The Dishwasher

The Dishwasher is a 2D animated thesis film that lasts 5 minutes and 27 seconds. This animated short film shows a story of a young man, Kai, who dreams of becoming an artist in the US; however, he has to wash dishes for his living. One day, the dish machine is out of control because of Kai’s careless mistake. The manager sees the situation and begins to blame Kai. While the manager yells at him, Kai notices a funny animation loop appearing on the spinning dishes, which makes him laugh. After the manager leaves, Kai looks at his face reflected in the window of the dish machine and it reminds him of the old times when he learned drawing at art school. This coincidental memory sparks Kai’s passion for making art on the plates. Then, he starts to dance and decides to treat his job differently

Privacy-Preserving Detection Method for Transmission Line Based on Edge Collaboration

Unmanned aerial vehicles (UAVs) are commonly used for edge collaborative computing in current transmission line object detection, where computationally intensive tasks generated by user nodes are offloaded to more powerful edge servers for processing. However, performing edge collaborative processing on transmission line image data may result in serious privacy breaches. To address this issue, we propose a secure single-stage detection model called SecYOLOv7 that preserves the privacy of object detecting. Based on secure multi-party computation (MPC), a series of secure computing protocols are designed for the collaborative execution of Secure Feature Contraction, Secure Bounding-Box Prediction and Secure Object Classification by two non-edge servers. Performance evaluation shows that both computational and communication overhead in this framework as well as calculation error significantly outperform existing works

A potential therapeutic drug for osteoporosis: prospect for osteogenic LncRNAs

Long non-coding RNAs (LncRNAs) play essential roles in multiple physiological processes including bone formation. Investigators have revealed that LncRNAs regulated bone formation through various signaling pathways and micro RNAs (miRNAs). However, several problems exist in current research studies on osteogenic LncRNAs, including sophisticated techniques, high cost for in vivo experiment, as well as low homology of LncRNAs between animal model and human, which hindered translational medicine research. Moreover, compared with gene editing, LncRNAs would only lead to inhibition of target genes rather than completely knocking them out. As the studies on osteogenic LncRNA gradually proceed, some of these problems have turned osteogenic LncRNA research studies into slump. This review described some new techniques and innovative ideas to address these problems. Although investigations on osteogenic LncRNAs still have obtacles to overcome, LncRNA will work as a promising therapeutic drug for osteoporosis in the near future

Case fatality risk of the first pandemic wave of novel coronavirus disease 2019 (COVID-19) in China

Objective To assess the case fatality risk (CFR) of COVID-19 in mainland China, stratified by region and clinical category, and estimate key time-to-event intervals. Methods We collected individual information and aggregated data on COVID-19 cases from publicly available official sources from December 29, 2019 to April 17, 2020. We accounted for right-censoring to estimate the CFR and explored the risk factors for mortality. We fitted Weibull, gamma, and lognormal distributions to time-to-event data using maximum-likelihood estimation. Results We analyzed 82,719 laboratory-confirmed cases reported in mainland China, including 4,632 deaths, and 77,029 discharges. The estimated CFR was 5.65% (95%CI: 5.50%-5.81%) nationally, with highest estimate in Wuhan (7.71%), and lowest in provinces outside Hubei (0.86%). The fatality risk among critical patients was 3.6 times that of all patients, and 0.8-10.3 fold higher than that of mild-to-severe patients. Older age (OR 1.14 per year; 95%CI: 1.11-1.16), and being male (OR 1.83; 95%CI: 1.10-3.04) were risk factors for mortality. The time from symptom onset to first healthcare consultation, time from symptom onset to laboratory confirmation, and time from symptom onset to hospitalization were consistently longer for deceased patients than for those who recovered. Conclusions Our CFR estimates based on laboratory-confirmed cases ascertained in mainland China suggest that COVID-19 is more severe than the 2009 H1N1 influenza pandemic in hospitalized patients, particularly in Wuhan. Our study provides a comprehensive picture of the severity of the first wave of the pandemic in China. Our estimates can help inform models and the global response to COVID-19

M7G methylated core genes (METTL1 and WDR4) and associated RNA risk signatures are associated with prognosis and immune escape in HCC

Abstract N7 methylguanosine (m7G) has a crucial role the development of hepatocellular carcinoma (HCC). This study aimed to investigate the impact of the m7G methylation core genes (METTL1 and WDR4) and associated RNA risk signatures on HCC. we found m7G methylation core genes (METTL1 and WDR4) were upregulated in four HCC cell lines, and downregulation of METTL1 and WDR4 attenuated HCC cell proliferation, migration, and invasion. Moreover, METTL1 and WDR4 are upregulated in HCC tissues, and that there is a significant positive correlation between them. METTL1 and WDR4 were identified as independent prognostic markers for HCC by employing overall survival (OS), disease-specific survival (DSS), Progression Free Interval survival (PFI), and univariate/multivariate Cox analyses. We identified 1479 coding RNAs (mRNAs) and 232 long non-coding RNAs (lncRNAs) associated with METTL1 / WDR4 by using weighted coexpression network analysis (WGCNA) and co-clustering analysis. The least absolute shrinkage and selection operator (lasso) were used to constructing mRNA and lncRNA risk signatures associated with the METTL1 / WDR4. These risk were independent poor prognostic factors in HCC. Furthermore, we found that METTL1 / WDR4 expression and mRNA / lncRNA risk scores were closely associated with TP53 mutations. Clinicopathological features correlation results showed that METTL1 / WDR4 expression and mRNA / lncRNA risk score were associated with the stage and invasion depth (T) of HCC. To predict the overall survival of HCC individuals, we constructed a nomogram with METTL1/WDR4 expression, mRNA/lncRNA risk score, and clinicopathological features. In addition, we combined single-cell sequencing datasets and immune escape-related checkpoints to construct an immune escape-related protein–protein interaction(PPI) network. In conclusion, M7G methylated core genes (METTL1 and WDR4) and associated RNA risk signatures are associated with prognosis and immune escape in HCC

Exe-Muscle: An Exercised Human Skeletal Muscle Gene Expression Database

Human muscle tissue undergoes dynamic changes in gene expression during exercise, and the dynamics of these genes are correlated with muscle adaptation to exercise. A database of gene expression changes in human muscle before and after exercise was established for data mining. A web-based searchable database, Exe-muscle, was developed using microarray sequencing data, which can help users to retrieve gene expression at different times. Search results provide a complete description of target genes or genes with specific expression patterns. We can explore the molecular mechanisms behind exercise science by studying the changes in muscle gene expression over time before and after exercise. Based on the high-throughput microarray data before and after human exercise, a human pre- and post-exercise database was created using web-based database technology, which researchers can use or share their gene expression data. The Exe-muscle database is accessible online

On the Reliability of a Solitary Wave Based Transducer to Determine the Characteristics of Some Materials

In the study presented in this article we investigated the feasibility and the reliability of a transducer design for the nondestructive evaluation (NDE) of the stiffness of structural materials. The NDE method is based on the propagation of highly nonlinear solitary waves (HNSWs) along a one-dimensional chain of spherical particles that is in contact with the material to be assessed. The chain is part of a built-in system designed and assembled to excite and detect HNSWs, and to exploit the dynamic interaction between the particles and the material to be inspected. This interaction influences the time-of-flight and the amplitude of the solitary pulses reflected at the transducer/material interface. The results of this study show that certain features of the waves are dependent on the modulus of elasticity of the material and that the built-in system is reliable. In the future the proposed NDE method may provide a cost-effective tool for the rapid assessment of materials’ modulus

Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization

To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold ξ and B-spline order n, were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (CCKur) constituted the objective function. Initially, the objective function CCKur was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely CC, Kur, MI, EE, EEMI, and CCKur, revealed that the synthetic measurement index CCKur and objective function fitness were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method

A Multiview Representation Learning Framework for Large-Scale Urban Road Networks

Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis

The risk of mpox importation and subsequent outbreak potential in Chinese mainland: a retrospective statistical modelling study

Abstract Background The 2022–2023 mpox (monkeypox) outbreak has spread rapidly across multiple countries in the non-endemic region, mainly among men who have sex with men (MSM). In this study, we aimed to evaluate mpox’s importation risk, border screening effectiveness and the risk of local outbreak in Chinese mainland. Methods We estimated the risk of mpox importation in Chinese mainland from April 14 to September 11, 2022 using the number of reported mpox cases during this multi-country outbreak from Global.health and the international air-travel data from Official Aviation Guide. We constructed a probabilistic model to simulate the effectiveness of a border screening scenario during the mpox outbreak and a hypothetical scenario with less stringent quarantine requirement. And we further evaluated the mpox outbreak potential given that undetected mpox infections were introduced into men who have sex with men, considering different transmissibility, population immunity and population activity. Results We found that the reduced international air-travel volume and stringent border entry policy decreased about 94% and 69% mpox importations respectively. Under the quarantine policy, 15–19% of imported infections would remain undetected. Once a case of mpox is introduced into active MSM population with almost no population immunity, the risk of triggering local transmission is estimated at 42%, and would rise to > 95% with over six cases. Conclusions Our study demonstrates that the reduced international air-travel volume and stringent border entry policy during the COVID-19 pandemic reduced mpox importations prominently. However, the risk could be substantially higher with the recovery of air-travel volume to pre-pandemic level. Mpox could emerge as a public health threat for Chinese mainland given its large MSM community. Graphical Abstrac