A dead reckoning localization method for in-pipe detector of water supply pipeline: an application to leak localization

Urban water supply pipeline system integrity is important for the urban life. The aim of the study reported in this paper is to locate the water pipeline leaks by using an in-pipe detector. In this study, a mathematical model is extracted from an actual inspection system. By using the homogeneous transformation theory, transformation matrix which is from carrier to a reference coordinate system is deduced, and then the global transformation matrix is obtained to describe the detector’s posture. Through measuring the distance increment of each sample time step in carrier coordinate system, the cumulative distance result is calculated. After combining the data of the inertial measurement unit (IMU) and odometer, the leak can be located. To improve the accuracy of leak localization, the magnetic markers are implemented about one in each 1 km distance, which provide reference points to be used to compensate accumulative error during the localization process. Furthermore, a dead reckoning localization method combining data of a micro electro-mechanical IMU, three odometers, and magnetic markers is proposed. To verify above localization algorithm, a simulation case study is conducted with the artificial error generated by the white noise. The simulation results show that the dead reckoning algorithm can effectively provide leak locations with a reasonable uncertainty. Based on this, an experimental platform was built in this study. The experimental results show that the relative error of leak locating achieves a reasonably good performanc

ART⋅\boldsymbol{\cdot}V: Auto-Regressive Text-to-Video Generation with Diffusion Models

We present ART$\boldsymbol{\cdot}$V, an efficient framework for auto-regressive video generation with diffusion models. Unlike existing methods that generate entire videos in one-shot, ART$\boldsymbol{\cdot}$V generates a single frame at a time, conditioned on the previous ones. The framework offers three distinct advantages. First, it only learns simple continual motions between adjacent frames, therefore avoiding modeling complex long-range motions that require huge training data. Second, it preserves the high-fidelity generation ability of the pre-trained image diffusion models by making only minimal network modifications. Third, it can generate arbitrarily long videos conditioned on a variety of prompts such as text, image or their combinations, making it highly versatile and flexible. To combat the common drifting issue in AR models, we propose masked diffusion model which implicitly learns which information can be drawn from reference images rather than network predictions, in order to reduce the risk of generating inconsistent appearances that cause drifting. Moreover, we further enhance generation coherence by conditioning it on the initial frame, which typically contains minimal noise. This is particularly useful for long video generation. When trained for only two weeks on four GPUs, ART$\boldsymbol{\cdot}$V already can generate videos with natural motions, rich details and a high level of aesthetic quality. Besides, it enables various appealing applications, e.g., composing a long video from multiple text prompts.Comment: 24 pages, 21 figures. Project page at https://warranweng.github.io/art.

A novel BRDT inhibitor NHWD870 shows potential as a male contraceptive in mice

Small molecule inhibitors of the bromodomain and extraterminal domain (BET) family proteins have emerged as a promising option for not only the treatment of multiple cancers but also for disturbing the process of sperm maturation with potential for use as a viable contraceptive target. In this paper, we report a new generation of BET family inhibitor, NHWD870, that provide a complete and reversible contraceptive effect in mice which is stronger than that of JQ1 and its synthesized derivatives. This study is hoped to lead to the clinical trial eventually

Development of a recombinase-aided amplification combined with a lateral flow dipstick assay for rapid detection of H7 subtype avian influenza virus

Avian influenza viruses (AIV) pose a significant persistent threat to the public health and safety. It is estimated that there have been over 100 outbreaks caused by various H7 subtypes of avian influenza viruses (AIV-H7) worldwide, resulting in over 33 million deaths of poultry. In this study, we developed a recombinase-aided amplification combined with a lateral flow dipstick assay for the detection of hemagglutinin (HA) genes to provide technical support for rapid clinical detection of AIV-H7. The results showed that the assay can complete the reaction within 30 min at a temperature of 39°C. Specificity tests demonstrated that there was no cross-reactivity with other common poultry pathogens, including Newcastle disease virus (NDV) and infections bronchitis virus (IBV). The detection limit of this assay was 1 × 101 copies/μL, while RT-qPCR method was 1 × 101 copies/μL, and RT-PCR was 1 × 102 copies/μL. The κ value of the RT-RAA-LFD and RT-PCR assay in 132 avian clinical samples was 0.9169 (p < 0.001). These results indicated that the developed RT-RAA-LFD assay had good specificity, sensitivity, stability and repeatability and may be used for rapid detection of AIV-H7 in clinical diagnosis

Preliminary Evidence of Sex Differences in Cortical Thickness Following Acute Mild Traumatic Brain Injury

The main objective of this study was to evaluate sex differences in cortical thickness after acute mild traumatic brain injury (mTBI) and its associations with clinical outcomes. Thirty-two patients with mTBI at acute phase (2.4 ± 1.3 days post-injury) and 30 healthy controls were enrolled. All the participants underwent comprehensive neurocognitive assessments and MRI to assess cortical thickness. Significant sex differences were determined by using variance analysis of factorial design. Relations between the cortical thickness and clinical assessments were measured with the Spearman Correlation. Results revealed that patients with mTBI had significantly reduced cortical thickness in the left entorhinal cortex while increased cortical thickness in the left precuneus cortex and right lateral occipital cortex, compared with healthy controls. The interaction effect of the group × sex on cortical thickness was significant. Female patients had significant thicker cortical thickness in the left caudal anterior cingulate cortex (ACC) than male patients and had higher scores on Posttraumatic stress disorder Checklist—Civilian Version (PCL-C). Spearman correlational analysis showed a significantly positive correlations between the cortical thickness of the left caudal ACC and PCL-C ratings in female patients. Sex differences in cortical thickness support its potential as a neuroimaging phenotype for investigating the differences in clinical profiles of mild TBI between women and men

Solution Structure and Dynamics of the I214V Mutant of the Rabbit Prion Protein

Background: The conformational conversion of the host-derived cellular prion protein (PrP C) into the disease-associated scrapie isoform (PrP Sc) is responsible for the pathogenesis of transmissible spongiform encephalopathies (TSEs). Various single-point mutations in PrP C s could cause structural changes and thereby distinctly influence the conformational conversion. Elucidation of the differences between the wild-type rabbit PrP C (RaPrP C) and various mutants would be of great help to understand the ability of RaPrP C to be resistant to TSE agents. Methodology/Principal Findings: We determined the solution structure of the I214V mutant of RaPrP C (91–228) and detected the backbone dynamics of its structured C-terminal domain (121–228). The I214V mutant displays a visible shift of surface charge distribution that may have a potential effect on the binding specificity and affinity with other chaperones. The number of hydrogen bonds declines dramatically. Urea-induced transition experiments reveal an obvious decrease in the conformational stability. Furthermore, the NMR dynamics analysis discloses a significant increase in the backbone flexibility on the pico- to nanosecond time scale, indicative of lower energy barrier for structural rearrangement. Conclusions/Significance: Our results suggest that both the surface charge distribution and the intrinsic backbone flexibility greatly contribute to species barriers for the transmission of TSEs, and thereby provide valuable hints fo

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters

Wear and Breakage Detection of Integral Spiral End Milling Cutters Based on Machine Vision

Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures