ROLE OF BLUEBERRY IN PANCREATIC β-CELLS

“We are what we eat.” Imbalanced diet is a major reason for obesity and consequently type-2 diabetes. A healthy and attractive diet is key to the control and prevention of obesity and type-2 diabetes. Among many fruit products, blueberry is rich in bioactive substances and possesses powerful antioxidant potential, which can protect against oxidant-induced and inflammatory cell damage and cytotoxicity. Blueberry has been found to improve insulin sensitivity in muscle and adipose, and thus reduce the risk of developing type 2 diabetes. However, whether blueberry affects β-cell function and growth were not fully evaluated. To study the effect of the whole blueberry on beta cell function, a modified high-fat diet supplemented with 4% (wt:wt) freeze-dried whole blueberry powder (HFD+B) was applied to the C57BL/6 male mice. Compared to the mice fed with high-fat diet (HFD), the addition of blueberry had no significant change in the body weight and glucose level. Interestingly, after 8 weeks feeding, the plasma insulin level was decreased significantly in mice fed with HFD+B compared to mice fed with HFD. In addition, mice fed with HFD+B had significantly increased glucose tolerance and insulin sensitivity. Moreover, the blueberry-supplemented diet prevents HFD-induced beta-cell expansion and preserves the islet structure. Taken together, our results indicated that blueberry-supplemented diet could significantly protect β-cell, restore HFD-induced impaired glucose homeostasis and attenuate the development of obesity, which will provide new insights into the effects of blueberry on beta-cell function and expand our understanding the importance of blueberry in treating and preventing obesity and diabetes

The Analysis and Calculation Method of Urban Rail Transit Carrying Capacity Based on Express-Slow Mode

Urban railway transport that connects suburbs and city areas is characterized by uneven temporal and spatial distribution in terms of passenger flow and underutilized carrying capacity. This paper aims to develop methodologies to measure the carrying capacity of the urban railway by introducing a concept of the express-slow mode. We first explore factors influencing the carrying capacity under the express-slow mode and the interactive relationships among these factors. Then we establish seven different scenarios to measure the carrying capacity by considering the ratio of the number of the express trains and the slow trains, the station where overtaking takes place, and the number of overtaking maneuvers. Taking Shanghai Metro Line 16 as an empirical study, the proposed methods to measure the carrying capacity under different express-slow mode are proved to be valid. This paper contributes to the literature by remodifying the traditional methods to measure the carrying capacity when different express-slow modes are applied to improve the carrying capacity of the suburban railway

Harmonizing Output Imbalance for semantic segmentation on extremely-imbalanced input data

Semantic segmentation is a high level computer vision task that assigns a label for each pixel of an image. It is challenging to deal with extremely-imbalanced data in which the ratio of target pixels to background pixels is lower than 1:1000. Such severe input imbalance leads to output imbalance for poor model training. This paper considers three issues for extremely-imbalanced data: inspired by the region-based Dice loss, an implicit measure for the output imbalance is proposed, and an adaptive algorithm is designed for guiding the output imbalance hyperparameter selection; then it is generalized to distribution-based loss for dealing with output imbalance; and finally a compound loss with our adaptive hyperparameter selection algorithm can keep the consistency of training and inference for harmonizing the output imbalance. With four popular deep architectures on our private dataset from three different input imbalance scales and three public datasets, extensive experiments demonstrate the competitive/promising performance of the proposed method.Comment: 18 pages, 13 figures, 2 appendixe

Whole blueberry protects pancreatic beta-cells in diet-induced obese mouse

Background Blueberry is rich in bioactive substances and possesses powerful antioxidant potential, which can protect against oxidant-induced and inflammatory cell damage and cytotoxicity. The aim of this study was to determine how blueberry affects glucose metabolism and pancreatic β-cell proliferation in high fat diet (HFD)-induced obese mice. Methods Wild type male mice at age of 4 weeks received two different kinds of diets: high-fat diet (HFD) containing 60% fat or modified HFD supplemented with 4% (wt:wt) freeze-dried whole blueberry powder (HFD + B) for 14 weeks. A separate experiment was performed in mice fed with low-fat diet (LFD) containing 10% fat or modified LFD + B supplemented with 4% (wt:wt) freeze-dried whole blueberry powder. The metabolic parameters including blood glucose and insulin levels, glucose and insulin tolerances were measured. Results Blueberry-supplemented diet significantly increased insulin sensitivity and glucose tolerance in HFD + B mice compared to HFD mice. However, no difference was observed in blood glucose and insulin sensitivity between LFD + B and LFD mice. In addition, blueberry increased β-cell survival and prevented HFD-induced β-cell expansion. The most important finding was the observation of presence of small scattered islets in blueberry treated obese mice, which may reflect a potential role of blueberry in regenerating pancreatic β-cells. Conclusions Blueberry-supplemented diet can prevent obesity-induced insulin resistance by improving insulin sensitivity and protecting pancreatic β-cells. Blueberry supplementation has the potential to protect and improve health conditions for both type 1 and type 2 diabetes patients

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models

Frequent copy number variations of PI3K/AKT pathway and aberrant protein expressions of PI3K subunits are associated with inferior survival in diffuse large B cell lymphoma

BACKGROUND: It has been reported that the PI3K/AKT signaling pathway is activated in diffuse large B-cell lymphoma (DLBCL), PI3K constitutive activation plays a crucial role in PI3K/AKT pathway. However, the copy number variations (CNVs) of PI3K subunits on gene level remain unknown in DLBCL. Therefore, the aim of the study is to investigate the CNV of PI3K subunits and their relationship with clinicopathological features exploring the possible mechanism underlying of PI3K activation in DLBCL. METHODS: CNV of 12 genes in the PI3K/AKT pathway was detected by NanoString nCounter in 60 de novo DLBCLs and 10 reactive hyperplasia specimens as controls. Meanwhile, immunohistochemistry (IHC) was performed to examine the expression of p110α, p110β, p110γ, p110δ, and pAKT on DLBCL tissue microarrays. RESULTS: All PI3K and AKT subunits, except for PIK3R1, had various CNVs in the form of copy number amplifications and copy number losses. Their rates were in the range of 8.3–20.0%. Of them PIK3CA and PIK3CB gene CNVs were significantly associated with decreased overall survival (P = 0.029 and P = 0.019, respectively). IHC showed that the frequency of strong positive expression of p110α, p110β, p110γ, and p110δ were 26.7%, 25.0%, 18.3%, and 25.0% respectively, and they were found to be associated with decreased survival (P = 0.022, P = 0.015, P = 0.015, and P = 0.008, respectively). Expression of p110α was not only significantly associated with CNVs of PIK3CA (P = 0.002) but also positively correlated with strong positive expression of pAKT (P = 0.026). CONCLUSIONS: CNV of PIK3CA is highly associated with aberrant p110α protein expression and subsequent activation of PI3K/AKT pathway. CNVs of PIK3CA and PIK3CB, and aberrant protein expression of p110 isoforms are of great important value for predicting inferior prognosis in DLBCL. Frequent CNVs of PI3K/AKT subunits may play an important role in the tumorigenesis of DLBCL

A miniaturised active thermography system for in-situ inspections

With the increase of the functionalisation, integration and complexity of industrial components and systems, deploying Non-Destructive Testing (NDT) devices for ‘in-situ’ inspection has become a major challenge for high-value assets. Due to the mismatching of size and volume between the existing inspection unit and the targeted complex object, inaccessibility and inapplicability have limited the applicability of NDT techniques. To address this challenge, this paper introduces a novel miniaturised active thermography system based on a commercial thermal imaging sensor featured with small size and low cost. Combining with different excitation sources, its detection performance on different types of defect of carbon fibre reinforced polymer (CFRP) is investigated and compared with an existing system. The results show that the proposed system can work with laser and flash effectively for degradation assessment although the detectability is compromised. Such a technique will play a unique role in the in-situ inspection where the space to deploy the device is limited

Automated diagnosis of pancreatic mucinous and serous cystic neoplasms with modality-fusion deep neural network using multi-modality MRIs

BackgroundPancreatic cystic neoplasms are increasingly diagnosed with the development of medical imaging technology and people’s self-care awareness. However, two of their sub-types, serous cystic neoplasms (SCN) and mucinous cystic neoplasms (MCN), are often misclassified from each other. Because SCN is primarily benign and MCN has a high rate of malignant transformation. Distinguishing SCN and MCN is challenging and essential.PurposeMRIs have many different modalities, complete with SCN and MCN diagnosis information. With the help of an artificial intelligence-based algorithm, we aimed to propose a multi-modal hybrid deep learning network that can efficiently diagnose SCN and MCN using multi-modality MRIs.MethodsA cross-modal feature fusion structure was innovatively designed, combining features of seven modalities to realize the classification of SCN and MCN. 69 Patients with multi-modalities of MRIs were included, and experiments showed performances of every modality.ResultsThe proposed method with the optimized settings outperformed all other techniques and human radiologists with high accuracy of 75.07% and an AUC of 82.77%. Besides, the proposed disentanglement method outperformed other fusion methods, and delayed contrast-enhanced T1-weighted MRIs proved most valuable in diagnosing SCN and MCN.ConclusionsThrough the use of a contemporary artificial intelligence algorithm, physicians can attain high performance in the complex challenge of diagnosing SCN and MCN, surpassing human radiologists to a significant degree