Data pooling efforts in Africa and Latin America.

Correspondenc

Impurity-enhanced solid-state amorphization : the Ni-Si thin film reaction altered by nitrogen

Solid-state amorphization, the growth of an amorphous phase during annealing, has been studied in a wide variety of thin film structures. Whereas research on the remarkable growth of such a metastable phase has mostly focused on strictly binary systems, far less is known about the influence of impurities on such reactions. In this paper, the influence of nitrogen, introduced via ion implantation, is studied on the solid-state amorphization reaction of thin (35 nm) Ni films with Si, using in situ x-ray diffraction (XRD), ex situ Rutherford backscattering spectrometry, XTEM, and synchrotron XRD. It is shown that due to small amounts of nitrogen (<2 at.%), an amorphous Ni-Si phase grows almost an order of magnitude thicker during annealing than for unimplanted samples. Nitrogen hinders the nucleation of the first crystalline phases, leading to a new reaction path: the formation of the metal-rich crystalline silicides is suppressed in favour of an amorphous Ni-Si alloy; during a brief temperature window between 330 and 350 degrees C, the entire film is converted to an amorphous phase. The first crystalline structure to grow is the orthorhombic NiSi phase. We demonstrate that this impurity-enchanced solid-state amorphization reaction occurs only under specific implantation conditions. In particular, the initial distribution of nitrogen upon implantation is crucial: sufficient nitrogen impurities must be present at the interface throughout the reaction. Introducing implantation damage without nitrogen impurities (e.g. by implanting a noble gas) does not cause the enhanced solid-state amorphization reaction. Moreover, we show that the stabilizing effect of nitrogen on amorphous Ni-Si films (with a composition ranging from 40% to 50% Si) is not restricted to thin film reactions, but is a general feature of the Ni-Si system

Ultracold polar molecules near quantum degeneracy

We report the creation and characterization of a near quantum-degenerate gas of polar $^{40}$K-$^{87}$Rb molecules in their absolute rovibrational ground state. Starting from weakly bound heteronuclear KRb Feshbach molecules, we implement precise control of the molecular electronic, vibrational, and rotational degrees of freedom with phase-coherent laser fields. In particular, we coherently transfer these weakly bound molecules across a 125 THz frequency gap in a single step into the absolute rovibrational ground state of the electronic ground potential. Phase coherence between lasers involved in the transfer process is ensured by referencing the lasers to two single components of a phase-stabilized optical frequency comb. Using these methods, we prepare a dense gas of $4\cdot10^4$ polar molecules at a temperature below 400 nK. This fermionic molecular ensemble is close to quantum degeneracy and can be characterized by a degeneracy parameter of $T/T_F=3$. We have measured the molecular polarizability in an optical dipole trap where the trap lifetime gives clues to interesting ultracold chemical processes. Given the large measured dipole moment of the KRb molecules of 0.5 Debye, the study of quantum degenerate molecular gases interacting via strong dipolar interactions is now within experimental reach

Dipolar collisions of polar molecules in the quantum regime

Ultracold polar molecules offer the possibility of exploring quantum gases with interparticle interactions that are strong, long-range, and spatially anisotropic. This is in stark contrast to the dilute gases of ultracold atoms, which have isotropic and extremely short-range, or "contact", interactions. The large electric dipole moment of polar molecules can be tuned with an external electric field; this provides unique opportunities such as control of ultracold chemical reactions, quantum information processing, and the realization of novel quantum many-body systems. In spite of intense experimental efforts aimed at observing the influence of dipoles on ultracold molecules, only recently have sufficiently high densities been achieved. Here, we report the observation of dipolar collisions in an ultracold molecular gas prepared close to quantum degeneracy. For modest values of an applied electric field, we observe a dramatic increase in the loss rate of fermionic KRb molecules due to ultrcold chemical reactions. We find that the loss rate has a steep power-law dependence on the induced electric dipole moment, and we show that this dependence can be understood with a relatively simple model based on quantum threshold laws for scattering of fermionic polar molecules. We directly observe the spatial anisotropy of the dipolar interaction as manifested in measurements of the thermodynamics of the dipolar gas. These results demonstrate how the long-range dipolar interaction can be used for electric-field control of chemical reaction rates in an ultracold polar molecule gas. The large loss rates in an applied electric field suggest that creating a long-lived ensemble of ultracold polar molecules may require confinement in a two-dimensional trap geometry to suppress the influence of the attractive dipolar interactions

Mechanisms of vesicular stomatitis virus inactivation by protoporphyrin ix, zinc- protoporphyrin ix, and mesoporphyrin ix

© 2017 American Society for Microbiology. All Rights Reserved.Virus resistance to antiviral therapies is an increasing concern that makes the development of broad-spectrum antiviral drugs urgent. Targeting of the viral envelope, a component shared by a large number of viruses, emerges as a promising strategy to overcome this problem. Natural and synthetic porphyrins are good candidates for antiviral development due to their relative hydrophobicity and pro-oxidant character. In the present work, we characterized the antiviral activities of protoprophyrin IX (PPIX), Zn-protoporphyrin IX (ZnPPIX), and mesoporphyrin IX (MPIX) against vesicular stomatitis virus (VSV) and evaluated the mechanisms involved in this activity. Treatment of VSV with PPIX, ZnPPIX, and MPIX promoted dose-dependent virus inactivation, which was potentiated by porphyrin photoactivation. All three porphyrins inserted into lipid vesicles and disturbed the viral membrane organization. In addition, the porphyrins also affected viral proteins, inducing VSV glycoprotein cross-linking, which was enhanced by porphyrin photoactivation. Virus incubation with sodium azide and α-tocopherol partially protected VSV from inactivation by porphyrins, suggesting that singlet oxygen (1O2) was the main reactive oxygen species produced by photoactivation of these molecules. Furthermore, 1O2 was detected by 9,10-dimethylanthracene oxidation in photoactivated porphyrin samples, reinforcing this hypothesis. These results reveal the potential therapeutic application of PPIX, ZnPPIX, and MPIX as good models for broad antiviral drug design.Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ; Brazil; grant number E-26/201.167/2014), the Conselho Nacional de Desenvolvimento Cientifico e Tecnológico (CNPq; Brazil; grant number 306669/2013-7), the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Brazil; grant number CsF 171/2012), the Fundacao para a Ciencia e Tecnologia-Ministério da Educação e Ciência (FCT-MEC; Portugal; project HIVERA/0002/2013), and Marie Skłodowska-Curie Actions (MSCA; European Commission project INPACT 644167). C.C.-O. acknowledges a Science without Borders postdoctoral fellowship from CAPES (171/2012), and J.M.F. acknowledges an FCT-MEC Ph.D. fellowship (SFRH/BD/70423/2010)info:eu-repo/semantics/publishedVersio

Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population.

Background: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence: This observational study provides a level IV evidence on prognosis after TBI

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model:A Netherlands consortium of dementia cohorts case study

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.</p

Data harmonization and federated learning for multi-cohort dementia research using the OMOP common data model:A Netherlands consortium of dementia cohorts case study

Background: Establishing collaborations between cohort studies has been fundamental for progress in health research. However, such collaborations are hampered by heterogeneous data representations across cohorts and legal constraints to data sharing. The first arises from a lack of consensus in standards of data collection and representation across cohort studies and is usually tackled by applying data harmonization processes. The second is increasingly important due to raised awareness for privacy protection and stricter regulations, such as the GDPR. Federated learning has emerged as a privacy-preserving alternative to transferring data between institutions through analyzing data in a decentralized manner. Methods: In this study, we set up a federated learning infrastructure for a consortium of nine Dutch cohorts with appropriate data available to the etiology of dementia, including an extract, transform, and load (ETL) pipeline for data harmonization. Additionally, we assessed the challenges of transforming and standardizing cohort data using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) and evaluated our tool in one of the cohorts employing federated algorithms. Results: We successfully applied our ETL tool and observed a complete coverage of the cohorts’ data by the OMOP CDM. The OMOP CDM facilitated the data representation and standardization, but we identified limitations for cohort-specific data fields and in the scope of the vocabularies available. Specific challenges arise in a multi-cohort federated collaboration due to technical constraints in local environments, data heterogeneity, and lack of direct access to the data. Conclusion: In this article, we describe the solutions to these challenges and limitations encountered in our study. Our study shows the potential of federated learning as a privacy-preserving solution for multi-cohort studies that enhance reproducibility and reuse of both data and analyses.</p

Physical structure of the envelopes of intermediate-mass protostars

Context: Intermediate mass protostars provide a bridge between low- and high-mass protostars. Furthermore, they are an important component of the UV interstellar radiation field. Despite their relevance, little is known about their formation process. Aims: We present a systematic study of the physical structure of five intermediate mass, candidate Class 0 protostars. Our two goals are to shed light on the first phase of intermediate mass star formation and to compare these protostars with low- and high-mass sources. Methods: We derived the dust and gas temperature and density profiles of the sample. We analysed all existing continuum data on each source and modelled the resulting SED with the 1D radiative transfer code DUSTY. The gas temperature was then predicted by means of a modified version of the code CHT96. Results: We found that the density profiles of five out of six studied intermediate mass envelopes are consistent with the predictions of the "inside-out" collapse theory.We compared several physical parameters, like the power law index of the density profile, the size, the mass, the average density, the density at 1000 AU and the density at 10 K of the envelopes of low-, intermediate, and high-mass protostars. When considering these various physical parameters, the transition between the three groups appears smooth, suggesting that the formation processes and triggers do not substantially differ