Miracle-dz/UniFAR-FTUs huggingface dataset:
Computational and Experimental Approaches Identify Beta-Blockers as Potential SARS-CoV-2 Spike Inhibitors

Finding antivirals for SARS-CoV-2 is still a major challenge, and many computational and experimental approaches have been employed to find a solution to this problem. While the global vaccination campaigns are the primary driver of controlling the current pandemic, orally bioavailable small-molecule drugs and biologics are critical to overcome this global issue. Improved therapeutics and prophylactics are required to treat people with circulating and emerging new variants, addressing severe infection, and people with underlying or immunocompromised conditions. The SARS-CoV-2 envelope spike is a challenging target for viral entry inhibitors. Pindolol presented a good docking score in a previous virtual screening using computational docking calculations after screening a Food and Drug Administration (FDA)-approved drug library of 2400 molecules as potential candidates to block the SARS-CoV-2 spike protein interaction with the angiotensin-converting enzyme 2 (ACE-2). Here, we expanded the computational evaluation to identify five beta-blockers against SARS-CoV-2 using several techniques, such as microscale thermophoresis, NanoDSF, and in vitro assays in different cell lines. These data identified carvedilol with a Kd of 364 ± 22 nM for the SARS-CoV-2 spike and in vitro activity (EC50 of 7.57 μM, CC50 of 18.07 μM) against SARS-CoV-2 in Calu-3 cells. We have shown how we can apply multiple computational and experimental approaches to find molecules that can be further optimized to improve anti-SARS-CoV-2 activity.
Generation and Characterization of Novel iPSC Lines from a Portuguese Family Bearing Heterozygous and Homozygous GRN Mutations

Mutations in granulin (GRN) have been associated with neurodegenerative diseases, such as frontotemporal lobar degeneration (FTLD) and neuronal ceroid lipofuscinosis (NCL). In Portugal, GRN mutations account for around half of all FTLD cases with known genetic origin. Here, we describe the generation and characterization of three human-induced pluripotent stem cell (hiPSC) lines from a Portuguese family harboring heterozygous and homozygous GRN mutation. hiPSCs were reprogrammed from human dermal fibroblasts by episomal nucleofection of the Yamanaka factors. The new generated lines were positive for pluripotency markers, could be further differentiated to cells expressing all trilineage markers, and presented a normal karyotype. They were also capable of differentiating into 3D brain organoids and presented a significant decrease in progranulin protein levels. Hence, these cell lines constitute suitable new tools to elucidate the pathophysiological mechanisms associated with the GRN mutations in the context of FTLD.
A functional central limit theorem for branching random walks, almost sure weak convergence, and applications to random trees

Let $W_{\infty}(\beta)$ be the limit of the Biggins martingale $W_n(\beta)$ associated to a supercritical branching random walk with mean number of offspring $m$. We prove a functional central limit theorem stating that as $n\to\infty$ the process $$ D_n(u):= m^{\frac 12 n} \left(W_{\infty}\left(\frac{u}{\sqrt n}\right) - W_{n}\left(\frac{u}{\sqrt n}\right) \right) $$ converges weakly, on a suitable space of analytic functions, to a Gaussian random analytic function with random variance. Using this result we prove central limit theorems for the total path length of random trees. In the setting of binary search trees, we recover a recent result of R. Neininger [Refined Quicksort Asymptotics, Rand. Struct. and Alg. , to appear], but we also prove a similar theorem for uniform random recursive trees. Moreover, we replace weak convergence in Neininger's theorem by the almost sure weak (a.s.w.) convergence of probability transition kernels. In the case of binary search trees, our result states that $$ L\left\{\sqrt{\frac{n}{2\log n}} \left(EPL_{\infty} - \frac{EPL_n-2n\log n}{n}\right)\Bigg | G_{n}\right\} \to \{\omega\mapsto N_{0,1}\}, \quad \text{a.s. w. },$$ where $EPL_n$ is the external path length of a binary search tree $X_n$ with $n$ vertices, $EPL_{\infty}$ is the limit of the R\'egnier martingale, and $L(\,\cdot\, |G_n)$ denotes the conditional distribution w.r. t. the $\sigma$-algebra $G_n$ generated by $X_1,\ldots,X_n$. A.s. w. convergence is stronger than weak and even stable convergence. We prove several basic properties of the a.s. w. convergence and study a number of further examples in which the a.s. w. convergence appears naturally. These include the classical central limit theorem for Galton-Watson processes and the P\'olya urn.
Diagnostic accuracy of CompCog: reaction time as a screening measure for mild cognitive impairment

ABSTRACT Background Reaction time is affected under different neurological conditions but has not been much investigated considering all types of mild cognitive impairment (MCI). Objective This study investigated the diagnostic accuracy of CompCog, a computerized cognitive screening battery focusing on reaction time measurements. Methods A sample of 52 older adults underwent neuropsychological assessments, including CompCog, and medical appointments, to be classified as a control group or be diagnosed with MCI. The accuracy of CompCog for distinguishing between the two groups was calculated. Results The results from diagnostic accuracy analyses showed that the AUCs of ROC curves were as high as 0.915 (CI 0.837-0.993). The subtest with the highest sensitivity and specificity (choice reaction time subtest) had 91.7% sensitivity and 89.3% specificity. The logistic regression final model correctly classified 92.3% of individuals, with 92.9% specificity and 91.7% sensitivity, and included only four variables from different subtests. Conclusions In summary, the study showed that reaction time assessed through CompCog is a good screening measure to differentiate between normal aging and MCI. Reaction time measurements in milliseconds were more accurate than correct answers. This test can form part of routine clinical tests to achieve the objectives of screening for MCI, indicating further procedures for investigation and diagnosis and planning interventions.
NEW EVOLUTIONARY ALGORITHMS AND THEIR APPLICATION TO ELECTRIC POWER SYSTEM OPERATIONAL ENGINEERING

In this research work, models will be developed for optimal dispatch problems applied in a smart grid environment. The models will be used to solve optimal dispatch problems and other problems such as: optimal power flow , unit commitment and short term hydrothermal scheduling for a smart grid environment. The main objectives of smart grid include the optimization of energy production, minimization of the production cost, integration of renewable energy resources and implementation of real time pricing and billing. Each problem will be effected by the objectives of the smart grid and the tools used to implement these objectives such as demand side management (DSM) and load forecasting. There are many constraints in the problems treated such as generators capacity, ramp rate limit, and prohibited operational zones. This research will define the changes in all these constraints. There is also the problem of valve point effects for thermal driven units which will change the fuel cost equation of the generators, which categorizes it as non-smooth and non-convex problem. The second objective of this work will be selecting a suitable algorithm or algorithms to solve the problems with different constraints. The algorithms tested are population based and are inspired by nature. A new heuristic optimization technique called Khums optimization algorithm is introduced and tested on some of the problems treated.
Intra-regional mobility of PhD students in the European Union: the outcomes of region-making policy?

ABSTRACT This paper examines the flow of intra-European Union (EU) students for doctoral (PhD) studies to identify reasons for differences in international student mobility and migration (ISM) among member states. Rather than conceptualising intra-EU PhD student ISM only through push–pull forces, we theorise the intra-EU PhD ISM is associated with relative levels of national resources or levels of capital. We investigate the intra-EU PhD ISM through dyadic country pairings allowing the use of Gravity models to estimate the effect of variables associated with stocks of capital ascribed to a country to the change in the number of PhD students. The findings of this study indicate while there is asymmetry among EU member states, investment in strengthening the higher education systems within individual EU countries can strengthen the overall cohesion and competitiveness of the EU in the global science competition. Thus, policy focused on enhancing developing national higher education systems can pay dividends throughout the EU.
Dynamics Analysis of a VLF Linear Antenna in an Anisotropic Magnetoplasma

The motion of a very-low-frequency (VLF: 3–30 kHz) orbiting antenna across the Earth’s magnetic field will generate the Lorentz force, which not only compromises the efficiency of the antenna, but also causes it to fall out of orbit over a long time. This article investigates the Lorentz force and dissipated power of a VLF space-borne linear antenna in an anisotropic magnetoplasma as well as the deorbiting time of the system. By considering the inhomogeneity of the current and the effect of ordinary and extraordinary waves, we systematically quantified and analyzed the Lorentz force on the antenna in an anisotropic scenario. The dissipated power and system deorbiting time are then determined by integrating antenna theory and electrodynamics. The results show that the Lorentz force and the power will increase with the antenna length and radius, whereas the deorbiting time will decrease accordingly. It is also found that as the geomagnetic inclination angle increases, the Lorentz force and the power will slightly decrease. The proposed method can estimate the Lorentz force, power, and deorbiting time for arbitrary antennas, low Earth orbital conditions, and VLF ionospheric parameters. This work may also help eliminate the adverse effect and evaluate the deorbiting performance of the VLF space-borne system from an analytical perspective.
Validation of Diabetes Knowledge Questionnaire (DKQ) in the Taiwanese Population — Concurrent Validity with Diabetes-Specific Quality of Life Questionnaire Module

Purpose Diabetes knowledge is important for people with type 2 diabetes mellitus (DM) to improve their health. Therefore, it is important to validate an instrument for assessing diabetes knowledge. The present study aimed to validate the 24-item Diabetes Knowledge Questionnaire (DKQ). Patients and Methods The 24-item DKQ and Diabetes-specific Quality of Life Module (DMQoL) were administered to 425 patients (mean±SD age=58.4±11.6) with type 2 DM. Results The 24-item DKQ was first examined for its factor structure using exploratory factor analysis (EFA). Items with low factors loadings were removed and 18 items were retained to make a DKQ-18. In DKQ-18, five factors were identified, which were named as diabetes etiology and symptoms (F1), intermediate nursing (F2), complications (F3), diet and treatment (F4), and elementary nursing (F5). The DKQ-18 had satisfactory internal consistency (Cronbach’s α= 0.732 and McDonald’s ω=0.748), good known-group validity (participants with a higher level of education showed better score in DKQ-18; participants with HbA1c ≤7 had better score in DKQ-18 compared to group of HbA1c level >8.5), acceptable test–retest reliability (r=0.69), adequate responsiveness (DKQ-18 can detect knowledge change), and concurrent validity with DMQoL. Conclusion The DKQ-18 is a valid measure for assessing diabetes knowledge. The DKQ-18 could evaluate participants’ diabetes knowledge and improve their diabetes knowledge and self-care through a diabetes team and serve as a tool to evaluate the knowledge of participants with type 2 DM.
On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights

In this paper, a novel Double Intuitionistic Fuzzy Synthetic Measure (DIFSM), based on intuitionistic fuzzy values for handling multi-criteria decision-making problems used to rank alternatives, is presented. In the studies, intuitionistic fuzzy sets (IFSs) represented uncertain, imprecise information or human judgment. The intuitionistic fuzzy sets can also reflect the approval, rejection, and hesitation of decision-makers. The degrees of satisfiability and non-satisfiability and uncertainty of each alternative with respect to a set of criteria are described by membership functions, non-membership functions, and hesitancy indexes, respectively. The aggregation algorithm DIFSM is inspired by Hellwig’s method based on two reference points: ideal point (pattern) and anti-ideal point (anti-pattern), measuring distances between the alternative and ideal point and distance between the ideal and anti-ideal point. The proposed methods take into consideration the entropy-based weights of criteria. An illustrative example is given to demonstrate the practicality and effectiveness of the proposed approach. Additionally, the comparative analysis results, using the DIFSM and the Intuitionistic Fuzzy TOPSIS-based framework, are presented.
GENERALIZED QUASILINEARIZATION AND HIGHER ORDER OF CONVERGENCE FOR FIRST ORDER INITIAL VALUE PROBLEMS

In this paper the method of generalized quasilinearization for initial value problems has been extended when the forcing function is the sum of hyperconvex and hyperconcave functions of order m, m 0. The cases when m is even and m is odd has been discussed separately. AMS (MOS) Subject Classication. 34A12, 34A34, 34A45. The method of quazilinearization introduced by Bellman and Kalaba (1, 2) yields iterates which are lower bounds to the solutions of the nonlinear problem when the forcing function f is convex. Furthermore, this monotone sequence of approximate solutions converges uniformly, monotonically, and quadratically to the unique solution of the nonlinear problem on the interval of existence. However, if f is concave a dual result can be developed which yields upper bounds to the solution of the nonlinear problem. Recently, the method of quasilinearization combined with the method of upper and lower solutions has been extended, generalized, and rened so as to include the cases when the forcing function is the sum of convex and concave functions. See (4) for details. The method is extremely useful in scientic computations due to its accelerated rate of convergence as in (5, 6). In (3) Cabada and Nieto have obtained a higher order of convergence (an order more than 2). The idea used is on the same lines as monotone method, which requires the nonlinearity of the iterates to be the same as that of the order of convergence. However, in (7) they have extended the quasilinearization method of Bellman and Kalaba to obtain a higher order of convergence when the forcing function is either hyperconvex or hyperconcave. Furthermore, the nonlinearity of the iterates are one less than that of the iterates in (3). In this paper we consider the situation when the forcing function is the sum of a hyperconvex and a hyperconcave function of the same order. Further, we consider all
Linguistic Determinism and the Innate Basis of Number

purposes it wili be useful to distinguish three general approaches what we will call empiri.ir*, weak nativism, and strong nativism. Empiricist accounts maintain that there ,r. 1.ro innate number-specifi. t.pi. sentations or number-specific cognitive systems of any kind and that the natural numbers are acquired on the basis of general cognitive resources that are responsible for the acquisition of a wide variety of concePts. Weak nativist accounts implicate considerably more innate strucfure, including number-related cognitive systerns and representations of approximate quantity, blt these accounts draw the line at concepts fot specific natural numbers. They maintain lhat even though concepts for the natural ,r.r-b.rs have a good deal of innate support, they have to be learned all the same. Finally, strong nativist accounts maintain that concePts for at least some sPecific natural number, ,i. innate and that these innate concepts are a crucial factor in the explanation of why the human mind is suited for mathematics. In one respect, weak and shong nativish are natural allies. Both help themselves to domain-specific innate shuchrre. Yet in other respects, weak nativists are closer to empiricists, since weak nativists and empiricists tend to agree that concePts for_the nahrral numbers are aculhrral achievement, like writing and agriculfure. They view these concePts not as part of our innate endowment but as fundamentally owing to invention and discoverya view we will refer to as the Cu lnral ConstntctThesis. Of course, there is no disputing that culture influences mathematical cognition. Culture affects the richness of our numerical knowledg. , the techniques we rely ttpon for exploiting numerical quantity, and the conventionrl-. rrrs we ,tr. for recording and communicating mathematical information. But in adhering to the Cultural Consnuct Thesis, weak nativists and empiricists embrace the
DEEP REVIEW ON ALOPECIA AREATA DIAGNOSIS FOR HAIR LOSS-RELATED AUTOIMMUNE DISORDER PROBLEM

Lots of women all over the globe are affected by thinning hair, and the number of females suffering from the disease is growing per year. Another important component in the development of thinning hair is genetics. One of the most important goals is to make a clinical condition. For example, in the area of medicine, categorization is critical since one of the primary goals of the doctor is to determine whether or not a patient suffers from an illness. Alopecia areata is a kind of chronic illness that causes baldness in the affected region. AA may cause baldness for a variety of causes thus, testing may be essential to confirm if it is the source of the loss of hair. Machine learning approaches have shown promise in a variety of fields, including dermatology, and may be useful in identifying alopecia areata for better prediction and diagnosis. Proper detection of an illness is also influenced by the fluctuating character of illness signs. Deep learning algorithms for identifying hair loss levels in males using facial pictures in this research. In this situation, a special training database, including face photos with varying degrees of baldness, has been generated. Furthermore, despite the limited accessibility of hairs in such images, a matching approach for mechanically categorizing face images to design categorization tables of male baldness from the medical field is provided. The outcomes of the experiments demonstrate the potential and efficiency for medical, security, and business apps. Related work in machine learning for hair illness categorization has also been addressed. The main objective of this study to analyze several machine learning and deep learning strategies for the identification of alopecia as well as in humans, as well as to determine the accuracy of extracting features methodologies.
Une étude comparative de quelques schémas d'approximation de type iterations sur les politiques

Nous considerons le probleme du controle optimal actualise a horizon infini formalise dans le cadre des processus de decision Markoviens. Nous nous focalisons sur plusieurs variations approchees du schema iteration sur les politiques: iterations sur les politiques approche (API), iterations sur les politiques conservatif (CPI), une adaptation naturelle de l'algorithme ''Policy Search by Dynamic Programming'' au cas de l'horizon infini (PSDP), et iterations sur les politiques non-stationnaires (NSPI). Pour tous ces algorithmes, nous decrivons des bornes de performance en fonction de l'erreur $\epsilon$ a chaque iteration, et faisons une comparaison en portant une attention particuliere aux coefficients de concentration impliques, au nombre d'iterations et a la memoire requis. Notre analyse souligne plusieurs points: 1) Les garanties de performance de CPI peuvent etre arbitrairement meilleures que celle d'API, mais au prix d'une augmentation---exponentielle en $\frac{1}{\epsilon}$---du nombre d'iterations. 2) PSDP combine les avantages d'API et CPI: sa garantie de performance est similaire a celle de CPI, et elle est obtenue en un nombre d'iterations identique a celui d'API. 3) Contrairement a API qui requiert une memoire constante, la memoire dont CPI et PSDP ont besoin est proportionnelle au nombre d'iterations, ce qui peut etre problematique lorsque le facteur d'actualisation $\gamma$ est proche de 1 ou lorsque l'erreur d'approximation $\epsilon$ est proche de 0; nous montrons que l'algorithme NSPI permet de faire un compromis entre la bonne memoire d'API et la meilleure performance de PSDP. Enfin, des simulations numeriques de ces schemas algorithmiques confirment notre analyse.
Understanding the customer benefits of customisation: Case surfboard

Customisation is one strategy to involve customers in the design process. However, qualitative empirical studies on customisation in different product contexts are scarce. This paper investigates the benefits customers perceive in craft customisation where they are actively involved in the customisation with the designer. The case of this study is a high involvement custom product, a surfboard. Findings derived from qualitative interviews with surfers (N=22) reveal the perceived benefits in the three distinct phases of customisation: pre-customisation, during which the decision to customise is made; customisation process, during which the product is customised; and product usage. The source of the found benefits are the product, process or customisation itself and they can be classified to functional, emotional, experiential, symbolic, aesthetic, personal, social, epistemic, creative, and hedonic. The benefits vary in occurrence during the different customisation phases. Based on the findings, we present a model for the benefits in the three phases of customisation. The model can be used when implementing craft customisation as a design strategy. Keywords: customisation, benefits of customisation, phases of customisation, high-involvement product, surfboard.
Psychological Distress in South African Healthcare Workers Early in the COVID-19 Pandemic: An Analysis of Associations and Mitigating Factors

While the global COVID-19 pandemic has been widely acknowledged to affect the mental health of health care workers (HCWs), attention to measures that protect those on the front lines of health outbreak response has been limited. In this cross-sectional study, we examine workplace contextual factors associated with how psychological distress was experienced in a South African setting where a severe first wave was being experienced with the objective of identifying factors that can protect against HCWs experiencing negative impacts. Consistent with mounting literature on mental health effects, we found a high degree of psychological distress (57.4% above the General Health Questionnaire cut-off value) and a strong association between perceived risks associated with the presence of COVID-19 in the healthcare workplace and psychological distress (adjusted OR = 2.35, p < 0.01). Our research indicates that both training (adjusted OR 0.41, 95% CI 0.21–0.81) and the reported presence of supportive workplace relationships (adjusted OR 0.52, 95% CI 0.27–0.97) were associated with positive outcomes. This evidence that workplace resilience can be reinforced to better prepare for the onset of similar outbreaks in the future suggests that pursuit of further research into specific interventions to improve resilience is well merited.