Miracle-dz/UniFAR-FTUs huggingface dataset:
Persisting Cryptococcus yeast species Vishniacozyma victoriae and Cryptococcus neoformans elicit unique airway inflammation in mice following repeated exposure

Background Allergic airway disease (AAD) is a growing concern in industrialized nations and can be influenced by fungal exposures. Basidiomycota yeast species such as Cryptococcus neoformans are known to exacerbate allergic airway disease; however, recent indoor assessments have identified other Basidiomycota yeasts, including Vishniacozyma victoriae (syn. Cryptococcus victoriae), to be prevalent and potentially associated with asthma. Until now, the murine pulmonary immune response to repeated V. victoriae exposure was previously unexplored. Objective This study aimed to compare the immunological impact of repeated pulmonary exposure to Cryptococcus yeasts. Methods Mice were repeatedly exposed to an immunogenic dose of C. neoformans or V. victoriae via oropharyngeal aspiration. Bronchoalveolar lavage fluid (BALF) and lungs were collected to examine airway remodeling, inflammation, mucous production, cellular influx, and cytokine responses at 1 day and 21 days post final exposure. The responses to C. neoformans and V. victoriae were analyzed and compared. Results Following repeated exposure, both C. neoformans and V. victoriae cells were still detectable in the lungs 21 days post final exposure. Repeated C. neoformans exposure initiated myeloid and lymphoid cellular infiltration into the lung that worsened over time, as well as an IL-4 and IL-5 response compared to PBS-exposed controls. In contrast, repeated V. victoriae exposure induced a strong CD4+ T cell-driven lymphoid response that started to resolve by 21 days post final exposure. Discussion C. neoformans remained in the lungs and exacerbated the pulmonary immune responses as expected following repeated exposure. The persistence of V. victoriae in the lung and strong lymphoid response following repeated exposure were unexpected given its lack of reported involvement in AAD. Given the abundance in indoor environments and industrial utilization of V. victoriae, these results highlight the importance to investigate the impact of frequently detected fungal organisms on the pulmonary response following inhalational exposure. Moreover, it is important to continue to address the knowledge gap involving Basidiomycota yeasts and their impact on AAD.
A new approach for analysing the fuzzy system reliability using intuitionistic fuzzy number

In general, fuzzy sets are used to analyse the fuzzy system reliability. In this paper, a new approach is developed for analysing the fuzzy system reliability of series and parallel systems using intuitionistic fuzzy set theory. For analysing the fuzzy system reliability, the definition of trapezoidal intuitionistic fuzzy number is introduced and arithmetic operations between two trapezoidal intuitionistic fuzzy numbers are also introduced. Also by considering several cases, it is shown that definitions of triangular intuitionistic fuzzy number, trapezoidal fuzzy number and triangular fuzzy number are the particular cases of the definition of trapezoidal intuitionistic fuzzy number introduced in this paper. For evaluating the fuzzy reliability of a series system and a parallel system, formulae are derived. For numerical verification of proposed approach, the fuzzy reliability of the basement flooding has been analysed using the proposed approach.
Metabolic imprint induced by seed halo-priming promotes a differential physiological performance in two contrasting quinoa ecotypes

“Memory imprint” refers to the process when prior exposure to stress prepares the plant for subsequent stress episodes. Seed priming is a strategy to change the performance of seedlings to cope with stress; however, mechanisms associated with the metabolic response are fragmentary. Salinity is one of the major abiotic stresses that affect crop production in arid and semiarid areas. Chenopodium quinoa Willd. (Amaranthaceae) is a promising crop to sustain food security and possesses a wide genetic diversity of salinity tolerance. To elucidate if the metabolic memory induced by seed halo-priming (HP) differs among contrasting saline tolerance plants, seeds of two ecotypes of Quinoa (Socaire from Atacama Salar, and BO78 from Chilean Coastal/lowlands) were treated with a saline solution and then germinated and grown under different saline conditions. The seed HP showed a more positive impact on the sensitive ecotype during germination and promoted changes in the metabolomic profile in both ecotypes, including a reduction in carbohydrates (starch) and organic acids (citric and succinic acid), and an increase in antioxidants (ascorbic acid and α-tocopherol) and related metabolites. These changes were linked to a further reduced level of oxidative markers (methionine sulfoxide and malondialdehyde), allowing improvements in the energy use in photosystem II under saline conditions in the salt-sensitive ecotype. In view of these results, we conclude that seed HP prompts a “metabolic imprint” related to ROS scavenger at the thylakoid level, improving further the physiological performance of the most sensitive ecotype.
Modifications of the electrodynamic interaction between Jupiter and Io due to mass loading effects

The electrodynamic interaction between Jupiter and Io is investigated by means of three-dimensional resistive MHD simulations with Jupiter being modeled as an aligned rotator situated in the origin of a cylindrical coordinate system. For numerical reasons, however, only qualitative statements can be made, which do not allow quantitative comparisons with observations. The main topic is the generation of electric currents parallel to the magnetic field. In consideration of the fact that some observational features cannot be qualitatively understood with present theories, the role of the plasma production from lo's atmosphere as well as by ionization in the plasma torus is investigated. If these two plasma sources are significantly cooler than the plasma environment, they can be shown to cause a torus corotational lag, in consequence of which a field-aligned current system develops. If microinstabilities occur along this system, anomalous resistivity regions may be built up where the electric field attains a component parallel to the magnetic field. Thus ionization within the torus might explain the observed decametric radiation upstream of Io's current position. Furthermore, plasma production from Io's atmosphere shows a strong influence on the MHD modes excited by the Io/torus interaction. The pressure signatures generated by slow mode perturbations disclose the formation of a tail-like structure in the wake behind Io. Thus previous conceptions about a new current systems in Io's wake can be confirmed by self-consistent MHD simulations. Finally, the correlations between this current and gradients in angular velocity and density are studied in detail.
Exercise sustains motor function in Parkinson's disease: Evidence from 109 randomized controlled trials on over 4,600 patients

Physical exercise has been widely identified as a supplementary therapy for Parkinson's disease (PD). Evaluating changes in motor function over long-term periods of exercise and comparing efficacy of various exercise types will enable a better understanding of the effects of exercise on PD. In the current study, a total of 109 studies that covered 14 types of exercise were included in the analyses, enrolling 4,631 PD patients. The results of meta-regression revealed that chronic exercise delays the progression of PD motor symptoms, mobility, and balance decline deterioration, whereas for the non-exercise PD groups, motor function progressively decline. Results from network meta-analyses suggest that dancing is the optimal exercise for general motor symptoms of PD. Furthermore, Nordic walking is the most efficient exercise to mobility and balance performance. The results from network meta-analyses also suggest that Qigong may have specific benefit in improving hand function. The findings of the current study provide further evidence that chronic exercise preserves the progression of motor function decline in PD and suggest that dancing, yoga, multimodal training, Nordic walking, aquatic training, exercise gaming, and Qigong are effective PD exercises. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=276264, identifier: CRD42021276264.
Convergence and stability of the FSR CNN model

Stability and convergency results are reported for a modified continuous-time CNN model. The signal range of the state variables is equal to the unitary interval, independently of the application, Stability and convergency properties are similar to those of the original model and, for given templates and offset coefficients. The results are generally identical. In addition, robustness and area-efficiency of VLSI implementations are significantly advantageous. <<ETX>>
Window length insensitive real-time EMG hand gesture classification using entropy calculated from globally parsed histograms

Electromyography (EMG) signal classification is vital to diagnose musculoskeletal abnormalities and control devices by motion intention detection. Machine learning assists both areas by classifying conditions or motion intentions. This paper proposes a novel window length insensitive EMG classification method utilizing the Entropy feature. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. The main goal of this study is to show that entropy can be used as the only feature for fast real-time classification of EMG signals of hand gestures. Additionally, the entropy feature can classify feature vectors of different sliding window lengths without including them in the training data. Many kinds of entropy feature succeeded in electroencephalography (EEG) and electrocardiography (ECG) classification research. However, to the best of our knowledge, the Entropy Feature proposed by Shannon stays untested for EMG classification to this day. All the machine learning models are tested on datasets NinaPro DB5 and the newly collected SingleMyo. As an initial analysis to test the entropy feature, classic Machine Learning (ML) models are trained on the NinaPro DB5 dataset. This stage showed that except for the K Nearest Neighbor (kNN) with high inference time, Support Vector Machines (SVM) gave the best validation accuracy. Later, SVM models trained with feature vectors created by 1 s (200 samples) sliding windows are tested on feature vectors created by 250 ms (50 samples) to 1500 ms (300 samples) sliding windows. This experiment resulted in slight accuracy differences through changing window length, indicating that the Entropy feature is insensitive to this parameter. Lastly, Locally Parsed Histogram (LPH), typical in standard entropy functions, makes learning hard for ML methods. Globally Parsed Histogram (GPH) was proposed, and classification accuracy increased from 60.35% to 89.06% while window length insensitivity is preserved. This study shows that Shannon’s entropy is a compelling feature with low window length sensitivity for EMG hand gesture classification. The effect of the GPH approach against an easy-to-make mistake LPH is shown. A real-time classification algorithm for the entropy features is tested on the newly created SingleMyo dataset.
Variable-rate contour-based interpolative vector quantization for image coding

In order to obtain perceptually enhanced interpolation, the authors propose a directional technique that follows edge contours to interpolate pixels along and near contours more accurately. This technique, referred to as variable rate contour based interpolative vector quantization (VQ), uses the directional information inherent in local gray level transitions and consists of an orientation estimator followed by directional interpolators. By combining a powerful VQ technique with decimation and directional interpolation, a very efficient image representation is obtained. An adaptive corrector stage using variable-rate VQ is added as a final stage of coding to improve the quality of the reconstructed image. Some relatively large amplitude errors tend to persist along image edges, and the adaptive scheme helps to correct the remaining errors in those regions selectively using an edge detector. Contour-based interpolative VQ yields fairly good reconstructed images at about 0.27 bit/pixel with a relatively simple vector coder structure. <<ETX>>
Stability Analysis of Bottle gourd (Lagenaria siceraria (Mol.) Standl.) in Southern Plains of Rajasthan (India)

Fifty-eight genotypes of bottle gourd were evaluated for stability in a Completely Randomized Block Design over three different environments in Rajasthan during summer and late Kharif 2021 (E2 & E3). According to the Eberhart and Russell model for stability analysis, P3 x P6 for days to first harvest, P2 x P9 , P3 x P6 , P3 x P9 , P4 x P7 , P3 x P7 , P1 x P3 for fruit length, P4 x P10, P2 x P5 , P1 x P6 forrind thickness, P1 x P6 , P6 x P8 , P9 x P10 for flesh thickness, P6 x P8 , P3 x P9 , P1 x P6 , P8 x P9 , P8 x P10, P4 x P6 for stem girth, P8 x P9 , P6 x P8 , P4 x P8 , P8 x P10 for vine length at final harvest, P4 x P5 and P5 x P6 for yield per vine, P1 x P5 and P1 x P3 for total sugar and P5 x P10, P5 x P9 , P9 x P10, P1 x P2 , P1 x P3 , P1 x P6 and P4 x P7 for non-reducing sugar were exhibited non-significant deviation from regression (S2 di ) and regression coefficient (bi 1) along with mean value higher than the population mean. It indicates that these hybrids would stablein favorable environment. The hybrid P8 x P10 and P9 x P10 for rind thickness and the hybrid P4 x P9 yield per vine were exhibited non-significant deviation from regression (S2 di ) and regression coefficient nearly equal to a unit (bi =1) along with mean value greater than the population mean, thereby indicated its average stability under different environment
Potential effects of shift work on skin autoimmune diseases

Shift work is associated with systemic chronic inflammation, impaired host and tumor defense and dysregulated immune responses to harmless antigens such as allergens or auto-antigens. Thus, shift workers are at higher risk to develop a systemic autoimmune disease and circadian disruption with sleep impairment seem to be the key underlying mechanisms. Presumably, disturbances of the sleep-wake cycle also drive skin-specific autoimmune diseases, but epidemiological and experimental evidence so far is scarce. This review summarizes the effects of shift work, circadian misalignment, poor sleep, and the effect of potential hormonal mediators such as stress mediators or melatonin on skin barrier functions and on innate and adaptive skin immunity. Human studies as well as animal models were considered. We will also address advantages and potential pitfalls in animal models of shift work, and possible confounders that could drive skin autoimmune diseases in shift workers such as adverse lifestyle habits and psychosocial influences. Finally, we will outline feasible countermeasures that may reduce the risk of systemic and skin autoimmunity in shift workers, as well as treatment options and highlight outstanding questions that should be addressed in future studies.
Successful new product development through external collaboration : the case of SMEs in the medical devices sector

This research focuses on the challenges faced by small and medium sized companies (SMEs) in New Product Development and the importance of the external network in this situation. The literature clearly identifies the internal and external organization as two factors that influence innovation performance. Literature mainly addressed the internal organization, but in an increasing degree also the external organization. Companies are no longer individual entities, but are rather actors that operate in a shared system with other companies and stakeholders. This movement is also reflected in this research, as we start off by studying the internal organization in a pilot study. Based on the findings from the pilot study we shift the focus to the external organization. Both quantitative and qualitative data from Dutch medical devices SMEs is used to empirically examine combinations of network characteristics that lead to high innovation performance. In this research, we 1) develop a measurement instrument for simultaneous measurement of several network characteristics and innovation performance, 2) study the relation between network characteristics and innovation performance and 3) examine which specific combination of network characteristics is related to high innovation performance. The research leads to new insights for both network and innovation management literature and offers practice a benchmark tool.
Robust estimation for radial basis functions

This paper presents a new learning algorithm for radial basis functions (RBF) neural network, based on robust statistics. The extention of the learning vector quantizer for second order statistics is one of the classical approaches in estimating the parameters of a RBF model. The paper provides a comparative study for these two algorithms regarding their application in probability density function estimation. The theoretical bias in estimating one-dimensional Gaussian functions are derived. The efficiency of the algorithm is shown in modelling two-dimensional functions. <<ETX>>
Immunotherapy Using Immunogenic Mimotopes Selected by Phage Display plus Amphotericin B Inducing a Therapeutic Response in Mice Infected with Leishmania amazonensis

Leishmania amazonensis can cause cutaneous and visceral clinical manifestations of leishmaniasis in infected hosts. Once the treatment against disease is toxic, presents high cost, and/or there is the emergence of parasite-resistant strains, alternative means through which to control the disease must be developed. In this context, immunotherapeutics combining known drugs with immunogens could be applied to control infections and allow hosts to recover from the disease. In this study, immunotherapeutics protocols associating mimotopes selected by phage display and amphotericin B (AmpB) were evaluated in L. amazonensis-infected mice. Immunogens, A4 and A8 phages, were administered alone or associated with AmpB. Other animals received saline, AmpB, a wild-type phage (WTP), or WTP/AmpB as controls. Evaluations performed one and thirty days after the application of immunotherapeutics showed that the A4/AmpB and A8/AmpB combinations induced the most polarized Th1-type immune responses, which reflected in significant reductions in the lesion’s average diameter and in the parasite load in the infected tissue and distinct organs of the animals. In addition, the combination also reduced the drug toxicity, as compared to values found using it alone. In this context, preliminary data presented here suggest the potential to associate A4 and A8 phages with AmpB to be applied in future studies for treatment against leishmaniasis.
A Fault Detection Algorithm Based on Artificial Neural Network Threshold Selection in Multi-Terminal DC Grids

The DC fault is one of the critical challenges in the High Voltage Multi-Terminal Direct Current (HV-MTDC) grid. The fixed value threshold used in some implementations may be affected by different grid conditions and thus causes errors in the fault detection procedure. This article offers a two-stage fault detection algorithm based on the integrated Artificial Neural Network (ANN) and Discrete Wavelet Transform (DWT) theory. The main innovations of the proposed method are: 1) Increasing the reliability of the fault detection operation by solving the fixed value threshold problems; 2) Offering high robustness against the wide range of fault resistances; 3) Providing bus protection capability and 4) Setting relays locally, and avoiding the requirement to high-speed communications links. Also, it offers DC fault detection in a short period of time. The performance of the proposed protection scheme has been ascertained via simulations in the MATLAB/Simulink environment. The results prove the robustness of the proposed method against noise disturbance, high resistance fault, and grid parameters variations.
Displaying cliques in graph drawings

Relational information represented by graphs can be found in various areas. Understanding completely connected groups of items is useful in studying relational information. However, when displayed in the form of a graph drawing, completely connected graphs contain quadratically many edges relative to the number of their vertices. This may increase the difficulty in identifying useful information, such as maximal cliques, in the graph. This thesis attempts to display the maximal cliques and the cliques contained in two or more maximal cliques in a given graph in an explicit and clear fashion. In order to achieve the goal, the thesis defines two models, the clique-star and the reduced-clique-star, that represent given input graphs. Both representations reduce the number of edges of the original graphs while maintaining the information about the maximal cliques. This thesis shows that six classes of graphs that can be represented by planar clique-star representations, and four classes of graphs that can be represented by planar reduced-clique-star representations. It also empirically shows that small graphs or either very sparse or very dense graphs maybe beneficially represented by planar clique-star or planar reduced-clique-star representations.
Collective tree exploration

An n-node tree has to be explored by k mobile agents (robots), starting at its root. Every edge of the tree must be traversed by at least one robot, and exploration must be completed as fast as possible. Even when the tree is known in advance, scheduling optimal collective exploration turns out to be NP-hard. We investigate the problem of how communication among robots influences the time for exploring unknown trees. To this end, we consider two scenarios. In the first scenario, robots can communicate by writing at the currently visited node previously acquired information, and reading information available at this node. In the second scenario robots are oblivious of one another, and each of them knows only the part of the tree previously explored by itself. We show that this difference of communication capability significantly influences time of collective exploration. Under the first scenario, we construct an exploration algorithm whose running time for any tree is only O(ks log k) larger than the optimal exploration time with full knowledge of the tree, while under the second scenario we prove that every algorithm works in time Ω(k) larger than optimal, for some trees. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 48(3), 166–177 2006
Probing nanoscale behavior of magnetic materials with soft X-ray spectromicroscopy

Abstract The magnetic properties of matter continue to be a vibrant research area driven both by scientific curiosity to unravel the basic physical processes which govern magnetism and the vast and diverse utilization of magnetic materials in current and future devices, e.g., in information and sensor technologies. Relevant length and time scales approach fundamental limits of magnetism and with state-of-the-art synthesis approaches we are able to create and tailor unprecedented properties. Novel analytical tools are required to match these advances and soft X-ray probes are among the most promising ones. Strong and element-specific magnetic X-ray dichroism effects as well as the nanometer wavelength of photons and the availability of fsec short and intense X-ray pulses at upcoming X-ray sources enable unique experimental opportunities for the study of magnetic behavior. This article provides an overview of recent achievements and future perspectives in magnetic soft X-ray spectromicroscopies which permit us to gain spatially resolved insight into the ultrafast spin dynamics and the magnetic properties of buried interfaces of advanced magnetic nanostructures.