Assessing the Impact of Defects on Lead-Free Perovskite-Inspired Photovoltaics via Photoinduced Current Transient Spectroscopy

Funder: Collaborative Innovation Center of Suzhou Nano Science & TechnologyFunder: Priority Academic Program Development of Jiangsu Higher Education Institutions; Id: http://dx.doi.org/10.13039/501100012246Funder: 111 Project; Id: http://dx.doi.org/10.13039/501100013314Funder: Joint International Research Laboratory of Carbon‐Based Functional Materials and DevicesThe formidable rise of lead-halide perovskite photovoltaics has energized the search for lead-free perovskite-inspired materials (PIMs) with related optoelectronic properties but free from toxicity limitations. The photovoltaic performance of PIMs closely depends on their defect tolerance. However, a comprehensive experimental characterization of their defect-level parameters—concentration, energy depth, and capture cross-section—has not been pursued to date, hindering the rational development of defect-tolerant PIMs. While mainstream, capacitance-based techniques for defect-level characterization have sparked controversy in lead-halide perovskite research, their use on PIMs is also problematic due to their typical near-intrinsic character. This study demonstrates on four representative PIMs (Cs3Sb2I9, Rb3Sb2I9, BiOI, and AgBiI4) for which Photoinduced Current Transient Spectroscopy (PICTS) offers a facile, widely applicable route to the defect-level characterization of PIMs embedded within solar cells. Going beyond the ambiguities of the current discussion of defect tolerance, a methodology is also presented to quantitatively assess the defect tolerance of PIMs in photovoltaics based on their experimental defect-level parameters. Finally, PICTS applied to PIM photovoltaics is revealed to be ultimately sensitive to defect-level concentrations <1 ppb. Therefore, this study provides a versatile platform for the defect-level characterization of PIMs and related absorbers, which can catalyze the development of green, high-performance photovoltaics.Royal Academy of Engineerin

Adaptive Strategy for the Statistical Analysis of Connectomes

We study an adaptive statistical approach to analyze brain networks represented by brain connection matrices of interregional connectivity (connectomes). Our approach is at a middle level between a global analysis and single connections analysis by considering subnetworks of the global brain network. These subnetworks represent either the inter-connectivity between two brain anatomical regions or by the intra-connectivity within the same brain anatomical region. An appropriate summary statistic, that characterizes a meaningful feature of the subnetwork, is evaluated. Based on this summary statistic, a statistical test is performed to derive the corresponding p-value. The reformulation of the problem in this way reduces the number of statistical tests in an orderly fashion based on our understanding of the problem. Considering the global testing problem, the p-values are corrected to control the rate of false discoveries. Finally, the procedure is followed by a local investigation within the significant subnetworks. We contrast this strategy with the one based on the individual measures in terms of power. We show that this strategy has a great potential, in particular in cases where the subnetworks are well defined and the summary statistics are properly chosen. As an application example, we compare structural brain connection matrices of two groups of subjects with a 22q11.2 deletion syndrome, distinguished by their IQ scores

Clinical utility of chromosomal microarray analysis in invasive prenatal diagnosis

Novel methodologies for detection of chromosomal abnormalities have been made available in the recent years but their clinical utility in prenatal settings is still unknown. We have conducted a comparative study of currently available methodologies for detection of chromosomal abnormalities after invasive prenatal sampling. A multicentric collection of a 1-year series of fetal samples with indication for prenatal invasive sampling was simultaneously evaluated using three screening methodologies: (1) karyotype and quantitative fluorescent polymerase chain reaction (QF-PCR), (2) two panels of multiplex ligation-dependent probe amplification (MLPA), and (3) chromosomal microarray-based analysis (CMA) with a targeted BAC microarray. A total of 900 pregnant women provided informed consent to participate (94% acceptance rate). Technical performance was excellent for karyotype, QF-PCR, and CMA (~1% failure rate), but relatively poor for MLPA (10% failure). Mean turn-around time (TAT) was 7 days for CMA or MLPA, 25 for karyotype, and two for QF-PCR, with similar combined costs for the different approaches. A total of 57 clinically significant chromosomal aberrations were found (6.3%), with CMA yielding the highest detection rate (32% above other methods). The identification of variants of uncertain clinical significance by CMA (17, 1.9%) tripled that of karyotype and MLPA, but most alterations could be classified as likely benign after proving they all were inherited. High acceptability, significantly higher detection rate and lower TAT, could justify the higher cost of CMA and favor targeted CMA as the best method for detection of chromosomal abnormalities in at-risk pregnancies after invasive prenatal sampling

The NMR restraints grid at BMRB for 5,266 protein and nucleic acid PDB entries

Several pilot experiments have indicated that improvements in older NMR structures can be expected by applying modern software and new protocols (Nabuurs et al. in Proteins 55:483–186, 2004; Nederveen et al. in Proteins 59:662–672, 2005; Saccenti and Rosato in J Biomol NMR 40:251–261, 2008). A recent large scale X-ray study also has shown that modern software can significantly improve the quality of X-ray structures that were deposited more than a few years ago (Joosten et al. in J. Appl Crystallogr 42:376–384, 2009; Sanderson in Nature 459:1038–1039, 2009). Recalculation of three-dimensional coordinates requires that the original experimental data are available and complete, and are semantically and syntactically correct, or are at least correct enough to be reconstructed. For multiple reasons, including a lack of standards, the heterogeneity of the experimental data and the many NMR experiment types, it has not been practical to parse a large proportion of the originally deposited NMR experimental data files related to protein NMR structures. This has made impractical the automatic recalculation, and thus improvement, of the three dimensional coordinates of these structures. We here describe a large-scale international collaborative effort to make all deposited experimental NMR data semantically and syntactically homogeneous, and thus useful for further research. A total of 4,014 out of 5,266 entries were ‘cleaned’ in this process. For 1,387 entries, human intervention was needed. Continuous efforts in automating the parsing of both old, and newly deposited files is steadily decreasing this fraction. The cleaned data files are available from the NMR restraints grid at http://restraintsgrid.bmrb.wisc.edu

Automated High-Content Live Animal Drug Screening Using C. elegans Expressing the Aggregation Prone Serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms

Network centrality and organizational aspirations: A behavioral interaction in the context of international strategic alliances

Whereas social network analysis has been associated with organizational aspirations, little is known on how firm's structural positioning, and particularly network centrality, affects organizational aspirations to engage in international strategic alliances (ISA). This study examines the impact of network centrality on firm's internationalization behavior within the ISA domain in response to the performance-aspiration gap. We build on social and behavioral perspectives to predict that network centrality and performance-based aspirations will be associated with the number of ISA the firm engages in. Using a sample of 7760 alliance collaborations from the top 81 global pharmaceutical firms for the period of 1991-2012, we find supporting evidence for most of our arguments

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe