Miracle-dz/UniFAR-FTUs huggingface dataset:
Perivascular Unit: This Must Be the Place. The Anatomical Crossroad Between the Immune, Vascular and Nervous System

Most neurological disorders seemingly have heterogenous pathogenesis, with overlapping contribution of neuronal, immune and vascular mechanisms of brain injury. The perivascular space in the brain represents a crossroad where those mechanisms interact, as well as a key anatomical component of the recently discovered glymphatic pathway, which is considered to play a crucial role in the clearance of brain waste linked to neurodegenerative diseases. The pathological interplay between neuronal, immune and vascular factors can create an environment that promotes self-perpetration of mechanisms of brain injury across different neurological diseases, including those that are primarily thought of as neurodegenerative, neuroinflammatory or cerebrovascular. Changes of the perivascular space can be monitored in humans in vivo using magnetic resonance imaging (MRI). In the context of glymphatic clearance, MRI-visible enlarged perivascular spaces (EPVS) are considered to reflect glymphatic stasis secondary to the perivascular accumulation of brain debris, although they may also represent an adaptive mechanism of the glymphatic system to clear them. EPVS are also established correlates of dementia and cerebral small vessel disease (SVD) and are considered to reflect brain inflammatory activity. In this review, we describe the “perivascular unit” as a key anatomical and functional substrate for the interaction between neuronal, immune and vascular mechanisms of brain injury, which are shared across different neurological diseases. We will describe the main anatomical, physiological and pathological features of the perivascular unit, highlight potential substrates for the interplay between different noxae and summarize MRI studies of EPVS in cerebrovascular, neuroinflammatory and neurodegenerative disorders.
Search and Bidding Costs

Abstract This paper studies consumer search behavior in markets where sellers must incur fixed costs to win a contract. We show that consumers face a commitment problem if their search intensities are not observed by sellers. Welfare is maximized when consumers can precommit to a limited number of searches, but in the absence of the ability to commit, consumers search more than the optimum. A decline in search costs can exacerbate the commitment problem and leave consumers potentially worse off.
Scientific citizens, smartphones and social media – reshaping the socio-spatial networks of participation: Insects, soil and food

Abstract The conjunction of citizen science and social media through the mediation of the smartphone is investigated in this Scientific Communication, following on from the last issue of the Moravian Geographical Reports (2019, Vol. 27, No. 4). Through a reconsideration of three previously published articles, in part written by the author, this paper reflects on these topics with regard to farmer innovation, local food networks and citizen-informed ecology. Each of these papers has used Twitter to gather data about practices of innovation and observation that have revealed new insights about innovation networks amongst farmers, urban-rural connections and insect behaviours. The reflections reported here are embedded in a discussion of the rise of the term ‘Citizen Science’. Recent experiences in areas as diverse as fisheries management and combating Ebola, have informed societal needs for greater engagement in finding inclusive, comprehensive solutions to urgent socio-ecological problems. This paper suggests a compositional approach to studies using citizen scientists and their data as a new avenue of practice and investigation.
A Second–Order TV–Based Coupling Model and an ADMM Algorithm for MR Image Reconstruction

Abstract Motivated by ideas from two-step models and combining second-order TV regularization in the LLT model, we propose a coupling model for MR image reconstruction. By applying the variables splitting technique, the split Bregman iterative scheme, and the alternating minimization method twice, we can divide the proposed model into several subproblems only related to second-order PDEs so as to avoid solving a fourth-order PDE. The solution of every subproblem is based on generalized shrinkage formulas, the shrink operator or the diagonalization technique of the Fourier transform, and hence can be obtained very easily. By means of the Barzilai–Borwein step size selection scheme, an ADMM type algorithm is proposed to solve the equations underlying the proposed model. The results of numerical implementation demonstrate the feasibility and effectiveness of the proposed model and algorithm.
Cytotoxic and Mutagenic Potential of Juglone: A Comparison of Free and Nano-encapsulated Form

Abstract Despite its evidenced beneficial herbicidal, antibacterial, antiviral, antifungal, and antioxidant effects, the application of juglone (5-hydroxy-1,4,-naphthoquinone) is limited due to its low water solubility and allelopathic and toxic effects. In recent years, research has aimed to overcome these limitations by increasing its solubility and controlling its release through nanoparticular systems. This is the first study to have synthesised and characterised juglone-loaded polymeric nanoparticles and compared them with free juglone for cytotoxicity in mouse (L929 fibroblasts) and alfalfa cells and for mutagenic potential in Salmonella typhimurium TA98/100. Mouse and plant cells treated with free and nano-encapsulated juglone showed a decrease in cell viability in a dose and time-dependent manner, but this effect was significantly lower with the nano-encapsulated form at lower doses. In the TA98 strain with S9, nano-encapsulated juglone did not exhibit mutagenic effects, unlike the free form. Since all results show that juglone encapsulation with polymeric nanoparticles reduced the toxic and mutagenic effects, it has a promising potential to be applied in medicine, food safety, and agriculture.
Game-Based Promotion of Motivation and Attention for Socio-Emotional Training in Autism

Abstract Caused by a deviance of their reward system, autistic people show attention deficits for learning content outside their special fields of interest. This can lead to significant problems, especially in formal learning situations. A promising approach to increase attention is the use of game-based learning concepts. The effect of individual playful aspects could be shown in existing learning systems. However, these do not provide consistent game experiences, which may result in a decreasing motivation for training. Therefore, this paper presents requirements as well as a related game concept to integrate the learning content with a playful narrative in order to promote motivation and attention for socio-emotional training.
Accuracy of boundary layer treatments at different Reynolds scales

Abstract Resistive forces associated to boundary layers (‘friction’) are usually out of scale in physical models of hydraulic structures, especially in the case of hydraulically smooth walls, generating distortions in the model results known as scale effects, that can be problematic in some relevant engineering problems. These scale effects can be quantified and corrected using suitable numerical models. In this paper the accuracy of using numerical simulation through the Reynolds Averaged Navier-Stokes (RANS) approximation in order to represent the head losses introduced by friction in hydraulically smooth walls is evaluated for a wide range of Reynolds scales. This is performed by comparing the numerical results for fully developed flow on circular pipes and between parallel plates against experimental results, using the most popular wall treatments. The associated numerical errors, mesh requirements and ranges of application are established for each treatment. It is shown that, when properly applied, RANS models are able to simulate the head losses produced by smooth wall friction accurately enough as to quantify the scale effects present in physical models. A methodology for upscaling physical model results to prototype scale, free of scale effects, is proposed.
Morphology of the Vascular Bodies of the Encephalon’s Ventricles of Cow (Bos Taurus Taurus)

Abstract The aim of the research was to establish the morphology of the vascular bodies of the encephalon ventricles of cow (Bos taurus taurus). Methods used: thin anatomical preparation, histological method of examination, transmission electron microscopy. Given the relationship, structure and general origin of the vascular bodies, they were divided into bodies III, IV and the lateral ventricles of the encephalon. This unit has topographical nature. Functionally, vascular bodies are an indivisible organ whose main function is the secretion of the cerebrospinal fluid, which maintains the constancy of the central nervous system internal environment. Three types of capillaries, differing in their morphology, were found in the composition of the vascular bodies’ villi in Bos taurus taurus.
Weighted Mean Squared Deviation Feature Screening for Binary Features

In this study, we propose a novel model-free feature screening method for ultrahigh dimensional binary features of binary classification, called weighted mean squared deviation (WMSD). Compared to Chi-square statistic and mutual information, WMSD provides more opportunities to the binary features with probabilities near 0.5. In addition, the asymptotic properties of the proposed method are theoretically investigated under the assumption logp=o(n). The number of features is practically selected by a Pearson correlation coefficient method according to the property of power-law distribution. Lastly, an empirical study of Chinese text classification illustrates that the proposed method performs well when the dimension of selected features is relatively small.
Optimal Digital Implementation of Fractional-Order Models in a Microcontroller

The growing number of operations in implementations of the non-local fractional differentiation operator is cumbersome for real applications with strict performance and memory storage requirements. This demands use of one of the available approximation methods. In this paper, the analysis of the classic integer- (IO) and fractional-order (FO) models of the brushless DC (BLDC) micromotor mounted on a steel rotating arms, and next, the discretization and efficient implementation of the models in a microcontroller (MCU) is performed. Two different methods for the FO model are examined, including the approximation of the fractional-order operator sν (ν∈R) using the Oustaloup Recursive filter and the numerical evaluation of the fractional differintegral operator based on the Grünwald–Letnikov definition and Short Memory Principle. The models are verified against the results of several experiments conducted on an ARM Cortex-M7-based STM32F746ZG unit. Additionally, some software optimization techniques for the Cortex-M microcontroller family are discussed. The described steps are universal and can also be easily adapted to any other microcontroller. The values for integral absolute error (IAE) and integral square error (ISE) performance indices, calculated on the basis of simulations performed in MATLAB, are used to evaluate accuracy.
Nonlinear Methods Most Applied to Heart-Rate Time Series: A Review

The heart-rate dynamics are one of the most analyzed physiological interactions. Many mathematical methods were proposed to evaluate heart-rate variability. These methods have been successfully applied in research to expand knowledge concerning the cardiovascular dynamics in healthy as well as in pathological conditions. Notwithstanding, they are still far from clinical practice. In this paper, we aim to review the nonlinear methods most used to assess heart-rate dynamics. We focused on methods based on concepts of chaos, fractality, and complexity: Poincaré plot, recurrence plot analysis, fractal dimension (and the correlation dimension), detrended fluctuation analysis, Hurst exponent, Lyapunov exponent entropies (Shannon, conditional, approximate, sample entropy, and multiscale entropy), and symbolic dynamics. We present the description of the methods along with their most notable applications.
Unification of the Nature’s Complexities via a Matrix Permanent—Critical Phenomena, Fractals, Quantum Computing, ♯P-Complexity

We reveal the analytic relations between a matrix permanent and major nature’s complexities manifested in critical phenomena, fractal structures and chaos, quantum information processes in many-body physics, number-theoretic complexity in mathematics, and ♯P-complete problems in the theory of computational complexity. They follow from a reduction of the Ising model of critical phenomena to the permanent and four integral representations of the permanent based on (i) the fractal Weierstrass-like functions, (ii) polynomials of complex variables, (iii) Laplace integral, and (iv) MacMahon master theorem.
Exploring the Phase Space of Multi-Principal-Element Alloys and Predicting the Formation of Bulk Metallic Glasses

Multi-principal-element alloys share a set of thermodynamic and structural parameters that, in their range of adopted values, correlate to the tendency of the alloys to assume a solid solution, whether as a crystalline or an amorphous phase. Based on empirical correlations, this work presents a computational method for the prediction of possible glass-forming compositions for a chosen alloys system as well as the calculation of their critical cooling rates. The obtained results compare well to experimental data for Pd-Ni-P, micro-alloyed Pd-Ni-P, Cu-Mg-Ca, and Cu-Zr-Ti. Furthermore, a random-number-generator-based algorithm is employed to explore glass-forming candidate alloys with a minimum critical cooling rate, reducing the number of datapoints necessary to find suitable glass-forming compositions. A comparison with experimental results for the quaternary Ti-Zr-Cu-Ni system shows a promising overlap of calculation and experiment, implying that it is a reasonable method to find candidates for glass-forming alloys with a sufficiently low critical cooling rate to allow the formation of bulk metallic glasses.
On the Evidence of Thermodynamic Self-Organization during Fatigue: A Review

In this review paper, the evidence and application of thermodynamic self-organization are reviewed for metals typically with single crystals subjected to cyclic loading. The theory of self-organization in thermodynamic processes far from equilibrium is a cutting-edge theme for the development of a new generation of materials. It could be interpreted as the formation of globally coherent patterns, configurations and orderliness through local interactivities by “cascade evolution of dissipative structures”. Non-equilibrium thermodynamics, entropy, and dissipative structures connected to self-organization phenomenon (patterning, orderliness) are briefly discussed. Some example evidences are reviewed in detail to show how thermodynamics self-organization can emerge from a non-equilibrium process; fatigue. Evidences including dislocation density evolution, stored energy, temperature, and acoustic signals can be considered as the signature of self-organization. Most of the attention is given to relate an analogy between persistent slip bands (PSBs) and self-organization in metals with single crystals. Some aspects of the stability of dislocations during fatigue of single crystals are discussed using the formulation of excess entropy generation.
Smart Collaborative Systems for Enabling Flexible and Ergonomic Work Practices [Industry Activities]

The manufacturing industry is among the top wealthgenerating sectors of the global economy and accounted for 15.3% and 10%, respectively, of the total European and American workforce in 2018 [1], [2]. Despite its crucial role, manufacturing is facing a critical challenge based on a reduction of skilled labor availability. This trend is impos- ing a bottleneck on growth due to the demands of an increasingly competitive market. The aging workforce is not helping this shortfall either, as the available workforce is less able to perform burdensome industrial tasks in an efficient and productive manner.
Predicting Biological Age and Sex using Spatial Temporal Graph CNN for functional MRI data

Functional Magnetic Resonance Imaging (fMRI) is a technique to measure brain activity by quantifying blood oxygen level (BOLD signal) (1). There have been different studies showing that human functional organization undergoes changes related to age and exhibits sexual dimorphism (2; 3; 4). We aim at leveraging deep learning techniques to develop a model that can be used to accurately predict the biological age and sex based on fMRI data. We used the state of art Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data. As the ﬁrst attempt to apply spatial-temporal convolution on fMRI data, we overcame the challenge of limited sample size and are able to get to get an accuracy of 78% for sex and 63% for age (binary classiﬁcation for young v/s old). These results manifest ST-GCN as the novel model to predict biological sex and age from fMRI data.
Many Views Are Not Enough: Designing for Synoptic Insights in Cultural Collections

Cultural object collections attract and delight spectators since ancient times. Yet, they also easily overwhelm visitors due to their perceptual richness and associated information. Similarly, digitized collections appear as complex, multifaceted phenomena, which can be challenging to grasp and navigate. Though visualizations can create various types of collection overviews for that matter, they do not easily assemble into a “big picture” or lead to an integrated understanding. We introduce coherence techniques to maximize connections between multiple views and apply them to the prototype PolyCube system of collection visualization: with map, set, and network visualizations it makes spatial, categorical, and relational collection aspects visible. For the essential temporal dimension, it offers four different views: superimposition, animation, juxtaposition, and space–time cube representations. A user study confirmed that better integrated visualizations support synoptic, cross-dimensional insights. An outlook is dedicated to the system's applicability within other arts and humanities data domains.
Filling of lactose-based formulations in a tamping-pin capsule filler

Abstract We studied three lactose-based formulations in terms of bulk powder properties and capsule-filling behavior in a tamping-pin capsule filling system, to which several mechanical adaptions were made for process optimization in light of future continuous production. The model formulations were thoroughly characterized and filled into size 1 capsules according a well-defined design of experiments (DoE). Overall, the three entirely different formulations were successfully filled within the selected design space. The fill weight and fill weight variability can be adjusted by fine-tuning the process settings, like the pin immersion depth and the maximum compaction pressure (pneumatic or spring-controlled), and by using the appropriate powder bed height and mechanical adaptions. This study demonstrated that selection of process parameters and mechanical adaptions could enhance the filling performance, especially in continuous production, since they reduce the powder volume in the process. Moreover, we showed that a tamping-pin system is capable of successfully filling a broad range of powders with various material characteristics and can potentially be used in a continuous production mode.
Effects of Microplastic Fibers and Drought on Plant Communities

Microplastics in soils can affect plant performance, as shown in studies using individual plants. However, we currently have no information about potential effects on plant community productivity and structure. In a plant community consisting of seven plant species that co-occur in temperate grassland ecosystems, we thus investigated the effect of microplastics (i.e., microfibers) and drought, a factor with which microfibers might interact, on plant productivity and community structure. Our results showed that at the community level, shoot and root mass decreased with drought but increased with microfibers, an effect likely linked to reduced soil bulk density, improved aeration, and better penetration of roots in the soil. Additionally, we observed that microfibers affected plant community structure. Species such as Calamagrostis, invasive in Europe, and the allelophatic Hieracium, became more dominant with microfibers, while species that potentially have the ability to facilitate the establishment of other plant species (e.g., Holcus), decreased in biomass. As microfibers affect plant species dominance, the examination of cascade effects on ecosystem functions should be a high priority for future research.
Lower Ca2+ enhances the K+-induced force depression in normal and HyperKPP mouse muscles

Ingestion of Ca2+ alleviates hyperkalemic periodic paralysis (HyperKPP) symptoms by an unknown mechanism. This study shows that lowering extracellular Ca2+ affects muscle contractility when the resting membrane potential is depolarized as in HyperKPP muscles and reduces the efficacy of salbutamol, a β2-adrenergic receptor agonist used to treat HyperKPP.
Oxidative Stress in Male Infertility: Causes, Effects in Assisted Reproductive Techniques, and Protective Support of Antioxidants

The spermatozoon is a highly specialized cell, whose main function is the transport of the intact male genetic material into the oocyte. During its formation and transit throughout male and female reproductive tracts, sperm cells are internally and externally surrounded by reactive oxygen species (ROS), which are produced from both endogenous and exogenous sources. While low amounts of ROS are known to be necessary for crucial physiological sperm processes, such as acrosome reaction and sperm–oocyte interaction, high levels of those species underlie misbalanced antioxidant-oxidant molecules, generating oxidative stress (OS), which is one of the most damaging factors that affect sperm function and lower male fertility potential. The present work starts by reviewing the different sources of oxidative stress that affect sperm cells, continues by summarizing the detrimental effects of OS on the male germline, and discusses previous studies addressing the consequences of these detrimental effects on natural pregnancy and assisted reproductive techniques effectiveness. The last section is focused on how antioxidants can counteract the effects of ROS and how sperm fertilizing ability may benefit from these agents.