Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a novel mathematical model that explains SARS-CoV-2 infection dynamics in Caco-2 cells

Mathematical modeling is widely used to study within-host viral dynamics. However, to the best of our knowledge, for the case of SARS-CoV-2 such analyses were mainly conducted with the use of viral load data and for the wild type (WT) variant of the virus. In addition, only few studies analyzed models for in vitro data, which are less noisy and more reproducible. In this work we collected multiple data types for SARS-CoV-2-infected Caco-2 cell lines, including infectious virus titers, measurements of intracellular viral RNA, cell viability data and percentage of infected cells for the WT and Delta variants. We showed that standard models cannot explain some key observations given the absence of cytopathic effect in human cell lines. We propose a novel mathematical model for in vitro SARS-CoV-2 dynamics, which included explicit modeling of intracellular events such as exhaustion of cellular resources required for virus production. The model also explicitly considers innate immune response. The proposed model accurately explained experimental data. Attenuated replication of the Delta variant in Caco-2 cells could be explained by our model on the basis of just two parameters: decreased cell entry rate and increased cytokine production rate.
The Impact of Ovarian Cancer on Life Expectancy in Japan

Abstract The purpose of this study was to determine how life expectancy is modified by ovarian cancer from 1950–2000. The contributions of ovarian cancer to life expectancy were estimated. The age characteristics of ovarian cancer were detected using the Gompertz relational mortality model. The patterns between years of potential life lost (YPLL) and mortality were obtained by fitting a linear regression equation to the natural logarithm of their ratios. YPLLs are substantially higher in Ireland than in Japan. However, the rates of change were much higher in Japan than in Ireland. YPLLs changed from 0.02 year in 1950 to 0.12 year in 2000. In Japan, there was a sixfold increase in the proportion of YPLLs for death from ovarian cancer relative to those for death from gynaecological cancers during the last half century. The impact of ovarian cancer on life expectancy clearly increased and the age-specific mortality tend to ageing.
Tracing Metallicity in High Redshift Quasars

Abstract We present two ongoing studies of gas phase abundances around high redshift quasars. First, we examine broad emission line (BEL) metallicities for 29 quasars with 2.3 < z < 4.6 and far-infrared (far-IR) luminosities (LFIR) from 1013.4 to ≤ 1012.2 L⊙, corresponding to star formation rates (SFRs) of 6740 to ≤ 1360 M⊙ yr−1. Quasar samples sorted by LFIR might represent an evolutionary sequence if SFRs in quasar hosts generally diminish across quasar lifetimes. We create three composite spectra from rest-frame ultra-violet Sloan Digital Sky Survey spectra with increasing far-IR luminosity. We measure the N V(λ1240)/C IV(λ1550) and Si IV(λ1397)+O IV](λ1402)/C IV(λ1550) emission line flux ratios for each composite and find uniformly high (~5-10 times solar) metallicities for the three composites, and no evidence for changes in metal enrichment with changes in ongoing SFR. Second, we present preliminary results from the largest ever survey of high resolution associated absorption line (AAL) region metallicities and physical properties in a sample of high redshift (z > 3) quasars. This includes five quasars with previously known AALs at z > 4 and two well measured z ~3 quasars with unusually rich absorption spectra. We determine well-constrained metallicities of about twice solar for five AAL systems. We find a range of lower limits for AAL metallicities in the z > 4 quasars from 1/100ths solar to 3 times solar. Overall, these results for typically super-solar gas-phase metallicities near quasars are consistent with evolutionary schemes where the major episodes of star formation in the host galaxies occur before the visibly luminous quasar phase. High SFRs (comparable to ULIRGs) in the host galaxies are not clearly linked to younger or chemically less mature quasar environments.
Big Data and Machine Learning to Improve European Grapevine Moth (Lobesia botrana) Predictions

Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
Inclusion of Red Macroalgae (Asparagopsis taxiformis) in Dairy Cow Diets Modulates Feed Intake, Chewing Activity and Estimated Saliva Secretion

Simple Summary Here, we studied how Asparagopsis taxiformis—an emerging ruminant feed additive with methane inhibitory effect—affected the feed intake of early lactating dairy cows given free access to total mixed ration and drinking water. Eating–rumination behaviour was recorded continuously for 11 days, using electronic sensors, on 15 lactating Norwegian Red dairy cows fed diets with graded levels (0, 0.125 and 0.25% on an organic matter basis) of the macroalgae. Our results indicated that the macroalgae reduced feed intake through reduced feed intake rate, and increased eating and chewing indices. Inclusion of the macroalgae increased the estimated saliva production per unit dry matter intake. To our knowledge, this is the first documentation on the eating–rumination behaviour of dairy cows given access to a diet containing this novel ingredient. Alternative modes of delivery of the ingredient, in contrast to what is commonly used now (i.e., blending it with a water–molasses mixture) is envisaged. Abstract The current study assessed the effects of red macroalgae Asparagopsis taxiformis (AT)—included as an enteric methane inhibitor—in dairy cow diets on feed intake and eating–rumination behaviour. Fifteen early lactating Norwegian Red dairy cows were offered ad libitum access to drinking water and a total mixed ration (TMR) composed of 35% concentrate feed and 65% grass silage on a dry matter (DM) basis. The experiment lasted for 74 days with the first 22 days on a common diet used as the covariate period. At the end of the covariate period, the cows were randomly allocated into one of three dietary treatments: namely, 0% AT (control), 0.125% AT and 0.25% AT in the TMR. The TMR was offered in individual feed troughs with AT blended in a 400 g (w/w) water–molasses mixture. Eating–rumination behaviour was recorded for 11 days using RumiWatchSystem after feeding the experimental diets for 30 days. The 0.25% AT inclusion significantly reduced the DM intake (DMI). Time (min/d) spent on eating and eating in a head-down position increased with the increasing AT level in the diet, whereas rumination time was not affected. The greater time spent on eating head-down with the 0.25% AT group resulted in a significantly higher chewing index (min/kg DMI). Estimated saliva production per unit DMI (L/kg DMI, SE) increased from 10.9 (0.4) in the control to 11.3 (0.3) and 13.0 (0.3) in the 0.125% and 0.25% AT groups, respectively. This aligned with the measured ruminal fluid pH (6.09, 6.14, and 6.37 in the control, 0.125% AT and 0.25% AT groups, respectively). In conclusion, either the level of the water–molasses mixture used was not sufficient to mask the taste of AT, or the cows used it as a cue to sort out the AT. Studies with relatively larger numbers of animals and longer adaptation periods than what we used here, with varied modes of delivery of the seaweed may provide novel strategies for administering the additive in ruminant diets.
Experiments on the rise of air bubbles in clean viscous liquids

A number of new experiments have been performed on the rise of air bubbles in clean mixtures of distilled water and pure, reagent grade, glycerine covering a range of the relevant parameter, the Morton number, Mo = gv4ρ3/σ3, of 1013. Here g is the acceleration due to gravity, v the kinematic viscosity, ρ the density and σ the surface tension of the mixture. In these careful measurements several scaling regimes have been found that have not been discussed before in the extensive literature on the subject. The transitions between these regimes have been delineated and attempts made to discuss the dynamical processes that might be important in each of them.
Modelling visual saliency using degree centrality

Visual attention is an indispensable component of complex vision tasks. A multi-scale, complex network-based approach for determining visual saliency is described. It uses degree centrality (conceptually and computationally the simplest among all the centrality measures) over a network of image regions to form a saliency map. The regions used in the network are multiscale in nature with scale selected automatically. Experimental evaluation establishes the superiority of the method over existing saliency methods, even in noisy environments.
Charge density wave transitions with multiple nesting in the monophosphate tungsten bronzes : P4W12O44 and P4W14O50

We present the first x-ray diffuse scattering investigation of P4W12O44 and P4W14O50, two members of a new series of tungsten bronzes which exhibit two-dimensional electronic properties. Surprisingly, at ambient temperature these materials show quasi-one-dimensional charge density wave (CDW) fluctuations. In P4W12O44 these fluctuations condense into two successive phase transitions at 124 K and 60 K where the electrical resistivity shows giant anomalies. In P4W14O50 they condense at 183 K into a triply incommensurate and nonsinusoidal modulation exhibiting harmonics up to the 6th order. Below 47 K another triply incommensurate and nonsinusoidal modulation is stabilized. The origin of these CDW transitions of quite different nature for the two bronzes are discussed in terms of original nesting mechanisms of the Fermi surface.
An Empirical Study on the Affecting Factors of Cloud-based ERP System Adoption in Iraqi SMEs

—This paper aims to investigate the main factors that have an impact on the adoption of cloud-based enterprise resource planning (ERP) among small-and medium-sized enterprises (SMEs) in the Republic of Iraq using TOE, DOI, and HOT-fit as a theoretical framework. Data was collected from 136 decision maker senior executives, and IT managers in SMEs in the Republic of Iraq. A web-based survey questionnaire was used for data collection processes. The research model and the derived hypotheses were tested using SPSS and SmartPLS. The findings indicate several specific factors have a significant effect on the adoption of cloud-based ERP. This conclusion can be utilized in enhancing the strategies for approaching cloud-based ERP by pinpointing the reasons why some SMEs choose to adopt this technology and success during the adoption phase, while others still do not go forward with the adoption. This study provides an overview and empirically shows the main determinants logistical factors that might face SMEs in the Republic of Iraq. The findings also help SMEs consider their information technologies investments when they think to adopt cloud-based ERP.
The Use of the Weibull Three-Parameter Model for Estimating Mean Wind Power Densities

Abstract The Weibull three-parameter model is discussed for estimation of mean wind power densities. This probability density function is a generalization of a number of more conventional density functions. Using wind speed observations, it is shown that this model generally gives a more reliable fit to the empirical wind speed frequency data than the density functions with one or two parameters. Wind power density estimations turn out to be strongly dependent on the hypothesized probability density function. The variation with height of the three parameters of the discussed model is investigated; no simple height dependence can be proposed.
Renormalization group, operator product expansion and anomalous scaling in models of turbulent advection

Recent progress on the anomalous scaling in models of turbulent heat and mass transport is reviewed with the emphasis on the approach based on the field-theoretic renormalization group (RG) and operator product expansion (OPE). In that approach, the anomalous scaling is established as a consequence of the existence in the corresponding field-theoretic models of an infinite number of ‘dangerous’ composite fields (operators) with negative critical dimensions, which are identified with the anomalous exponents. This allows one to calculate the exponents in a systematic perturbation expansion, similar to the ε expansion in the theory of critical phenomena. The RG and OPE approach is presented in a self-contained way for the example of a passive scalar field (temperature, concentration of an impurity, etc) advected by a self-similar Gaussian velocity ensemble with vanishing correlation time, the so-called Kraichnan's rapid-change model, where the anomalous exponents are known up to order O(ε3). Effects of anisotropy, compressibility and the correlation time of the velocity field are discussed. Passive advection by non-Gaussian velocity field governed by the stochastic Navier–Stokes equation and passively advected vector (e.g. magnetic) fields are considered.
Longitudinal assessment of cognitive function in the APPswe/PS1dE9 mouse model of Alzheimer’s-related beta-amyloidosis

Preclinical models of Alzheimer's disease (AD)-related cognitive decline can be useful for developing therapeutics. The current study longitudinally assessed short-term memory, using a delayed matching-to-position (DMTP) task, and attention, using a 3-choice serial reaction time (3CSRT) task, from approximately 18 weeks of age through death or 72 weeks of age in APPswe/PS1dE9 mice, a widely used mouse model of AD-related amyloidosis. Both transgenic (Tg) and non-Tg mice exhibited improvements in DMTP accuracy over time. Breaks in testing reduced DMTP accuracy but accuracy values quickly recovered in both Tg and non-Tg mice. Both Tg and non-Tg mice exhibited high accuracy in the 3CSRT task with breaks in testing briefly reducing accuracy values equivalently in the 2 genotypes. The current results raise the possibility that deficits in Tg APPswe/PS1dE9 mice involve impairments in learning rather than declines in established performances. A better understanding of the factors that determine whether deficits develop will be useful for designing evaluations of potential pharmacotherapeutics and may reveal interventions for clinical application.
Immune Cells Release MicroRNA-155 Enriched Extracellular Vesicles That Promote HIV-1 Infection

The hallmark of HIV-1 infection is the rapid dysregulation of immune functions. Recent investigations for biomarkers of such dysregulation in people living with HIV (PLWH) reveal a strong correlation between viral rebound and immune activation with an increased abundance of extracellular vesicles (EVs) enriched with microRNA-155. We propose that the activation of peripheral blood mononuclear cells (PBMCs) leads to an increased miR-155 expression and production of miR-155-rich extracellular vesicles (miR-155-rich EVs), which can exacerbate HIV-1 infection by promoting viral replication. PBMCs were incubated with either HIV-1 (NL4.3Balenv), a TLR-7/8 agonist, or TNF. EVs were harvested from the cell culture supernatant by differential centrifugation, and RT-qPCR quantified miR-155 in cells and derived EVs. The effect of miR-155-rich EVs on replication of HIV-1 in incubated PBMCs was then measured by viral RNA and DNA quantification. HIV-1, TLR7/8 agonist, and TNF each induced the release of miR-155-rich EVs by PBMCs. These miR-155-rich EVs increased viral replication in PBMCs infected in vitro. Infection with HIV-1 and inflammation promote the production of miR-155-rich EVs, enhancing viral replication. Such autocrine loops, therefore, could influence the course of HIV-1 infection by promoting viral replication.
Small-Sample DIF Estimation Using SIBTEST, Cochran’s Z, and Log-Linear Smoothing

Minimum sample sizes of about 200 to 250 per group are often recommended for differential item functioning (DIF) analyses. However, there are times when sample sizes for one or both groups of interest are smaller than 200 due to practical constraints. This study attempts to examine the performance of Simultaneous Item Bias Test (SIBTEST), Cochran’s Z test, and log-linear smoothing with these methods in DIF detection accuracy at a number of small-sample and ability distribution combinations. Effects of item parameters and DIF magnitudes are also investigated. Results show that when ability distributions between groups are identical, Type I error for these DIF methods can be adequately controlled at all sample sizes, and their power to detect a large amount of unidirectional DIF can be tolerably high (power > .6) when sample size is not too small (at least 100 per group). When ability distributions are different, Type I inflation is higher for easier items and larger sample sizes, and power depends on DIF direction. Log-linear smoothing with SIBTEST tends to lower both Type I error rate and power. The effect of smoothing with Cochran’s Z test is not as consistent. Implications of the findings are discussed.
An Open-Set Modulation Recognition Scheme With Deep Representation Learning

This letter proposes a deep representation learning based automatic modulation recognition (AMR) algorithm in the open-set recognition (OSR) regime. The challenging recognition risk of unknown modulation classes is first analyzed for most state-of-the-art approaches, and interesting insights into this problem is then provided. Based on this, an open-set AMR scheme is proposed with a combination of feature representation and classification, where a triplet loss function from metric learning is employed for the representor to form distinct clusters for <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> known modulation classes. Then, the degree of membership is calculated via extreme value theory (EVT) by modeling the distance between known training data to its corresponding clustering center, followed by <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> binary classifiers. Comprehensive experiments on public dataset confirm that the proposed scheme outperforms the other state-of-the-arts in terms of both balanced accuracy and openness.
Tryptophan and Substance Abuse: Mechanisms and Impact

Addiction, the continuous misuse of addictive material, causes long-term dysfunction in the neurological system. It substantially affects the control strength of reward, memory, and motivation. Addictive substances (alcohol, marijuana, caffeine, heroin, methamphetamine (METH), and nicotine) are highly active central nervous stimulants. Addiction leads to severe health issues, including cardiovascular diseases, serious infections, and pulmonary/dental diseases. Drug dependence may result in unfavorable cognitive impairments that can continue during abstinence and negatively influence recovery performance. Although addiction is a critical global health challenge with numerous consequences and complications, currently, there are no efficient options for treating drug addiction, particularly METH. Currently, novel treatment approaches such as psychological contingency management, cognitive behavioral therapy, and motivational enhancement strategies are of great interest. Herein, we evaluate the devastating impacts of different addictive substances/drugs on users′ mental health and the role of tryptophan in alleviating unfavorable side effects. The tryptophan metabolites in the mammalian brain and their potential to treat compulsive abuse of addictive substances are investigated by assessing the functional effects of addictive substances on tryptophan. Future perspectives on developing promising modalities to treat addiction and the role of tryptophan and its metabolites to alleviate drug dependency are discussed.
SYSTEM IDENTIFICATION IN TUMOR GROWTH MODELING USING SEMI-EMPIRICAL EIGENFUNCTIONS

A tumor growth model based on a parametric system of partial differential equations is considered. The system corresponds to a phenomenological description of a multi-species population evolution. A velocity field taking into account the volume increase due to cellular division is introduced and the mechanical closure is provided by a Darcy-type law. The complexity of the biological phenomenon is taken into account through a set of parameters included in the model that need to be calibrated. To this end, a system identification method based on a low-dimensional representation of the solution space is introduced. We solve several idealized identification cases corresponding to typical situations where the information is scarce in time and in terms of observable fields. Finally, applications to actual clinical data are presented.
Acoustic Absorption by Sunspots

The paper presents the initial results of a series of observations designed to probe the nature of sunspots by detecting their influence on high-degree p-mode oscillations in the surrounding photosphere. The analysis decomposes the observed oscillations into radially propagating waves described by Hankel functions in a cylindrical coordinate system centered on the sunspot. From measurements of the differences in power between waves traveling outward and inward, it is demonstrated that sunspots appear to absorb as much as 50 percent of the incoming acoustic waves. It is found that for all three sunspots observed, the amount of absorption increases linearly with horizontal wavenumber. The effect is present in p-mode oscillations with wavelengths both significantly larger and smaller than the diameter of the sunspot umbrae. Actual absorption of acoustic energy of the magnitude observed may produce measurable decreases in the power and lifetimes of high-degree p-mode oscillations during periods of high solar activity. 10 references.