Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Surveillance and Decolonization of Methicillin-Resistant Staphylococcus aureus on Admission to Neonatal Intensive Care Units in Hong Kong: A Cost-Effectiveness Analysis

Objective. To examine potential clinical outcomes and cost of active methicillin-resistant Staphylococcus aureus (MRSA) surveillance with and without decolonization in neonatal intensive care units (NICUs) from the perspective of healthcare providers in Hong Kong. Design. Decision analysis modeling. Setting. NICU. Patients. Hypothetical cohort of patients admitted to an NICU. Methods. We designed a decision tree to simulate potential outcomes of active MRSA surveillance with and without decolonization in patients admitted to an NICU. Outcome measures included total direct medical cost per patient, MRSA infection rate, and MRSA-associated mortality rate. Model inputs were derived from the literature. Sensitivity analyses evaluated the impact of uncertainty in all model variables. Results. In the base-case analysis, active surveillance plus decolonization showed a lower expected MRSA infection rate (0.911% vs 1.759%), MRSA-associated mortality rate (0.223% vs 0.431%), and total cost per patient (USD 47,294 vs USD 48,031) compared with active surveillance alone. Sensitivity analyses showed that active surveillance plus decolonization cost less and had lower event rates if the incidence risk ratio of acquiring MRSA infections in carriers after decolonization was less than 0.997. In 10,000 Monte Carlo simulations, active surveillance plus decolonization was significantly less costly than active surveillance alone 99.9% of the time, and both the MRSA infection rate and the MRSA-associated mortality rate were significantly lower 99.9% of the time. Conclusions. Active surveillance plus decolonization for patients admitted to NICUs appears to be cost saving and effective in reducing the MRSA infection rate and the MRSA-associated mortality rate if addition of decolonization to active surveillance reduces the risk of MRSA infection. Infect Control Hosp Epidemiol 2012;33(10):1024-1030
Permutations of Strongly Self-Absorbing C*-algebras

Let G be a finite group acting on {1,. ,n}. For any C*-algebra A, this defines an action of \alpha of G on A^{\otimes n}. We show that if A tensorially absorbs a UHF algebra of infinite type, the Jiang-Su algebra, or is approximately divisible, then A \times_{\alpha} G has the corresponding property as well.
Laplace transform, dynamics and spectral geometry

We consider vector fields X on a closed manifold M with rest points of Morse type. For such vector fields we define the property of expo- nential growth. A cohomology class � 2 H 1 (M; R) which is Lyapunov for X defines counting functions for isolated instantons and closed trajectories. If X has exponential growth property we show, under a mild hypothesis generi- cally satisfied, that these counting functions can be recovered from the spectral geometry associated to (M,g,! ) where g is a Riemannian metric and ! is a closed one form representing �, cf Theorems 3 and 4 in sectionn 1.6. This is done with the help of Dirichlet series and their Laplace transform.
Politics as usual? Assessing the extent and content of candidate-level online campaigning at the 2015 UK general election

ABSTRACT There has been much work done investigating the adoption of online campaigns in UK general election campaigns. Although some research has focussed on the candidate-level (Gibson, R., W. Lusoli, and S. Ward. 2008. “Nationalizing and Normalizing the Local? A Comparative Analysis of Online Candidate Campaigning in Australia and Britain.” Journal of Information Technology and Politics 4: 15–30; Lee, B. 2014. “Window Dressing 2.0: Constituency-level Web Campaigns in the 2010 UK General Election.” Politics 34 (1): 45–57; Southern, R. 2015. “Is Web 2.0 Providing a Voice for Outsiders? A Comparison of Personal Web Site and Social Media Use by Candidates at the 2010 UK General Election. ” Journal of Information Technology & Politics 12 (1): 1–17), this is an under-researched area. This is despite early web-campaign scholars (Margolis, M., and D. Resnick. 2000. Politics as Usual: The Cyberspace Revolution. Sage) maintaining that e-campaigning could provide the most important advantages to campaigns at the candidate level. In light of this, the paper aims to provide the most comprehensive study of candidate-level online campaigning carried out in the UK to date. This paper employs original data, measuring website and social media use by candidates during the 2015 UK general election campaign. These data allow for a detailed explanation of the normalization thesis, one of the leading theories in the field of e-campaigns. The findings here are significant as there are several instances where normalization does not hold, suggesting that online tools are contributing to campaign change. Green Party candidates subverted normalization to a significant degree, particularly on social media. Assessing the campaign content, a quarter of candidates adopted more than one interactive feature on their website and furthermore, interactive use of Twitter was the most common type of Twitter use.
Nontraded Goods and Macroeconomic Policy Under a Fixed Exchange Rate

I. Introduction, 469—II. The analytical framework, 469. —III. Fiscal policy, 473. —IV. Exchange rate adjustments, 475. —V. Summary, 478. —Appendix A: Proof of Proposition 1,479. —Appendix B: Proof of Proposition 2,480.
Comprehensive Assessment of Sociodemographic and Behavioral Risk Factors for Mycoplasma genitalium Infection in Women

Background: Neisseria gonorrhoeae and Chlamydia trachomatis are characterized by different risk factors, thus control strategies for each also differ. In contrast, risk factors for Mycoplasma genitalium have not been well characterized. Methods: Between 2000 and 2006, 1090 women ages 14 to 45 attending the Public Health-Seattle & King County Sexually Transmitted Diseases Clinic in Seattle, WA, underwent clinical examination and computer-assisted survey interview. M. genitalium was detected by transcription mediated amplification from self-obtained vaginal swab specimens. C. trachomatis and N. gonorrhoeae were detected by culture from cervical swab specimens. Results: Prevalent M. genitalium infection was detected in 84 women (7.7%), C. trachomatis in 63 (5.8%), and N. gonorrhoeae in 26 (2.4%). Age <20 and nonwhite race were associated with increased risk for all 3 organisms. In addition, risk for M. genitalium was higher for women with a black partner (adjusted odds ratio [AOR]: 3.4; 95% confidence interval = 1.83–6.29), those never married (AOR: 2.6; 1.08–6.25), using Depo-Provera (AOR: 2.3; 1.19–4.46), and smoking (AOR: 1.7; 1.03–2.83). Drug use, history of STI in the past year, ≤high school education, meeting and having intercourse the same day, anal sex, douching, and hormonal contraception were associated with N. gonorrhoeae or C. trachomatis, but not with M. genitalium. Number of partners was not associated with any of the 3 organisms. Conclusions: The limited number of risk factors for prevalent infection common to all 3 pathogens suggests that M. genitalium may circulate in different sexual networks than N. gonorrhoeae or C. trachomatis. The predominance of sociodemographic risk factors for M. genitalium, rather than high-risk sexual behaviors, suggests broad-based testing may be the most effective control strategy.
KRAS and BRAF mutation analysis can be reliably performed on aspirated cytological specimens of metastatic colorectal carcinoma

N. K. B. Pang, M. E. Nga, S. Y. Chin, T. ‐M. Ismail, G. L. Lim, R. Soong and M. Salto‐Tellez KRAS and BRAF mutation analysis can be reliably performed on aspirated cytological specimens of metastatic colorectal carcinoma
Development of a pharmacokinetic/pharmacodynamic/disease progression model in NC/Nga mice for development of novel anti-atopic dermatitis drugs

Abstract 1. JHL45, a novel immune modulator against atopic dermatitis (AD), was synthesized from decursin isolated from Angelica gigas. The goal is to evaluate the lead compound using quantitative modeling approaches to novel anti-AD drug development. 2. We tested the anti-inflammatory effect of JHL45 by in vitro screening, characterized its in vitro pharmacokinetic (PK) properties. The dose-dependent efficacy of JHL45 was developed using a pharmacokinetics/pharmacodynamics/disease progression (PK/PD/DIS) model in NC/Nga mice. 3. JHL45 has drug-like properties and pharmacological effects when administered orally to treat atopic dermatitis. The developed PK/PD/DIS model described well the rapid metabolism of JHL45, double-peak phenomenon in the PK of decursinol and inhibition of IgE generation by compounds in NC/Nga mice. Also, a quantitative model was developed and used to elucidate the complex interactions between serum IgE concentration and atopic dermatitis symptoms. 4. Our findings indicate that JHL45 has good physicochemical properties and powerful pharmacological effects when administered orally for treatment of AD in rodents.
Mapping the Membrane-aqueous Border for the Voltage-sensing Domain of a Potassium Channel*

Voltage-sensing domains (VSDs) play diverse roles in biology. As integral components, they can detect changes in the membrane potential of a cell and couple these changes to activity of ion channels and enzymes. As independent proteins, homologues of the VSD can function as voltage-dependent proton channels. To sense voltage changes, the positively charged fourth transmembrane segment, S4, must move across the energetically unfavorable hydrophobic core of the bilayer, which presents a barrier to movement of both charged species and protons. To reduce the barrier to S4 movement, it has been suggested that aqueous crevices may penetrate the protein, reducing the extent of total movement. To investigate this hypothesis in a system containing fully functional channels in a native environment with an intact membrane potential, we have determined the contour of the membrane-aqueous border of the VSD of KvAP in Escherichia coli by examining the chemical accessibility of introduced cysteines. The results revealed the contour of the membrane-aqueous border of the VSD in its activated conformation. The water-inaccessible regions of S1 and S2 correspond to the standard width of the membrane bilayer (∼28 Å), but those of S3 and S4 are considerably shorter (≥40%), consistent with aqueous crevices pervading both the extracellular and intracellular ends. One face of S3b and the entire S3a were water-accessible, reducing the water-inaccessible region of S3 to just 10 residues, significantly shorter than for S4. The results suggest a key role for S3 in reducing the distance S4 needs to move to elicit gating.
Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.
CM-GAN: A Cross-Modal Generative Adversarial Network for Imputing Completely Missing Data in Digital Industry

Multimodal data fusion analysis is essential to model the uncertainty of environment awareness in digital industry. However, due to communication failure and cyberattack, the sampled time-series data often have the issue of data missing. In some extreme cases, part of units are unobservable for a long time, which results in complete data missing (CDM). To impute missing data, many models have been proposed. However, they cannot address the CDM issue, because no observation data of the unobservable units can be obtained in this case. Thus, to address the CDM issue, a novel cross-modal generative adversarial network (CM-GAN) is proposed in this article. It combines the cross-modal data fusion technique and the deep adversarial generation technique to construct a cross-modal data generator. This generator can generate long-term time-series data from widely existing spatio-temporal modal data in modern industrial system, and then impute missing value by replacing them with generated data. To test the performance of CM-GAN, extensive experiments are conducted on photovoltaic (PV) power output dataset. Compared with other baseline models, the performance of CM-GAN is generally better and reaches the state-of-the-art level. Moreover, sufficient ablation studies are conducted to present the contribution of the cross-modal data fusion technique and show the reasonability of parameter settings of CM-GAN. Apart from this, some prediction experiments are also conducted. The results show that the PV data recovered by CM-GAN can provide more predictability information for improving the prediction accuracy of deep learning model.
Predicting Level-Dependent Changes in Concurrent Vowel Scores Using the 2D-CNN Models

Differences in fundamental frequencies (F0s) are an important cue for segregating multiple speakers. However, the ability to avail this cue for identification varies with sound levels. For different-and same-F0 conditions, the identification scores of both vowels increased from low- to mid-levels and then reduced at higher levels for younger adults with normal hearing (YNH). These subjects benefited from the F0 difference; however, this benefit varied across the levels. The current study aims to develop a deep-neural-network (DNN) model that can predict the level-dependent changes in concurrent-vowel scores for YNH subjects. This DNN-model includes two-dimensional convolutional neural networks (2D-CNNs) in parallel, with the same architecture, to predict the concurrent-vowel scores. The input layer was neural responses of the auditory-nerve model to concurrent vowels. The 2D-CNN models were trained and validated using the subsets of the concurrent vowels from 50, 65, and 75 dB SPL for both F0 conditions, using the batch gradient descent algorithm. The trained model was then fine-tuned with a single epoch. The 2D-CNN models were evaluated against vowel levels (25 to 85 dB SPL) and F0 conditions. Compared with previous models, the current model accurately predicts the level-dependent changes in concurrent vowel scores for different-and same-F0 conditions.
Automatic selection of eye tracking variables in visual categorization in adults and infants

Visual categorization and learning of visual categories exhibit early onset, however the underlying mechanisms of early categorization are not well understood. The main limiting factor for examining these mechanisms is the limited duration of infant cooperation (10-15 minutes), which leaves little room for multiple test trials. With its tight link to visual attention, eye tracking is a promising method for getting access to the mechanisms of category learning. But how should researchers decide which aspects of the rich eye tracking data to focus on? To date, eye tracking variables are generally handpicked, which may lead to biases in the eye tracking data. Here, we propose an automated method for selecting eye tracking variables based on analyses of their usefulness to discriminate learners from non-learners of visual categories. We presented infants and adults with a category learning task and tracked their eye movements. We then extracted an over-complete set of eye tracking variables encompassing durations, probabilities, latencies, and the order of fixations and saccadic eye movements. We compared three statistical techniques for identifying those variables among this large set that are useful for discriminating learners form non-learners: ANOVA ranking, Bayes ranking, and L1 regularized logistic regression. We found remarkable agreement between these methods in identifying a small set of discriminant variables. Moreover, the same eye tracking variables allow us to classify category learners from non-learners among adults and 6- to 8-month-old infants with accuracies above 71%.
CHARACTERIZATION AND TYPOLOGY OF GUINEA FOWL (NUMIDA MELEAGRIS) FARMING SYSTEMS IN BURKINA FASO.

Fabiola G. Traoré 1 , * Amadou Traoré 1 , Balé Bayala 2 , Guiguibaza K. Dayo 3 , Arnaud S. Tapsoba 1 , Albert Soudré 4 , Moumouni Sanou 1 , Kisito Tindano 5 and Hamidou H. Tamboura 1 . 1. Institut de l’Environnement et de Recherches Agricoles (INERA)-Laboratoire de Biologie et de Santé animales, 04 BP 8645 Ouagadougou 04, Burkina Faso. Tél: +226 25 34 02 70. 2. Université de Ouagadougou, Unité de Formation et de Recherches en sciences de la vie et de la terre, 03 BP 7021 Ouagadougou 03 Burkina Faso. Tél: +226 25 30 70 63. 3. Centre International de Recherches Développement sur l’Elevage en zones Subhumides; 01 BP 454 BoboDioulasso 01, Burkina Faso. Tél. : +226 20 97 20 53 / 20 97 27 95. 4. Université Norbert Zongo, UFR Sciences et Techniques, BP 376 Koudougou, Burkina Faso. 5. Ecole Nationale de l’Elevage et de la Santé Animale (ENESA), 03 BP 7023 Ouagadougou 04. Manuscript Info Abstract . Manuscript History
Studies of the Antarctic stratosphere during IPY

A. R. Klekociuk, Australian Antarctic Division, Australia (andrew.klekociuk@aad.gov.au) M. C. Pitts, NASA Langley Research Center, USA (michael.c.pitts@nasa.gov) S. P. Alexander, Australian Antarctic Division, Australia (simon.alexander@aad.gov.au) C. David, L’Institut Pierre-Simon Laplace, France (christine.david@aero.jussieu.fr) M. Snels, Institute of Atmospheric Sciences and Climate, Italy (m.snels@isac.cnr.it) P. von der Gathen, Alfred Wegener Institute for Polar and Marine Research, Germany (peter.von.der.gathen@awi.de)
ASR - articulatory speech recognition

The hidden Markov model (HMM) has proven to be the model which has made large-vocabulary automatic speech recognition (ASR) possible. The HMM is robust, versatile and has at its disposal a host of efficient algorithms which deal with training, speaker adaptation and recognition. However, there is nothing uniquely speech orientated about the HMM. In fact, certain assumptions are made of speech which are known to be untrue. For example, speech is modelled as a piecewise stationary process when we know it to be continuous. Also, co-articulation, which should be a rich source of information, simply provides unwanted variation. This variation is generally taken into account by modelling every phone in every context which in turn leads to problems of data sparcity, making elaborate parameter tying schemes necessary. Speech is generally modelled in a parametrised version of the acoustic domain, which is natural given that this is the data we have most ready access to. Any practical speech recogniser must of course take acoustic waveforms as input, however to take these in isolation from the production mechanism which created them ignores a rich source of prior knowledge. We propose that modelling speech in the articulatory domain using linear dynamic models (see section 4) will address some of these issues. The data here consists of trajectories which evolve smoothly over time, namely coordinates of points on the articulators. Effects such as coarticulation and assimilation are most simply described in articulatory terms, as opposed to in acoustic terms where they are confounded with the representation. Models that work in the articulatory domain are therefore able to explicitly model these phenomena. We have access to real articulatory data, collected by Alan Wrench at Queen Margaret College, Edinburgh (see [1] for further details). This has been used to train neural networks to recover articulatory traces from the acoustics. In our experiments we have used both real and automatically recovered articulation.
PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy

The resurgence of cancer immunotherapy stems from an improved understanding of the tumor microenvironment. The PD-1/PD-L1 axis is of particular interest, in light of promising data demonstrating a restoration of host immunity against tumors, with the prospect of durable remissions. Indeed, remarkable clinical responses have been seen in several different malignancies including, but not limited to, melanoma, lung, kidney, and bladder cancers. Even so, determining which patients derive benefit from PD-1/PD-L1–directed immunotherapy remains an important clinical question, particularly in light of the autoimmune toxicity of these agents. The use of PD-L1 (B7-H1) immunohistochemistry (IHC) as a predictive biomarker is confounded by multiple unresolved issues: variable detection antibodies, differing IHC cutoffs, tissue preparation, processing variability, primary versus metastatic biopsies, oncogenic versus induced PD-L1 expression, and staining of tumor versus immune cells. Emerging data suggest that patients whose tumors overexpress PD-L1 by IHC have improved clinical outcomes with anti-PD-1–directed therapy, but the presence of robust responses in some patients with low levels of expression of these markers complicates the issue of PD-L1 as an exclusionary predictive biomarker. An improved understanding of the host immune system and tumor microenvironment will better elucidate which patients derive benefit from these promising agents. Mol Cancer Ther; 14(4); 847–56. ©2015 AACR.