Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic diversity and evolutionary history of the Tyrrhenian treefrog Hyla sarda (Anura: Hylidae): adding pieces to the puzzle of Corsica-Sardinia biota

The Corsica–Sardinia archipelago is a hotspot of Mediterranean biodiversity. Although tempo and mode of arrival of species to this archipelago and phylogenetic relationships with continental species have been investigated in many taxa, very little is known about the current genetic structure and evolutionary history subsequent to arrival. In the present study, we investigated genetic variation within and among populations of the Tyrrhenian treefrog Hyla sarda, a species endemic to the Corsica–Sardinia microplate and the surrounding islands, by means of allozyme electrophoresis. Low genetic divergence (mean DNEI = 0.01) and no appreciable differences in the levels and distribution of genetic variability (HE: 0.06–0.09) were observed among all but one populations (Elba). Historical demographic and isolation-by-distance analyses indicated that this diffused genetic homogeneity could be the result of recent demographic expansion. Along with paleoenvironmental data, such expansion could have occurred during the last glacial phase, when wide and persistent land bridges connected the main islands and a widening of lowland areas occurred. This scenario is unprecedented among Corsica–Sardinia species. Together with the lack of concordance even among the few previously studied species, this suggests either that species had largely independent responses to paleoenvironmental changes, or that most of the history of assembly of the Corsica– Sardinia biota is yet to be written. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2011, 103, 159–167.
Characterize and Evaluate Scientific Domain and Domain Context Knowledge Map

Domain knowledge map, a.k. a. , scholarly network, construction as an important method can describe the significant characters of a selected domain. In this research, we will address three fundamental problems for scholarly network generation. Firstly, two different methods will be investigated to associate keywords on the graph: Co-occur Domain Distance and Citation Probability Distribution Distance. Secondly, this paper will construct domain (core journals and conference proceedings) knowledge and domain referral (domain citation) scholarly networks, and propose a novel method to integrate those graphs by optimizing the nodes and their linkage. Finally, the paper will propose an innovative method to evaluate the accuracy and coverage of scholarly networks based on training keyword oriented Labeled-LDA model and validate different domain or domain referral graphs.
Evaluation of Low-end Virtual Reality Content of Cultural Heritage: A Preliminary Study with Eye Movement

The affordances of virtual reality (VR) have made it widely adopted for presenting content of cultural heritage in digital libraries. In recent years, non-specialists including university students were involved in creating low-end VR content using low-cost equipment and software. Among various design options, the question as to which ones could be more effective in presenting cultural heritage remains. This study aims to evaluate and compare the effectiveness of user-created VR content of cultural heritage with different designs, through collecting and analyzing self-report and eye movement data from end users. Results show that the presence of text annotations in the VR content helped users understand the cultural heritage being presented, whereas users' visual attention was largely attracted to the text annotations and additional images when the VR content contained such visual information. This preliminary study also explores the feasibility of using the eye-tracking method to analyze user interactions with VR content of cultural heritage. The results provide empirical evidence on the effects of different designs of user-created VR content on end users' understanding of cultural heritage.
Long-term patient-reported symptoms of COVID-19: an analysis of social media data

As the COVID-19 virus continues to infect people across the globe, there is little understanding of the long term implications for recovered patients. There have been reports of persistent symptoms after confirmed infections on patients even after three months of initial recovery. While some of these patients have documented follow-ups on clinical records, or participate in longitudinal surveys, these datasets are usually not publicly available or standardized to perform longitudinal analyses on them. Therefore, there is a need to use additional data sources for continued follow-up and identification of latent symptoms that might be underreported in other places. In this work we present a preliminary characterization of post-COVID-19 symptoms using social media data from Twitter. We use a combination of natural language processing and clinician reviews to identify long term self-reported symptoms on a set of Twitter users.
School Well-Being and Drug Use in Adolescence

This research is part of the last study Drugs and School IX developed in the Basque Country (Spain) by the Instituto Deusto de Drogodependencias (Deusto Institute of Drug Addiction) of the University of Deusto (The study had the support of the Public Health and Addictions Directorate of the Deputy Health Ministry from the Health Department of the Basque Country.) and the data gathered by means of cluster sampling in two stages. The sample is made up of N = 6,007 girls and boys ranging from 12 to 22 years of age in secondary education, and the aims, on the basis of those parameters, are as follows: (1) describe the reality of drug consumption and some psychosocial variables in this sample, as well as analyze several relations between variables; (2) analyze the role of school well-being (SWB), self-esteem, and self-concept regarding consumption; (3) take a close look at the moderating role of age and gender on the relationship of school well-being, self-concept, and self-esteem with consumption; and (4) understand the existing interaction between all these variables, by studying the moderating role of self-esteem and self-concept in the influence of school well-being on consumption. With the use of a correlation, hierarchical regression, and mediation analysis with SPSS (v. 26) and Amos (v. 26) applications, three main conclusions were reached. Firstly, educational and academic well-being, academic self-concept, and self-esteem seem to play the role of protecting factors in adolescence, whereas assertiveness is linked to a higher consumption level. Secondly, academic self-concept has a mediating effect between well-being and consumption. Some of these relations are moderated by the variables of gender and age. Thirdly, age and gender are very relevant sociodemographic variables that must be taken into account in order to understand this phenomenon. Age has shown its covariant effect, which is especially relevant in the influence of academic well-being measured as being held back years. It has also proved to be important in order to understand its experiential or experimental and transitory character. Moreover, significant differences in consumptions have been found based on gender.
UAV Flight Dynamics: Design and Development of a Framework for Flight Data Processing and Analysis

The Center for Aerospace Research, working on the leading edge of the next generation of unmanned aircraft, carries out an extensive activity of flight testing with the objective of studying new configurations and systems to fulfil missions in the most efficient way. Since the acquisition of several projects by collaborators such as Boeing, Bombardier and the Canadian Department of Defence, the number of flight tests to be performed has increased substantially, requiring a radical change in the data handling method used to analyse the numerous log files generated. The project developed in this thesis proposes to solve this issue with the creation of a customized flight data post processor software, capable of managing all the data produced from the flight tests. This software not only provides a unique tool to handle and process all the flight data, but also integrates a variety of features in order to extend the analysis capability. The functions implemented range from simple data extrapolation tools, to complex frequency analysis algorithms used to study the dynamic response of the aircraft. Moreover the software offers a sequence of functions to perform a complete tuning process of the autopilot with the objective of increasing the control efficiency, by removing the oscillatory response caused by erroneous parameters. To complete the project a study of a new type of flight mission plan is conducted in order to improve the dynamic analysis of the aircraft performed by the advanced tools in the software.
There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks

Artificial Intelligence (AI) plays a fundamental role in the modern world, especially when used as an autonomous decision maker. One common concern nowadays is “how trustworthy the AIs are. ” Human operators follow a strict educational curriculum and performance assessment that could be exploited to quantify how much we entrust them. To quantify the trust of AI decision makers, we must go beyond task accuracy especially when facing limited, incomplete, misleading, controversial or noisy datasets. Toward addressing these challenges, we describe DeepTrust, a Subjective Logic (SL) inspired framework that constructs a probabilistic logic description of an AI algorithm and takes into account the trustworthiness of both dataset and inner algorithmic workings. DeepTrust identifies proper multi-layered neural network (NN) topologies that have high projected trust probabilities, even when trained with untrusted data. We show that uncertain opinion of data is not always malicious while evaluating NN's opinion and trustworthiness, whereas the disbelief opinion hurts trust the most. Also trust probability does not necessarily correlate with accuracy. DeepTrust also provides a projected trust probability of NN's prediction, which is useful when the NN generates an over-confident output under problematic datasets. These findings open new analytical avenues for designing and improving the NN topology by optimizing opinion and trustworthiness, along with accuracy, in a multi-objective optimization formulation, subject to space and time constraints.
Real-Time Efficient Detection in Vision Based Static Hand Gesture Recognition

The focus on Human-Computer Interaction (HCI) research is increasing day by day, due to the increasing requirement of intelligent input devices. Hand Gesture Recognition is a small sub-field but presents a significant number of applications and consumer products. Most researches target on the feasibility of recognition systems but give less weight to the device resources, so the cost and time. The time-consuming complicated algorithms' use is limited to special purpose devices such as expensive gaming consoles. The use of such systems in low cost embedded hardware in realtime circumstances is required, with the comfortability to use it. In this paper, we design an efficient real-time keyboard-like HCI using Static HGR. We have proposed and implemented new methods to reduce the time consumption while maintaining the high accuracy of 90% with scale and rotation invariance. Also, to maintain the comfort of use, we have eliminated complicated gestures and used only 11 gestures as input gesture set.
Model-Driven Ethical, Social and Environmental Accounting

Ethical, social and environmental accounting is the practice of assessing organisations' performances in sustainability and business ethics topics. The organisations typically publish the results in a sustainability or non-financial report. We aim at offering a novel perspective from which researchers investigate, practitioners apply and policy-makers regulate ethical, social and environmental accounting (ESEA). The large quantity of ESEA methods and tools causes managerial problems, affecting the identity of social enterprises and complicating policy making. We will develop a domain-specific modelling language to specify existing ESEA methods and capture the advantages of model-driven engineering. We will create a repository where method models can be stored. These models contain the data structure and configuration of the methods. We will also develop openESEA, a run-time model interpreter that automatically executes ESEA method models. We will offer features to allow organisations to tailor the methods to their needs, to support model management operations, and to compare existing methods to inform policy makers about their similarities and differences. This project combines expertise in information science and social entrepreneurship with the intention to pave the way to future research avenues in ESEA and, eventually, to profound changes towards a fair and sustainable economy.
THE EFFECT OF TEMPERATURE ON THE OIL CONTENT AND FATTY ACID COMPOSITION OF THE OILS FROM SEVERAL OIL SEED CROPS

Plants of rape, safflower, sunflower, flax, and castor bean were grown at temperatures of 10, 16, 21, and 26.5 °C for the period of seed development. Oil content of sunflower, safflower, and castor bean was not affected by temperature. Highest oil content in rape and flax was found at the lowest temperature and a continual decrease was observed with increases in temperature. Fatty acid composition of the oil from safflower and castor bean was not affected by a change in temperature. In the other three species the amount of the more highly unsaturated fatty acids decreased as the temperature was increased. This decrease was accompanied by an increase in oleic acid. The levels of saturated fatty acids in all of the species were not affected by changes in temperature.
The performance of Polylepis australis trees along their entire altitudinal range: implications of climate change for their conservation

Altitudinal gradients are expected to heavily influence the general performance of mountain tree species. For this study we evaluated vitality, radial growth and reproductive efficiency in Polylepis australis (Rosaceae) throughout its complete altitudinal range in central Argentina. We selected seven forest fragments ranging from 900 to 2700 m a.s. l. In the field, we subjectively assigned vitality and seed productivity indexes to 12 P. australis trees per fragment. In addition, we cored and assessed for radial growth 10–18 trees per altitudinal level. In the laboratory, we individually weighed seeds from four or five trees from each altitudinal level and divided into two seed mass classes (light and heavy). We then tested for germination and monitored their seedlings for survival. Seedlings of three trees per altitudinal level were harvested after 40 days and assessed for dry mass. Seedlings of one to two trees per altitudinal level were monitored for survival until both leaves and roots had died. Our main results showed two types of responses to altitude: a unimodal relationship with an optimum at intermediate altitudes of around 1800 m a.s. l. for tree vitality, radial growth, seed productivity and seed mass; and an increase with altitude for in‐vitro seed germination and seedling survival. A rise in temperature due to climate change could restrict P. australis to the upper most altitudes, where conservation of these forests will be a priority.
A Novel CD206 Targeting Peptide Inhibits Bleomycin Induced Pulmonary Fibrosis in Mice

Activated M2 polarized macrophages are drivers of pulmonary fibrosis in several clinical scenarios such as Acute Respiratory Disease Syndrome (ARDS) and Idiopathic Pulmonary Fibrosis (IPF), through the production of inflammatory and fibrosis-inducing cytokines. In this study, we investigated the effect of targeting the CD206 receptor with a novel fragment of a Host Defense Peptide (HDP), RP-832c to decrease cytokines that cause fibrosis. RP-832c selectively binds to CD206 on M2 polarized bone marrow derived macrophages (BMDM) in vitro, resulting in a time-dependent decrease in CD206 expression, and a transient increase in M1 marker TNFα, which resolves over a 24hr period. To elucidate the antifibrotic effect of RP-832c, we used a murine model of bleomycin (BLM) -induced early-stage pulmonary fibrosis. RP-832c significantly reduced bleomycin-induced fibrosis in a dosage dependent manner, as well as decreased CD206, TGF-β1 and α-SMA expression in mouse lungs. Interestingly we did not observe any changes in the resident alveolar macrophage marker CD170 expression. Similarly, in an established model of lung fibrosis, RP-832c significantly decreased fibrosis in the lung, as well as significantly decreased inflammatory cytokines TNFα, IL-6, IL-10, INF-γ, CXCL1/2, and fibrosis markers TGF-β1 and MMP-13. In comparison with FDA approved drugs, Nintedanib and Pirfenidone, RP-832c exhibited a similar reduction in fibrosis compared to Pirfenidone, and to a greater extent than Nintedanib, with no apparent toxicities observed on body weight or blood chemistry. In summary, RP-832c is a potential agent to mitigate the overactivity of M2 macrophages in pathogenesis several pulmonary fibrotic diseases, including SARS-CoV-2 induced lung fibrosis.
How Do Humans Perform in Multiple Object Tracking With Unstable Features

In real-world scenarios, objects’ surface features sometimes change as they move, impairing the continuity of objects. However, it is still unknown how our visual system adapts to this dynamic change. Hence, the present study investigated the role of feature changes in attentive tracking through a modified multiple object tracking (MOT) task. The feature heterogeneity and feature stability were manipulated in two experiments. The results from Experiment 1 showed that the tracking performance under feature-changed condition was lower than that under the feature-fixed condition only when the objects were four colors grouped or all unique, suggesting that the performance decrease was moderated by the feature heterogeneity. In Experiment 2, we further examined this effect by manipulating the frequency of feature change. The results showed that when the target set was one color or two colors grouped (the color grouping for the distractor set corresponded with it), the tracking performance decreased significantly as the feature-change frequency increased. However, this trend was not the case when the objects were of the same color or eight unique colors. In addition, a relatively consistent effect appeared both in Experiments 1 and 2. When objects have unique features, the tracking performance decreased significantly as the increase of feature heterogeneity in each frequency of feature changes. Taken together, we concluded that unstable features could be utilized in attentive tracking, and the extent to which the observers relied on surface feature information to assist tracking depended on the level of feature heterogeneity and the frequency of feature change.
Systemic inflammation causes microglial dysfunction with a mixed AD-like pathology

Background Alzheimer’s disease (AD) is the primary cause of cognitive deficit in elderly humans. Late-onset AD (LOAD) is sporadic, multifactorial, non-Mendelian accounting at present for 95% of the cases in contrast to the genetic form. Risk factors for sporadic AD include Gene: Environment interactions. There is increasing evidence that lifestyle and environmental stress such as infection and chronic inflammation are underlying culprits of neurodegenerative dementia. Dementias that share or mimic pathological processes of AD include cerebrovascular diseases, Lewy body disease, TDP-43 proteinopathy. To date, very few mouse models reproduce the pathophysiological progression of mixed-vascular-AD, while the majority of studies have employed transgenic animals reproducing the familial form. Methods We have re-engineered the Polyinosinic:polycytidylic acid (PolyI:C) sterile infection model in wildtype C57Bl6 mice to obtain chronic low-grade systemic inflammation. We have conducted a cross-sectional analysis of aging PolyI:C and Saline control mice (3 months, 6 months, 9 months and 16 months), taking the hippocampus as a reference brain region, based on its vulnerability, and compared the brain aging phenotype to AD progression in humans with mild AD, severe AD and Controls (CTL), parallely in Vascular dementia (VaD) patient specimens. Results We found that PolyI:C mice display both peripheral and central inflammation with a peak at 6 months, associated with memory deficits. The hippocampus is characterized by a pronounced and progressive tauopathy. In PolyI:C brains, microglia undergo aging-dependent morphological rearrangements progressively adopting a phagocytic phenotype. Transcriptomic analysis reveals a profound change in gene expression over the course of aging, with a peak in differential expression at 9 months. We confirm that the proinflammatory marker Lcn2 is one of the genes with the strongest upregulation in PolyI:C mice upon aging. Validation in brains from patients with increasing severity of AD and VaD shows a reproducibility of some gene targets in vascular dementia specimens rather than AD ones, in which only GFAP is strongly increased at the severe stages. Conclusions The PolyI:C model of sterile infection demonstrates that peripheral chronic inflammation is sufficient to cause neuropathological processes resembling a mixed-VaD-AD phenotype, with progressive tau hyperphosphorylation, changes in microglia morphology, astrogliosis and gene reprogramming reflecting increased neuroinflammation, vascular remodeling and the loss of neuronal functionality seen to some extent in humans.
MAPS integrates overlooked regulation of actin-targeting effector SteC into the virulence control network of Salmonella small RNA PinT

A full understanding of the contribution of small RNAs (sRNAs) to bacterial virulence demands knowledge of their target suites under infection-relevant conditions. Here, we take an integrative approach to capturing targets of the Hfq-associated sRNA PinT, a known post-transcriptional timer of the two major virulence programs of Salmonella enterica. Using MS2 affinity purification and RNA-sequencing (MAPS), we identify PinT ligands in bacteria under in-vitro conditions mimicking specific stages of the infection cycle, and in bacteria growing inside macrophages. This reveals PinT-mediated translational inhibition of the secreted effector kinase SteC, which had gone unnoticed in previous target searches. Using genetic, biochemical, and microscopic assays, we provide evidence for PinT-mediated repression of steC mRNA, eventually preventing actin rearrangements in infected host cells. Our findings support the role of PinT as a central post-transcriptional regulator in Salmonella virulence and illustrate the need for complementary methods to reveal the full target suites of sRNAs.