Mental Health Needs & Barriers: Assessment of Latinos in Las Vegas

Research suggests that the prevalence of mental illness in Latinos is not necessarily uncommon and that economic concerns may be an important factor in determining the type of services Latinos are likely to seek (Kouyoumdjian, 2003). For Latinos, mental health disorders such as depression and anxiety have higher rates than the general population but the rate at which Latinos seek treatment is strikingly lower (Barrio, 2008). Observations regarding treatment engagement rates conclude that Latinos also have significantly higher probability of terminating treatments prematurely (Kouyoumdjian, 2003). Aim: to examine the mental health needs and barriers to treatment present in the Latino community of Las Vegas, Nevada

Greenpreneurs: A handbook for trainers

This handbook is designed for educators who want to design and deliver a highly interactive learning project aimed at helping people in the renewable energy and energy efficient buildings sectors develop their entrepreneurial capacities. The Greenpreneurs approach is student-centred and student-led, based on learning by doing

Surface Morphologies in a Mars-Analog Ca-Sulfate Salar, High Andes, Northern Chile

Salar de Pajonales, a Ca-sulfate salt flat in the Chilean High Andes, showcases the type of polyextreme environment recognized as one of the best terrestrial analogs for early Mars because of its aridity, high solar irradiance, salinity, and oxidation. The surface of the salar represents a natural climate-transition experiment where contemporary lagoons transition into infrequently inundated areas, salt crusts, and lastly dry exposed paleoterraces. These surface features represent different evolutionary stages in the transition from previously wetter climatic conditions to much drier conditions today. These same stages closely mirror the climate transition on Mars from a wetter early Noachian to the Noachian/Hesperian. Salar de Pajonales thus provides a unique window into what the last near-surface oases for microbial life on Mars could have been like in hypersaline environments as the climate changed and water disappeared from the surface. Here we open that climatological window by evaluating the narrative recorded in the salar surface morphology and microenvironments and extrapolating to similar paleosettings on Mars. Our observations suggest a strong inter-dependence between small and large scale features that we interpret to be controlled by extrabasinal changes in environmental conditions, such as precipitation-evaporation-balance changes and thermal cycles, and most importantly, by internal processes, such as hydration/dehydration, efflorescence/deliquescence, and recrystallization brought about by physical and chemical processes related to changes in groundwater recharge and volcanic processes. Surface structures and textures record a history of hydrological changes that impact the mineralogy and volume of Ca-sulfate layers comprising most of the salar surface. Similar surface features on Mars, interpreted as products of freeze-thaw cycles, could, instead, be products of water-driven, volume changes in salt deposits. On Mars, surface manifestations of such salt-related processes would point to potential water sources. Because hygroscopic salts have been invoked as sources of localized, transient water sufficient to support terrestrial life, such structures might be good targets for biosignature exploration on Mars

Ribosomal RNA 2′O-methylation as a novel layer of inter-tumour heterogeneity in breast cancer

International audienceRecent epitranscriptomics studies unravelled that ribosomal RNA (rRNA) 2′O-methylation is an additional layer of gene expression regulation highlighting the ribosome as a novel actor of translation control. However, this major finding lies on evidences coming mainly, if not exclusively, from cellular models. Using the innovative next-generation RiboMeth-seq technology, we established the first rRNA 2′O-methylation landscape in 195 primary human breast tumours. We uncovered the existence of compulsory/stable sites, which show limited inter-patient variability in their 2′O-methylation level, which map on functionally important sites of the human ribosome structure and which are surrounded by variable sites found from the second nucleotide layers. Our data demonstrate that some positions within the rRNA molecules can tolerate absence of 2′O-methylation in tumoral and healthy tissues. We also reveal that rRNA 2′O-methylation exhibits intra- and inter-patient variability in breast tumours. Its level is indeed differentially associated with breast cancer subtype and tumour grade. Altogether, our rRNA 2′O-methylation profiling of a large-scale human sample collection provides the first compelling evidence that ribosome variability occurs in humans and suggests that rRNA 2′O-methylation might represent a relevant element of tumour biology useful in clinic. This novel variability at molecular level offers an additional layer to capture the cancer heterogeneity and associates with specific features of tumour biology thus offering a novel targetable molecular signature in cancer

Development of a framework for genotyping bovine-derived Cryptosporidium parvum, using a multilocus fragment typing tool

Background: There is a need for an integrated genotyping approach for C. parvum; no sufficiently discriminatory scheme to date has been fully validated or widely adopted by veterinary or public health researchers. Multilocus fragment typing (MLFT) can provide good differentiation and is relatively quick and cheap to perform. A MLFT tool was assessed in terms of its typeability, specificity, precision (repeatability and reproducibility), accuracy and ability to genotypically discriminate bovine-derived Cryptosporidium parvum. Methods: With the aim of working towards a consensus, six markers were selected for inclusion based on their successful application in previous studies: MM5, MM18, MM19, TP14, MS1 and MS9. Alleles were assigned according to the fragment sizes of repeat regions amplified, as determined by capillary electrophoresis. In addition, a region of the GP60 gene was amplified and sequenced to determine gp60 subtype and this was added to the allelic profiles of the 6 markers to determine the multilocus genotype (MLG). The MLFT tool was applied to 140 C. parvum samples collected in two cross-sectional studies of UK calves, conducted in Cheshire in 2004 (principally dairy animals) and Aberdeenshire/Caithness in 2011 (beef animals). Results: Typeability was 84 %. The primers did not amplify tested non-parvum species frequently detected in cattle. In terms of repeatability, within- and between-run fragment sizes showed little variability. Between laboratories, fragment sizes differed but allele calling was reproducible. The MLFT had good discriminatory ability (Simpson’s Index of Diversity, SID, was 0.92), compared to gp60 sequencing alone (SID 0.44). Some markers were more informative than others, with MS1 and MS9 proving monoallelic in tested samples. Conclusions: Further inter-laboratory trials are now warranted with the inclusion of human-derived C. parvum samples, allowing progress towards an integrated, standardised typing scheme to enable source attribution and to determine the role of livestock in future outbreaks of human C. parvum

Diagnosis of bipolar disorders and body mass index predict clustering based on similarities in cortical thickness-ENIGMA study in 2436 individuals

AIMS: Rates of obesity have reached epidemic proportions, especially among people with psychiatric disorders. While the effects of obesity on the brain are of major interest in medicine, they remain markedly under-researched in psychiatry. METHODS: We obtained body mass index (BMI) and magnetic resonance imaging-derived regional cortical thickness, surface area from 836 bipolar disorders (BD) and 1600 control individuals from 14 sites within the ENIGMA-BD Working Group. We identified regionally specific profiles of cortical thickness using K-means clustering and studied clinical characteristics associated with individual cortical profiles. RESULTS: We detected two clusters based on similarities among participants in cortical thickness. The lower thickness cluster (46.8% of the sample) showed thinner cortex, especially in the frontal and temporal lobes and was associated with diagnosis of BD, higher BMI, and older age. BD individuals in the low thickness cluster were more likely to have the diagnosis of bipolar disorder I and less likely to be treated with lithium. In contrast, clustering based on similarities in the cortical surface area was unrelated to BD or BMI and only tracked age and sex. CONCLUSIONS: We provide evidence that both BD and obesity are associated with similar alterations in cortical thickness, but not surface area. The fact that obesity increased the chance of having low cortical thickness could explain differences in cortical measures among people with BD. The thinner cortex in individuals with higher BMI, which was additive and similar to the BD-associated alterations, may suggest that treating obesity could lower the extent of cortical thinning in BD

Global warming and recurrent mass bleaching of corals

During 2015–2016, record temperatures triggered a pan-tropical episode of coral bleaching, the third global-scale event since mass bleaching was first documented in the 1980s. Here we examine how and why the severity of recurrent major bleaching events has varied at multiple scales, using aerial and underwater surveys of Australian reefs combined with satellite-derived sea surface temperatures. The distinctive geographic footprints of recurrent bleaching on the Great Barrier Reef in 1998, 2002 and 2016 were determined by the spatial pattern of sea temperatures in each year. Water quality and fishing pressure had minimal effect on the unprecedented bleaching in 2016, suggesting that local protection of reefs affords little or no resistance to extreme heat. Similarly, past exposure to bleaching in 1998 and 2002 did not lessen the severity of bleaching in 2016. Consequently, immediate global action to curb future warming is essential to secure a future for coral reefs

Mega-analysis of association between obesity and cortical morphology in bipolar disorders:ENIGMA study in 2832 participants

Background: Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact. Methods: We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations. Results: BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI. Conclusions: We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p