Miracle-dz/UniFAR-FTUs huggingface dataset:
Higher‐temperature‐adapted dengue virus serotype 2 strain exhibits enhanced virulence in AG129 mouse model

The factors that drive dengue virus (DENV) evolution, and selection of virulent variants are yet not clear. Higher environmental temperature shortens DENV extrinsic incubation period in mosquitoes, increases human transmission, and plays a critical role in outbreak dynamics. In the present study, we looked at the effect of temperature in altering the virus virulence. We found that DENV cultured at a higher temperature in C6/36 mosquito cells was significantly more virulent than the virus grown at a lower temperature. In a mouse model, the virulent strain induced enhanced viremia and aggressive disease with a short course, hemorrhage, severe vascular permeability, and death. Higher inflammatory cytokine response, thrombocytopenia, and severe histopathological changes in vital organs such as heart, liver, and kidney were hallmarks of the disease. Importantly, it required only a few passages for the virus to acquire a quasi‐species population harboring virulence‐imparting mutations. Whole genome comparison with a lower temperature passaged strain identified key genomic changes in the structural protein‐coding regions as well as in the 3′UTR of the viral genome. Our results point out that virulence‐enhancing genetic changes could occur in the dengue virus genome under enhanced growth temperature conditions in mosquito cells.
Scaling Fuzzy Models

Scaling fuzzy learning systems can be a challenge, because the search space for fuzzy models is larger than that of crisp models. Here, we are concerned with scaling fuzzy systems as the size of the data grows. There are now many collections of data that are terabytes in size and we are moving towards petabyte collections such as a digital Sloan sky survey (Giannella et al., 2006, Gray and Szalay, 2004). AbsTRACT
Global behavior of delay differential equations model of HIV infection withapoptosis

In this paper, a class of delay differential equations model of HIV infection dynamics with nonlinear transmissions and apoptosis induced by infected cells is proposed, and then the global properties of the model are considered. It shows that the infection-free equilibrium of the model is globally asymptotically stable if the basic reproduction number $R_{0}<1$, and globally attractive if $R_{0}=1$. The positive equilibrium of the model is locally asymptotically stable if $R_{0}>1$. Furthermore, it also shows that the model is permanent, and some explicit expressions for the eventual lower bounds of positive solutions of the model are given.
Failure Mechanism of an Embankment due to Overflowing from a Reservoir

Experiments using small embankments were carried out to observe and understand the failure of an embankment due to overflow. Noncohesive silty sand was used to construct the embankments. The experimental results were quite similar to previous studies. Headcut advance was a key behavior of the failure as reported by the previous studies. In this paper, theoretical concepts for headcut advance and profiles of an embankment during overtopping failure are also discussed.
Nonsmooth Schur-Newton methods for nonsmooth saddle point problems.

We introduce and analyze nonsmooth Schur-Newton methods for a class of nonsmooth saddle point problems. The method is able to solve problems where the primal energy decomposes into a convex smooth part and a convex separable but nonsmooth part. The method is based on nonsmooth Newton techniques for an equivalent unconstrained dual problem. Using this we show that it is globally convergent even for inexact evaluation of the linear subproblems.
New Studies In Deontic Logic Norms Actions And The Foundations Of Ethics

Thank you for reading new studies in deontic logic norms actions and the foundations of ethics. As you may know, people have look hundreds times for their chosen novels like this new studies in deontic logic norms actions and the foundations of ethics, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their laptop.
Left Handed Metamaterial Design for Microstrip Antenna Application

The speculation and invention of Left-Handed Meta Material (LH MTM) had sparked the interest of many researchers globally and was said to help increase the gain and directivity of the microstrip antenna. This paper discusses the design, simulation, fabrication and measurement of LH MTM in order to proof the claims. As of the structure design, the combination of the Square Rectangular Split Ring and the Thin Wire Structure was used for fabrication purposes on an FR-4 Board using the Wet Etching Technique. From the simulation, the gain of the antenna had increased to 1.2 dB while in the measurement, 2 dB and 4 dB of increment were observed in the maximum power received of the antenna for the E-Field and H- Field respectively. This had proven that the focusing effect of the LH MTM really enhance the gain of the antenna. On the other hand, the Return Loss (S11) had improved by 9.62 dB and 12.29 dB in the simulation and measurement results respectively. Therefore, we could also conclude that LH MTM also contributed to the betterment of the matching of the antenna.
Bayesian curve fitting for transient signals by using smooth transition regression models

This communication addresses the problem of fitting time series with smooth transition regression models. These models are of interest to characterize transient signals in the context of system monitoring and diagnosis. Within this modelling, time series are segmentated by sequences of piecewise constant polynomial regression models. Moreover, smooth transitions between each segment are obtained by introducing some smooth, monotically increasing parametric transition functions. It allows one to give a synthetic representation of signals composed by smooth transitions between different regimes. Estimation of the parameters of these models appears to be an ill-posed problem. Direct optimization algorithms are not robust enough with regard to the initial parameters guess. Therefore, to achieve parameters estimation, we introduce a Bayesian framework. Appropriate priors for the unknown model parameters are introduced to penalize a data-driven criterion built from the likelihood of the observations. As the resulting posterior probability distributions do not admit closed-form analytical expressions, Markov Chain Monte Carlo (MCMC) sampling methods are derived to obtain the standard Bayesian estimators of the model parameters. Results are shown for synthetic and real appliance load monitoring data. Page 1 of 15
Theory and technology of SPASERs

Spaser is an acronym for surface plasmon amplification by stimulated emission of radiation. A spaser is effectively a nanoscale laser with subwavelength dimensions and a low-Q plasmonic resonator, which sustains its oscillations using stimulated emission of surface plasmons. The concept of stimulated emission to sustain plasmonic oscillations in a resonator was first described by David Bergman and Mark Stockman in 2003. Using a unified notation, we provide an up-to-date literature review of the major developments and latest advances in spaser theory and carry out a systematic exposition of some of the key results useful to understand the operation of spasers. Our presentation covers both semiclassical and quantum-mechanical formulations of spaser models as well as various designs and technologies demonstrated/suggested to illustrate key aspects of this technology. Even though many advances have already been made in spaser technology, there are many hurdles that need to be overcome to bring this technology up to the level of modern laser technology. We take especially great care to highlight the main challenges facing various spaser designs and the limitations of widely used methods and materials. This review is written for both specialists in the field and a general engineering–physics–chemistry readership.
Deciphering genetic basis of developmental and agronomic traits by integrating high‐throughput optical phenotyping and genome‐wide association studies in wheat

Summary Dissecting the genetic basis of complex traits such as dynamic growth and yield potential is a major challenge in crops. Monitoring the growth throughout growing season in a large wheat population to uncover the temporal genetic controls for plant growth and yield‐related traits has so far not been explored. In this study, a diverse wheat panel composed of 288 lines was monitored by a non‐invasive and high‐throughput phenotyping platform to collect growth traits from seedling to grain filling stage and their relationship with yield‐related traits was further explored. Whole genome re‐sequencing of the panel provided 12.64 million markers for a high‐resolution genome‐wide association analysis using 190 image‐based traits and 17 agronomic traits. A total of 8327 marker‐trait associations were detected and clustered into 1605 quantitative trait loci (QTLs) including a number of known genes or QTLs. We identified 277 pleiotropic QTLs controlling multiple traits at different growth stages which revealed temporal dynamics of QTLs action on plant development and yield production in wheat. A candidate gene related to plant growth that was detected by image traits was further validated. Particularly, our study demonstrated that the yield‐related traits are largely predictable using models developed based on i‐traits and provide possibility for high‐throughput early selection, thus to accelerate breeding process. Our study explored the genetic architecture of growth and yield‐related traits by combining high‐throughput phenotyping and genotyping, which further unravelled the complex and stage‐specific contributions of genetic loci to optimize growth and yield in wheat.
Langevin Diffusion Transitional Markov Chain Monte Carlo with an Application to Pharmacodynamics

We propose an algorithm for the sampling of the posterior distribution in Bayesian inference problems. The algorithm builds on the Transitional Markov Chain Monte Carlo (TMCMC) method with independent MC chains following the manifold Metropolis Adjusted Langevin transition kernels (mMALA). The algorithm combines the advantages of population based sampling algorithms -- global exploration of the parameter space, parallelizability, reliable estimator of evidence -- with the advantages of Langevin diffusion based Metropolis-Hastings MCMC -- efficient exploration of local space. Here, we use a bias free version of the TMCMC algorithm, namely the BASIS algorithm. We show that the proposed sampling scheme outperforms the existing algorithm in a test case of multidimensional Gaussian mixtures and the posterior distribution of a Bayesian inference problem in an 8 dimensional pharmacodynamics model.
White Label Fragrances: Struggles of Small Business in Botswana

In over the years, new players have entered the fragrance market to bring the essence of Africa into fragrances. The case discusses one such niche luxury brand, White Label Fragrances that incorporates indigenous materials to produce African-flavored perfumes. The brand’s distinct product segments include personal fragrances, a male grooming range, body care, and home aroma solutions. The personal fragrances consist of 16 variants catered for both males and females. The company differentiates itself by providing scents to capture the essence of Africa. The case also illustrates the timeline of achievements and challenges of the company starting from inception till date followed by targets for 2025. A Fishbone analysis demonstrates the root causes of the problems faced by the company. Finally, the case portrays the goals that the brand plans to achieve and probable strategies it could implement to take White Label Fragrances to a global level.
Challenges in Obstetric Morbidity and Mortality, With Special Reference to India: A Literature Review

The purpose of this article was to conduct a review on the determinants of maternal morbidity and mortality, obstetric health scenario, especially after the implementation of the Janani Surksha Yojana (JSY) and recognise the existing gaps in research in obstetric health and healthcare utilisation in India. The articles were retrieved using search engines like Google Scholar and PubMed, using specific combination of terms along with the grey literature available in national and regional health databases. Although the current medical knowledge and interventions are capable to tackle most physical preconditions responsible for maternal deaths, yet the utilisation of healthcare facilities depends on the available health infrastructure, human resources and quality of services that are accessible and acceptable to the beneficiaries. Acceptability especially is impacted by the health-seeking behaviour of a community. The implementation of JSY is believed to contribute to the decline of maternal mortality ratio, yet maternal health outcome reports gross inequity in terms of utilisation of antenatal care and postnatal care at many sub-national levels. Thus, micro-level studies are required to understand critically the level of utilisation of formal healthcare facilities, in order to ensure an unscathed maternal health profile throughout India.
Studying the Downstream Effects of Fact-Checking on Social Media: Experiments on Correction Formats, Belief Accuracy, and Media Trust

Repeated exposure to misinformation not only reduces the accuracy of people’s beliefs, but it also decreases confidence in institutions such as the news media. Can fact-checking—journalism’s main weapon against misinformation—worsen or ameliorate distrust in journalists and the media? To answer this question, we conducted two pre-registered experiments in Chile (total N = 1,472) manipulating message and receiver factors known to regulate the persuasiveness of fact-checks: transparency elements, arousing images, and political alignment. The results of both studies show that, across message formats, fact-checks are similarly effective at reducing people’s misperceptions. However, these positive effects on belief accuracy come at a cost: Compared to control groups, users exposed to political fact-checks trust news less and perceive the media as more biased, especially after reading corrections debunking pro-attitudinal misinformation. We close with a discussion of the theoretical and practical implications of these findings.
Algorithms and hegemony in the workplace: Negotiating design and values in an Italian television platform

In recent years, several scholars have highlighted the necessity to scrutinize the practices and material settings in which algorithmic models are designed, in order to unpack the working activities and socio-cultural constructs underlying their production and deployment process. Drawing on a multisited ethnography, this paper investigates the practices of tech workers within the corporate environment of an internet television platform, the hierarchical relationships between different professional figures, and how these individuals frame algorithms and contribute to the enactment of these systems with their activities. Findings highlight the hierarchical organization of tech work and the subordination of operative figures to the goals imposed by business clients and to both internal and external forms of control. Specifically, it emerges how the subalternity of tech workers is materially and discursively constructed and forms of causal, dispositional and facilitative power exerted on them. In this environment, frictions, negotiations as well as concealing strategies by tech workers regarding the design and meaning of algorithms emerge, thus showing their cultural, contingent and multiple composition. Within the framework of Giddens’ structure/agency cycle, it is shown how everyday working activities and relationships contribute to the reproduction of hegemonic arrangements in the workplace, and how these hegemonic arrangements are at the core of algorithmic production, thus playing a key role in the framing, construction and enactment of these systems.
Speaking Stata: Shading Zones on Time Series and Other Plots

Background shading of time series and other plots is a common need. For example, we often wish to identify particular periods of recession or war or other distinct conditions. The shading should be laid down before the data elements (for example, data points or lines). Several methods of shading can be effective. Tips are included for automating the production of a series of similar graphs.
Similarity indices based on link weight assignment for link prediction of unweighted complex networks

Many link prediction methods have been proposed for predicting the likelihood that a link exists between two nodes in complex networks. Among these methods, similarity indices are receiving close attention. Most similarity-based methods assume that the contribution of links with different topological structures is the same in the similarity calculations. This paper proposes a local weighted method, which weights the strength of connection between each pair of nodes. Based on the local weighted method, six local weighted similarity indices extended from unweighted similarity indices (including Common Neighbor (CN), Adamic-Adar (AA), Resource Allocation (RA), Salton, Jaccard and Local Path (LP) index) are proposed. Empirical study has shown that the local weighted method can significantly improve the prediction accuracy of these unweighted similarity indices and that in sparse and weakly clustered networks, the indices perform even better.
Spatial correspondence in relative space regulates serial dependence

Our perception is often attracted to what we have seen before, a phenomenon called ‘serial dependence. ’ Serial dependence can help maintain a stable perception of the world, given the statistical regularity in the environment. If serial dependence serves this presumed utility, it should be pronounced when consecutive elements share the same identity when multiple elements spatially shift across successive views. However, such preferential serial dependence between identity-matching elements in dynamic situations has never been empirically tested. Here, we hypothesized that serial dependence between consecutive elements is modulated more effectively by the spatial correspondence in relative space than by that in absolute space because spatial correspondence in relative coordinates can warrant identity matching invariantly to changes in absolute coordinates. To test this hypothesis, we developed a task where two targets change positions in unison between successive views. We found that serial dependence was substantially modulated by the correspondence in relative coordinates, but not by that in absolute coordinates. Moreover, such selective modulation by the correspondence in relative space was also observed even for the serial dependence defined by previous non-target elements. Our findings are consistent with the view that serial dependence subserves object-based perceptual stabilization over time in dynamic situations.
Reconstructing the evolutionary history of human, simian, and prosimian immunodeficiency viruses

While paleovirological evidence supports the idea that the ancestors of modern simian (SIV) and prosimian (pSIV) immunodeficiency viruses have been evolving in nonhuman primates for millions of years, standard molecular clock methods calibrated using contemporary sequences significantly underestimate their times of origin. This discrepancy is attributed to the time-dependent nature of evolutionary rate estimates whereby the rate of virus evolution varies with respect to the time window of its measurement. While biogeographical calibrations may provide better estimates across intermediate timescales, there is currently no unifying framework that can allow inference of lentiviral ages across all relevant timescales. Here we showed that by correcting for such time-dependent rate effects using the recently developed Prisoner-of-War evolutionary model, we can successfully reconstruct the evolutionary history of human, simian, and prosimian immunodeficiency viruses across vastly different timescales and allowed estimates of the age of lineages that gave rise to HIV-1 and HIV-2. The model also predicted that the most recent common ancestor of SIV and pSIV lived around 21 million years ago, suggesting that the most likely explanation for the origins of primate lentiviruses is a terrestrial transfer of lentiviruses between African and Malagasy primates during the last episode of colonisation of Madagascar. Infections have entered via a land bridge in a group of mammals or through a nonprimate vector or transfer mediated by an aerial vector species. We also found that the most recent common ancestor of SIVs lived nearly a million years ago and that some SIV lineages codiverged with their hosts for several hundreds of thousands of years. The predicted long evolutionary timescales of SIVs and potential for close virus/host co-adaptation is consistent with their reduced or minimal pathogenicity in their native hosts, contrasting with the very recent evolutionary origins of highly pathogenic HIV strains in humans.
SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis

Background In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. Results To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. Conclusion SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan. Key Points A deep learning model was trained to detect and segment spheroids in images from microscopes and Incucytes. The model performed well on both types of images with the total loss decreasing significantly during the training process. A web tool called SpheroScan was developed to facilitate the analysis of spheroid images, which includes prediction and visualization modules. SpheroScan is efficient and scalable, making it possible to handle large datasets with ease. SpheroScan is user-friendly and accessible to researchers, making it a valuable resource for the analysis of spheroid image data.