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The purpose of this study was to investigate the influence of increasing sulfate concentrations on chromium removal, to evaluate the effect of the presence of Cr(VI) on sulfate removal by Streptomyces sp. MC1 and to analyze the differential protein expression profile in the presence of this metal for the identification of proteins repressed or overexpressed. In the presence of Cr(VI) but in the absence of sulfate ions, bacterial growth was negligible, showing the Cr(VI) toxicity for this bacterium. However, the sulfate presence stimulated bacterium growth and Cr(VI) removal, regardless of its concentrations. Streptomyces sp. MC1 showed ability to remove chromium and sulfate simultaneously. Also, the sulfate presence favored the decrease of total chromium concentration from supernatants reaching a decrease of 50% at 48 h. In presence of chromium, seven proteins were down‐expressed and showed homology to proteins involved in protein biosynthesis, energy production and free radicals detoxification while two proteins involved in oxidation‐reduction processes identified as dihydrolipoamide dehydrogenase and S‐adenosyl‐l‐methionine synthase were overexpressed.

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

Hepatic steatosis is a key initiating event in the pathogenesis of alcohol-associated liver disease (ALD), the most detrimental organ damage resulting from alcohol use disorder. However, the mechanisms by which alcohol induces steatosis remain incompletely understood. We have previously found that alcohol binging impairs brain insulin action, resulting in increased adipose tissue lipolysis by unrestraining sympathetic nervous system (SNS) outflow. Here, we examined whether an impaired brain-SNS-adipose tissue axis drives hepatic steatosis through unrestrained adipose tissue lipolysis and increased lipid flux to the liver.We examined the role of lipolysis, and the brain-SNS-adipose tissue axis and stress in alcohol induced hepatic triglyceride accumulation in a series of rodent models: pharmacological inhibition of the negative regulator of insulin signaling protein-tyrosine phosphatase 1β (PTP1b) in the rat brain, tyrosine hydroxylase (TH) knockout mice as a pharmacogenetic model of sympathectomy, adipocyte specific adipose triglyceride lipase (ATGL) knockout mice, wildtype (WT) mice treated with β3 adrenergic agonist or undergoing restraint stress.Intracerebral administration of a PTP1b inhibitor, inhibition of adipose tissue lipolysis and reduction of sympathetic outflow ameliorated alcohol induced steatosis. Conversely, induction of adipose tissue lipolysis through β3 adrenergic agonism or by restraint stress worsened alcohol induced steatosis.Brain insulin resistance through upregulation of PTP1b, increased sympathetic activity, and unrestrained adipose tissue lipolysis are key drivers of alcoholic steatosis. Targeting these drivers of steatosis may provide effective therapeutic strategies to ameliorate ALD.

Background:  We previously found that activation of the glial cell line‐derived neurotrophic factor (GDNF) pathway in the ventral tegmental area (VTA) reduces ethanol‐drinking behaviors. In this study, we set out to assess the contribution of endogenous GDNF or its receptor GFRα1 to the regulation of ethanol‐related behaviors.Methods:  GDNF and GFRα1 heterozygote mice (HET) and their wild‐type littermate controls (WT) were used for the studies. Ethanol‐induced hyperlocomotion, sensitization, and conditioned place preference (CPP), as well as ethanol consumption before and after a period of abstinence were evaluated. Blood ethanol concentration (BEC) was also measured.Results:  We observed no differences between the GDNF HET and WT mice in the level of locomotor activity or in sensitization to ethanol‐induced hyperlocomotion after systemic injection of a nonhypnotic dose of ethanol and in BEC. However, GDNF and GFRα1 mice exhibited increased place preference to ethanol as compared with their WT littermates. The levels of voluntary ethanol or quinine consumption were similar in the GDNF HET and WT mice, however, a small but significant increase in saccharin intake was observed in the GDNF HET mice. No changes were detected in voluntary ethanol, saccharin or quinine consumption of GFRα1 HET mice as compared with their WT littermates. Interestingly, however, both the GDNF and GFRα1 HET mice consumed much larger quantities of ethanol after a period of abstinence from ethanol as compared with their WT littermates. Furthermore, the increase in ethanol consumption after abstinence was found to be specific for ethanol as similar levels of saccharin intake were measured in the GDNF and GFRα1 HET and WT mice after abstinence.Conclusions:  Our results suggest that endogenous GDNF negatively regulates the rewarding effect of ethanol and ethanol‐drinking behaviors after a period of abstinence.

Aims The aim of this study was to determine whether the sequential application of povidone iodine-alcohol (PVI) followed by chlorhexidine gluconate-alcohol (CHG) would reduce surgical wound contamination to a greater extent than PVI applied twice in patients undergoing spinal surgery. Patients and Methods A single-centre, interventional, two arm, parallel group randomised controlled trial was undertaken, involving 407 patients who underwent elective spinal surgery. For 203 patients, the skin was disinfected before surgery using PVI (10% [w/w (1% w/w available iodine)] in 95% industrial denatured alcohol, povidone iodine; Videne Alcoholic Tincture) twice, and for 204 patients using PVI once followed by CHG (2% [w/v] chlorhexidine gluconate in 70% [v/v] isopropyl alcohol; Chloraprep with tint). The primary outcome measure was contamination of the wound determined by aerobic and anaerobic bacterial growth from samples taken after disinfection. Results The detection of viable bacteria in any one of the samples taken after disinfection (culture-positive) was significantly lower in the group treated with both PVI and CHG than in the group treated with PVI alone (59 (29.1%) versus 85 (41.7%), p = 0.009; odds ratio 0.574; 95% confidence interval, 0.380 to 0.866). Conclusions Antisepsis of the skin with the sequential application of PVI and CHG more effectively reduces the contamination of a surgical wound than PVI alone. Cite this article: Bone Joint J 2017;99-B:1354–65.

ABSTRACTPrenatal ethanol exposure causes a variety of cognitive deficits that have a persistent impact on quality of life, some of which may be explained by ethanol-induced alterations in interneuron function. Studies from several laboratories, including our own, have demonstrated that a single binge-like ethanol exposure during the equivalent to the third trimester of human pregnancy leads to acute apoptosis and long-term loss of interneurons in the rodent retrosplenial cortex (RSC). The RSC is interconnected with the hippocampus, thalamus, and other neocortical regions and plays distinct roles in visuospatial processing and storage, as well as retrieval of hippocampal-dependent episodic memories. Here we used slice electrophysiology to characterize the acute effects of ethanol on GABAergic neurotransmission in the RSC of neonatal mice, as well as the long-term effects of neonatal ethanol exposure on parvalbumin-interneuron mediated neurotransmission in adolescent mice. Mice were exposed to ethanol using vapor inhalation chambers. In postnatal day (P) 7 mouse pups, ethanol unexpectedly failed to potentiate GABAAreceptor-mediated synaptic transmission. Binge-like ethanol exposure of P7 mice expressing channel rhodopsin in parvalbumin-positive interneurons enhanced the peak amplitudes, asynchronous activity and total charge, while decreasing the rise-times of optically-evoked GABAAreceptor-mediated inhibitory postsynaptic currents in adolescent animals. These effects could partially explain the learning and memory deficits that have been documented in adolescent and young adult mice exposed to ethanol during the third trimester-equivalent developmental period.

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Primary input data was collected from the following sources: Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA) Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes. All data is provided in CSV format. Nothing proprietary is required. 

Variation in food resources can result in dramatic fluctuations in the body condition of animals dependent on those resources. Decreases in body mass can disrupt patterns of energy allocation and impose stress, thereby altering immune function. In this study we investigated links between changes in body mass of captive cane toads (Rhinella marina), their circulating white blood cell populations, and their performance in immune assays. Captive toads that lost weight over a 3-month period had increased levels of monocytes and heterophils and reduced levels of eosinophils. Basophil and lymphocyte levels were unrelated to changes in mass. Because individuals that lost mass had higher heterophil levels but stable lymphocyte levels, the ratio of these cell types was also higher, partially consistent with a stress response. Phagocytic ability of whole blood was higher in toads that lost mass, due to increased circulating levels of phagocytic cells. Other measures of immune performance were unrelated to mass change. These results highlight the challenges faced by invasive species as they expand their range into novel environments which may impose substantial seasonal changes in food availability that were not present in the native range. Individuals facing energy restrictions may shift their immune function towards more economical and general avenues of combating pathogens.

{"references": ["Liu, Z., Ciais, P., Deng, Z., Lei, R., Davis, S. J., Feng, S., Zheng, B., Cui, D., Dou, X., Zhu, B., Guo, R., Ke, P., Sun, T., Lu, C., He, P., Wang, Y., Yue, X., Wang, Y., Lei, Y., Zhou, H., Cai, Z., Wu, Y., Guo, R., Han, T., Xue, J., Boucher, O., Boucher, E., Chevallier, F., Tanaka, K., Wei, Y., Zhong, H., Kang, C., Zhang, N., Chen, B., Xi, F., Liu, M., Br\u00e9on, F.-M., Lu, Y., Zhang, Q., Guan, D., Gong, P., Kammen, D. M., He, K. & Schellnhuber, H. J. (2020). Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic. Nature Communications 11, 5172 (2020). https://doi.org/10.1038/s41467-020-18922-7", "Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., & van Vuuren, D. P. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Climatic Change, 109(1\u20132), 213\u2013241. https://doi.org/10.1007/s10584-011-0156-z", "Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P. & Wilbanks, T. J. (2010). The next generation of scenarios for climate change research and assessment. Nature, 463(7282), 747\u2013756. https://doi.org/10.1038/nature08823", "Myhre, G., Highwood, E. J., Shine, K. P., & Stordal, F. (1998). New estimates of radiative forcing due to well mixed greenhouse gases. Geophysical Research Letters, 25(14), 2715\u20132718. https://doi.org/10.1029/98gl01908", "Strassmann, K. M. and Joos, F. (2018). The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle\u2013climate simulations, Geosci. Model Dev., 11, 1887\u20131908, https://doi.org/10.5194/gmd-11-1887-2018", "Thomas, M. A., and Lin, T. (2018). A dual model for emulation of thermosteric and dynamic sea-level change. Climatic Change, 148(1\u20132), 311\u2013324. https://doi.org/10.1007/s10584-018-2198-y"]} Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections. Earth's Future. In Press. Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO2 emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community. We acknowledge the Carbon Monitor initiative (Liu et al., 2020) for providing the COVID-19 CO2 sectoral emission data used to construct the proposed TEPs. In addition, we acknowledge the developers of the BernSCM (Strassmann and Joos, 2018) that was utilized in this study to relate TEP CO2 emissions to their respective CO2 atmospheric concentrations. Furthermore, we thank the Texas Tech University McNair Scholars Program and the Multi-Hazard Sustainability (HazSus) research group for guidance and support throughout the course of this study. Analyses presented herein were performed using the RedRaider computing cluster at Texas Tech University. We thank the team at the High Performance Computing Center (HPCC) for their generous support. In addition, the equipment support from the Vice President for Research & Innovation for T.L.'s HazSus Research Group is gratefully acknowledged.