SPIDER-WEB enables stable, repairable, and encryptible algorithms under arbitrary local biochemical constraints in DNA-based storage

DNA has been considered as a promising medium for storing digital information. Despite the biochemical progress in DNA synthesis and sequencing, novel coding algorithms need to be constructed under the specific constraints in DNA-based storage. Many functional operations and storage carriers were introduced in recent years, bringing in various biochemical constraints including but not confined to long single-nucleotide repeats and abnormal GC content. Existing coding algorithms are not applicable or unstable due to more local biochemical constraints and their combinations. In this paper, we design a graph-based architecture, named SPIDER-WEB, to generate corresponding graph-based algorithms under arbitrary local biochemical constraints. These generated coding algorithms could be used to encode arbitrary digital data as DNA sequences directly or served as a benchmark for the follow-up construction of coding algorithms. To further consider recovery and security issues existing in the storage field, it also provides pluggable algorithmic patches based on the generated coding algorithms: path-based correcting and mapping shuffling. They provide approaches for probabilistic error correction and symmetric encryption respectively.Comment: 30 pages; 12 figures; 2 table

Optimization of Axial Resolution

Abstract: To improve the axial resolution of ultrasonic elastography, by taking the advantage of code excitation and frequency compounding, multi-frequency with coded excitation for elastography (FCCE) was proposed. FCCE adopts the Chirp signal excitation scheme and strikes a balance in the selection of sub-signal bandwidth, the bandwidth overlap and the number of sub-strain image based on theoretical derivation, so as to further improve the axial resolution of elastic image. On MATLAB, multi-frequency with coded excitation for elastography was implemented and compared with short pulse. Experiments have proved that, compared with the short pulse, the elastographic signal-to-noise ratio (SNRe) and contrast-to-noise ratio (CNRe) were improved significantly. Moreover, probing depth, axial resolution and target detection were improved too. Therefore, the FCCE technology can effectively improve the elastography quality and can be applied to ultrasonic clinical trials. Copyright © 2014 IFSA Publishing, S. L

A Survey of Neural Trees

Neural networks (NNs) and decision trees (DTs) are both popular models of machine learning, yet coming with mutually exclusive advantages and limitations. To bring the best of the two worlds, a variety of approaches are proposed to integrate NNs and DTs explicitly or implicitly. In this survey, these approaches are organized in a school which we term as neural trees (NTs). This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability. We first propose a thorough taxonomy of NTs that expresses the gradual integration and co-evolution of NNs and DTs. Afterward, we analyze NTs in terms of their interpretability and performance, and suggest possible solutions to the remaining challenges. Finally, this survey concludes with a discussion about other considerations like conditional computation and promising directions towards this field. A list of papers reviewed in this survey, along with their corresponding codes, is available at: https://github.com/zju-vipa/awesome-neural-treesComment: 35 pages, 7 figures and 1 tabl

Evolving Neural Networks through a Reverse Encoding Tree

NeuroEvolution is one of the most competitive evolutionary learning frameworks for designing novel neural networks for use in specific tasks, such as logic circuit design and digital gaming. However, the application of benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT) remains a challenge, in terms of their computational cost and search time inefficiency. This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently. Using RET, two types of approaches -- NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search encoding (GS-NEAT) -- have been designed to solve problems in benchmark continuous learning environments such as logic gates, Cartpole, and Lunar Lander, and tested against classical NEAT and FS-NEAT as baselines. Additionally, we conduct a robustness test to evaluate the resilience of the proposed NEAT algorithms. The results show that the two proposed strategies deliver improved performance, characterized by (1) a higher accumulated reward within a finite number of time steps; (2) using fewer episodes to solve problems in targeted environments, and (3) maintaining adaptive robustness under noisy perturbations, which outperform the baselines in all tested cases. Our analysis also demonstrates that RET expends potential future research directions in dynamic environments. Code is available from https://github.com/HaolingZHANG/ReverseEncodingTree.Comment: Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC) 2020. Lecture Presentatio

Optimization of Axial Resolution in Ultrasound Elastography

To improve the axial resolution of ultrasonic elastography, by taking the advantage of code excitation and frequency compounding, multi-frequency with coded excitation for elastography (FCCE) was proposed. FCCE adopts the Chirp signal excitation scheme and strikes a balance in the selection of sub-signal bandwidth, the bandwidth overlap and the number of sub-strain image based on theoretical derivation, so as to further improve the axial resolution of elastic image. On MATLAB, multi- frequency with coded excitation for elastography was implemented and compared with short pulse. Experiments have proved that, compared with the short pulse, the elastographic signal-to-noise ratio (SNRe) and contrast-to-noise ratio (CNRe) were improved significantly. Moreover, probing depth, axial resolution and target detection were improved too. Therefore, the FCCE technology can effectively improve the elastography quality and can be applied to ultrasonic clinical trials

Comprehensive treatments of tungsten slags in China: A critical review

As a critical and strategic metal, tungsten is widely used in the fields of machinery, mining and military industry. With most of the tungsten resources reserves in the world, China is the largest producer and exporter of tungsten. This has resulted in the generation of a huge amount of tungsten slag (slag) stored in China. This slag always contains not only valuable elements, such as tungsten (W), scandium (Sc), tin (Sn), niobium (Nb) and tantalum (Ta), but also toxic elements, such as arsenic (As), lead (Pb), chromium (Cr) and mercury (Hg). Due to a lack of developed technologies, most of these slags cannot be treated safely, which results in a waste of resources and serious environmental and ecological risks. In this review we briefly describe the distribution and proportion of tungsten deposits in China, the tungsten extraction process and the properties of tungsten slag. We also mainly discuss the comprehensive treatments for the valuable and toxic slag, including the amounts of valuable metal elements that can be recovered and the stabilization of toxic elements. These aspects are summarized in a comparison of their advantages and disadvantages. In particular, we focus on the efforts to analyze the relationship between the existing processes and attempts to establish a comprehensive technology to treat tungsten slag and also suggest areas for future research

Energy Saving Characteristics of a Winch System Driven by a Four-Quadrant Hydraulic Pump

In this study, an integrated system of winch driving and potential energy recovery using a four-quadrant pump was proposed, aimed at the large amount of recoverable gravitational potential energy in a winch system. The proposed system changed the original open system into a closed-structure part, using a four-quadrant pump to drive the winch, and an open-structure part, using an open hydraulic pump to balance torque. The closed-structure and open-structure parts were coaxial, and connected with the engine through the transfer case, which was able to make full use of the four-quadrant pump characteristics. It was able to achieve flow regeneration when the weight was lowered, and to achieve direct use of gravitational potential energy. The AMESim model of the original and proposed systems was further established according to a working characteristics analysis of the energy consumption of the winch-driving system. The simulation results verified that the proposed system kept good controllability while recovering potential energy. An experimental prototype was built; the test results showed that, compared with the original winch system, the proposed system increased lifting speed and reduced fuel consumption significantly. Additionally, diesel consumption was reduced by 87% in the descending process

The synthesis and photophysical properties of zinc (II) phthalocyanine bearing poly(aryl benzyl ether) dendritic substituents

National Natural Science Foundation of China [20604007]; Natural Science Foundation of Fujian [2008J0078, 2007F30103, 2009J01020]; Key Foundation for Technology Department [200710013]; Science Research Foundation of Ministry of Health & United Fujian ProvZinc (II) phthalocyanines carrying four poly(aryl benzyl ether) dendritic substituents with terminal cyano and carboxylic acid functionalities were synthesized and characterized using elemental analysis, (1)H NMR, IR, UV vis and matrix-assisted laser-desorption ionization time-of-flight spectra. Phthalocyanines with terminal cyano groups were essentially non-aggregated in common organic solvents whilst the aqueous aggregation tendency of those which contained terminal carboxylic acid groups decreased with increasing size of the dendron. Photoinduced electron transfer in these compounds was investigated using a fluorescence quenching method, employing both neutral and cationic quenchers. Upon excitation of the dendritic subunits in DMSO, the cyano compounds underwent intermolecular energy transfer from the excited dendritic subunits to the phthalocyanine core which acted as an energy trap. (C) 2010 Elsevier Ltd. All rights reserved

Table1_Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis.docx

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.</p