Miracle-dz/UniFAR-FTUs huggingface dataset:
Vaccination against Borna Disease: Overview, Vaccine Virus Characterization and Investigation of Live and Inactivated Vaccines

(1) Background: Vaccination of horses and sheep against Borna disease (BD) was common in endemic areas of Germany in the 20th century but was abandoned in the early 1990s. The recent occurrence of fatal cases of human encephalitis due to Borna disease virus 1 (BoDV-1) has rekindled the interest in vaccination. (2) Methods: The full genomes of the BD live vaccine viruses “Dessau” and “Giessen” were sequenced and analyzed for the first time. All vaccination experiments followed a proof-of-concept approach. Dose-titration infection experiments were performed in rabbits, based on both cell culture- and brain-derived viruses at various doses. Inactivated vaccines against BD were produced from concentrated cell culture supernatants and investigated in rabbits and horses. The BoDV-1 live vaccine “Dessau” was administered to horses and antibody profiles were determined. (3) Results: The BD live vaccine viruses “Dessau” and “Giessen” belong to clusters 3 and 4 of BoDV-1. Whereas the “Giessen” virus does not differ substantially from field viruses, the “Dessau” virus shows striking differences in the M gene and the N-terminal part of the G gene. Rabbits infected with high doses of cell-cultured virus developed neutralizing antibodies and were protected from disease, whereas rabbits infected with low doses of cell-cultured virus, or with brain-derived virus did not. Inactivated vaccines were administered to rabbits and horses, following pre-defined vaccination schemes consisting of three vaccine doses of either adjuvanted or nonadjuvanted inactivated virus. Their immunogenicity and protective efficacy were compared to the BD live vaccine “Dessau”. Seventy per cent of horses vaccinated with the BD live vaccine “Dessau” developed neutralizing antibodies after vaccination. (4) Conclusion: Despite a complex evasion of immunological responses by bornaviruses, some vaccination approaches can protect against clinical disease. For optimal effectiveness, vaccines should be administered at high doses, following vaccination schemes consisting of three vaccine doses as basic immunization. Further investigations are necessary in order to investigate and improve protection against infection and to avoid side effects.
Analytical and Experimental Evaluation of the Joints in Bi-Based Superconducting Tape for the Feeder Cable

Electric railways have several problems due to the electrical resistance of feeder lines, such as transmission loss and voltage drop. In order to solve these problems, we have been developing a superconducting feeder cable, which enables zero-resistance power transmission. Due to restrictions during transportation, the length of one superconducting cable is at most 500 m, although the average distance between substations is about several kilometers. Hence, to introduce superconducting feeder cables between substations, it is required to join superconducting feeder cables to each other at a railway site. This article reports analytical and experimental results of joining tests of the superconducting tape, assuming the joining of superconducting feeder cables at railway sites, and gives standards for the joining of superconducting feeder cables.
ASSESSMENT AND PERFORMANCE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR GAS SENSING E-NOSE SYSTEMS

E-noses that combine machine learning and gas sensor arrays (GSAs) are widely used for the detection and identification of various gases. GSAs produce signals that provide vital information about the exposed gases for the machine learning algorithms, rendering them indispensable within the smart-gas sensing arena. In this work, we present a detailed assessment of several machine learning techniques employed for the detection of gases and estimation of their concentrations. The modeling and predictive analysis conducted in this paper are based on kNN, ANN, Decision Trees, Random Forests, SVM and other ensembling-based techniques. Predictive models are implemented and tested on three different MoX gas sensor-based experimental datasets as reported in the literature. The assessment includes a delineated analysis of the different models’ performance followed by a detailed comparison against results found in the literature. It highlights factors that play a pivotal role in machine learning for gas sensing and sheds light on the predictive capability of different machine learning approaches applied on experimental GSA datasets.
On the forcing of time-mean flows by transient, small-amplitude eddies

Abstract Small-amplitude, transient disturbances to a steady, zonally asymmetric basic state are considered, for barotropic (or baroclinic, quasi-geostrophic) flow on a beta-plane. An explicit expression is derived for the eddy vorticity (or potential vorticity) flux in terms of the basic velocity and basic vorticity (or potential vorticity), and transient and nonconservative eddy effects, plus an identically nondivergent term. The divergence of the time mean of this flux provides the forcing of the time-mean flow. The theory generalizes a “nonacceleration” result due to Plumb. In the case of conservative flow, the relevant expressions can be obtained in Eulerian form; however, for nonconservative flow it appears to be necessary to introduce the Lagrangian fluid particle displacements. Possible applications of the theory to the interpretation of atmospheric and model data are mentioned.
CONSTRUCTION AND VALIDATION OF LEADERSHIP ARCHETYPES SCALE FOR BUSINESS LEADERSHIP

Literature suggests that people hold different perceptions of leadership, particularly ideal leadership, and associated characteristics. Building on this, it was proposed that an instrument that could assess local views or ‘archetypes’ of what a leader should be like can be helpful in Pakistani organizational settings. Owing to the dearth of a reliable and valid scale for assessment of leadership archetypes in business settings, this study aimed to develop the Leadership Archetypes Scale. Initially item pool of 90 statements was formulated with the aid of previous literature and interviews with 30 business sector employees. Advice from subject matter experts resulted in the final selection of 81 items which were put through an EFA after obtaining data form 365 employees. A five-factor solution containing 57 items was suggested and it was confirmed through a CFA on an independent sample (N = 359). Keywords: Leadership, archetypes, Leadership Archetype Scale, Business leadership
Magnetic Polyethyleneimine Nanoparticles Fabricated via Ionic Liquid as Bridging Agents for Laccase Immobilization and Its Application in Phenolic Pollutants Removal

In this study, polyethyleneimine was combined with magnetic Fe3O4 nanoparticles through the bridging of carboxyl-functionalized ionic liquid, and laccase was loaded onto the carrier by Cu2+ chelation to achieve laccase immobilization (MCIL–PEI–Cu–lac). The carrier was characterized by Fourier transform infrared spectroscopy, scanning electron microscope, thermogravimetric analysis, X-ray diffraction analysis, magnetic hysteresis loop and so on. MCIL–PEI–Cu–lac has good immobilization ability; its loading and activity retention could reach 52.19 mg/g and 91.65%, respectively. Compared with free laccase, its thermal stability and storage stability have been significantly improved, as well. After 6 h of storage at 60 °C, 51.45% of the laccase activity could still be retained, and 81.13% of the laccase activity remained after 1 month of storage at 3 °C. In the pollutants removal test, the removal rate of 2,4-dichlorophenol (10 mg/L) by MCIL–PEI–Cu–lac could reach 100% within 10 h, and the removal efficiency could still be maintained 60.21% after repeated use for 8 times. In addition, MCIL–PEI–Cu–lac also has a good removal effect on other phenolic pollutants (such as bisphenol A, phenol, 4-chlorophenol, etc.). Research results indicated that an efficient strategy for laccase immobilization to biodegrade phenolic pollutants was developed.
Vanishing one-loop. beta. function in gauged N>4 supergravity

In O(N) extended supergravity theories, the usual arguments for one-loop finiteness (modulo topological terms) cease to apply when the internal symmetry is gauged because of the appearance of a cosmological constant related to the gauge coupling e. For N 4 results in a cancellation of these infinities, implying, in particular a vanishing one-loop . beta. (e) function.
Estimation of the Force of Infection from Current Status Data Using Generalized Linear Mixed Models

Abstract Based on sero-prevalence data of rubella, mumps in the UK and varicella in Belgium, we show how the force of infection, the age-specific rate at which susceptible individuals contract infection, can be estimated using generalized linear mixed models (McCulloch & Searle, 2001). Modelling the dependency of the force of infection on age by penalized splines, which involve fixed and random effects, allows us to use generalized linear mixed models techniques to estimate both the cumulative probability of being infected before a given age and the force of infection. Moreover, these models permit an automatic selection of the smoothing parameter. The smoothness of the estimated force of infection can be influenced by the number of knots and the degree of the penalized spline used. To determine these, a different number of knots and different degrees are used and the results are compared to establish this sensitivity. Simulations with a different number of knots and polynomial spline bases of different degrees suggest – for estimating the force of infection from serological data – the use of a quadratic penalized spline based on about 10 knots.
Lateral stiffness: A new nanomechanical measurement for the determination of shear strengths with friction force microscopy

We present a technique to measure the lateral stiffness of the nanometer-sized contact formed between a friction force microscope tip and a sample surface. Since the lateral stiffness of an elastic contact is proportional to the contact radius, this measurement can be used to study the relationship between friction, load, and contact area. As an example, we measure the lateral stiffness of the contact between a silicon nitride tip and muscovite mica in a humid atmosphere (55% relative humidity) as a function of load. Comparison with friction measurements confirms that friction is proportional to contact area and allows determination of the shear strength.
Measures of dependence for censored survival data

In the linear regression model with normal errors the squared product-moment correlation provides the standard measure of dependence between the explanatory variable and the response variable. Using the concept of information gain, a measure of dependence can also be defined for more general regression models used in survival analysis, such as the Weibull regression model or Cox's proportional hazards model. Further, this measure of dependence can be conveniently estimated even when the response variable is subject to censoring, and a simple approximation is available.
Combining simulation and machine learning for the management of healthcare systems

The growing research trends in the field of artificial intelligence have largely impacted the healthcare sector. Thanks to the high predictive power of machine learning approaches, new tools to support the clinical decision-making can be designed. However, since the demand for healthcare services is complex and highly changing, as it is affected by external unpredictable factors such as the CoViD-19, the reliability and robustness of such predictive tools is highly dependent on their capability of varying and adapting the forecasting in accordance with variations in environmental factors and health needs. In this work, we propose a combined simulation and machine learning approach to study the robustness and adaptability of predictive tools for healthcare management. Discrete event simulation is employed to simulate a generic healthcare service. The patients’ length of stay (LOS) is monitored as a performance indicator of the care process. Three machine learning algorithms have been tested to predict the LOS in different simulated scenarios obtained by varying the level of demand for the healthcare service. The predictability of the tested algorithms has been studied in terms of mean errors. Preliminary results suggest that abrupt changes in the healthcare demand have a negative impact on the performance of the machine learning algorithms, which are not prone to adapt decisions to the surrounding environment. The design of novel intelligent health system, which aim to integrate artificial intelligence tools in the clinical decision-making process, should take into account these limitations. In this sense the use of simulation can be beneficial in the assessment of the new generation of decision support systems in healthcare.
The Validity of Benchmark Dose Limit Analysis for Estimating Permissible Accumulation of Cadmium

Cadmium (Cd) is a toxic metal pollutant that accumulates, especially in the proximal tubular epithelial cells of kidneys, where it causes tubular cell injury, cell death and a reduction in glomerular filtration rate (GFR). Diet is the main Cd exposure source in non-occupationally exposed and non-smoking populations. The present study aimed to evaluate the reliability of a tolerable Cd intake of 0.83 μg/kg body weight/day, and its corresponding toxicity threshold level of 5.24 μg/g creatinine. The PROAST software was used to calculate the lower 95% confidence bound of the benchmark dose (BMDL) values of Cd excretion (ECd) associated with injury to kidney tubular cells, a defective tubular reabsorption of filtered proteins, and a reduction in the estimated GFR (eGFR). Data were from 289 males and 445 females, mean age of 48.1 years of which 42.8% were smokers, while 31.7% had hypertension, and 9% had chronic kidney disease (CKD). The BMDL value of ECd associated with kidney tubular cell injury was 0.67 ng/L of filtrate in both men and women. Therefore, an environmental Cd exposure producing ECd of 0.67 ng/L filtrate could be considered as Cd accumulation levels below which renal effects are likely to be negligible. A reduction in eGFR and CKD may follow when ECd rises from 0.67 to 1 ng/L of filtrate. These adverse health effects occur at the body burdens lower than those associated with ECd of 5.24 µg/g creatinine, thereby arguing that current health-guiding values do not provide a sufficient health protection.
GARP Regulates the Immune Capacity of a Human Autologous Platelet Concentrate

Autologous platelet concentrates, like liquid platelet rich fibrin (iPRF), optimize wound healing; however, the underlying immunological mechanisms are poorly understood. Platelets, the main cellular component of iPRF, highly express the protein, Glycoprotein A repetitions predominant (GARP), on their surfaces. GARP plays a crucial role in maintaining peripheral tolerance, but its influence on the immune capacity of iPRF remains unclear. This study analyzed the interaction of iPRF with immune cells implicated in the wound healing process (human monocyte derived macrophages and CD4+ T cells) and evaluated the distinct influence of GARP on these mechanisms in vitro. GARP was determined to be expressed on the surface of platelets and to exist as a soluble factor in iPRF. Platelets derived from iPRF and iPRF itself induced a regulatory phenotype in CD4+ T cells, shown by increased expression of Foxp3 and GARP as well as decreased production of IL-2 and IFN-γ. Application of an anti-GARP antibody reversed these effects. Additionally, iPRF polarized macrophages to a “M0/M2-like” phenotype in a GARP independent manner. Altogether, this study demonstrated for the first time that the immune capacity of iPRF is mediated in part by GARP and its ability to induce regulatory CD4+ T cells.
Laser Patterning Method for Integrated Type a-Si Solar Cell Submodules

A laser patterning method was investigated as a fabrication method for integrated-type amorphous-silicon (a-Si) solar cell submodules. A three-dimensional thermal analysis of a multilayer structure was performed to determine the selective scribing conditions for each layer of an a-Si solar cell. The optimum laser power densities calculated from a three-dimensional thermal analysis were confirmed by the experiments. It was found that not only transparent conductive oxide and a-Si films, but also the metal electrodes of the integrated-type a-Si solar cell submodule were selectively scribed. The total output power of an a-Si solar cell submodule patterned by optimum laser-power densities was 9% higher than that achieved by a conventional patterning method.
Wheat and Rice beyond Phenolic Acids: Genetics, Identification Database, Antioxidant Properties, and Potential Health Effects

Wheat and rice play a vital role in human nutrition and food security. A better understanding of the potential health benefits associated with consuming these cereals, combined with studies by plant scientists and food chemists to view the entire food value chain from the field, pre and post-harvest processing, and subsequent “fork” consumption, may provide the necessary tools to optimize wheat and rice production towards the goal of better human health improvement and food security, providing tools to better adapt to the challenges associated with climate change. Since the available literature usually focuses on only one food chain segment, this narrative review was designed to address the identities and concentration of phenolics of these cereal crops from a farm-to-fork perspective. Wheat and rice genetics, phenolic databases, antioxidant properties, and potential health effects are summarized. These cereals contain much more than phenolic acids, having significant concentrations of flavonoids (including anthocyanins) and proanthocyanidins in a cultivar-dependent manner. Their potential health benefits in vitro have been extensively studied. According to a number of in vivo studies, consumption of whole wheat, wheat bran, whole rice, and rice bran may be strategies to improve health. Likewise, anthocyanin-rich cultivars have shown to be very promising as functional foods
A Novel Multi-Criteria Local Latin Hypercube Refinement System for Commutation Angle Improvement in IPMSMs

The commutation angle, γ, of an interior permanent magnet synchronous motor's (IPMSM) vector diagram, plays an important role in compensating the back electromotive force (back-EMF); both under phase current variations and an extended speed range, is required by the application. This commutation angle is defined as the angle between the fundamental of the motor phase current and the fundamental of the back-EMF. It can be utilised to provide a compensating effect in IPMSMs. This is due to the reluctance torque component being dependent on the commutation angle of the phase current even before entering the extended speed range. A real-time maximum torque per current and voltage strategy is demonstrated to find the trajectory and optimum commutation angles, γ, where the level of accuracy depends on the application and available computational speed. A magnet volume reduction using a novel multi-criteria local Latin hypercube refinement (MLHR) sampling system is also presented to improve the optimisation process. The proposed new technique minimises the magnet mass to motor torque density whilst maintaining a similar phase current level. A mapping of γ allows the determination of the optimum angles, as shown in this paper. The 3rd generation Toyota Prius IPMSM is considered as the reference motor, where the rotor configuration is altered to allow for an individual assessment.
Standing electromagnetic solitons in hot ultra-relativistic electron-positron plasmas

Using a one-dimensional self-consistent fluid model, we investigate standing relativistic bright solitons in hot electron-positron plasmas. The positron dynamics is taken into account. A set of nonlinear coupled differential equations describing the evolution of electromagnetic waves in fully relativistic two-fluid plasma is derived analytically and solved numerically. As a necessary condition for the existence of standing solitons the system should be relativistic. For the case of ultra-relativistic plasma, we investigate non-drifting bright solitary waves. Detailed discussions of the acceptable solutions are presented. New single hump non-trivial symmetric solutions for the scalar potential were found, and single and multi-nodal symmetric and anti-symmetric solutions for the vector potential are presented. It is shown that for a fixed value of the fluid velocity excited modes with more zeros in the profile of the vector potential show a higher magnitude for the scalar potential. An increase in the plasma fluid velocity also increases the magnitude of the scalar potential. Furthermore, the Hamiltonian and the first integral of the system are given.
A new approach to limited angle tomography using the compressed sensing framework

The limited angle problem is a well-known problem in computed tomography. It is caused by missing data over a certain angle interval, which make an inverse Radon transform impossible. In daily routine this problem can arise for example in tomosynthesis, C-arm CT or dental CT. In the last years there has been a big development in the field of compressed sensing algorithms in computed tomography, which deal very good with incomplete data. The most popular way is to integrate a minimal total variation norm in form of a cost function into the iteration process. To find an exact solution of such a constrained minimization problem, computationally very demanding higher order algorithms should be used. Due to the non perfect sparsity of the total variation representation, reconstructions often show the so called staircase effect. The method proposed here uses the solutions of the iteration process as an estimation for the missing angle data. Compared to a pure compressed sensing-based algorithm we reached much better results within the same number of iterations and could eliminate the staircase effect. The algorithm is evaluated using measured clinical datasets.
On the Boundary Value Problem for Systems of Ordinary Differential Equations with a Singularity of the Second Kind

A Fredholm theory for linear boundary value problems $t^\alpha y' = G(t)y + g(t),\,0 < t \leqq 1,\,y \in C[0,1] \cap C^1 (0,1],\,\alpha \geqq 1$; $B_0 y(0) + B_1y(1) = \gamma $ is established, together with existence and regularity results for continuous solutions of nonlinear systems of ordinary differential equations $t^\alpha y' = f(t,y)$. This theory is applied to boundary value problems on infinite intervals and is illustrated by two examples. Finally, some fundamental properties of the generalized linear eigenvalue problem $t^\alpha y' - (G(t) - \lambda H(t))y = 0,\,0 < t \leqq 1,\,y \in C[0,1] \cap C^1 (0,1]$; $B_0 y(0) + B_1 y(1) = 0$, are derived.
A New Algorithm for the 0-1 Knapsack Problem

We present a new algorithm for the optimal solution of the 0-1 Knapsack problem, which is particularly effective for large-size problems. The algorithm is based on determination of an appropriate small subset of items and the solution of the corresponding "core problem": from this we derive a heuristic solution for the original problem which, with high probability, can be proved to be optimal. The algorithm incorporates a new method of computation of upper bounds and efficient implementations of reduction procedures. The corresponding Fortran code is available. We report computational experiments on small-size and large-size random problems, comparing the proposed code with all those available in the literature.