Miracle-dz/UniFAR-FTUs huggingface dataset:
Vasculo-Neuronal Coupling and Neurovascular Coupling at the Neurovascular Unit: Impact of Hypertension

Components of the neurovascular unit (NVU) establish dynamic crosstalk that regulates cerebral blood flow and maintain brain homeostasis. Here, we describe accumulating evidence for cellular elements of the NVU contributing to critical physiological processes such as cerebral autoregulation, neurovascular coupling, and vasculo-neuronal coupling. We discuss how alterations in the cellular mechanisms governing NVU homeostasis can lead to pathological changes in which vascular endothelial and smooth muscle cell, pericyte and astrocyte function may play a key role. Because hypertension is a modifiable risk factor for stroke and accelerated cognitive decline in aging, we focus on hypertension-associated changes on cerebral arteriole function and structure, and the molecular mechanisms through which these may contribute to cognitive decline. We gather recent emerging evidence concerning cognitive loss in hypertension and the link with vascular dementia and Alzheimer’s disease. Collectively, we summarize how vascular dysfunction, chronic hypoperfusion, oxidative stress, and inflammatory processes can uncouple communication at the NVU impairing cerebral perfusion and contributing to neurodegeneration.
The Spatial Dimension of COVID-19: The Potential of Earth Observation Data in Support of Slum Communities with Evidence from Brazil

The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.
The Spatial-Comprehensiveness (S-COM) Index: Identifying Optimal Spatial Extents in Volunteered Geographic Information Point Datasets

Social media and other forms of volunteered geographic information (VGI) are used frequently as a source of fine-grained big data for research. While employing geographically referenced social media data for a wide array of purposes has become commonplace, the relevant scales over which these data apply to is typically unknown. For researchers to use VGI appropriately (e.g., aggregated to areal units (e.g., neighbourhoods) to elicit key trend or demographic information), general methods for assessing the quality are required, particularly, the explicit linkage of data quality and relevant spatial scales, as there are no accepted standards or sampling controls. We present a data quality metric, the Spatial-comprehensiveness Index (S-COM), which can delineate feasible study areas or spatial extents based on the quality of uneven and dynamic geographically referenced VGI. This scale-sensitive approach to analyzing VGI is demonstrated over different grains with data from two citizen science initiatives. The S-COM index can be used both to assess feasible study extents based on coverage, user-heterogeneity, and density and to find feasible sub-study areas from a larger, indefinite area. The results identified sub-study areas of VGI for focused analysis, allowing for a larger adoption of a similar methodology in multi-scale analyses of VGI.
Opportunities of frugality in the post-corona era

Objective of this discussion paper is twofold: First, we assess the likely impact of the Corona crisis on the economic and societal choices of people, especially in relation to voluntary simplicity. Second, we contextualize the impact of those choices in the field of innovation management. Taking a normative-conceptual perspective we seek to understand in how far frugality, and inter alia, frugal innovations can play a role in better managing the after-effects of the Corona crisis and what implications arise out of this for the relevant societal stakeholders, especially for corporates, policy makers and consumers. Taking note of historical discussions of the role of frugality as a "virtuell", we propose that the currently ongoing fourth renaissance of frugality needs to - and is likely to - emerge as a megatrend that may shape a frugal "AGE" resulting in "affordable green excellence" as the dominant innovation paradigm. To realize this potential, however, it is necessary to move away from the unidimensional focus on monetary affordability of frugal solutions. There is a need for developing a more comprehensive and multidimensional understanding of affordability that is targeted at ensuring financial, societal, infrastructural and ecological affordability of frugal products, services, technologies and business models. Frugal innovators and entrepreneurs should not merely target "significant cost reduction" in relation to existing substitute products but rather aim at ensuring "high affordability" that will open up new business arenas with means of breakthrough innovations and technological excellence. This paradigm shift requires action from all relevant stakeholders including policymakers.
TasRec: A Framework for Task Recommendation in Crowdsourcing

Lately, crowdsourcing has emerged as a viable option of getting work done by leveraging the collective intelligence of the crowd. With many tasks posted every day, the size of crowdsourcing platforms is growing exponentially. Hence, workers face an important challenge of selecting the right task. Despite the task filtering criteria available on the platform to select the right task, crowd workers find it difficult to choose the most relevant task and must glean through the filtered tasks to find the relevant tasks. In this paper, we propose a framework for recommending tasks to workers. The proposed framework evaluates the worker’s fitment over the tasks based on worker’s preference, past tasks (s)he has performed, and tasks done by similar workers. We evaluated our approach on the datasets collected from popular crowdsourcing platform. Our experimental results based on 5,000 tasks and 3,000 workers show that the recommendation made by our framework is significantly better as compared to the baseline approach.
Immersive IDE: Towards Leveraging Virtual Reality for creating an Immersive Software Development Environment

Positive affects have been shown to be positively correlated with developer productivity. Moreover, the environment in which the developer sits is important as it has a direct bearing on her emotions. However, in typical software development project setups, it is difficult to mold the surroundings of a developer according to her own needs or wishes. Moreover, large project areas may tend to have multiple distractions that may further negatively affect the developer. Multiple studies have shown that Virtual Reality can be an effective medium to induce positive emotions, with the capability to immerse oneself into virtually created environs, allowing for countless opportunities to surround a developer with what she would want. In this paper, we present our approach to allow a developer to choose her own surrounding environment to work in (say a beach, a park or in space! ), while allowing for a real-time feed of her workstation/tools to be embedded in the same for a seamless experience. We believe that this approach will enable better mood/engagement and lower distractions/stress, which may lead to higher productivity and a balanced sense of developer well-being.
A Preliminary Systematic Mapping on Software Engineering for Robotic Systems: A Software Quality Perspective

Robotic systems have been increasingly employed in everyday tasks. Considering that software plays a crucial point in robot systems, to investigate how software engineering concepts in a software quality perspective can improve robotic systems. In this work, we present a systematic mapping to identify and classify the state-of-art of software engineering for robotic systems in a quality software perspective. We selected and systematically analyzed a final set of 35 primary studies extracted from an automated search on Scopus digital library. This work presents three main contributions. Firstly, we organize a catalogue of research studies about software engineering, more specifically software quality applied in robotic systems. Next, we systematically analyze software quality areas used in robotic systems. Finally, we discuss insights into research opportunities and gaps in software engineering to robotic systems for future studies. As a result, we observed that there are studies in the robotic systems area, addressing in a combined way, software engineering approaches and software quality aspects. The less investigated software quality aspect is security. Due to this fact, we presented an overview of the state-of-art on blockchain applying in robotics systems. Blockchain brings opportunities for changing the ways that robots interact with humans. Finally, we identify research opportunities and gaps in software quality on robotic systems, presenting an overview for future studies.
Ceria Nanoparticles Decrease UVA-Induced Fibroblast Death Through Cell Redox Regulation Leading to Cell Survival, Migration and Proliferation

Exposure to ultraviolet radiation is a major contributor to premature skin aging and carcinogenesis, which is mainly driven by overproduction of reactive oxygen species (ROS). There is growing interest for research on new strategies that address photoaging prevention, such as the use of nanomaterials. Cerium oxide nanoparticles (nanoceria) show enzyme-like activity in scavenging ROS. Herein, our goal was to study whether under ultraviolet A rays (UVA)-induced oxidative redox imbalance, a low dose of nanoceria induces protective effects on cell survival, migration, and proliferation. Fibroblasts cells (L929) were pretreated with nanoceria (100 nM) and exposed to UVA radiation. Pretreatment of cells with nanoceria showed negligible cytotoxicity and protected cells from UVA-induced death. Nanoceria also inhibited ROS production immediately after irradiation and for up to 48 h and restored the superoxide dismutase (SOD) activity and GSH level. Additionally, the nanoceria pretreatment prevented apoptosis by decreasing Caspase 3/7 levels and the loss of mitochondrial membrane potential. Nanoceria significantly improved the cell survival migration and increased proliferation, over a 5 days period, as compared with UVA-irradiated cells, in wound healing assay. Furthermore, it was observed that nanoceria decreased cellular aging and ERK 1/2 phosphorylation. Our study suggests that nanoceria might be a potential ally to endogenous, antioxidant enzymes, and enhancing the redox potentials to fight against UVA-induced photodamage and consequently modulating the cells survival, migration, and proliferation.
Hybrid embedding and joint training of stacked encoder for opinion question machine reading comprehension

Opinion question machine reading comprehension (MRC) requires a machine to answer questions by analyzing corresponding passages. Compared with traditional MRC tasks where the answer to every question is a segment of text in corresponding passages, opinion question MRC is more challenging because the answer to an opinion question may not appear in corresponding passages but needs to be deduced from multiple sentences. In this study, a novel framework based on neural networks is proposed to address such problems, in which a new hybrid embedding training method combining text features is used. Furthermore, extra attention and output layers which generate auxiliary losses are introduced to jointly train the stacked recurrent neural networks. To deal with imbalance of the dataset, irrelevancy of question and passage is used for data augmentation. Experimental results show that the proposed method achieves state-of-the-art performance. We are the biweekly champion in the opinion question MRC task in Artificial Intelligence Challenger 2018 (AIC2018).
Zebrafish Shoaling, Its Behavioral and Neurobiological Mechanisms, and Its Alteration by Embryonic Alcohol Exposure: A Review

Social cognition and social behaviors are complex phenomena that involve numerous brain areas and underlying neurobiological mechanisms. Embryonic alcohol exposure may lead to the development of Fetal Alcohol Spectrum Disorder (FASD), a disorder that manifests with varying symptoms including abnormal social behavior and other cognitive deficits. Animal models have been utilized to mimic aspects of the disease and to study potential underlying mechanisms. The zebrafish is a relative newcomer in this field but has been suggested as an optimal compromise between system complexity and practical simplicity for modeling FASD. Importantly, due to external fertilization and development of the embryo outside the mother and subsequent lack of parental care, this species allows precise control of the timing and dose of alcohol delivery during embryonic development. Furthermore, the zebrafish is a highly social species and thus may be particularly appropriate for the analysis of embryonic alcohol-induced alterations in this context. Here, we provide a succinct review focusing on shoaling, a prominent form of social behavior, in zebrafish. We summarize what is known about its behavioral mechanisms and underlying neurobiological processes, and how it is altered by exposure to ethanol during embryonic development. Lastly, we briefly consider possible future directions of research that would help us better understand the relationship between the behavioral expression and molecular basis of embryonic ethanol-induced social deficits in fish and humans.
Inter-characters associations in pearl millet (Pennisetum glaucum L.R.Br.) germplasm in hot-arid climate.

The forty six accessions of pearl millet germplasm collected from different places were studied during Kharif 2012 and 2013 in a Randomized Complete Block Design in the hot arid climate of Rajasthan. The study was conducted to assess the magnitude of presence of genetic variability and inter-characters associations of the characters namely, number of tillers per plant, plant height (cm), panicle length (cm), panicle diameter (cm), number of leaves per plant, days to 50 per cent flowering and test weight (cm). The result showed that genetic variability was present among the accessions for all studied characters. The estimated PCV (%) and GCV (%) were moderate to high for all characters. The broad sense heritability (%) and genetic advance (% of mean) estimates were moderate to high for all studied characters. The number of tillers per plant, plant height (cm), panicle length (cm), panicle diameter (cm), number of leaves per plant, days to 50 per cent flowering and test weight (cm) had positive and significant genotypic, phenotypic and environmental correlation co-efficients with grain yield per plant. The presence of genetic variability, high heritability and genetic advance and association with grain yield of all studied characters amenable them for selection and enhancement of grain yield.
Predicting Stack Overflow Question Tags: A Multi-Class, Multi-Label Classification

This work proposes to predict the tags assigned for the posts on Stack Overflow platform. The raw data was obtained from the stackexchange.com including more than 50K posts and their associated tags given by the users. The posts' questions and titles are pre-processed, and the sentences in the posts are further transformed into features via Latent Dirichlet Allocation. The problem is a multi-class and multi-label classification and hence, we propose 1) one-against-all models for 15 most popularly used tags, and 2) a combined multi-tag classifier for finding the top K tags for a single post. Three algorithms are used to train the one-against-all classifiers to decide to what extent a post belongs to a tag. The probabilities of each post belonging to a tag are then combined to give the results of the multi-tag classifier with the best performing algorithm. The performance is compared with a baseline approach (kNN). Our multi-tag classifier achieves 55% recall and 39% F1-score.
Designing Digital Engagements: Approaches to creative practice and adaptable programming for archaeological visualisation

The processes of archaeological visualisation exist at the intersection of art practice and archaeological interpretation, often involving complex negotiations between stakeholders and practitioners. This paper reflects upon the authors’ experiences developing interactive mixed media content for public outreach from two case study archaeological excavations: the SERF Hillforts Project in Strathearn, Scotland and the Nunalleq Archaeology Project in southwest Alaska. Each presented unique challenges in the integration of layered multivocal narratives in the context of ongoing archaeological excavations. This included evolving scientific interpretations, co-design with stakeholders and a cycle of feedback. Creative design and software development were a core part of the collaborative process that resulted in these interactive digital interfaces. Here we explore how collaborative creative practice influenced the design choices that were made and the programming paradigms that were used.
Proposing a Supply Chain Collaboration Framework for Synchronous Flow Implementation in the Automotive Industry: A Moroccan Case Study

The present paper reports on studying synchronous flow implementation, as a lean supply chain tools, through a collaborative relationship with suppliers. This involves consolidating with a new contribution to the development and application of a supply chain collaboration framework between automotive constructor and first-tier equipment suppliers to achieve the synchronous flow of components. The objective is to provide the automotive companies with a decision-making tool for selecting strategic suppliers to collaborate with, examining the collaboration context in terms of motivators, drivers, and barriers and evaluating the collaboration performance. Therefore, our contribution is structured as follows. As a first step, an overview of papers reporting on collaboration, lean supply chain, and synchronous flow is provided to identify the key elements of successful collaboration relationships. As a result, a preliminary framework is elaborated. The second step described the case study of a leading automotive firm “RENAULT” and its suppliers in Morocco. Based on semi-structured interviews conducted with participants from these companies, the preliminary framework was improved. The next section discusses the obtained results as well as the improved framework. Finally, conclusions and suggestions for further works are included.
How Online Forums Complement Task Documentation in Software Crowdsourcing

An issue in software crowdsourcing is the quality of the task documentation and the high number of registered crowd workers to solve tasks but few submitted solutions only. This happens because uncommunicated or misunderstood requirements can lead crowd workers to deliver a solution that does not meet the customers' requirements or, worse, to give up submitting a solution. In this paper, we present an empirical study in which we analyzed task documentation and online forums messages associated with 25 Software Crowdsourcing (SW CS) challenges. The findings corroborate that weak documentation is a challenge in SW CS. Meanwhile, online forums allow crowd workers to gather additional technical and operational information that is not present in the official task documentation. We provide a stepping stone towards understanding the interplay between requirements and communication, to make it possible to improve SW CS development processes, practices, and tools.
Fitness and Predating Potential of Antilochus coquebertii (Hemiptera: Pyrrhocoridae): A Predator of the Red Cotton Bug (Dysdercus koenigii)

ABSTRACT: Antilochus coquebertii is one of the most important predators of Dysdercus koenigii. The present study examines for the first time the fitness (using age-stage and two-sex life table parameters), morphometrics and predating potential of A. coquebertii fed on the second instar of D. koenigii under laboratory conditions. Results indicate that female adults of A. coquebertii consumed significantly higher numbers of second instar D. koenigii (229.81 ± 14.36) when compared to male adults (118.47 ± 10.77). The body length (16.10±0.40 mm) and width (6.60 ± 0.20 mm) of female adults of A. coquebertii were significantly higher if compared to body length (14.40 ± 0.40 mm) and width (5.80 ± 0.40 mm) of male adults. The developmental period of A. coquebertii from egg to female adult (76.09 ± 3.51 days) was significantly longer than egg to male adult (64.00 ± 1.87 days). The fecundity (number of eggs/female) of A. coquebertii was 108.67 ± 3.67 per female. The intrinsic rate of increase (r) and finite rate of increase (λ) per day of A. coquebertii were recorded with values of 5.87 and 1.06 days, respectively. The gross reproductive rate (GRR) and net reproductive rate (R0) of A. coquebertii were 23.17 offspring and 18.56 offspring/individual, respectively. The information obtained in this study will be useful for the development of future research projects focused on this predator against D. koenigii under natural conditions. Based on our results, we suggest that A. coquebertii will be a good biological control agent against the second instar of D. koenigii in cotton fields.
Magnetic Hamiltonian parameter estimation using deep learning techniques

Deep learning can provide a better complementary link between the theoretical models and experimental results in magnetism. Understanding spin textures in magnetic systems is extremely important to the spintronics and it is vital to extrapolate the magnetic Hamiltonian parameters through the experimentally determined spin. It can provide a better complementary link between theories and experimental results. We demonstrate deep learning can quantify the magnetic Hamiltonian from magnetic domain images. To train the deep neural network, we generated domain configurations with Monte Carlo method. The errors from the estimations was analyzed with statistical methods and confirmed the network was successfully trained to relate the Hamiltonian parameters with magnetic structure characteristics. The network was applied to estimate experimentally observed domain images. The results are consistent with the reported results, which verifies the effectiveness of our methods. On the basis of our study, we anticipate that the deep learning techniques make a bridge to connect the experimental and theoretical approaches not only in magnetism but also throughout any scientific research.