bluuebunny/crossref_metadata_2025_split huggingface dataset:
Eco-Friendly Pharmaceutical Analysis of Rifaximin in Tablets by HPLC-MS and Microbiological Turbidimetry
Authors: Kogawa Ana Carolina , Mendonça Jacqueline Nakau , Lopes Norberto Peporine , Salgado Hérida Regina Nunes 
Published: April 2021

Abstract
Rifaximin, an antimicrobial used for the treatment of various diseases, lacks analytical methods in official compendia for evaluation of the final product. This paper presents an eco-friendly protocol for rifaximin tablets by high performance liquid chromatography coupled with mass spectrometry (HPLC-MS). The method was completely validated according to the International Conference on Harmonization guidelines and developed following the concept of Quality by Design. The separation was achieved using a C18 column, purified water +0.1% glacial acetic acid and ethyl alcohol, 52:48 (v/v), as mobile phase, 0.9 mL min−1 at 290 nm and ambient room temperature. Mass spectral analyses were performed using electrospray ionization (ESI) ion source and ion trap mass analyzer. The method was linear over the concentration range of 5–50 μg mL−1. The sample was subjected to acidic, basic, neutral, oxidative and photolytic degradation. Degradation products did not interfere in the quantification of the rifaximin, so the method can be considered indicative of stability. Degradation products were also evaluated individually by microbiological method using Escherichia coli. The validated method could be used promisingly as green analytical strategies for detection and quantification of rifaximin in tablets.
High Infection Secondary Attack Rates of Severe Acute Respiratory Syndrome Coronavirus 2 in Dutch Households Revealed by Dense Sampling
Authors: Reukers Daphne F M , van Boven Michiel , Meijer Adam , Rots Nynke , Reusken Chantal , Roof Inge , van Gageldonk-Lafeber Arianne B , van der Hoek Wim , van den Hof Susan 
Published: April 2021

Abstract

Background
Indoor environments are considered one of the main settings for transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Households in particular represent a close-contact environment with high probability of transmission between persons of different ages and roles in society.

Methods
Households with a laboratory-confirmed SARS-CoV-2 positive case in the Netherlands (March-May 2020) were included. At least 3 home visits were performed during 4-6 weeks of follow-up, collecting naso- and oropharyngeal swabs, oral fluid, feces and blood samples from all household members for molecular and serological analyses. Symptoms were recorded from 2 weeks before the first visit through to the final visit. Infection secondary attack rates (SAR) were estimated with logistic regression. A transmission model was used to assess household transmission routes.

Results
A total of 55 households with 187 household contacts were included. In 17 households no transmission took place; in 11 households all persons were infected. Estimated infection SARs were high, ranging from 35% (95% confidence interval [CI], 24%-46%) in children to 51% (95% CI, 39%-63%) in adults. Estimated transmission rates in the household were high, with reduced susceptibility of children compared with adolescents and adults (0.67; 95% CI, .40-1.1).

Conclusion
Estimated infection SARs were higher than reported in earlier household studies, presumably owing to our dense sampling protocol. Children were shown to be less susceptible than adults, but the estimated infection SAR in children was still high. Our results reinforce the role of households as one of the main multipliers of SARS-CoV-2 infection in the population.
A Virtual Network Embedding Algorithm Based On Double-Layer Reinforcement Learning
Authors: Li Meng , Lu MeiLian 
Published: April 2021

AbstractVirtual network embedding (VNE) algorithms dominate the effectiveness of resource sharing in network virtualization. Heuristic embedding algorithms generally make embedding decisions by artificially specified strategies, in which the node importance is measured by simply summing or multiplying several node attributes. However, the contributions of different attributes may be combined through complex functional relationships. The reinforcement learning-based VNE algorithms can optimize node embedding. However, the existing algorithms only consider the local node attributes, and only simple shortest path-based embedding policy is adopted for link embedding, resulting limited embedding effects. To overcome the above defects, we propose a double-layer reinforcement learning-based VNE algorithm (DRL-VNE). In DRL-VNE, both the global and local node attributes are extracted to represent the status of network nodes, then a policy network is constructed to optimize node embedding, and the other policy network is designed to optimize link embedding. The performance of DRL-VNE is evaluated under different network scenarios and is compared with that of heuristic and machine learning-based VNE algorithms. Simulation results show that in hierarchical network scenario, the request acceptance ratio and the resource utilization of DRL-VNE are respectively improved by 14% and by 27% compared with the best performance comparison algorithm.
Austropuccinia psidii, causing myrtle rust, has a gigabase-sized genome shaped by transposable elements
Authors: Tobias Peri A , Schwessinger Benjamin , Deng Cecilia H , Wu Chen , Dong Chongmei , Sperschneider Jana , Jones Ashley , Luo Zhenyan , Zhang Peng , Sandhu Karanjeet , Smith Grant R , Tibbits Josquin , Chagné David , Park Robert F 
Published: April 2021

AbstractAustropuccinia psidii, originating in South America, is a globally invasive fungal plant pathogen that causes rust disease on Myrtaceae. Several biotypes are recognized, with the most widely distributed pandemic biotype spreading throughout the Asia-Pacific and Oceania regions over the last decade. Austropuccinia psidii has a broad host range with more than 480 myrtaceous species. Since first detected in Australia in 2010, the pathogen has caused the near extinction of at least three species and negatively affected commercial production of several Myrtaceae. To enable molecular and evolutionary studies into A. psidii pathogenicity, we assembled a highly contiguous genome for the pandemic biotype. With an estimated haploid genome size of just over 1 Gb (gigabases), it is the largest assembled fungal genome to date. The genome has undergone massive expansion via distinct transposable element (TE) bursts. Over 90% of the genome is covered by TEs predominantly belonging to the Gypsy superfamily. These TE bursts have likely been followed by deamination events of methylated cytosines to silence the repetitive elements. This in turn led to the depletion of CpG sites in TEs and a very low overall GC content of 33.8%. Compared to other Pucciniales, the intergenic distances are increased by an order of magnitude indicating a general insertion of TEs between genes. Overall, we show how TEs shaped the genome evolution of A. psidii and provide a greatly needed resource for strategic approaches to combat disease spread.
A mixed-methods study of American Millennials’ views about celebrity endorsement of foods and beverages
Authors: Zhou Mi , Kraak Vivica 
Published: April 2021

SummaryMore than one-third of American Millennial adults have obesity, and a significant amount of their household budget is spent on purchasing energy-dense and nutrient-poor food and beverage products. Consumers’ brand awareness and purchasing behaviors are influenced by celebrity credibility measured by trustworthiness, expertise and attractiveness; and celebrity ‘fit’ between products, brands and consumer’ self-image. This empirical mixed-methods study combined Q methodology with questionnaires to explore the shared and distinct viewpoints of demographically diverse Millennial adults about celebrity endorsement of food and beverage products or marketing campaigns in the United States (USA). Millennials (n = 40; aged 26–39 years) sorted photo images (n = 48) of US celebrities associated with branded food and beverage product endorsements on a 9-point normal distribution scale from ‘most trusted’ (+4) to ‘most distrusted’ (−4). Participants also completed a 4-item post Q-sort questionnaire to interpret their thoughts during the card sorting process, and a 3-item questionnaire to examine their views about celebrity credibility, ‘fit’ and multiple brand and product endorsements. Three distinct viewpoints were identified that included: (i) healthy lifestyle champions who trusted celebrities associated with healthy products or campaigns; (ii) female role-model admirers who trusted female celebrities associated with positive social impacts and (ii) African-American celebrity fans who trusted African-American celebrities who endorsed any brand or products. Qualitative analysis of the questionnaire identified the potential negative influence of celebrity endorsement for unhealthy products on Millennials’ dietary behaviors. Businesses and organizations should carefully select credible celebrities trusted by Millennials to encourage food and beverage products associated with a healthy diet.
Reflection on modern methods: shared-parameter models for longitudinal studies with missing data
Authors: Griswold Michael E , Talluri Rajesh , Zhu Xiaoqian , Su Dan , Tingle Jonathan , Gottesman Rebecca F , Deal Jennifer , Rawlings Andreea M , Mosley Thomas H , Windham B Gwen , Bandeen-Roche Karen 
Published: April 2021

AbstractA primary goal of longitudinal studies is to examine trends over time. Reported results from these studies often depend on strong, unverifiable assumptions about the missing data. Whereas the risk of substantial bias from missing data is widely known, analyses exploring missing-data influences are commonly done either ad hoc or not at all. This article outlines one of the three primary recognized approaches for examining missing-data effects that could be more widely used, i.e. the shared-parameter model (SPM), and explains its purpose, use, limitations and extensions. We additionally provide synthetic data and reproducible research code for running SPMs in SAS, Stata and R programming languages to facilitate their use in practice and for teaching purposes in epidemiology, biostatistics, data science and related fields. Our goals are to increase understanding and use of these methods by providing introductions to the concepts and access to helpful tools.
The maximum likelihood ensemble filter for computational flame and fluid dynamics
Authors: Wang Yijun , Guzik Stephen , Zupanski Milija , Gao Xinfeng 
Published: April 2021

Abstract
The numerical solution of partial differential equations that govern fluid dynamics with turbulence and combustion is challenging due to the multiscale nature of the dynamical system and the need to resolve small-scale physical features. In addition, the uncertainties in the dynamical system, including those in the physical models and parameters, initial and boundary conditions and numerical methods, impact the computational fluid dynamics (CFD) prediction of turbulence and chemical reactions. To improve the CFD prediction, this study focuses on the development and application of a maximum likelihood ensemble filter (MLEF), an ensemble-based data assimilation (DA), for flows featuring combustion and/or turbulence. MLEF finds the optimal analysis and its uncertainty by maximizing the posterior probability density function. The novelty of the study lies in the combination of advanced DA and CFD methods for a new comprehensive application to predict engineering fluid dynamics. The study combines important aspects, including an ensemble-based DA with analysis and uncertainty estimation, an augmented control vector that simultaneously adjusts initial conditions and model empirical parameters and an application of DA to CFD modeling of combustion and flows with complex geometry. The DA performance is validated by a turbulent Couette flow. The new CFD–DA system is then applied to solve the time-evolving shear-layer mixing with methane-air combustion and the turbulent flow over a bluff-body geometry. Results demonstrate the improvement of estimates of model parameters and the uncertainty reduction in initial conditions (ICs) for CFD modeling of flames and flows by the MLEF method.
A dual self-monitored reconstruction scheme on theTV-regularized inverse conductivity problem
Authors: Markaki Vanessa , Kourounis Drosos , Charalambopoulos Antonios 
Published: April 2021

AbstractRecently in Charalambopoulos et al. (2020), we presented a methodology aiming at reconstructing bounded total variation ($TV$) conductivities via a technique simulating the so-called half-quadratic minimization approach, encountered in Aubert & Kornprobst (2002, Mathematical Problems in Image Processing. New York, NY: Springer). The method belongs to a duality framework, in which the auxiliary function $\omega (x)$ was introduced, offering a tool for smoothing the members of the admissible set of conductivity profiles. The dual variable $\omega (x)$, in that approach, after every external update, served in the formation of an intermediate optimization scheme, concerning exclusively the sought conductivity $\alpha (x)$. In this work, we develop a novel investigation stemming from the previous approach, having though two different fundamental components. First, we do not detour herein the $BV$-assumption on the conductivity profile, which means that the functional under optimization contains the $TV$ of $\alpha (x)$ itself. Secondly, the auxiliary dual variable $\omega (x)$ and the conductivity $\alpha (x)$ acquire an equivalent role and concurrently, a parallel pacing in the minimization process. A common characteristic between these two approaches is that the function $\omega (x)$ is an indicator of the conductivity’s ‘jump’ set. A fortiori, this crucial property has been ameliorated herein, since the reciprocal role of the elements of the pair $(\alpha ,\omega )$ offers a self-monitoring structure very efficient to the minimization descent.
Cell culture selections reveal favourable drug resistance profiles for doravirine and islatravir
Authors: Brenner Bluma G , Oliveira Maureen , Ibanescu Ruxandra-Ilinca , Routy Jean-Pierre , Thomas Réjean 
Published: April 2021

Abstract

Background
The newer generation NNRTIs, including doravirine and rilpivirine, were designed to show high potency and overcome K103N, Y181C and G190A resistance.

Objectives
To assess emergent resistance to doravirine and rilpivirine, alone and paired with lamivudine or islatravir through in vitro drug selections.

Methods
Subtype B (n = 3), non-B subtype (n = 3), and pNL4.3 viral isolates were passaged in cord blood mononuclear cells with progressively increasing concentrations of drug(s). Genotypic analysis compared the acquisition and accumulation of drug resistance mutations at weeks 8 and 24 following drug pressure. Cell-based phenotypic assays assessed cross-resistance patterns to NNRTIs by acquired resistance mutations.

Results
Doravirine pressure resulted in the acquisition of V108I (6/7) and V106A/I/M (5/7) mutations at weeks 8, followed by F227L (4/7), Y318F (4/7), M230L (2/7) or L234I (2/7) by weeks 24. In contrast, rilpivirine resulted in E138K (5/7) followed by L100I (3/7), K101E (1/7), or M230L (1/7). Doravirine resistance pathways retained susceptibility to rilpivirine, whereas rilpivirine resistance conferred intermediate resistance (12–152-fold) to doravirine. Dual selections with islatravir or lamivudine delayed and diminished emergent resistance to doravirine, resulting in V108I (9/15) with fewer or no other changes at weeks 24. There was a lesser delay in emergent resistance to rilpivirine when combined with islatravir or lamivudine. The M184V mutation did not arise in dual selections with islatravir or lamivudine.

Conclusions
Doravirine showed a more robust resistance profile compared with other NNRTIs. The long intracellular half-life of islatravir and delayed acquisition of resistance in dual selections provide an opportunity for long-acting treatment options.
Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults
Authors: Chun Matthew , Clarke Robert , Cairns Benjamin J , Clifton David , Bennett Derrick , Chen Yiping , Guo Yu , Pei Pei , Lv Jun , Yu Canqing , Yang Ling , Li Liming , Chen Zhengming , Zhu Tingting ,   the China Kadoorie Biobank Collaborative Group
Published: April 2021

Abstract

Objective
To compare Cox models, machine learning (ML), and ensemble models combining both approaches, for prediction of stroke risk in a prospective study of Chinese adults.

Materials and Methods
We evaluated models for stroke risk at varying intervals of follow-up (&lt;9 years, 0–3 years, 3–6 years, 6–9 years) in 503 842 adults without prior history of stroke recruited from 10 areas in China in 2004–2008. Inputs included sociodemographic factors, diet, medical history, physical activity, and physical measurements. We compared discrimination and calibration of Cox regression, logistic regression, support vector machines, random survival forests, gradient boosted trees (GBT), and multilayer perceptrons, benchmarking performance against the 2017 Framingham Stroke Risk Profile. We then developed an ensemble approach to identify individuals at high risk of stroke (&gt;10% predicted 9-yr stroke risk) by selectively applying either a GBT or Cox model based on individual-level characteristics.

Results
For 9-yr stroke risk prediction, GBT provided the best discrimination (AUROC: 0.833 in men, 0.836 in women) and calibration, with consistent results in each interval of follow-up. The ensemble approach yielded incrementally higher accuracy (men: 76%, women: 80%), specificity (men: 76%, women: 81%), and positive predictive value (men: 26%, women: 24%) compared to any of the single-model approaches.

Discussion and Conclusion
Among several approaches, an ensemble model combining both GBT and Cox models achieved the best performance for identifying individuals at high risk of stroke in a contemporary study of Chinese adults. The results highlight the potential value of expanding the use of ML in clinical practice.
14 Use of Airway Pressure Relief Ventilation (APRV) in Burn Patients With and Without Inhalation Injury
Authors: Foster Kevin N , Buchanan Dylon , Durr Timothy , Richey Karen J 
Published: April 2021

Abstract

Introduction
Burn patients often require ventilator management because of large % TBSA injury, the presence of inhalation injury, and/or other factors. Airway pressure relief ventilation (APRV) offers several advantages over conventional ventilation modes including improved alveolar recruitment, better oxygenation and hemodynamics, preservation of spontaneous breathing, and possibly less ventilator-induced lung injury. This study reviews the use of APRV as the primary ventilator mode in burn patients with and without inhalation injury.

Methods
A retrospective chart review of patients admitted to the burn center and requiring APRV ventilation over a ten-year period was performed. Data collected included demographic data, burn injury data, ventilator settings, arterial blood gas data, and development of ventilator-associated pneumonia (VAP).

Results
There were 411 patients identified over the ten-year period. Mean age was 46 years, and mean % TBSA burned was 33. Seventy-three percent were male. One-half (51%) of patients had an inhalation injury. Mean hospital length of stay was 32 days with 22 mean ventilator days. Average number of surgeries was 4.4 per patient. Mean high pressure (P high) was 23 mm Hg. Mean FiO2 was 88% on post-injury day (PID) 1, 65% on day PID 2, and 45% thereafter. Mean P/F ratio was 333. Mean pH was 7.40, mean pCO2 was 40 mmHg, and mean HCO3 was 25 mm Hg. Forty-six percent of patients met criteria for diagnosis of VAP.

Conclusions
These data demonstrate that burn patients requiring mechanical ventilation can be safely and effectively managed with APRV. Oxygenation, carbon dioxide removal, normal acid-base status, and excellent P/F ratios were maintained with relatively low ventilator settings such as peak airway pressure and FiO2. Patients were able to breathe spontaneously when able and were easily liberated form the ventilator at the appropriate time.
19 Nebulized Tobramycin is Associated with Decreased Pneumonia After Inhalation Injury
Authors: Chin Theresa L , Frerk Rita , Joe Victor C , Bernal Nicole O 
Published: April 2021

Abstract

Introduction
Management of inhalation injury is largely supportive and consists primarily of mechanical ventilation, bronchodilators, muscarinic receptor antagonists, and inhaled mucolytics and anticoagulants. Patients with inhalation injury are at high risk for pneumonia. Nebulized tobramycin is well supported in the use of chronic lung infections in patients with cystic fibrosis (CF). We hypothesize that after inhalation injury patients empirically treated with nebulized tobramycin (NT) have decreased incidence of pneumonia.

Methods
A protocol for a standardized 7-day course of nebulized treatments with bronchodilators, inhaled mucolytics combined with inhaled anticoagulants and Nebulized tobramycin was developed. Starting in May 2013, all patients that had a clinical diagnosis of inhalation documented by the attending physician were started on the 7-day treatment course. Patients with inhalation injury from 2009–2019 were retrospectively reviewed for treatment of inhalation injury. Univariate analysis and multiple logistic regression were performed using Stata.

Results
Of 90 patients with inhalation injury, median age was 52 (IQR:31–60) with 27% (n=23) women and median TBSA of 10%(IQR:0.7–35%). The median length of stay was 21 days (IQR:6–47). Median ventilator days were lower in patients treated with NT (8.5, IQR:4–21) compared to patients that did not receive NT (10, IQR:3–23) but was not significant (p=0.85). However, of those that received NT, the presence of pneumonia was significantly lower compared to patients not treated with NT (p=0.032). (Table 1) Patients who were not treated with NT developed pneumonia earlier (day 5, IQR:3–7) than patients treated with NT (day 10, IQR 6–16, p=0.02) After adjusting for grade of inhalation injury, patients who did not receive NT were 2.7 times as likely to get pneumonia compared to patients who received NT (p=0.037).

Conclusions
NT is a prophylactic strategy for pneumonia in patients with CF, well documented to be safe. It allows for concentrated antibacterial coverage localized to the area of infection without significant systemic absorption. After seeing ~60% incidence of pneumonia in inhalation patients, we developed a strategy that avoided systemic prophylactic treatment. Initial data shows empiric administration of NT in patients with inhalation injury may abrogate the development pneumonia. Additional research and clinical trials are needed to better understand the role of inhaled tobramycin in the management of inhalation injury.