The Influence of Preoperative Hydronephrosis on the Prognosis after Radical Cystectomy among Patients with Different Pathological Stages of Bladder Cancer.

INTRODUCTION: Preoperative hydronephrosis is closely associated with the prognosis of patients with bladder cancer. This study assesses the effect of preoperative hydronephrosis on the prognosis after radical cystectomy (RC) among patients with different pathological stages of bladder urothelial carcinoma. METHODS: We retrospectively analyzed the clinical data of 231 patients who underwent RC because of bladder urothelial carcinoma at our institution from January 2013 to December 2017. The overall survival (OS) in patients with or without preoperative hydronephrosis was followed up and compared, and the prognostic role that preoperative hydronephrosis played in patients with different pathological stages of bladder cancer was analyzed. Multivariate analysis was performed with the help of Cox proportional hazards regression models, the postoperative survival was analyzed with the help of Kaplan-Meier plots and log-rank test, and the p values of multiple testing were corrected using the Bonferroni correction. RESULTS: Of 231 patients, 96 were patients with preoperative hydronephrosis and 115 patients had died by the end of the follow-up. Survival analysis found the 3- and 5-year survival rates after radical surgery of patients with preoperative hydronephrosis were significantly lower than those of patients without preoperative hydronephrosis (p < 0.001). Multivariate analysis found preoperative hydronephrosis, T stage of tumor, and lymphatic metastasis were independent influencing factors of postoperative OS (p < 0.05). Survival analysis of subgroups according to pathological stages found in pT3-4N0M0 patients had a significant difference in postoperative survival between the group with preoperative hydronephrosis and the group without preoperative hydronephrosis (p < 0.0001). CONCLUSION: The results indicate that preoperative hydronephrosis mainly affects postoperative OS in the patients whose pathological stage of bladder cancer is pT3-4N0M0.

A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation.

The conservation of avian diversity plays a critical role in maintaining ecological balance and ecosystem function, as well as having a profound impact on human survival and livelihood. With species' continuous and rapid decline, information and intelligent technology have provided innovative knowledge about how functional biological diversity interacts with environmental changes. Especially in complex natural scenes, identifying bird species with a real-time and accurate pattern is vital to protect the ecological environment and maintain biodiversity changes. Aiming at the fine-grained problem in bird image recognition, this paper proposes a fine-grained detection neural network based on optimizing the YOLOV5 structure via a graph pyramid attention convolution operation. Firstly, the Cross Stage Partial (CSP) structure is introduced to a brand-new backbone classification network (GPA-Net) for significantly reducing the whole model's parameters. Then, the graph pyramid structure is applied to learn the bird image features of different scales, which enhances the fine-grained learning ability and embeds high-order features to reduce parameters. Thirdly, YOLOV5 with the soft non-maximum suppression (NMS) strategy is adopted to design the detector composition, improving the detection capability for small targets. Detailed experiments demonstrated that the proposed model achieves better or equivalent accuracy results, over-performing current advanced models in bird species identification, and is more stable and suitable for practical applications in biodiversity conservation.

Rabbit manure compost as a peat substitute for compound growing media: Proportioning optimization according to physiochemical characteristics and seedling effects.

A large amount of rabbit manure is produced with the development of the rabbit industry, which will cause environmental pollution without proper treatment. Rabbit manure compost may be suitable for seedling cultivation, considering its low moisture, low heavy metal, high lignocellulose, and good fertilizer effect. In this study, a pre-proportioning test of growing media was conducted to optimize the ratio of perlite and vermiculite with peat/rabbit manure compost according to their physicochemical properties. Then, based on the results of the first proportioning optimization, the mixing ratio of rabbit manure compost and peat was further optimized using a bioassay. In this bioassay, salt-tolerant calendula (Calendula officinalis L.) and salt-intolerant cucumber (Cucumis sativus L.) were selected as test plants. The seedling effects (e.g., seedling emergence percentage, plant growth parameters, plant biomass, and nutrient effects) were evaluated. It was shown in the results that the rabbit manure compound growing media could be used for the seedlings, and suitable seedling performance was obtained with the increase of the total porosity (5.0%-61.2%), organic matter content (8.3%-39.9%), and nutrient elements from the rabbit manure compost. From the perspective of seedling emergence, there was no significant difference between rabbit manure compound media and peat treatment, in which the highest emergence percentages were >90%. At the same time, the nutrient performance of plant aboveground was significantly increased in rabbit manure compound growing media compared to peat treatment. In particular, the contents of P and Mg were increased by 31%-141.4% and 80.4%-107.8% for calendula and by 82.6%-117.4% and 35.1%-67.6% for cucumber, respectively. It was indicated in the two-step optimization that the rabbit manure compost proportion of 30%-50% (that is, 60%-100% instead of peat) was more suitable. Additionally, the greenhouse gas emission could be reduced by using rabbit manure compost replacing peat, and the greenhouse gas emission reduction potential would be 3.65 × 105-4.06 × 108 kg CO2-equivalent/year in China, which has important ecological significance.

A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests.

Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.

Deep-Stacking Network Approach by Multisource Data Mining for Hazardous Risk Identification in IoT-Based Intelligent Food Management Systems.

Food quality and safety issues occurred frequently in recent years, which have attracted more and more attention of social and international organizations. Considering the increased quality risk in the food supply chain, many researchers have applied various information technologies to develop real-time risk identification and traceability systems (RITSs) for preferable food safety guarantee. This paper presents an innovative approach by utilizing the deep-stacking network method for hazardous risk identification, which relies on massive multisource data monitored by the Internet of Things timely in the whole food supply chain. The aim of the proposed method is to help managers and operators in food enterprises to find accurate risk levels of food security in advance and to provide regulatory authorities and consumers with potential rules for better decision-making, thereby maintaining the safety and sustainability of food product supply. The verification experiments show that the proposed method has the best performance in terms of prediction accuracy up to 97.62%, meanwhile achieves the appropriate model parameters only up to 211.26 megabytes. Moreover, the case analysis is implemented to illustrate the outperforming performance of the proposed method in risk level identification. It can effectively enhance the RITS ability for assuring food supply chain security and attaining multiple cooperation between regulators, enterprises, and consumers.