Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimate of the actual number of COVID-19 cases from the analysis of deaths.

Using the calculated values for the Infection Fatality Rate (IFR) of COVID-19 it is possible to estimate the prevalence of cases of infection in the city of Buenos Aires, Argentina, throughout the pandemic. The use of confirmed cases as a metric and their replacement by more reliable parameters such as death figures are discussed. The results are analyzed according to age ranges and possible sources of error in the estimates are established.
AMPK in the Ventromedial Nucleus of the Hypothalamus: A Key Regulator for Thermogenesis

Obesity has become a global health issue, but effective therapies remain very limited. Adaptive thermogenesis promotes weight loss by dissipating energy in the form of heat, thereby representing a promising target to counteract obesity. Notably, the regulation of thermogenesis is tightly orchestrated by complex neuronal networks, especially those in the hypothalamus. Recent evidence highlights the importance of adenosine monophosphate-activated protein kinase (AMPK) within the ventromedial nucleus of the hypothalamus (VMH) in modulating thermogenesis. Various molecules, such as GLP-1, leptin, estradiol, and thyroid hormones, have been reported to act on the VMH to inhibit AMPK, which subsequently increases thermogenesis through the activation of the sympathetic nervous system (SNS). In this review, we summarize the critical role of AMPK within the VMH in the control of energy balance, focusing on its contribution to thermogenesis and the associated mechanisms.
Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals

Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)]. First, the structural features and connectivity features of the θ-, α-, and β-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the θ-, α-, and β-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.
Entity deduplication in big data graphs for scholarly communication

PurposeSeveral online services offer functionalities to access information from “big research graphs” (e.g. Google Scholar, OpenAIRE, Microsoft Academic Graph), which correlate scholarly/scientific communication entities such as publications, authors, datasets, organizations, projects, funders, etc. Depending on the target users, access can vary from search and browse content to the consumption of statistics for monitoring and provision of feedback. Such graphs are populated over time as aggregations of multiple sources and therefore suffer from major entity-duplication problems. Although deduplication of graphs is a known and actual problem, existing solutions are dedicated to specific scenarios, operate on flat collections, local topology-drive challenges and cannot therefore be re-used in other contexts.Design/methodology/approachThis work presents GDup, an integrated, scalable, general-purpose system that can be customized to address deduplication over arbitrary large information graphs. The paper presents its high-level architecture, its implementation as a service used within the OpenAIRE infrastructure system and reports numbers of real-case experiments.FindingsGDup provides the functionalities required to deliver a fully-fledged entity deduplication workflow over a generic input graph. The system offers out-of-the-box Ground Truth management, acquisition of feedback from data curators and algorithms for identifying and merging duplicates, to obtain an output disambiguated graph.Originality/valueTo our knowledge GDup is the only system in the literature that offers an integrated and general-purpose solution for the deduplication graphs, while targeting big data scalability issues. GDup is today one of the key modules of the OpenAIRE infrastructure production system, which monitors Open Science trends on behalf of the European Commission, National funders and institutions.
An analytical toast to wine: Using stacked generalization to predict wine preference

Due to the intricacies surrounding taste profiles, one's view of good wine is subjective. Therefore, it is advantageous to provide a more objective, data‐driven way to assess wine preferences. Motivated by a previous study that modeled wine preferences using machine learning algorithms, this work presents an ensemble approach to predict a wine sample's quality level given its physiochemical properties. Results show the proposed framework out‐performs many sophisticated models including the one recommended by the motivational study. Moreover, the proposed framework offers a simple variable importance strategy to gain insight as to the relevance of the predictor variables and is applied to both simulated and real data. Given the predictive power of using ensembles, especially when they can be interpretable, practitioners can use the following approach to provide an accurate and inferential perspective towards demystifying wine preferences.
Overview of MediaEval 2019: Insights for Wellbeing TaskMultimodal Personal Health Lifelog Data Analysis

This paper provides a description of the MediaEval 2019 "Multi-modal personal health lifelog data analysis". The purpose of this task is to develop approaches that process the environment data to obtain insights about personal wellbeing. Establishing the association between people’s wellbeing and properties of the surrounding environment which is vital for numerous research. Our task focuses on the internal associations of heterogeneous data. Participants are expected to process the mixed environment data(e.p weather, air pollution, lifelog images, etc.) to tackle two challenging subtasks. The first one is to develop a hypothesis about the associations within the heterogeneous data and build a system that is able to correctly replace segments of data that have been removed. The second one is to develop approaches to automatically predict personal AQI (Air Quality Index) at specific positions and time durations.
The upper bound of multi-source DOA information in sensor array and its application in performance evaluation

Direction of arrival (DOA) estimation has been discussed extensively in the array signal processing field. In this paper, the authors focus on the multi-source DOA information which is defined as the mutual information between the DOA and the received signal contaminated by complex additive white Gaussian noise. A theoretical expression of DOA information with multiple sources is derived for the uniform linear array. At high SNRs and under the sparse-source assumption obtained is the upper bound of DOA information contained in K sparse sources which can be regarded as the sum of all single-source information minus the uncertainty of sources’ order log K ! Moreover, because of the uncertainty of multi-sources’ order, the posteriori probability distribution of DOA no longer obeys single peak Gaussian distribution so that the mean square error is unsuitable in evaluating the performance of multi-dimensional parameter estimation. Consequently, entropy error (EE) is used as a new performance evaluation metric, whose relationship with DOA information is given.
Association of Cytomegalovirus DNA and Immunologic Markers of Cardiovascular Disease

Abstract Background Persons living with human immunodeficiency virus (HIV) (PLWH) with high cytomegalovirus (CMV)–specific interferon (IFN) γ response have increased numbers of endothelium homing receptor (CX3CR1)+–expressing cells that are associated with cardiovascular disease. The current study was performed to investigate the effect of cellular levels of CMV DNA on these markers. Methods Eighty paired peripheral blood mononuclear cell samples were collected ≥12 months apart from 40 CMV-seropositive PLWH with suppressed HIV RNA, who started antiretroviral therapy at median of 3-months of infection. The samples were assessed for CMV-specific IFN-γ response by means of enzyme-linked immunospot assay, and participants were classified as low responders (LRs) or high responders (HRs) based on IFN-γ production (≤100 or >100 spot-forming units [SFUs]/105 cells). Results Of the 40 participants, 26 (65%) were HRs and 14 (35%) LRs at baseline, which did not change over time or by CMV levels (median at first/second time points, 383/308 SFUs/106 cells for HRs vs 21/41 SFUs/106 for LRs). A decrease in IFN-γ over time was associated with higher CMV DNA levels (P < .01). High CMV response was also associated with increased CD28+CD27−CD4+ T cells expressing CX3CR1 (P < .001). Similarly, increased IFN-γ production was associated with increased CMV-specific CX3CR1+CD28+CD27−CD4+ and CD8+ T cells (P < .001). Conclusions These findings demonstrate that levels of CMV-specific IFN-γ response in PLWH are stable over time, and that HRs have increased circulating T cells expressing CX3CR1 that may put them at increased risk of cardiovascular disease and other inflammatory diseases.
Critical literature review on straggler node

In today’s era, Big data processing capabilities is a challenging task and Hadoop’s mapreduce is one of the effective framework to process the big data. But Hadoop itself struggle due to straggler node. This paper goes through a critical review of literature which through light on how to efficiently and proactively handle the straggler node or nodes
Detecting and differentiating leg bouncing behaviour from everyday movements using tri-axial accelerometer data

Leg bouncing is assumed to be related to anxiety, engrossment, boredom, excitement, fatigue, impatience, and disinterest. Objective detection of this behaviour would enable researching its relation to different mental and emotional states. However, differentiating this behaviour from other movements is less studied. Also, it is less known which sensor placements are best for such detection. We collected recordings of everyday movements, including leg bouncing, from six leg bouncers using tri-axial accelerometers at three leg positions. Using a Random Forest Classifier and data collected at the ankle, we could obtain a 90% accuracy in the classification of the recorded everyday movements. Further, we obtained a 94% accuracy in classifying four types of leg bouncing. Based on the subjects' opinion on leg bouncing patterns and experience with wearables, we discuss future research opportunities in this domain.
Molecular mechanisms behind mRNA localization in axons

Messenger RNA (mRNA) localization allows spatiotemporal regulation of the proteome at the subcellular level. This is observed in the axons of neurons, where mRNA localization is involved in regulating neuronal development and function by orchestrating rapid adaptive responses to extracellular cues and the maintenance of axonal homeostasis through local translation. Here, we provide an overview of the key findings that have broadened our knowledge regarding how specific mRNAs are trafficked and localize to axons. In particular, we review transcriptomic studies investigating mRNA content in axons and the molecular principles underpinning how these mRNAs arrived there, including cis-acting mRNA sequences and trans-acting proteins playing a role. Further, we discuss evidence that links defective axonal mRNA localization and pathological outcomes.
Overwintering in the cabbage root fly Delia radicum : a dynamic model of temperature-dependent dormancy and post-dormancy development

1. Emergence data on adult Delia radicum from overwintering dormant pupae were used to develop a temperature-driven stage dynamics model to analyse development during winter in this species. 2. The model suggests that overwintering has three phases: two in which no morphological development occurs, followed by a third phase or metamorphosis of the pupae to the adult stage (i.e. post-dormancy) culminating in the emergence of the adult. It is proposed that each phase has a different response to temperature and different optima. 3. The biology of the model is in accord with the empirical knowledge about overwintering in this species, and predicted well the patterns of emergence of adults from six cohorts of dormant pupae reared under different temperature regimes.
Exponential stability in Mindlin’s Form II gradient thermoelasticity with microtemperatures of type III

In this paper, we derive a nonlinear strain gradient theory of thermoelastic materials with microtemperatures taking into account micro-inertia effects as well. The elastic behaviour is assumed to be consistent with Mindlin’s Form II gradient elasticity theory, while the thermal behaviour is based on the entropy balance of type III postulated by Green and Naghdi for both temperature and microtemperatures. The work is motivated by increasing use of materials having microstructure at both mechanical and thermal levels. The equations of the linear theory are also obtained. Then, we use the semigroup theory to prove the well-posedness of the obtained problem. Because of the coupling between high-order derivatives and microtemperatures, the obtained equations do not have exponential decay. A frictional damping for the elastic component, whose form depends on the micro-inertia, is shown to lead to exponential stability for the type III model.
Navigating cross-cultural research: methodological and ethical considerations

The intensifying pace of research based on cross-cultural studies in the social sciences necessitates a discussion of the unique challenges of multi-sited research. Given an increasing demand for social scientists to expand their data collection beyond WEIRD (Western, educated, industrialized, rich and democratic) populations, there is an urgent need for transdisciplinary conversations on the logistical, scientific and ethical considerations inherent to this type of scholarship. As a group of social scientists engaged in cross-cultural research in psychology and anthropology, we hope to guide prospective cross-cultural researchers through some of the complex scientific and ethical challenges involved in such work: (a) study site selection, (b) community involvement and (c) culturally appropriate research methods. We aim to shed light on some of the difficult ethical quandaries of this type of research. Our recommendation emphasizes a community-centred approach, in which the desires of the community regarding research approach and methodology, community involvement, results communication and distribution, and data sharing are held in the highest regard by the researchers. We argue that such considerations are central to scientific rigour and the foundation of the study of human behaviour.
Effects of Increased Precipitation and Nitrogen Deposition on Methane Uptake of Alpine Meadow in Qinghai-Tibet Plateau: in situ Experiments

ABSTRACT The methane (CH4) fluxes in grasslands are sensitive to changes in precipitation and soil nitrogen concentrations, which was poorly understood, especially on the Tibetan Plateau. Therefore, an experiment was conducted from May 2012 to October 2013, by using the static chamber technique and gas chromatography method. We set up five treatments: an increased 2 g m-2 NH4NO3 treatment (2gN); an increased 4 g m-2 NH4NO3 treatment (4gN); and treatment with precipitation increased by 20% (Pre), and added 2 g m-2 NH4NO3 and precipitation (20%) treatment (N+Pre), and a control treatment (CK). The five treatments showed decreasing CH4 uptake rates in the following order: CK (71.66 ± 6.6 µg m-2 h-1) > N+Pre (58.57 ± 3.7 µg m-2 h-1) > Pre (52.66 ± 2.3 µg m-2 h-1) > 2gN (47.63 ± 3.1 µg m-2 h-1) > 4gN (39.12 ± 3.3 µg m-2 h-1). The 2gN and 4gN treatment resulted in 33.5% and 45.4% lower CH4 uptake than the CK treatment, respectively. The path analysis indicated that the above-ground biomass and litter fall were the most important factor promoting and limiting the CH4 uptake rate of alpine meadow, respectively.
Experiments in Non-Personalized Future Blood Glucose Level Prediction

In this study we investigate the need for training future blood glucose level prediction models at the individual level (i.e. per patient). Speciﬁcally, we train various model classes: linear models, feed-forward neural networks, recurrent neural networks, and recurrent neural networks incorporating attention mechanisms, to predict future blood glucose levels using varying time series history lengths and data sources. We also compare methods of handling missing time series data during training. We found that relatively short history lengths provided the best results: a 30 minute history length proved optimal in our experiments. We observed long short-term memory (LSTM) networks performed better than linear and feed-forward neural networks, and that including an attention mechanism in the LSTM model further improved performance, even when processing sequences with relatively short length. We observed models trained using all of the available data outperformed those trained at the individual level. We also observed models trained using all of the available data, except for the data contributed by a given patient, were as effective at predicting the patient’s future blood glucose levels as models trained using all of the available data. These models also sig-niﬁcantly outperformed models trained using the patient’s data only. Finally, we found that including sequences with missing values during training produced models that were more robust to missing values.
What’s in a Name? Naming Big Science Data in Named Data Networking

Data naming is the most critical construct of Named Data Networking (NDN). The way a piece of content is named has profound impacts on content discovery, routing of user requests, data retrieval, and security. Besides, the naming of individual pieces of content seriously affects how 𝑡ℎ𝑒 𝑛𝑒𝑡𝑤𝑜𝑟𝑘 behaves. While names are ubiquitous in NDN, the design choices for content names and how they affect the network have largely been overlooked. NDN applications and protocols usually name content to fit their particular application scenarios, often derived from existing naming conventions. However, these ad-hoc naming schemes often ignore the impact of these names on the network and the applications themselves. Drawing upon our experience in applying NDN to multiple science domains, we point out different exiting naming schemes in scientific communities, how we translated these names into NDN names, and the effect of naming on the network. Based on these observations, we provide a set of naming guidelines for future scientific applications and network operators supporting those applications.
iCDN: An NDN-based CDN

Despite the close philosophy between content delivery networks (CDN) and named-data networks (NDN), no solution has realized a large-scale NDN-based CDN yet. In this paper, we void the popular belief that any NDN network can be expanded to serve as a CDN and introduce iCDN, a scalable, resilient, and high-performance CDN using NDN technology. We evaluate different aspects of iCDN over the Abilene topology against the global NDN testbed solution and show why iCDN is a promising design to build a large-scale NDN-based CDN.
Multi-Worker NFD: an NFD-compatible High-speed NDN Forwarder

The NDN Forwarding Deamon (NFD) has been a reference forwarder implementation of Named Data Networking. Its good modularity and extensibility make it easy to experiment with new ideas, but it shows limited forwarding performance even with multi-core CPUs because its forwarding structure uses only single core. We present the Multi-Worker NFD (MW-NFD), an NFD-compatible NDN forwarder with parallel forwarding capability on multi-core CPUs. Since the NFD's forwarding architecture is maintained as is, MW-NFD inherits the advantages (modularity and extensibility) of NFD, and it is fully compatible with NFD and existing NDN applications. In this demo, we shows that MW-NFD can yield high forwarding performance, about 13 times higher than NFD's performance.
Prognosis Prediction in Covid-19 Patients from Lab Tests and X-ray Data through Randomized Decision Trees

AI and Machine Learning can offer powerful tools to help in the fight against Covid-19. In this paper we present a study and a concrete tool based on machine learning to predict the prognosis of hospitalised patients with Covid-19. In particular we address the task of predicting the risk of death of a patient at different times of the hospitalisation, on the base of some demographic information, chest X-ray scores and several laboratory findings. Our machine learning models use ensembles of decision trees trained and tested using data from more than 2000 patients. An experimental evaluation of the models shows good performance in solving the addressed task.
Effect of calcium intake on iron absorption and hematologic status: A systematic review and dose-response meta-analysis of randomized trials and case-cross-over studies

Background: The interaction between dietary (and supplementary) divalent ions has been a long-standing issue in human nutrition research. Developing optimal calcium and iron supplementation recommendation needs detailed knowledge of the potential trade-offs between: a) the clinical effects of concurrent intake on iron absorption and hematological indices, and b) the potentially negative effects of separated ingestion on adherence to either or both iron and calcium supplements. Human clinical studies have examined the effects of calcium intake on iron status, but there are no meta-analyses or recent reviews summarizing the findings. Objective: We aimed to summarize the literature on the effect of calcium consumption from meals and supplements on iron indices in humans, and quantify the pooled effects. Design: Peer-reviewed randomized and case-cross-over studies were included in this review. Result: The negative effect of calcium intake was statistically significant in short-term iron absorption studies but the effect magnitude was low (weighted mean difference (WMD) = -5.57%, (95% CI: -7.09, -4.04)). The effect of calcium on iron status was mixed. There was a quadratic dose-response relationship between calcium intake and serum ferritin concentration. Higher daily calcium intake was associated with a modest reduction in serum ferritin concentration. There was, however, no reduction in hemoglobin concentration (WMD = 1.22g/L, 95% CI: 0.37, 2.07). Conclusion: The existing body of studies is insufficient to make recommendations with high confidence due to heterogeneity in design, limitations of ferritin as an iron biomarker and lack of intake studies in pregnant women. Prescribing separation of prenatal calcium and iron supplements in free living individuals is unlikely to affect the anemia burden. There is a need for effectiveness trials comparing the effects of prescribing separated intake to concurrent intake, with functional end-points as primary outcomes, and adherence to each supplement as intermediate outcomes.