Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention Bias for Angry Faces in Children with Social Phobia

Attention bias towards threatening stimuli is a well-established cognitive correlate of anxiety disorders. In tasks using angry faces, accumulating results link paediatric anxiety to biased attention. Nevertheless, at least in childhood social phobia, there is mixed evidence regarding the direction of this bias as some findings suggest that socially anxious children show increased attention bias towards threat, whereas other evidence suggests they have an increased bias away from threat. The present study examined attention bias for angry (and happy) faces in 27 children with a principal diagnosis of social phobia and 27 non-clinical healthy volunteers between 5 and 13 years of age. Children completed a visual probe task in which pairs of angry and neutral faces and happy and neutral faces were presented for 500 ms followed by an asterisk probe in either the same or opposite location as the emotional face. Participants were instructed to respond quickly by key-press to indicate the position of the probe. Results showed that only socially phobic children with high symptom severity showed a significant attention bias towards angry faces relative to neutral ones. In contrast, children with social phobia with lower levels of symptom severity showed a significant attention bias away from angry faces. Non-anxious volunteers showed no significant bias for angry faces. There were no significant between-group differences for happy faces. Results are discussed in terms of prior work on attention and emotion regulation.
Gradient sensing via cell communication.

Experimental evidence lends support to the conjecture that cell-to-cell communication plays a role in the gradient sensing of chemical species by certain chains of cells. Models have been formulated to explore this idea. For cells with no identifiable sensing structure, Mugler et al. [Proc. Natl. Acad. Sci. (U.S.A.) 113, E689 (2016)10.1073/pnas.1509597112] have defined a particular local excitation, global inhibition (LEGI) model that pits nearest-neighbor communication against local reactions in a noisy environment to suggest how this sensing capability might arise in a physical system. In this study, we generalize the nearest-neighbor communication mechanism in the aforementioned LEGI model in order to explore the extent to which the gradient sensing characteristics depend on the parametrization of the communication itself, as well as on the cell size, the radius of influence of neighboring cells, and the influence of the background noise. Using our generalization and a collection of particular candidate communication models, we find that the precision of gradient sensing is indeed sensitive to the particular communication model, and we derive physical and analytic explanations for these results. The framework established and the associated results should prove useful in understanding the appropriateness of particular cell-to-cell communication models in gradient sensing studies.
Convergence of Pitch Accents in a Shadowing Task

In the present study, a corpus of short German sentences collected in a shadowing task was examined with respect to pitch accent realization. The pitch accents were parameterized with the PaIntE model, which describes the f 0 contour of intonation events concerning their height, slope, and temporal alignment. Convergence was quantiﬁed as decrease in Euclidean distance, and hence increase in similarity, between the PaIntE parameter vectors. This was assessed for three stimulus types: natural speech, diphone based speech synthesis, or hidden Markov model (HMM) based speech synthesis. The factors tested in the analysis were experimental phase – was the sentence uttered be-fore or while shadowing the model, accent type – a distinction was made between prenuclear and nuclear pitch accents, and sex of speaker & shadowed model . For the natural and HMM stimuli, Euclidean distance decreased in the shadowing task. This convergence effect did not depend on the accent type. However, prenuclear pitch accents showed generally lower values in Euclidean distance than nuclear pitch accents. Whether the sex of the speaker and the shadowed model matched did not explain any variance in the data. For the diphone stimuli, no convergence of pitch accents was observed.
Detecting Linguistic Change Based on Word Co-occurrence Patterns

Diachronic linguistic analysis focuses on detecting elements of language change over time. This change can take different forms, as for instance certain words could show a slow increase or decrease in frequency over time as these become more popular or obsolete. We are interested in sudden change in words that are attested for in every time slice of the overall examined time period. In particular, we are trying to relate the change in frequency of words that are always there to words that emerge around certain points in time and only remain frequent for shorter periods of time, suggesting they are more prone to sudden changes in popular topics or could be influenced by historical events. This addresses the question of how the more regular word expressions’ frequencies are influenced by new clusters of words appearing and disappearing. Although there might be links to collocation analysis, words that occur frequently next to each other are not of primary interest here, but rather words that are conceptually related, where one is causing or affecting the frequency of the other, which causes them vary in a similar fashion. We use statistical change point analysis for identification of significant change over time and seek to validate our findings by randomly extracting example sentences from the data.
Broad Impairment of Executive Functions in Patients with ParkinsonÃÂÃÂ¢ÃÂÃÂÃÂÃÂs disease: A Meta-Analysis

Abstract Objective: Executive functions (EF) impairments have been observed in patients with Parkinson’s disease (PD). However, the pattern of EF deficits in this population remains unclear. This article aimed to examine the influence of PD on different EF domains through meta-analysis of published data. Methods: This article aimed to compare the EFs of PD patients with those of healthy controls (CON) in different EF domains. We searched electronic databases for articles reporting comparisons of EF performance between non-demented/non-depressed PD patients and CON. Accordingly, we identified 140 studies investigating 6 EF domains (attention, inhibition, planning, reasoning, shifting and working memory) in 4683 PD patients and 4247 CON. Results Results showed that PD patients exhibited impaired attention (Hedges’ g= -0.48), inhibition (Hedges’ g=-0.48), planning (Hedges’ g = -0.49), reasoning (Hedges’ g = -0.31), shifting (Hedges’ g = -0.55) and working memory (Hedges’ g = -0.53). They exhibited a moderately impaired overall EF (Hedges’ g = -0.49). EF deficits were not moderated by age, years of education, disease severity, motor deficits, disease duration, medication dose or global cognition. Conclusions: The findings suggest that among PD patients, EFs in which reasoning is least affected are broadly impaired
RIAACT: A robust approach to adjustable autonomy for human-multiagent teams

When human-multiagent teams act in real-time uncertain domains, adjustable autonomy (dynamic transferring of decisions between human and agents) raises four key challenges. First, the human and agents may di ﬀ er signiﬁcantly in their worldviews, leading to in-consistencies in their decisions. Unfortunately, even when such in-consistencies arise, previous adjustable autonomy work has rigidly assumed the infallibility of human decisions, leading to degradation in team performance. Second, these human-multiagent teams must operate and plan in real-time with deadlines combined with uncertain duration of human actions. Third, the team must plan for the optimal time to interrupt actions; unfortunately previous models of continuous time planning under uncertainty assume non-interruptible actions. Fourthly, adjustable autonomy in teams is an inherently distributed and complex problem that cannot be solved optimally and completely online. To address these challenges and build a working multiagent system, our paper presents a solution (RIAACT) that integrates four key ideas. First, our adjustable autonomy framework models resolution of inconsistencies between human and agent view, rather than assuming the human to be in-fallible. Second, agents plan their interactions in continuous time, avoiding a discretized time model and less e ﬃ cient solutions. Third, we introduce a new Interruptible Time dependent Markov Decision Problem (ITMDP) model for planning with interruptible actions in continuous time. Fourth, our hybrid approach combines non-decision-theoretic algorithms for coordination with the ITMDPs, to avoid the complexities of distributed ITMDPs. These contributions have been realized in a complex disaster response simulation system and extensive experimental results are presented.
Triangle Mesh Generation by Edge Splitting and Angle-based Smoothing

We describe a new mesh generation algorithm that involves only edge splits, Delaunay edge swaps, and an angle-based smoothing procedure. Given a coarse mesh and a bivariate sizing function that restricts maximum edge length, we seek to satisfy the edge length constraints with as few additional vertices as possible and with maximum triangle quality. We alternate between a refinement step in which long edges are bisected, and an optimization procedure that combines Delaunay edge swaps with a simple and efficient smoothing method. Test results show the method to be very effective.
Integration and Management of Wired/Wireless Networks Based on the HART Concept

This paper describes integration and management of wired/wireless networks in industrial plants. First, the technical requirements that justify the need for integration of wired HART devices with WirelessHART network within a modern control system in the process industry with already installed devices are specified. Then, model of integrated wired/wireless network was designed and for the same model diagrams of communication between nodes obtained using TrueTime simulator are shown. Obtained experimental results of planning and management of integrated industrial network using Emerson's AMS Wireless SNAP-ON application, which justify the use of wireless adapter for integration HART devices with WirelessHART network, are shown at the end of the paper.
Exposure to Arsenic Alters the Microbiome of Larval Zebrafish

Exposure to environmental toxins such as heavy metals can perturb the development and stability of microbial communities associated with human or animal hosts. Widespread arsenic contamination in rivers and riparian habitats therefore presents environmental and health concerns for populations living near sources of contamination. To investigate how arsenic affects host microbiomes, we sequenced and characterized the microbiomes of twenty larval zebrafish exposed to three concentrations of arsenic that are found in contaminated water—low (10 ppb), medium (50 ppb), and high (100 ppb) for 20 days. We found that even a small concentration of arsenic changed the overall microbial composition, structure and diversity of microbial communities, causing dysbiosis in developing larval zebrafish microbiota. In addition, we found that a high concentration of arsenic also increased the abundance of a class 1 integron, an integrase-dependent system facilitating the horizontal transfer of genes conferring resistance to heavy metals and antibiotics.
Predicting Energy Cost using Portable Physiological Sensors

Introduction Lower-limb robotic assistive devices, (e.g., exoskeletons and prostheses), provide net-positive power to the gait cycle, which reduces the amount of biological power the user must provide. Thus, the effectiveness of such devices is often quantified as a reduction in the individual’s energetic cost [1–3]. Measures of energy cost have also been used to identify optimal parameter settings (e.g., actuation timing) for powered assistive devices in real time [4, 5]. The current ‘state-of-the-art’ method for estimating energetic cost during walking is indirect calorimetry. The user wears a mask that covers his or her nose and mouth, and an embedded flowmeter measures oxygen consumption and carbon dioxide production. However, this method is unsuitable for longterm data collection due to the cumbersome equipment, and the measurements obtained breath-by-breath are sparsely sampled, noisy, and dynamically delayed from the true energetic demands of the body. Due to these challenges, it is common practice to collect 5-6 minutes of respiratory measurements per condition, and to average the last 2-3 minutes of steady-state measurements to yield one estimate of ‘ground truth’ energetic cost. Given that quick and accurate estimates of energetic cost are highly valuable to the design and evaluation of assistive robotic devices, it would be beneficial to estimate energy cost using other sensors with less variability and better temporal resolution.
Sleep disturbances and depression severity in patients with Parkinson's disease

Parkinson's disease (PD) is a multisystem movement disorder associated with sleep disturbance and depression. Sleep disturbances and depression severity share a bidirectional association. This association may be greater in individuals who are more vulnerable to the deleterious consequences of sleep disturbance and depression severity. We investigated whether the association between sleep disturbances and depression severity is greater in patients with PD than in matched controls (MC).
Effects of Caffeic Acid Phenethyl Ester on Retinal Damage After Traumatic Brain Injury

Head trauma damages the optic nerve visual function and visual acuity.Effects of head trauma on the retina was investigated with biochemical, histological and immunohistochemical respects.The study was conducted on 30 rats with three grou ps: group 1 was control group (n=10). Second group was head-traumatized group (n=10) and last group was head-traumatized+Caffeic ac i phenethyl ester (CAPE, i.p. 20ml/kg/day). Upon head was traumatized, CAPE was applied to trauma+CAPE group and then for the following four days. At the end of 5th day, rats were anesthetized with ketamine hydroxide and then blood samples were taken fo r biochemical analysis. MDA and GSH-Px values were compared. After blood sample, total eyes of rats were dissected for histopatho logical and immunohistochemical analysis. In trauma group, degeneration in retinal photoreceptor cells, disintegrity and in inner and o uter nuclear layers, hypertrophy in ganglion cells, and hemorrhage in blood vessels were observed. In the group treated with CAPE, l esser degeneration in photoreceptor cells, regular appearances of inner and outer nuclear layers, mild hemorrhage in blood vessels of ganglionic cell layer were observed. The apoptotic changes caused by trauma seen in photoreceptor and ganglionic cells were decreased and cellular organization was preserved due to CAPE treatment. CAPE was thought to induce healing process on traumatic damages.
Real Time Facial Expression Recognition Using Webcam and SDK Affectiva

Facial expression is an essential part of communication. For this reason, the issue of human emotions evaluation using a computer is a very interesting topic, which has gained more and more attention in recent years. It is mainly related to the possibility of applying facial expression recognition in many fields such as HCI, video games, virtual reality, and analysing customer satisfaction etc. Emotions determination (recognition process) is often performed in 3 basic phases: face detection, facial features extraction, and last stage - expression classification. Most often you can meet the so-called Ekman’s classification of 6 emotional expressions (or 7 - neutral expression) as well as other types of classification - the Russell circular model, which contains up to 24 or the Plutchik’s Wheel of Emotions. The methods used in the three phases of the recognition process have not only improved over the last 60 years, but new methods and algorithms have also emerged that can determine the ViolaJones detector with greater accuracy and lower computational demands. Therefore, there are currently various solutions in the form of the Software Development Kit (SDK). In this publication, we point to the proposition and creation of our system for real-time emotion classification. Our intention was to create a system that would use all three phases of the recognition process, work fast and stable in real time. That’s why we’ve decided to take advantage of existing Affectiva SDKs. By using the classic webcamera we can detect facial landmarks on the image automatically using the Software Development Kit (SDK) from Affectiva. Geometric feature based approach is used for feature extraction. The distance between landmarks is used as a feature, and for selecting an optimal set of features, the brute force method is used. The proposed system uses neural network algorithm for classification. The proposed system recognizes 6 (respectively 7) facial expressions, namely anger, disgust, fear, happiness, sadness, surprise and neutral. We do not want to point only to the percentage of success of our solution. We want to point out the way we have determined this measurements and the results we have achieved and how these results have significantly influenced our future research direction.
A Document Profile for Improving Information Retrieval Systems

This work is funded by a FPU predoctoral fellowship granted by University of Alicante (UAFPU2015-5999), and has also been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government, Ministerio de Educacion, Cultura y Deporte and Ayudas Fundacion BBVA a equipos de investigacion cientifica 2016 through the projects TIN2015-65100-R, TIN2015-65136-C2-2-R, PROMETEOII/2014/001, GRE16-01: “Plataforma inteligente para recuperacion, analisis y representacion de la informacion generada por usuarios en Internet” and ASAP.
UniqueID: Decentralized Proof-of-Unique-Human

Bitcoin and Ethereum are novel mechanisms for decentralizing the concept of money and computation. Extending decentralization to the human identity concept, we can think of using blockchain for creating a list of verified human identities with a one-person-one-ID property. UniqueID is a Decentralized Autonomous Organization(DAO) for maintaining human identities such that every physical human entity can have no more that one account. One part of this identity is simply the user's claim on one of his unique, permanent, and measurable characteristics -biometrics. Blockchain has proved its integrity as a platform for storing and performing computations on such claims. The biggest challenge here is to ensure that the user has submitted his own valid biometric data. Human verifiers can check if there is any inconsistency in other users' data, by peer-to-peer checks. For preventing bad behavior and centralization in the verification process, UniqueID benefits from novel governance mechanisms to choose verifiers and punish unjust ones. Also, there are incentives for honest verifiers and users by newly generated tokens. We show how the users' privacy can be preserved by using state-of-the-art cryptographic techniques, and so they can use their identity without any concerns for votings, financial and banking purposes, social media accounts, reputation systems etc.
The Enzymatic Core of the Parkinson’s Disease-Associated Protein LRRK2 Impairs Mitochondrial Biogenesis in Aging Yeast

Mitochondrial dysfunction is a prominent trait of cellular decline during aging and intimately linked to neuronal degeneration during Parkinson’s disease (PD). Various proteins associated with PD have been shown to differentially impact mitochondrial dynamics, quality control and function, including the leucine-rich repeat kinase 2 (LRRK2). Here, we demonstrate that high levels of the enzymatic core of human LRRK2, harboring GTPase as well as kinase activity, decreases mitochondrial mass via an impairment of mitochondrial biogenesis in aging yeast. We link mitochondrial depletion to a global downregulation of mitochondria-related gene transcripts and show that this catalytic core of LRRK2 localizes to mitochondria and selectively compromises respiratory chain complex IV formation. With progressing cellular age, this culminates in dissipation of mitochondrial transmembrane potential, decreased respiratory capacity, ATP depletion and generation of reactive oxygen species. Ultimately, the collapse of the mitochondrial network results in cell death. A point mutation in LRRK2 that increases the intrinsic GTPase activity diminishes mitochondrial impairment and consequently provides cytoprotection. In sum, we report that a downregulation of mitochondrial biogenesis rather than excessive degradation of mitochondria underlies the reduction of mitochondrial abundance induced by the enzymatic core of LRRK2 in aging yeast cells. Thus, our data provide a novel perspective for deciphering the causative mechanisms of LRRK2-associated PD pathology.
DDR 2 receptor promotes MMP-2 – mediated proliferation and invasion by hepatic stellate cells

Hepatic fibrosis, or scar deposition in response to chronic injury, is similar in all forms of liver disease (1). Accumulation of fibrillar, or type I, collagen occurs in the subendothelial space between hepatocytes and endothelial cells, where it replaces a low-density basement membrane–like matrix containing type IV collagen. This conversion of the subendothelial matrix to one rich in fibrillar collagen is a pivotal event mediating the loss of differentiated function characteristic of progressive liver disease. Hepatic stellate cells are the major source of fibrosis following their two-stage activation from quiescent vitamin A–rich cells to proliferative and fibrogenic myofibroblasts (1). Initiation of this cascade is ascribed to paracrine stimulation by Kupffer and endothelial cells. In contrast, perpetuation comprises a broad and largely autocrine conversion that includes enhanced proliferation, fibrogenesis, migration, contractility, cytokine release, and production of matrix proteases (2). The accumulation of type I collagen has a direct activating effect on stellate cells through an unknown mechanism (3). Stellate cell activation is further accelerated by upregulation of matrix metalloproteinase 2 (MMP-2) activity, because this enzyme degrades the normal subendothelial matrix, hastening its replacement by fibrillar collagen (4). The activity of MMP-2 is tightly regulated by specific inhibitors, including TIMP-2 (tissue inhibitor of metalloproteinase-2) and by activators including membrane-type matrix metallproteinase-1 (MT1-MMP) (5, 6). Major features of stellate cell activation are mediated by the induction of transmembrane growth factor receptors with kinase activity or receptor tyrosine kinases (RTKs). For example, stellate cell proliferation and migration is preceded by a rapid induction of the β-PDGF receptor (7, 8), as well as by mitogenic responses to epidermal growth factor (9) and fibroblast growth factor (10). To identify additional RTKs induced during stellate cell activation, previously we performed homology PCR using primers derived from the conserved kinase domain of RTKs and a template consisting of activation-specific cDNAs from rat stellate cells (11). This approach yielded several RTKs, including a partial cDNA encoding the discoidin domain receptor 2 (DDR2), also known as Tyro10 or TKT (12–14). The DDR subfamily (which includes DDR1 and DDR2) has several features that distinguish it from other RTKs. DDRs signal in response to collagens rather than soluble peptide growth factors, and they display a relatively slow onset of phosphorylation, occurring in hours rather than minutes (15, 16). The