141 research outputs found

    Drosophila Single-minded Represses Gene Transcription by Activating the Expression of Repressive Factors

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    AbstractThe Drosophila single-minded gene controls CNS midline cell development by both activating midline gene expression and repressing lateral CNS gene expression in the midline cells. The mechanism by which Single-minded represses transcription was examined using the ventral nervous system defective gene as a target gene. Transgenic-lacZ analysis of constructs containing fragments of the ventral nervous system defective regulatory region identified sequences required for lateral CNS transcription and midline repression. Elimination of Single-minded:Tango binding sites within the ventral nervous system defective gene did not affect midline repression. Mutants of Single-minded that removed the DNA binding and transcriptional activation regions abolished ventral nervous system defective repression, as well as transcriptional activation of other genes. The replacement of the Single-minded transcriptional activation region with a heterologous VP16 transcriptional activation region restored the ability of Single-minded to both activate and repress transcription. These results indicate that Single-minded indirectly represses transcription by activating the expression of repressive factors. Single-minded provides a model system for how regulatory proteins that act only as transcriptional activators can control lineage-specific transcription in both positive and negative modes

    Development of Morphological Diversity of Dendrites in Drosophila by the BTB-Zinc Finger Protein Abrupt

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    Morphological diversity of dendrites contributes to specialized functions of individual neurons. In the present study, we examined the molecular basis that generates distinct morphological classes of Drosophila dendritic arborization (da) neurons. da neurons are classified into classes I to IV in order of increasing territory size and/or branching complexity. We found that Abrupt (Ab), a BTB-zinc finger protein, is expressed selectively in class I cells. Misexpression of ab in neurons of other classes directed them to take the appearance of cells with smaller and/or less elaborated arbors. Loss of ab functions in class I neurons resulted in malformation of their typical comb-like arbor patterns and generation of supernumerary branch terminals. Together with the results of monitoring dendritic dynamics of ab-misexpressing cells or ab mutant ones, all of the data suggested that Ab endows characteristics of dendritic morphogenesis of the class I neurons

    Individual, Provider, and System Risk Factors for Breast and Cervical Cancer Screening Among Underserved Black, Latina, and Arab Women

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    Background: Socioeconomic and racial/ethnic disparities in breast and cervical cancer screening persist. An exploratory study was conducted to better understand co-occurring risk factors in underserved groups that could inform interventions to improve screening adherence. The objective of this study was to examine associations between breast and cervical cancer screening adherence and co-occurring risk factors in three racial/ethnic groups of underserved women. Methods: Black, Latina, and Arab women (N=514), ages 21 to 70 years, were enrolled into the Kin KeeperSM randomized controlled trial in communities around Detroit, Michigan. We used participant baseline assessments (e.g., demographic characteristics, health literacy) to explore screening risks using an additive approach and multivariate logistic analyses. Results: For black women, having more health literacy risks were associated with reduced odds of a clinical breast exam (CBE), mammogram, and Papanicolaou (Pap) test; more competing priorities were associated with reduced odds of a Pap test; lack of doctor mammogram recommendation was significantly associated with decreased odds of CBE. For Latina women, lack of doctor recommendations were significantly associated with decreased odds of CBE, mammogram, and Pap test. For Arab women, lack of doctor recommendations were significantly associated with decreased odds of CBE, mammogram, and Pap test; more competing priorities were significantly associated with reduced odds of CBE and Pap test. All results were significant at p<0.05. Conclusions: Characteristics associated with breast and cervical screening adherence differs among Black, Latina, and Arab underserved women. Interventions to improve screening should be tailored for racial/ethnic groups with particular attention to competing survival priorities, health literacy risks factors, and provider recommendations.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140125/1/jwh.2013.4397.pd

    Inhibition of Cellular Protein Secretion by Norwalk Virus Nonstructural Protein p22 Requires a Mimic of an Endoplasmic Reticulum Export Signal

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    Protein trafficking between the endoplasmic reticulum (ER) and Golgi apparatus is central to cellular homeostasis. ER export signals are utilized by a subset of proteins to rapidly exit the ER by direct uptake into COPII vesicles for transport to the Golgi. Norwalk virus nonstructural protein p22 contains a YXΦESDG motif that mimics a di-acidic ER export signal in both sequence and function. However, unlike normal ER export signals, the ER export signal mimic of p22 is necessary for apparent inhibition of normal COPII vesicle trafficking, which leads to Golgi disassembly and antagonism of Golgi-dependent cellular protein secretion. This is the first reported function for p22. Disassembly of the Golgi apparatus was also observed in cells replicating Norwalk virus, which may contribute to pathogenesis by interfering with cellular processes that are dependent on an intact secretory pathway. These results indicate that the ER export signal mimic is critical to the antagonistic function of p22, shown herein to be a novel antagonist of ER/Golgi trafficking. This unique and well-conserved human norovirus motif is therefore an appealing target for antiviral drug development

    Revisiting the dynamic risk profile of cardiovascular/non-cardiovascular multimorbidity in incident atrial fibrillation patients and five cardiovascular/non-cardiovascular outcomes: A machine-learning approach.

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    BackgroundPatients with atrial fibrillation (AF) usually have a heterogeneous co-morbid history, with dynamic changes in risk factors impacting on multiple adverse outcomes. We investigated a large prospective cohort of patients with multimorbidity, using a machine-learning approach, accounting for the dynamic nature of comorbidity risks and incident AF.MethodsUsing machine-learning, we studied a prospective US cohort using medical/pharmacy databases of 1 091 911 patients, with an incident AF cohort of 14 078 and non-AF cohort of 1 077 833 enrolled in the 4-year study. Five incident clinical outcomes (heart failure, stroke, myocardial infarction, major bleeding, and cognitive impairment) were examined in relationship to AF status (AF vs non-AF), diverse multi-morbid (conditions and medications) history, and demographic parameters (age and gender), with supervised machine-learning techniques.ResultsComplex inter-relationships of various comorbidities were uncovered for AF cases, leading to 6-fold higher risk of heart failure relative to the non-AF cohort (OR 6.02, 95% CI 5.72-6.33), followed by myocardial infarction (OR=2.68), stroke (OR=2.19), and major bleeding (OR=1.36). Supervised machine learning algorithms on the original populations yielded comparable results for both neural network and logistic regression algorithms in terms of discriminant validity, with c-indexes for incident adverse outcomes: heart failure (0.924, 95%CI 0.923-0.925), stroke (0.871, 95%CI 0.869-0.873), myocardial infarction (0.901, 95% CI 0.899-0.903), major bleeding (0.700, 95%CI 0.697-0.703), and cognitive impairment (0.919, 95% CI 0.9170.921). External calibration of all models demonstrated a good fit between the predicted probabilities and observed events. Decision curve analysis demonstrated that the obtained models were much more clinically useful than the "treat all" strategy.ConclusionsComplex multimorbidity relationships uncovered using a machine learning approach for incident AF cases have major consequences for integrated care management, with implications for risk stratification and adverse clinical outcomes. This approach may facilitate automated approaches in the presence of multimorbidity, potentially helping decision making

    Ursinus College Alumni Journal, August 1968

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    A special responsibility • Capp quips cleverly at commencement • Alumni Day \u2768: The happy time; The business of the day; Election winners take office; Two distinguished graduates honored; A salute to individuals & organizations; Picture of a perfect day • Weighing the worth of Ursinus • What is a college? • Alumni award winner recalls Ursinus past • Alumni classes support college in 1967-1968 • Call it animal faith, if you like • All-Ursinus anniversary drive • From the President • Dr. James E. Wagner retires as vice-president • Development department increases staff • Campus clippings: New faculty members; Chapter scholars; Tax tip on the surcharge; Board elects three • Class notebook • Weddings • Births • In memoriamhttps://digitalcommons.ursinus.edu/alumnijournal/1092/thumbnail.jp

    The Public Health Exposome: A Population-Based, Exposure Science Approach to Health Disparities Research

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    The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures “get under the skin”. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training

    Brain Responses to Words in 2-Year-Olds with Autism Predict Developmental Outcomes at Age 6

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    Autism Spectrum Disorder (ASD) is a developmental disability that affects social behavior and language acquisition. ASD exhibits great variability in outcomes, with some individuals remaining nonverbal and others exhibiting average or above average function. Cognitive ability contributes to heterogeneity in autism and serves as a modest predictor of later function. We show that a brain measure (event-related potentials, ERPs) of word processing in children with ASD, assessed at the age of 2 years (N = 24), is a broad and robust predictor of receptive language, cognitive ability, and adaptive behavior at ages 4 and 6 years, regardless of the form of intensive clinical treatment during the intervening years. The predictive strength of this brain measure increases over time, and exceeds the predictive strength of a measure of cognitive ability, used here for comparison. These findings have theoretical implications and may eventually lead to neural measures that allow early prediction of developmental outcomes as well as more individually tailored clinical interventions, with the potential for greater effectiveness in treating children with ASD
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