To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
The role of neurological proteins in the development of bipolar disorder (BD) and schizophrenia (SCZ) remains elusive now. The current study aims to explore the potential genetic correlations of plasma neurological proteins with BD and SCZ.
By using the latest genome-wide association study (GWAS) summary data of BD and SCZ (including 41,917 BD cases, 11,260 SCZ cases, and 396,091 controls) derived from the Psychiatric GWAS Consortium website (PGC) and a recently released GWAS of neurological proteins (including 750 individuals), we performed a linkage disequilibrium score regression (LDSC) analysis to detect the potential genetic correlations between the two common psychiatric disorders and each of the 92 neurological proteins. Two-sample Mendelian randomisation (MR) analysis was then applied to assess the bidirectional causal relationship between the neurological proteins identified by LDSC, BD and SCZ.
LDSC analysis identified one neurological protein, NEP, which shows suggestive genetic correlation signals for both BD (coefficient = −0.165, p value = 0.035) and SCZ (coefficient = −0.235, p value = 0.020). However, those association did not remain significant after strict Bonferroni correction. Two sample MR analysis found that there was an association between genetically predicted level of NEP protein, BD (odd ratio [OR] = 0.87, p value = 1.61 × 10−6) and SCZ (OR = 0.90, p value = 4.04 × 10−6). However, in the opposite direction, there is no genetically predicted association between BD, SCZ, and NEP protein level.
This study provided novel clues for understanding the genetic effects of neurological proteins on BD and SCZ.
The nature and impact of ZnO buffer layers on the initial stages of the hydride vapor phase epitaxy (HVPE) of GaN have been studied by x-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), x-ray diffraction (XRD) and photoluminescence (PL). During pre-growth heating, the surface ZnO layer was found to both desorb from ZnO-coated sapphire and react with the underlying sapphire surface forming a thin ZnAl2O4 alloy layer between ZnO and sapphire surface. This ZnO-derived surface promotes the initial nucleation of the GaN and markedly improves material surface morphology, quality and growth reproducibility.
The development of new chemically based growth techniques has opened the range of possible GaN applications. This paper reviews some of the challenges in the chemically based growth of GaN and related materials. Ammonothermal-based growth, hydride vapor phase epitaxy and metal organic vapor phase epitaxy (MOVPE) are chemically complex systems wherein the underlying mechanisms of growth are not well understood at present. All these systems require substantial experimental and theoretical efforts to determine the nature and kinetics of GaN growth. In the case of metal organic vapor phase epitaxy, the application of computational techniques based on density functional theory have augmented the more conventional experimental approaches to determining the growth chemistry. These chemical reaction schemes, when combined with computational thermal-fluid models of the reactor environment, provide the opportunity to predict growth rates, uniformity and eve ntually materials properties.
Email your librarian or administrator to recommend adding this to your organisation's collection.