Search

Background Urinary bladder cancer (UBC) is a common malignancy of the urinary tract; however, the mechanism underlying its high recurrence and responses to immunotherapy remains unclear, making clinical outcome predictions difficult. Epigenetic alterations, especially DNA methylation, play important roles in bladder cancer development and are increasingly being investigated as biomarkers for diagnostic or prognostic predictions. However, little is known about hydroxymethylation since previous studies based on bisulfite-sequencing approaches could not differentiate between 5mC and 5hmC signals, resulting in entangled methylation results. Methods Tissue samples of bladder cancer patients who underwent laparoscopic radical cystectomy (LRC), partial cystectomy (PC), or transurethral resection of bladder tumor (TURBT) were collected. We utilized a multi-omics approach to analyze both primary and recurrent bladder cancer samples. By integrating various techniques including RNA sequencing, oxidative reduced-representation bisulfite sequencing (oxRRBS), reduced-representation bisulfite sequencing (RRBS), and whole exome sequencing, a comprehensive analysis of the genome, transcriptome, methylome, and hydroxymethylome landscape of these cancers was possible. Results By whole exome sequencing, we identified driver mutations involved in the development of UBC, including those in FGFR3, KDMTA, and KDMT2C. However, few of these driver mutations were associated with the down-regulation of programmed death-ligand 1 (PD-L1) or recurrence in UBC. By integrating RRBS and oxRRBS data, we identified fatty acid oxidation-related genes significantly enriched in 5hmC-associated transcription alterations in recurrent bladder cancers. We also observed a series of 5mC hypo differentially methylated regions (DMRs) in the gene body of NFATC1, which is highly involved in T-cell immune responses in bladder cancer samples with high expression of PD-L1. Since 5mC and 5hmC alternations are globally anti-correlated, RRBS-seq-based markers that combine the 5mC and 5hmC signals, attenuate cancer-related signals, and therefore, are not optimal as clinical biomarkers. Conclusions By multi-omics profiling of UBC samples, we showed that epigenetic alternations are more involved compared to genetic mutations in the PD-L1 regulation and recurrence of UBC. As proof of principle, we demonstrated that the combined measurement of 5mC and 5hmC levels by the bisulfite-based method compromises the prediction accuracy of epigenetic biomarkers.

The widely recommended fracture prediction tool FRAX was developed based on and for the general population. Although several adjusted FRAX methods were suggested for type 2 diabetes (T2DM), they still need to be evaluated in T2DM cohort.This study was undertaken to develop a prediction model for Chinese diabetes fracture risk (CDFR) and compare its performance with those of FRAX.In this retrospective cohort study, 1730 patients with T2DM were enrolled from 2009.08 to 2013.07. Major osteoporotic fractures (MOFs) during follow-up were collected from Electronic Health Records (EHRs) and telephone interviews. Multivariate Cox regression with backward stepwise selection was used to fit the model. The performances of the CDFR model, FRAX, and adjusted FRAX were compared in the aspects of discrimination and calibration.6.3% of participants experienced MOF during a median follow-up of 10 years. The final model (CDFR) included 8 predictors: age, gender, previous fracture, insulin use, diabetic peripheral neuropathy (DPN), total cholesterol, triglycerides, and apolipoprotein A. This model had a C statistic of 0.803 (95%CI 0.761-0.844) and calibration χThe CDFR model has good performance in 10-year MOF risk prediction in T2DM, especially in patients with insulin use or DPN. Future work is needed to validate our model in external cohort(s).

In industrial production, personal protective equipment (PPE) protects workers from accidental injuries. However, wearing PPE is not strictly enforced among workers due to all kinds of reasons. To enhance the monitoring of workers and thus avoid safety accidents, it is essential to design an automatic detection method for PPE. In this paper, we constructed a dataset called FZU-PPE for our study, which contains four types of PPE (helmet, safety vest, mask, and gloves). To reduce the model size and resource consumption, we propose a lightweight object detection method based on deep learning for superfast detection of whether workers are wearing PPE or not. We use two lightweight methods to optimize the network structure of the object detection algorithm to reduce the computational effort and parameters of the detection model by 32% and 25%, respectively, with minimal accuracy loss. We propose a channel pruning algorithm based on the BN layer scaling factor γ to further reduce the size of the detection model. Experiments show that the automatic detection of PPE using our lightweight object detection method takes only 9.5 ms to detect a single video frame and achieves a detection speed of 105 FPS. Our detection model has a minimum size of 1.82 MB and a model size compression rate of 86.7%, which can meet the strict requirements of memory occupation and computational resources for embedded and mobile devices. Our approach is a superfast detection method for green edge computing.

Visual trackers have achieved a high-level performance from deep features, but many limitations remain. Online trackers suffer from low speed while using deep features for parameter updating, and deep trackers trained offline demonstrate data hunger. To meet these challenges, our work aims to mine the target representation capability of a pre-trained model and presents deep convolutional descriptor aggregation (DCDA) for visual tracking. Based on spatial and semantic priors, we propose an edge-aware selection (EAS) and a central-aware selection (CAS) method to aggregate the accuracy-aware and robustness-aware features. To make full use of the scene context, our method is derived from one-shot learning by designing a dedicated regression process that is capable of predicting discriminative model in a few iterations. By exploiting robustness feature aggregation, the accuracy feature aggregation, and the discriminative regression, our DCDA with Siamese tracking architecture not only enhances the target prediction capacity, but also achieves a low-cost reuse of the pre-trained model. Comprehensive experiments on OTB-100, VOT2016, VOT2017, VOT2020, NFS30, and NFS240 show that our DCDA tracker achieves state-of-the-art performance with a high running speed of 65 FPS. The source code and all the experimental results of this work will be made public at https://github.com/Gitlyz007/DCDA_Tracker .

BackgroundThe generation time distribution, reflecting the time between successive infections in transmission chains, is one of the fundamental epidemiological parameters for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution, reflecting the time between illness onsets of infector and infectee. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true.MethodsWe analyzed data on observed incubation period and serial interval distributions in China, during January and February 2020, under different sampling approaches, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures.ResultsWe analyzed data on a total of 2989 confirmed cases for COVID-19 during January 1 to February 29, 2020 in Mainland China. During the study period, the empirical forward serial interval decreased from a mean of 8.90 days to 2.68 days. The estimated mean backward incubation period of infectors increased from 3.77 days to 9.61 days, and the mean forward incubation period of infectees also increased from 5.39 days to 7.21 days. The estimated mean forward generation time decreased from 7.27 days (95% confidence interval: 6.42, 8.07) to 4.21 days (95% confidence interval: 3.70, 4.74) days by January 29. We used simulations to examine the sensitivity of our modelling approach to a number of assumptions and alternative dynamics.ConclusionsThe proposed method can provide more reliable estimation of the temporal variation in the generation time distribution, enabling proper assessment of transmission dynamics.

This study examined the effects of Stmn2 on phenotype transformation of vascular smooth muscle in vascular injury via RNA sequencing and experimental validation. Total RNA was extracted for RNA sequencing after 1, 3 and 5 days of injury to screen the differentially expressed genes (DEGs). Western blot was used to detect the protein expression of Stmn2 and its associated targets. The morphological changes of carotid arteries in rats were examined by hematoxylin and eosin (HE Stmn2 overexpression significantly up-regulated the expression of osteopontin and α-SMA and vimentin in VSMCs. The results of morphology analysis and immunostaining also showed that Stmn2 overexpression promoted the intima thickening and enhanced the proliferating cell nuclear antigen expression in the injured vascular tissues. In conclusion, our results implied that Stmn2 may play a potential role in vascular injury, which may be associated with VSMC phenotype transformation. Further studies are warranted to determine detailed molecular mechanisms of Stmn2 in vascular injury.

A class of fractional stochastic neutral functional differential equation is analyzed in this paper. With the utilization of the fractional calculations, semigroup theory, fixed point technique and stochastic analysis theory, a sufficient condition of the existence for p-mean almost periodic solution is obtained, which are supported by two examples.