Joint Deep Modeling of Users and Items Using Reviews for Recommendation

A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.Comment: WSDM 201

Structure of Transmembrane Helix 8 and Possible Membrane Defects in CFTR

The cystic fibrosis transmembrane conductance regulator (CFTR) is an ion channel that regulates the flow of anions across epithelia. Mutations in CFTR cause cystic fibrosis. CFTR belongs to the ATP-binding cassette transporter superfamily, and gating is controlled by phosphorylation and ATP binding and hydrolysis. Recently obtained ATP-free and ATP-bound structures of zebrafish CFTR revealed an unwound segment of transmembrane helix (TM) 8, which appears to be a unique feature of CFTR not present in other ATP-binding cassette transporter structures. Here, using μs-long molecular dynamics simulations, we investigate the interactions formed by this TM8 segment with nearby helices in both ATP-free and ATP-bound states. We highlight ATP-dependent interactions as well as the structural role of TM8 in maintaining the functional architecture of the pore via interactions common to both the ATP-bound and ATP-free state. The results of the molecular dynamics simulations are discussed in the context of the gating mechanism of CFTR

Simulations of a single membrane between two walls using a Monte Carlo method

Quantitative theory of interbilayer interactions is essential to interpret x-ray scattering data and to elucidate these interactions for biologically relevant systems. For this purpose Monte Carlo simulations have been performed to obtain pressure P and positional fluctuations sigma. A new method, called Fourier Monte-Carlo (FMC), that is based on a Fourier representation of the displacement field, is developed and its superiority over the standard method is demonstrated. The FMC method is applied to simulating a single membrane between two hard walls, which models a stack of lipid bilayer membranes with non-harmonic interactions. Finite size scaling is demonstrated and used to obtain accurate values for P and sigma in the limit of a large continuous membrane. The results are compared with perturbation theory approximations, and numerical differences are found in the non-harmonic case. Therefore, the FMC method, rather than the approximations, should be used for establishing the connection between model potentials and observable quantities, as well as for pure modeling purposes.Comment: 10 pages, 10 figure

Optimal antithrombotic treatment of patients with atrial fibrillation undergoing percutaneous coronary intervention:triple therapy is too much!

Patients with atrial fibrillation who undergo a coronary intervention are eligible for both anticoagulation and (dual) antiplatelet therapy ((D) APT). An optimal balance has to be found to reduce the thromboembolic risk (i.e. stroke, systemic embolism and myocardial infarction) and to minimise the increased risk of bleeding with concomitant use of an anticoagulant and (D) APT. Owing to a lack of evidence, the guideline recommendations are predominantly based on expert opinion. Current evidence indicates that the combination of a non-vitamin K oral anticoagulant (NOAC) and clopidogrel is safer than vitamin-K oral antagonists plus DAPT, which increases the risk of bleeding, without clear advantages in regard to efficacy. Concerning whether (N) OACs should be combined with single APT rather than DAPT, the findings of the WOEST, PIONEER AF-PCI and RE-DUAL PCI trials seem to favour a combination with clopidogrel only, thus omitting aspirin. Choosing the optimal treatment strategies for individual patients on NOACs and (D) APT will remain a challenge for clinicians, though triple therapy seems to be the less favourable option owing to the increased risk of bleeding

Electrostatics of ions inside the nanopores and trans-membrane channels

A model of a finite cylindrical ion channel through a phospholipid membrane of width $L$ separating two electrolyte reservoirs is studied. Analytical solution of the Poisson equation is obtained for an arbitrary distribution of ions inside the trans-membrane pore. The solution is asymptotically exact in the limit of large ionic strength of electrolyte on the two sides of membrane. However, even for physiological concentrations of electrolyte, the electrostatic barrier sizes found using the theory are in excellent agreement with the numerical solution of the Poisson equation. The analytical solution is used to calculate the electrostatic potential energy profiles for pores containing charged protein residues. Availability of a semi-exact interionic potential should greatly facilitate the study of ionic transport through nanopores and ion channels

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Screening for Atrial Fibrillation in Sub-Saharan Africa:A Health Economic Evaluation to Assess the Feasibility in Nigeria

Background: Cardiovascular disease reflects a major burden of non-communicable disease in Sub-Saharan Africa (SSA). Early detection and treatment of atrial fibrillation (AF), as a preventive measure against stroke, is currently not in the scope of the World Health Organization recommendation to reduce cardiovascular disease. Objective: We hypothesized that screening for AF would be an important approach to determine the true AF prevalence in the general population in African countries and to identify asymptomatic AF patients at risk for stroke to optimize prevention. Methods: A decision analytic model was developed to study the health-economic impact of AF screening in Nigeria over a life-time horizon. The patient population explored in the model was a population of newly detected AF cases that would be diagnosed with a one-time systematic screening for AF with a single lead ECG device in community health centres across Nigeria. Conclusions: The health gain per newly detected AF patient (N = 31,687) was 0.41 QALY at a cost of $5,205 per patient with 100$12,587 per QALY gained. The intervention was cost-effective with a 99.9% warfarin use with an ICER of $1,363 per QALY gained. The total cost of a single screening session was$7.3 million for the total screened population in Nigeria or $1.60 per patient screened. Screening for AF to detect AF patients in need for stroke prevention can be a cost-effective intervention in the Sub-Saharan region, depending on type of anticoagulant used and drug costs