Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming Drug Resistance and Treating Advanced Prostate Cancer

Most of the prostate cancers (PCa) in advanced stage will progress to castration-resistant prostate cancer (CRPC). Within CRPC group, 50-70% of the patients will develop bone metastasis in axial and other regions of the skeleton. Once PCa cells spread to the bone, currently, no treatment regimens are available to eradicate the metastasis, and cancer-related death becomes inevitable. In 2012, it is estimated that there will be 28,170 PCa deaths in the United States. Thus, PCa bone metastasis-associated clinical complications and treatment resistance pose major clinical challenges. In this review, we will present recent findings on the molecular and cellular pathways that are responsible for bone metastasis of PCa. We will address several novel mechanisms with a focus on the role of bone and bone marrow microenvironment in promoting PCa metastasis, and will further discuss why prostate cancer cells preferentially metastasize to the bone. Additionally, we will discuss novel roles of several key pathways, including angiogenesis and extracellular matrix remodeling in bone marrow and stem cell niches with their relationship to PCa bone metastasis and poor treatment response. We will evaluate how various chemotherapeutic drugs and radiation therapies may allow aggressive PCa cells to gain advantageous mutations leading to increased survival and rendering the cancer cells to become resistant to treatment. The novel concept relating several key survival and invasion signaling pathways to stem cell niches and treatment resistance will be reviewed. Lastly, we will provide an update of several recently developed novel drug candidates that target metastatic cancer microenvironments or niches, and discuss the advantages and significance provided by such therapeutic approaches in pursuit of overcoming drug resistance and treating advanced PCa.
How to write short cases for assessing problem-solving skills.

In assessment of problem solving the use of short case-based testing is a promising development. In this approach an examination consists of large numbers of short cases each of which contain a small number of questions. These questions are aimed at essential decisions.Writing such cases, however, is not easy. In this article a description of this type of examination is provided.Also strategies and pitfalls are described in writing these cases. These strategies pertain to the selection of essential decisions, the careful writing of cases and questions and the selection of question formats.
Beyond the WiFi: Introducing RFID system using IPv6

RFID System suffers from limited address space and local mobility. Moreover, it is a monopoly business with few vendors, which are trying to dominate the market with proprietary standard of RFID reader. The proposed system will replace the expensive RFID reader with cheap Wireless Network Interface Card (WNIC). For the purpose, an innovative scheme for RFID tagging system is introduced which will be benefited by well-defined WiFi protocol. IPv6 (Internet Protocol version 6) address will be used as product identifier. This will provide a universal identity of the objects with seamless global mobility. The EPC (Electronic Product Code) will directly map to IPv6 address by using an auto configuration method. So that 64 bit EPC will take the place of the EUI-64 portion of IPv6 address. The proposed system suggests the mechanism of reducing significant cost, physical location detection and usage of global unique address, which will also be compatible with existing EPC addressing scheme.
Changing associations of episiotomy and anal sphincter injury across risk strata: results of a population-based register study in Finland 2004–2011

Objectives To evaluate the changing association between lateral episiotomy and obstetric anal sphincter injury (OASIS) for women with low and high baseline risk of OASIS. Design A population-based register study. Setting Data gathered from the Finnish Medical Birth Register for the years 2004−2011. Participants All women with spontaneous vaginal or vacuum-assisted singleton births in Finland (n=384 638). Main outcome measure OASIS incidence. Results During the study period, the incidence of OASIS increased from 1.3% to 1.7% in women with first vaginal births, including women admitted for first vaginal birth after a prior caesarean section and from 0.1% to 0.3% in women with at least one prior birth, whereas episiotomy rates declined from 56.7% to 45.5% and 10.1– 5.3%, respectively. At the study onset, when episiotomy was used more widely, it was negatively associated with OASIS in women with first vaginal births, but as episiotomy use declined it became positively associated with OASIS. Women with episiotomy were complicated by OASIS with clearly higher risk scores than women without episiotomy suggesting that episiotomy was clearly protective against OASIS. OASIS occurred with lower mean risk scores among women with and without episiotomy over time. However, OASIS incidences increased only among women with episiotomy, whereas it decreased or remained among women without episiotomy. Conclusions The cross-over effect between episiotomy and OASIS could be explained by increasing disparity in baseline OASIS risk between treated and untreated women, since episiotomy use declined most in women at low OASIS risk. Episiotomy rate can be safely reduced in low-risk women but interestingly along with the policy change the practice to cut the episiotomy became less protective among high-risk women.
DESIGN AND IMPLEMENTATION OF WSRF-COMPLIANT GRID SERVICES FOR MINING FUZZY ASSOCIATION RULES

Data mining is a widely used approach for the transformation of large amounts of data to useful patterns and knowledge. Fuzzy association rules mining is a data mining technique which tries to nd association rules without the e ect of sharp boundary problems when data contains continuous and categorical attributes. Grid data mining is a new concept, which allows the data mining process to be deployed and used in a data grid environment where data and service resources are geographically distributed. In this paper, a grid service for mining fuzzy association rules is developed. The service is implemented based on recently proposed Data Mining Grid Architecture (DMGA) and uses the Web Service Resource Framework (WSRF). Experimental evaluations, after implementing and deploying the service, show the e ectiveness and acceptable performance of the proposed grid service. Additionally, in this study, a new algorithm, namely FFDM, is developed to mine fuzzy association rules without raw data exchange, using the distributed storage of data grid environments. Empirical evaluation of FFDM reveals the scalability and eciency of the proposed method, in addition to the advantages of minimum messaging and providing privacy of data.
Rewriting-based Check of Chase Termination

The Chase is a fixpoint algorithm enforcing satisfaction of data dependencies (also called constraints) in databases. It has been proposed more than thirty years ago [2,18] and has seen a revival of interest in recent years in both database theory and practical applications. Indeed, the availability of data coming from different sources easily results in inconsistent or incomplete data (i.e., data not satisfying data dependencies) and, therefore, techniques for fixing inconsistencies are crucial [1,3,5,7,8,13,17]. The chase algorithm is used, directly or indirectly, on an everyday basis by people who design databases, and it is used in commercial systems to reason about the consistency and correctness of a data design. New applications of the chase in meta-data management, ontological reasoning, data exchange, data cleaning, and query optimization have been proposed as well [6,9]. The chase algorithm solves possible violations of constraints by inserting new tuples, possibly containing null values [4]. The following example shows a case where a given database does not satisfy a set of tuple generating dependencies (TGDs) and the application of the chase algorithm produces a new consistent database by adding tuples with nulls.
Alignment of core point for shoeprint analysis and retrieval

The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. The proposed method firstly detects contour points in a black-and-white shoeprint image. Those reliable contour points are selected to simulate the left and right sidelines of the shoeprint by curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern description of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. The highest = 0.726 is obtained from the first four Zernike moments with the radius = 90 pixels and three baselines. The experimental results also show that Zernike method in any orders always outperforms the compared moment invariants and GLCM method. This study has verified that the proposed method can effectively align the shoeprints for pattern comparison.
Ethnic Differences in Physiological Cardiac Adaptation to Intense Physical Exercise in Highly Trained Female Athletes

Background— Ethnicity is an important determinant of cardiovascular adaptation in athletes. Studies in black male athletes reveal a higher prevalence of electric repolarization and left ventricular hypertrophy than observed in white males; these frequently overlap with those observed in cardiomyopathy and have important implications in the preparticipation cardiac screening era. There are no reports on cardiac adaptation in highly trained black females, who comprise an increasing population of elite competitors. Methods and Results— Between 2004 and 2009, 240 nationally ranked black female athletes (mean age 21±4.6 years old) underwent 12-lead ECG and 2-dimensional echocardiography. The results were compared with 200 white female athletes of similar age and size participating in similar sports. Black athletes demonstrated greater left ventricular wall thickness (9.2±1.2 versus 8.6±1.2 mm, P<0.001) and left ventricular mass (187.2±42 versus 172.3±42 g, P=0.008) than white athletes. Eight black athletes (3%) exhibited a left ventricular wall thickness >11 mm (12 to 13 mm) compared with none of the white athletes. All athletes revealed normal indices of systolic and diastolic function. Black athletes exhibited a higher prevalence of T-wave inversions (14% versus 2%, P<0.001) and ST-segment elevation (11% versus 1%, P<0.001) than white athletes. Deep T-wave inversions (−0.2 mV) were observed only in black athletes and were confined to the anterior leads (V1 through V3). Conclusions— Systematic physical exercise in black female athletes is associated with greater left ventricular hypertrophy and higher prevalence of repolarization changes than in white female athletes of similar age and size participating in identical sporting disciplines. However, a maximal left ventricular wall thickness >13 mm or deep T-wave inversions in the inferior and lateral leads are rare and warrant further investigation.
Study on the key quality parameter decision for multi-process by CART method

It has the features of data missing and nonlinear in multi-process manufacturing the quality data, the quality knowledge mining and key parameter decision-making based on CART method is carried out. Then the object data model, implementation method is discussed. At last, an application case in Alumina Ceramic Tube production is given which shows the CART method has strong applicability in multi-process manufacturing.
Systematically Engineering Self-Organizing Systems: The SodekoVS Approach

Self-organizing systems promise new software quality attributes that are very hard to obtain using standard software engineering approaches. In accordance with the visions of e.g. autonomic computing and organic computing, self-organizing systems promote self-adaptability as one major property helping to realize software that can manage itself at runtime. In this respect, self-adaptability can be seen as a necessary foundation for realizing e.g. self* properties such as self-configuration or self-protection. However, the systematic development of systems exhibiting such properties challenges current development practices. The SodekoVS project addresses the challenge to purposefully engineer adaptivity by proposing a new approach that considers the system architecture as well as the software development methodology as integral intertwined aspects for system construction. Following the proposed process, self-organizing dynamics, inspired by biological, physical and social systems, can be integrated into applications by composing modules that distribute feedback control structures among system entities. These compositions support hierarchical as well as completely decentralized solutions without a single point of failure. This novel development conception is supported by a reference architecture, a tailored programming model as well as a library of ready to use self-organizing patterns. The key challenges, recent research activities, application scenarios as well as intermediate results are discussed.
Endocytosis in Trypanosoma cruzi

Endocytic activity is particularly intense in Trypanosoma cruzi epimastigotes, while in amastigotes and trypo- mastigotes it is untraceable. Cargo molecules enters through the cytostome or flagellar pocket at the parasite anterior re- gion, goes along a branched early endosomal network of tubules and vesicles spread from nuclear periphery to the pos- terior pole, until delivery to reservosomes, the final compartment. Reservosomes are acid compartments that store protein and lipid cargo and also accumulate lysosomal hydrolases, modulating digestive activity. Although T. cruzi infective forms are unable to uptake molecules, its lysosome related organelles represent a potential targets for anti-parasitic chemotherapy.
PyRhO: A Multiscale Optogenetics Simulation Platform

Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.
Association of systemic concentrations of macrophage migration inhibitory factor with impaired glucose tolerance and type 2 diabetes: results from the Cooperative Health Research in the Region of Augsburg, Survey 4 (KORA S4).

OBJECTIVE Macrophage migration inhibitory factor (MIF) is a central cytokine in innate immunity. MIF expression can be regulated by glucose and insulin, but data on the association with type 2 diabetes are sparse. The aim of this study was to test whether MIF is associated with impaired glucose tolerance (IGT) and type 2 diabetes and whether these associations are independent of metabolic and immunological risk factors and to compare the associations of MIF and IGT/type 2 diabetes with those of C-reactive protein (CRP) and interleukin-6 (IL-6) with IGT/type 2 diabetes. RESEARCH DESIGN AND METHODS The Cooperative Health Research in the Region of Augsburg/Kooperative Gesundheitsforschung im Raum Augsburg, Survey 4 (KORA S4) is a population-based survey performed in Southern Germany (1999-2001). Of 1,653 participants aged 55-74 years, 236 patients with type 2 diabetes, 242 subjects with IGT, and 244 normoglycemic control subjects matched for age and sex were included in this cross-sectional study. Serum concentrations of MIF were measured by enzyme-linked immunosorbent assay. RESULTS Serum MIF concentrations are highly increased in individuals with IGT and type 2 diabetes. The associations of MIF with IGT and type 2 diabetes were independent of classical risk factors and of CRP and IL-6 and were much stronger before and after multivariate adjustment than the associations of CRP and IL-6 with IGT and type 2 diabetes. CONCLUSIONS Our data suggest that elevations of systemic MIF concentrations precede the onset of type 2 diabetes. This finding may be relevant because MIF has been reported to contribute to the development of type 2 diabetes-related diseases such as atherosclerosis and cancer.
SLA-Driven State Monitoring for Cloud Services

Witnessing the great growth of cloud services, providers are faced with the state monitoring for a diversity of services from federated clouds. Since the consumers might have individual SLA requirements on its applications, the state monitoring of service has become a demanding work in the cloud. In this paper, we present a SLA-driven state monitoring framework for cloud service based on the matrix factorization model, which employs a boosting WCGD algorithm to optimize the monitoring results. Our approach not only contributes to improving the monitoring accuracy, but also takes less overhead during the communication. As demonstrated in our experiment, our framework proves to be efficient and adapts to the dynamical network environment. Compared with existing approaches, we are the first to conduct the state monitoring based on the matrix factorization model.
HFIaaS: A proposed FPGA Infrastructure as a Service framework using High-Level Synthesis

This paper presents a framework, called FHIaaS, to provide FPGA Infrastructure as a Service (IaaS) in the cloud. The framework contains two parts, VHDL part, and High-Level Synthesis (HLS) part. The former provides an IaaS including FPGAs and a VHDL synthesis tool. The latter is an HLS tool to convert C programs to VHDL and a VHDL simulator for user-side verification of HLS-driven VHDL codes. Therefore, this framework can provide a service for changing C programs to VHDL codes, simulating the codes, synthesizing the codes, and finally running on FPGAs. In this experimental evaluation, PandA HLS tool is used and CHStone is applied on a VC707 board for workload evaluations. All the applied tests have been done for showing the scalability, the amount of overhead, and the constraints of Virtual Machines (VMs) creations. Furthermore, applied workload for VMs and a Physical Machine (PM) have been compared. The VMs were placed on the same PM to compare them. It has been found that HFIaaS with two VMs was 1.3× better than the PM in total execution time. This study may show that employing this architecture could also be useful for Software as a Service (SaaS). Therefore, considering the proposed architecture, an automatic acceleration system may achieve using HLS tools on a SaaS for full hardware based on FPGA designs.
Performance Evaluation of Transmission Algorithms for CSMA/ID WDM Ring Networks

As data traffic increase rapidly, IP over WDM (Wavelength Division Multiplexing) transport will replace conventional SONET transport in current metropolitan area networks. Such networks will require new MAC protocols to efficiently share network bandwidth among multiple network nodes. This research project describes and evaluates a novel Carrier Sense Multiple Access with Idle Detection (CSMA/ID) MAC protocols for IP over WDM ring networks. The paper focuses on the influence of performance for the packet scheduling algorithms included FIFO, OPF_CA and OPF_CF. An analytical model is developed to evaluate the performance of the protocol, with simulation results showing good network efficiency and will be an excellent candidate for next-generation metro networks.
Lessons learnt from the analysis of large-scale corporate databases

This paper presents the lessons learnt during the analysis of the corporate databases developed by IBM Global Services (Australia). IBM is rated as CMM level 5. Following CMM level 4 and above practices, IBM designed several software metrics databases with associated data collection and reporting systems to manage its corporate goals. However, IBM quality staff believed the data were not as useful as they had expected. NICTA staff undertook a review of IBM's statistical process control procedures and found problems with the databases mainly due to a lack of links between the different data tables. Such problems might be avoided by using M3P variant of the GQM paradigm to define a hierarchy of goals, with project goals at the lowest level, then process goals and corporate goals at the highest level. We propose using E-R models to identify problems with existing databases and to design databases once goals have been defined.
Virtualization as an enabler for security in mobile devices

Virtualization, a well established technology in the desktop and server domain, is currently investigated and analyzed with respect to its potential within mobile devices. The paper argues that mobile devices, which are targeting a completely open setup (e.g. Linux based), are facing severe security challenges. Thus, virtualization will be discussed as an enabler for security in mobile devices. Virtualization approaches and elements are discussed in detail taking this use case and existing limitations of mobile embedded devices into account.
A Real-Time Traffic Information Prediction Model Based on AOSVR and On-Line Learning

Acquiring the real-time information about traffic flow is one of the important steps toward the realization of ITS. In this paper, we propose a real-time traffic prediction model with warm start by integrating an accurate on-line support vector regression (AOSVR) with a corrected on-line learning algorithm that is used for improving computational rate. The forecasting implementation has showed that the proposed model is faster and more exact than AOSVR with both cold and warm start when it is applied to an actual real-time forecasting scheme