Formation and Disruption of W-Phase in High-Entropy Alloys

High-entropy alloys (HEAs) are single-phase systems prepared from equimolar or near-equimolar concentrations of at least five principal elements. The combination of high mixing entropy, severe lattice distortion, sluggish diffusion and cocktail effect favours the formation of simple phases—usually a bcc or fcc matrix with minor inclusions of ordered binary intermetallics. HEAs have been proposed for applications in which high temperature stability (including mechanical and chemical stability under high temperature and high mechanical impact) is required. On the other hand, the major challenge to overcome for HEAs to become commercially attractive is the achievement of lightweight alloys of extreme hardness and low brittleness. The multicomponent AlCrCuScTi alloy was prepared and characterized using powder X-ray diffraction (PXRD), scanning-electron microscope (SEM) and atomic-force microscope equipped with scanning Kelvin probe (AFM/SKP) techniques. Results show that the formation of complex multicomponent ternary intermetallic compounds upon heating plays a key role in phase evolution. The formation and degradation of W-phase, Al2Cu3Sc, in the AlCrCuScTi alloy plays a crucial role in its properties and stability. Analysis of as-melted and annealed alloy suggests that the W-phase is favoured kinetically, but thermodynamically unstable. The disruption of the W-phase in the alloy matrix has a positive effect on hardness (890 HV), density (4.83 g·cm−3) and crack propagation. The hardness/density ratio obtained for this alloy shows a record value in comparison with ordinary heavy refractory HEAs

MicroRNA clusters integrate evolutionary constraints on expression and target affinities : the miR-6/5/4/286/3/309 cluster in Drosophila

This research was supported by the Hong Kong Research Grant Council GRF Grant (14103516), The Chinese University of Hong Kong Direct Grant (4053248), and TUYF Charitable Trust (6903957) (JHLH).A striking feature of microRNAs is that they are often clustered in the genomes of animals. The functional and evolutionary consequences of this clustering remain obscure. Here, we investigated a microRNA cluster miR-6/5/4/286/3/309 that is conserved across drosophilid lineages. Small RNA sequencing revealed expression of this microRNA cluster in Drosophila melanogaster leg discs, and conditional overexpression of the whole cluster resulted in leg appendage shortening. Transgenic overexpression lines expressing different combinations of microRNA cluster members were also constructed. Expression of individual microRNAs from the cluster resulted in a normal wild-type phenotype, but either the expression of several ancient microRNAs together (miR-5/4/286/3/309) or more recently evolved clustered microRNAs (miR-6-1/2/3) can recapitulate the phenotypes generated by the whole-cluster overexpression. Screening of transgenic fly lines revealed down-regulation of leg patterning gene cassettes in generation of the leg-shortening phenotype. Furthermore, cell transfection with different combinations of microRNA cluster members revealed a suite of downstream genes targeted by all cluster members, as well as complements of targets that are unique for distinct microRNAs. Considered together, the microRNA targets and the evolutionary ages of each microRNA in the cluster demonstrates the importance of microRNA clustering, where new members can reinforce and modify the selection forces on both the cluster regulation and the gene regulatory network of existing microRNAs.PostprintPeer reviewe

Direct patterning of gold nanoparticles using flexographic printing for biosensing applications

In this paper, we have presented the use of flexographic printing techniques in the selective patterning of gold nanoparticles (AuNPs) onto a substrate. Highly uniform coverage of AuNPs was selectively patterned on the substrate surface, which was subsequently used in the development of a glucose sensor. These AuNPs provide a biocompatible site for the attachment of enzymes and offer high sensitivity in the detection of glucose due to their large surface to volume ratio. The average size of the printed AuNPs is less than 60 nm. Glucose sensing tests were performed using printed carbon-AuNP electrodes functionalized with glucose oxidase (GOx). The results showed a high sensitivity of 5.52 μA mM−1 cm−2 with a detection limit of 26 μM. We have demonstrated the fabrication of AuNP-based biosensors using flexographic printing, which is ideal for low-cost, high-volume production of the devices

Asthma Treatments and Mental Health Visits After a Food and Drug Administration Label Change for Leukotriene Inhibitors

Purpose In 2009, the US Food and Drug Administration (FDA) mandated a label change for leukotriene inhibitors (LTIs) to include neuropsychiatric adverse events (eg, depression and suicidality) as a precaution. This study investigated how this label change affected the use of LTIs and other asthma controller medications, mental health visits, and suicide attempts. Methods We analyzed data (2005–2010) from 5 large health plans in the US Population-Based Effectiveness in Asthma and Lung Diseases (PEAL) Network. The study cohort included children and adolescents (n = 30,000), young adults (n = 20,000), and adults (n = 90,000) with asthma. We used interrupted time series to examine changes in rates of LTI dispensings, non-LTI dispensings, mental health visits, and suicide attempts (using a validated algorithm based on a combination of diagnoses of injury or poisoning and psychiatric conditions). Findings The label change was associated with abrupt reductions in LTI use among all age groups (relative reductions of 8.3%, 15.1%, and 6.0% among adolescents, young adults, and adults, respectively, compared with expected rates at 1 year after the warnings). Although we detected immediate offset increases in non-LTI asthma medication use, these increases were not sustained among adolescents and young adults. There were small increases in mental health visits among LTI users. Implications The FDA label change for LTIs communicated possible risk of neuropsychiatric events. Communication and enhanced awareness may have increased reporting of mental health symptoms among young adults and adults. It is important to assess intended and unintended consequences of FDA warnings and label changes

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study.

Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18-36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus-specifically, intervals between ultrasound visits-rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20-30 weeks gestational age window with a prediction interval 3-5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal. Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre

Monthly hemostatic factor variability in women and men

Hormonal status influences hemostatic factors including fibrinogen, factor VII and plasminogen activator inhibitor (PAI-1), and concentrations differ among men, premenopausal and postmenopausal women. This study examines how phases of the menstrual cycle influence variability of fibrinogen, factor VII and PAI-1

Whole genome sequencing for the genetic diagnosis of heterogenous dystonia phenotypes

Introduction: Dystonia is a clinically and genetically heterogeneous disorder and a genetic cause is often difficult to elucidate. This is the first study to use whole genome sequencing (WGS) to investigate dystonia in a large sample of affected individuals. Methods: WGS was performed on 111 probands with heterogenous dystonia phenotypes. We performed analysis for coding and non-coding variants, copy number variants (CNVs), and structural variants (SVs). We assessed for an association between dystonia and 10 known dystonia risk variants. Results: A genetic diagnosis was obtained for 11.7% (13/111) of individuals. We found that a genetic diagnosis was more likely in those with an earlier age at onset, younger age at testing, and a combined dystonia phenotype. We identified pathogenic/likely-pathogenic variants in ADCY5 (n = 1), ATM (n = 1), GNAL (n = 2), GLB1 (n = 1), KMT2B (n = 2), PRKN (n = 2), PRRT2 (n = 1), SGCE (n = 2), and THAP1 (n = 1). CNVs were detected in 3 individuals. We found an association between the known risk variant ARSG rs11655081 and dystonia (p = 0.003). Conclusion: A genetic diagnosis was found in 11.7% of individuals with dystonia. The diagnostic yield was higher in those with an earlier age of onset, younger age at testing, and a combined dystonia phenotype. WGS may be particularly relevant for dystonia given that it allows for the detection of CNVs, which accounted for 23% of the genetically diagnosed cases. © 2019 The Author