As technology continues towards smaller, thinner and lighter devices, more stringent demands are placed on thin polymer films as diffusion barriers, dielectric coatings, electronic packaging and so on. Therefore, there is a growing need for testing platforms to rapidly determine the mechanical properties of thin polymer films and coatings. We introduce here an elegant, efficient measurement method that yields the elastic moduli of nanoscale polymer films in a rapid and quantitative manner without the need for expensive equipment or material-specific modelling. The technique exploits a buckling instability that occurs in bilayers consisting of a stiff, thin film coated onto a relatively soft, thick substrate. Using the spacing of these highly periodic wrinkles, we calculate the film's elastic modulus by applying well-established buckling mechanics. We successfully apply this new measurement platform to several systems displaying a wide range of thicknessess (nanometre to micrometre) and moduli (MPa to GPa).

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Purpose of Review Artificial intelligence (AI) technology holds both great promise to transform mental healthcare and potential pitfalls. This article provides an overview of AI and current applications in healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. Recent Findings We reviewed 28 studies of AI and mental health that used electronic health records (EHRs), mood rating scales, brain imaging data, novel monitoring systems (e.g., smartphone, video), and social media platforms to predict, classify, or subgroup mental health illnesses including depression, schizophrenia or other psychiatric illnesses, and suicide ideation and attempts. Collectively, these studies revealed high accuracies and provided excellent examples of AI's potential in mental healthcare, but most should be considered early proof-of-concept works demonstrating the potential of using machine learning (ML) algorithms to address mental health questions, and which types of algorithms yield the best performance. Summary As AI techniques continue to be refined and improved, it will be possible to help mental health practitioners re-define mental illnesses more objectively than currently done in the DSM-5, identify these illnesses at an earlier or prodromal stage when interventions may be more effective, and personalize treatments based on an individual's unique characteristics. However, caution is necessary in order to avoid over-interpreting preliminary results, and more work is required to bridge the gap between AI in mental health research and clinical care.

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As demand for smaller, more powerful, and energy-efficient devices continues, conventional patterning technologies are pushing up against fundamental limits. Block copolymers (BCPs) are considered prime candidates for a potential solution via directed self-assembly of nanostructures. We introduce here a facile directed self-assembly method to rapidly fabricate unidirectionally aligned BCP nanopatterns at large scale, on rigid or flexible template-free substrates via a thermally induced dynamic gradient soft-shear field. A localized differential thermal expansion at the interface between a BCP film and a confining polydimethylsiloxane (PDMS) layer due to a dynamic thermal field imposes the gradient soft-shear field. PDMS undergoes directional expansion (along the annealing direction) in the heating zone and contracts back in the cooling zone, thus setting up a single cycle of oscillatory shear (maximum lateral shear stress ∼12 × 10(4) Pa) in the system. We successfully apply this process to create unidirectional alignment of BCP thin films over a wide range of thicknesses (nm to μm) and processing speeds (μm/s to mm/s) using both a flat and patterned PDMS layer. Grazing incidence small-angle X-ray scattering measurements show absolutely no sign of isotropic population and reveal ≥99% aligned orientational order with an angular spread Δθ(fwhm) ≤ 5° (full width at half-maximum). This method may pave the way to practical industrial use of hierarchically patterned BCP nanostructures.

Ghrelin is an endogenous ligand for the GH secretagogue receptor, produced and secreted mainly from the stomach. Ghrelin stimulates GH release and induces positive energy balances. Previous studies have reported that ghrelin inhibits apoptosis in several cell types, but its antiapoptotic effect in neuronal cells is unknown. Therefore, we investigated the role of ghrelin in ischemic neuronal injury using primary hypothalamic neurons exposed to oxygen-glucose deprivation (OGD). Here we report that treatment of hypothalamic neurons with ghrelin inhibited OGD-induced cell death and apoptosis. Exposure of neurons to ghrelin caused rapid activation of ERK1/2. Ghrelin-induced activation of ERK1/2 and the antiapoptotic effect of ghrelin were blocked by chemical inhibition of MAPK, phosphatidylinositol 3 kinase, protein kinase C, and protein kinase A. Ghrelin attenuated OGD-induced activation of c-Jun NH2-terminal kinase and p-38 but not ERK1/2. We also investigated ghrelin regulation of apoptosis at the mitochondrial level. Ghrelin protected cells from OGD insult by inhibiting reactive oxygen species generation and stabilizing mitochondrial transmembrane potential. In addition, ghrelin-treated cells showed an increased Bcl-2/Bax ratio, prevention of cytochrome c release, and inhibition of caspase-3 activation. Finally, in vivo administration of ghrelin significantly reduced infarct volume in an animal model of ischemia. Our data indicate that ghrelin may act as a survival factor that preserves mitochondrial integrity and inhibits apoptotic pathways.