A systematic review of transitions between cigarette and smokeless tobacco product use in the United States

Abstract Background Smokeless tobacco use is becoming an increasingly important public health issue in the US and may influence cigarette smoking behavior. Systematic information on transitions between smokeless tobacco and cigarette use in the US is limited. Methods We conducted a systematic review of published literature on transitions between smokeless tobacco and cigarette use in the US. We searched PubMed, Web of Science and EbscoHost databases for all published articles from January 2000 to March 2014 that presented estimates of transitions in US youth and adult study populations over time between at least one of the following tobacco use states: exclusive cigarette smoking, exclusive smokeless tobacco use, dual use of both products, and use of neither product. We excluded non-English language studies, studies published before 2000, clinical trials, controlled cessation programs, and clinical studies or evaluations of smokeless tobacco cessation programs. Results The review identified six studies on US populations published since 2000 with longitudinal data on some or all of the transitions that users can undergo between smokeless tobacco and cigarette use. There was considerable heterogeneity across studies in design and tobacco use definitions. Despite these differences, the existing data indicate that switching behaviors from exclusive smoking to exclusive smokeless tobacco use are limited (adults: 0%-1.4%, adolescents: 0.8%-3.8%) but may be more common from exclusive smokeless tobacco use to exclusive smoking (adults: 0.9%-26.6%, adolescents: 16.6%-25.5%). Among adults, exclusive cigarette smoking was generally stable and consistent (79.7% to 87.6%) over follow-up across studies but less stable in adolescents (46.8%-78.7%). Exclusive smokeless tobacco use was less stable than exclusive cigarette smoking over time (adults: 59.4%-76.6%, adolescents: 26.2%-44.8%). Conclusion This review provides published estimates of the proportions of adults and adolescents transitioning between tobacco use categories from the most recently available studies on longitudinal transitions between smokeless tobacco and cigarettes in the US. These data can be used to track tobacco use behaviors and evaluate their effect on public health; however, the data for these studies were generally collected more than a decade ago. Additional research including nationally representative longitudinal estimates using consistent definitions and designs, would improve understanding of current tobacco transition behaviors.http://deepblue.lib.umich.edu/bitstream/2027.42/110803/1/12889_2015_Article_1594.pd

Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and Artificial Neural Networks.

Cuticular hydrocarbons were extracted daily from the larvae of two closely related blowflies Calliphora vicina and Calliphora vomitoria (Diptera: Calliphoridae). The hydrocarbons were then analysed using Gas Chromatography-Mass Spectrometry (GC-MS), with the aim of observing changes within their chemical profiles in order to determine the larval age. The hydrocarbons were examined daily for each species from 1day old larvae until pupariation. The results show significant chemical changes occurring from the younger larvae to the post-feeding larvae. With the aid of a multivariate statistical method (Principal Component Analysis and Artificial Neural Networks), samples were clustered and classified, allowing for the larval age to be established. Results from this study allowed larvae to be aged to the day with at worst, 87% accuracy, which suggests there is great potential for the use of cuticular hydrocarbons present on larvae to give an indication of their age and hence potentially a valuable tool for minimum PMI estimations

The potential shared role of inflammation in insulin resistance and schizophrenia:a bidirectional two-sample mendelian randomization study

BACKGROUND: Insulin resistance predisposes to cardiometabolic disorders, which are commonly comorbid with schizophrenia and are key contributors to the significant excess mortality in schizophrenia. Mechanisms for the comorbidity remain unclear, but observational studies have implicated inflammation in both schizophrenia and cardiometabolic disorders separately. We aimed to examine whether there is genetic evidence that insulin resistance and 7 related cardiometabolic traits may be causally associated with schizophrenia, and whether evidence supports inflammation as a common mechanism for cardiometabolic disorders and schizophrenia. METHODS AND FINDINGS: We used summary data from genome-wide association studies of mostly European adults from large consortia (Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) featuring up to 108,557 participants; Diabetes Genetics Replication And Meta-analysis (DIAGRAM) featuring up to 435,387 participants; Global Lipids Genetics Consortium (GLGC) featuring up to 173,082 participants; Genetic Investigation of Anthropometric Traits (GIANT) featuring up to 339,224 participants; Psychiatric Genomics Consortium (PGC) featuring up to 105,318 participants; and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium featuring up to 204,402 participants). We conducted two-sample uni- and multivariable mendelian randomization (MR) analysis to test whether (i) 10 cardiometabolic traits (fasting insulin, high-density lipoprotein and triglycerides representing an insulin resistance phenotype, and 7 related cardiometabolic traits: low-density lipoprotein, fasting plasma glucose, glycated haemoglobin, leptin, body mass index, glucose tolerance, and type 2 diabetes) could be causally associated with schizophrenia; and (ii) inflammation could be a shared mechanism for these phenotypes. We conducted a detailed set of sensitivity analyses to test the assumptions for a valid MR analysis. We did not find statistically significant evidence in support of a causal relationship between cardiometabolic traits and schizophrenia, or vice versa. However, we report that a genetically predicted inflammation-related insulin resistance phenotype (raised fasting insulin (raised fasting insulin (Wald ratio OR = 2.95, 95% C.I, 1.38–6.34, Holm-Bonferroni corrected p-value (p) = 0.035) and lower high-density lipoprotein (Wald ratio OR = 0.55, 95% C.I., 0.36–0.84; p = 0.035)) was associated with schizophrenia. Evidence for these associations attenuated to the null in multivariable MR analyses after adjusting for C-reactive protein, an archetypal inflammatory marker: (fasting insulin Wald ratio OR = 1.02, 95% C.I, 0.37–2.78, p = 0.975), high-density lipoprotein (Wald ratio OR = 1.00, 95% C.I., 0.85–1.16; p = 0.849), suggesting that the associations could be fully explained by inflammation. One potential limitation of the study is that the full range of gene products from the genetic variants we used as proxies for the exposures is unknown, and so we are unable to comment on potential biological mechanisms of association other than inflammation, which may also be relevant. CONCLUSIONS: Our findings support a role for inflammation as a common cause for insulin resistance and schizophrenia, which may at least partly explain why the traits commonly co-occur in clinical practice. Inflammation and immune pathways may represent novel therapeutic targets for the prevention or treatment of schizophrenia and comorbid insulin resistance. Future work is needed to understand how inflammation may contribute to the risk of schizophrenia and insulin resistance

Grassy–herbaceous land moderates regional climate effects on honey bee colonies in the Northcentral US

The lack of seasonally sustained floral resources (i.e. pollen and nectar) is considered a primary global threat to pollinator health. However, the ability to predict the abundance of flowering resources for pollinators based upon climate, weather, and land cover is difficult due to insufficient monitoring over adequate spatial and temporal scales. Here we use spatiotemporally distributed honey bee hive scales that continuously measure hive weights as a standardized method to assess nectar intake. We analyze late summer colony weight gain as the response variable in a random forest regression model to determine the importance of climate, weather, and land cover on honey bee colony productivity. Our random forest model predicted resource acquisition by honey bee colonies with 71% accuracy, highlighting the detrimental effects of warm, wet regions in the Northcentral United States on nectar intake, as well as the detrimental effect of years with high growing degree day accumulation. Our model also predicted that grassy–herbaceous natural land had a positive effect on the summer nectar flow and that large areas of natural grassy–herbaceous land around apiaries can moderate the detrimental effects of warm, wet climates. These patterns characterize multi-scale ecological processes that constrain the quantity and quality of pollinator nutritional resources. That is, broad climate conditions constrain regional floral communities, while land use and weather act to further modify the quantity and quality of pollinator nutritional resources. Observing such broad-scale trends demonstrates the potential for utilizing hive scales to monitor the effects of climate change on landscape-level floral resources for pollinators. The interaction of climate and land use also present an opportunity to manage for climate-resilient landscapes that support pollinators through abundant floral resources under climate change

Age estimation of Calliphora (Diptera: Calliphoridae) larvae using cuticular hydrocarbon analysis and Artificial Neural Networks

Cuticular hydrocarbons were extracted daily from the larvae of two closely related blowflies Calliphora vicina and Calliphora vomitoria (Diptera: Calliphoridae). The hydrocarbons were then analysed using Gas Chromatography–Mass Spectrometry (GC–MS), with the aim of observing changes within their chemical profiles in order to determine the larval age. The hydrocarbons were examined daily for each species from 1 day old larvae until pupariation. The results show significant chemical changes occurring from the younger larvae to the post-feeding larvae. With the aid of a multivariate statistical method (Principal Component Analysis and Artificial Neural Networks), samples were clustered and classified, allowing for the larval age to be established. Results from this study allowed larvae to be aged to the day with at worst, 87% accuracy, which suggests there is great potential for the use of cuticular hydrocarbons present on larvae to give an indication of their age and hence potentially a valuable tool for minimum PMI estimations

Adult fly age estimations using cuticular hydrocarbons and Artificial Neural Networks in forensically important Calliphoridae species

Blowflies (Diptera: Calliphoridae) are forensically important as they are known to be one of the first to colonise human remains. The larval stage is typically used to assist a forensic entomologists with adult flies rarely used as they are difficult to age because they remain morphologically similar once they have gone through the initial transformation upon hatching. However, being able to age them is of interest and importance within the field. This study examined the cuticular hydrocarbons (CHC) of Diptera: Calliphoridae species Lucilia sericata, Calliphora vicina and Calliphora vomitoria. The CHCs were extracted from the cuticles of adult flies and analysed using Gas Chromatography–Mass Spectrometry (GC–MS). The chemical profiles were examined for the two Calliphora species at intervals of day 1, 5, 10, 20 and 30 and up to day 10 for L. sericata. The results show significant chemical changes occurring between the immature and mature adult flies over the extraction period examined in this study. With the aid of a Principal Component Analysis (PCA) and Artificial Neural Networks (ANN), samples were seen to cluster, allowing for the age to be established within the aforementioned time frames. The use of ANNs allowed for the automatic classification of novel samples with very good performance. This was a proof of concept study, which developed a method allowing to age post-emergence adults by using their chemical profiles

Mentoring Impact on Leader Efficacy Development: A Field Experiment

While practitioners and scholars tout the importance of mentorship in leader development, few studies have empirically determined whether mentoring actually positively impacts a leader’s development, and if so, in what ways. In a longitudinal field experiment, we examined how a targeted mentorship program that unfolded over 6 months enhanced the development of protégés’ leader efficacy and performance. Results showed that the targeted mentorship intervention increased protégés’ level of leader efficacy more than a comparison intervention that was based on a more eclectic leadership education program delivered in a group setting. Leader efficacy then predicted rated leader performance. Both protégés’ preferences for feedback and trust in the mentor served as important moderators in contributing to the development of leader efficacy. Findings from this longitudinal field experiment could be used by educational institutions and other organizations to enhance their mentorship programs in content, focus, and evaluation of impact

EN-BIRTH Data Collector Training - Handbook and Manual

The EN-BIRTH study aims to validate selected newborn and maternal indicators for routine facility-based tracking of coverage and quality of care for use at district, national and global levels. The item contains the EN-BIRTH_Trainer's Manual (14 June 2017) and EN-BIRTH_Training Handbook (23 May 2017)