R-spondin1 synergizes with Wnt3A in inducing osteoblast differentiation and osteoprotegerin expression

AbstractR-spondins are a new group of Wnt/β-catenin signaling agonists, however, the role of these proteins in bone remains unclear. We reported herein that R-sponin1 (Rspo1) acted synergistically with Wnt3A to activate Wnt/β-catenin signaling in the uncommitted mesenchymal C2C12 cells. Furthermore, we found that Rspo1 at concentrations as low as 10ng/ml synergized strongly with Wnt3A to induce C2C12 osteoblastic differentiation and osteoprotegerin expression. These events were blocked by Wnt/β-catenin signaling antagonist Dickkopf-1. Finally, we demonstrated that Rspo1 synergized with Wnt3A to induce primary mouse osteoblast differentiation. Together, these findings suggest that Rpos1 may play an important role in bone remodeling

Adaptive Resonance Theory (ART) for social media analytics

This chapter presents the ART-based clustering algorithms for social media analytics in detail. Sections 3.1 and 3.2 introduce Fuzzy ART and its clustering mechanisms, respectively, which provides a deep understanding of the base model that is used and extended for handling the social media clustering challenges. Important concepts such as vigilance region (VR) and its properties are explained and proven. Subsequently, Sects. 3.3-3.7 illustrate five types of ART adaptive resonance theory variants, each of which addresses the challenges in one social media analytical scenario, including automated parameter adaptation, user preference incorporation, short text clustering, heterogeneous data co-clustering and online streaming data indexing. The content of this chapter is several prior studies, including Probabilistic ART [15

On the implications of aerosol liquid water and phase separation for modeled organic aerosol mass

Water is an important component of PM2.5 Many traditional SOA species are highly soluble and thus can be considered extractable Water can influence the partitioning of compounds traditionally considered insoluble in models Organic aerosol takes up water according to RH Organic aerosol interacts with inorganic water Deviations in ideality (solubility) must be considered

Multiple tumor suppressors regulate a HIF-dependent negative feedback loop via ISGF3 in human clear cell renal cancer.

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1, KDM5C/JARID1C, SETD2, and/or BAP1, remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/-cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC. © 2018, Liao et al

Individualised Transcranial Magnetic Stimulation Targeting of the Left Dorsolateral Prefrontal Cortex for Enhancing Cognition: A Randomised Controlled Trial

Repetitive transcranial magnetic stimulation (rTMS) has been demonstrated to produce cognitive enhancing effects across different neuropsychiatric disorders; however, so far, these effects have been limited. This trial investigated the efficacy of using a novel individualised approach to target the left dorsolateral prefrontal cortex (L-DLPFC) for enhancing cognitive flexibility based on performance on a cognitive task. First, forty healthy participants had their single target site at the L-DLPFC determined based on each individual’s performance on a random letter generation task. Participants then received, in a cross-over single-blinded experimental design, a single session of intermittent theta burst stimulation (iTBS) to their individualised DLPFC target site, an active control site and sham iTBS. Following each treatment condition, participants completed the Task Switching task and Colour–Word Stroop test. There was no significant main effect of treatment condition on the primary outcome measure of switch reaction times from the Task Switching task [F = 1.16 (2, 21.6), p = 0.33] or for any of the secondary cognitive outcome measures. The current results do not support the use of our novel individualised targeting methodology for enhancing cognitive flexibility in healthy participants. Research into alternative methodological targeting approaches is required to further improve rTMS’s cognitive enhancing effects

Globally enhanced mercury deposition during the end-Pliensbachian extinction and Toarcian OAE: A link to the Karoo-Ferrar Large Igneous Province

The Mesozoic Era featured emplacement of a number of Large Igneous Provinces (LIPs), formed by the outpouring of millions of cubic kilometres of basaltic magma. The radiometric ages of several Mesozoic LIPs coincide with the dates of Oceanic Anoxic Events (OAEs). As a result of these coincidences, a causal link has been suggested, but never conclusively proven. This study explores the use of mercury as a possible direct link between the Karoo-Ferrar LIP and the coeval Toarcian OAE (T-OAE). Mercury is emitted to the atmosphere as a trace constituent of volcanic gas, and may be distributed globally before being deposited in sediments. Modern marine deposits show a strong linear correlation between mercury and organic-matter content. Results presented here indicate departures from such a simple linear relationship in sediments deposited during the T-OAE, and also during the Pliensbachian-Toarcian transition (an event that saw elevated benthic extinctions and carbon-cycle perturbations prior to the T-OAE). A number of depositional settings illustrate an increased mercury concentration in sediments that record one or both events, suggesting a rise in the depositional flux of this element. Complications to this relationship may arise from very organic-rich sediments potentially overprinting any Hg/TOC signal, whereas environments preserving negligible organic matter may leave no record of mercury deposition. However, the global distribution of coevally elevated Hg-rich levels suggests enhanced atmospheric mercury availability during the Early Toarcian, potentially aided by the apparent affinity of Hg for terrestrial organic matter, although the relative importance of aquatic vs terrestrial fixation of Hg in governing these enrichments remains uncertain. A perturbation in atmospheric Hg is most easily explained by enhanced volcanic output. It is suggested that extrusive igneous activity caused increased mercury flux to the Early Toarcian sedimentary realm, supporting the potential of this element as a proxy for ancient volcanism. This interpretation is consistent with a relationship between LIP formation and a perturbed carbon cycle during the Pliensbachian-Toarcian transition and T-OAE. The recording of these two distinct Hg excursions may also indicate that the Karoo-Ferrar LIP released volatiles in temporally distinct episodes, due either to multiple phases of magmatic emplacement or sporadic release of thermogenic gaseous products from intrusion of igneous material into volatile-rich lithologies.We acknowledge NERC (NE/G01700X/1) and the Leverhulme Trust for funding

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data

Machine learning models are a powerful theoretical tool for analyzing data from quantum simulators, in which results of experiments are sets of snapshots of many-body states. Recently, they have been successfully applied to distinguish between snapshots that can not be identified using traditional one and two point correlation functions. Thus far, the complexity of these models has inhibited new physical insights from this approach. Here, using a novel set of nonlinearities we develop a network architecture that discovers features in the data which are directly interpretable in terms of physical observables. In particular, our network can be understood as uncovering high-order correlators which significantly differ between the data studied. We demonstrate this new architecture on sets of simulated snapshots produced by two candidate theories approximating the doped Fermi-Hubbard model, which is realized in state-of-the art quantum gas microscopy experiments. From the trained networks, we uncover that the key distinguishing features are fourth-order spin-charge correlators, providing a means to compare experimental data to theoretical predictions. Our approach lends itself well to the construction of simple, end-to-end interpretable architectures and is applicable to arbitrary lattice data, thus paving the way for new physical insights from machine learning studies of experimental as well as numerical data.Comment: 7 pages, 4 figures + 13 pages of supplemental materia

Electromagnetically Induced Transparency and Slow Light with Optomechanics

Controlling the interaction between localized optical and mechanical excitations has recently become possible following advances in micro- and nano-fabrication techniques. To date, most experimental studies of optomechanics have focused on measurement and control of the mechanical subsystem through its interaction with optics, and have led to the experimental demonstration of dynamical back-action cooling and optical rigidity of the mechanical system. Conversely, the optical response of these systems is also modified in the presence of mechanical interactions, leading to strong nonlinear effects such as Electromagnetically Induced Transparency (EIT) and parametric normal-mode splitting. In atomic systems, seminal experiments and proposals to slow and stop the propagation of light, and their applicability to modern optical networks, and future quantum networks, have thrust EIT to the forefront of experimental study during the last two decades. In a similar fashion, here we use the optomechanical nonlinearity to control the velocity of light via engineered photon-phonon interactions. Our results demonstrate EIT and tunable optical delays in a nanoscale optomechanical crystal device, fabricated by simply etching holes into a thin film of silicon (Si). At low temperature (8.7 K), we show an optically-tunable delay of 50 ns with near-unity optical transparency, and superluminal light with a 1.4 microseconds signal advance. These results, while indicating significant progress towards an integrated quantum optomechanical memory, are also relevant to classical signal processing applications. Measurements at room temperature and in the analogous regime of Electromagnetically Induced Absorption (EIA) show the utility of these chip-scale optomechanical systems for optical buffering, amplification, and filtering of microwave-over-optical signals.Comment: 15 pages, 9 figure