Prevention and Trust Evaluation Scheme Based on Interpersonal Relationships for Large-Scale Peer-To-Peer Networks

Peer reviewedPublisher PD

Serum and Adipose Tissue mRNA Levels of ATF3 and FNDC5/Irisin in Colorectal Cancer Patients With or Without Obesity

Objectives: To explore the activating transcription factor 3 (ATF3) and fibronectin type III domain-containing protein 5 (FNDC5)/irisin protein levels in serum and mRNA levels in subcutaneous and visceral white adipose tissue (sWAT and vWAT) in normal-weight (NW) and overweight/obese (OW/OB) patients with colorectal cancer (CRC).Methods: 76 CRC patients and 40 healthy controls were recruited. Serum ATF3 and irisin levels were detected by using ELISA kits, and the mRNA expression levels in sWAT and vWAT were measured by performing RT-qPCR.Results: The serum ATF3 levels were greater by 37.2%, whereas the irisin levels were lower by 23.3% in NW+CRC patients compared with those in healthy controls. CRC was independently associated with both ATF3 and irisin levels. The probability of CRC greater by 22.3-fold in individuals with high ATF3 levels compared with those with low ATF3 levels, whereas the risk of CRC in subjects with high irisin levels was lower by 78.0% compared to the risk in those with low irisin levels after adjustment for age, gender, BMI, and other biochemical parameters. Serum ATF3 and irisin could differentiate CRC patients from controls with receiver operating characteristic (ROC) curve areas of 0.745 (95% CI, 0.655–0.823) and 0.656 (95% CI, 0.561–0.743), respectively. The combination of ATF3 and irisin exhibited improved diagnosis value accuracy with ROC curve areas of 0.796 (95% CI, 0.710–0.866) as well as 72.6% sensitivity and 80.0% specificity.Conclusion: Increased ATF3 and reduced irisin levels were observed in sera from CRC patients. Individuals with high ATF3 and low irisin levels were more likely to have CRC. ATF3 and irisin represent potential diagnostic biomarkers for CRC patients

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks

The supervised training of deep neural networks (DNNs) by noisy labels has been studied extensively in image classification but much less in image segmentation. So far, our understanding of the learning behavior of DNNs trained by noisy segmentation labels remains limited. In this study, we address this deficiency in both binary segmentation of biological microscopy images and multi-class segmentation of natural images. We classify segmentation labels according to their noise transition matrices (NTM) and compare performance of DNNs trained by different types of labels. When we randomly sample a small fraction (e.g., 10%) or flipping a large fraction (e.g., 90%) of the ground-truth labels to train DNNs, their segmentation performance remains largely the same. This indicates that DNNs learn structures hidden in labels rather than pixel-level labels per se in their supervised training for semantic segmentation. We call these hidden structures "meta-structures". When we use labels with different perturbations to the meta-structures to train DNNs, their performance in feature extraction and segmentation degrades consistently. In contrast, addition of meta-structure information substantially improves performance of an unsupervised model in binary semantic segmentation. We formulate meta-structures mathematically as spatial density distributions and quantify semantic information of different types of labels, which we find to correlate strongly with ranks of their NTM. We show theoretically and experimentally how this formulation explains key observed learning behavior of DNNs

Improved Degree Search Algorithms in Unstructured P2P Networks

Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P) networks. Breadth-first search (BFS) and depth-first search (DFS) are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD) and memory function preference degree algorithm (PD). We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times

Ileal Transposition Surgery Decreases Fat Mass and Improves Glucose Metabolism in Diabetic GK Rats: Possible Involvement of FGF21

Objective: Ileal transposition (IT) surgery has been reported to improve glucose and lipid metabolism, and fibroblast growth factor 21 (FGF21) is a powerful metabolic regulator. In the present study, we aimed to investigate the effects of IT surgery on metabolism and its possible relationship with the FGF21 signaling pathway in diabetic Goto-Kakizaki (GK) rats.Methods: Ten-week-old male GK rats were subjected to IT surgery with translocation of a 10 cm ileal segment to the proximal jejunum (IT group) or sham surgery without the ileum transposition (Sham-IT group). Rats in the no surgery group did not receive any surgical intervention. Six weeks later, body weight, fat mass, fasting blood glucose (FBG), and serum levels of FGF21 and leptin were measured. The expression of the FGF21 signaling pathway and white adipose tissue (WAT) browning-related genes in the WAT and liver were evaluated by real-time reverse transcription polymerase chain reaction (RT-qPCR) and western blot.Results: IT surgery significantly decreased the body weights and FBG levels and increased the insulin sensitivity of GK rats. The total WAT mass of the IT rats showed a 41.5% reduction compared with the Sham-IT rats, and serum levels of FGF21 and leptin of the IT rats decreased by 26.3 and 61.7%, respectively (all P < 0.05). The mRNA levels of fibroblast growth factor receptor 1 (FGFR1) and its co-receptor β klotho (KLB) in the perirenal WAT (pWAT) of the IT rats were 1.4- and 2.4-fold that of the Sham-IT rats, respectively, and the FGFR1 protein levels were 1.7-fold of the Sham-IT rats (all P < 0.05). In accordance with the pWAT, the protein levels of FGFR1 and KLB in the epididymal WAT (eWAT) of the IT rats notably increased to 3.0- and 3.9-fold of the Sham-IT rats (P < 0.05). Furthermore, uncoupling protein 1 (UCP1) protein levels in the eWAT and pWAT of the IT rats also increased to 2.2- and 2.3-fold of the Sham-IT rats (P < 0.05). However, the protein levels of FGFR1 and KLB in the subcutaneous WAT (sWAT) of the IT rats decreased by 34.4 and 72.1%, respectively, compared with the Sham-IT rats (P < 0.05). In addition, the protein levels of FGF21 and KLB in the livers of IT rats were 3.9- and 2.3-fold of the Sham-IT rats (all P < 0.05).Conclusions: IT surgery significantly decreased fat mass and improved glucose metabolism in diabetic GK rats. These beneficial roles of IT surgery were probably associated with its stimulatory action on the expression of FGFR1 and KLB in both the eWAT and the pWAT, thereby promoting UCP1 expression in these tissues

table_1_Circulating and Adipose Tissue mRNA Levels of Zinc-α2-Glycoprotein, Leptin, High-Molecular-Weight Adiponectin, and Tumor Necrosis Factor-Alpha in Colorectal Cancer Patients With or Without Obesity.docx

Objectives<p>To explore zinc-α2-glycoprotein (ZAG), leptin, high-molecular-weight adiponectin (HMW-ADPN), and tumor necrosis factor-alpha (TNF-α) levels in serum and subcutaneous and visceral white adipose tissue (sWAT and vWAT) among normal weight (NW) and overweight/obese (OW/OB) patients with colorectal cancer (CRC).</p>Methods<p>A total of 76 Chinese CRC patients (42 NW + CRC, 34 OW/OB + CRC) and 40 healthy controls were recruited. Serum levels of the adipokines of interest were measured by an enzyme-linked immunosorbent assay method, and their mRNA levels in sWAT and vWAT were determined by reverse transcription quantitative PCR methods.</p>Results<p>Serum ZAG levels in the NW + CRC group were significantly increased by 11.7% compared with the healthy controls. Serum leptin levels in the OW/OB + CRC group were found to be increased by 57.7%, while HMW-ADPN levels were decreased by 23.5% when compared with the NW + CRC group of CRC patients. Additionally, ZAG mRNA levels in sWAT were significantly reduced by 78.8% in OB + CRC in comparison with NW + CRC patients. ZAG mRNA levels were negatively associated with body mass index (BMI) in sWAT but positively correlated with BMI in vWAT. TNF-α mRNA levels in vWAT of OB + CRC patients were significantly increased by 2.8-fold when compared with NW + CRC patients. In particular, CRC was independently associated with serum ZAG levels. The risk of CRC in participants with high tertile serum ZAG levels was 5.84-fold higher than in those with low tertile ZAG levels after adjusting for age, gender, and other confounders [odds ratio (OR) = 6.84, 95% confidence interval (CI) 1.70–27.54, P = 0.03]. The CRC risk in participants with high tertile leptin levels was only 10.7% of those with low tertile leptin levels (OR = 0.11, 95% CI 0.01–0.89, P = 0.04). The area under the receiver operating characteristic (ROC) curve of ZAG was 0.66 (95% CI 0.54–0.77, P < 0.05). At the cutoff value of 1.42 µg/mL serum ZAG, the sensitivity and specificity for differentiating patients with CRC from controls were 62.2 and 69.2%, respectively.</p>Conclusion<p>Serum ZAG levels were significantly increased in CRC patients. Subjects with higher circulating ZAG and lower leptin levels were more likely to have CRC than those with lower ZAG and higher leptin levels. Serum ZAG might be a potential diagnostic biomarker for CRC in the Chinese population.</p