Factors Affecting Biodiversity Protection in the Mediterranean Basin

Earth’s biodiversity includes all extant species; however, species are not evenly distributed across the planet. Species tend to be clustered in densely populated areas known as “biodiversity hotspots;” species which inhabit only a single area are also termed “endemic,” and tend to be highly vulnerable to population-reducing changes in their environment. Biodiversity hotspots are considered priorities for conservation if the area has a high rate of endemism as well as a notable and continual habitat loss (Noss et al., 2015). Preventing biodiversity loss is a complex and multi-level decision-making process about setting priorities and defining clear biodiversity protection areas. Biodiversity loss, or the loss of entire species or sub-populations in an area, can be driven by multiple processes, including land use changes, climate change, and the introduction of invasive species (Plexida et al. 2018). The Mediterranean Basin is one such hotspot, transecting multiple countries surrounding the Mediterranean Sea, including European, Middle Eastern, and North African countries with different systems of government and cultural perceptions of environmental resources and biodiversity. Furthermore, the basin is one the most species-rich biodiversity hotspots on Earth in terms of endemic vascular plants and has high rates of endemism for amphibians and fish, as well as being an important migration corridor for many bird species (Cuttelod et al., 2008). The hotspot is at high risk for continued biodiversity loss due to 53 several human-driven factors including population increase and government-level environmental policies (Grainger, 2003)

The effect of air pollution on children’s health: a comparative study between La Plata and Bahía Blanca, Buenos Aires Province, Argentina

We present the results of a study of outdoor air quality in two comparable regions of Buenos Aires province (Argentina), La Plata and Bahía Blanca, developed jointly by researchers of National University in both cities, and of the Hospital of Bahía Blanca, between 2009 and 2011. Both regions are characterized by a large petrochemical complex and a village with outstanding traffic. In this study, we measure levels of volatile organic compounds (VOCs), particulate matter suspended in air (PM) in air outdoor and affectation of respiratory system in children between 6 and 12 years. Also, analysis of the effect of the air pollution exposure was done thought the calculation of potentially increased life time cancer risk (LCR) in children. In both regions, including three areas: urban, industrial and residential (reference area), 20 VOCs were sampled by passive monitoring (3M 3500), and determined by GC/FID, comprising n-alkanes, cycloalkanes, aromatics, chlorinated compounds, terpenoids and ketones; particulate matter (PM10) was token using a low flow sampler MiniVol TAS, and spirometry were performed, using a portable spirometer. The collected data show higher levels of PM10 in Bahía Blanca, both in the industrial zone and urban areas, industrial area of Bahía Blanca with very bad air quality, associable with a 5% increased in mortality. The levels of total VOCs found in the residential area for both regions are comparable. Spirometry parameters of children living in industrial area evidence respiratory disease respect to urban and residential areas.Fil: Colman Lerner, Jorge Esteban. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Morales, A.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Aguilar, M.. Universidad Nacional de La Plata. Fundación Ciencias Exactas; ArgentinaFil: Giuliani, Daniela Silvana. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Orte, Marcos Agustín. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ditondo, J.. Hospital Interzonal General de Agudos “Dr. José Penna”; ArgentinaFil: Dodero, Veronica Isabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; ArgentinaFil: Massolo, L.. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; ArgentinaFil: Sanchez, Erica Yanina. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Matamoros, N.. Universidad Nacional de La Plata. Facultad de Ingenieria; ArgentinaFil: Porta, A.. Universidad Nacional de La Plata. Facultad de Ciencias Exactas; Argentin

Estimación de la distribución de fuentes de emisión de dióxido de azufre en la ciudad de Bahía Blanca

Una adecuada gestión del medio ambiente requiere de la complementación de metodologías que permitan no sólo caracterizar la calidad del aire y conocer sus efectos en la salud, sino también reconocer las principales fuentes de emisión de los contaminantes de interés y sus contribuciones, de manera de poder definir una estrategia que permita conciliar los procesos productivos con la salud de la población. En este trabajo se implementa el modelo NTA (Nonparametric Trajectory Analysis) en el software libre Python, para estimar la distribución de fuentes de emisión y la concentración promedio del dióxido de azufre en la ciudad de Bahía Blanca (Provincia de Buenos Aires, Argentina). La herramienta es considerada un híbrido de los modelos receptores, dado que utiliza mediciones tomadas en el centro de monitoreo y datos meteorológicos locales. Para tal objetivo, se hace uso de la información de calidad de aire de dióxido de azufre, de acceso libre y gratuito en el sitio web del municipio, junto con datos del Servicio Meteorológico Nacional (SMN), descritos en el mismo período temporal. En función de la información recopilada, se aplica la herramienta a distintos períodos temporales, complementando el estudio con la elaboración de la rosa de los vientos para el mismo periodo de interés. Esta herramienta permitió conocer con más detalle el comportamiento previsto del patrón direccional del contaminante, a partir de la estimación de la zona de mayor de distribución de fuentes. Particularmente, se evidenciaron altas concentraciones al noroeste de la estación de monitoreo, en mayor medida para los meses fríos del año. Si bien Petrobras Argentina S.A. representa solo el 17.2% anual de las emisiones de SO2, es destacable la relevancia que tienen los vientos predominantes en la dispersión de los contaminantes, y consecuente en la definición de las áreas de impacto. Como fortaleza de la herramienta, se destaca la posibilidad de proyectar zonas de mayor amenaza en función de la meteorología local, de modo de optimizar los recursos disponibles aplicados a campañas de monitoreo, al acotar la potencial región de emisión.Centro de Investigaciones del MedioambienteCentro de Investigación y Desarrollo en Ciencias Aplicada

Detección de regiones de procedencia de dióxido de azufre frente a eventos de concentraciones elevadas, en Gran La Plata

La Organización Mundial de la Salud (OMS) establece que la contaminación del aire representa un importante riesgo medioambiental para la salud. La población bonaerense (ARG) constituye una de aquellas que viven en lugares donde no se respetan las directrices de la OMS sobre la calidad del aire, y las escasas políticas de monitoreo impiden abordar esta problemática con seriedad. Un paso inicial implica reconocer la calidad el aire que respiramos y detectar los factores que contribuyen a la mala calidad del mismo (meteorología local y fuentes de emisión). Este trabajo propone una metodología capaz de detectar eventos donde la calidad del aire es insalubre según niveles guía propuestos por organismos competentes, y determinar así las zonas potenciales de procedencia mediante la aplicación del modelo de receptor híbrido NTA (Nonparametric Trajectory Analysis). Para su implementación, se analizó una base de datos meteorológicos y de concentraciones atmosféricas de dióxido de azufre, registrados en la UTN-FRLP, para el período 1999-2003. Los resultados muestran los eventos considerados insalubres acorde al Índice de calidad de aire (ICA) de la U.S.EPA, asociados a las potenciales zonas geográficas de procedencia, siendo el polo petroquímico la fuente areal principal de emisión. La fortaleza de la metodología radica en visualizar en tiempo real, la región potencial de emisión frente a la detección de una mala calidad del aire en un sitio de monitoreo. Asimismo, su capacidad de relacionar emisión y condiciones meteorológicas locales, para señalar a aquellas fuentes potenciales de aporte que no necesariamente deben ser las que más emisiones presentan en la región estudio.Centro de Investigaciones del MedioambienteCentro de Investigación y Desarrollo en Ciencias AplicadasCentro de Investigaciones Óptica

Agnostic Pathway/Gene Set Analysis of Genome-Wide Association Data Identifies Associations for Pancreatic Cancer

Background Genome-wide association studies (GWAS) identify associations of individual single-nucleotide polymorphisms (SNPs) with cancer risk but usually only explain a fraction of the inherited variability. Pathway analysis of genetic variants is a powerful tool to identify networks of susceptibility genes. Methods We conducted a large agnostic pathway-based meta-analysis of GWAS data using the summary-based adaptive rank truncated product method to identify gene sets and pathways associated with pancreatic ductal adenocarcinoma (PDAC) in 9040 cases and 12 496 controls. We performed expression quantitative trait loci (eQTL) analysis and functional annotation of the top SNPs in genes contributing to the top associated pathways and gene sets. All statistical tests were two-sided. Results We identified 14 pathways and gene sets associated with PDAC at a false discovery rate of less than 0.05. After Bonferroni correction (P Conclusion Our agnostic pathway and gene set analysis integrated with functional annotation and eQTL analysis provides insight into genes and pathways that may be biologically relevant for risk of PDAC, including those not previously identified.Peer reviewe

Three new pancreatic cancer susceptibility signals identified on chromosomes 1q32.1, 5p15.33 and 8q24.21.

Genome-wide association studies (GWAS) have identified common pancreatic cancer susceptibility variants at 13 chromosomal loci in individuals of European descent. To identify new susceptibility variants, we performed imputation based on 1000 Genomes (1000G) Project data and association analysis using 5,107 case and 8,845 control subjects from 27 cohort and case-control studies that participated in the PanScan I-III GWAS. This analysis, in combination with a two-staged replication in an additional 6,076 case and 7,555 control subjects from the PANcreatic Disease ReseArch (PANDoRA) and Pancreatic Cancer Case-Control (PanC4) Consortia uncovered 3 new pancreatic cancer risk signals marked by single nucleotide polymorphisms (SNPs) rs2816938 at chromosome 1q32.1 (per allele odds ratio (OR) = 1.20, P = 4.88x10 -15), rs10094872 at 8q24.21 (OR = 1.15, P = 3.22x10 -9) and rs35226131 at 5p15.33 (OR = 0.71, P = 1.70x10 -8). These SNPs represent independent risk variants at previously identified pancreatic cancer risk loci on chr1q32.1 ( NR5A2), chr8q24.21 ( MYC) and chr5p15.33 ( CLPTM1L- TERT) as per analyses conditioned on previously reported susceptibility variants. We assessed expression of candidate genes at the three risk loci in histologically normal ( n = 10) and tumor ( n = 8) derived pancreatic tissue samples and observed a marked reduction of NR5A2 expression (chr1q32.1) in the tumors (fold change -7.6, P = 5.7x10 -8). This finding was validated in a second set of paired ( n = 20) histologically normal and tumor derived pancreatic tissue samples (average fold change for three NR5A2 isoforms -31.3 to -95.7, P = 7.5x10 -4-2.0x10 -3). Our study has identified new susceptibility variants independently conferring pancreatic cancer risk that merit functional follow-up to identify target genes and explain the underlying biology

Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer.

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene

ENIGMA-anxiety working group : Rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Altres ajuts: Anxiety Disorders Research Network European College of Neuropsychopharmacology; Claude Leon Postdoctoral Fellowship; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, 44541416-TRR58); EU7th Frame Work Marie Curie Actions International Staff Exchange Scheme grant 'European and South African Research Network in Anxiety Disorders' (EUSARNAD); Geestkracht programme of the Netherlands Organization for Health Research and Development (ZonMw, 10-000-1002); Intramural Research Training Award (IRTA) program within the National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, MH002781); National Institute of Mental Health under the Intramural Research Program (NIMH-IRP, ZIA-MH-002782); SA Medical Research Council; U.S. National Institutes of Health grants (P01 AG026572, P01 AG055367, P41 EB015922, R01 AG060610, R56 AG058854, RF1 AG051710, U54 EB020403).Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders

The “Diabetes Comorbidome”: A Different Way for Health Professionals to Approach the Comorbidity Burden of Diabetes

(1) Background: The disease burden related to diabetes is increasing greatly, particularly in older subjects. A more comprehensive approach towards the assessment and management of diabetes’ comorbidities is necessary. The aim of this study was to implement our previous data identifying and representing the prevalence of the comorbidities, their association with mortality, and the strength of their relationship in hospitalized elderly patients with diabetes, developing, at the same time, a new graphic representation model of the comorbidome called “Diabetes Comorbidome”. (2) Methods: Data were collected from the RePoSi register. Comorbidities, socio-demographic data, severity and comorbidity indexes (Cumulative Illness rating Scale CIRS-SI and CIRS-CI), and functional status (Barthel Index), were recorded. Mortality rates were assessed in hospital and 3 and 12 months after discharge. (3) Results: Of the 4714 hospitalized elderly patients, 1378 had diabetes. The comorbidities distribution showed that arterial hypertension (57.1%), ischemic heart disease (31.4%), chronic renal failure (28.8%), atrial fibrillation (25.6%), and COPD (22.7%), were the more frequent in subjects with diabetes. The graphic comorbidome showed that the strongest predictors of death at in hospital and at the 3-month follow-up were dementia and cancer. At the 1-year follow-up, cancer was the first comorbidity independently associated with mortality. (4) Conclusions: The “Diabetes Comorbidome” represents the perfect instrument for determining the prevalence of comorbidities and the strength of their relationship with risk of death, as well as the need for an effective treatment for improving clinical outcomes