Detection Of Sequences Research Articles

Maritime Small Target Detection Based on Appearance Stability and Depth-Normalized Motion Saliency in Infrared Video With Dense Sunglints

Maritime infrared (IR) small target detection in dense sunglint environments has always been challenging. It is difficult for existing methods to distinguish the small target from dense and strong sea clutter because the sea clutter caused by the sunglints has similar spatial-temporal characteristics to small targets. This paper proposes a method based on appearance stability and depth-normalized motion saliency (AS-DNMS) for small target detection in IR videos or sequences. First, on each frame of the IR video, the isotropic salience measure (ISM) based on principal curvature filtering is proposed for target enhancement, and an adaptive threshold is used to extract a stable number of candidate targets. Then, we use improved pipeline filtering to form trajectory chains of the candidate targets extracted from consecutive frames. To improve the matching accuracy of pipeline filtering, we propose an inverse optical flow method to predict the local images of the candidate targets. Third, the motion vectors of the candidate targets are extracted and depth-normalized. According to the principle of projection perspective, a depth normalization model based on the position of the sea-sky line is established, providing a simple and low-cost solution for acquiring depth information of maritime targets under sea-sky backgrounds. Finally, the appearance stability measure (ASM) and the depth-normalized motion saliency measure (DNMSM) are calculated to construct the AS-DNMS joint feature. A double asymptote decision function is employed to determine the real target. The experimental results show that our method has better detection performance in dense sunglint environments than the baseline methods.

Architecture of dual emissive three-dimensional nanostructure composites containing graphitic 2D sheets and iron oxide nanoparticles: detection of short single-stranded DNA sequences.

Here, we report an effect driven by repetitive heating and cooling; as a result, 2D and 1D nanomaterials are parallelly produced locally in a single reactor from the same precursors. Afterward, more repetitive heating and cooling induced the self-folding approach of a 2D nanomaterial with a 1D nanomaterial, giving them a self-assembled biconcave disk-shaped 3D nanostructure. The microscopy and spectroscopy studies reveal that the nanostructure has a diameter of nearly 200 nm and is composed of Fe, C, O and incorporated N and P. This 3D nanostructure composite shows red-shifted dual emission (430 nm and 500 nm) at two different excitations (350 nm and 450 nm), accompanied by a rare large Stokes shift (LSS), and it was employed in the detection of targeted short single-stranded DNA sequences (ssDNA). Upon the addition of target DNA, the specific binding of 3D nanostructure probes with the target triggers variations (off/on) of two signals, and by considering the decreased emission (fluorescence quenching) at 500 nm, we can detect the target ssDNA at the single-molecule level. The change of fluorescence intensity and the concentration of complementary target ssDNA sequences show a better linear relationship than a single emission-based probe, and the limit of detection (LOD) was as low as 0.47 nM.

SARS-CoV-2 Infections, Clinical Characteristics, and Related Risk Factors: The First 8 Months Surveillance Study Conducted in Southwest Ethiopia.

Coronavirus disease 2019 (COVID-19) is caused by a severe acute respiratory syndrome coronavirus (SARS-CoV-2) and became pandemic after emerging in Wuhan, China, in December 2019. We aimed to investigate the prevalence, clinical characteristics, and risk factors for SARS-CoV-2 infections in districts of southwest Ethiopia. A study was conducted on COVID-19 surveillance data in the diagnostic center of the southwest district of Ethiopia from July 1, 2020, to February 30, 2021. A total of 10 618 nasopharyngeal specimens were tested for SARS-COV-2 using the detection of unique sequences of virus RNA by reverse transcriptase PCR. Data were entered into Epidata version 3.1 and analyzed using SPSS version 25. Logistic regression was used to determine the relationship between COVID-19 and risk factors, with a significance level of P = 0.05. A total of 10 618 individuals were tested for SARS-CoV-2. Of these, 419 (3.9%) patients tested positive for SARS-CoV-2. Among a total of 419 patients who tested positive for SARS-CoV-2, 80.2% were asymptomatic, 264 (63.0%) were males, and 233 (55.6%) were aged 19 to 35 years. Comorbidity was present in 37 (8.8%). The risk of getting SARS-CoV-2 infections was increased with male sex (AOR = 1.248; 95% CI: 1.007, 1.547), health workers (AOR = 3.187; 95% CI: 1.960, 5.182), prisoners (AOR = 2.118; 95% CI: 1.104, 4.062), and comorbid conditions (AOR = 2.972; 95% CI: 1.649, 5.358), such as diabetes (AOR = 4.765; 95% CI: 1.977-11.485) and other respiratory problems (AOR = 3.267; 95% CI: 1.146-9.317). Despite the fact that overall laboratories confirmed prevalence of SARS-CoV-2 infections in the study area was low and dynamic, it was spread to all zones of the study area. This highlights the importance of implementing the most effective public health strategies to prevent the further spread and reduce the burden of SARS-CoV-2 infections.

A tolane-modified 5-ethynyluridine as a universal and fluorogenic photochemical DNA crosslinker.

We report the fluorescent nucleoside ToldU and its application as a photoresponsive crosslinker in three different DNA architectures with enhanced fluorescence emission of the crosslinked products. The fluorogenic ToldU crosslinking reaction enables the assembly of DNA polymers in a hybridization chain reaction for the concentration-dependent detection of a specific DNA sequence.

Skim-Nanopore sequencing for routine genomic evaluation and bacterial pathogen detection in cattle

Context Genotyping-by-sequencing, the use of sequence reads to genotype single-nucleotide polymorphisms (SNPs), has seen an increase in popularity as a tool for genomic prediction. Oxford Nanopore Technologies (Nanopore) sequencing is an emerging technology that produces long sequence reads in real-time. Recent studies have established the ability for low-coverage Nanopore sequence data to be used for genomic prediction. However, the value proposition of Nanopore sequencing for individuals could be improved if both genotyping and disease diagnosis are achieved from a single sample. Aims This study aimed to demonstrate that Nanopore sequencing can be used for both rapid genotyping and as a disease diagnostic tool using the same sample in livestock. Methods Total DNA extracts from nasal swabs collected from 48 feedlot cattle presenting with clinical signs of bovine respiratory disease (BRD) were sequenced using the Nanopore PromethION sequencer. After 24 h of sequencing, genotypes were imputed and genomic estimated breeding values (GEBVs) for four traits were derived using 641 163 SNPs and corresponding SNP effects. These GEBVs were compared with GEBVs derived from SNP array genotypes and calculated using the same SNP effects. Unmapped sequence reads were classified into taxa using Kraken2 and compared with quantitative real-time polymerase chain reaction (qPCR) results for five BRD-associated pathogens of interest. Key results Sequence-derived genotypes for 46 of the 48 animals were produced in 24 h and GEBV correlations ranged between 0.92 and 0.94 for the four traits. Eleven different BRD-associated pathogens (two viruses and nine bacterial species) were detected in the samples using Nanopore sequence data. A significant (P < 0.001) relationship between Nanopore and qPCR results was observed for five overlapping species when a maximum threshold cycle was used. Conclusions The results of this study indicated that 46 cattle genomes can be multiplexed and accurately genotyped for downstream genomic prediction by using a single PromethION flow cell (ver. R9.4) in 24 h. This equates to a cost of AUD35.82 per sample for consumables. The concordance between qPCR results and pathogen proportion estimates also indicated that some pathogenic species, in particular bacterial species, can be accurately identified from the same test. Implications Using Nanopore sequencing, routine genotyping and disease detection in livestock could be combined into one cost-competitive test with a rapid turnaround time.

Semi-Thermal Asymmetric Reverse PCR (STARP) Genotyping.

PCR-based individual Single nucleotide polymorphism (SNP) genotyping methods are preferred due to their flexibility, high-throughput, and improved accuracy. Semi-thermal asymmetric reverse PCR (STARP) is one of the SNP genotyping methods developed to reduce operational cost with improved platform compatibility. STARP is a unique method which can be used either as a gel-free SNP genotyping by detection of fluorescent signals or polyacrylamide gel-based size separation. SNP assay designing using sequence information and detection methods of STARP are described in detail.

P562: Sequence and copy number variant detection in autosomal recessive conditions utilizing tiered-testing approach

P562: Sequence and copy number variant detection in autosomal recessive conditions utilizing tiered-testing approach

The Unique Genetic Mutation Characteristics Based on Large Panel Next-Generation Sequencing (NGS) Detection in Multiple Primary Lung Cancers (MPLC) Patients.

With the wide application of multislice spiral computed tomography (CT), the frequency of detection of multiple lung cancer is increasing. This study aimed to analyze gene mutations characteristics in multiple primary lung cancers (MPLC) using large panel next-generation sequencing (NGS) assays. Patients with MPLC surgically removed from the Affiliated Hospital of Guangdong Medical University from Jan 2020 to Dec 2021 enrolled the study. NGS sequencing of large panels of 425 tumor-associated genes was performed. The 425 panel sequencing of 114 nodules in 36 patients showed that epidermal growth factor receptor (EGFR) accounted for the largest proportion (55.3%), followed by Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) (9.6%), v-Raf murine sarcoma viral oncogene homolog B1 (BRAF), and Kirsten rat sarcoma viral oncogene (KRAS) (8.8%). Fusion target variation was rare (only 2, 1.8%). ERBB2 Y772_A775dup accounted for 73%, KRAS G12C for about 18%, and BRAF V600E for only 10%. AT-rich interaction domain 1A (ARID1A) mutations were significantly higher in invasive adenocarcinoma (IA) which contained solid/micro-papillary malignant components (p = 0.008). The tumor mutation burden (TMB) distribution was low, with a median TMB of 1.1 MUTS/Mb. There were no differences in the TMB distribution of different driver genes. In addition, 97.2% of MPLC patients (35/36) had driver gene mutations, and 47% had co-mutations, mainly in IA (45%) and invasive adenocarcinoma (MIA) (37%) nodule, with EGFR (39.4%), KRAS (9.1%), ERBB2 (6.1%), tumor protein 53 (TP53) (6.1%) predominately. MPLC has a unique genetic mutation characteristic that differs from advanced patients and usually presents with low TMB. Comprehensive NGS helps to diagnose MPLC and guides the MPLC clinical treatment. ARID1A is significantly enriched in IA nodules containing micro-papillary/solid components, suggesting that these MPLC patients may have a poor prognosis.

Molecular and Genetic Study for Detection of Helicobacter pylori Virulence Genes among Patients with Diffuse Large B-Cell Lymphoma

Background: The bacteria Helicobacter pylori (Hp) has supposed to confirm an oncogenic consequence with the evolution of malignancy events in stomach, effectiveness of Hp consider a top risk factor of gastric-diffuse large B-cell lymphoma (DLBCL). Aims: the aim of this study was to observe the correlation between virulence factors, especially cytotoxin association gene A (CagA) and the progress of gastric DLBCL. Materials and Methods: Biopsy samples (50) involved in this study (28 were men, and 22 women, with a mean age up to 57.64 years), among gastric-DLBCL patients, with (25) control (were 14 men and 11 women) without DLBCL, tested at Medical City Teaching Hospital and Teaching Laboratories, study conducted from January 1st to September 30, 2022. Diagnosis was by molecular methods included DNA extraction for Hp by STR-Format kit (BIORON Diagnostics GmbHm In den Rauhweiden 20,67354 Germany), and “real-time polymerase chain reaction” methods using the (ABI PRISM 7700) Sequence Detection System, also applied the primers and probes, for Hp-CagA. Results: The mean of age was 55.7 ± 2 in DLBCL, while in control were 61.82 + 6, CagA +ve 66.66 + 2. In gastric DLBCL, the red blood cell count, white blood cell count, platelets count, and hemoglobin were all decline in DLBCL, not in the control group, lactate dehydrogenase levels in gastric DLBCL patients were rise (322 U/L), while less in control, while albumin (322g/dl) was less in gastric DLBCL patients. Hp 16S rRNA was detected relating to CagA variety, 66% (33/50) were CagA positive with highly significant in 23 patients with DLBCL (P = 0.004), and 10 in control patients. CagA gene expression was highly expression in gastric DLBCL patients with range up to (+), 8 (++), and 12 (+++), while in the control group was 5 (+), 2 (++), and 3 (+++), respectively. Conclusions: This present study fixed high frequency of CagA gene in gastric DLBCL patients. Moreover, confirmed this gene is significantly associated with gastric DLBCL in patients with Hp. Moreover, genotype EPIYA-ABC motif is related with the high risk of gastric DLBCL.

A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems

A deep learning approach is proposed to detect data and system anomalies using high-resolution continuous point-on-wave (CPOW) or phasor measurements. Both the anomaly and anomaly-free measurement models are assumed to have unknown temporal dependencies and probability distributions. Historical training samples are assumed for the anomaly-free model, while no training samples are available for the anomaly measurements. By transforming the anomaly-free observations into uniform independent and identically distributed sequences via a generative adversarial network, the proposed approach deploys a uniformity test for anomaly detection at the sensor level. A distributed detection scheme that combines sensor level detections at the control center is also proposed which combines local detections to form more reliable detections. Numerical results demonstrate significant improvement over the state-of-the-art solutions for various bad-data cases using real and synthetic CPOW and PMU data sets.

Vibrational Radar Backscatter Communications Theory and Bound

Vibrational radar backscatter communication (VRBC) utilizes millimeter-wave radar vibrometry to receive message signals from vibrating surfaces. So long as multiple transponding surfaces are separable by the system radar in angle and/or range, VRBC can simultaneously detect, isolate, and decode messages from these multiple sources. For anything-to-vehicle communications, VRBC is therefore a scalable, low-latency approach which leverages existing automotive radars to increase situational awareness without requiring any additional use of the RF spectrum. In this paper, we describe the modeling and processing of VRBC signals. We show that knowledge of surface resonances and coding constraints can be used to improve system performance by incorporating them into a Markov model. We provide insight into the rate capabilities of VRBC by providing a theoretical upper bound that incorporates the presence of intersymbol interference (ISI). Simulation results compare our upper bound to achievable communication rates using single symbol vs sequence detection maximum likelihood methods for a given symbol length, set of symbol excitations, and VRBC radar pulse repetition interval (PRI).

ALLELIC AND GENOTYPIC FREQUENCIES OF THE CSN-2 GENE IN A POPULATION OF DAIRY CATTLE IN THE NORTHERN REGION OF THE STATE OF TOCANTINS

The present study aimed to verify allelic and genotypic frequencies of the ß-casein genes in a population of dairy cattle in the north of the state of Tocantins in order to identify animals capable of producing ß-casein A2 in preference to its counterpart. To this end, five herds belonging to the cities of Araguaína, Arapoema, Araguatins, Colinas do Tocantins, and Palmas were selected, totaling 1,109 dairy cattle of mixed and/or Gir dairy breed. Samples for two markers of polymorphic regions were characterized and confirmed by real-time PCR, using an ABI Prism® 7500 sequence detection system (Applied Biosystems). Allelic and genotypic frequencies were determined using the TaqMan ™ detection system, in which the primer and probe emit different fluorescence signals for each allele of the polymorphism. The observed frequencies indicated a prevalence of the A2 allele (72.59%) compared to the A1 allele (27.41%). Considering the genotypic constitution of the animals, there was a greater correspondence for A2A2 (54.55%) compared to A1A1 (9.38%) and A1A2 (36.06%). This denotes a promising future for the development of economic activities focused on the production of type-A2 milk in the northern region of the state of Tocantins. Keywords: Beta casein A1. Beta casein A2. Bovine milk. Hypersensitivity.

User Interface-Based Repeated Sequence Detection Method for Authentication

In this paper, we propose an authentication method that use mouse and keystroke dynamics to enhance online privacy and security. The proposed method identifies personalized repeated user interface (UI) sequences by analyzing mouse and keyboard data. To this end, an Apriori algorithm based on the keystroke-level model (KLM) of the human–computer interface domain was used. The proposed system can detect repeated UI sequences based on KLM for authentication in the software. The effectiveness of the proposed method is verified through access testing using commercial applications that require intensive UI interactions. The results show using our cognitive mouse-and-keystroke dynamics system can complement authentication at the application level.

Anomaly Detection of Consumption in Hotel Units: A Case Study Comparing Isolation Forest and Variational Autoencoder Algorithms

Buildings are responsible for a high percentage of global energy consumption, and thus, the improvement of their efficiency can positively impact not only the costs to the companies they house, but also at a global level. One way to reduce that impact is to constantly monitor the consumption levels of these buildings and to quickly act when unjustified levels are detected. Currently, a variety of sensor networks can be deployed to constantly monitor many variables associated with these buildings, including distinct types of meters, air temperature, solar radiation, etc. However, as consumption is highly dependent on occupancy and environmental variables, the identification of anomalous consumption levels is a challenging task. This study focuses on the implementation of an intelligent system, capable of performing the early detection of anomalous sequences of values in consumption time series applied to distinct hotel unit meters. The development of the system was performed in several steps, which resulted in the implementation of several modules. An initial (i) Exploratory Data Analysis (EDA) phase was made to analyze the data, including the consumption datasets of electricity, water, and gas, obtained over several years. The results of the EDA were used to implement a (ii) data correction module, capable of dealing with the transmission losses and erroneous values identified during the EDA’s phase. Then, a (iii) comparative study was performed between a machine learning (ML) algorithm and a deep learning (DL) one, respectively, the isolation forest (IF) and a variational autoencoder (VAE). The study was made, taking into consideration a (iv) proposed performance metric for anomaly detection algorithms in unsupervised time series, also considering computational requirements and adaptability to different types of data. (v) The results show that the IF algorithm is a better solution for the presented problem, since it is easily adaptable to different sources of data, to different combinations of features, and has lower computational complexity. This allows its deployment without major computational requirements, high knowledge, and data history, whilst also being less prone to problems with missing data. As a global outcome, an architecture of a platform is proposed that encompasses the mentioned modules. The platform represents a running system, performing continuous detection and quickly alerting hotel managers about possible anomalous consumption levels, allowing them to take more timely measures to investigate and solve the associated causes.

Quick Handover in 5G for High Speed Railways and Highways Using Forward Handover and PN Sequence Detection

Quick Handover in 5G for High Speed Railways and Highways Using Forward Handover and PN Sequence Detection

CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome.

The global outbreak of the monkeypox virus (MPXV) highlights the need for rapid and cost-effective MPXV detection tools to effectively monitor and control the monkeypox disease. Herein, we demonstrated a portable CRISPR-Cas-based system for naked-eye detection of MPXV. The system harnesses the high selectivity of CRISPR-Cas12 and the isothermal nucleic acid amplification potential of recombinase polymerase amplification. It can detect both the current circulating MPXV clade and the original clades. We reached alimit of detection (LoD) of 22.4 aM (13.5 copies/µl) using a microtiter plate reader, while the visual LoD of the system is 75 aM (45 copies/µl) in a two-step assay, which is further reduced to 25 aM (15 copies/µl) in a one-pot system. We compared our results with quantitative polymerase chain reaction andobtained satisfactory consistency. For clinical application, we demonstrated a sensitive and precise visual detection method with attomolar sensitivity and a sample-to-answer time of 35 min.

The mitigative effect of isorhamnetin against type 2 diabetes via gut microbiota regulation in mice.

In order to demonstrate the effects of isorhamnetin (IH) on the symptoms of type 2 diabetes mellitus (T2DM) and the role of gut microbiota in this process, an T2DM mouse model was established via a high-fat diet and streptozotocin. After 6 weeks of IH intervention and diabetes phenotype monitoring, the mice were dissected. We detected blood indicators and visceral pathology. Contents of the cecum were collected for 16S rRNA sequencing and short chain fatty acid (SCFAs) detection. The results showed that after IH intervention, the body weight of type 2 diabetic mice was gradually stabilized, fasting blood glucose was significantly decreased, and food intake was reduced (P < 0.05). Isorhamnetin significantly increased the level of SCFAs and decreased the levels of blood lipids and inflammatory factors in mice (P < 0.05). 16S rRNA sequencing results showed that Lactobacillus were significantly decreased and Bacteroidales S24-7 group_norank were significantly increased (P < 0.05). Interestingly, gut microbiota was significantly correlated with inflammatory factors, blood lipids, and SCFAs (P < 0.05). Taken together, our data demonstrated that isorhamnetin could improve the diabetic effects in T2DM mice, which might be mediated by gut microbiota.

The Effect of Breast Milk Microbiota on the Composition of Infant Gut Microbiota: A Cohort Study.

Evidence shows that breast milk microbiota and an infant's gut microbiota are related. This study aimed to compare the effects of breast milk microbiota on the construction and colonization of gut microbiota in newborns. In this study, 23 healthy infants were selected and divided into a breastfeeding group (13) and a mixed feeding group (10) based on the feeding method within one month of age. Infant fecal and breast milk samples were collected on the day of birth (0 day) and 30 days after birth (30 days) for 16S rRNA second-generation sequencing and SCFA detection. The results showed that Firmicutes and Actinobacteriota on day 0 and Firmicutes and Proteobacteria on 30 d dominated breast milk gut microbiota. There were correlations between the breast milk microbiota and the infant gut microbiota in each group (p &lt; 0.05). Additionally, breast milk microbiota correlated more significantly with infants' SCFAs in the breastfeeding group than in the mixed feeding group. This study showed that breast milk microbiota partially influences the construction of infant gut microbiota, with some key strains having a crucial influence, such as Lactobacillus, Bifidobacterium, and Enterobacter. However, the effect of breast milk microbiota on infant gut microbiota is not through direct strain transmission but has been indirectly influenced, which may be related to the cross-feeding effect mediated by SCFAs.

An Ultrasensitive PCR-Based CRISPR-Cas13a Method for the Detection of Helicobacter pylori.

The rapid and simple detection of Helicobacter pylori (H. pylori) is essential for its clinical eradication. Although various methods for detecting H. pylori have been well established, such as endoscopy in combination with histology or culture, rapid urease test (RUT) and molecular tests using clinical specimens, it is of great importance to develop an ultrasensitive and accurate nucleic acid detection platform and apply it to identify H. pylori. To meet these demands, a novel method based on PCR and CRISPR-Cas13a, called PCR-Cas13a, was developed and validated using the DNA of 84 clinical strains and 71 clinical specimens. PCR primers for the pre-amplification of conservative sequence and CRISPR RNA (crRNA) for the detection of specific sequence were designed according to the principle. The designed primers and crRNA were specific to H. pylori, and the assay showed a high degree of specificity compared with other common pathogens. Our detection system can screen H. pylori with a limit of 2.2 copies/μL within 30 mins after PCR amplification. Using a coincidence analysis with traditional methods, our method exhibited 100% accuracy for the detection of H. pylori. Furthermore, its diagnostic performance was compared, in parallel with a q-PCR. The PCR-Cas13a demonstrates 98% sensitivity and 100% specificity. Moreover, our approach had a lower limit of detection (LOD) than q-PCR. Herein, we present a diagnostic system for the highly sensitive screening of H. pylori and distinguish it from other pathogens. All the results demonstrated that this PCR-based CRISPR assay has wide application prospects for the detection of H. pylori and other slow-growth pathogens.

Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing.

The multi-attribute method (MAM) is a liquid chromatography-mass spectrometry (LC-MS)-based method that is used to directly characterize and monitor numerous product quality attributes (PQAs) at the amino acid level of a biopharmaceutical product. MAM enables identity testing based on primary sequence verification, detection and quantitation of post-translational modifications and impurities. This ability to simultaneously and directly determine PQAs of therapeutic proteins makes MAM a more informative, streamlined and productive workflow than conventional chromatographic and electrophoretic assays. MAM relies on proteolytic digestion of the sample followed by reversed-phase chromatographic separation and high-resolution LC-MS analysis in two phases. First, a discovery study to determine quality attributes for monitoring is followed by the creation of a targeted library based on high-resolution retention time plus accurate mass analysis. The second aspect of MAM is the monitoring phase based on the target peptide library and new peak detection using differential analysis of the data to determine the presence, absence or change of any species that might affect the activity or stability of the biotherapeutic. The sample preparation process takes between 90 and 120 min, whereas the time spent on instrumental and data analyses might vary from one to several days for different sample sizes, depending on the complexity of the molecule, the number of attributes to be monitored and the information to be detailed in the final report. MAM is developed to be used throughout the product life cycle, from process development through upstream and downstream processes to quality control release or under current good manufacturing practices regulations enforced by regulatory agencies.