The X-linked tumor suppressor TSPX downregulates cancer-drivers/oncogenes in prostate cancer in a C-terminal acidic domain dependent manner.

TSPX is a tumor suppressor gene located at Xp11.22, a prostate cancer susceptibility locus. It is ubiquitously expressed in most tissues but frequently downregulated in various cancers, including lung, brain, liver and prostate cancers. The C-terminal acidic domain (CAD) of TSPX is crucial for the tumor suppressor functions, such as inhibition of cyclin B/CDK1 phosphorylation and androgen receptor transactivation. Currently, the exact role of the TSPX CAD in transcriptional regulation of downstream genes is still uncertain. Using different variants of TSPX, we showed that overexpression of either TSPX, that harbors a CAD, or a CAD-truncated variant (TSPX[∆C]) drastically retarded cell proliferation in a prostate cancer cell line LNCaP, but cell death was induced only by overexpression of TSPX. Transcriptome analyses showed that TSPX or TSPX[∆C] overexpression downregulated multiple cancer-drivers/oncogenes, including MYC and MYB, in a CAD-dependent manner and upregulated various tumor suppressors in a CAD-independent manner. Datamining of transcriptomes of prostate cancer specimens in the Cancer Genome Atlas (TCGA) dataset confirmed the negative correlation between the expression level of TSPX and those of MYC and MYB in clinical prostate cancer, thereby supporting the hypothesis that the CAD of TSPX plays an important role in suppression of cancer-drivers/oncogenes in prostatic oncogenesis

The PstI/RsaI and DraI polymorphisms of CYP2E1 and head and neck cancer risk: a meta-analysis based on 21 case-control studies

<p>Abstract</p> <p>Background</p> <p><it>CYP2E1 </it>encodes a member of the cytochrome P450 superfamily of enzymes which play a central role in activating and detoxifying many carcinogens and endogenous compounds thought to be involved in the development of cancer. The PstI/RsaI and DraI polymorphism are two of the most commonly studied polymorphisms of the gene for their association with risk of head and neck cancer, but the results are conflicting.</p> <p>Methods</p> <p>We performed a meta-analysis using 21 eligible case-control studies with a total of 4,951 patients and 6,071 controls to summarize the data on the association between the <it>CYP2E1 </it>PstI/RsaI and DraI polymorphism and head and neck cancer risk, especially by interacting with smoking or alcohol.</p> <p>Results</p> <p>Compared with the wild genotype, the OR was 1.96 (95% CI: 1.33-2.90) for PstI/RsaI and 1.56 (95% CI: 1.06-2.27) for DraI polymorphism respectively. When stratified according to ethnicity, the OR increased in the Asians for both polymorphisms (OR = 2.04, 95% CI: 1.32-3.15 for PstI/RsaI; OR = 2.04, 95% CI: 1.27-3.29 for DraI), suggesting that the risk is more pronounced in Asians.</p> <p>Conclusion</p> <p>Our meta-analysis suggests that individuals with the homozygote genotypes of PstI/RsaI or DraI polymorphism might be associated with an increased risk of head and neck cancer, especially in Asians.</p

Ultra-thin transmissive crystalline silicon high-contrast grating metasurfaces

Dielectric metasurfaces made from crystalline silicon, titanium dioxide, gallium nitride and silicon nitride have developed rapidly for applications in the visible wavelength regime. High performance metasurfaces typically require the realisation of subwavelength, high aspect ratio nanostructures, the fabrication of which can be challenging. Here, we propose and demonstrate the operation of high performance metasurfaces in ultra-thin (100 nm) crystalline silicon at the wavelength of 532 nm. Using optical beam analysis, we discuss fabrication complexity and show that our approach is more fabrication-tolerant than the nanofin approach, which has so far produced the highest performance metasurfaces, but may be difficult to manufacture, especially when using nanoimprint lithography

Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants

Differentiated Content Acquisition Integrating Network Coding and Edge Computing for Industrial Internet

The construction of the Industrial Internet has become a concrete implementation and an essential starting point in accelerating the digital transformation and intelligent upgrading of industrial manufacturing enterprises. The problem of addressing and forwarding for resource-constrained devices restricts data acquisition and dissemination on the Industrial Internet. In order to retrieve content reasonably, we propose IDEANE, an identity-differentiated content acquisition and multipath forwarding scheme with network coding and edge computing. In IDEANE, content requests are disseminated based on the identity and location information carried in a multi-identifier network, which can improve the efficiency of interest message requests and reduce idle links in the network. Moreover, the content acquisition computation is offloaded to multiple edge nodes, and the encoded data are transmitted to the edge nodes for recovery. IDEANE offloads the less demanding computing tasks to the edge nodes close to the content requesters, to relieve the pressure on providers. In addition, a collaboration method among multiple edge nodes is also studied. Multiple edge nodes collaborate to support the mobility of content requesters, save energy consumption in terminal devices, reduce transmission latency, and ensure data security. The experimental results show that IDEANE can avoid duplicated transmission, reduce the network link overhead, improve network throughput, and enhance network robustness and reliability

Unified Fusion Rules for Multisensor Multihypothesis Network Decision Systems

In this paper, we present a fusion rule for distributed multihypothesis decision systems where communication patterns among sensors are given and the fusion center may also observe data. It is a specific form of the most general fusion rule, independent of statistical characteristics of observations and decision criteria, and thus, is called a unified fusion rule of the decision system. To achieve globally optimum performance, only sensor rules need to be optimized under the proposed fusion rule for the given conditional distributions of observations and decision criterion. Following this idea, we present a systematic and efficient scheme for generating optimum sensor rules and hence, optimum fusion rules, which reduce computation tremendously as compared with the commonly used exhaustive search. Numerical examples are given, which support the above results and provide a guideline on how to assign sensors to nodes in a signal detection networks with a given communication pattern. In addition, performance of parallel and tandem networks is compared

Linear minimum variance estimation fusion

This paper shows that a general multisensor unbiased linearly weighted estimation fusion essentially is the linear minimum variance (LMV) estimation with linear equality constraint, and the general estimation fusion formula is developed by extending the Gauss-Markov estimation to the random parameter under estimation. First, we formulate the problem of distributed estimation fusion in the LMV setting. In this setting, the fused estimator is a weighted sum of local estimates with a matrix weight. We show that the set of weights is optimal if and only if it is a solution of a matrix quadratic optimization problem subject to a convex linear equality constraint. Second, we present a unique solution to the above optimization problem, which depends only on the covariance matrix k . Third, if a priori information, the expectation and covariance, of the estimated quantity is unknown, a necessary and sufficient condition for the above LMV fusion becoming the best unbiased LMV estimation with known prior information as the above is presented. We also discuss the generality and usefulness of the LMV fusion formulas developed. Finally, we provide an off-line recursion of k for a class of multisensor linear systems with coupled measurement noises

Extended Dampster-Shafer Combination Rules Based On Random Set Theory

The Dampster-Shafer combination rule has been widely discussed and used recently since it is a convenient and promising method to combine multi-source information with their own confidence degrees/evidences. On the other hand, it has been criticized and debated upon its some counterintuitive behavior and too restrictive requirement, such as the confidence degrees independency cross disparate sources. To clarify the theoretical essence of the Dampster-Shafer combination rule and provide the direction how to solve these problems, the Dampster-Shafer combination rule is formulated based on random set theory first. Then, under this framework, all possible combination rules are presented, and these combination rules based on correlated sensor confidence degrees (evidence supports) are proposed. The optimal Bayes combination rule is given finally