Meta-Analysis of Effects of Sodium-Glucose Cotransporter 2 Inhibitors on Cardiovascular Outcomes and All-Cause Mortality Among Patients With Type 2 Diabetes Mellitus

The benefit or risk of sodium glucose cotransporter 2 (SGLT2) inhibitors on cardiovascular (CV) outcomes in patients with type 2 diabetes mellitus has not been established. We aimed to assess the comparative CV safety and mortality risk associated with the use of SGLT2 inhibitors. PubMed, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov were systematically searched up to January 27, 2016, to identify randomized controlled trials (RCTs) with the use of SGLT2 inhibitors of at least 24 weeks of duration. The primary outcomes included all-cause mortality and major adverse cardiovascular events. A random-effects network meta-analysis was performed to calculate the odds ratio (OR) with 95% CI. We identified 37 eligible trials involving 29,859 patients that compared 3 SGLT2 inhibitors (canagliflozin, dapagliflozin, and empagliflozin) to placebo and other active antidiabetic treatments. Of all direct and indirect comparisons, only empagliflozin compared with placebo was significantly associated with lower risk of all-cause mortality (OR 0.67, 95% CI 0.56 to 0.81) and major adverse cardiovascular events (OR 0.81, 95% CI 0.70 to 0.93). However, the significant effect of empagliflozin was largely driven by one large randomized trial (EMPA-REG OUTCOME trial). Neither dapagliflozin nor canagliflozin was significantly associated with any harm. In conclusion, current RCT evidence suggests that 3 common SGLT2 inhibitors are not associated with increased risk of all-cause mortality and CV outcomes when used to treat patients with type 2 diabetes mellitus. Although empagliflozin may have a protective effect, further confirmative data from rigorous RCTs are needed

Bayesian Knowledge-driven Critiquing with Indirect Evidence

Conversational recommender systems (CRS) enhance the expressivity and personalization of recommendations through multiple turns of user-system interaction. Critiquing is a well-known paradigm for CRS that allows users to iteratively refine recommendations by providing feedback about attributes of recommended items. While existing critiquing methodologies utilize direct attributes of items to address user requests such as 'I prefer Western movies', the opportunity of incorporating richer contextual and side information about items stored in Knowledge Graphs (KG) into the critiquing paradigm has been overlooked. Employing this substantial knowledge together with a well-established reasoning methodology paves the way for critique-based recommenders to allow for complex knowledge-based feedback (e.g., 'I like movies featuring war side effects on veterans') which may arise in natural user-system conversations. In this work, we aim to increase the flexibility of critique-based recommendation by integrating KGs and propose a novel Bayesian inference framework that enables reasoning with relational knowledge-based feedback. We study and formulate the framework considering a Gaussian likelihood and evaluate it on two well-known recommendation datasets with KGs. Our evaluations demonstrate the effectiveness of our framework in leveraging indirect KG-based feedback (i.e., preferred relational properties of items rather than preferred items themselves), often improving personalized recommendations over a one-shot recommender by more than 15%. This work enables a new paradigm for using rich knowledge content and reasoning over indirect evidence as a mechanism for critiquing interactions with CRS

Letter of Intent: Jinping Neutrino Experiment

Jinping Neutrino Experiment (Jinping) is proposed to significantly improve measurements on solar neutrinos and geoneutrinos in China Jinping Laboratory - a lab with a number of unparalleled features, thickest overburden, lowest reactor neutrino background, etc., which identify it as the world-best low-energy neutrino laboratory. The proposed experiment will have target mass of 4 kilotons of liquid scintillator or water-based liquid scintillator, with a fiducial mass of 2 kilotons for neutrino-electron scattering events and 3 kilotons for inverse-beta interaction events. A number of initial sensitivities studies have been carried out, including on the transition phase for the solar neutrinos oscillation from the vacuum to the matter effect, the discovery of solar neutrinos from the carbon-nitrogen-oxygen (CNO) cycle, the resolution of the high and low metallicity hypotheses, and the unambiguous separation on U and Th cascade decays from the dominant crustal anti-electron neutrinos in China.Comment: Proposal for the Jinping Neutrino Experimen

FALCON: Faithful Neural Semantic Entailment over ALC Ontologies

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i.e., a prototypical and expressive DL, or its extensions. The main task that explores ALC ontologies is to compute semantic entailment. We developed FALCON, a Fuzzy ALC Ontology Neural reasoner, which uses fuzzy logic operators to generate model structures for arbitrary ALC ontologies, and uses multiple model structures to compute faithful semantic entailments. Theoretical results show that FALCON faithfully approximates semantic entailment over ALC ontologies and therefore endows neural networks with world models and the ability to reason over them. Experimental results show that FALCON enables approximate reasoning, paraconsistent reasoning (reasoning with inconsistencies), and improves machine learning in the biomedical domain by incorporating knowledge expressed in ALC

SRDA-Net: Super-Resolution Domain Adaptation Networks for Semantic Segmentation

Recently, Unsupervised Domain Adaptation was proposed to address the domain shift problem in semantic segmentation task, but it may perform poor when source and target domains belong to different resolutions. In this work, we design a novel end-to-end semantic segmentation network, Super-Resolution Domain Adaptation Network (SRDA-Net), which could simultaneously complete super-resolution and domain adaptation. Such characteristics exactly meet the requirement of semantic segmentation for remote sensing images which usually involve various resolutions. Generally, SRDA-Net includes three deep neural networks: a Super-Resolution and Segmentation (SRS) model focuses on recovering high-resolution image and predicting segmentation map; a pixel-level domain classifier (PDC) tries to distinguish the images from which domains; and output-space domain classifier (ODC) discriminates pixel label distributions from which domains. PDC and ODC are considered as the discriminators, and SRS is treated as the generator. By the adversarial learning, SRS tries to align the source with target domains on pixel-level visual appearance and output-space. Experiments are conducted on the two remote sensing datasets with different resolutions. SRDA-Net performs favorably against the state-of-the-art methods in terms of accuracy and visual quality. Code and models are available at https://github.com/tangzhenjie/SRDA-Net

Intelligent Exploration for User Interface Modules of Mobile App with Collective Learning

A mobile app interface usually consists of a set of user interface modules. How to properly design these user interface modules is vital to achieving user satisfaction for a mobile app. However, there are few methods to determine design variables for user interface modules except for relying on the judgment of designers. Usually, a laborious post-processing step is necessary to verify the key change of each design variable. Therefore, there is a only very limited amount of design solutions that can be tested. It is timeconsuming and almost impossible to figure out the best design solutions as there are many modules. To this end, we introduce FEELER, a framework to fast and intelligently explore design solutions of user interface modules with a collective machine learning approach. FEELER can help designers quantitatively measure the preference score of different design solutions, aiming to facilitate the designers to conveniently and quickly adjust user interface module. We conducted extensive experimental evaluations on two real-life datasets to demonstrate its applicability in real-life cases of user interface module design in the Baidu App, which is one of the most popular mobile apps in China.Comment: 10 pages, accepted as a full paper in KDD 202

Hmgb1-IL-23-IL-17-IL-6-Stat3 Axis Promotes Tumor Growth in Murine Models of Melanoma

In order to understand how tumor cells can escape immune surveillance mechanisms and thus develop antitumor therapies, it is critically important to investigate the mechanisms by which the immune system interacts with the tumor microenvironment. In our current study, IL-17 deficiency results in reduced melanoma tumor size, diminished numbers of proliferating cells and blood vessels, and decreased percentage of CD11b(+)Gr-1(+) MDSCs in tumor tissues. IL-17 promotes IL-6 induction and Stat3 activation. Treatment of Stat3 inhibitor WP1066 in B16-F10 tumor cells inoculated wild-type mice inhibits tumor growth. Additional administration of recombinant IL-6 into B16-F10 tumor-bearing IL-17(−/−) mice results in markedly increased tumor size and p-Stat3 expression, whereas additional recombinant IL-17 administration into B16-F10 tumor-bearing wild-type mice treated with anti-IL-6 mAb does not significantly alter the tumor growth and p-Stat3 expression. In our further study, blockade of Hmgb1-RAGE pathway inhibits melanoma tumor growth and reduces production of IL-23 and IL-17. All these data suggest that Hmgb1-IL-23-IL-17-IL-6-Stat3 axis plays a pivotal role in tumor development in murine models of melanoma, and blocking any portion of this axis will attenuate melanoma tumor growth

Exploring Query Understanding for Amazon Product Search

Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search

Effect of titanium carbide particles in electrolyte on the properties of microarc oxidation layer on tc4 alloy

With the use of titanium carbide (TiC) particles as additives in the electrolyte of the phosphate-silicate system, Microarc Oxidation (MAO) layers were prepared on TC4 alloy. The formation process, phase, morphology, and microhardness of the MAO layers were analyzed, and the friction performance of different layers was tested under dry friction by using stainless steel ball as a counter-body. The results showed that TiC particles participated in the MAO layer formation process and increased the thickness of the layer. The MAO layer prepared in the base electrolyte was mainly composed of rutile and anatase TiO2, and the addition of TiC led to the appearance of the TiC phase in the MAO layer, increasing the compactness of the MAO layer and reducing the number of holes. The MAO layer obtained in the electrolyte with 6 g/L TiC achieved the best corrosion resistance, 1.4 times that of the layer formed in the base electrolyte. When the TiC content is 9 g/L, the average hardness of the MAO layer is 690 HV, which is 65% higher than that of the base oxide layer; the wear volume is 0.81 mm3, and the anti-wear resistance is 1.60 times higher than that of the base MAO layer

Research progress on S-palmitoylation modification mediated by the ZDHHC family in glioblastoma

S-Palmitoylation has been widely noticed and studied in a variety of diseases. Increasing evidence suggests that S-palmitoylation modification also plays a key role in Glioblastoma (GBM). The zDHHC family, as an important member of S-palmitoyltransferases, has received extensive attention for its function and mechanism in GBM which is one of the most common primary malignant tumors of the brain and has an adverse prognosis. This review focuses on the zDHHC family, essential S-palmitoyltransferases, and their involvement in GBM. By summarizing recent studies on zDHHC molecules in GBM, we highlight their significance in regulating critical processes such as cell proliferation, invasion, and apoptosis. Specifically, members of zDHHC3, zDHHC4, zDHHC5 and others affect key processes such as signal transduction and phenotypic transformation in GBM cells through different pathways, which in turn influence tumorigenesis and progression. This review systematically outlines the mechanism of zDHHC family-mediated S-palmitoylation modification in GBM, emphasizes its importance in the development of this disease, and provides potential targets and strategies for the treatment of GBM. It also offers theoretical foundations and insights for future research and clinical applications