Localization and Functional Analysis of the Calcium Permeable Melastatin-like Channel TRPM3

TRPM3 is a highly conserved melastatin-like transient receptor potential (TRP) gene with direct orthologs in all chordates and close homologs in all bilateral animals. So far, little is known about its biological role, activation mechanism, expression pattern and the functional role of its domains. The aim of this work was to clone a cDNA of mouse TRPM3, to express it in in vitro overexpression models and evaluate its function, to raise antisera against TRPM3 in order to determine its cellular expression profile in the mouse and, finally, to obtain a knock-out allele in the mouse in order to assess its biological role. 1. In this work, we cloned two cDNAs of mTRPM3, both starting with the unique starting exon we had identifed in a 5-RACE study. One variant is 1337 amino acids long and has a shorter C-terminal domain than the other, which has 1719 amino acid residues. Both splice variants are readily expressable in HEK-293 cells. Plasma membrane localization was greatly augmented by stable inducible expression in differentiated, polarized MDCK cells, where mTRPM31719 resided in the basolateral compartment. When overexpressed in HEK-293 cells, TRPM3 was unresponsive to hypo- or hyperosmolar stimuli, D-erythro-sphingosine and protocols used to elicit store-operated calcium entry. Instead, we observed a robust constitutive calcium entry through TRPM3, which was seen with both splice variants and abolished by point mutations in the pore domain. 2. We confirmed by means of fluorescence resonance energy transfer (FRET) and co-immunoprecipitation that TRPM3, like other TRP channels, forms multimeric channel complexes. The C-terminus of TRPM3 was found to attach to the plasma membrane through a palmitoylation of a dual-cysteine motif, a modification that quantitatively affected calcium entry through TRPM3. In a systematic yeast two hybrid screen of a kidney library against the C-terminus of TRPM3, we identified a number of candidate interaction partners of TRPM3, including alpha-B crystallin and protein kinase inhibitor gamma (PKIG). 3. We investigated the TRPM3 expression pattern by a combination of Northern blots, Western blots and lacZ stainings in a mouse model based on the GeneTrap approach. We found TRPM3 to be mainly enriched in eye and brain tissues, but, unlike human TRPM3, very low levels in kidney. Using lacZ staining of cryostat sections, TRPM3 was detected mainly in sensory tissues like the bipolar and gangion cell layers of the eye, the embryonic ear bubble and dorsal root ganglia. In the brain, a considerable enrichment of TRPM3 gene activity was seen, among others, in various neuron populations of the hippocampus formation (dentate gyrus, CA1, CA3), the Purkinje cells layer, and the neocortex. Thus, TRPM3 is mainly expressed in cells of ectodermal origin. 4. We characterized a mouse model in which TRPM3 had been targeted by a gene trap insertion. We confirmed the gene trap insertion by means of PCR, we confirmed the absence of the protein in eye tissues by means of a Western blot using a newly made peptide rabbit antiserum, and we isogenized the genetical background for skin coloration markers using Mendelian crosses. A basic study of TRPM3s phenotype showed the absence of morphological and overall behavioural defects and complete viability of the mutant. Further assays addressing the visual abilities of the TRPM3-/- mouse showed no signs of blindness. In summary, we cloned cDNAs of TRPM3, expressed them in vitro and found that TRPM3 is calcium-permeable and constitutively active. TRPM3 is palmitoylated on its C-terminus and interacts with a variety of proteins we identified by a yeast two hybrid approach, many of them novel in this regard. TRPM3 is highly enriched in sensory and central nervous tissues. The TRPM3-/- mouse is viable without any major neurological or sensory defect as far as tested. Further investigation on tissues which will have to address the specific function of TRPM3 where it is enriched

Asymptotic Stability and Exponential Stability of Impulsive Delayed Hopfield Neural Networks

A criterion for the uniform asymptotic stability of the equilibrium point of impulsive delayed Hopfield neural networks is presented by using Lyapunov functions and linear matrix inequality approach. The criterion is a less restrictive version of a recent result. By means of constructing the extended impulsive Halanay inequality, we also analyze the exponential stability of impulsive delayed Hopfield neural networks. Some new sufficient conditions ensuring exponential stability of the equilibrium point of impulsive delayed Hopfield neural networks are obtained. An example showing the effectiveness of the present criterion is given

Understanding Convolution for Semantic Segmentation

Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. Here we show how to improve pixel-wise semantic segmentation by manipulating convolution-related operations that are of both theoretical and practical value. First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase. This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation. We evaluate our approaches thoroughly on the Cityscapes dataset, and achieve a state-of-art result of 80.1% mIOU in the test set at the time of submission. We also have achieved state-of-the-art overall on the KITTI road estimation benchmark and the PASCAL VOC2012 segmentation task. Our source code can be found at https://github.com/TuSimple/TuSimple-DUC .Comment: WACV 2018. Updated acknowledgements. Source code: https://github.com/TuSimple/TuSimple-DU

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels

A quantum neural network (QNN) is a parameterized mapping efficiently implementable on near-term Noisy Intermediate-Scale Quantum (NISQ) computers. It can be used for supervised learning when combined with classical gradient-based optimizers. Despite the existing empirical and theoretical investigations, the convergence of QNN training is not fully understood. Inspired by the success of the neural tangent kernels (NTKs) in probing into the dynamics of classical neural networks, a recent line of works proposes to study over-parameterized QNNs by examining a quantum version of tangent kernels. In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization. As a result of the deviation, we prove the at-most sublinear convergence for QNNs with Pauli measurements, which is beyond the explanatory power of any kernel regression dynamics. We then present the actual dynamics of QNNs in the limit of over-parameterization. The new dynamics capture the change of convergence rate during training and implies that the range of measurements is crucial to the fast QNN convergence

Drosophila TRPM Channel Is Essential for the Control of Extracellular Magnesium Levels

The TRPM group of cation channels plays diverse roles ranging from sensory signaling to Mg2+ homeostasis. In most metazoan organisms the TRPM subfamily is comprised of multiple members, including eight in humans. However, the Drosophila TRPM subfamily is unusual in that it consists of a single member. Currently, the functional requirements for this channel have not been reported. Here, we found that the Drosophila TRPM protein was expressed in the fly counterpart of mammalian kidneys, the Malpighian tubules, which function in the removal of electrolytes and toxic components from the hemolymph. We generated mutations in trpm and found that this resulted in shortening of the Malpighian tubules. In contrast to all other Drosophila trp mutations, loss of trpm was essential for viability, as trpm mutations resulted in pupal lethality. Supplementation of the diet with a high concentration of Mg2+ exacerbated the phenotype, resulting in growth arrest during the larval period. Feeding high Mg2+ also resulted in elevated Mg2+ in the hemolymph, but had relatively little effect on cellular Mg2+. We conclude that loss of Drosophila trpm leads to hypermagnesemia due to a defect in removal of Mg2+ from the hemolymph. These data provide the first evidence for a role for a Drosophila TRP channel in Mg2+ homeostasis, and underscore a broad and evolutionarily conserved role for TRPM channels in Mg2+ homeostasis

Investment decision making along the B&R using critic approach in probabilistic hesitant fuzzy environment

The Belt and Road (B&R) Initiative receives enthusiastic response, the aim of which is to develop cooperative partnerships with countries along the routes and build a community of common destiny. So far, Chinese companies have invested in many different countries along the B&R. Generally, the investment decision making problems are characterized by high risk and uncertainty. Then how to make an appropriate investment decision will be a thorny issue. In this paper, probabilistic hesitant fuzzy set (PHFS) is used for handling uncertainty in multiple attribute decision making (MADM), and the criteria importance through intercriteria correlation (CRITIC) approach is extended to obtain attribute weights, no matter whether the weight information is incompletely known or not. Considering that the existing probabilistic hesitant fuzzy distance measures fail to meet the condition of distance measure, a new distance between PHFSs is proposed and applied to investment decision making for countries along the B&R. In the last, comparative analyses are performed to illustrate the advantages of the presented approach

An Automated Vulnerability Detection Framework for Smart Contracts

With the increase of the adoption of blockchain technology in providing decentralized solutions to various problems, smart contracts have become more popular to the point that billions of US Dollars are currently exchanged every day through such technology. Meanwhile, various vulnerabilities in smart contracts have been exploited by attackers to steal cryptocurrencies worth millions of dollars. The automatic detection of smart contract vulnerabilities therefore is an essential research problem. Existing solutions to this problem particularly rely on human experts to define features or different rules to detect vulnerabilities. However, this often causes many vulnerabilities to be ignored, and they are inefficient in detecting new vulnerabilities. In this study, to overcome such challenges, we propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract since the source code of smart contracts are rarely available in public. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result. We conduct comprehensive experiments on large-scale benchmarks, and the quantitative results demonstrate the effectiveness and efficiency of our approach

miRNA-378 reverses chemoresistance to cisplatin in lung adenocarcinoma cells by targeting secreted clusterin

Cisplatin resistance is a major obstacle in the treatment of NSCLC, and its mechanism has not been fully elucidated. The objectives of the study were to determine the role of miR-378 in the sensitivity of lung adenocarcinoma cells to cisplatin (cDDP) and its working mechanism. With TargetScan and luciferase assay, miR-378 was found to directly target sCLU. miR-378 and sCLU were regulated in A549/cDDP and Anip973/cDDP cells to investigate the effect of miR-378 on the sensitivity and apoptotic effects of cDDP. The effect of miR-378 upregulation on tumor growth was analyzed in a nude mouse xenograft model. The correlation between miR-378 and chemoresistance was tested in patient samples. We found that upregulation of miR-378 in A549/cDDP and Anip973/cDDP cells significantly down-regulated sCLU expression, and sensitized these cells to cDDP. miR-378 overexpression inhibited tumor growth and sCLU expression in a xenograft animal model. Analysis of human lung adenocarcinoma tissues revealed that the cDDP sensitive group expressed higher levels of miR-378 and lower levels of sCLU. miR-378 and sCLU were negatively correlated. To conclude, we identified sCLU as a novel miR-378 target, and we showed that targeting sCLU via miR-378 may help disable the chemoresistance against cisplatin in lung adenocarcinoma cells