A Rotation Meanout Network with Invariance for Dermoscopy Image Classification and Retrieval

The computer-aided diagnosis (CAD) system can provide a reference basis for the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs) can not only extract visual elements such as colors and shapes but also semantic features. As such they have made great improvements in many tasks of dermoscopy images. The imaging of dermoscopy has no principal orientation, indicating that there are a large number of skin lesion rotations in the datasets. However, CNNs lack rotation invariance, which is bound to affect the robustness of CNNs against rotations. To tackle this issue, we propose a rotation meanout (RM) network to extract rotation-invariant features from dermoscopy images. In RM, each set of rotated feature maps corresponds to a set of outputs of the weight-sharing convolutions and they are fused using meanout strategy to obtain the final feature maps. Through theoretical derivation, the proposed RM network is rotation-equivariant and can extract rotation-invariant features when followed by the global average pooling (GAP) operation. The extracted rotation-invariant features can better represent the original data in classification and retrieval tasks for dermoscopy images. The RM is a general operation, which does not change the network structure or increase any parameter, and can be flexibly embedded in any part of CNNs. Extensive experiments are conducted on a dermoscopy image dataset. The results show our method outperforms other anti-rotation methods and achieves great improvements in dermoscopy image classification and retrieval tasks, indicating the potential of rotation invariance in the field of dermoscopy images

Effects of Dioscorea polystachya \u27yam gruel\u27 on the cognitive function of diabetic rats with focal cerebral ischemia-reperfusion injury via the gut-brain axis

© 2020 Pang et al. Published by IMR press. Focal cerebral ischemia-reperfusion injury is closely related to hyperglycemia and gut microbiota imbalance, while gut microbiota contributes to the regulation of brain function through the gut-brain axis. Previous studies in patients with diabetes have found that \u27yam gruel\u27 is a classic medicated diet made from Dioscorea polystachya, increases the content of Bifidobacterium, regulates oxidative stress, and reduces fasting blood glucose levels. The research reported here investigated the effects of \u27yam gruel\u27 on the cognitive function of streptozotocin-induced diabetic rats with focal cerebral ischemia-reperfusion injury and explored the mechanism underlying the role of the gut-brain axis in this process. \u27Yam gruel\u27 was shown to improve cognitive function as indicated by increased relative content of probiotic bacteria, and short-chain fatty acids in the intestinal tract and cerebral cortex reduced oxidative stress and inflammatory response and promotion of the expression of neurotransmitters and brain-derived neurotrophic factor. Thus, it is concluded that \u27yam gruel\u27 has a protective effect on cognitive function via a mechanism related to the gut-brain axis

Endogenous SIRT6 in platelets negatively regulates platelet activation and thrombosis

Thromboembolism resulting from platelet dysfunction constitutes a significant contributor to the development of cardiovascular disease. Sirtuin 6 (SIRT6), an essential NAD+-dependent enzyme, has been linked to arterial thrombosis when absent in endothelial cells. In the present study, we have confirmed the presence of SIRT6 protein in anucleated platelets. However, the precise regulatory role of platelet endogenous SIRT6 in platelet activation and thrombotic processes has remained uncertain. Herein, we present compelling evidence demonstrating that platelets isolated from SIRT6-knockout mice (SIRT6−/−) exhibit a notable augmentation in thrombin-induced platelet activation, aggregation, and clot retraction. In contrast, activation of SIRT6 through specific agonist treatment (UBCS039) confers a pronounced protective effect on platelet activation and arterial thrombosis. Moreover, in platelet adoptive transfer experiments between wild-type (WT) and SIRT6−/− mice, the loss of SIRT6 in platelets significantly prolongs the mean thrombus occlusion time in a FeCl3-induced arterial thrombosis mouse model. Mechanistically, we have identified that SIRT6 deficiency in platelets leads to the enhanced expression and release of proprotein convertase subtilisin/kexin type 9 (PCSK9), subsequently activating the platelet activation-associated mitogen-activated protein kinase (MAPK) signaling pathway. These findings collectively unveil a novel protective role of platelet endogenous SIRT6 in platelet activation and thrombosis. This protective effect is, at least in part, attributed to the inhibition of platelet PCSK9 secretion and mitogen-activated protein kinase signaling transduction. Our study provides valuable insights into the intricate interplay between SIRT6 and platelet function, shedding light on potential therapeutic avenues for managing thrombotic disorders

Amniocytes can serve a dual function as a source of iPS cells and feeder layers

Clinical barriers to stem-cell therapy include the need for efficient derivation of histocompatible stem cells and the zoonotic risk inherent to human stem-cell xenoculture on mouse feeder cells. We describe a system for efficiently deriving induced pluripotent stem (iPS) cells from human and mouse amniocytes, and for maintaining the pluripotency of these iPS cells on mitotically inactivated feeder layers prepared from the same amniocytes. Both cellular components of this system are thus autologous to a single donor. Moreover, the use of human feeder cells reduces the risk of zoonosis. Generation of iPS cells using retroviral vectors from short- or long-term cultured human and mouse amniocytes using four factors, or two factors in mouse, occurs in 5–7 days with 0.5% efficiency. This efficiency is greater than that reported for mouse and human fibroblasts using similar viral infection approaches, and does not appear to result from selective reprogramming of Oct4+ or c-Kit+ amniocyte subpopulations. Derivation of amniocyte-derived iPS (AdiPS) cell colonies, which express pluripotency markers and exhibit appropriate microarray expression and DNA methylation properties, was facilitated by live immunostaining. AdiPS cells also generate embryoid bodies in vitro and teratomas in vivo. Furthermore, mouse and human amniocytes can serve as feeder layers for iPS cells and for mouse and human embryonic stem (ES) cells. Thus, human amniocytes provide an efficient source of autologous iPS cells and, as feeder cells, can also maintain iPS and ES cell pluripotency without the safety concerns associated with xenoculture

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050

© 2016 The Author(s). Background: Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Methods: Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Results: Average malaria incidence was 0.107 per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R2 = 0.825) and 17.102 % for test data (R2 = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. Conclusions: The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas

Basic Statistical Analysis and Modelling ofEvaluation Data for Teaching

This thesis proposes a novel numerical scoring system, which efficiently evaluates the teaching effectiveness of the lecturers. Based upon the scores given in the student evaluation of teaching (SET), this numerical scoring system employes the factor score of one-factor model of data and yields the instructor rankings result as output. The other purpose of this paper is to discover determinants of SET scores, especially to examine whether factors which are normatively irrelevant to teaching quality matter or not. Results indicate that communication skill of lecturer & students’ reaction, course attributes and quality of lecture notes are three most significant factors which determines the student response to ”general overall ratings” of the course. The study suggests that class size and class meeting time also have some influence on that

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are not available, the errors of high-precision INS also disperse rapidly, similar to MEMS-INS when GNSS signals would be unavailable for a long time, leading to a serious degradation of the navigation accuracy. This paper presents a new AI-assisted method for the integrated high-precision INS/GNSS navigation system. The position increments during GNSS outage are predicted by the convolutional neural network-gated recurrent unit (CNN-GRU). In the process, the CNN is utilized to quickly extract the multi-dimensional sequence features, and GRU is used to model the time series. In addition, a new real-time training strategy is proposed for practical application scenarios, where the duration of the GNSS outage time and the motion state information of the vehicle are taken into account in the training strategy. The real road test results verify that the proposed algorithm has the advantages of high prediction accuracy and high training efficiency

A Novel Method for AI-Assisted INS/GNSS Navigation System Based on CNN-GRU and CKF during GNSS Outage

In the fields of positioning and navigation, the integrated inertial navigation system (INS)/global navigation satellite systems (GNSS) are frequently employed. Currently, high-precision INS typically utilizes fiber optic gyroscopes (FOGs) and quartz flexural accelerometers (QFAs) rather than MEMS sensors. But when GNSS signals are not available, the errors of high-precision INS also disperse rapidly, similar to MEMS-INS when GNSS signals would be unavailable for a long time, leading to a serious degradation of the navigation accuracy. This paper presents a new AI-assisted method for the integrated high-precision INS/GNSS navigation system. The position increments during GNSS outage are predicted by the convolutional neural network-gated recurrent unit (CNN-GRU). In the process, the CNN is utilized to quickly extract the multi-dimensional sequence features, and GRU is used to model the time series. In addition, a new real-time training strategy is proposed for practical application scenarios, where the duration of the GNSS outage time and the motion state information of the vehicle are taken into account in the training strategy. The real road test results verify that the proposed algorithm has the advantages of high prediction accuracy and high training efficiency