariate showed a significant relationship between plasma GDF-15 level and time to wean off MV (hazard ratio, 0.549 [95% confidence level, 0.382–0.789]). The GDF-15 level at ICU admission predicted late recovery. Conclusion GDF-15 and IL-6 derived from proteomics analysis were related with disease severity of COVID-19. Their values were higher in sepsis than in COVID-19 and were associated with prognosis in sepsis. In COVID-19 patients treated in the ICU, GDF-15 was associated with the time to wean off MV and better predicted late recovery.
Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security.
Numerical and Experimental Characterization of LoRa-based Helmet-to-UAV links on Flat Lands

—The use of the LoRa communication protocol in a new generation of transceivers is attractive for search and rescue (SaR) procedures because they can operate in harsh environments covering vast areas while maintaining a low power consumption. The possibility of wearing helmets equipped with LoRa-radios and installing LoRa transceivers in unmanned aerial vehicles (UAVs) will accelerate the localization of the targets, probably unconscious. In this paper, the achievable communication ranges of such links are theoretically and experimentally evaluated by considering the possible positions of the helmet wearer (standing or lying) on a ﬂat ﬁeld, representing a simple SaR scenario. Simulations and experimental tests demonstrated that, for the standing position, the ground-bounce multi-path produces strong ﬂuctuations of the received power versus the Tx-Rx distances. Such ﬂuctuations can be kept conﬁned within 100 m from the target by lowering the UAV altitude. Instead, for a more critical lying position, the received power proﬁle is monotonic and nearly insensitive to the posture. For all the considered cases, the signal emitted by the body-worn transceiver can be exploited to localize the helmet wearer based on its strength, and it is theoretically detectable by the UAV radio up to 5 km on ﬂat terrain.