Miracle-dz/UniFAR-FTUs huggingface dataset:
Concussion rates and effects on player performance in Major League Baseball players

Background Major League Baseball (MLB) players are throwing the ball faster and hitting harder than ever before. Although some safety measures have been implemented, by decreasing the 15 days on the disabled list (DL) to the 7 days on the DL, concussion rates remain high across positions and may impact player performance. Our hypothesis was, there would be an increase in concussion incidence following implementation of the 7 day DL, but this would not have a negative impact on player’s postconcussion performance. Study design This is a descriptive epidemiology study. Methods The concussed players from 2005 to 2016 were identified from the MLB DL and verified using established new sources. Position-specific performance metrics from before and after injuries were gathered and compared to assess effects of the injury. Postconcussion performance metrics were compared before and after the 7-day DL rule implementation. Results A total of 112 concussed players were placed on the DL. For all position players, the batting average (BA) and on-base percentage (OBP) showed a nonsignificant decline after injury (P=0.756). Although performance statistics for pitchers declined on average, the trend was not statistically significant. Postinjury BA and OBP did not significantly change before (0.355) and after (0.313) the 7-day DL rule change in 2011 (P=0.162). Conclusion The incidence of reported concussion has increased with the 7-day DL rule change. Concussion incidence was highest in catchers and pitchers compared with all other players. The most common causes identified as being hit by pitch or struck by a foul ball or foul tip. While new league rules prevent collisions with catchers at home plate, injury by a foul tip was the most common cause for concussion. The shortened time spent on the DL did not negatively impact player’s performance. Further research on protective helmets for catchers may reduce concussion incidence.
Nitrogen input by bamboos in neotropical forest: a new perspective

Background Nitrogen (N) is an important macronutrient that controls the productivity of ecosystems and biological nitrogen fixation (BNF) is a major source of N in terrestrial systems, particularly tropical forests. Bamboo dominates theses forests, but our knowledge regarding the role of bamboo in ecosystem functioning remains in its infancy. We investigated the importance of a native bamboo species to the N cycle of a Neotropical forest. Methods We selected 100 sample units (100 m2 each) in a pristine montane Atlantic Forest, in Brazil. We counted all the clumps and live culms of Merostachys neesii bamboo and calculated the specific and total leaf area, as well as litter production and respective N content. Potential N input was estimated based on available data on BNF rates for the same bamboo species, whose N input was then contextualized using information on N cycling components in the study area. Results With 4,000 live culms ha−1, the native bamboo may contribute up to 11.7 kg N ha−1 during summer (January to March) and 19.6 kg N ha−1 in winter (July to September). When extrapolated for annual values, M. neesii could contribute more than 60 kg N ha−1y−1. Discussion The bamboo species’ contribution to N input may be due to its abundance (habitat availability for microbial colonization) and the composition of the free-living N fixer community on its leaves (demonstrated in previous studies). Although some N is lost during decomposition, this input could mitigate the N deficit in the Atlantic Forest studied by at least 27%. Our findings suggest that M. neesii closely regulates N input and may better explain the high diversity and carbon stocks in the area. This is the first time that a study has investigated BNF using free-living N fixers on the phyllosphere of bamboo.
Moment Matching for Multi-Source Domain Adaptation

Conventional unsupervised domain adaptation (UDA) assumes that training data are sampled from a single domain. This neglects the more practical scenario where training data are collected from multiple sources, requiring multi-source domain adaptation. We make three major contributions towards addressing this problem. First, we collect and annotate by far the largest UDA dataset, called DomainNet, which contains six domains and about 0.6 million images distributed among 345 categories, addressing the gap in data availability for multi-source UDA research. Second, we propose a new deep learning approach, Moment Matching for Multi-Source Domain Adaptation (M3SDA), which aims to transfer knowledge learned from multiple labeled source domains to an unlabeled target domain by dynamically aligning moments of their feature distributions. Third, we provide new theoretical insights specifically for moment matching approaches in both single and multiple source domain adaptation. Extensive experiments are conducted to demonstrate the power of our new dataset in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model. Dataset and Code are available at http://ai.bu. edu/M3SDA/
Recursive decentralized localization for multi-robot systems with asynchronous pairwise communication

This paper provides a fully decentralized algorithm for collaborative localization based on the extended Kalman filter. The major challenge in decentralized collaborative localization is to track inter-robot dependencies, which is particularly difficult when sustained synchronous communication between the robots cannot be guaranteed. Current approaches suffer from the need for particular communication schemes, extensive bookkeeping of measurements, overly conservative assumptions, or the restriction to specific measurement models. This paper introduces a localization algorithm that is able to approximate the inter-robot correlations while fulfilling all of the following conditions: communication is limited to two robots that obtain a relative measurement, the algorithm is recursive in the sense that it does not require storage of measurements and each robot maintains only the latest estimate of its own pose, and it supports generic measurement models. The fact that the proposed approach can handle these particularly difficult conditions ensures that it is applicable to a wide range of multi-robot scenarios. We provide mathematical details on our approximation. Extensive experiments carried out using real-world datasets demonstrate the improved performance of our method compared with several existing approaches.
Mitigating TCP Incast Issue in Cloud Data Centres using Software-Defined Networking (SDN): A Survey

Transmission Control Protocol (TCP) is the most widely used protocol in the cloud data centers today. However, cloud data centers using TCP experience many issues as TCP was designed based on the assumption that it would primarily be used in Wide Area Networks (WANs). One of the major issues with TCP in the cloud data centers is the Incast issue. This issue arises because of the many-to-one communication pattern that commonly exists in the modern cloud data centers. In many-to-one communication pattern, multiple senders simultaneously send data to a single receiver. This causes packet loss at the switch buffer which results in TCP throughput collapse that leads to high Flow Completion Time (FCT). Recently, Software-Defined Networking (SDN) has been used by many researchers to mitigate the Incast issue. In this paper, a detailed survey of various SDN based solutions to the Incast issue is carried out. In this survey, various SDN based solutions are classified into four categories i.e. TCP Receive Window based solutions, Tuning TCP Parameters based solutions, Quick Recovery based solutions and Application Layer based solutions. All the solutions are critically evaluated in terms of their principles, advantages, and shortcomings. Another important feature of this survey is to compare various SDN based solutions with respect to different performance metrics e.g. maximum number of concurrent senders supported, calculation of delay at the controller etc. These performance metrics are important for deployment of any SDN based solution in modern cloud data centers. In addition, future research directions are also discussed in this survey that can be explored to design and develop better SDN based solutions to the Incast issue.
Explainable and Explicit Visual Reasoning Over Scene Graphs

We aim to dismantle the prevalent black-box neural architectures used in complex visual reasoning tasks, into the proposed eXplainable and eXplicit Neural Modules (XNMs), which advance beyond existing neural module networks towards using scene graphs --- objects as nodes and the pairwise relationships as edges --- for explainable and explicit reasoning with structured knowledge. XNMs allow us to pay more attention to teach machines how to ``think'', regardless of what they ``look''. As we will show in the paper, by using scene graphs as an inductive bias, 1) we can design XNMs in a concise and flexible fashion, i.e., XNMs merely consist of 4 meta-types, which significantly reduce the number of parameters by 10 to 100 times, and 2) we can explicitly trace the reasoning-flow in terms of graph attentions. XNMs are so generic that they support a wide range of scene graph implementations with various qualities. For example, when the graphs are detected perfectly, XNMs achieve 100% accuracy on both CLEVR and CLEVR CoGenT, establishing an empirical performance upper-bound for visual reasoning; when the graphs are noisily detected from real-world images, XNMs are still robust to achieve a competitive 67.5% accuracy on VQAv2.0, surpassing the popular bag-of-objects attention models without graph structures.
Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data

We consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions. We use an attentional convolutional neural network to learn parsimonious signatures of individual cardiovascular response from data recorded at the minute level resolution over several months on a cohort of 80k people. We demonstrate internal validity by showing that signatures generated on an individual's 2017 data generalize to predict minute-level heart rate from physical activity and sleep for the same individual in 2018, outperforming several time-series forecasting baselines. We also show external validity demonstrating that signatures outperform plain resting heart rate (RHR) in predicting variables associated with cardiovascular functions, such as age and Body Mass Index (BMI). We believe that the computed cardiovascular signatures have utility in monitoring cardiovascular health over time, including detecting abnormalities and quantifying recovery from acute events.
Evaluating the individuality of animal‐habitat relationships

Abstract Examining the ways in which animals use habitat and select resources to satisfy their life history requirements has important implications for ecology, evolution, and conservation. The advent of radio‐tracking in the mid‐20th century greatly expanded the scope of animal‐habitat modeling. Thereafter, it became common practice to aggregate telemetry data collected on a number of tagged individuals and fit one model describing resource selection at the population level. This convention, however, runs the risk of masking important individuality in the nature of associations between animals and their environment. Here, we investigated the importance of individual variation in animal‐habitat relationships via the study of a highly gregarious species. We modeled elk (Cervus elaphus) location data, collected from Global Positioning System (GPS) collars, using Bayesian discrete choice resource selection function (RSF) models. Using a high‐performance computing cluster, we batch‐processed these models at the level of each individual elk (n = 88) and evaluated the output with respect to: (a) the composition of parameters in the most supported models, (b) the estimates of the parameters featured in the global models, and (c) spatial maps of the predicted relative probabilities of use. We detected considerable individual variation across all three metrics. For instance, the most supported models varied with respect to parameter composition with a range of seven to 17 and an average of 14.4 parameters per individual elk. The estimates of the parameters featured in the global models also varied greatly across individual elk with little conformity detected across age or sex classes. Finally, spatial mapping illustrated stark differences in the predicted relative probabilities of use across individual elk. Our analysis identifies that animal‐habitat relationships, even among the most gregarious of species, can be highly variable. We discuss the implications of our results for ecology and present some guiding principles for the development of RSF models at the individual‐animal level.
A Rhodnius prolixus Insulin Receptor and Its Conserved Intracellular Signaling Pathway and Regulation of Metabolism

The insulin signaling pathway is a modulator of metabolism in insects and can regulate functions associated with growth and development, as well as lipid and carbohydrate balance. We have previously reported the presence of an insulin-like peptide and an insulin-like growth factor in Rhodnius prolixus, which are involved in the homeostasis of lipids and carbohydrates in post-feeding and non-feeding periods. In the present study, we have characterized the first insulin receptor (IR) to be discovered in R. prolixus, Rhopr-IR, and investigated its intracellular signaling cascade and its role in nutrient control. We identified a candidate protein sequence within R. prolixus putative peptidome and predicted its conserved features using bioinformatics. Tissue-specific expression analyses indicated that the Rhopr-IR transcript is differentially-expressed in all tissues tested, with the highest values observed in the central nervous system (CNS). Treatment of insects with the IR kinase activator BpV(phen), glucose, or porcine insulin resulted in the activation of protein phosphorylation in the fat body, and stimulated the phosphorylation of protein kinase Akt, an evolutionarily conserved key regulator of the intracellular insulin signaling cascade. We also observed activation of Akt and phosphorylation of its downstream targets glycogen synthase kinase 3 β (GSK3β) and the transcription factor FOXO for several days following a blood meal. We used dsRNA to knockdown transcript expression and examined the resulting effects on metabolism and intracellular signaling. Furthermore, knockdown of the Rhopr-IR transcript increased lipid levels in the hemolymph, while reducing lipid content in the fat body. Interestingly, the levels of carbohydrates in the hemolymph and in the fat body did not show any alterations. The activation of Akt and phosphorylation of FOXO were also reduced in knockdown insects, while the phosphorylation pattern of GSK3β did not change. Our results support the identification of the first IR in R. prolixus and suggest that Rhopr-IR signaling is involved in hemolymph nutrient homeostasis and fat body storage both in post-feeding and in non-feeding stages. These metabolic effects are likely regulated by the activation of Akt and downstream cascades similar to mammalian insulin signaling pathways.
Age-Related Alterations in Electroencephalography Connectivity and Network Topology During n-Back Working Memory Task

The study of the healthy brain in elders, especially age-associated alterations in cognition, is important to understand the deficits created by Alzheimer's disease (AD), which imposes a tremendous burden on individuals, families, and society. Although, the changes in synaptic connectivity and reorganization of brain networks that accompany aging are gradually becoming understood, little is known about how normal aging affects brain inter-regional synchronization and functional networks when items are held in working memory (WM). According to the classic Sternberg WM paradigm, we recorded multichannel electroencephalography (EEG) from healthy adults (young and senior) in three different conditions, i.e., the resting state, 0-back (control) task, and 2-back task. The phase lag index (PLI) between EEG channels was computed and then weighted and undirected network was constructed based on the PLI matrix. The effects of aging on network topology were examined using a brain connectivity toolbox. The results showed that age-related alteration was more prominent when the 2-back task was engaged, especially in the theta band. For the younger adults, the WM task evoked a significant increase in the clustering coefficient of the beta-band functional connectivity network, which was absent in the older adults. Furthermore, significant correlations were observed between the behavioral performance of WM and EEG metrics in the theta and gamma bands, suggesting the potential use of those measures as biomarkers for the evaluation of cognitive training, for instance. Taken together, our findings shed further light on the underlying mechanism of WM in physiological aging and suggest that different EEG frequencies appear to have distinct functional correlates in cognitive aging. Analysis of inter-regional synchronization and topological characteristics based on graph theory is thus an appropriate way to explore natural age-related changes in the human brain.
Efficient grounding of abstract spatial concepts for natural language interaction with robot platforms

Our goal is to develop models that allow a robot to efficiently understand or “ground” natural language instructions in the context of its world representation. Contemporary approaches estimate correspondences between language instructions and possible groundings such as objects, regions, and goals for actions that the robot should execute. However, these approaches typically reason in relatively small domains and do not model abstract spatial concepts such as as “rows,” “columns,” or “groups” of objects and, hence, are unable to interpret an instruction such as “pick up the middle block in the row of five blocks. ” In this paper, we introduce two new models for efficient natural language understanding of robot instructions. The first model, which we call the adaptive distributed correspondence graph (ADCG), is a probabilistic model for interpreting abstract concepts that require hierarchical reasoning over constituent concrete entities as well as notions of cardinality and ordinality. Abstract grounding variables form a Markov boundary over concrete groundings, effectively de-correlating them from the remaining variables in the graph. This structure reduces the complexity of model training and inference. Inference in the model is posed as an approximate search procedure that orders factor computation such that the estimated probable concrete groundings focus the search for abstract concepts towards likely hypothesis, pruning away improbable portions of the exponentially large space of abstractions. Further, we address the issue of scalability to complex domains and introduce a hierarchical extension to a second model termed the hierarchical adaptive distributed correspondence graph (HADCG). The model utilizes the abstractions in the ADCG but infers a coarse symbolic structure from the utterance and the environment model and then performs fine-grained inference over the reduced graphical model, further improving the efficiency of inference. Empirical evaluation demonstrates accurate grounding of abstract concepts embedded in complex natural language instructions commanding a robotic torso and a mobile robot. Further, the proposed approximate inference method allows significant efficiency gains compared with the baseline, with minimal trade-off in accuracy.
Influence of Physical Exercise on Advanced Glycation End Products Levels in Patients Living With the Human Immunodeficiency Virus

Introduction: Combined antiretroviral therapy (cART) used to treat acquired immunodeficiency virus (HIV) induces a number of adverse effects, such as insulin resistance and dyslipidemia, which ultimately increases the cardiovascular risk. Advanced glycation end products (AGEs) have been implicated in the etiology of cardiovascular diseases, diabetes and other chronic diseases. It is known that physical exercise improves the lipid profile, insulin resistance and reduces the risk of cardiovascular diseases. However, the impact of physical exercise on AGE levels in HIV-infected patients has not been so far investigated. Therefore, this study compared AGEs levels in people with and without HIV and verified the effect of physical training on serum AGE levels. Methods: Participants were initially assigned into three groups: healthy control (CTL, n = 35), physically inactive HIV-infected (In-HIV, n = 33) and physically active HIV-infected (Ac-HIV, n = 19). The In-HIV group underwent physical training for 3 months, consisting of 60-min sessions of multimodal supervised exercise (aerobic, resistance and flexibility) with moderate intensity (50–80% heart rate reserve), performed 3 times/week. AGEs were measured in serum by fluorescence spectrometry. Results: At baseline, serum AGEs fluorescence level was significantly higher in inactive HIV-patients when compared to controls or active HIV-patients (In-HIV: 0.93 ± 0.08 vs. controls: 0.68 ± 0.13 and Ac-HIV: 0.59 ± 0.04 A.U.; P < 0.001). Triglycerides were also higher in In-HIV than CTL (182.8 ± 102 vs. 132.8 ± 52.3 mg/dL; P < 0.05). Waist circumference was lower in Ac-HIV, compared to In-HIV and controls (83.9 ± 10.4 vs. 92.9 ± 13.5 and 98.3 ± 12.4, respectively; P < 0.05). Body mass, fasting blood glucose, LDL, HDL, and total cholesterol were similar between groups. After training, AGE levels decreased (Baseline: 0.93 ± 0.08 vs. 3 months follow-up: 0.59 ± 0.04 AU; P < 0.001), no further difference being detected vs. CTL or Ac-HIV. Conclusion: HIV-infected patients under cART exhibited elevated AGEs levels compared to healthy individuals and physically active patients. Short-term aerobic training of moderate intensity counteracted this condition.
Ecological Succession of Sulfur-Oxidizing Epsilon- and Gammaproteobacteria During Colonization of a Shallow-Water Gas Vent

In this study, we integrated geochemical measurements, microbial diversity surveys and physiological characterization of laboratory strains to investigate substrate-attached filamentous microbial biofilms at Tor Caldara, a shallow-water gas vent in the Tyrrhenian Sea. At this site, the venting gases are mainly composed of CO2 and H2S and the temperature at the emissions is the same as that of the surrounding water. To investigate the composition of the total and active fraction of the Tor Caldara biofilm communities, we collected established and newly formed filaments and we sequenced the 16S rRNA genes (DNA) and the 16S rRNA transcripts (cDNA). Chemoautotrophic sulfur-oxidizing members of the Gammaproteobacteria (predominantly Thiotrichales) dominate the active fraction of the established microbial filaments, while Epsilonproteobacteria (predominantly Sulfurovum spp.) are more prevalent in the young filaments. This indicates a succession of the two communities, possibly in response to age, sulfide and oxygen concentrations. Growth experiments with representative laboratory strains in sulfide gradient medium revealed that Sulfurovum riftiae (Epsilonproteobacteria) grew closer to the sulfide source than Thiomicrospira sp. (Gammaproteobacteria, Thiotrichales). Overall, our findings show that sulfur-oxidizing Epsilonproteobacteria are the dominant pioneer colonizers of the Tor Caldara biofilm communities and that Gammaproteobacteria become prevalent once the community is established. This succession pattern appears to be driven - among other factors - by the adaptation of Epsilon- and Gammaproteobacteria to different sulfide concentrations.