Miracle-dz/UniFAR-FTUs huggingface dataset:
More Aspects of Polya Tree Distributions for Statistical Modelling

The definition and elementary properties of Polya tree distributions are reviewed. Two theorems are presented showing that Polya trees can be constructed to concentrate arbitrarily closely about any desired pdf, and that Polya tree priors can put positive mass in every relative entropy neighborhood of every positive density with finite entropy, thereby satisfying a consistency condition. Such theorems are false for Dirichlet processes. Models are constructed combining partially specified Polya trees with other information such as monotonicity or unimodality. It is shown how to compute bounds on posterior expectations over the class of all priors with the given specifications. A numerical example is given. A theorem of Diaconis and Freedman about Dirichlet processes is generalized to Polya trees, allowing Polya trees to be the models for errors in regression problems. Finally, empirical Bayes models using Dirichlet processes are generalized to Polya trees. An example from Berry and Christensen is reanalyzed with a Polya tree model.
Between Fragmentation and Dialogue. Twitter Communities and Political Debate About the Swiss “Nuclear Withdrawal Initiative”

ABSTRACT As social media sites have become significant arenas for political debate in recent years, this paper investigates the political debate that took place on Twitter in the wake of the Swiss referendum on the Nuclear Withdrawal Initiative, which was conducted in November 2016. Using Twitter data of 3000 users, we could identify 7 distinct communities that varied in terms of their size, position, and contribution to the debate. The most dominant communities in the debate were the Conservative Mainstream and the Green-Left communities. Moreover, our findings show that communities supporting and opposing the initiative differ with regard to the wording they use in their tweets. Finally, our results reveal an active exchange both within and, even more importantly, between the communities, which indicates a rather pluralistic and internally connected debate. Thus, our findings clearly contrast with those of prior studies concerning political debates on social media that identified pronounced echo chambers.
Adaptive Low Power Listening for Wireless Sensor Networks

Most sensor networks require application-specific network-wide performance guarantees, suggesting the need for global and flexible network optimization. The dynamic and nonuniform local states of individual nodes in sensor networks complicate global optimization. Here, we present a cross-layer framework for optimizing global power consumption and balancing the load in sensor networks through greedy local decisions. Our framework enables each node to use its local and neighborhood state information to adapt its routing and MAC layer behavior. The framework employs a flexible cost function at the routing layer and adaptive duty cycles at the MAC layer in order to adapt a node's behavior to its local state. We identify three state aspects that impact energy consumption: 1) number of descendants in the routing tree, 2) radio duty cycle, and 3) role. We conduct experiments on a test-bed of 14 mica2 sensor nodes to compare the state representations and to evaluate the framework's energy benefits. The experiments show that the degree of load balancing increases for expanded state representations. The experiments also reveal that all state representations in our framework reduce global power consumption in the range of one-third for a time-driven monitoring network and in the range of one-fifth for an event-driven target tracking network.
The Transmogrifier C hardware description language and compiler for FPGAs

The Transmogrifier C hardware description language is almost identical to the C programming language, making it attractive to the large community of C-language programmers. This paper describes the semantics of the language and presents a Transmogrifier C compiler that targets the Xilinx 4000 FPGA. The compiler is operational and has produced several working circuits, including a graphics display driver.
THE INFLUENCE OF QUALITY ON E-COMMERCE SUCCESS – AN EMPIRICAL APPLICATION OF THE DELONE AND MCLEAN IS SUCCESS MODEL

This research addresses difficulties in measuring e-commerce success by implementing the DeLone and McLean (D&M) model of IS success (1992, 2003) in an e-commerce environment. This research considers the influence of quality on e-commerce success by measuring the information quality and system quality attributes of an e-commerce system and the intention to use, user satisfaction and intention to transact from a sample of respondents. This research provides an empirical e-commerce application of the updated IS success model proposed by DeLone and McLean (2003). This paper found significant relationships between Information Quality and System Quality and three success dimensions: intention to use, user satisfaction and intention to transact. It found the following information and system quality constructs to be most important in predicting e-commerce success: ease of understanding, personalisation and reliability. In particular, that reliability is more important than usability where transactions are concerned and security is important to transactional zones of e-commerce systems, but is not the most important factor.
Elevated Expression of miR-210 Predicts Poor Survival of Cancer Patients: A Systematic Review and Meta-Analysis

Background MiRNAs are important regulators of different biological processes, including tumorigenesis. MiR-210 is a potential prognostic factor for survival in patients with cancer according to previous clinical researches. We conducted a systematic review and meta-analysis to summarize the significance of increased miR-210 expression in the prognosis of indicated cancers. Methodology/Principal Findings The present systematic review and meta-analysis of 16 researches included 1809 patients with 7 different types of cancers from 7 countries, and aimed to explore the association between miR-210 expression and the survival of cancer patients. Over-expression of miR-210 may predict poor overall survival (OS, HR = 1.33, 95% CI: 0.85–2.09, P = 0.210), but the effect was not significant. While the predictive effect on disease-free survival (DFS, HR = 1.89, 95% CI: 1.30–2.74, P = 0.001), progression-free survival (PFS, HR = 1.20, 95% CI: 1.05–1.38, P = 0.007) and relapse-free survival(RFS, HR = 4.42, 95% CI: 2.14–9.15, P = 0.000) for patients with breast cancer, primary head and neck squamous cell carcinoma (HNSCC), renal cancer, soft-tissue sarcoma, pediatric osteosarcoma, bladder cancer or glioblastoma was certain. Subgroup analysis showed the limited predictive effect of over-expressed miR-210 on breast cancer OS (HR = 1.63, 95% CI: 0.47–5.67, P = 0.443), breast cancer DFS (HR = 2.03, 95% CI: 0.90–4.57, P = 0.088), sarcoma OS (HR = 1.24, 95% CI: 0.20–7.89, P = 0.818) and renal cancer OS (HR = 1.16, 95% CI: 0.27–4.94, P = 0.842). Conclusions/Significance This systematic review and meta-analysis suggests that miR-210 has a predictive effect on survival of patients with studied cancer types as indexed by disease-free survival, progression-free survival and relapse-free survival. While the predictive effect on overall survival, breast cancer overall survival, breast cancer disease-free survival, sarcoma overall survival and renal cancer overall survival was not statistically significant.
Association of outpatient alcohol and drug treatment with health care utilization and cost: revisiting the offset hypothesis.

OBJECTIVE This study examines the hypothesis that treatment reduces medical utilization and costs of patients with substance use problems. METHOD Adult patients (N = 1.011; 67% men) entering the outpatient chemical dependency recovery program at Sacramento Kaiser Permanente over a 2-year period were recruited into the study. Medical utilization and costs were examined for 18 months prior and 18 months after intake. To account for overall changes in utilization and cost, an age, gender and length-of-enrollment matched nonpatient control group (N = 4,925) was selected from health-plan members living in the same service area. Multivariate analyses controlling for age and gender were conducted using generalized estimating equation methods, allowing for correlation between repeated measures and nonnormal distributions of the outcome variable. RESULTS The treatment cohort was less likely to be hospitalized (odds ratio [OR] = 0.59; p < .01) and there was a trend for having spent fewer days (rate ratio [RR] = 0.77; p < .10) in the hospital in the posttreatment period compared to pretreatment period. These patients were also less likely to visit the emergency room (ER) (OR = 0.64; p < .01) and had fewer ER visits (RR = 0.81; p < .01) following treatment. Inpatient, ER and total medical costs declined by 35%, 39% and 26%, respectively (p < .01). Reductions in cost were greater for the treatment cohort when compared with the matched sample (p < .05). Among women, there were significant reductions (p < .05) in inpatient, ER and total costs for the study cohort when compared with the matched sample; among men, the reductions in inpatient and ER cost (but not total cost) were significantly larger (p < .05) for the study cohort when compared with the matched sample. For the treatment cohort, the change in medical cost was not significantly different by gender. Changes in cost were significantly different across the various age groups (p < .05) for the study cohort and the matched sample. Among those in the group aged 40-49 years, the decline in cost for study cohort was significantly larger (p < .05) than for the matched sample. CONCLUSIONS For patients with substance use disorders entering treatment, there was a substantial decline in inappropriate utilization and cost (hospital and ER) in the posttreatment period. The disaggregated pattern of posttreatment decline in utilization and cost is suggestive of long-term reductions that warrant a longer follow-up.
PENGEMBANGAN MODEL PENERIMAAN SOCIAL MEDIA MARKETING DI KALANGAN MAHASISWA

The internet has changed the life style of people and also the way people to communicate. After the existence of the internet, people can buy products from his home or office. Prior to the internet, people get information about a product through written advertising, but now the information can be quickly obtained or disseminated by the presence of second-generation internet, or Web 2.0 Customers can exchange information and influence each otherin product selection. This could happen through social media. Because the huge number of the social media users make companies use this medium, not only to inform but also to sell. Not all entrepreneurs succeed in this online business, but many also fail. This study proposes model developed by the Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA) to determine the factors that could affect the purchase intention online through Social Media Marketing. The data obtained from this study is 748, which is obtained through a question naire distributed at public universities and private individual in three cities in Indonesia (Surabaya, Malang and Banjarmasin). Confirmatory factor analysis was conducted to test the construct validity and reliability for each variable. By using Structural Equation Modeling (SEM) showed that the most influential factor on purchase intention is perceived use fulness, while subjective normin fluenced the purchase intention but had no effect on perceived usefulness. Keywords: social media marketing, structural equation modeling, technology acceptance models, theory of reasoned action
Toward a Physically Based Gravity Wave Source Parameterization in a General Circulation Model

Abstract Middle atmospheric general circulation models (GCMs) must employ a parameterization for small-scale gravity waves (GWs). Such parameterizations typically make very simple assumptions about gravity wave sources, such as uniform distribution in space and time or an arbitrarily specified GW source function. The authors present a configuration of the Whole Atmosphere Community Climate Model (WACCM) that replaces the arbitrarily specified GW source spectrum with GW source parameterizations. For the nonorographic wave sources, a frontal system and convective GW source parameterization are used. These parameterizations link GW generation to tropospheric quantities calculated by the GCM and provide a model-consistent GW representation. With the new GW source parameterization, a reasonable middle atmospheric circulation can be obtained and the middle atmospheric circulation is better in several respects than that generated by a typical GW source specification. In particular, the interannual NH stratospher.
Picornavirus Receptor Down-Regulation by Plasminogen Activator Inhibitor Type 2

ABSTRACT Therapeutic interference with virus-cell surface receptor interactions represents a viable antiviral strategy. Here we demonstrate that cytoplasmic expression of the serine protease inhibitor (serpin), plasminogen activator inhibitor type 2 (PAI-2), affords a high level of protection from lytic infection by multiple human picornaviruses. The antiviral action of PAI-2 was mediated primarily through transcriptional down-regulation of the following virus receptors: intercellular adhesion molecule 1 (ICAM-1, a cellular receptor for the major group of rhinoviruses), decay-accelerating factor (a cellular receptor for echoviruses and coxsackieviruses), and to a lesser extent the coxsackie-adenovirus receptor protein (a cellular receptor for group B coxsackieviruses and group C adenoviruses). Expression of related cell surface receptors, including membrane cofactor protein and the poliovirus receptor, remained unaffected. These findings suggest that PAI-2 and/or related serpins may form the basis of novel antiviral strategies against picornavirus infections and also therapeutic interventions against ICAM-1-mediated respiratory inflammation.
Spectral Regression: A Unified Approach for Sparse Subspace Learning

Recently the problem of dimensionality reduction (or, subspace learning) has received a lot of interests in many fields of information processing, including data mining, information retrieval, and pattern recognition. Some popular methods include principal component analysis (PCA), linear discriminant analysis (LDA) and locality preserving projection (LPP). However, a disadvantage of all these approaches is that the learned projective functions are linear combinations of all the original features, thus it is often difficult to interpret the results. In this paper, we propose a novel dimensionality reduction framework, called Unified Sparse Subspace Learning (USSL), for learning sparse projections. USSL casts the problem of learning the projective functions into a regression framework, which facilitates the use of different kinds of regularizes. By using a L1-norm regularizer (lasso), the sparse projections can be efficiently computed. Experimental results on real world classification and clustering problems demonstrate the effectiveness of our method.
Low response to clopidogrel is associated with cardiovascular outcome after coronary stent implantation.

AIMS To assess whether low response to clopidogrel influences cardiovascular outcome after coronary stent implantation in a consecutively measured cohort of patients with coronary stent implantation. METHODS AND RESULTS A total of 379 consecutive patients with symptomatic coronary artery disease (CAD), (stable angina n = 206 and acute coronary syndrome, n = 173) treated with percutaneous coronary stenting were enrolled in this trial. Responsiveness to clopidogrel was assessed by ADP (20 micromol/L)-induced aggregometry at least 6 h (mean 34.8+/-25.9 h) after administration of a loading dose of 600 mg clopidogrel. Platelet inhibition < 30% was defined as low response to clopidogrel. At 3-month follow-up, the primary outcome of a combined major cardiovascular event including non-fatal myocardial infarction, non-fatal ischaemic stroke, or cardiovascular death was evaluated. Twenty-two patients (5.8%) were classified as low responders. Compared with patients who adequately responded to clopidogrel, a low responder had a significantly higher risk of major cardiovascular events [22.7 vs. 5.6%; odds ratio, 4.9; 95% confidence interval (CI), 1.66-14.96; P = 0.004]. After adjustment for other factors influencing cardiovascular outcome, low response to clopidogrel and severe left ventricular dysfunction were independently associated with a major cardiovascular event within 3 months (hazard ratio for low response to clopidogrel, 3.71; 95% CI, 1.08-12.69; P = 0.037). CONCLUSION Low response to clopidogrel in patients with symptomatic CAD treated by stenting significantly enhances the occurrence of cardiovascular events and death. The evaluation of low response to clopidogrel may help to identify patients at increased risk who may benefit from intensified antiplatelet strategy.
CYTOTOXIC IMMUNE CELLS WITH SPECIFICITY FOR DEFINED SOLUBLE ANTIGENS

Spleen cells from mice immunized against ovalbumin (OA) or dinitrophenylated mouse serum albumin (DM) were found to be specifically cytotoxic in vitro towards target cells (chicken red blood cells) coated with these antigens. Inhibition of specific cytotoxicity was observed when free soluble antigen was added to the incubation mixtures. DM-immune cell cytotoxicity could be specifically and completely inhibited by DNP-lysine and was thus shown to be hapten specific. Complete and specific inhibition was also observed for OA-immune cell cytotoxicity using OA as inhibitor, but compared with the inhibition curves obtained with DNP-lysine, the OA cytotoxicity inhibition curves were shifted by a factor of about one hundred towards lower molar inhibitor concentrations. Very similar results were observed when the serum antibodies of DM- and OA-immune animals were analyzed by passive hemagglutination inhibition. With increasing time after immunization, both cytotoxicity inhibition curves and agglutination inhibition curves, shifted to lower antigen or hapten concentrations. Specific cytotoxicity against antigen-coated target cells was induced in nonimmune spleen cells (a) by serum from immune animals, and (b) by supernatants from in vitro immune cell cultures. In both instances, the factor which induced antigen-specific cytotoxic activity could be absorbed on anti-mouse Ig columns, thus demonstrating its immunoglobulin nature. The ability of target cell bound antibodies to induce cytotoxicity in nonimmune spleen cells was restricted to the 7S antibody class.
Optimization and Analysis on Fuzzy SVM for Targets Classification in Forest

Abstract: In this paper, machine learning technology was introduced to forestry machine, and harvesting targets data were classified based on fuzzy support vector machine (FSVM). Fuzzy membership function largely determines the classifier results, so a new membership function was proposed to improve classification performance. Clustering center was selected based on a K-means algorithm, then the membership function was determined via comparing distance between samples to the positive and negative clustering centers in feature space, respectively. It has good learning ability and generalization performance by the experiment with common SVM and representative Fuzzy-SVM. Besides, this model is applied in harvesting target detection, which improves classification accuracy and satisfies with the request of forestry machine.
Prognostic Factors of Papillary and Follicular Carcinomas in Japan Based on Data of Kuma Hospital

There are some important prognostic factors for papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC). In this paper, clinicopathological features significantly affecting patient prognosis are described based on our data as well as others. Distant metastasis at diagnosis is the most important prognostic factor for both PTC and FTC. Other than that, preoperative and intraoperative findings are important to evaluate the biological behavior of PTC. Extrathyroid extension, large lymph-node metastasis, and extranodal tumor extension that can be evaluated preoperatively or intraoperatively are significant prognostic factors for PTC patients. In contrast, pathological findings are important not only for diagnosis of FTC, but also for the evaluation of its biological character. Grade of invasiveness (minimally or widely invasive) and degree of differentiation (well differentiated or including a poorly differentiated component) greatly affect the prognosis of FTC patients.
Visualization of self-delivering hydrophobically modified siRNA cellular internalization

siRNAs are a new class of therapeutic modalities with promising clinical efficacy that requires modification or formulation for delivery to the tissue and cell of interest. Conjugation of siRNAs to lipophilic groups supports efficient cellular uptake by a mechanism that is not well characterized. Here we study the mechanism of internalization of asymmetric, chemically stabilized, cholesterol-modified siRNAs (sd-rxRNAs®) that efficiently enter cells and tissues without the need for formulation. We demonstrate that uptake is rapid with significant membrane association within minutes of exposure followed by the formation of vesicular structures and internalization. Furthermore, sd-rxRNAs are internalized by a specific class of early endosomes and show preferential association with epidermal growth factor (EGF) but not transferrin (Tf) trafficking pathways as shown by live cell TIRF and structured illumination microscopy (SIM). In fixed cells, we observe ∼25% of sd-rxRNA co-localizing with EGF and <5% with Tf, which is indicative of selective endosomal sorting. Likewise, preferential sd-rxRNA co-localization was demonstrated with EEA1 but not RBSN-containing endosomes, consistent with preferential EGF-like trafficking through EEA1-containing endosomes. sd-rxRNA cellular uptake is a two-step process, with rapid membrane association followed by internalization through a selective, saturable subset of the endocytic process. However, the mechanistic role of EEA1 is not yet known. This method of visualization can be used to better understand the kinetics and mechanisms of hydrophobic siRNA cellular uptake and will assist in further optimization of these types of compounds for therapeutic intervention.
Physically Inspired Neural Network Model for RF Power Amplifier Behavioral Modeling and Digital Predistortion

In this paper, a novel two hidden layers artificial neural network (2HLANN) model is proposed to predict the dynamic nonlinear behavior of wideband RF power amplifiers (PAs). Starting with a generic low-pass equivalent circuit of the PA, several circuit transformations are applied in order to build an appropriate artificial neural network structure and improve the modeling accuracy. This approach culminates in the development of a real-valued and feed-forward 2HLANN-based model. The parameters (number of neurons, memory depth, etc.) of the proposed model and the back propagation learning algorithm (learning rate, momentum term, etc.) used for its training were carefully studied and thoughtfully chosen to ensure the generality of the constructed model. The validation of the proposed models in mimicking the behavior of a 250-W Doherty amplifier driven with a 20-MHz bandwidth signal is carried out in terms of its accuracy in predicting its output spectrum, dynamic AM/AM and AM/PM characteristics, and in minimizing the normalized mean square error. In addition, the linearization of the Doherty PA using the 2HLANN enabled attaining an output power of up to 46.5 dBm and an average efficiency of up to 40% coupled with an adjacent channel power ratio higher than 50 dBc.