Kinetically-derived maximal dose (KMD) indicates lack of human carcinogenicity of ethylbenzene.

The kinetically-derived maximal dose (KMD) is defined as the maximal external dose at which kinetics are unchanged relative to lower doses, e.g., doses at which kinetic processes are not saturated. Toxicity produced at doses above the KMD can be qualitatively different from toxicity produced at lower doses. Here, we test the hypothesis that neoplastic lesions reported in the National Toxicology Program's (NTP) rodent cancer bioassay with ethylbenzene are a high-dose phenomenon secondary to saturation of elimination kinetics. To test this, we applied Bayesian modeling on kinetic data for ethylbenzene from rats and humans to estimate the Vmax and Km for the Michaelis-Menten equation that governs the elimination kinetics. Analysis of the Michaelis-Menten elimination curve generated from those Vmax and Km values indicated KMD ranges for venous ethylbenzene of 8-17 mg/L in rats and 10-18 mg/L in humans. Those venous concentrations are produced by inhalation concentrations of around 200 ppm ethylbenzene, which is well above typical human exposures. These KMD estimates support the hypothesis that neoplastic lesions seen in the NTP rodent bioassay occur secondary to saturation of ethylbenzene elimination pathways and are not relevant for human risk assessment. Thus, ethylbenzene does not pose a credible cancer risk to humans under foreseeable exposure conditions. Cancer risk assessments focused on protecting human health should avoid endpoint data from rodents exposed to ethylbenzene above the KMD range and future toxicological testing should focus on doses below the KMD range.

A Bayesian nonparametric model for classification of longitudinal profiles.

Across several medical fields, developing an approach for disease classification is an important challenge. The usual procedure is to fit a model for the longitudinal response in the healthy population, a different model for the longitudinal response in the diseased population, and then apply Bayes' theorem to obtain disease probabilities given the responses. Unfortunately, when substantial heterogeneity exists within each population, this type of Bayes classification may perform poorly. In this article, we develop a new approach by fitting a Bayesian nonparametric model for the joint outcome of disease status and longitudinal response, and then we perform classification through the clustering induced by the Dirichlet process. This approach is highly flexible and allows for multiple subpopulations of healthy, diseased, and possibly mixed membership. In addition, we introduce an Markov chain Monte Carlo sampling scheme that facilitates the assessment of the inference and prediction capabilities of our model. Finally, we demonstrate the method by predicting pregnancy outcomes using longitudinal profiles on the human chorionic gonadotropin beta subunit hormone levels in a sample of Chilean women being treated with assisted reproductive therapy.

A novel approach to calculating the kinetically derived maximum dose.

The kinetically derived maximal dose (KMD) provides a toxicologically relevant upper range for the determination of chemical safety. Here, we describe a new way of calculating the KMD that is based on sound Bayesian, theoretical, biochemical, and toxicokinetic principles, that avoids the problems of relying upon the area under the curve (AUC) approach that has often been used. Our new, mathematically rigorous approach is based on converting toxicokinetic data to the overall, or system-wide, Michaelis-Menten curve (which is the slope function for the toxicokinetic data) using Bayesian methods and using the "kneedle" algorithm to find the "knee" or "elbow"-the point at which there is diminishing returns in the velocity of the Michaelis-Menten curve (or acceleration of the toxicokinetic curve). Our work fundamentally reshapes the KMD methodology, placing it within the well-established Michaelis-Menten theoretical framework by defining the KMD as the point where the kinetic rate approximates the Michaelis-Menten asymptote at higher concentrations. By putting the KMD within the Michaelis-Menten framework, we leverage existing biochemical and pharmacological concepts such as "saturation" to establish the region where the KMD is likely to exist. The advantage of defining KMD as a region, rather than as an inflection point along the curve, is that a region reflects uncertainty and clarifies that there is no single point where the curve is expected to "break;" rather, there is a region where the curve begins to taper off as it approaches the asymptote (Vmax in the Michaelis-Menten equation).

A Bayesian Mixture Cure Rate Model for Estimating Short-Term and Long-Term Recidivism.

Mixture cure rate models have been developed to analyze failure time data where a proportion never fails. For such data, standard survival models are usually not appropriate because they do not account for the possibility of non-failure. In this context, mixture cure rate models assume that the studied population is a mixture of susceptible subjects who may experience the event of interest and non-susceptible subjects that will never experience it. More specifically, mixture cure rate models are a class of survival time models in which the probability of an eventual failure is less than one and both the probability of eventual failure and the timing of failure depend (separately) on certain individual characteristics. In this paper, we propose a Bayesian approach to estimate parametric mixture cure rate models with covariates. The probability of eventual failure is estimated using a binary regression model, and the timing of failure is determined using a Weibull distribution. Inference for these models is attained using Markov Chain Monte Carlo methods under the proposed Bayesian framework. Finally, we illustrate the method using data on the return-to-prison time for a sample of prison releases of men convicted of sexual crimes against women in England and Wales and we use mixture cure rate models to investigate the risk factors for long-term and short-term survival of recidivism.

Statistical optimisation of growth conditions and diesel degradation by the Antarctic bacterium, Rhodococcus sp. strain AQ5‒07.

Petroleum pollution is a major concern in Antarctica due to the persistent nature of its hydrocarbon components coupled with the region's extreme environmental conditions, which means that bioremediation approaches are largely inapplicable at present. The current study assessed the ability of the psychrotolerant phenol-degrader, Rhodococcus sp. strain AQ5-07, to assimilate diesel fuel as the sole carbon source. Factors expected to influence the efficiency of diesel degradation, including the initial hydrocarbon concentration, nitrogen source concentration and type, temperature, pH and salinity were studied. Strain AQ5-07 displayed optimal cell growth and biodegradation activity at 1% v/v initial diesel concentration, 1 g/L NH4Cl concentration, pH 7 and 1% NaCl during one-factor-at-a-time (OFAT) analyses. Strain AQ5-07 was psychrotolerant based on its optimum growth temperature being near 20 °C. In conventionally optimised media, strain AQ5-07 showed total petroleum hydrocarbons (TPH) mineralisation of 75.83%. However, the optimised condition for TPH mineralisation predicted through statistical response surface methodology (RSM) enhanced the reduction to 90.39% within a 2 days incubation. Our preliminary data support strain AQ5-07 being a potential candidate for real-field soil bioremediation by specifically adopting sludge-phase bioreactor system in chronically cold environments such as Antarctica. The study also confirmed the utility of RSM in medium optimisation.

Biosurfactant Production and Growth Kinetics Studies of the Waste Canola Oil-Degrading Bacterium Rhodococcus erythropolis AQ5-07 from Antarctica.

With the progressive increase in human activities in the Antarctic region, the possibility of domestic oil spillage also increases. Developing means for the removal of oils, such as canola oil, from the environment and waste "grey" water using biological approaches is therefore desirable, since the thermal process of oil degradation is expensive and ineffective. Thus, in this study an indigenous cold-adapted Antarctic soil bacterium, Rhodococcus erythropolis strain AQ5-07, was screened for biosurfactant production ability using the multiple approaches of blood haemolysis, surface tension, emulsification index, oil spreading, drop collapse and "MATH" assay for cellular hydrophobicity. The growth kinetics of the bacterium containing different canola oil concentration was studied. The strain showed ß-haemolysis on blood agar with a high emulsification index and low surface tension value of 91.5% and 25.14 mN/m, respectively. Of the models tested, the Haldane model provided the best description of the growth kinetics, although several models were similar in performance. Parameters obtained from the modelling were the maximum specific growth rate (qmax), concentration of substrate at the half maximum specific growth rate, Ks% (v/v) and the inhibition constant Ki% (v/v), with values of 0.142 h-1, 7.743% (v/v) and 0.399% (v/v), respectively. These biological coefficients are useful in predicting growth conditions for batch studies, and also relevant to "in field" bioremediation strategies where the concentration of oil might need to be diluted to non-toxic levels prior to remediation. Biosurfactants can also have application in enhanced oil recovery (EOR) under different environmental conditions.

Marginal likelihood estimation of negative binomial parameters with applications to RNA-seq data.

RNA-Seq data characteristically exhibits large variances, which need to be appropriately accounted for in any proposed model. We first explore the effects of this variability on the maximum likelihood estimator (MLE) of the dispersion parameter of the negative binomial distribution, and propose instead to use an estimator obtained via maximization of the marginal likelihood in a conjugate Bayesian framework. We show, via simulation studies, that the marginal MLE can better control this variation and produce a more stable and reliable estimator. We then formulate a conjugate Bayesian hierarchical model, and use this new estimator to propose a Bayesian hypothesis test to detect differentially expressed genes in RNA-Seq data. We use numerical studies to show that our much simpler approach is competitive with other negative binomial based procedures, and we use a real data set to illustrate the implementation and flexibility of the procedure.

An asymptotic approximation to the N-mixture model for the estimation of disease prevalence.

N-mixture models are probability models that estimate abundance using replicate observed counts while accounting for imperfect detection. In this article, we propose an asymptotic approximation to the N-mixture model which efficiently estimates large abundances without the computational limitations of the generalized N-mixture model introduced by Dail and Madsen in 2011. It has been suggested in the literature that N-mixture models do not perform well when counts from the same sites show weak patterns of population dynamics. Our proposed model addresses this issue by using the asymptotic information matrix to diagnose model parameter estimability and to derive parameter standard errors. A simulation study show that this model performs almost as well as the Dail-Madsen Generalized N-mixture model at low abundances and improves on it at higher abundances. We illustrate the procedure using two data sets: the American robin data from Dail and Madsen (2011), and counts of chlamydia cases in the state of Oregon from 2007-2016. The chlamydia data exhibit very large abundances and demonstrate the potential usefulness of the proposed model for disease surveillance data.

Inactivation model and risk-analysis design for apple juice processing by high-pressure CO2.

Sigmoidal microbial survival curves are observed in high-pressure carbon dioxide (HPCD) pasteurization treatments. The objectives of this study were to use the Gompertz primary model to describe the inactivation in apple juice of the pathogen Escherichia coli CGMCC1.90 and to apply probabilistic engineering to select HPCD treatments meeting at least 5 log10 reductions (SV ≥ 5) at 95% confidence. This required secondary models for the temperature (T, °C) and pressure (P, MPa) dependence of the Gompertz model parameters. The expressions [Formula: see text] and [Formula: see text] selected using goodness-of-fit measures and assessments based on Akaike and Bayesian information criteria were consistent with proposed mechanistic models for HPCD bactericidal effects. Monte Carlo simulations accounting for the variability and uncertainty of the parameter b and c estimates were used to predict SV values for a given time, temperature and CO2 pressure combination and desired confidence boundary. A similar approach used to estimate process times meeting SV ≥ 5 at 95% confidence for a given temperature and CO2 pressure combination, showed that HPCD processes met this requirement only for relatively long processing times, i.e., 35-124 min in the experimental range of 32-42 °C and 10-30 MPa. Therefore, further HPCD research is required to reduce processing time.

Predicting pregnancy outcomes using longitudinal information: a penalized splines mixed-effects model approach.

We propose a semiparametric nonlinear mixed-effects model (SNMM) using penalized splines to classify longitudinal data and improve the prediction of a binary outcome. The work is motivated by a study in which different hormone levels were measured during the early stages of pregnancy, and the challenge is using this information to predict normal versus abnormal pregnancy outcomes. The aim of this paper is to compare models and estimation strategies on the basis of alternative formulations of SNMMs depending on the characteristics of the data set under consideration. For our motivating example, we address the classification problem using a particular case of the SNMM in which the parameter space has a finite dimensional component (fixed effects and variance components) and an infinite dimensional component (unknown function) that need to be estimated. The nonparametric component of the model is estimated using penalized splines. For the parametric component, we compare the advantages of using random effects versus direct modeling of the correlation structure of the errors. Numerical studies show that our approach improves over other existing methods for the analysis of this type of data. Furthermore, the results obtained using our method support the idea that explicit modeling of the serial correlation of the error term improves the prediction accuracy with respect to a model with random effects, but independent errors. Copyright © 2017 John Wiley & Sons, Ltd.

Conservative Approach for the Esthetic Management of Multiple Interdental Spaces: A Systematic Approach.

PURPOSE: To describe a conservative approach using resin-based composites following a buccolingual layering technique with a customized silicon index for the management of multiple diastemas. CLINICAL CONSIDERATIONS: This clinical article describes the case of a patient with their anterior teeth esthetically compromised by multiple diastemas, incisal wear, and dull/low-value cervical composite resin restorations that were managed with nanofilled composite resin using the "buccolingual technique" with a customized silicon index made from a wax-up to build up the restorations. The first layer of composite placed lingually that represents the enamel replacement was placed directly on the silicon index so that it provides in one single step the lingual profile and the position of the incisal edge of the restoration. Then, dentine and effect composite resin can be applied in a precise three-dimensional configuration. CONCLUSIONS: To solve esthetic dental problems, as anterior diastemas, in a very conservative and even reversible way, the use of direct resin composites for layering is an excellent choice, but should be performed based on simple and reproducible techniques, as the buccolingual technique. CLINICAL SIGNIFICANCE: The clinical technique described in this paper shows the advantages of a conservative approach to correct diastemas on maxillary anterior teeth. The application of these techniques can not only help achieve optimal esthetics, but also avoid the removal of extensive dental hard tissue and achieve a predictable final result, especially in esthetically demanding cases.

Effects of heavy metals on bacterial growth parameters in degradation of phenol by an Antarctic bacterial consortium.

Antarctica has often been perceived as a pristine continent until the recent few decades as pollutants have been observed accruing in the Antarctic environment. Irresponsible human activities such as accidental oil spills, waste incineration and sewage disposal are among the primary anthropogenic sources of heavy metal contaminants in Antarctica. Natural sources including animal excrement, volcanism and geological weathering also contribute to the increase of heavy metals in the ecosystem. A microbial growth model is presented for the growth of a bacterial cell consortium used in the biodegradation of phenol in media containing different metal ions, namely arsenic (As), cadmium (Cd), aluminium (Al), nickel (Ni), silver (Ag), lead (Pb) and cobalt (Co). Bacterial growth was inhibited by these ions in the rank order of Al < As < Co < Pb < Ni < Cd < Ag. Greatest bacterial growth occurred in 1 ppm Al achieving an OD600 of 0.985 and lowest in 1 ppm Ag with an OD600 of 0.090. At a concentration of 1.0 ppm, Ag had a considerable effect on the bacterial consortium, inhibiting the degradation of phenol, whereas this concentration of the other metal ions tested had no effect on degradation. The biokinetic growth model developed supports the suitability of the bacterial consortium for use in phenol degradation.

Contribution of hospitals and clinical services to global warming: protocol for a scoping review.

INTRODUCTION: Climate change is one of the greatest threats to public health today, placing considerable pressure on the healthcare sector. During patient care processes, hospital facilities contribute to greenhouse gas emissions through the use of greater resources and higher energy consumption. Consequently, there is growing interest among researchers, universities, organisations and governments to study the impact of the healthcare sector on the environment and to develop strategies to mitigate its effects. The aim of this scoping review is to determine the extent and nature of current literature on global warming from hospitals and clinical services, and ways in which they contribute to its effect. Planning and execution of future research are possible once those areas with existing gaps are identified. METHODS AND ANALYSIS: A broad literature search will be carried out to illustrate the ways in which hospitals and clinical services, processes and activities contribute to climate change. Our protocol was drafted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. The final protocol was registered prospectively with the Open Science Framework. All identified studies indexed in Medline, Scopus and Embase will be examined. ETHICS AND DISSEMINATION: This project is literature-based research; therefore, it does not require ethical approval. The results will be presented to researchers as well as policymakers in this particular area, via conferences, webinars, podcasts and online events. A peer-reviewed publication will be submitted to specific journals of interest.

Antarctic heavy metal pollution and remediation efforts: state of the art of research and scientific publications.

In Antarctica, human activities have been reported to be the major cause of the accumulation of heavy metal contaminants. A comprehensive bibliometric analysis of publications on heavy metal contamination in Antarctica from year 2000 to 2020 was performed to obtain an overview of the current landscape in this line of research. A total of 106 documents were obtained from Scopus, the largest citation database. Extracted data were analysed, and VOSviewer software was used to visualise trends. The result showed an increase in publications and citations in the past 20 years indicating the rising interest on heavy metal contamination in the Antarctic region. Based on the analysis of keywords, the publications largely discuss various types of heavy metals found in the Antarctic water and sediment. The analysis on subject areas detects multiple disciplines involved, wherein the environmental science was well-represented. The top countries and authors producing the most publication in this field were from Australia, China, Brazil and Chile. Numerous efforts have been exercised to investigate heavy metal pollution and its mitigation approaches in the region in the past decades. This paper not only is relevant for scholars to understand the development status and trends in this field but also offers clear insights on the future direction of Antarctic heavy metal contamination and remediation research.

Harnessing Diesel-Degrading Potential of an Antarctic Microalga from Greenwich Island and Its Physiological Adaptation.

Microalgae are well known for their metal sorption capacities, but their potential in the remediation of hydrophobic organic compounds has received little attention in polar regions. We evaluated in the laboratory the ability of an Antarctic microalga to remediate diesel hydrocarbons and also investigated physiological changes consequent upon diesel exposure. Using a polyphasic taxonomic approach, the microalgal isolate, WCY_AQ5_1, originally sampled from Greenwich Island (South Shetland Islands, maritime Antarctica) was identified as Tritostichococcus sp. (OQ225631), a recently erected lineage within the redefined Stichococcus clade. Over a nine-day experimental incubation, 57.6% of diesel (~3.47 g/L) was removed via biosorption and biodegradation, demonstrating the strain's potential for phytoremediation. Fourier transform infrared spectroscopy confirmed the adsorption of oil in accordance with its hydrophobic characteristics. Overall, degradation predominated over sorption of diesel. Chromatographic analysis confirmed that the strain efficiently metabolised medium-chain length n-alkanes (C-7 to C-21), particularly n-heneicosane. Mixotrophic cultivation using diesel as the organic carbon source under a constant light regime altered the car/chl-a ratio and triggered vacuolar activities. A small number of intracellular lipid droplets were observed on the seventh day of cultivation in transmission electron microscopic imaging. This is the first confirmation of diesel remediation ability in an Antarctic green microalga.

Human-associated extended-spectrum ß-lactamase in the Antarctic.

Escherichia coli bacteria with extended-spectrum ß-lactamase (ESBL) type CTX-M resistance were isolated from water samples collected close to research stations in Antarctica. The isolates had bla(CTX-M-1) and bla(CTX-M-15) genotypes and sequence types (ST) indicative of a human-associated origin. This is the first record of ESBL-producing enterobacteria from Antarctica.

Analytical categories to describe deficit attributions in deep disagreements between citizens and experts.

Disagreements often arise from citizen-expert collaboration, as both agents share a different epistemic worldview. Fogelin, following Wittgenstein, proposed that some disagreements (i.e. deep disagreements) cannot be rationally solved when participants share different forms of life. Citizen-expert is an exemplar of this sort of disagreement. Moreover, deep disagreements are often followed by deficit attributions from one of the agents to the other, regarding their epistemic understanding, credibility, and motives. Articulating the notions of deep disagreements and deficit attributions, as well as reviewing the complementary concept of epistemic injustice, we have constructed analytical categories that allow us to understand two things: (1) how deficit attributions operate in dialogical contexts of deep disagreements and (2) what types of deficit attributions we can find. We expect that this characterization can serve to analyze citizen-expert dialogues and the pursuit of more modest and inclusive forms of conversation.

Oil Palm's Empty Fruit Bunch as a Sorbent Material in Filter System for Oil-Spill Clean Up.

Oil pollution such as diesel poses a significant threat to the environment. Due to this, there is increasing interest in using natural materials mainly from agricultural waste as organic oil spill sorbents. Oil palm's empty fruit bunch (EFB), a cost-effective material, non-toxic, renewable resource, and abundantly available in Malaysia, contains cellulosic materials that have been proven to show a good result in pollution treatment. This study evaluated the optimum screening part of EFB that efficiently absorbs oil and the physicochemical characterisation of untreated and treated EFB fibre using Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscopy (SEM). The treatment conditions were optimised using one-factor-at-a-time (OFAT), which identified optimal treatment conditions of 170 °C, 20 min, 0.1 g/cm3, and 10% diesel, resulting in 23 mL of oil absorbed. The predicted model was highly significant in statistical Response Surface Methodology (RSM) and confirmed that all the parameters (temperature, time, packing density, and diesel concentration) significantly influenced the oil absorbed. The predicted values in RSM were 175 °C, 22.5 min, 0.095 g/cm3, and 10%, which resulted in 24 mL of oil absorbed. Using the experimental values generated by RSM, 175 °C, 22.5 min, 0.095 g/cm3, and 10%, the highest oil absorption achieved was 24.33 mL. This study provides further evidence, as the data suggested that RSM provided a better approach to obtain a high efficiency of oil absorbed.

Offspring of first-generation hatchery steelhead trout (Oncorhynchus mykiss) grow faster in the hatchery than offspring of wild fish, but survive worse in the wild: Possible mechanisms for inadvertent domestication and fitness loss in hatchery salmon.

Salmonid fish raised in hatcheries often have lower fitness (number of returning adult offspring) than wild fish when both spawn in the wild. Body size at release from hatcheries is positively correlated with survival at sea. So one explanation for reduced fitness is that hatcheries inadvertently select for trait values that enhance growth rate under the unnatural environment of a hatchery, but that are maladaptive in the wild environment. A simple prediction of this hypothesis is that juveniles of hatchery origin should grow more quickly than fish of wild origin under hatchery conditions, but should have lower survival under wild conditions. We tested that hypothesis using multiple full sibling families of steelhead (Oncorhynchus mykiss) that were spawned using either two wild parents (WxW) or two first-generation hatchery (HxH) parents. Offspring from all the families were grown together under hatchery conditions and under semi-natural conditions in artificial streams. HxH families grew significantly faster in the hatchery, but had significantly lower survival in the streams. That we see this tradeoff after only a single generation of selection suggests that the traits involved are under very strong selection. We also considered one possible alteration to the hatchery environment that might reduce the intensity of selection among families in size at release. Here we tested whether reducing the fat content of hatchery feed would reduce the variance among families in body size. Although fish raised under a low-fat diet were slightly smaller, the variation among families in final size was unchanged. Thus, there is no evidence that reducing the fat content of hatchery feed would reduce the opportunity for selection among families on size at release.

Research Trends of Biodegradation of Cooking Oil in Antarctica from 2001 to 2021: A Bibliometric Analysis Based on the Scopus Database.

In the present age, environmental pollution is multiplying due to various anthropogenic activities. Pollution from waste cooking oil is one of the main issues facing the current human population. Scientists and researchers are seriously concerned about the oils released from various activities, including the blockage of the urban drainage system and odor issues. In addition, cooking oil is known to be harmful and may have a carcinogenic effect. It was found that current research studies and publications are growing on these topics due to environmental problems. A bibliometric analysis of studies published from 2001 to 2021 on cooking oil degradation was carried out using the Scopus database. Primarily, this analysis identified the reliability of the topic for the present-day and explored the past and present progresses of publications on various aspects, including the contributing countries, journals and keywords co-occurrence. The links and interactions between the selected subjects (journals and keywords) were further visualised using the VOSviewer software. The analysis showed that the productivity of the publications is still developing, with the most contributing country being the United States, followed by China and India with 635, 359 and 320 publications, respectively. From a total of 1915 publications, 85 publications were published in the Journal of Agricultural and Food Chemistry. Meanwhile, the second and third of the most influential journals were Bioresource Technology and Industrial Crops and Products with 76 and 70 total publications, respectively. Most importantly, the co-occurrence of the author's keywords revealed "biodegradation", "bioremediation", "vegetable oil" and "Antarctic" as the popular topics in this study area, especially from 2011 to 2015. In conclusion, this bibliometric analysis on the degradation of cooking oil may serve as guide for future avenues of research in this area of research.