Phase II Proof-of-Concept Trial of the Orexin Receptor Antagonist Filorexant (MK-6096) in Patients with Major Depressive Disorder.

BackgroundWe evaluated the orexin receptor antagonist filorexant (MK-6096) for treatment augmentation in patients with major depressive disorder.MethodsWe conducted a 6-week, double-blind, placebo-controlled, parallel-group, Phase II, proof-of-concept study. Patients with major depressive disorder (partial responders to ongoing antidepressant therapy) were randomized 1:1 to once-daily oral filorexant 10 mg or matching placebo.ResultsDue to enrollment challenges, the study was terminated early, resulting in insufficient statistical power to detect a prespecified treatment difference; of 326 patients planned, 129 (40%) were randomized and 128 took treatment. There was no statistically significant difference in the primary endpoint of change from baseline to week 6 in Montgomery Asberg Depression Rating Scale total score; the estimated treatment difference for filorexant-placebo was -0.7 (with negative values favoring filorexant) (P=.679). The most common adverse events were somnolence and suicidal ideation.ConclusionsThe interpretation of the results is limited by the enrollment, which was less than originally planned, but the available data do not suggest efficacy of orexin receptor antagonism with filorexant for the treatment of depression. (Clinical Trial Registry: clinicaltrials.gov: NCT01554176)

Liat1, an arginyltransferase-binding protein whose evolution among primates involved changes in the numbers of its 10-residue repeats

The arginyltransferase Ate1 is a component of the N-end rule pathway, which recognizes proteins containing N-terminal degradation signals called N-degrons, polyubiquitylates these proteins, and thereby causes their degradation by the proteasome. At least six isoforms of mouse Ate1 are produced through alternative splicing of Ate1 pre-mRNA. We identified a previously uncharacterized mouse protein, termed Liat1 (ligand of Ate1), that interacts with Ate1 but does not appear to be its arginylation substrate. Liat1 has a higher affinity for the isoforms Ate1^(1A7A) and Ate1^(1B7A). Liat1 stimulated the in vitro N-terminal arginylation of a model substrate by Ate1. All examined vertebrate and some invertebrate genomes encode proteins sequelogous (similar in sequence) to mouse Liat1. Sequelogs of Liat1 share a highly conserved ∼30-residue region that is shown here to be required for the binding of Liat1 to Ate1. We also identified non-Ate1 proteins that interact with Liat1. In contrast to Liat1 genes of nonprimate mammals, Liat1 genes of primates are subtelomeric, a location that tends to confer evolutionary instability on a gene. Remarkably, Liat1 proteins of some primates, from macaques to humans, contain tandem repeats of a 10-residue sequence, whereas Liat1 proteins of other mammals contain a single copy of this motif. Quantities of these repeats are, in general, different in Liat1 of different primates. For example, there are 1, 4, 13, 13, 17, and 17 repeats in the gibbon, gorilla, orangutan, bonobo, neanderthal, and human Liat1, respectively, suggesting that repeat number changes in this previously uncharacterized protein may contribute to evolution of primates

Designing hollow nano gold golf balls.

Hollow/porous nanoparticles, including nanocarriers, nanoshells, and mesoporous materials have applications in catalysis, photonics, biosensing, and delivery of theranostic agents. Using a hierarchical template synthesis scheme, we have synthesized a nanocarrier mimicking a golf ball, consisting of (i) solid silica core with a pitted gold surface and (ii) a hollow/porous gold shell without silica. The template consisted of 100 nm polystyrene beads attached to a larger silica core. Selective gold plating of the core followed by removal of the polystyrene beads produced a golf ball-like nanostructure with 100 nm pits. Dissolution of the silica core produced a hollow/porous golf ball-like nanostructure

Deep learning predictions of galaxy merger stage and the importance of observational realism

Machine learning is becoming a popular tool to quantify galaxy morphologies and identify mergers. However, this technique relies on using an appropriate set of training data to be successful. By combining hydrodynamical simulations, synthetic observations, and convolutional neural networks (CNNs), we quantitatively assess how realistic simulated galaxy images must be in order to reliably classify mergers. Specifically, we compare the performance of CNNs trained with two types of galaxy images, stellar maps and dust-inclusive radiatively transferred images, each with three levels of observational realism: (1) no observational effects (idealized images), (2) realistic sky and point spread function (semirealistic images), and (3) insertion into a real sky image (fully realistic images). We find that networks trained on either idealized or semireal images have poor performance when applied to survey-realistic images. In contrast, networks trained on fully realistic images achieve 87.1 per cent classification performance. Importantly, the level of realism in the training images is much more important than whether the images included radiative transfer, or simply used the stellar maps (⁠87.1 per cent compared to 79.6 per cent accuracy, respectively). Therefore, one can avoid the large computational and storage cost of running radiative transfer with a relatively modest compromise in classification performance. Making photometry-based networks insensitive to colour incurs a very mild penalty to performance with survey-realistic data (⁠86.0 per cent with r-only compared to 87.1 per cent with gri). This result demonstrates that while colour can be exploited by colour-sensitive networks, it is not necessary to achieve high accuracy and so can be avoided if desired. We provide the public release of our statistical observational realism suite, REALSIM, as a companion to this paper