Guava Leaf Extracts Inhibit 3T3-L1 Adipocyte Differentiation Via Activating AMPK

The guava tree (psidium guajava linn.) is commonly used not only as food but also as folk medicine. In our previous studies, we showed that oral administration of guava leaf extracts (GLE) had beneficial anti-obesity effects using metabolic syndrome model rats. However, we did not clarify molecular mechanism by which GLE administration leads to anti-obesity effect. This study was designed to evaluate the mechanism of anti-obesity by GLE using 3T3-L1 pre-adipocyte cell lines. We found that GLE significantly inhibited 3T3-L1 differentiation via down-regulation of adipogenic transcription factors and markers. Mitotic clonal expansion, which is essential for adipose differentiation, was also depressed in the early phase. Interestingly, GLE increased the phosphorylation of AMPK on 3T3-L1 cells and, by pretreatment with AMPK siRNA, the GLE treatment group showed restored adipocyte differentiation. In conclusion, these results showed that GLE is capable of inhibiting adipocyte differentiation via AMPK activation and therefore it may prevent obesity in vivo

Extracting hydrothermally altered information using WorldView-3 data: a case study of Huitongshan, NW Gansu, China

Introduction: The Huitongshan skarn-type deposits, in which ore bodies primarily occur in the outer contact zone between K-feldspar granite and marble in the Beishan area, are evidently related to hydrothermal alteration of the surrounding rock. Key mineral alteration processes include serpentinization, epidotization, chloritization, carbonatization, jarosite, ferritization, and hematitization.Methods: WorldView-3 (WV-3), a satellite-recorded high-spatial resolution multispectral image, has been widely used in the exploration and prediction of different types of deposits around the world. In this study, WV-3 multispectral images were used to extract the spatial distribution data of the main altered minerals in the Huitongshan area. Dedicated radiometric calibration, atmospheric correction, and image fusion were used to pre-process the extracted spectral information related to hydrothermal alteration. In addition, directed principal component analysis (PCA) and a unique mineral index were designed based on the effective use of the WV-3 data band corresponding to the spectral absorption characteristics of altered minerals.Results: The findings of this study show that the PCA model and mineral index pro-posed herein are reliable both in theory and for practically obtaining extraction information. Additionally, the WV-3 data are well suited for identifying hydroxy-bearing alterations with rich short-wave infrared bands that distinguish Fe-OH–bearing alterations from Mg-OH–bearing alterations. The results obtained were applied to identify potential targets for skarn-type copper deposits and the implementation of prospecting practices.Discussion: This study provides a basis for the application of WV-3 data as an important and effective tool for alteration information extraction and determination of prospecting practice, thereby proving the validity of multispectral remote sensing images in mineral resource exploration

To What Extent is Internet Activity Predictive of Psychological Well-Being?

Background: Healthy internet activity (eg, making use of eHealth and online therapy) is positively associated with well-being. However, unhealthy internet activity (too much online time, problematic internet use/PIU, internet dependency/ID, etc.) is associated with reduced well-being, loneliness, and other related negative aspects. While most of the evidence is correlational, some research also shows that internet activity can be predictive for well-being. Objective: The aim of this article is to elaborate on the question as to what extent internet activity is predictive of psychological well-being by means of (a) a scoping review and (b) theoretical understanding which model the interrelation of internet activity and psychological well-being. Methodology: We searched different electronic databases such as Web of Science by using the search terms "Internet" OR "App" OR "digital" OR "online" OR "mobile application" AND "Use" OR "Activity" OR "Behavior" OR "Engagement" AND "Well-being" OR "Loneliness" for (a, the scoping review) or CCAM for (b, the theoretical understanding). Results: The scoping review (a) summarizes recent findings: the extent to which internet activity is predictive for well-being depends on the internet activity itself: internet activity facilitating self-management is beneficial for well-being but too much internet activity, PIU and ID are detrimental to well-being. To understand (b) why, when and how internet activity is predictive for well-being, theoretical understanding and a model are required. While theories on either well-being or internet activity exist, not many theories take both aspects into account while also considering other behaviors. One such theory is the Compensatory Carry-Over Action Model (CCAM) which describes mechanisms on how internet use is related to other lifestyle behaviors and well-being, and that individuals are driven by the goal to adopt and maintain well-being - also called higher-level goals - in the CCAM. There are few studies testing the CCAM or selected aspects of it which include internet activity and well-being. Results demonstrate the potentials of such a multifactorial, sophisticated approach: it can help to improve health promotion in times of demographic change and in situations of lacking personnel resources in health care systems. Conclusion and recommendation: Suggestions for future research are to employ theoretical approaches like the CCAM and testing intervention effects, as well as supporting individuals in different settings. The main aim should be to perform healthy internet activities to support well-being, and to prevent unhealthy internet activity. Behavior management and learning should accordingly aim at preventing problematic internet use and internet dependency

A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworks

Federated learning (FL) is a distributed machine learning (ML) paradigm, allowing multiple clients to collaboratively train shared machine learning (ML) models without exposing clients' data privacy. It has gained substantial popularity in recent years, especially since the enforcement of data protection laws and regulations in many countries. To foster the application of FL, a variety of FL frameworks have been proposed, allowing non-experts to easily train ML models. As a result, understanding bugs in FL frameworks is critical for facilitating the development of better FL frameworks and potentially encouraging the development of bug detection, localization and repair tools. Thus, we conduct the first empirical study to comprehensively collect, taxonomize, and characterize bugs in FL frameworks. Specifically, we manually collect and classify 1,119 bugs from all the 676 closed issues and 514 merged pull requests in 17 popular and representative open-source FL frameworks on GitHub. We propose a classification of those bugs into 12 bug symptoms, 12 root causes, and 18 fix patterns. We also study their correlations and distributions on 23 functionalities. We identify nine major findings from our study, discuss their implications and future research directions based on our findings

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202

An Approximate Proximal Point Algorithm for Maximal Monotone Inclusion Problems

This paper presents and analyzes a strongly convergent approximate proximal point algorithm for finding zeros of maximal monotone operators in Hilbert spaces. The proposed method combines the proximal subproblem with a more general correction step which takes advantage of more information on the existing iterations. As applications, convex programming problems and generalized variational inequalities are considered. Some preliminary computational results are reported

Global Stability of Positive Periodic Solutions and Almost Periodic Solutions for a Discrete Competitive System

A discrete two-species competitive model is investigated. By using some preliminary lemmas and constructing a Lyapunov function, the existence and uniformly asymptotic stability of positive almost periodic solutions of the system are derived. In addition, an example and numerical simulations are presented to illustrate and substantiate the results of this paper