Current exposure of Italian women of reproductive age to PFOS and PFOA: a human biomonitoring study

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) concentrations were determined in serum samples collected in 2011-2012 from 549 nulliparous Italian women of reproductive age who resided in six different Italian Regions. Assessment of exposure to perfluorinated compounds was part of a large human biomonitoring study (Project Life Plus "Womenbiopop") that aimed at examining the exposure of women of reproductive age to priority organic pollutants. The median concentrations of PFOS and PFOA were 2.43, and 1.55ngg-1, respectively. Significant differences in the concentrations of both compounds were observed among the six Regions. Women from central Italy had the highest levels of both compounds, followed by women from northern Italy, and southern Italy. No differences in the PFOS concentrations were found between women from urban/industrial areas and women from rural areas, whereas the levels of PFOA were significantly higher in women residing in urban/industrial areas than in women residing in rural areas. Taken together, the observed concentrations confirm that the overall exposure of the Italian population is among the lowest observed in industrialized countries. A downward temporal trend in exposure was observed for both compounds when comparing the results from the present study with those assessed in a study conducted in 2008

Large-Scale Atomistic Simulations of Environmental Effects on the Formation and Properties of Molecular Junctions

Using an updated simulation tool, we examine molecular junctions comprised of benzene-1,4-dithiolate bonded between gold nanotips, focusing on the importance of environmental factors and inter-electrode distance on the formation and structure of bridged molecules. We investigate the complex relationship between monolayer density and tip separation, finding that the formation of multi-molecule junctions is favored at low monolayer density, while single-molecule junctions are favored at high density. We demonstrate that tip geometry and monolayer interactions, two factors that are often neglected in simulation, affect the bonding geometry and tilt angle of bridged molecules. We further show that the structures of bridged molecules at 298 and 77 K are similar.Comment: To appear in ACS Nano, 30 pages, 5 figure

The Influence of Molecular Adsorption on Elongating Gold Nanowires

Using molecular dynamics simulations, we study the impact of physisorbing adsorbates on the structural and mechanical evolution of gold nanowires (AuNWs) undergoing elongation. We used various adsorbate models in our simulations, with each model giving rise to a different surface coverage and mobility of the adsorbed phase. We find that the local structure and mobility of the adsorbed phase remains relatively uniform across all segments of an elongating AuNW, except for the thinning region of the wire where the high mobility of Au atoms disrupts the monolayer structure, giving rise to higher solvent mobility. We analyzed the AuNW trajectories by measuring the ductile elongation of the wires and detecting the presence of characteristic structural motifs that appeared during elongation. Our findings indicate that adsorbates facilitate the formation of high-energy structural motifs and lead to significantly higher ductile elongations. In particular, our simulations result in a large number of monatomic chains and helical structures possessing mechanical stability in excess of what we observe in vacuum. Conversely, we find that a molecular species that interacts weakly (i.e., does not adsorb) with AuNWs worsens the mechanical stability of monatomic chains.Comment: To appear in Journal of Physical Chemistry

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.The brainlife.io project development and operations were supported by awards to F.P.: grant nos. NIH NIBIB 01EB029272, R01EB030896NSF and R01EB030896; NSF BCS 1734853 and 1636893; ACI 1916518, IIS 1912270; a gift from the Kavli Foundation; Wellcome Trust grant no. 226486/Z/22/Z and a Microsoft Investigator Fellowship. Additional funding was provided to support data collection used by the team, research that used brainlife.io or infrastructure that supported the platform: grant no. NIMH UM1NS132207 BRAIN CONNECTS: Center for Mesoscale Connectomics (Principal Investigator K. Ugurbil), grant no. NIMH R01MH133701 (C.R.). NSF grant award nos. 2004877 (S.V.-B.),1541335 and 2232628 (S.M.), 1445604 and 2005506 (D.Y.H.),1341698 and 1928224 (M. Norman), 1445606 (S.T.B.), 1928147 (S.Sanalevici). NIH grant award nos. 1U54MH091657 (HCP data, Principal Investigators D. Van Essen and K. Ugurbil), U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, 01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, 01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, 24DA041147 (ABCD Study, multiple Principal Investigators), P41EB017183 (J.V.), NIH NIBIB R01EB030896 (A.P.) and ANR-20-NEUC-0004-01 (mulitple Principal Investigators). Multiple philanthropic contributions to the HBN (M. Milham)

Discovering privileged topologies of molecular knots with self-assembling models

Despite the several available strategies to build complex supramolecular constructs, only a handful of different molecular knots have been synthesised so far. Here, in response to the quest for further designable topologies, we use Monte Carlo sampling and molecular dynamics simulations, informed by general principles of supramolecular assembly, as a discovery tool for thermodynamically and kinetically accessible knot types made of helical templates. By combining this approach with the exhaustive enumeration of molecular braiding patterns applicable to more general template geometries, we find that only few selected shapes have the closed, symmetric and quasi-planar character typical of synthetic knots. The corresponding collection of admissible topologies is extremely restricted. It covers all known molecular knots but it especially includes a limited set of novel complex ones that have not yet been obtained experimentally, such as 10124 and 15n41185, making them privileged targets for future self-assembling experiments

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed

Variability in the analysis of a single neuroimaging dataset by many teams

Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fact that no two teams chose identical workflows to analyze the data. This flexibility resulted in sizeable variation in hypothesis test results, even for teams whose statistical maps were highly correlated at intermediate stages of their analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Importantly, meta-analytic approaches that aggregated information across teams yielded significant consensus in activated regions across teams. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset. Our findings show that analytic flexibility can have substantial effects on scientific conclusions, and demonstrate factors related to variability in fMRI. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for multiple analyses of the same data. Potential approaches to mitigate issues related to analytical variability are discussed