Miracle-dz/UniFAR-FTUs huggingface dataset:
The Role of Matrix Proteins in Cardiac Pathology

The extracellular matrix (ECM) and ECM-regulatory proteins mediate structural and cell-cell interactions that are crucial for embryonic cardiac development and postnatal homeostasis, as well as organ remodeling and repair in response to injury. These proteins possess a broad functionality that is regulated by multiple structural domains and dependent on their ability to interact with extracellular substrates and/or cell surface receptors. Several different cell types (cardiomyocytes, fibroblasts, endothelial and inflammatory cells) within the myocardium elaborate ECM proteins, and their role in cardiovascular (patho)physiology has been increasingly recognized. This has stimulated robust research dissecting the ECM protein function in human health and disease and replicating the genetic proof-of-principle. This review summarizes recent developments regarding the contribution of ECM to cardiovascular disease. The clear importance of this heterogeneous group of proteins in attenuating maladaptive repair responses provides an impetus for further investigation into these proteins as potential pharmacological targets in cardiac diseases and beyond.
Alien or Native? How to Distinguish Feces of Fallow and Roe Deer Using Central Poland as a Case Study

Simple Summary Fallow and roe deer are both game species, and therefore it is crucial to monitor their population locally and on a country scale. The method of pellet group count is commonly used for estimating population trends of ungulates; however, in the case of the two species, the misidentification rate can be high due to their similar body size. Our aim was to determine a metrical threshold between pellet groups of roe deer (native species) and fallow deer (alien species) to be applied during fieldwork. We measured the number of feces in the group, the length and width of five randomly selected feces from each pellet group and the length/width ratio. Roe deer pellets were shorter, narrower and less elongated than those of fallow deer; yet, length was found to be the best discriminant. The most accurate threshold was 1.2 cm. The mean number of pellets in a group was lower for roe deer than for fallow deer. A value of 50–52 pellets best differentiated between the two species. We therefore concluded that, on the basis of morphometric measurement, it is possible to distinguish roe and fallow deer feces. Abstract The method of pellet group count is commonly used for estimating population trends of ungulates; however, in the case of species of similar body size, the misidentification rate can be high. Our aim was to find a metrical threshold between pellet groups of roe deer (native species) and fallow deer (alien species) to be applied during fieldwork. The study was conducted in spring 2020 and 2021 in central Poland (lowlands) in areas were only roe or fallow deer occurred. We measured the number of feces in the group, the length and width of five randomly selected feces from each pellet group and the length/width ratio. Roe deer pellets were shorter, narrower and less elongated than those of fallow deer; yet, length was found to be the best discriminant. The most accurate threshold was 1.2 cm, i.e., 12–15% of pellets were over/below this value. The mean number of pellets in a group was lower for roe deer (39.6, SE = 1.6) than for fallow deer (64.5, SE = 1.5). A value of 50–52 pellets best differentiated between the two species. To conclude, combining these two measurements could be an objective method to distinguish between pellet groups of the two species.
Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.
Unexpected events during survey design and trust in the police: a systematic review

Objectives The current review has two aims: (1) to synthesize the impact of unexpected events on trust in police across different contexts and types of events, and (2) to evaluate the methodological characteristics of each study with attention to the assumptions for causal inference. Methods We conducted a pre-registered narrative systematic review on 12 independent studies. Results Studies closely adhering to causal inference assumption checks (i.e., excludability and ignorability) find significant changes in trust in police following incidents of police (non) violence and protest. Still, excludability is assessed and addressed less rigorously than ignorability in the included studies. Conclusion Regarding the procedural justice framework, this provides some causal evidence that vicarious (positive and negative) experiences can shape short-term assessments of public trust in police. We furthermore highlight issues related to design and power, statistical conclusion validity, and the evaluation of assumptions to detect threats to internal validity. Supplementary Information The online version contains supplementary material available at 10.1007/s11292-022-09508-y.
Online Wind-Atlas Databases and GIS Tool Integration for Wind Resource Assessment: A Spanish Case Study

Renewable energy sources are becoming increasingly integrated into the electricity-generation sector, being eco-friendly solutions, decreasing global warming, and improving the energy transition process. Among the different renewables, wind energy is considered a mature, clean, renewable, and inexhaustible technology as well, becoming one of the main resources in a sustainable framework. Aiming to evaluate the wind resource, scientific contributions have mostly presented a common basis: historical data campaigns of the wind resource mainly considering wind speed—including the module, direction, standard deviation, etc. However, online wind-atlas databases are becoming tools widely used for both wind-resource assessment and optimal wind-power locations. Under this framework, this study analyzed and compared such online wind data sources and their integration with GIS tools for optimal wind-resource-assessment purposes. The proposed methodology identified the corresponding wind-atlas databases directly on their websites and indirectly through the wind data used in relevant contributions about the optimal location of wind sites. Our contribution to the scientific community is thus the review and comparison of these atlas databases for reducing the barrier to access wind data—including GIS-tool-integration analysis. The limitations raised by civil societies, particularly regarding environmental and bird concerns, were not included in this study. Nevertheless, the authors are aware of these concerns and limitations. A Spanish case study was also included in this work, comparing both estimated and collected wind-atlas databases in terms of wind-resource assessment.
Churn Prediction Model of Telecom Users Based on XGBoost

: As the cost of accessing a telecom operator’s network continues to decrease, user churn after arrears occurred repeatedly, which has brought huge economic losses to operators and reminded them that it is significant to identify users who are likely to churn in advance. Machine learning can form a series of judgment rules by summarizing a large amount of data, and telecom user data naturally has the advantage of user scale, which can provide data support for learning algorithms. XGBoost is an improved gradient boosting algorithm, and in this paper, we explore how to use the algorithm to train an efficient model and use this model one month in advance to predict whether users will churn. Our work is mainly divided into two aspects: (1) By completing data exploration, feature engineering and data preprocessing, we obtained a data set that can be used to train a prediction model and features that can effectively predict user churn. And using these features and data sets, two prediction models were trained based on Random Forest and XGBoost. (2) According to the business needs of telecom operators, we continuously evaluated and optimized these models. And by comparing the test results of the two models, we proved that the XGBoost model performs better for the precision and recall of user churn.
Ontology: A tool for Organization of Knowledge

Knowledge organization (KO) is a core subject in Library and Information Science (LIS) studies. Classification, cataloguing, and indexing traditionally formed its backbone. The faceted classification schemes with some variations are being used to classify documents on topics at micro level. With the uncontrollable flow of information on the Internet, there is an urgent need to capture and organize the information so as to retrieve it in a meaningful way. ontology is one of the tools for study in LIS. The present paper gives a brief overview of ontology for organizing the vast stock of knowledge on the internet with the view to bringing conceptual understanding.
The Arginine Catabolism-Derived Amino Acid l-ornithine Is a Chemoattractant for Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, opportunistic bacterial pathogen among patients with cystic fibrosis, asthma, and chronic obstructive pulmonary disease. During the course of these diseases, l-ornithine, a non-proteinogenic amino acid, becomes more abundant. P. aeruginosa is chemotactic towards other proteinogenic amino acids. Here, we evaluated the chemotaxis response of P. aeruginosa towards l-ornithine. Our results show that l-ornithine serves as a chemoattractant for several strains of P. aeruginosa, including clinical isolates, and that the chemoreceptors involved in P. aeruginosa PAO1 are PctA and PctB. It seems likely that P. aeruginosa’s chemotactic response to l-ornithine might be a common feature and thus could potentially contribute to pathogenesis processes during colonization and infection scenarios.
Heart Rate Variability and Pain: A Systematic Review

Background and Objective: Heart rate variability (HRV) as an index of the autonomic nervous system appears to be related to reactivity to experimental pain stimuli. HRV could better explain the contributions of sympathetic and parasympathetic activity response to nociceptive stimulation. The aim of this study was to systematically review and synthesize the current evidence on HRV in relation to the experience of pain in experimental tasks. Databases and Data Treatment: Studies indexed in the PubMed, PsycINFO, MEDLINE, WebOfScience, and Scopus databases were reviewed for eligibility. Studies on the autonomic response (i.e., HRV) to experimentally induced pain in healthy adults were included. Different methods of pain induction were considered (e.g., thermal, pressure, and electrical). Data were synthesized considering the association between HRV and both pain induction and subjective measures of pain. Results: Seventy-one studies were included. The results underline significant change in both the sympathetic and parasympathetic autonomic nervous systems during the painful stimulation independent of the pain induction method. The autonomic reaction to pain could be affected by several factors, such as sex, age, body mass index, breathing patterns, the intensity of the stimulation, and the affective state. Moreover, an association between the autonomic nervous system and the subjective experience of pain was found. Higher parasympathetic activity was associated with better self-regulation capacities and, accordingly, a higher pain inhibition capacity. Conclusions: HRV appears to be a helpful marker to evaluate nociceptive response in experimentally induced pain. Future studies are also needed in clinical samples to understand better the interindividual changes of autonomic response due to pain stimuli.
Managers and telework in public sector organizations during a crisis

Abstract Through the use of a narrative-interview approach and contingency theory as research methods, this study explores the teleworking behaviors of N = 41 public managers when remotely managing and leading their organizations as virtual bureaucracies during the Covid-19 lockdown. Its findings suggest that their role set was underscored by communal and supervisory tasks. Managers were confronted with environmental challenges of emotional nature (e.g., anxieties). But they also associated teleworking with improving their work-life balance and staffing work process. Simply put, teleworking was perceived with having both positive and negative organizational outcomes. Drawing from the implications of these findings, this study proposes recommendations for future research.
Highly Efficient Photothermal Reduction of CO2 on Pd2Cu Dispersed TiO2 Photocatalyst and Operando DRIFT Spectroscopic Analysis of Reactive Intermediates

The photocatalytic conversion of CO2 to fuels using solar energy presents meaningful potential in the mitigation of global warming, solar energy conversion, and fuel production. Photothermal catalysis is one promising approach to convert chemically inert CO2 into value-added chemicals. Herein, we report the selective hydrogenation of CO2 to ethanol by Pd2Cu alloy dispersed TiO2 (P25) photocatalyst. Under UV-Vis irradiation, the Pd2Cu/P25 showed an efficient CO2 reduction photothermally at 150 °C with an ethanol production rate of 4.1 mmol g−1 h−1. Operando diffuse reflectance infrared Fourier transform (DRIFT) absorption studies were used to trace the reactive intermediates involved in CO2 hydrogenation in detail. Overall, the Cu provides the active sites for CO2 adsorption and Pd involves the oxidation of H2 molecule generated from P25 and C–C bond formation.
On the Integration of Enabling Wireless Technologies and Sensor Fusion for Next-Generation Connected and Autonomous Vehicles

The automotive industry is transitioning towards intelligent, connected, and autonomous vehicles to avoid traffic congestion, conflicts, and collisions with increased driver safety. Connected and autonomous vehicles (CAV) must be aware of their surroundings and act as per their environment. Communication infrastructure can be vital in transmitting necessary information to peers and receiving critical information for timely decisions. This article provides a comprehensive review of the topic, covering the aspects of enabling wireless technologies and sensor fusion. The article reviews data acquisition using various sensing devices such as RADAR (Radio Detection and Ranging), LiDAR (Light Detection and Ranging), cameras, and multi-modal sensor fusion of the acquired data after signal processing. Thereafter, it reviews the communication and networking infrastructure for intra- and inter-vehicle communication and related technologies. For each of these themes, research challenges and future directions have been identified.
Concept Analysis of Interpersonal Skills in Nursing

Objective: To determine a broad definition of the term interpersonal skills in nursing. Materials and methods: This theoretical study follows Walker and Avant’s method of concept analysis to examine the defining attributes of the concept. A literature search on interpersonal aspects of nursing was conducted in databases, e-books, and institutional repositories to recognize the uses, attributes, antecedents, consequences, and empirical referents of the concept. Experts on professional and relationship skills were consulted between 2009 and 2019. Results: This information was analyzed, concluding that interpersonal skills in nursing are defined as the abilities of professional nurses to express their knowledge, abilities, attitudes, and values in relationships with colleages, patients, and their relatives and achieve a humane and person- centered practice. Conclusion: The concept of interpersonal skills in nursing is limited, given a lack of clarity in practice and research on the indistinct use of terms related to interpersonal skills and interpersonal knowledge. This analysis advances nursing theory towards practice and research.
Recent Advances in Wearable Optical Sensor Automation Powered by Battery versus Skin-like Battery-Free Devices for Personal Healthcare—A Review

Currently, old-style personal Medicare techniques rely mostly on traditional methods, such as cumbersome tools and complicated processes, which can be time consuming and inconvenient in some circumstances. Furthermore, such old methods need the use of heavy equipment, blood draws, and traditional bench-top testing procedures. Invasive ways of acquiring test samples can potentially cause patient discomfort and anguish. Wearable sensors, on the other hand, may be attached to numerous body areas to capture diverse biochemical and physiological characteristics as a developing analytical tool. Physical, chemical, and biological data transferred via the skin are used to monitor health in various circumstances. Wearable sensors can assess the aberrant conditions of the physical or chemical components of the human body in real time, exposing the body state in time, thanks to unintrusive sampling and high accuracy. Most commercially available wearable gadgets are mechanically hard components attached to bands and worn on the wrist, with form factors ultimately constrained by the size and weight of the batteries required for the power supply. Basic physiological signals comprise a lot of health-related data. The estimation of critical physiological characteristics, such as pulse inconstancy or variability using photoplethysmography (PPG) and oxygen saturation in arterial blood using pulse oximetry, is possible by utilizing an analysis of the pulsatile component of the bloodstream. Wearable gadgets with “skin-like” qualities are a new type of automation that is only starting to make its way out of research labs and into pre-commercial prototypes. Flexible skin-like sensing devices have accomplished several functionalities previously inaccessible for typical sensing devices due to their deformability, lightness, portability, and flexibility. In this paper, we studied the recent advancement in battery-powered wearable sensors established on optical phenomena and skin-like battery-free sensors, which brings a breakthrough in wearable sensing automation.
Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting

Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder–decoder architecture for spatial–temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial–temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial–temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.
Biogenic methane cycling is controlled by microbial cohorts

The generation and consumption of methane by aquatic microbial communities is an important contribution to the global carbon budget. We sought to broaden understanding of consortia members and interactions by combining multiple methods including analysis of natural and cultivated microbial communities. By analysing the microbial community composition and co-occurrence patterns of a lake time-series we were able to identify potential consortia involved in methane cycling. In combination with methane flux, we also analysed the community composition and co-occurrence patterns of reduced microbial model communities with inoculum from the same lake. While the network analyses confirmed many known associations, when combined with results from mixed cultures, we noted new players in methane cycling. Cultivated model communities were shown to be an effective method to explore the rarer but still important players in methane cycling and for identifying new putative members. Here we show that using multiple methods to approach the complex problem of methane cycling consortia yields not just insights into the known taxa but highlights potential new members creating new hypotheses to be tested.
CoRa: An Innovative Software for Raman Spectroscopy

An innovative software CoRa with friendly user interface for Raman spectroscopy has been developed. The interface and functionality of CoRa have been discussed in this paper. Two examples of practical usage of CoRa for data analysis of amorphous Ge3Sb2Te6 and CdP2 singly crystal have been demonstrated.