Output Sequence Research Articles

Abstract 3750: Design and experimental validation of WG00446_MET_A methylation next generation sequencing panel using ion AmpliSeq technology

Abstract Background: DNA methylation is the most recognized epigenetic mark that leads to a massive distortion in cancer cells. It has been observed that a large number of DNA hypermethylation events co-occur in groups of genes, thus providing a growth advantage to the cell in promoting cell differentiation and neoplastic transformation. Here we designed and performed a first step of validation of a novel next-generation sequencing (NGS) targeted methylation panel that can simultaneously evaluate the methylation levels of multiple cancer-related genes. Methods: The WG00446_MET_A AmpliSeq™ Methylation Panel for Cancer Research was designed using the Ion AmpliSeq technology to amplify 152 regions with an amplicon size range of 125-175bp, that covers a total of 1107 promoter and intragenic CpGs of 18 cancer-related genes. The performance of the panel was assessed by running in duplicate commercially available fully methylated and unmethylated control human gDNA samples and a variable mixture of them to simulate 10%, 25%, 75%, and 90% of global methylation. The libraries (manual preparation using the Ion AmpliSeq Plus Library Kit) were loaded on 530 chips and run on the GeneStudio S5 Sequencer (Thermo Fisher Scientific). The sequencing output was analyzed using the methylation_analysis plugin, available on Ion Torrent Suite v5.16 (Thermo Fisher Scientific). Results: The WG00446_MET_A panel allowed to run 6 samples per 530 chips to reach an observed mean target depth >2,000X (observed reads on-targets: >80%; average number of mapped reads: >450,000/sample). The conversion efficiency, determined by spiking-in Unmethylated Lambda DNA into each sample before the bisulfite conversion process, was >97% for all samples. The observed % of global methylation for all CpGs were >98% and <2% for fully methylated and unmethylated gDNA samples, respectively. The observed results for the variable mixtures were in agreement with what expected. Conclusions: The methylation-specific NGS analysis using the WG00446_MET_A panel represents a feasible method for a fast and multiplexed screening of cancer patients by a high-throughput approach. It will offer the opportunity to design a more robust algorithm for disease prediction for those cancer patients with a low quantity of biological material available. The orthogonal testing on DNA extracted by FFPE and frozen samples is ongoing with a subset of amplicons from the WG00446_MET_A panel. Citation Format: Federico Pio Fabrizio, Stefano Castellana, Angelo Sparaneo, Tommaso Mazza, Flavia Centra, Domenico Trombetta, Vito Michele Fazio, Lucia Anna Muscarella. Design and experimental validation of WG00446_MET_A methylation next generation sequencing panel using ion AmpliSeq technology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3750.

An H∞ approach to data‐driven fault estimation, and isolation for Hammerstein‐Wiener systems

AbstractThis article considers the fault estimation problem in a data‐driven setup for a class of nonlinear systems called Hammerstein‐Wiener. The fault estimation problem is converted to a finite‐horizon minimax optimization problem. To estimate faults, a subspace predictor is utilized to predict the output sequence. Our proposed method is robust against disturbances. The proposed method is briefly expressed as an algorithm that can be implemented by MATLAB software. The suggested algorithm is applied to a numerical MIMO example to verify its validity.

Improvement of Diagnostic Yield by an Additional Amplicon Module to Hybridization-Based Next-Generation Sequencing Panels

Many approaches aimed at improving next-generation sequencing output for clinical purposes exist. However, sequencing gaps or misalignments for regions that are difficult to cover because of their low complexity or high homology still exist. Our aim was to improve the yield of sequencing data. A hybridization-based next-generation sequencing library was pooled with custom add-on amplicon-based libraries processed by the same commercial test and run in parallel and sequenced simultaneously. Formulas and steps for proper amplicon pooling (250 to 7000 bp) and final library merging are presented. The novel strategy was tested on selected archetypal situations: diagnostics of a gene with many pseudogenes, a genomic region surrounded by Alu repeats, simple one-time addition of an extra gene, and mosaicism detection. The sequence of all supplemented genomic regions was traced with reasonable coverage at the background of a hybridization captured library. The flexible add-on module expands the possibilities of routine diagnostics. The technical solution makes it possible to mix amplicons that differ significantly in size and process them in one tube simultaneously with samples of the hybridization-based panel. The proposed approach reduces turnaround time and increases diagnostic yield.

Optimal output tracking control of linear discrete-time systems with unknown dynamics by adaptive dynamic programming and output feedback

This paper discusses the output-feedback-based model-free optimal output tracking control problem of discrete-time systems with completely unknown system models under mild assumptions. The only information that allows utilisation is the system output and the reference output. To overcome these challenges, the paper aims to solve a model-free optimal output regulation problem for achieving optimal output tracking control; solving an optimal output regulation problem is equivalent to solving a linear quadratic regulation (LQR) problem and a constrained static optimisation problem. The state reconstruction is first given to represent the system state in terms of the input and output sequences. A data-driven value iteration (VI) algorithm is then proposed to iteratively approximate the solution to the discrete-time algebraic Riccati equation (ARE) of the corresponding LQR problem on the basis of input and output data. Next, based on the iterative solutions with respect to the ARE, a model-free solution is provided for the corresponding constrained static optimisation problem. Finally, an alternative reference system equivalent to the original reference system is established to avoid the requirement of having the knowledge of the reference system dynamics and the reference state. A numerical example is employed to demonstrate the effectiveness of the proposed control scheme.

Studying and analyzing the performance of photovoltaic system by using fuzzy logic controller

The main objective of this paper is to implement a circuit-based simulation model of a photovoltaic (PV) cell in order to investigate the electrical behavior of the practical cell with respect to some changes in weather parameters such as irradiation and temperature. The research focuses on using a simulation model to achieve the maximum power of solar energy by using the maximum power point tracking (MPPT) controller. The circuit simulation model consists mainly of three subsystems: PV cells: DC/DC converter; and MPPT controller-based logic fuzzy control. Dynamic analysis of the system is carried out and the results are recorded. The maximum power control function is achieved with the appropriate power control of the power inverter. Fuzzy logic controller has been used to perform MPPT functions to get maximum power from the PV panel. The proposed circuit was implemented in MATLAB/Simulink and the results show that the output sequence is non-linear and almost constant current to the open circuit voltage and the power has maximum motion to voltage for certain environmental conditions.

Application of Attention Mechanism with Prior Information in Natural Language Processing

When using deep learning methods to model natural language, a recurrent neural network that can map input sequences to output sequences is usually used. Considering that natural language contains more complicated syntactic structures, and the performance of cyclic neural networks in long sentence processing will decrease, scholars have introduced an attention mechanism into the model, which has improved the above problems to a certain extent. The existing attention mechanism still has some shortcomings, such as the inability to explicitly obtain the known syntactic structure information in the sentence, and the poor interpretability of the output probability. In response to the above problems, this article will improve the attention mechanism in the recurrent neural network model. Firstly, the prior information in the natural language sequence is constructed as a graph model through syntactic analysis and other means, and then the graph structure regularization term is introduced into the sparse mapping. A new function netmax is constructed to replace the softmax function in the traditional attention mechanism, thereby improving the performance of the model and making the degree of association. The input values corresponding to larger input samples are closer, making the output of the attention mechanism easier to understand. The innovation of this paper mainly lies in that the weight calculation method which can be widely used in the attention mechanism is proposed by combining the deep learning model with statistical knowledge, which opens a channel to introduce the prior information for the deep learning model in natural language processing tasks.

Analysis of the long-read sequencing data using computational tools confirms the presence of 5-methylcytosine in the Saccharomyces cerevisiae genome

Modification of DNA bases plays important roles in the epigenetic regulation of eukaryotic gene expression. Among the different types of DNA methylation, 5-methylcytosine (5mC) is common in higher eukaryotes. Although bisulfite sequencing is the established detection method for this modification, newer methods, such as Oxford nanopore sequencing, have been developed as quick and reliable alternatives. An earlier study using sensitive liquid chromatography tandem mass spectrometry (LC-MS/MS) indicated the presence of 5mC at very low concentration in Saccharomyces cerevisiae. More recently, a comprehensive study of the yeast genome found 40 5mC sites using the computational tool Nanopolish on nanopore sequencing output raw data. In the present study, we are trying to validate the prediction of the 5mC modifications in yeast with Nanopolish and two other nanopore software tools, Tombo and DeepSignal. Using publicly available genome sequencing data, we compared the open-access computational tools, including Tombo, Nanopolish and DeepSignal, for predicting 5mC. Our results suggest that these tools are indeed capable of predicting DNA 5mC modifications at a specific location from Oxford nanopore sequencing data. We also predicted that 5mC present in the S. cerevisiae genome might be located predominantly at the RDN locus of chromosome 12.

Conversion of Sign Language Video to Text and Speech

Abstract: Sign Language recognition (SLR) is a significant and promising technique to facilitate communication for hearingimpaired people. Here, we are dedicated to finding an efficient solution to the gesture recognition problem. This work develops a sign language (SL) recognition framework with deep neural networks, which directly transcribes videos of SL sign to word. We propose a novel approach, by using Video sequences that contain both the temporal as well as the spatial features. So, we have used two different models to train both the temporal as well as spatial features. To train the model on the spatial features of the video sequences we use the (Convolutional Neural Networks) CNN model. CNN was trained on the frames obtained from the video sequences of train data. We have used RNN(recurrent neural network) to train the model on the temporal features. A trained CNN model was used to make predictions for individual frames to obtain a sequence of predictions or pool layer outputs for each video. Now this sequence of prediction or pool layer outputs was given to RNN to train on the temporal features. Thus, we perform sign language translation where input video will be given, and by using CNN and RNN, the sign shown in the video is recognized and converted to text and speech. Keywords: CNN (Convolutional Neural Network), RNN(Recurrent Neural Network), SLR(Sign Language Recognition), SL(Sign Language).

Stochastic Cell- and Bit-Discard Technique to Improve Randomness of a TRNG

This paper presents a post-processing algorithm for a true random number generator (TRNG). Once the randomness of security key generation deteriorates for any reason, the entire chain of the security system can be compromised, increasing the odds of it being exploited by an attacker to retrieve information. Considering the change in the distribution of the RNG output sequence due to variations in the operating environment or the occurrence of aging phenomena in silicon-integrated circuits, a robust post-processing algorithm must be applied to an intrinsic TRNG to ensure the sustainability of a security system. Targeting high-level cryptography systems complying with the NIST 800-22a requirements, the proposed algorithm significantly improves the Hamming weight (HW) and successfully passes the NIST criteria while sacrificing approximately 20% of the entire number of available bits. The proposed algorithm improves the randomness of the TRNG through a sequential cell- and bit-level discarding technique, a cell-discard method, and focuses on improving the overall HW of the TRNG while the subsequent bit- discard method performs a Chi-square (χ2) test. To prove the concept, we programmed the proposed algorithm in a FPGA and configured the output of the manufactured TRNG chip to be post-processed and stored into on-board memory in real time. For five different ring-oscillator-based TRNG prototypes (fully custom designed in the 65 nm CMOS process), the failed intrinsic TRNG output sequences were respectively post-processed, resulting in all surpassing the NIST 800-22a requirements.

A CRISPR view of hematopoietic stem cells: Moving innovative bioengineering into the clinic.

Clustered regularly interspaced short palindromic repeats (CRISPR)/Cas genome engineering has emerged as a powerful tool to modify precise genomic sequences with unparalleled accuracy and efficiency. Major advances in CRISPR technologies over the last 5 years have fueled the development of novel techniques in hematopoiesis research to interrogate the complexities of hematopoietic stem cell (HSC) biology. In particular, high throughput CRISPR based screens using various “flavors” of Cas coupled with sequencing and/or functional outputs are becoming increasingly efficient and accessible. In this review, we discuss recent achievements in CRISPR‐mediated genomic engineering and how these new tools have advanced the understanding of HSC heterogeneity and function throughout life. Additionally, we highlight how these techniques can be used to answer previously inaccessible questions and the challenges to implement them. Finally, we focus on their translational potential to both model and treat hematological diseases in the clinic.

Design and analysis of a three-dimensional discrete memristive chaotic map with infinitely wide parameter range

In this paper, a new discrete memristive chaotic map with infinitely wide parameter range is designed. Firstly, a discrete memristor based on a triangular wave function is constructed. The memristor conforms to the definition of generalized memristor, and a new three-dimensional memristive chaotic map is designed based on it. Numerical simulations show that the map has complex dynamic behavior. An improved perturbation method is proposed to estimate the output sequence of the chaotic system. At the same time, it is proved mathematically that the new map can always be in chaotic or hyperchaotic state with infinitely wide parameter range under certain conditions. By observing the Lyapunov exponent spectrum and the phase diagram, it is found as the absolute value of the parameter increases, the output range and ergodicity of the new map are also enhanced. We demonstrate that the new map has an initial-boosting behavior that depends on the initial conditions of the memristor. By changing the initial values of the memristor, we can control the appearance of attractor at different locations without loss. At the same time, this paper analyzes the mechanism of the discrete memristive chaotic map generating initial-boosting behavior, puts forward a method to make ordinary chaotic maps easier to obtain this behavior. Finally, the DSP hardware platform is used to implement the new map, which proves the physical existence and realizability of the map.

Memristor-based PUF for lightweight cryptographic randomness

Physical unclonable functions (PUF) are cryptographic primitives employed to generate true and intrinsic randomness which is critical for cryptographic and secure applications. Thus, the PUF output (response) has properties that can be utilized in building a true random number generator (TRNG) for security applications. The most popular PUF architectures are transistor-based and they focus on exploiting the uncontrollable process variations in conventional CMOS fabrication technology. Recent development in emerging technology such as memristor-based models provides an opportunity to achieve a robust and lightweight PUF architecture. Memristor-based PUF has proven to be more resilient to attacks such as hardware reverse engineering attacks. In this paper, we design a lightweight and low-cost memristor PUF and verify it against cryptographic randomness tests achieving a unique, reliable, irreversible random sequence output. The current research demonstrates the architecture of a low-cost, high endurance Cu/HfO_2/p^{++}Si memristor-based PUF (MR-PUF) which is compatible with advanced CMOS technologies. This paper explores the 15 NIST cryptographic randomness tests that have been applied to our Cu/HfO_2/p^{++}Si MR-PUF. Moreover, security properties such as uniformity, uniqueness, and repeatability of our MR-PUF have been tested in this paper and validated. Additionally, this paper explores the applicability of our MR-PUF on block ciphers to improve the randomness achieved within the encryption process. Our MR-PUF has been used on block ciphers to construct a TRNG cipher block that successfully passed the NIST tests. Additionally, this paper investigated MR-PUF within a new authenticated key exchange and mutual authentication protocol between the head-end system (HES) and smart meters (SM)s in an advanced metering infrastructure (AMI) for smartgrids. The authenticated key exchange protocol utilized within the AMI was verified in this paper to meet the essential security when it comes to randomness by successfully passing the NIST tests without a post-processing algorithm.

Intelligent Data-Driven Decision-Making Method for Dynamic Multisequence: An E-Seq2Seq-Based SCUC Expert System

Under the background of the rapid change of energy technology and the deep integration of artificial intelligence into the power system, it is of great significance to study the intelligent decision-making method of security-constrained unit commitment (SCUC) with high adaptability and high accuracy. Thus, in this article, an expanded sequence-to-sequence (E-Seq2Seq)-based data-driven SCUC expert system for dynamic multiple-sequence mapping samples is proposed. First, dynamic multiple-sequence mapping samples of SCUC are reconstructed by analyzing the input–output sequence characteristics. Then, an E-Seq2Seq approach with a multiple-encoder–decoder architecture and a fully connected extension layer is proposed. On this basis, the simple recurrent unit is introduced as a neuron of the E-Seq2Seq approach to construct deep learning models, and an intelligent data-driven expert system for SCUC is further developed. The proposed approach has been simulated on a typical IEEE 118-bus system and a practical system in Hunan province in China. The results indicate that the proposed approach could possess strong generality, high solution accuracy, and efficiency over traditional methods.

Wavelength-resolved photoelectrochemical biosensor triggered by cascade signal amplification reactions for RNA methylation analysis on a single interface

Among ribonucleic acid (RNA) methylation modifications, those of m6A and m5C are the two most abundant forms, and the bias of m6A and m5C modification levels plays an essential role in many biological processes. Therefore, constructing a mechanism that can simultaneously analyze the two modifications would be potentially very significant. Herein, we propose a wavelength-resolved photoelectrochemical (PEC) biosensor based on cascade signal amplification reactions for m6A-RNA and m5C-RNA analysis on a single interface. First, a target sequence was transformed with antibody-functionalized magnetic beads to obtain the initial sequences, triggering the 3D DNA nanomachine to transform and amplify targets by generating many output sequences. Subsequently, the output sequences were combined with rolling-circle-amplification (RCA) primer scaffolds, and then a DNA origami structure was formed based on RCA. A DNA origami could immobilize many photoelectrochemical mediators to amplify photocurrent responses. As expected, the proposed PEC biosensor exhibited excellent analytical performances for both m6A-RNA and m5C-RNA. The detection limits were as low as 1.96 fM and 7.37 pM, respectively. The successful fabrication of the PEC biosensor opened new possibilities for epigenetic research and the diagnosis and treatment of methylation-related diseases.

Pump control based pulse on demand operation of frequency doubled Nd:YVO4

Directly pump controlled pulse on demand operation of an intracavity frequency doubled Nd:YVO4 laser is presented, capable of generating arbitrary delay between pulses in the output sequence with the upper limit in MHz range. Micro pulses on demand were achieved by the gain switching technique and intracavity doubling through the optimization between the conversion efficiency and achievable repetition rate. Further, the article presents first study of a high modulation intracavity doubled Nd:YVO4 laser utilizing the crystals high emission cross section for fast gain switching. In the free space experimental setup, the highest repetition rate over 2 MHz and frequency doubling efficiency of 30% were achieved with the further scaling possible using optically bonded tightly packed architecture. The demonstrated pulse on demand operation of a compact micro pulsed laser at 532 nm enables full synchronization with the application.

A reconfigurable PUF structure with dual working modes based on entropy separation model

Privacy Protection Mutual Authentication (PPMA) in the Internet of Things uses physical unclonable functions (PUFs) and true random number generators (TRNGs) as security primitives, providing a lightweight solution for key generation and protection. At present, the designs for achieving PUF and TRNG simultaneously on field programmable gate array (FPGA) lack theoretical support, and have poor randomness and stability. Therefore, this paper provides a separation model of PUF entropy (manufacturing delay) and TRNG entropy (jitter delay) based on ring oscillators (ROs). Based on this model, a reconfigurable PUF structure is proposed, which can be reconstructed to a TRNG by changing the working mode. The proposed structure not only improves the area utilization, but also realizes the low-power consumption. Experiments on Xilinx Spartan-6 FPGAs and Xilinx Virtex-6 FPGAs show that the proposed structure has wonderful performance. The PUF has excellent uniqueness (average resolution is 50.21%) and reliability (the average repeatability rate is 96.98%). TRNG's output sequences have high randomness and pass all National Institute of Standards and Technology (NIST) tests (average pass rate is 99%).

Video Person Re-Identification with Frame Sampling-Random Erasure and Mutual Information-Temporal Weight Aggregation.

Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and temporal weight aggregation of mutual information of partial and global features. First, for the case in which the target person is interfered or partially occluded, the frame sampling–random erasure (FSE) method is used for data enhancement to effectively alleviate the occlusion problem, improve the generalization ability of the model, and match persons more accurately. Second, to further improve the re-ID accuracy of video-based persons and learn more discriminative feature representations, we use a ResNet-50 network to extract global and partial features and fuse these features to obtain frame-level features. In the time dimension, based on a mutual information–temporal weight aggregation (MI–TWA) module, the partial features are added according to different weights and the global features are added according to equal weights and connected to output sequence features. The proposed method is extensively experimented on three public video datasets, MARS, DukeMTMC-VideoReID, and PRID-2011; the mean average precision (mAP) values are 82.4%, 94.1%, and 95.3% and Rank-1 values are 86.4%, 94.8%, and 95.2%, respectively.

Tap water as a natural vehicle for microorganisms shaping the human gut microbiome.

Fresh potable water is an indispensable drink which humans consume daily in substantial amounts. Nonetheless, very little is known about the composition of the microbial community inhabiting drinking water or its impact on our gut microbiota. In the current study, an exhaustive shotgun metagenomics analysis of the tap water microbiome highlighted the occurrence of a highly genetic biodiversity of the microbial communities residing in fresh water and the existence of a conserved core tap water microbiota largely represented by novel microbial species, representing microbial dark matter. Furthermore, genome reconstruction of this microbial dark matter from water samples unveiled homologous sequences present in the faecal microbiome of humans from various geographical locations. Accordingly, investigation of the faecal microbiota content of a subject that daily consumed tap water for 3 years provides proof for horizontal transmission and colonization of water bacteria in the human gut.

Hardware architecture of a digital piecewise linear chaotic map with perturbation for pseudorandom number generation

Piecewise linear chaotic map (PWLCM) is a simple chaotic system popularly used for generating pseudorandom numbers (PRN). Digital implementation of PWLCM degrades the randomness of the pseudorandom number due to the finite precision effect. The current paper presents a digital 1-dimensional PWLCM that mitigates the degradation of digital chaos by cascading the chaotic map with a three-stage XORed shift register that perturbs the output of the PWLCM. We further perturb the output of the shift register using a one-stage XOR post-processing. We also present the hardware architecture of the proposed system to generate a high throughput PRN sequence. The designed architecture is synthesized on Xilinx Zynq 7000 Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC) using TSMC (Taiwan Semiconductor Manufacturing Company) 90 nm CMOS technology. We experimentally validate the randomness of the output sequence using NIST test suite 800–22. In addition, we also perform security analyses such as phase space, key sensitivity, correlation, and information entropy. The statistical and security analysis confirms the efficiency of the proposed design in terms of randomness. The synthesis result demonstrates the achieved throughput as 1.296 Gbps at 81 MHz operating frequency on Zynq 7000 FPGA, whereas a throughput of 1.696 Gbps at 106 MHz operating frequency is achieved on ASIC.

Universal Decoding for the Typical Random Code and for the Expurgated Code

We provide two results concerning the optimality of the stochastic-mutual information (SMI) decoder, which chooses the estimated message according to a posterior probability mass function, which is proportional to the exponentiated empirical mutual information induced by the channel output sequence and the different codewords. First, we prove that the error exponents of the typical random codes under the optimal maximum likelihood (ML) decoder and the SMI decoder are equal. As a corollary to this result, we also show that the error exponents of the expurgated codes under the ML and the SMI decoders are equal. These results strengthen the well-known result due to Csiszár and Körner, according to which, the ML and the maximum-mutual information (MMI) decoders achieve equal random-coding error exponents, since the error exponents of the typical random code and the expurgated code are strictly higher than the random-coding error exponents, at least at low coding rates. The universal optimality of the SMI decoder, in the random-coding error exponent sense, is easily proven by commuting the expectation over the channel noise and the expectation over the ensemble. This commutation can no longer be carried out, when it comes to typical and expurgated exponents. Therefore, the proof of the universal optimality of the SMI decoder must be completely different and it turns out to be highly non-trivial.