STEM Exploration

After school club for middle school students consisting of individual and partner activities that will building teamwork and problem-solving skills. The club will also introduce the students to many different areas of STEM

Stroke Quality Measures in Mexican Americans and Non-Hispanic Whites

Mexican Americans (MAs) have been shown to have worse outcomes after stroke than non-Hispanic Whites (NHWs), but it is unknown if ethnic differences in stroke quality of care may contribute to these worse outcomes. We investigated ethnic differences in the quality of inpatient stroke care between MAs and NHWs within the population-based prospective Brain Attack Surveillance in Corpus Christi (BASIC) Project (February 2009- June 2012). Quality measures for inpatient stroke care, based on the 2008 Joint Commission Primary Stroke Center definitions were assessed from the medical record by a trained abstractor. Two summary measure of overall quality were also created (binary measure of defect-free care and the proportion of measures achieved for which the patient was eligible). 757 individuals were included (480 MAs and 277 NHWs). MAs were younger, more likely to have hypertension and diabetes, and less likely to have atrial fibrillation than NHWs. MAs were less likely than NHWs to receive tPA (RR: 0.72, 95% confidence interval (CI) 0.52, 0.98), and MAs with atrial fibrillation were less likely to receive anticoagulant medications at discharge than NHWs (RR 0.73, 95% CI 0.58, 0.94). There were no ethnic differences in the other individual quality measures, or in the two summary measures assessing overall quality. In conclusion, there were no ethnic differences in the overall quality of stroke care between MAs and NHWs, though ethnic differences were seen in the proportion of patients who received tPA and anticoagulant at discharge for atrial fibrillation

Sleep‐disordered breathing and poststroke outcomes

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150527/1/ana25515_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150527/2/ana25515.pd

The SCID Mouse Model for Identifying Virulence Determinants in Coxiella burnetii

Coxiella burnetii is an intracellular, zoonotic pathogen that is the causative agent of Q fever. Infection most frequently occurs after inhalation of contaminated aerosols, which can lead to acute, self-limiting febrile illness or more serve chronic infections such as hepatitis or endocarditis. Macrophages are the principal target cells during infection where C. burnetii resides and replicates within a unique phagolysosome-like compartment, the Coxiella-containing vacuole (CCV). The first virulence determinant described as necessary for infection was full-length lipopolysaccarride (LPS); spontaneous rough mutants (phase II) arise after passage in immuno-incompetent hosts. Phase II C. burnetii are attenuated in immuno-competent animals, but are fully capable of infecting a variety of host cells in vitro. A clonal strain of the Nine Mile isolate (RSA439, clone 4), has a 26 KDa chromosomal deletion that includes LPS biosynthetic genes and is uniquely approved for use in BL2/ABL2 conditions. With the advances of axenic media and genetic tools for C. burnetii research, the characterization of novel virulence determinants is ongoing and almost exclusively performed using this attenuated clone. A major problem with predicting essential virulence loci with RSA439 is that, although some cell-autonomous phenotypes can be assessed in tissue culture, no animal model for assessing pathogenesis has been defined. Here we describe the use of SCID mice for predicting virulence factors of C. burnetii, in either independent or competitive infections. We propose that this model allows for the identification of mutations that are competent for intracellular replication in vitro, but attenuated for growth in vivo and predict essential innate immune responses modulated by the pathogen during infection as a central pathogenic strategy

Prevalence and Course of Depression During the First Year After Mild to Moderate Stroke

Background: This study examined the prevalence and longitudinal course of depression during the first year after mild to moderate stroke. Methods and Results: We identified patients with mild to moderate ischemic stroke or intracerebral hemorrhage (National Institutes of Health Stroke Scale score <16) and at least 1 depression assessment at 3, 6, or 12 months after stroke (n=648, 542, and 533, respectively) from the Brain Attack Surveillance in Corpus Christi project (2014–2016). Latent transition analysis was used to examine temporal profiles of depressive symptoms assessed by the 8‐item Patient Health Questionnaire between 3 and 12 months after stroke. Mean age was 65.6 years, 49.4% were women, and 56.7% were Mexican Americans. The prevalence of depression after stroke was 35.3% at 3 months, decreased to 24.9% at 6 months, and remained stable at 25.7% at 12 months. Approximately half of the participants classified as having depression at 3 or 6 months showed clinical improvement at the next assessment. Subgroups with distinct patterns of depressive symptoms were identified, including mild/no symptoms, predominant sleep disturbance and fatigue symptoms, affective symptoms, and severe/all symptoms. A majority of participants with mild/no symptoms retained this symptom pattern over time. The probability of transitioning to mild/no symptoms was higher before 6 months compared with the later period, and severe symptoms were more likely to persist after 6 months compared with the earlier period. Conclusions: The observed dynamics of depressive symptoms suggest that depression after stroke tends to persist after 6 months among patients with mild to moderate stroke and should be continually monitored and appropriately managed

The Type IV Secretion System Effector Protein CirA Stimulates the GTPase Activity of RhoA and Is Required for Virulence in a Mouse Model of Coxiella burnetii Infection

Coxiella burnetii, the etiological agent of Q fever in humans, is an intracellular pathogen that replicates in an acidified parasitophorous vacuole derived from host lysosomes. Generation of this replicative compartment requires effectors delivered into the host cell by the Dot/Icm type IVb secretion system. Several effectors crucial for C. burnetii intracellular replication have been identified, but the host pathways coopted by these essential effectors are poorly defined, and very little is known about how spacious vacuoles are formed and maintained. Here we demonstrate that the essential type IVb effector, CirA, stimulates GTPase activity of RhoA. Overexpression of CirA in mammalian cells results in cell rounding and stress fiber disruption, a phenotype that is rescued by overexpression of wild-type or constitutively active RhoA. Unlike other effector proteins that subvert Rho GTPases to modulate uptake, CirA is the first effector identified that is dispensable for uptake and instead recruits Rho GTPase to promote biogenesis of the bacterial vacuole. Collectively our results highlight the importance of CirA in coopting host Rho GTPases for establishment of Coxiella burnetii infection and virulence in mammalian cell culture and mouse models of infection

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Common genetic variation and susceptibility to partial epilepsies: a genome-wide association study

Partial epilepsies have a substantial heritability. However, the actual genetic causes are largely unknown. In contrast to many other common diseases for which genetic association-studies have successfully revealed common variants associated with disease risk, the role of common variation in partial epilepsies has not yet been explored in a well-powered study. We undertook a genome-wide association-study to identify common variants which influence risk for epilepsy shared amongst partial epilepsy syndromes, in 3445 patients and 6935 controls of European ancestry. We did not identify any genome-wide significant association. A few single nucleotide polymorphisms may warrant further investigation. We exclude common genetic variants with effect sizes above a modest 1.3 odds ratio for a single variant as contributors to genetic susceptibility shared across the partial epilepsies. We show that, at best, common genetic variation can only have a modest role in predisposition to the partial epilepsies when considered across syndromes in Europeans. The genetic architecture of the partial epilepsies is likely to be very complex, reflecting genotypic and phenotypic heterogeneity. Larger meta-analyses are required to identify variants of smaller effect sizes (odds ratio <1.3) or syndrome-specific variants. Further, our results suggest research efforts should also be directed towards identifying the multiple rare variants likely to account for at least part of the heritability of the partial epilepsies. Data emerging from genome-wide association-studies will be valuable during the next serious challenge of interpreting all the genetic variation emerging from whole-genome sequencing studies