Deep LSTM with Guided Filter for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets

Simvastatin reduces atherogenesis and promotes the expression of hepatic genes associated with reverse cholesterol transport in apoE-knockout mice fed high-fat diet

<p>Abstract</p> <p>Background</p> <p>Statins are first-line pharmacotherapeutic agents for hypercholesterolemia treatment in humans. However the effects of statins on atherosclerosis in mouse models are very paradoxical. In this work, we wanted to evaluate the effects of simvastatin on serum cholesterol, atherogenesis, and the expression of several factors playing important roles in reverse cholesterol transport (RCT) in apoE-/- mice fed a high-fat diet.</p> <p>Results</p> <p>The atherosclerotic lesion formation displayed by oil red O staining positive area was reduced significantly by 35% or 47% in either aortic root section or aortic arch en face in simvastatin administrated apoE-/- mice compared to the control. Plasma analysis by enzymatic method or ELISA showed that high-density lipoprotein-cholesterol (HDL-C) and apolipoprotein A-I (apoA-I) contents were remarkably increased by treatment with simvastatin. And plasma lecithin-cholesterol acyltransferase (LCAT) activity was markedly increased by simvastatin treatment. Real-time PCR detection disclosed that the expression of several transporters involved in reverse cholesterol transport, including macrophage scavenger receptor class B type I, hepatic ATP-binding cassette (ABC) transporters ABCG5, and ABCB4 were induced by simvastatin treatment, the expression of hepatic ABCA1 and apoA-I, which play roles in the maturation of HDL-C, were also elevated in simvastatin treated groups.</p> <p>Conclusions</p> <p>We demonstrated the anti-atherogenesis effects of simvastatin in apoE-/- mice fed a high-fat diet. We confirmed here for the first time simvastatin increased the expression of hepatic ABCB4 and ABCG5, which involved in secretion of cholesterol and bile acids into the bile, besides upregulated ABCA1 and apoA-I. The elevated HDL-C level, increased LCAT activity and the stimulation of several transporters involved in RCT may all contribute to the anti-atherosclerotic effect of simvastatin.</p

Concurrent serum lead levels and cognitive function in older adults

IntroductionIn this study, we investigated the relationship between serum lead levels and cognitive functioning in a sample of older adults in the US.MethodUsing the National Health and Nutrition Examination Survey (NHANES) 2011–2013, a total of 768 older adults aged ≥60 years were included in the analysis. Lead concentrations in the whole blood samples were assessed using mass spectrometry. We used the immediate and delayed memory portions of the Consortium to Establish a Registry for Alzheimer's Disease Word Learning Subtest (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) to assess the participants' cognitive performance. Using sample averages and standard deviations (SDs), we computed test-specific and global cognition z-scores. To assess the relationships between the quartiles of serum lead levels and cognitive performance, we built multiple linear regression models and adjusted for covariates, including age, sex, race/ethnicity, education, depressive symptoms, alcohol usage, and body mass index.ResultsThe average age of the participants was 69.6 (SD 6.6) years. Approximately half of the participants were women (52.6%), non-Hispanic white (52.0%), and had completed at least some college education (51.8%). The average serum lead concentration was 1.8 g/dL (SD 1.6) for these participants. The results of multiple linear regression using individuals in the lowest serum lead quantile as a reference group revealed that the serum lead level was not associated with test-specific (CERAD-WL, AFT, and DSST) or global cognitive z-scores.ConclusionsIn older adults, concurrent serum lead concentration is not related to cognitive performance. Early or continuous lead exposure may exert a greater effect on the etiology of accelerated cognitive decline with old age

Shared decision making in sarcopenia treatment

The implementation of shared decision making (SDM) in management of sarcopenia is still in its nascent stage, especially compared to other areas of medical research. Accumulating evidence has highlighted the importance of SDM in older adults care. The current study overviews general SDM practices and explores the potential advantages and dilemmas of incorporating these concepts into sarcopenia management. We present common patient decision aids available for sarcopenia management and propose future research directions. SDM can be effectively integrated into daily practice with the aid of structured techniques, such as the “seek, help, assess, reach, evaluate” approach, “making good decisions in collaboration” questions, “benefits, risks, alternatives, doing nothing” tool, or “multifocal approach to sharing in shared decision making.” Such techniques fully consider patient values and preferences, thereby enhancing adherence to and satisfaction with the intervention measures. Additionally, we review the barriers to and potential solutions to SDM implementation. Further studies are required to investigate measurement and outcomes, coordination and cooperation, and digital technology, such as remote SDM. The study concludes that sarcopenia management must go beyond the single dimension of “Paternalism” choice. Integrating SDM into clinical practice offers promising opportunities to improve patient care, with patient-centered care and partnership of care approaches positively impacting treatment outcomes

On the Negative Effects of Trend Noise and Its Applications in Side-Channel Cryptanalysis

Abstract. Side-channel information leaked during the execution of cryptographic modules usually contains various noises. Normally, these noises have negative effects on the performance of side-channel attacks exploiting noisy leakages. Therefore, to reduce noise in leakages usually serves to be an effective approach to enhance the performance of side-channel attacks. However, most existing noise reduction methods treat all noises as a whole, instead of identifying and dealing with each of them individually. Motivated by this, this paper investigates the feasibility and implications of identifying trend noise from any other noises in side-channel acquisitions and then dealing with it accordingly. Specifically, we discuss the effectiveness of applying least square method (LSM for short) to remove inherent trend noise in side-channel leakages, and also clarify the limited capability of existing noise reduction methods in dealing with trend noise. For this purpose, we perform a series of correlation power analysis attacks, as a case of study, against a set of real power traces, published in the second stage of international DPA contest which provides a public set of original power traces without any preprocessing, from an unprotected FPGA implementation of AES encryption. The experimental results firmly confirmed the soundness and validity of our analysis and observations

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug&ndash;target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

Background. Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients. Methods. Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features. Results. CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P=0.003) and ASPM (HR: 1.8, P<0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P=0.052) was not statistically significant. CCNB2 (HR: 1.5, P=0.006), TOP2A (HR: 1.7, P<0.001), and ASPM (HR: 1.6, P=0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P<0.05). CCNB2 expression was associated with tumor stage (P=0.01), and ASPM expression was associated with new tumor events (P=0.03). Conclusion. Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC