Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating Reelin promotes inflammation and modulates disease activity in acute and long COVID-19 cases

Thromboembolic complications and excessive inflammation are frequent in severe COVID-19, potentially leading to long COVID. In non-COVID studies, we and others demonstrated that circulating Reelin promotes leukocyte infiltration and thrombosis. Thus, we hypothesized that Reelin participates in endothelial dysfunction and hyperinflammation during COVID-19. We showed that Reelin was increased in COVID-19 patients and correlated with the disease activity. In the severe COVID-19 group, we observed a hyperinflammatory state, as judged by increased concentration of cytokines (IL-1α, IL-4, IL-6, IL-10 and IL-17A), chemokines (IP-10 and MIP-1β), and adhesion markers (E-selectin and ICAM-1). Reelin level was correlated with IL-1α, IL-4, IP-10, MIP-1β, and ICAM-1, suggesting a specific role for Reelin in COVID-19 progression. Furthermore, Reelin and all of the inflammatory markers aforementioned returned to normal in a long COVID cohort, showing that the hyperinflammatory state was resolved. Finally, we tested Reelin inhibition with the anti-Reelin antibody CR-50 in hACE2 transgenic mice infected with SARS-CoV-2. CR-50 prophylactic treatment decreased mortality and disease severity in this model. These results demonstrate a direct proinflammatory function for Reelin in COVID-19 and identify it as a drug target. This work opens translational clinical applications in severe SARS-CoV-2 infection and beyond in auto-inflammatory diseases.
Cold Cas: reevaluating the occurrence of CRISPR/Cas systems in Mycobacteriaceae

Bacterial CRISPR/Cas systems target foreign genetic elements such as phages and regulate gene expression by some pathogens, even in the host. The system is a marker for evolutionary history and has been used for inferences in Mycobacterium tuberculosis for 30 years. However, knowledge about mycobacterial CRISPR/Cas systems remains limited. It is believed that Type III-A Cas systems are exclusive to Mycobacterium canettii and the M. tuberculosis complex (MTBC) of organisms and that very few of the >200 diverse species of non-tuberculous mycobacteria (NTM) possess any CRISPR/Cas system. This study sought unreported CRISPR/Cas loci across NTM to better understand mycobacterial evolution, particularly in species phylogenetically near the MTBC. An analysis of available mycobacterial genomes revealed that Cas systems are widespread across Mycobacteriaceae and that some species contain multiple types. The phylogeny of Cas loci shows scattered presence in many NTM, with variation even within species, suggesting gains/losses of these loci occur frequently. Cas Type III-A systems were identified in pathogenic Mycobacterium heckeshornense and the geological environmental isolate Mycobacterium SM1. In summary, mycobacterial CRISPR/Cas systems are numerous, Type III-A systems are unreliable as markers for MTBC evolution, and mycobacterial horizontal gene transfer appears to be a frequent source of genetic variation.
Virtual reality check: a comparison of virtual reality, screen-based, and real world settings as research methods for HRI

To reduce costs and effort, experiments in human-robot interaction can be carried out in Virtual Reality (VR) or in screen-based (SB) formats. However, it is not well examined whether robots are perceived and experienced in the same way in VR and SB as they are in the physical world. This study addresses this topic in a between-subjects experiment, measuring trust and engagement of an interaction with a mobile service robot in a museum scenario. Measures were made in three different settings, either the real world, in VR or in a game-like SB and then compared with an ANOVA. The results indicate, that neither trust nor engagement differ dependent on the experimental setting. The results imply that both VR and SB are eligible ways to explore the interaction with a mobile service robot, if some peculiarities of each medium are taken into account.
Chronic HCV infection promotes cytotoxicity in antigen-specific CD8+ T cells regardless of virus specificity

Introduction Despite advancements in hepatitis C virus (HCV) infection treatment, HCV still represents a significant public health burden. Besides progressive hepatic damage, viral persistence has lasting effects on innate and adaptive immune responses. Lack of a complete understanding of the factors driving an effective HCV response contributes to the failure to develop a vaccine for prevention. This study advances the existing knowledge on HCV-specific CD8+ T cells and describes the impact of current or past HCV infection on CD8+ T cells specific for other viruses. Methods We used barcoded-dextramers to identify and sort CD8+ T cells specific for HCV, cytomegalovirus, and influenza, and characterized them using single-cell RNA sequencing technology. Our cohort included chronic (cHCV), spontaneously resolved (rHCV), and subjects undergoing direct-acting antiviral (DAA) therapy. Results We show that HCV-specific CD8+ T cells have cytotoxic features in patients with cHCV, which is progressively reduced with DAA therapy and persists 12 weeks after treatment completion. We also observe a shift in the CD8+ T cell phenotype on DAA treatment, with decreased effector memory and exhausted cell signatures. In rHCV, we also detected a smaller proportion of effector memory cells compared to cHCV. The proportion of CD8+ exhausted T cells in cHCV and rHCV subjects was comparable. Moreover, we also observed that non-HCV virus-specific CD8+ T cells exhibit robust cytotoxic traits during cHCV infection. Discussion Altogether, our findings suggest that cHCV infection promotes cytotoxicity in CD8+ T cells regardless of virus specificity. The immunological changes caused by cHCV infection in CD8+ T cells may contribute to worsening the ongoing hepatic damage caused by HCV infection or exacerbate the immune response to possible co-infections. Our data provide a resource to groups exploring the underlying mechanisms of HCV-specific T cell spontaneous and treatment-induced resolution to inform the development of effective vaccines against HCV infection.
Critical action to redress systemic oppression: a person-centered approach

Introduction In 2020, public outcry against police brutality prompted many social media users to post black squares and use the hashtag #BlackLivesMatter (BLM). Many of the people who posted these squares were engaging in performative action in the sense that they failed to engage with BLM’s history and core principles. Drawing from a critical action framework, the current research seeks to more deeply understand what drives people to engage in more versus less impactful forms of action to resist systemic oppression. Methods We employed a mixed-methods and person-centered methodological approach with the goal of providing nuanced information about factors that distinguish among individuals who engage in different forms of action. Participants were 359 undergraduates who reported that they engaged in some form of action to support BLM. Findings Latent profile analysis identified three subgroups (i.e., latent classes) in the larger sample, which we labeled (1) intentional action, (2) intermediate action, and (3) passive action. Participants in each latent class differed from one another in their sociopolitical attitudes, sociodemographic background, and level of action to support BLM. Through the qualitative coding process, the research team unearthed three overarching themes and a range of subthemes that help to explain why the members of each class engaged in different forms of action. Discussion We conclude by proposing a flexible intervention that may motivate individuals to engage in critical action to support BLM.
Fuzzy H_∞, robust feedback control for uncertain nonlinear system with time-delay based on fuzzy model

The problem of the fuzzy H∞ state feedback control for a class of uncertain nonlinear systems with input and state time delay was addressed. The uncertain Takagi-Sugeno (T-S) fuzzy model with time delay was adopted for fuzzy modeling of nonlinear system. The systematic design procedures based on LMI for the fuzzy robust controller design were given and some sufficient conditions were derived for the existence of fuzzy H∞ state feedback controllers such that the closed-loop system was asymptotically stable and the effect of the disturbance input on the controlled output was reduced to a prescribed level. Simulation results show the effectiveness of the proposed method.
Aerosol Radiative Forcing and Forcing Efficiency in the UVB for Regions Affected by Saharan and Asian Mineral Dust

The influence of mineral dust on ultraviolet energy transfer is studied for two different mineralogical origins. The aerosol radiative forcing DF and the forcing efficiency at the surface DF eff in the range 290–325 nm were estimated in ground-based stations affected by the Saharan and Asian deserts during the dusty seasons. UVB solar measurements were taken from the World Ozone and Ultraviolet Data Center (WOUDC) for four Asian stations (2000–04) and from the Santa Cruz Observatory, Canary Islands (2002– 03), under Gobi and Sahara Desert influences, respectively. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth at 550 nm was used to characterize the aerosol load t, whereas the aerosol index provided by the Total Ozone Mapping Spectrometer (TOMS) sensor was employed to identify the mineral dust events. The DF is strongly affected by the aerosol load, the values found being comparable in both regions during the dusty seasons. Under those conditions, DF values as large as 21.29 6 0.53 W m 22 (t550 5 0.48 6 0.24) and 21.43 6 0.38 W m 22 (t550 5 0.54 6 0.26) were reached under Saharan and Asian dust conditions, respectively. Nevertheless, significant differences have been observed in the aerosol radiative forcing per unit of aerosol optical depth in the slant path, tS. The maximum DF eff values associated with dust influences were 21.55 6 0.20 W m 22 t 2 1 S550 for the Saharan region and 20.95 6 0.11 W m 22 t 2 1 S550 in the Asian area. These results may be used as a benchmark database for establishing aerosol corrections in UV satellite products or in global climate model estimations.
NWP Experiments with a Gridpoint Semi-Lagrangian Semi-ImplicitGlobal Model at NCEP

Abstract A series of 5-day forecasts for both winter and summer initial conditions have been performed using a fairly high resolution version (0.9375° latitude–longitude grid with 28 vertical levels) of a gridpoint semi-Lagrangian global forecast model with second-order semi-implicit off-centering and a time step of 1800 s. Most of the physics included in the model are taken from the NCEP operational MRF model. The forecasts are evaluated by comparing the anomaly correlations computed using the NCEP operational global analyses with those for the operational MRF forecasts. It is found that the skill scores of the semi-Lagrangian model are fairly close to those of the operational MRF model despite the handicap of not having its own analyses forecast cycle. The forecasts are also used to get an estimate of the climate drift of the model. It is found that the drift is similar to but somewhat larger than that for the NCEP MRF model. In the second part of the paper, a spatially averaged Eulerian treatment of or.
Unstable feature relevance in classification tasks

Skrypnyk, Iryna Unstable Feature Relevance in Classification Tasks Jyvaskyla: University of Jyvaskyla, 2011, 232 p. (Jyvaskyla Studies in Computing, ISSN 1456-5390;152) ISBN 978-951-39-4607-4 (nid.) ISBN 978-951-39-4608-1 (PDF) Finnish summary Diss. Over the last decade, data mining has gone through a significant transformation influenced by advanced data collection technologies. Today data mining faces the challenge of dealing with increasingly complex data structures. As a result, data often exhibits instability in measured attribute values (features). In other words, the set of relevant features is not the same through the entire set of domain examples. Considering this problem from another angle, data includes regions with local properties that, in particular, differ from each other with regard to the feature relevance profiles. Global models, therefore, cannot reflect the essential knowledge about the data structure. This thesis presents a description of the unstable feature relevance problem in classification tasks, elaborating the concept of heterogeneous classification problems and introducing different types of feature space heterogeneity. It also suggests a multi-model solution derived from the definition of a subproblem as a group of instances with easier class discrimination and lower complexity in the subspace of locally relevant features. The solution is presented within an ensemble learning framework. The search strategies, suggested for decomposition of classification problems with unstable feature relevance, express different levels of granularity with respect to classes. Evaluation of the candidate subproblems is executed through profiles of feature relevance. These profiles are vectors of weights obtained from feature merit measures and, alternatively, a result of distance metric adaptation. Additional measures of complexity, including class boundaries and density-based measures, are suggested to evaluate decomposition and to serve as preliminary heterogeneity tests. This research contributes towards reaching complementary data analysis goals on classification problems and revealing important insights on the data structure and its complexity. The effects on classification performance were studied through numerous experiments on synthetic, benchmark, and real data from a biomedical research domain. It was found that extraction of subproblems is possible in many cases and it provides meaningful data partitioning results. In many cases it also leads to improvement in predictive performance.
Variational Gaussian approximation for the magnetic Schrödinger equation

In the present paper we consider the semiclassical magnetic Schr\"odinger equation, which describes the dynamics of particles under the influence of a magnetic field. The solution of the time-dependent Schr\"odinger equation is approximated by a single Gaussian wave packet via the time-dependent Dirac--Frenkel variational principle. For the approximation we derive ordinary differential equations of motion for the parameters of the variational solution. Moreover, we prove $L^2$-error bounds and observable error bounds for the approximating Gaussian wave packet.
Forecasting European Ozone Air Pollution With Transformers

Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy to reduce the risk to health, such as air quality warnings. However, forecasting ozone is a difficult problem, as surface ozone concentrations are controlled by a number of physical and chemical processes which act on varying timescales. Accounting for these temporal dependencies appropriately is likely to provide more accurate ozone forecasts. We therefore deploy a state-of-the-art transformer-based model, the Temporal Fusion Transformer, trained on observational station data from three European countries. In four-day test forecasts of daily maximum 8-hour ozone, the novel approach is highly skilful (MAE = 4.6 ppb, R = 0.82), and generalises well to two European countries unseen during training (MAE = 4.9 ppb, R = 0.79). The model outperforms standard machine learning models on our data, and compares favourably to the published performance of other deep learning architectures tested on different data. We illustrate that the model pays attention to physical variables known to control ozone concentrations, and that the attention mechanism allows the model to use relevant days of past ozone concentrations to make accurate forecasts.
Processing of Flight Information based on Approximation with Analytical Connections (short paper)

The conflict of airships is understood as their convergence in space and time, during which there is a violation of the specified minimum separation distance (echeloning). An increase in the intensity of flights inevitably leads to an increase in conflicts between airships. The minimum separation distance between aircraft is determined by their protective spatial zone with regulated geometry. Thanks to the proposed mathematical and software, it became possible to reduce the protective space zone and thereby reduce the minimum permissible interval between aircraft and increase flight safety, as well as to optimize the structure of airspace to improve throughput and thereby increase flight safety.
The informative value of Key Audit Matters in the audit report: understanding the audit form and matter type impact

This paper examines whether the readability (and hence informative value) of Key Audit Matters (KAM) varies according to the audit firm and the KAM type disclosed, thus providing stakeholders with different information quality for effective decision making. Our analysis shows that the informative value of published descriptions of KAM matter and KAM audit procedures varies according to the audit firm responsible. This informative value also depends on whether the KAM concerns entity-level or accounting-level risks. This study contributes to stakeholder theory and to the literature on audit report quality by showing how the characteristics of the audit firm and the type/content of KAM presented impact on the informative value of the expanded audit report. Furthermore, we suggest that audit firms may increase or decrease the informative value of the audit report according to the type of KAM disclosure made.
Amphipathic Solvent-Assisted Synthetic Strategy for Random Lamellae of the Clinoptilolites with Flower-like Morphology and Thinner Nanosheet for Adsorption and Separation of CO2 and CH4

The random lamellae of the synthetic CP were synthesized with a hydrothermal approach using o-Phenylenediamine (OPD) as a modifier. The decreases in the order degree of the CP synthesized in the presence of the OPD resulted from the loss of long-range order in a certain direction. Subsequently, the ultrasonic treatment and washing were conducive to further facilitate the disordered arrangements of its lamellae. The possible promotion mechanism regarding the nucleation and growth behaviors of the sol-gel particles was proposed. The fractal evolutions of the aluminosilicate species with crystallization time implied that the aluminosilicate species became gradually smooth to rough during the crystallization procedures since the amorphous structures transformed into flower-like morphologies. Their gas adsorption and separation performances indicated that the adsorption capacity of CO2 at 273 K reached up to 2.14 mmol·g−1 at 1 bar, and the selective factor (CO2/CH4) up to 3.4, much higher than that of the CPs synthesized without additive OPD. The breakthrough experiments displayed a longer breakthrough time and enhancement of CO2 uptake, showing better performance for CO2/CH4 separation. The cycling test further highlighted their efficiency for CO2/CH4 separation.
The Best Deep Ensembles Sacrifice Predictive Diversity

Ensembling remains a hugely popular method for increasing the performance of a given class of models. In the case of deep learning, the benefits of ensembling are often attributed to the diverse predictions of the individual ensemble members. Here we investigate a tradeoff between diversity and individual model performance, and find that—surprisingly—encouraging diversity during training almost always yields worse ensembles. We show that this tradeoff arises from the Jensen gap between the single model and ensemble losses, and show that Jensen gap is a natural measure of diversity for both the mean squared error and cross entropy loss functions. Our results suggest that to reduce the ensemble error, we should move away from efforts to increase predictive diversity
MoTe2: Weyl and Line Node Topological Metal

Topological metals exhibit unremovable degeneracies in the band structure close to the Fermi level. These degeneracies can come in many types, the most basic of which are point (Weyl) and line nodes, and are associated with a wide range of phenomena from chiral anomalies to Fermi arcs. Here we show that MoTe2, in its low-temperature orthorhombic structure, realizes both type-II Weyl points and nodal lines close to the Fermi level, in a complex topological band structure. The Weyl nodes occur between several different bands, and the nodal lines are protected by mirror symmetry. We present an X-ray diffraction study of this new orthorhombic phase of MoTe2 taken at 100 K, and experimentally determine its lattice parameters. Using this input in ab initio and topological calculations, we predict a series of strain-driven topological phase transitions in this compound, opening a wide range of possible experimental realizations of different topological semimetal phases. Crucially, with no strain, the number of observable surface Fermi arcs in this material is 2 - the smallest number of arcs consistent with time-reversal symmetry.
Handwritten Ukrainian Character Recognition using a Convolutional Neural Networks and Synthetic Dataset

Abstract text. This paper considers several options for the architecture of convolutional neural networks for the recognition of isolated handwritten Ukrainian characters and numbers, which were trained using a synthetic dataset built on the basis of a set of handwritten and cursive fonts. Comparison of the results of recognition of several variants of images containing handwritten letters and numbers using neural networks with different architectures showed that an increase in the number of convolutional layers leads to a decrease in the frequency of erroneous character recognition. The size of the training dataset significantly affects the reliability of character recognition. The data sets used in the work contained from 192 to 2304 samples per class. The upper limit of the number of samples per class is close to the limit that provides acceptable recognition accuracy. Reducing the sample size by reducing the number of samples per class leads to a significant decrease in recognition accuracy (from 90% recognition accuracy of elements of real inscriptions to 40-60% with a 4-fold decrease in sample size).
A Semantics for Causing, Enabling, and Preventing Verbs Using Structural Causal Models

When choosing how to describe what happened, we have a number of causal verbs at our disposal. In this paper, we develop a model-theoretic formal semantics for nine causal verbs that span the categories of CAUSE , ENABLE , and PREVENT . We use structural causal models (SCMs) to represent participants’ mental construction of a scene when assessing the correctness of causal expressions relative to a presented context. Furthermore, SCMs enable us to model events relating both the physical world as well as agents’ mental states. In experimental evaluations, we find that the proposed semantics exhibits a closer alignment with human evaluations in comparison to prior accounts of the verb families.
COMMUNITY DETECTION WITH CELLULAR AUTOMATA

Cellular-automata was used in this work to detect community structure.The method was based on a definition of "strong community" which was given by Radicchi in 2004.Very good data were obtained in the detection of community structure under this definition.When applied to classical manual network with 128 vertices,the accuracy was 100%. The complexity of the method was O(n2lgn).