MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views

We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM). MHR-Net aims to find a set of reasonable reconstructions for a 2D view, and it also selects the most likely reconstruction from the set. To deal with the challenging unsupervised generation of non-rigid shapes, we develop a new Deterministic Basis and Stochastic Deformation scheme in MHR-Net. The non-rigid shape is first expressed as the sum of a coarse shape basis and a flexible shape deformation, then multiple hypotheses are generated with uncertainty modeling of the deformation part. MHR-Net is optimized with reprojection loss on the basis and the best hypothesis. Furthermore, we design a new Procrustean Residual Loss, which reduces the rigid rotations between similar shapes and further improves the performance. Experiments show that MHR-Net achieves state-of-the-art reconstruction accuracy on Human3.6M, SURREAL and 300-VW datasets.Comment: Accepted to ECCV 202

Short-term exposure to indoor PM2.5 in office buildings and cognitive performance in adults: An intervention study

Impacts of exposure to particulate matter can be wide-ranging, with some evidence suggesting potential impacts on nervous system, cognition, and productivity. However, most evidence to date addresses ambient exposure and chronic outcomes with limited research on indoor short-term exposure to PM2.5 and cognitive performance. Hence, the aim of this study was to evaluate if there is a relationship between short-term exposure to indoor PM2.5 within the workplace context and cognitive performance in adults. A randomized single-blind cross-over trial was conducted in an urban mixed-mode ventilated office in Beijing (China). Sixty eligible employees participated in the study and fifty-five valid responses were obtained. Cognitive performance was assessed with a validated neurological battery test during intervention and control conditions. Portable air purifiers were placed on the subjects' workstations and used in the intervention condition to control PM2.5 levels at the subjects’ breathing zone whereas in the control condition, the air purifiers were present but switched off. Average PM2.5 levels were respectively 18.0 μg/m³ and 3.7 μg/m³ in the control and intervention condition. In each condition, cognitive performance testing started five to 7 h after arriving in the office. The results showed office workers had significantly better performance for 9 out of the 16 cognitive skills during the intervention, compared to the control condition, with the most consistent effect in the memory domain. This study adds evidence that elevated PM2.5 levels can detrimentally affect cognitive performance even during short-term indoor exposure. Further research is needed on the potential impact of other air pollutants, including ultrafine particles, and on the possible role of sound and air movement from the air purifiers

Effects of earthworm antimicrobial peptides and probiotics on intestinal flora of yellow-feathered broilers

Earthworm antimicrobial peptides combined with probiotics were supplemented to the yellow-feathered broiler diet, and the cecal intestinal flora was subjected to 16S rDNA Qualcomm sequencing. The results showed that the total number of species in S1 (control group) was significantly higher than S2 (earthworm antimicrobial peptides) and S3 (Compound probiotic preparation), S4 (earthworm antibacterial peptide + composite probiotic preparation) (P<0.05), the colony structure of S1 is significantly different from S2, S3, and S4 (P<0.05). At the phylum level of each test group, the dominant bacterial groups were Firmicutes, Bacteroides, and Proteobacteria. Among them, S2, S3, and S4 were significantly higher than S1 in the relative abundance of Firmicutes and Bacteroides (P<0.05), the relative abundance of Proteobacteria was significantly lower than S1 (P<0.05). At the genus level, S2, S3, and S4 were significantly higher than S1 in the relative abundance of Lactobacillus (P0.05). Earthworm antimicrobial peptides combined with compound probiotics can increase the number of beneficial bacteria and reduce the number of harmful bacteria to regulate intestinal flora, indicating that earthworm antimicrobial peptides combined with compound probiotics can be used as new green antibiotics in animal production

Performance of long-term CT and PET/CT surveillance for detection of distant recurrence in patients with resected stage IIIA-D melanoma

Background Follow-up for patients with resected stage IIIA–D melanoma may include computed tomography (CT) or positron emission tomography (PET)/CT imaging to identify distant metastases. The aim of this study was to evaluate the test performance over follow-up time, of structured 6- and 12-monthly follow-up imaging schedules in these patients. Methods We conducted a retrospective analysis of consecutive resected stage IIIA–D melanoma patients from Melanoma Institute Australia (2000–2017). Patients were followed until a confirmed diagnosis of distant metastasis, end of follow-up schedule, or death. Test accuracy was evaluated by cross-classifying the results of the test against a composite reference standard of histopathology, cytology, radiologic imaging, and/or clinical follow-up, and then quantified longitudinally using logistic regression models with random effects. Results In total, 1373 imaging tests were performed among 332 patients. Distant metastases were detected in 110 (33%) patients during a median follow-up of 61 months (interquartile range 38–86), and first detected by imaging in 86 (78%) patients. 152 (68%) patients had at least one false-positive result. Sensitivity of the schedule over 5 years was 79% [95% confidence interval (CI) 70–86%] and specificity was 88% (95% CI 86–90%). There was no evidence of a significant difference in test performance over follow-up time or by American Joint Committee on Cancer (AJCC) substage. The positive predictive value ranged between 33 and 48% over follow-up time, reflecting a ratio of 1:2 false-positives per true-positive finding. Conclusions Regular 6- or 12-monthly surveillance imaging using CT or PET/CT has reasonable and consistent sensitivity and specificity over 5-year follow-up for resected stage IIIA–D melanoma patients. These data are useful when discussing the risks and benefits of long-term follow-up

Boletín Oficial de la Provincia de Oviedo: Número 180 - 1945 agosto 11

Model averaging is commonly used to allow for model uncertainty in parameter estimation. In the frequentist setting, a model-averaged estimate of a parameter is a weighted mean of the estimates from the individual models, with the weights being based on an information criterion, such as AIC. A Wald confidence interval based on this estimate often performs poorly, as its sampling distribution is generally non-normal and estimation of the standard error is problematic. A natural alternative is to use a bootstrap approach. The current method is based on the percentile method, which bootstraps the estimate from the best model. Little previous research has been carried out to assess its coverage properties. These issues demonstrate the need for further development into the interval estimation when making multi-model inference. In this thesis, we propose a new method for constructing a model-averaged confidence interval using studentized bootstrap. We carry out simulations when the data are either normal or lognormal. Our results suggest that the studentized bootstrap interval provides the best coverage for approximately the same interval width as competing intervals in the lognormal setting. It is also shown to be the best approach for both normal and lognormal data when the sample size is small. Furthermore, we consider the use of model-averaging in the analysis of overdispersed count data. In a single-model setting, the default method for dealing with overdispersion is to either use a quasi-Poisson approach, or to fit the data using a negative binomial model. We examine the performance of the two methods in a model-averaging setting. Our results suggest that the quasi-Poisson approach leads to a better coverage rate, even when a negative binomial model is used to generate the data

Studentized bootstrap model-averaged tail area intervals.

In many scientific studies, the underlying data-generating process is unknown and multiple statistical models are considered to describe it. For example, in a factorial experiment we might consider models involving just main effects, as well as those that include interactions. Model-averaging is a commonly-used statistical technique to allow for model uncertainty in parameter estimation. In the frequentist setting, the model-averaged estimate of a parameter is a weighted mean of the estimates from the individual models, with the weights typically being based on an information criterion, cross-validation, or bootstrapping. One approach to building a model-averaged confidence interval is to use a Wald interval, based on the model-averaged estimate and its standard error. This has been the default method in many application areas, particularly those in the life sciences. The MA-Wald interval, however, assumes that the studentized model-averaged estimate has a normal distribution, which can be far from true in practice due to the random, data-driven model weights. Recently, the model-averaged tail area Wald interval (MATA-Wald) has been proposed as an alternative to the MA-Wald interval, which only assumes that the studentized estimate from each model has a N(0, 1) or t-distribution, when that model is true. This alternative to the MA-Wald interval has been shown to have better coverage in simulation studies. However, when we have a response variable that is skewed, even these relaxed assumptions may not be valid, and use of these intervals might therefore result in poor coverage. We propose a new interval (MATA-SBoot) which uses a parametric bootstrap approach to estimate the distribution of the studentized estimate for each model, when that model is true. This method only requires that the studentized estimate from each model is approximately pivotal, an assumption that will often be true in practice, even for skewed data. We illustrate use of this new interval in the analysis of a three-factor marine global change experiment in which the response variable is assumed to have a lognormal distribution. We also perform a simulation study, based on the example, to compare the lower and upper error rates of this interval with those for existing methods. The results suggest that the MATA-SBoot interval can provide better error rates than existing intervals when we have skewed data, particularly for the upper error rate when the sample size is small

A descriptive analysis of transitions from smoking to electronic nicotine delivery system (ENDS) use: a daily diary investigation.

OBJECTIVES: We aimed to examine patterns in smoking and electronic nicotine delivery system (ENDS) use over an extended period of time (up to 20 weeks) in people who smoked and who had never previously made a successful quit attempt using an ENDS. We conducted a longitudinal mixed-methods study in Dunedin, New Zealand, during 2018 and 2019. Purposively selected participants ( = 45; age (≥18 years), gender, ethnicities, cigarettes/day) who wished to quit smoking. INTERVENTIONS: Participants were provided with a second-generation ENDS device (vape pen or starter "tank" device) at the start of their quit attempt, and asked to complete smartphone-based daily diary surveys assessing smoking and ENDS use. OUTCOME MEASURES: Sunburst plots and a sequence plot were used to describe weekly and daily patterns of smoking and ENDS use (smoking only, ENDS use only, dual use, abstinent). RESULTS: The most frequently reported movements among participants, classified according to their study week behaviour, occurred between dual use and exclusive ENDS use (and vice versa). A smaller group reported moving from dual use to exclusive smoking (and often back to dual use), and a small number reported moving between abstinence and different ENDS and smoked tobacco usage behaviours. Data visualisations focussing on those participants who had provided data during each of weeks 9-12 indicate that only a minority reported sustained dual use; instead, most participants indicated varied smoked tobacco and ENDS use, which included periods of dual use. CONCLUSIONS: The considerable variety observed within and between study participants suggests that high variability is typical rather than exceptional. Transitions from smoking to ENDS use may involve considerable periods of dual use, which is likely to be dynamic and potentially sustained over several months

WbuB, a glycosyltransferase family 4 protein, regulates the activation of NLRP3 inflammasome and contributes to the virulence of Aeromonas hydrophila CCL1

Aeromonas hydrophila is an opportunistic pathogen of fishes and aquatic animals. A. hydrophila has a strong ability to disrupt gut integrity and cause inflammation and septicemia in fish, however, the mechanism is not fully understood. In this study, we identified the function of a galactosaminogalactan (GAG) synthase (named WbuB) in the pathogenic A. hydrophila CCL1. WbuB belongs to the family 4 of glycosyltransferases (GT4) and is composed of 407 amino acids (aa). For virulence analysis, the mutant which has an in-frame deletion of the WbuB gene in CCL1 was created (named ΔWbuB). ΔWbuB had the decreased biofilm formation, as well as adhesion ability and cytotoxicity. Animal infection study in crucian carps showed that, compared to CCL1, ΔWbuB caused the decreased expression levels of ASC, NLRP3, caspase-1 and IL-1β in gut. Moreover, the expression levels of ZO-1 and Occludin were increased by ΔWbuB infection. In line with the results, the intestinal permeability and tissue dissemination capacity of ΔWbuB were attenuated significantly. These lost virulence capacities of ΔWbuB were restored by complementation with the WbuB gene. Taken together, these results indicate that WbuB is essential for the activation of NLRP3 inflammasome and is a novel virulent factor for tight junction barrier during A. hydrophila infection

An indicator system for assessing the impact of human activities on river structure

The impact of human activities on river systems has reached a state in which they can no longer be considered to be controlled only by natural processes of the Earth but also by anthropogenic forces in many regions of world. However, most approaches for assessing the impact still consider human activities to be an external influence in the study of feedback mechanisms between human activities and river systems, and they are usually complicated and specialized. This paper aims to develop an indicator system that includes the artificial water surface ratio (AWSR), artificial water surface density ratio (AWSDR), disruption of longitudinal connectivity ratio (DLCR), artificial river ratio (ARR), sinuosity of artificial cutoff (SAC), channelization ratio (CR), artificial levee ratio (ALR), road along river ratio (RARR), artificial sediment transport ratio (ASTR) and the integrated river structure impact index (IRSII) to quantitatively assess the impact of human activities on river structure by comparing natural and anthropogenic forces in a river system. The Nanliu River basin under the dramatic impact of human activities was selected to validate the indicator system. The case results showed the Nanliu River has become an anthropogenic river according to its high IRSII value (greater than 0.49). The artificial water surface area and artificial sediment discharge were 2.96 (AWSR = 2.96) and 2.51 (ASTR = 2.51) times of the outcome of natural process. The river was dramatically blocked by human-made buildings (DLCR = 68.63) and artificial channel accounts for 54.23% of total length of natural river (ARR = 0.54). Within the river basin, upper and lower sub-basins in areas with a higher economic level were more affected by human activities. Future management of the Nanliu River basin should focus on restoration of river connectivity, sand mining and irrigation issues. Unlike most previous assessment methods, the approach developed in this study is simple and readily comprehensible by the public and decision makers, cost-effective for long-term monitoring and not subject to the reference condition

Earthquake forecasting from paleoseismic records

Abstract Forecasting large earthquakes along active faults is of critical importance for seismic hazard assessment. Statistical models of recurrence intervals based on compilations of paleoseismic data provide a forecasting tool. Here we compare five models and use Bayesian model-averaging to produce time-dependent, probabilistic forecasts of large earthquakes along 93 fault segments worldwide. This approach allows better use of the measurement errors associated with paleoseismic records and accounts for the uncertainty around model choice. Our results indicate that although the majority of fault segments (65/93) in the catalogue favour a single best model, 28 benefit from a model-averaging approach. We provide earthquake rupture probabilities for the next 50 years and forecast the occurrence times of the next rupture for all the fault segments. Our findings suggest that there is no universal model for large earthquake recurrence, and an ensemble forecasting approach is desirable when dealing with paleoseismic records with few data points and large measurement errors