Sequences Of Different Lengths Research Articles

Validation of use of the miniPCR thermocycler for Ebola and Zika virus detection.

The development of point-of-care (POC) diagnostic systems has received well-deserved attention in recent years in the scientific literature, and many experimental systems show great promise in real settings. However, in the case of an epidemic emergency (or a natural disaster), the first line of response should be based on commercially available and validated resources. Here, we compare the performance and ease of use of the miniPCR, a recently commercially available compact and portable PCR device, and a conventional thermocycler for the diagnostics of viral nucleic acids. We used both thermocyclers to detect and amplify Ebola and Zika DNA sequences of different lengths (in the range of 91 to 300 nucleotides) at different concentrations (in the range of ~50 to 4.0 x 108 DNA copies). Our results suggest that the performance of both thermocyclers is quite similar. Moreover, the portability, ease of use, and reproducibility of the miniPCR makes it a reliable alternative for point-of-care nucleic acid detection and amplification.

Physiological Function Assessment Based on Kinect V2

This paper presented a framework based on fog computing for convenient and efficient Physiological Function Assessment, which consists of three parts: 1)measuring the degree of joint mobility; 2) investigating the abnormality of actions of upper limbs; and 3) abnormal gait detection for lower limbs. Especially, we introduced semi-automatic Rapid Upper Limb Assessment (RULA) using Kinect v2 for the upper limb motion evaluation. Since a specific action can be described by action sequences of different length, we used dynamic time warping (DTW) to find the similarity between action sequences with different length. Traditional DTW algorithm does not work well when the action sequences are long and complex. To address this problem, we improved the DTW method by modifying the mapping relationship and limiting the computation space. Our modified DTW algorithm was evaluated on a standard 3D action dataset (SYSU 3D HOI) and Human Upper Action dataset (HUA), achieving the accuracy of 83.75%, 89.50%, respectively. The result is significantly better than the traditional DTW and the reported methods. In our previous work, we described the framework and how to make physiological function assessment. The goal of this paper is to 1) enrich the experiments of previous work and 2) introduce the framework of using RULA for physiological function assessment. All the tests have been done in this framework based on fog computing.

Scalable, variable-length similarity search in data series

Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data series indexes enable fast similarity search, all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this work, we propose ULISSE , the first data series index structure designed for answering similarity search queries of variable length. Our contribution is two-fold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length (irrespective of Z-normalization). Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk based index visits and in-memory sequential scans. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times (and up to orders of magnitude) more efficient in terms of both space and time cost, when compared to competing approaches.

Scalable, variable-length similarity search in data series

Data series similarity search is an important operation and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data series indexes enable fast similarity search, all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this work, we propose ULISSE, the first data series index structure designed for answering similarity search queries of variable length. Our contribution is two-fold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length (irrespective of Z-normalization). Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk based index visits and in-memory sequential scans. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times (and up to orders of magnitude) more efficient in terms of both space and time cost, when compared to competing approaches.

A method based on the Levenshtein distance metric for the comparison of multiple movement patterns described by matrix sequences of different length

This paper introduces a new method for calculating distances between matrix sequences of different length. Matrix sequences are often used in spatiotemporal analysis, a field of research combining topological frameworks and temporal reasoning. The Qualitative Trajectory Calculus, a spatiotemporal calculus using matrix sequences, is used as an example in this paper. Since current approaches for comparing sequences are not suitable to calculate distances between matrix sequences of different length, a new method is proposed. The method is based on sequence alignment and uses the Levenshtein distance metric as starting point. The method is presented in general at first, followed by an application to QTC-matrix sequences and a three cushion billiards movement pattern detection example. Possible applications in expert and intelligent systems are discussed, such as human-robot interaction with multiple robots, and automated traffic analysis of (autonomous) vehicle interactions. The proposed method, however, can be applied in a broad range of domains, whenever distances need to be calculated between matrix sequences of different length.

Physicochemical properties of cationic nanoemulsions and liposomes obtained by microfluidization complexed with a single plasmid or along with an oligonucleotide: Implications for CRISPR/Cas technology

In this study, we investigated the effects of the association of a single plasmid or its co-complexation along with an oligonucleotide on the physicochemical properties of cationic nanoemulsions and liposomes intended for gene editing. Formulations composed of DOPE, DOTAP, DSPE-PEG (liposomes), MCT (nanoemulsions), and water were obtained by microfluidization. DSPE-PEG was found to play a crucial role on the size and polydispersity index of nanocarriers. Nucleic acids were complexated by adsorption at different charge ratios. No significant differences were noticed in the physicochemical properties of nanocarriers (i.e. droplet size, polydispersity index, or zeta potential) when a single plasmid or both plasmid and oligonucleotide were adsorbed to the formulations. Transmission electron microscopy photomicrographs suggested round nanostructures with the nucleic acids and DSPE-PEG enfolding the surface. Complexes at +4/−1 charge ratio protected nucleic acids against DNase I degradation. The oligonucleotide seemed to be released from the liposomal complexes, while nanoemulsions only released the plasmid after 24 and 48 h of incubation in DMEM supplemented or not. In vitro experiments demonstrated that complexes were highly tolerable to human fibroblasts, Hep-G2, and HEK-293 cells, demonstrating also an uptake ability of about 30%, 30%, and 90%, respectively, no matter what the formulation or the combination of nucleic acids used. Transfection efficiency of the formulations was around 25% in human fibroblasts, 32% in HEK-293, and 15% in Hep-G2 cells. The overall results demonstrated the behavior of liposomes and nanoemulsions complexed with a plasmid or a mixture of a plasmid and an oligonucleotide, and demonstrated that the association with one or two nucleic acids sequences of different length does not seem to interfere in the physicochemical characteristics of complexes or in the uptake capacity by three different types of cells.

A Matrixed Speech-in-Noise Test to Discriminate Favorable Listening Conditions by Means of Intelligibility and Response Time Results.

The primary aim of this study was to develop and examine the potentials of a new speech-in-noise test in discriminating the favorable listening conditions targeted in the acoustical design of communication spaces. The test is based on the recognition and recall of disyllabic word sequences. A secondary aim was to compare the test with current speech-in-noise tests, assessing its benefits and limitations. Young adults (19-40 years old), self-reporting normal hearing, were presented with the newly developed Words Sequence Test (WST; 16 participants, Experiment 1) and with a consonant confusion test and a sentence recognition test (Experiment 2, 36 participants randomly assigned to the 2 tests). Participants performing the WST were presented with word sequences of different lengths (from 2 up to 6 words). Two listening conditions were selected: (a) no noise and no reverberation, and (b) reverberant, steady-state noise (Speech Transmission Index: 0.47). The tests were presented in a closed-set format; data on the number of words correctly recognized (speech intelligibility, IS) and the response times (RTs) were collected (onset RT, single words' RT). It was found that a sequence composed of 4 disyllabic words ensured both the full recognition score in quiet conditions and a significant decrease in IS results when noise and reverberation degraded the speech signal. RTs increased with the worsening of the listening conditions and the number of words of the sequence. The greatest onset RT variation was found when using a sequence of 4 words. In the comparison with current speech-in-noise tests, it was found that the WST maximized the IS difference between the selected listening conditions as well as the RT increase. Overall, the results suggest that the new speech-in-noise test has good potentials in discriminating conditions with near-ceiling accuracy. As compared with current speech-in-noise tests, it appears that the WST with a 4-word sequence allows for a finer mapping of the acoustical design target conditions of public spaces through accuracy and onset RT data.

LSTM with sentence representations for document-level sentiment classification

Abstract Recently, due to their ability to deal with sequences of different lengths, neural networks have achieved a great success on sentiment classification. It is widely used on sentiment classification. Especially long short-term memory networks. However, one of the remaining challenges is to model long texts to exploit the semantic relations between sentences in document-level sentiment classification. Existing Neural network models are not powerful enough to capture enough sentiment messages from relatively long time-steps. To address this problem, we propose a new neural network model (SR-LSTM) with two hidden layers. The first layer learns sentence vectors to represent semantics of sentences with long short term memory network, and in the second layer, the relations of sentences are encoded in document representation. Further, we also propose an approach to improve it which first clean datasets and remove sentences with less emotional polarity in datasets to have a better input for our model. The proposed models outperform the state-of-the-art models on three publicly available document-level review datasets.

Assessment of UV-C-induced water disinfection by differential PCR-based quantification of bacterial DNA damage

Currently the gold standard for validating the efficiency of UV-C disinfection of water is biodosimetry. This method allows the assessment of operating conditions under which a UV reactor can be operated safely, but relies on the addition of a live reference organism into the water matrix. Whereas biodosimetry enables off-line testing prior to installation of UV reactors into water treatment plants, it is not compatible with on-site tests. To measure disinfection efficiency, we lent a principle for the assessment of genotoxicity originally developed for cancer research. The approach utilizes the amplification of gene sequences of different lengths by quantitative PCR. The principle is based on the fact that UV-C-induced DNA damage suppresses the PCR amplification of long DNA sequences stronger than it does in case of short DNA sequences. Comparison of threshold cycles of samples taken before and after UV disinfection allows the calculation of the number of PCR-effective DNA lesions. A clear dose-response relationship was obtained with laboratory-grown and -irradiated E. coli suspensions. The extent of DNA damage was less pronounced in bacteria naturally contained in drinking water from works applying UV-C, which was presumably due to distinct UV susceptibilities of different bacterial species. Indeed, DNA sequencing of bacterial populations before and after reactor passage revealed a strong shift of amplifiable sequences from proteobacteria towards highly UV-resistant actinobacteria. Interestingly, the original water microbiome was quickly re-established after passage through a clean water tank suggesting that UV exposure only perturbed the bacterial community for a short time.

Variation in the Marking of Text Organization in French Research Articles: From Short and Specific to Extended and Vague

Adopting a text linguistic, corpus-based approach, this article studies variation in the marking of text organization. Why is text organization sometimes signaled very precisely, while sometimes signaling does not occur at all? The focus is on a particular mode of text organization, taking the form of text sequences, i.e. structures at least partially signaled by markers of addition or order, such as first, the last example. The material consists of 90 research articles in French with manually XML-annotated text sequences (XML = Extensible Markup Language). The results highlight the variation in the marking and show several factors affecting it. In shorter sequences, the marking is typically explicit and precise, while in longer ones explicit marking is more often omitted; when used, only vague markers, signaling simple addition, are present. In addition, different markers tend to be used in the signaling of sequences of different lengths.

Biochemical and structural features of extracellular vesicle-binding RNA aptamers.

Extracellular vesicles are particles in mammalian body fluids that have attracted considerable attention as biomarkers for various diseases. In the present study, the authors isolated RNA aptamers with an affinity for extracellular vesicles from two library pools that encoded randomized sequences of different lengths. After the several rounds of selection, two conserved motifs are identified in the sequences that are obtained by next-generation sequencing. Most of the sequences were predicted to adopt a secondary structure that consisted of a non-conserved stem structure and a conserved loop sequence. Two minimal similar sequences are synthesized and confirmed the ability of these sequences to bind to extracellular vesicles. Circular dichroism spectroscopy and melting temperature analysis demonstrated that the aptamers were able to form a G-quadruplex structure in their loop regions and these structures were stabilized by potassium ions. Consistent with these structural data, the affinity of each aptamer for extracellular vesicles was dependent on potassium ions. The aptamers that were identified may be useful molecular tools for the development of diagnostic methods that utilize body fluids, such as blood, saliva and urine.

GA Optimized HMM Error Model for OFDM

Finite-state HMM error models are an established powerful tool for capturing temporal characteristics of fading channels. They make network simulations much more practical and fast while developing and testing higher layer wireless system protocols and designing interleaving and FEC schemes. This paper has introduced two new Genetic Algorithm (GA) based approaches, namely, GA-S and GA-B as an alternative to HMM error models, for the accurate learning of error statistics of autocorrelation function (ACF) and error-run distributions. Validity comparisons of fit obtained show that the GA method is better than both the conventional Baum–Welch algorithm (BWA) and Semi-hidden Markov model (SHMM) methods, even when the target sequences are as short as 1000 in length. The independent elements of the [A] and [B] parameters of BWA used as chromosomes of the population space are used for error generation within the Genetic Algorithm. Mean square error of statistical properties of the error sequences is used to determine fitness of the chromosomes unlike other works using average log-likelihood ratio. Applicability has been tested by numerical simulations using error sequences of different lengths as well as target sequences of fixed length from an OFDM transceiver system under different fading rates. A T s spaced time-delay model of the propagation channel with a fixed power delay profile has been used. For slow faded channels or when long sequences of error free intervals poses difficulties for BWA training, the GA method proves to be an excellent alternative for fast and accurate modeling of the error bursts. Unlike the computationally cumbersome BWA or the simplified SHMM approach, the GA model is capable of arriving at desired levels of accuracy with two to three states, in contrast to the Markov models needing a much higher number of states.

Performance evaluation of chaotic spreading sequences on software-defined radio

In this paper, bit error performance evaluation of chaotic sequences is presented. Performance of chaotic sequences is evaluated on multiple access spread spectrum system model for USRP software defined radio. LabVIEW is used to perform numerical simulations and bit error rate analysis. Chaotic sequences of different lengths are generated using one-dimensional, two-dimensional, and three-dimensional maps. It is expected that sequences generated from complex maps will have better multiple access performance. Performance of chaotic sequences is evaluated against orthogonal sequences in multiple access scenario. Using Golay figure of merit and Pearson correlation coefficient, analysis of correlation properties and a comparison is performed on orthogonal and chaotic sequences for correlation properties comparison. The results show that chaotic sequences from two-dimensional maps have lower bit error rate than orthogonal and other chaotic sequences. Comparison of correlation properties shows that sequences with a low figure of merit and low Pearson coefficient have lower bit error rate. Those results can be used to generate chaotic sequences with desired correlation properties.

Prediction of Manipulation Actions

By looking at a person’s hands, one can often tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor’s intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded videos of subjects performing different manipulation actions on the same object, such as “squeezing”, “flipping”, “washing”, “wiping” and “scratching” with a sponge. In psychophysical experiments, we evaluated human observers’ skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input image patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets show that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithms to predict in real time what and how a dexterous action is performed.

UV–Visible Spectroscopy-Based Quantification of Unlabeled DNA Bound to Gold Nanoparticles

DNA-functionalized gold nanoparticles have been increasingly applied as sensitive and selective analytical probes and biosensors. The DNA ligands bound to a nanoparticle dictate its reactivity, making it essential to know the type and number of DNA strands bound to the nanoparticle surface. Existing methods used to determine the number of DNA strands per gold nanoparticle (AuNP) require that the sequences be fluorophore-labeled, which may affect the DNA surface coverage and reactivity of the nanoparticle and/or require specialized equipment and other fluorophore-containing reagents. We report a UV-visible-based method to conveniently and inexpensively determine the number of DNA strands attached to AuNPs of different core sizes. When this method is used in tandem with a fluorescence dye assay, it is possible to determine the ratio of two unlabeled sequences of different lengths bound to AuNPs. Two sizes of citrate-stabilized AuNPs (5 and 12 nm) were functionalized with mixtures of short (5 base) and long (32 base) disulfide-terminated DNA sequences, and the ratios of sequences bound to the AuNPs were determined using the new method. The long DNA sequence was present as a lower proportion of the ligand shell than in the ligand exchange mixture, suggesting it had a lower propensity to bind the AuNPs than the short DNA sequence. The ratio of DNA sequences bound to the AuNPs was not the same for the large and small AuNPs, which suggests that the radius of curvature had a significant influence on the assembly of DNA strands onto the AuNPs.

A Time Flexible Kernel framework for video-based activity recognition

This work deals with the challenging task of activity recognition in unconstrained videos. Standard methods are based on video encoding of low-level features using Fisher Vectors or Bag of Features. However, these approaches model every sequence into a single vector with fixed dimensionality that lacks any long-term temporal information, which may be important for recognition, especially of complex activities. This work proposes a novel framework with two main technical novelties: First, a video encoding method that maintains the temporal structure of sequences and second a Time Flexible Kernel that allows comparison of sequences of different lengths and random alignment. Results on challenging benchmarks and comparison to previous work demonstrate the applicability and value of our framework.

Test Compaction by Sharing of Functional Test Sequences Among Logic Blocks

This paper describes a test compaction procedure that considers a set of functional test sequences for a set of logic blocks in a design. The logic blocks may have different numbers of primary inputs, and functional test sequences of different lengths. The procedure expands the test sequences such that every sequence is applicable to every logic block. It then concatenates and compacts the sequences into a single sequence. In this process, it considers the logic blocks one at a time to avoid the need to store and simulate all the logic blocks simultaneously. The resulting test sequence can be applied to all the logic blocks in parallel. The experimental results demonstrate the levels of test compaction that can be achieved by this approach. The results also demonstrate that the resulting sequence has an improved coverage of faults that were not targeted during the generation of the functional test sequences or the test compaction procedure.

Robust semantic text similarity using LSA, machine learning, and linguistic resources

Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines latent semantic analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM 2013 task on Semantic Textual Similarity, our best performing system ranked first among the 89 submitted runs. In the SemEval-2014 task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014 task on Cross-Level Semantic Similarity, we ranked first in Sentence---Phrase, Phrase---Word, and Word---Sense subtasks and second in the Paragraph---Sentence subtask.

ROLE OF BREAK CLUSTER REGION (BCR) - ABELSON MURINE LEUKIMIA (ABL) EXAMINATION IN CHRONIC MYELOGENOUS LEUKEMIA (CML)

Chronic myelogenous leukemia (CML) is a clonal bone marrow stem cell disorder associated with a characteristic chromosomal translocation called the Philadelphia chromosome which caused a proliferation of mature granulocytes (neutrophils, eosinophils and basophils) and their precursors, increasing unregulated growth of predominantly myeloid cells in the bone marrow and its accumulation in the blood. As myeloproliferative disease, Chronic Myelogenous Leukemia or CML is a malignancy of the sixth-highest, reaching 15% of all blood malignancies in adults with an incidence of 1.1 per 100,000 population (Ugroseno, 2012). The CML diagnosis is made based on a presence of Philadelphia chromosome due to the existence of a reciprocal translocation of chromosomes 9 and chromosome 22 t (9.22), and raises the fusion of Break Cluster Region (BCR) gene of chromosome 22 on band q11 by Abelson Murine Leukemia (ABL). The fused BCR-ABL gene has BCR sequences of different length, so it produces a protein that has a different molecular weight. Despite having different length of BCR sequences, however, the length of fuses ABL gene sequence is constant. Associated with this different BCR sequence length are the three variations of the BCR-ABL gene fusion. The first variation is a Major Break Cluster (M-BCR), the BCR gene break is found in exon 2 in e13-E14 region. This type of CML is the fusion of BCR exon b2 or b3 to ABL exon a2, forming two major transcripts of the b2a2 or b3a2, which has a protein product with 210 kD weight or referred to as p210. The second variation is Minor BCR (m-BCR), which has e1a2 fusion. CML with BCR-ABL gene fusion of this type has a protein product with a molecular weight of 190 kDa or called p190. The third variation is micro-BCR (m-BCR), with BCR gene break between exons E19 and e20b that form mRNA transcripts e19a2, with BCR-ABL protein P230. This fused gene can be detected with qualitative multiplex PCR.

An improved model for whole genome phylogenetic analysis by Fourier transform

DNA sequence similarity comparison is one of the major steps in computational phylogenetic studies. The sequence comparison of closely related DNA sequences and genomes is usually performed by multiple sequence alignments (MSA). While the MSA method is accurate for some types of sequences, it may produce incorrect results when DNA sequences undergone rearrangements as in many bacterial and viral genomes. It is also limited by its computational complexity for comparing large volumes of data. Previously, we proposed an alignment-free method that exploits the full information contents of DNA sequences by Discrete Fourier Transform (DFT), but still with some limitations. Here, we present a significantly improved method for the similarity comparison of DNA sequences by DFT. In this method, we map DNA sequences into 2-dimensional (2D) numerical sequences and then apply DFT to transform the 2D numerical sequences into frequency domain. In the 2D mapping, the nucleotide composition of a DNA sequence is a determinant factor and the 2D mapping reduces the nucleotide composition bias in distance measure, and thus improving the similarity measure of DNA sequences. To compare the DFT power spectra of DNA sequences with different lengths, we propose an improved even scaling algorithm to extend shorter DFT power spectra to the longest length of the underlying sequences. After the DFT power spectra are evenly scaled, the spectra are in the same dimensionality of the Fourier frequency space, then the Euclidean distances of full Fourier power spectra of the DNA sequences are used as the dissimilarity metrics. The improved DFT method, with increased computational performance by 2D numerical representation, can be applicable to any DNA sequences of different length ranges. We assess the accuracy of the improved DFT similarity measure in hierarchical clustering of different DNA sequences including simulated and real datasets. The method yields accurate and reliable phylogenetic trees and demonstrates that the improved DFT dissimilarity measure is an efficient and effective similarity measure of DNA sequences. Due to its high efficiency and accuracy, the proposed DFT similarity measure is successfully applied on phylogenetic analysis for individual genes and large whole bacterial genomes.