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Qi Shen1, Shuangwei Zou1, Bo Sheng1, Menghuang Zhao1, Lu-Zhe Sun2, Xueqiong Zhu1 1Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, People’s Republic of China; 2Department of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USACorrespondence: Lu-Zhe SunDepartment of Cell Systems and Anatomy, University of Texas Health Science Center at San Antonio, San Antonio, TX, USAEmail sunl@uthscsa.eduXueqiong ZhuDepartment of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, No. 109 Xueyuan Xi Road, Wenzhou, Zhejiang 325027, People’s Republic of ChinaTel +86 5 778 800 2796Fax +86 5 778 800 2560Email zjwzzxq@163.comPurpose: To investigate the role of IGF-1 signaling pathway in the treatment of uterine leiomyomas with mifepristone.Patients and methods: From October 2015 to December 2018, 50 patients with uterine leiomyoma were included in this study. Overexpression or siRNA of IGF-1 in primary human uterine leiomyoma cells were treated with or without mifepristone. MTT was used to evaluate cell viability in assays of cell proliferation and cytotoxicity. IGF-1 expression in the cells was measured with real-time RT-PCR and Western blotting and manipulated with lentivirus ectopic overexpression or siRNA silencing.Results: Inhibition of cell viability by mifepristone was found dependent on drug concentration and treatment time. IGF-1 and phosphorylation-ERK1/2 expression were decreased, while phosphorylation-AKT expression was increased after mifepristone treatment. IGF-1 significantly promoted cell growth, while IGF-1 knockdown and mifepristone showed synergistic inhibition effects on cell growth. The overexpression of IGF-1 reversed the inhibition of cell growth and ERK1/2 phosphorylation but showed no effect on AKT phosphorylation.Conclusion: Our study for the first time demonstrated that IGF-1 signaling via E

U radu je na temelju slobodne površinske energije, ocijenjena osjetljivost asfalta s aluminijevim trihidratom na vlagu. Analizirana su fizikalna svojstva i zapaljivost uzoraka s dodatkom aluminijeva trihidrata. Za potrebe istraživanja izrađeni su uzorci asfalta modificiranog stiren-butadien-stirenom s različitim udjelima ATH-a (0-14 %). Na uzorcima s dodatkom ATH-a analizirano je povećanje viskoznosti, točka razmekšanja, otpornost bitumena prema kolotraženju, granični indeks kisika i indeks penetracije. Iz aspekta mikromehanizama, ATH u velikoj mjeri nepovoljno utječe na oštećenje asfaltne mješavine uslijed djelovanja vlage., The moisture susceptibility of asphalt containing alumina trihydrate (ATH) was assessed through the surface free energy. Physical properties and flame retardancy of ATH samples were also investigated. For research purposes SBS modified asphalt samples with different dosages (0-14 %) of ATH were prepared. Increased viscosity, softening point limiting oxygen index and lower penetration ductility, were investigated for the ATH modified samples. ATH has a significant negative effect on the moisture-induced damage potential of asphalt mixture from the view of micromechanisms.

In the era of big data and cloud computing, large amounts of data are generated from user applications and need to be processed in the datacenter. Data-parallel computing frameworks, such as Apache Spark, are widely used to perform such data processing at scale. Specifically, Spark leverages distributed memory to cache the intermediate results, represented as Resilient Distributed Datasets (RDDs). This gives Spark an advantage over other parallel frameworks for implementations of iterative machine learning and data mining algorithms, by avoiding repeated computation or hard disk accesses to retrieve RDDs. By default, caching decisions are left at the programmer's discretion, and the LRU policy is used for evicting RDDs when the cache is full. However, when the objective is to minimize total work, LRU is woefully inadequate, leading to arbitrarily suboptimal caching decisions. In this paper, we design an algorithm for multi-stage big data processing platforms to adaptively determine and cache the most valuable intermediate datasets that can be reused in the future. Our solution automates the decision of which RDDs to cache: this amounts to identifying nodes in a direct acyclic graph (DAG) representing computations whose outputs should persist in the memory. Our experiment results show that our proposed cache optimization solution can improve the performance of machine learning applications on Spark decreasing the total work to recompute RDDs by 12%.

Yizuo Song, Yi Liu, Min Lin, Bo Sheng, Xueqiong Zhu Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325027, China Purpose: To evaluate the efficacy and safety of neoadjuvant platinum-based chemotherapy during pregnancy in women with cervical cancer. Methods: The PubMed, Embase, and Cochrane Library databases were fully searched to find eligible studies regarding platinum use during pregnancy in women with cervical cancer from January 1980 to September 2018. Data were extracted from the selected studies independently by two authors. Descriptive statistics were calculated for categorical data (frequency and percentage) and numeration data (mean and SD for normally distributed data and median and range for abnormally distributed data). Survival analyses were performed using Kaplan–Meier survival curves and log-rank tests to estimate overall survival and progression-free survival for all patients. Results: A total of 39 studies including 88 cervical cancer patients with platinum administration during pregnancy were selected in this meta-analysis, and 64 women provided International Federation of Gynecology and Obstetrics stage information. Among the latter, 56 of 64 (87.5%) were diagnosed with early stages (I and IIA) and the remaining 8 of 64 (12.5%) had advanced stages (IIB, III, and IV). In relation to cisplatin, 86 pregnant women were identified, whereas only 2 pregnant women with carboplatin application were retrieved. Overall, 88 newborns were delivered from 84 pregnancies, including two sets of twins and one set of triplets, among which 71 neonates (71 of 88, 80.7%) were completely healthy at birth. All children were healthy at the end of follow-up (median 17 months, range 0–149.5 months), except one who was diagnosed with retroperitoneal embryonal rhabdomyosarcoma at 5 years old and one who had acute myeloid leukemia at 22 months of age. At the end of follow-up (range 4.75&nda

Offloading resource-intensive jobs to the cloud and nearby users is a promising approach to enhance mobile devices. This paper investigates a hybrid offloading system that takes both infrastructure-based networks and Ad-hoc networks into the scope. Specifically, we propose EDOS, an edge assisted offloading system that consists of two major components, an Edge Assistant (EA) and Offload Agent (OA). EA runs on the routers/towers to manage registered remote cloud servers and local service providers and OA operates on the users' devices to discover the services in proximity. We present the system with a suite of protocols to collect the potential service providers and algorithms to allocate tasks according to user-specified constraints. To evaluate EDOS, we prototype it on commercial mobile devices and evaluate it with both experiments on a small-scale testbed and simulations. The results show that EDOS is effective and efficient for offloading jobs., Comment: The 26th International Conference on Computer Communications and Networks (ICCCN 2017)

We introduce a game-theoretic framework to ex- plore revenue sharing in an Edge-Cloud computing system, in which computing service providers at the edge of the Internet (edge providers) and computing service providers at the cloud (cloud providers) co-exist and collectively provide computing resources to clients (e.g., end users or applications) at the edge. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. To achieve high system-level efficiency, the manager of the system adopts a task distribution mechanism to maximize the total revenue received from clients and also adopts a revenue sharing mechanism to split the received revenue among computing servers (and hence service providers). Under those system-level mechanisms, service providers attempt to game with the system in order to maximize their own utilities, by strategically allocating their resources (e.g., computing servers). Our framework models the competition among the providers in an Edge-Cloud system as a non-cooperative game. Our simulations and experiments on an emulation system have shown the existence of Nash equilibrium in such a game. We find that revenue sharing mechanisms have a significant impact on the system-level efficiency at Nash equilibria, and surprisingly the revenue sharing mechanism based directly on actual contributions can result in significantly worse system efficiency than Shapley value sharing mechanism and Ortmann proportional sharing mechanism. Our framework provides an effective economics approach to understanding and designing efficient Edge-Cloud computing systems.