Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.
The traditional Chinese herbal medicine Sho-saiko-to is a mixture of seven herbal preparations that has long been used in the treatment of chronic liver disease. Various clinical trials have shown that Sho-saiko-to protects against the development of hepatocellular carcinoma in cirrhotic patients. However, the mechanism by which Sho-saiko-to protects hepatocytes against hepatic fibrosis and carcinoma is not yet known. Basic science studies have demonstrated that Sho-saiko-to reduces hepatocyte necrosis and enhances liver function. Sho-saiko-to significantly inhibits hepatic fibrosis by inhibiting the activation of stellate cells, the major producers of collagen in the liver, as well as by inhibiting hepatic lipid peroxidation, promoting matrix degradation, and suppressing extracellular matrix (ECM) accumulation. Furthermore, clinical trials have shown that Sho-saiko-to lowers the rate of hepatocellular carcinoma (HCC) development in patients with cirrhosis and increases the survival of patients with HCC. Unfortunately, some case reports have shown the side effects of Sho-saiko-to. Most of the side effects were interstitial pneumonia and acute respiratory failure induced by Sho-saiko-to in Japan. As a result of analyzing these case reports, the incidence and risk are increased by co-administration of interferon, duration of medication, and, high in an elderly population. This review discusses the properties of Sho-saiko-to with regards to the treatment of chronic liver diseases and suggests the side effects of Sho-saiko-to.
In this paper we present a general notion of Fisher's linear discriminant analysis that extends the classical multivariate concept to situations that allow for function-valued random elements. The development uses a bijective mapping that connects a second order process to the reproducing kernel Hilbert space generated by its within class covariance kernel. This approach provides a seamless transition between Fisher's original development and infinite dimensional settings that lends itself well to computation via smoothing and regularization. Simulation results and real data examples are provided to illustrate the methodology.
Background
Crataegus pinnatifida (Chinese hawthorn) has long been used as a herbal medicine in Asia and Europe. It has been used for the treatment of various cardiovascular diseases such as myocardial weakness, tachycardia, hypertension and arteriosclerosis. In this study, we investigated the anti-inflammatory effects of Crataegus pinnatifida ethanolic extracts (CPEE) on Th2-type cytokines, eosinophil infiltration, expression of matrix metalloproteinase (MMP)-9, and other factors, using an ovalbumin (OVA)-induced murine asthma model.Methods/Principal FindingAirways of OVA-sensitized mice exposed to OVA challenge developed eosinophilia, mucus hypersecretion and increased cytokine levels. CPEE was applied 1 h prior to OVA challenge. Mice were administered CPEE orally at doses of 100 and 200 mg/kg once daily on days 18–23. Bronchoalveolar lavage fluid (BALF) was collected 48 h after the final OVA challenge. Levels of interleukin (IL)-4 and IL-5 in BALF were measured using enzyme-linked immunosorbent (ELISA) assays. Lung tissue sections 4 µm in thickness were stained with Mayer’s hematoxylin and eosin for assessment of cell infiltration and mucus production with PAS staining, in conjunction with ELISA, and Western blot analyses for the expression of MMP-9, intercellular adhesion molecule (ICAM)-1 and vascular cell adhesion molecule (VCAM)-1 protein expression. CPEE significantly decreased the Th2 cytokines including IL-4 and IL-5 levels, reduced the number of inflammatory cells in BALF and airway hyperresponsiveness, suppressed the infiltration of eosinophil-rich inflammatory cells and mucus hypersecretion and reduced the expression of ICAM-1, VCAM-1 and MMP-9 and the activity of MMP-9 in lung tissue of OVA-challenged mice.ConclusionsThese results showed that CPEE can protect against allergic airway inflammation and can act as an MMP-9 modulator to induce a reduction in ICAM-1 and VCAM-1 expression. In conclusion, we strongly suggest the feasibility of CPEE as a therapeutic drug for allergic asthma.
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