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This work is to analyze a nonlocal dispersal model governed by a Volterra type integral and two space integrals. A weighted integral is included, and an existence result of solutions for this model is proved. We assume a suitably Hartman-type sign condition and use a sufficiently regular measure of noncompactness. Finally, the degree theory referring to condensing operators is applied.

Abstract Background Nutritional status and inflammation are closely associated with poor outcome in malignant tumors. However, the prognostic impact of postoperative in these variables on breast cancer (BC) remains inconclusive. We aimed to determine whether prognostic nutritional index (PNI), systemic immune‐inflammation index (SII), neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) affect two long-term outcomes among patients after curative resection of BC. Methods We retrospectively reviewed 508 patients with BC treated with curative surgery between February 5, 2013 and May 26, 2020. All patients were divided into 3 groups based on tertiles (T1-T3) of PNI, SII, NLR, and PLR. The effects of four indexes on disease-free survival (DFS) and overall survival (OS) have been evaluated using Cox proportional hazards models and Kaplan–Meier method. Results Compared with PNI-lowest cases, patients with highest PNI showed significantly longer DFS (multivariate adjusted hazard ratio [HR] = 0.37, 95% confident interval [CI] 0.19–0.70, P for trend = 0.002), whereas higher PLR seemed to be marginally associated with poorer DFS (P for trend = 0.086 and 0.074, respectively). Subgroup analyses indicate the potential modification effects of family history of BC and radiotherapy on the prognosis value of PNI to DFS in BC patients (P for interaction = 0.004 and 0.025, respectively). In addition, the levels of three inflammatory indices, namely SII, NLR, and PLR might be positively related with increased age at diagnosis (all P for trend

Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the proportion of labeled data is relatively low.Extending some advanced data augmentation method in supervised learning to be used in a semi-supervised learning setting is one of the ideas to solve this problem.Based on the consistency-based semi-supervised learning method MixMatch,a semi-supervised learning method AutoMixMatch based on automated mixed sample data augmentation techniques is proposed,which uses a modified automatic data augmentation technique in the data augmentation phase,and a mixed-sample algorithm is proposed to enhance the utilization of unlabeled samples in the sample mixing phase.The performance of the proposed method is evaluated through image classification experiments.In image classification benchmark datasets,the proposed method outperforms several mainstream semi-supervised classification methods in three labeled sample proportions,which validates the effectiveness of the method.In addition,the proposed method performs better with a very low proportion of labeled data to the training data (only 0.05%),and the classification error rate of the proposed method on the SVHN dataset is 30.17% lower than that of MixMatch.

Chitin is one of the most abundant polymers in nature, with chitinous biomass often discarded as food waste and marine debris. To explore an effective way to degrade chitin, in this work, anaerobic sludge was inoculated at the anode of a two-chamber microbial fuel cell (MFC), and chitin was degraded via anaerobic respiration and fermentation. The results showed that the anaerobic sludge could degrade chitin under both the anaerobic respiration and fermentation modes, with similar degradation rates (7.10 ± 0.96 and 6.96 ± 0.23 C-mg/L·d−1). The open-circuit voltage and output current density could roughly reflect the degradation of chitin in the MFC. The maximum current density generated through the anaerobic sludge degradation of chitin via anaerobic respiration was 160 mA/m2, and the maximum power density was 26.29 mW/m2. The microbial sequencing results revealed substantially different microbial community profiles, with electroactive bacteria (EAB) flora and fermentative bacteria (Longilinea) as the main microbial groups that degraded chitin via anaerobic respiration and fermentation, respectively. Therefore, anaerobic sludge may be a good choice for the treatment of refractory biomass due to its abundant electroactive and fermentative flora. [ABSTRACT FROM AUTHOR]