Miracle-dz/UniFAR-FTUs huggingface dataset:
Vegetables with High-Nitrate Content Significantly Increase Plasma Nitrate and Nitrite Concentrations but Do Not Significantly Reduce Systolic Blood Pressure in Young Healthy Men

Aims: To investigate the effects of supplementation with high-nitrate and low-nitrate vegetables on plasma nitrate and nitrite concentrations, blood pressure and the oxygen demand of moderateintensity exercise. Study Design: A randomized, cross-over design. Place and Duration of Study: Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, between January 2011 and March 2012. Study Protocol Ashworth et al.; EJNFS, 12(6): 67-82, 2020; Article no.EJNFS. 58073 68 Methodology: 15 non-smoking, physically active healthy men (age 25 ± 6 years, BMI 24 ± 4 kg/m 2 ) were randomized to receive a 2-week supply of high-nitrate or low-nitrate vegetables, with a 2-week ‘wash-out’ period in between. Clinic blood pressure, plasma nitrate and nitrite concentrations and physiological responses to moderate-intensity exercise tests were measured before and after each 2-week intervention. Nitrate intake was calculated using nutritional analysis of reported vegetables consumed. Results: Participants consumed significantly more dietary nitrate on the high-nitrate diet (417 ± 139 mg/day) than the low-nitrate diet (26 ± 11 mg/day). The high-nitrate diet supplied 5.5 mg nitrate/kg body weight, exceeding the Acceptable Daily Intake (ADI) of 3.7 mg nitrate/kg body weight. Supplementation with high-nitrate vegetables significantly increased plasma nitrate concentrations (baseline; 30 ± 20 μM, after high-nitrate vegetables; 129 ± 87 μM) and plasma nitrite concentrations (baseline; 119 ± 35 nM, after high-nitrate vegetables; 227 ± 89 nM) but did not significantly change systolic blood pressure or the physiological response to moderate exercise. There were significant correlations between diastolic blood pressure and plasma nitrite concentrations (low-nitrate diet; r = 0.63, high-nitrate diet, r = 0.56). Conclusion: Supplementation with high-nitrate vegetables above the ADI significantly increased plasma nitrate and nitrate concentrations but did not significantly reduce systolic blood pressure or the physiological response to moderate exercise. Plasma nitrite concentrations significantly correlated with diastolic blood pressure after high-nitrate and low-nitrate diets.
The effect of organic matter (OM) quality on the redox stability of OM-Fe association in freshwater sediments

Abstract. Redox sensitive iron (Fe) oxyhydroxides participate in the biogeochemical cycling of nutrients and trace metals. Their co-precipitation with organic matter (OM) leads to environmentally relevant OM-Fe associations. The stability of OM in such associations is still uncertain. It has been proposed that OM either increases the stability of the complex against reductive dissolution or enhance the dissolution (both abiotic and biotically) of Fe oxyhydroxides. The OM character, in terms of specific functional groups binding to Fe, could be a critical factor determining the fate and stability of OM-Fe. Here, sediment samples from a vertical profile of a mesotrophic freshwater reservoir were treated using a sequential extraction scheme designed to distinguish Fe oxyhydroxides of different redox reactivity based on dissolution kinetics. To assess the reactivity and stability of the complexes, special attention was payed to the determination of redox active vs. stable fractions of Fe and the corresponding dissolved organic matter (DOM) components sequentially extracted. The quality of the extracted DOM was evaluated using UV-VIS and fluorescence spectroscopy together with the PARAFAC model. A selectivity model was used to ascertain the quality of OM preferentially associated with the different redox stable Fe oxyhydroxides. Accordingly, we found that humic-like substances render the OM-Fe associations redox labile, while non-humic substances enhance the stability of the associations. These findings improve the understanding required for predicting the fate of OM-Fe associations in freshwater sediments with different organic matter sources and characters.
Early Stages of Secondary Succession on the Coastal Plain, New Jersey

A study of the flora and vegetation associated with early secondary succession was initiated in March, 1961, and concluded in October, 1963. The year prior to site clearing was devoted to reconnaissance of selected sites, collection of flora, and examination of soil morphology. First and second years after clearing were devoted to floristic survey, and quadrat studies of vegetation of the seven cleared sites. Four of the sites (Warren Grove, New Lisbon, Atsion, and Batsto) lie in the vegetation region known as the Pine Region or Pine Barrens. The remaining three sites (Manahawkin, Tuckahoe, and Millville) are on the Pine Re.gion fringe. A total of 198 species of vascular plants were found during the first year and 127 during the second year. Of these, 24% of the first-year and 23% of the second-year flora were naturalized species. Hemicryptophyte (45, 48%) was the leading life form category and therophytes (30, 24%) were of second importance. Herbaceous vegetation of the cleared sites was separable into five categories: (1) dominance by annuals during first and second year (Tuckahoe), (2) dominance by annuals first year and by perennials the second year (Manahawkin), (3) dominance by perennials first year and by annuals the second year (Millville and Batsto), (4) dominance by perennials first and second year (New Lisbon and Atsion), and (5) no dominance (Warren Grove). No first-year field in the Pine Region supported an annual "weed" stage; two of the three Pine Region fringe sites on heavier soils with better moisture relations and recent agricultural history did have an annual "weed" stage during the first year. It appeared that land use was the factor principally responsible for the vegetation patterns. Since all but three woody species had developed from residual vegetative parts, no generalization could be made regarding the importance of woody species that invade by seed. Each community appeared to represent a special example made possible by the species present prior to clearing and the degree of clearing disturbance to the vegetation.
Growth Rate, Feed Conversion, and Dietary Metabolizable Energy in Response to Virginiamycin Supplementation of Different Diets

Abstract Virginiamycin at a dietary concentration of 22 ppm stimulated growth rate and improved efficiency of feed conversion in broilers fed different diets. The dietary variations were practical ones which would alter the milieu of the intestinal bacteria. They involved the use of wheat vs. corn as principal dietary cereal, different levels of fat, and different levels of protein. All the diets supported similar performance and there were no significant interactions in response between diet and antibiotic. Body weights at 53 days of age averaged 5% heavier in the virginiamycin-fed birds and the average rate of improvement in feed conversion efficiency was 5.4%. At 26 days respective values for the above were 10.3% and 9.5%. The energy metabolized from the diets was increased by 7.2% when the diets contained virginiamycin.
RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation

The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers. Our work argues that existing benchmarks used for this task are mainly composed of trivial cases, in which referents can be identified with simple phrases. Our analysis relies on a new categorization of the phrases in the DAVIS-2017 and Actor-Action datasets into trivial and non-trivial REs, with the non-trivial REs annotated with seven RE semantic categories. We leverage this data to analyze the results of RefVOS, a novel neural network that obtains competitive results for the task of language-guided image segmentation and state of the art results for language-guided VOS. Our study indicates that the major challenges for the task are related to understanding motion and static actions.
Understanding Self-supervised Learning with Dual Deep Networks

We propose a novel theoretical framework to understand self-supervised learning methods that employ dual pairs of deep ReLU networks (e.g., SimCLR, BYOL). First, we prove that in each SGD update of SimCLR, the weights at each layer are updated by a \emph{covariance operator} that specifically amplifies initial random selectivities that vary across data samples but survive averages over data augmentations. We show this leads to the emergence of hierarchical features, if the input data are generated from a hierarchical latent tree model. With the same framework, we also show analytically that in BYOL, the combination of BatchNorm and a predictor network creates an implicit contrastive term, acting as an approximate covariance operator. Additionally, for linear architectures we derive exact solutions for BYOL that provide conceptual insights into how BYOL can learn useful non-collapsed representations without any contrastive terms that separate negative pairs. Extensive ablation studies justify our theoretical findings.
Pathways for producing binary black holes with large misaligned spins in the isolated formation channel

Binary black holes (BBHs) can form from the collapsed cores of isolated high-mass binary stars. The masses and spins of these BBHs are determined by the complicated interplay of phenomena such as tides, winds, accretion, common-envelope evolution (CEE), supernova (SN) natal kicks, and stellar core-envelope coupling. The gravitational waves emitted during the mergers of BBHs depend on their masses and spins and can thus constrain these phenomena. We present a simplified model of binary stellar evolution and identify regions of the parameter space that produce BBHs with large spins misaligned with their orbital angular momentum. In Scenario A (B) of our model, stable mass transfer (SMT) occurs after Roche-lobe overflow (RLOF) of the more (less) massive star, while CEE follows RLOF of the less (more) massive star. Each scenario is further divided into Pathways 1 and 2 depending on whether the core of the more massive star collapses before or after RLOF of the less massive star. If the stellar cores are weakly coupled to their envelopes, highly spinning BBHs can be produced if natal spins greater than 10% of the breakup value are preserved during the Wolf-Rayet (WR) stage. BBHs can alternatively acquire large spins by tidal synchronization during the WR stage in Scenario A or accretion onto the initially more massive star in Scenario B. BBH spins can become highly misaligned if the SN kicks are comparable to the orbital velocity which can more easily be achieved in Pathway A1 where the SN of the more massive star precedes CEE.
Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks

Mathematical models in epidemiology strive to describe the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. Since high-fidelity models are often quite complex and analytically intractable, their applicability to real data depends on powerful estimation algorithms. Moreover, uncertainty quantification in such models is far from trivial, and different types of uncertainty are often confounded. With this work, we introduce a novel coupling between epidemiological models and specialized neural network architectures. This coupling results in a powerful Bayesian inference framework capable of principled uncertainty quantification and efficient amortized inference once the networks have been trained on simulations from an arbitrarily complex model. We illustrate the utility of our framework by applying it to real Covid-19 cases from entire Germany and German federal states.
Megasporogenesis, microsporogenesis and development of female and male gametophyte of Pistacia chinensis Bunge.

The megasporogenesis,microsporogenesis and the development of female and male gametophyte of Pistacia chinensis Bunge were studied with the method of paraffin sectioning and the whole stain-clearing.The results showed that:Pistacia chinensis had 4～5 stamens in every floscule and 4 pollen sacs in every anther,the anther wall was composed of epidermis,fibrous endothecium,middle layer and glandular tapetum which was corresponds to glandular type.The cytokinesis belonged to simultaneous type after meiosis of the microspore mother cell,and their tetards were tetrahedral or isobilateral types,and a few dyads were observed at tetrad stage.The mature pollen grain which had 2～4 germinal apertures was of the 2-celled type,circular and nearly circular.The abnormal development was not found in the course of microsporogenesis and male gametophyte development.There is one ovary and one ovule.The ovule is single or double integuments,crassinucellate ovules which were anatropous.The polygonum type of mature embryo sac was detected.
Did circoviruses intermediate the recombination between bat and pangolin coronaviruses, yielding SARS-CoV-2?

Since the first reports of a coronavirus (CoV) disease 2019 (COVID-19) caused by severe acute respiratory syndrome virus (SARS-CoV-2) in Wuhan, Hubei province, China, scientists are working around the clock to find sound answers to the issue of its origin. While the number of scientific articles on SARS-CoV-2 is increasing, there are still many gaps as to its origin. All studies failed to find a coronavirus in other animals that is more similar to human SARS-COV2 than the bat virus, considered to be the primary reservoir. In this paper we address a new hypothesis, based on a possible recombination between a DNA and SARS-CoV viruses, to explain the rise of SRAS-CoV-2. By comparing SARS-CoV-2 and related CoVs with circoviruses (CVs), we found strong sequence similarity of the genomic region at the 3-end of Bat-CoV ORF1a and the origin of replication (Ori) of porcine CV type 2 (PCV2), as well as similar RNA secondary structures of the region encompassing the cleavage site of CoV S gene with the PCV2 Ori. This constitutes a primary evidence that supports a possible recombination, which occurrence might explain the origin of SARS-CoV-2.
Effect of Temperature on Sprint Performance in the Frog Xenopus laevis and the Salamander Necturus maculosus

Sprint performance is strongly temperature dependent in the mudpuppy Necturus maculosus and the African clawed frog Xenopus laevis. In experiments designed to control the effects of physical training and temperature acclimation on sprint performance, the time to swim 1.5 m was lowest at 15 C in Necturus, and at 27 C in Xenopus, and increased as temperature was either raised or lowered from these optima. These temperatures are similar to those selected by these species in laboratory thermal gradients.
GeoD: Consensus-based Geodesic Distributed Pose Graph Optimization

We present a consensus-based distributed pose graph optimization algorithm for obtaining an estimate of the 3D translation and rotation of each pose in a pose graph, given noisy relative measurements between poses. The algorithm, called GeoD, implements a continuous time distributed consensus protocol to minimize the geodesic pose graph error. GeoD is distributed over the pose graph itself, with a separate computation thread for each node in the graph, and messages are passed only between neighboring nodes in the graph. We leverage tools from Lyapunov theory and multi-agent consensus to prove the convergence of the algorithm. We identify two new consistency conditions sufficient for convergence: pairwise consistency of relative rotation measurements, and minimal consistency of relative translation measurements. GeoD incorporates a simple one step distributed initialization to satisfy both conditions. We demonstrate GeoD on simulated and real world SLAM datasets. We compare to a centralized pose graph optimizer with an optimality certificate (SE-Sync) and a Distributed Gauss-Seidel (DGS) method. On average, GeoD converges 20 times more quickly than DGS to a value with 3.4 times less error when compared to the global minimum provided by SE-Sync. GeoD scales more favorably with graph size than DGS, converging over 100 times faster on graphs larger than 1000 poses. Lastly, we test GeoD on a multi-UAV vision-based SLAM scenario, where the UAVs estimate their pose trajectories in a distributed manner using the relative poses extracted from their on board camera images. We show qualitative performance that is better than either the centralized SE-Sync or the distributed DGS methods.
On the Advantage of Coherent LoRa Detection in the Presence of Interference

It has been shown that the coherent detection of long range (LoRa) signals only provides marginal gains of around 0.7 dB on the additive white Gaussian noise (AWGN) channel. However, ALOHA-based massive Internet-of-Things systems, including LoRa, often operate in the interference-limited regime. Therefore, in this work, we examine the performance of the LoRa modulation with coherent detection in the presence of interference from another LoRa user with the same spreading factor. We derive rigorous symbol- and frame error rate (FER) expressions as well as bounds and approximations for evaluating the error rates. The error rates predicted by these approximations are compared against error rates found by Monte Carlo simulations and shown to be very accurate. We also compare the performance of LoRa with coherent and noncoherent receivers and we show that the coherent detection of LoRa is significantly more beneficial in interference scenarios than in the presence of only AWGN. For example, we show that coherent detection leads to a 2.5-dB gain over the standard noncoherent detection for a signal-to-interference ratio (SIR) of 3 dB and up to a 10-dB gain for an SIR of 0 dB. Moreover, we show that with coherent detection it is easier to obtain useful and relevant FER values even for negative SIR values.
Defining and Estimating Subgroup Mediation Effects with Semi-Competing Risks Data

In many medical studies, an ultimate failure event such as death is likely to be affected by the occurrence and timing of other intermediate clinical events. Both event times are subject to censoring by loss-to-follow-up but the nonterminal event may further be censored by the occurrence of the primary outcome, but not vice versa. To study the effect of an intervention on both events, the intermediate event may be viewed as a mediator, but conventional definition of direct and indirect effects is not applicable due to semi-competing risks data structure. We define three principal strata based on whether the potential intermediate event occurs before the potential failure event, which allow proper definition of direct and indirect effects in one stratum whereas total effects are defined for all strata. We discuss the identification conditions for stratum-specific effects, and proposed a semiparametric estimator based on a multivariate logistic stratum membership model and within-stratum proportional hazards models for the event times. By treating the unobserved stratum membership as a latent variable, we propose an EM algorithm for computation. We study the asymptotic properties of the estimators by the modern empirical process theory and examine the performance of the estimators in numerical studies.
CardioGAN: Attentive Generative Adversarial Network with Dual Discriminators for Synthesis of ECG from PPG

Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an adversarial model which takes PPG as input and generates ECG as output. The proposed network utilizes an attention-based generator to learn local salient features, as well as dual discriminators to preserve the integrity of generated data in both time and frequency domains. Our experiments show that the ECG generated by CardioGAN provides more reliable heart rate measurements compared to the original input PPG, reducing the error from 9.74 beats per minute (measured from the PPG) to 2.89 (measured from the generated ECG).
MaterialGAN

We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements. This is a fundamentally under-constrained problem, and previous work has relied on using various regularization priors or on capturing many images to produce plausible results. In this work, we present MaterialGAN, a deep generative convolutional network based on StyleGAN2, trained to synthesize realistic SVBRDF parameter maps. We show that MaterialGAN can be used as a powerful material prior in an inverse rendering framework: we optimize in its latent representation to generate material maps that match the appearance of the captured images when rendered. We demonstrate this framework on the task of reconstructing SVBRDFs from images captured under flash illumination using a hand-held mobile phone. Our method succeeds in producing plausible material maps that accurately reproduce the target images, and outperforms previous state-of-the-art material capture methods in evaluations on both synthetic and real data. Furthermore, our GAN-based latent space allows for high-level semantic material editing operations such as generating material variations and material morphing.