Massive neutrinos and dark energy

We consider the impact of the Heidelberg-Moscow claim for a detection of neutrino mass on the determination of the dark energy equation of state. By combining the Heidelberg-Moscow result with the WMAP 3-years data and other cosmological datasets we constrain the equation of state to -1.67< w <-1.05 at 95% c.l., While future data are certainly needed for a confirmation of the controversial Heildelberg-Moscow claim, our result shows that future laboratory searches for neutrino masses may play a crucial role in the determination of the dark energy properties.Comment: 3 pages, 1 figure, Talk given by Paolo Serra at the Neutrino Oscillation Workshop NOW2006, Otranto, Italy, September 9-16 200

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Population surveillance approach to detect and respond to new clusters of COVID-19

Background: To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. Methods: We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. Results: In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level—assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. Conclusion: This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak

Approche de surveillance de la population afin de détecter les nouveaux agrégats de cas de COVID-19 et y répondre

Contexte : Pour maintenir le contrôle de l’épidémie de maladie à coronavirus 2019 (COVID-19) lorsque les mesures de confinement seront levées, il sera essentiel de renforcer les mesures de santé publique alternatives. En ce qui concerne la surveillance, il sera nécessaire de détecter rapidement une grande proportion de tous nouveaux cas afin de pouvoir les isoler, et retrouver et mettre en quarantaine les personnes qui y ont été exposées. Nous présentons ici une approche mathématique qui peut être utilisée pour déterminer combien d’échantillons doivent être recueillis par unité de surface et par unité de temps pour détecter de nouveaux agrégats de cas de COVID-19 à un stade suffisamment précoce pour contrôler une éclosion. Méthodes : Nous présentons une méthode de détermination de la taille de l’échantillon qui utilise une approche de pondération relative. Compte tenu du fait que les résultats du test de diagnostic de la COVID-19 provenant de sous-populations ont contribué à détecter la maladie à un niveau de prévalence seuil pour contrôler l’éclosion, il s’agissait de 1) déterminer si le nombre prévu d’échantillons hebdomadaires fournis par la surveillance actuelle des infections virales respiratoires fondée sur les soins de santé peut fournir une taille d’échantillon déjà adéquate pour détecter de nouveaux agrégats de cas de COVID-19 et, dans le cas contraire, 2) de déterminer combien d’échantillons hebdomadaires supplémentaires étaient nécessaires à partir d’un échantillonnage volontaire. Résultats : Lors d’une démonstration de notre méthode à une fréquence hebdomadaire et à l’échelle des provinces et territoires canadiens, nous avons constaté que seuls les provinces et les territoires les plus peuplés disposaient d’un nombre suffisant de dépistages provenant des visites médicales en raison de maladies respiratoires pour détecter la COVID-19 à notre niveau de prévalence cible — qui doit être suffisamment élevé pour identifier et contrôler les nouveaux agrégats de cas. En outre, la détection de la COVID-19 est plus efficace (moins d’échantillons requis) lorsque la surveillance se concentre sur l’exigence de tests de dépistage de patients symptomatiques par les services des soins de santé. Dans les populations volontaires : plus les taux de contact sont élevés, plus le niveau de prévalence attendu est élevé, et moins il faut d’échantillons pour détecter la COVID-19 à un seuil prédéterminé. Conclusion : Cette étude présente une stratégie de surveillance ciblée, combinant des échantillons de surveillance passive et active, afin de déterminer le nombre d’échantillons à recueillir par unité de surface et par unité de temps pour détecter de nouveaux agrégats de cas de COVID-19. L’objectif de cette stratégie est de permettre une détection suffisamment précoce pour contrôler une éclosion

Canada-United States-Mexico Trilateral Cooperation on Childhood Obesity Initiative

ABSTRACT Childhood obesity is an important public health problem that affects countries in the Americas. In 2014, Pan American Health Organization (PAHO) Member States agreed on a Plan of Action for the Prevention of Obesity in Children and Adolescents in an effort to address the impact of this disorder in the Americas region. The interventions laid out in this regional plan are multi-faceted and require multi-sectoral partnerships. Building on a strong history of successful trilateral collaboration, Canada, Mexico, and the United States formed a partnership to address the growing epidemic of childhood obesity in the North American region. This collaborative effort, known as the Trilateral Cooperation on Childhood Obesity Initiative, is the first initiative in the region to address chronic noncommunicable diseases by bringing together technical and policy experts, with strong leadership and support from the secretaries and ministers of health. The Initiative’s goals include increasing levels of physical activity and reducing sedentary behavior through 1) increased social mobilization and citizen engagement, 2) community- based outreach, and 3) changes to the built (man-made) environment. This article describes the background and development process of the Initiative; specific goals, activities, and actions achieved to date; and opportunities and next steps. This information may be useful for those forming other partnerships designed to address childhood obesity or other complex public health challenges in the region

The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing

We characterize the landscape of somatic mutations-mutations occurring after fertilization-in the human brain using ultra-deep (~250×) whole-genome sequencing of prefrontal cortex from 59 donors with autism spectrum disorder (ASD) and 15 control donors. We observe a mean of 26 somatic single-nucleotide variants per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.4 mutations, followed by 2-3 mutations in subsequent generations. This suggests that a typical individual possesses ~80 somatic single-nucleotide variants present in ≥2% of cells-comparable to the number of de novo germline mutations per generation-with about half of individuals having at least one potentially function-altering somatic mutation somewhere in the cortex. ASD brains show an excess of somatic mutations in neural enhancer sequences compared with controls, suggesting that mosaic enhancer mutations may contribute to ASD risk

Large mosaic copy number variations confer autism risk

Although germline de novo copy number variants are known causes of autism spectrum disorder (ASD), the contribution of mosaic (early-developmental) copy number variants (mCNVs) has not been explored. Here, we assessed the contribution of mCNVs to ASD by ascertaining mCNVs in genotype array intensity data from 12,077 ASD probands and 5,500 unaffected siblings. We detected 46 mCNVs in probands and 19 mCNVs in siblings affecting 2.8–73.8% of cells. Probands carried a significant burden of large (>4 Mb) mCNVs, which were detected in 25 probands but only 1 sibling (OR=11.4, 95% CI=1.5–84.2, P=7.4×10(−4)). Event size positively correlated with severity of ASD symptoms (P=0.016). Surprisingly, we did not observe mosaic analogues of the short de novo CNVs recurrently observed in ASD (e.g. 16p11.2). We further experimentally validated two mCNVs in post-mortem brain tissue from 59 additional probands. These results indicate that mosaic CNVs contribute a previously unexplained component of ASD risk