DdRADSeq in a Field Setting v3

Aberrant DNA methylation frequently occurs at gene promoters during cancer progression. It is important to identify these loci because they are often misregulated and drive tumorigenesis. Bisulfite sequencing is the most direct and highest resolution assay for identifying aberrant promoter methylation. Recently, genomic capture methods have been combined with next-generation sequencing to enable genome-scale surveys of methylation in individual samples. However, it is challenging to validate candidate loci identified by these approaches because an efficient method to bisulfite sequence more than 50 differentially methylated loci across a large number of samples does not exist. To address this problem, we developed Bisulfite Patch PCR, which enables highly multiplexed bisulfite PCR and sequencing across many samples. Using this method, we successfully amplified 100% of 94 targeted gene promoters simultaneously in the same reaction. By incorporating sample-specific DNA barcodes into the amplicons, we analyzed 48 samples in a single run of the 454 Life Sciences (Roche) FLX sequencer. The method requires small amounts of starting DNA (250 ng) and does not require a shotgun library construction. The method was highly specific; 90% of sequencing reads aligned to targeted loci. The targeted promoters were from genes that are frequently mutated in breast and colon cancer, and the samples included breast and colon tumor and adjacent normal tissue. This approach allowed us to identify nine gene promoters that exhibit tumor-specific DNA methylation defects that occur frequently in colon and breast cancer. We also analyzed single nucleotide polymorphisms to observe DNA methylation that accumulated on specific alleles during tumor development. This method is broadly applicable for studying DNA methylation across large numbers of patient samples using next-generation sequencing.

Introduction: Whole genome sequencing (WGS) of influenza viruses is important for preparing vaccines and coping with newly emerging viruses. However, WGS is difficult to perform using conventional next-generation sequencers in developing countries, where facilities are often inadequate. In this study, we developed a high-throughput WGS method for influenza viruses in clinical specimens with the MinION portable sequencer.Methods: Whole genomes of influenza A and B viruses were amplified by multiplex RT-PCR from 13 clinical specimens collected in Tokyo, Japan. Barcode tags for multiplex MinION sequencing were added with each multiplex RT-PCR amplicon by nested PCR with custom barcoded primers. All barcoded amplicons were mixed and multiplex sequencing using the MinION sequencer with 1D2 sequencing kit. In addition, multiplex RT-PCR amplicons generated from each clinical specimen were sequenced using the Illumina MiSeq platform to validate the performance of MinION sequencer. The accuracy, recall, and precision rates of MinION sequencing were calculated by comparing the results of variant calling in the Illumina MiSeq platform and MinION sequencer.Results: Whole genomes of influenza A and B viruses were successfully amplified by multiplex RT-PCR from 13 clinical samples. We identified 6 samples as influenza type A virus H3N2 subtype and 7 as influenza B virus Yamagata lineage using the Illumina MiSeq platform. The overall accuracy, recall, and precision rates of the MinION sequencer were, respectively 99.95%, 89.41%, and 97.88% from 1D reads and 99.97%, 93.28%, and 99.86% from 1D2 reads.Conclusion: We developed a novel WGS method for influenza A and B viruses. It is necessary to improve read accuracy and analytical tools in order to better utilize the MinION sequencer for real-time monitoring of genetic rearrangements and for evaluation of newly emerging viruses.

We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of this framework is inspired by Momentum Contrast (MoCo), which uses a dictionary to store current and past batches to build a large set of encoded samples. As we find it less effective to use past positive samples which may be highly inconsistent to the encoded feature property formed with the current positive samples, MoReID is designed to use only a large number of negative samples stored in the dictionary. However, if we train the model using the widely used Triplet loss that uses only one sample to represent a set of positive/negative samples, it is hard to effectively leverage the enlarged set of negative samples acquired by the MoReID framework. To maximize the advantage of using the scaled-up negative sample set, we newly introduce Hard-distance Elastic loss (HE loss), which is capable of using more than one hard sample to represent a large number of samples. Our experiments demonstrate that a large number of negative samples provided by MoReID framework can be utilized at full capacity only with the HE loss, achieving the state-of-the-art accuracy on three re-ID benchmarks, VeRi-776, Market-1501, and VeRi-Wild.KeywordsRe-identificationLarge-scale negative samplesMomentum encoderMoReIDHE loss

Zucchini yellow mosaic virus (ZYMV) isolates from cucurbit crops growing in northern Australia and East Timor were investigated to establish possible genetic connectivity between crop viruses in Australia and Southeast Asia. Leaves from symptomatic plants of pumpkin (Cucurbita moschata and C. maxima), melon (Cucumis melo), and zucchini (C. pepo) were sampled near Broome, Darwin, and Kununurra in northern Australia. Leaves from symptomatic plants of cucumber (C. sativus) and pumpkin sampled in East Timor were sent to Australia on FTA cards. These samples were subjected to high-throughput sequencing and 15 complete new ZYMV genomic sequences obtained. When their nucleotide sequences were compared with those of 48 others from GenBank, the East Timorese and Kununurra sequences (three per location) and single earlier sequences from Singapore and Reunion Island were all in major phylogroup B. The seven Broome and two Darwin sequences were in minor phylogroups I and II, respectively, within larger major phylogroup A. When coat protein (CP) nucleotide sequences from the 15 new genomes and 47 Australian isolates sequenced previously were compared with 331 other CP sequences, the closest genetic match for a sequence from Kununurra was with an East Timorese sequence (95.5% nucleotide identity). Analysis of the 63 complete genomes found firm recombination events in 12 (75%) and 2 (4%) sequences from northern Australia or Southeast Asia versus the rest of the world, respectively; therefore, the formers' high recombination frequency might reflect adaptation to tropical conditions. Both parents of the recombinant Kununurra sequence were East Timorese. Phylogenetic analysis, nucleotide sequence identities, and recombination analysis provided clear evidence of genetic connectivity between sequences from Kununurra and East Timor. Inoculation of a Broome isolate to zucchini and watermelon plants reproduced field symptoms observed in northern Australia. This research has important biosecurity implications over entry of damaging viral crop pathogens not only into northern Australia but also moving between Australia's different agricultural regions.

Detection of mutation of the p53 gene with high sensitivity by fluorescence-based PCR-SSCP analysis using low-pH buffer and an automated DNA sequencer in a large number of DNA samples

Cytidine is an industrially useful precursor for the production of antiviral compounds and a variety of industrial compounds. Interest in the microbial production of cytidine has grown recently and high-throughput screening of cytidine over-producers is an important approach in large-scale industrial production using microorganisms. An enzymatic assay for cytidine was developed combining cytidine deaminase (CDA) and indophenol method. CDA catalyzes the cleavage of cytidine to uridine and NH3, the latter of which can be accurately determined using the indophenol method. The assay was performed in 96-well plates and had a linear detection range of cytidine of 0.058 - 10 mM. This assay was used to determine the amount of cytidine in fermentation flasks and the results were compared with that of High Perfomance Liquid Chromatography (HPLC) method. The detection range of the CDA method is not as wide as that of the HPLC, furthermore the correlation factor of CDA method is not as high as that of HPLC. However, it was suitable for the detection of large numbers of crude samples and was applied to high-throughput screening for high cytidine-producing strains using 96-well deep-hole culture plates. This assay was proved to be simple, accurate, specific and suitable for cytidine detection and high-throughput screening of cytidine-producing strains in large numbers of samples (96 well or more).

Globally, an estimated 131 million new cases of chlamydial infection occur annually. Chlamydia trachomatis infection can cause permanent damage to the fallopian tubes in woman, resulting in infertility and a risk of ectopic pregnancy. There is a great need for a vaccine against Chlamydia trachomatis and as a result there is a need for assays to evaluate functional immune responses for use in future clinical trials and epidemiological studies. Antibodies play a crucial role in the defense against infection and can be protective by several functions, including phagocytosis and neutralization. Vaccine development could greatly benefit from a method to measure functional C. trachomatis‐specific antibodies in a large number of samples. In the current in vitro antibody protection assays, which measure the capacity of antibodies to facilitate phagocytic uptake of C. trachomatis, the phagocytosed bacteria have to be counted manually. This is both labor demanding, time consuming, and it prevents high‐throughput usage of this method. In this study, we, therefore, developed a simple and rapid flow cytometry based assay to measure the capacity of antibodies to mediate Fc‐receptor dependent phagocytosis. This method is highly reproducible and suitable to analyze large numbers of clinical and nonclinical samples. © 2018 The Authors. Cytometry Part A Published by Wiley Periodicals, Inc. on behalf of ISAC.

Texas Hold'em has received considerable attention from researchers as a typical representative of imperfect information games. In the last 5 years, Texas Hold'em artificial intelligence (AI), which adopts algorithmic game theory as its core technology, has achieved many breakthroughs, with Libratus, DeepStack, Pluribus, and other Texas Hold'em AIs achieving the ability to beat top human players in heads-up and multiplayer no-limit Texas Hold'em. In the past 2 years, researchers have begun exploring the use of neural networks to build decision-making models to reduce the dependence of model training on expert knowledge. AlphaHoldem is an essential representative of these neural networks, beating Slumbot through end-to-end neural networks. However, AlphaHoldem does not fully consider game rules and other game information, and thus, the model's training relies on a large number of sampling and massive samples, making its training process considerably complicated. In this study, we propose DeepHoldem, an efficient end-to-end Texas Hold'em AI that combines algorithmic game theory and game information. DeepHoldem uses reinforcement learning to simulate the self-play process of the counterfactual regret minimization algorithm. DeepHoldem can efficiently converge to the Nash equilibrium by considering player and opponent hand information when encoding the game state and incorporating the attention module into the network structure to achieve the correlation extraction of game action sequences, and thus, fully utilize game state information. In addition, DeepHoldem incorporates the concept of supervised learning into the training process, dramatically improving the training efficiency of the model and expanding its application scope. Experimental results show that after about 4 days of training, DeepHoldem beats Slumbot at a level of 2.1 mbb/h, and the single decision time is less than 0.1 seconds.

Nowadays, computer-based face recognition is a mature and reliable mechanism that is significantly used in many access control scenarios along with other biometric methods. Face recognition consists of two subtasks including Face Verification and Face Identification. By comparing a pair of images, Face Verification determines whether those images are related to one person or not; and Face Identification has to identify a specific face within a set of available faces in the database. There are many challenges in face recognition such as angle, illumination, pose, facial expression, noise, resolution, occlusion and the few number of one-class samples with several classes. In this paper, we are carrying out face recognition by utilizing transfer learning in a siamese network which consists of two similar CNNs. In the siamese network, a pair of two face images is given to the network as input, then the network extracts the features of this pair of images and finally, it determines whether the pair of images belongs to one person or not by using a similarity criterion. The results show that the proposed model is comparable with advanced models that are trained on datasets containing large numbers of samples. furthermore, it improves the accuracy of face recognition in comparison with methods which are trained using datasets with a few number of samples, and the mentioned accuracy is claimed to be 95.62% on LFW dataset.

With the wide deployment of wireless networks, channel state information (CSI)-based joint recognition of location and activity boasts a wide range of application prospects in the fields of smart homes, remote health monitoring, and security alarm. However, the existing algorithms for joint recognition of location and activity can achieve high recognition accuracy only when trained on a large number of samples. To address this problem, this study proposes a novel, small-sample joint “location and activity” recognition algorithm based on residual networks (ResNets) with multiattention layers, referred to as the SSJR algorithm. In the algorithm, a small number of training samples are expanded via subtraction between CSI amplitude data of multiple links and horizontal flipping of images. Furthermore, a peak-seeking algorithm is proposed to decompose CSI data into location and activity recognition components, both of which are input into the constructed ResNets with multiattention layers for joint recognition. The algorithm can not only expand number of samples but also accurately extract and enhance the features of each category of samples. Experimental results indicate that the SSJR algorithm achieves an average recognition accuracy of 96.83% with only five training samples per category, demonstrating both its practicality and feasibility for human-computer interaction applications.

Although reinforcement learning (RL) has been successfully deployed in a variety of tasks, learning speed remains a fundamental problem for applying RL in complex environments. Transfer learning aims to ameliorate this shortcoming by speeding up learning through the adaptation of previously learned behaviors in similar tasks. Transfer techniques often use an inter-task mapping, which determines how a pair of tasks are related. Instead of relying on a hand-coded inter-task mapping, this paper proposes a novel transfer learning method capable of autonomously creating an inter-task mapping by using a novel combination of sparse coding, sparse projection learning and sparse Gaussian processes. We also propose two new transfer algorithms (TrLSPI and TrFQI) based on least squares policy iteration and fitted-Q-iteration. Experiments not only show successful transfer of information between similar tasks, inverted pendulum to cart pole, but also between two very different domains: mountain car to cart pole. This paper empirically shows that the learned inter-task mapping can be successfully used to (1) improve the performance of a learned policy on a fixed number of environmental samples, (2) reduce the learning times needed by the algorithms to converge to a policy on a fixed number of samples, and (3) converge faster to a near-optimal policy given a large number of samples.

Based on the standardized precipitation index data of 89 meteorological stations in southwest China (Sichuan Province, Yunnan Province, Guizhou Province, Chongqing) during 1961-2010, probability model containing drought duration and drought severity is established by using the theory of run and the Copula function. The influences of the drought sample number on the distribution parameters, the probability and drought return period are discussed. The result shows that the stability of distribution parameters needs larger sample number. The sample number is greater than 50 in some regions and the requirements for sample number of each parameter is not consistent. The sample number of severity distribution parameters is largest. The probability and return period obtained in the case where the sample number is about 10 have no significant difference (the significant level is 0.05) from those in the case where the sample number is 40 in most of region. With the results used as the standard, statistical model can greatly reduce the requirements for the sample number. And then it demonstrates that the distribution function of drought duration and drought severity can still be established in the lack of measurement data and the inconsistency between starting and ending time. Climate warming has no influence on the minimum of sample number. The fluctuation is mostly between -5 to 5. Statistical model has a certain stability. Meanwhile, the division of climate state reduces the need for distribution test sample number and makes it easier to build model.

In the past few weeks we have been very involved jn setting up new QemSCANs, MLA's and analytical scanning systems and it Is amazing how many times we had to explain the relationship between accuracy, magnification, scan speed, resolution and the resulting time taken to scan over a number of samples. Let me explain. All the above systems are used to scan over a number of geological samples using a scanning electron microscope and EDX analytical systems. In most cases the users are simply looking for a phase/type of mineral within each sample. They then need to analyse it. The main criteria being that they scan through a large number of such samples in as short a time as possible.

Standardisation of mycotoxin sampling procedures: an urgent necessity

The adoption of database screening using high-resolution liquid chromatography/mass spectrometry data is promising as a river water monitoring and surveillance tool but depends on the ability to perform reliable data processing on a large number of samples in a unified workflow. Strategies to minimize errors have been proposed but automated procedures are rare. High-resolution LC/ESI-QTOFMS/MS in data-dependent MS2 acquisition mode was performed for the analysis of surface water samples by direct injection. Data processing was achieved with software tools written in R. A database containing MS2 spectra of 693 compounds formed the basis of the workflow. Standard mixes and a time series of 361 samples of river water were analyzed and processed with the optimized workflow. Using the database and a mix of 70 standards for testing, it was found that an identification strategy including (i) mass, (ii) retention time, and (iii) MS2 spectral matching achieved a two- to three-fold improvement in the fraction of false positives compared with using only two criteria, while the number of false negatives remained low. The optimized workflow was applied to the sample series of river water. In total, 135 compounds were identified by a library match. The developed automated database screening approach minimizes the proportion of false positives, while still allowing for the screening of hundreds of water samples for hundreds of compounds in a single run.