Bioinformatic analysis of Streptococcus uberis genes and genomes

Streptococcus uberis is a Gram-positive, catalase-negative member of the family Streptococcaceae and is an important environmental pathogen primarily responsible for a significant amount of bovine intramammary infections. This thesis describes the sequencing and comparison of multiple strains from clinical and sub-clinical infections. Following de novo assembly, these are compared to the single reference strain (0140J). The assemblies of strains sequenced with two technologies (Illumina and SOLiD) were compared. From these assemblies, annotation allowed the comparison of gene content, the pan and core genomes and gene gain/loss of coding sequences associated with clustered regularly interspaced short palindromic repeats (CRISPRs), prophage and bacteriocin production. Identification of sequence variants allowed identification of highly conserved and highly variant genes. Inferred intraspecies and interspecies (host-S. uberis) protein-protein interaction networks revealed pathways of bovine proteins enriched with potentially interacting pathogen proteins. These identified known and predicted pathways and also novel interaction partners. This was the first “whole-genome” comparison of multiple S. uberis strains isolated from clinical vs non-clinical intramammary infections including the type virulent vs non-virulent strains. These data allowed the first insight into potential evolutionary forces behind virulence differences

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Genomic landscape of prominent XDR Acinetobacter clonal complexes from Dhaka, Bangladesh

Background: Acinetobacter calcoaceticus-A. baumannii (ACB) complex pathogens are known for their prevalence in nosocomial infections and extensive antimicrobial resistance (AMR) capabilities. While genomic studies worldwide have elucidated the genetic context of antibiotic resistance in major international clones (ICs) of clinical Acinetobacter spp., not much information is available from Bangladesh. In this study, we analysed the AMR profiles of 63 ACB complex strains collected from Dhaka, Bangladesh. Following this, we generated draft genomes of 15 of these strains to understand the prevalence and genomic environments of AMR, virulence and mobilization associated genes in different Acinetobacter clones. Results: Around 84% (n = 53) of the strains were extensively drug resistant (XDR) with two showing pan-drug resistance. Draft genomes generated for 15 strains confirmed 14 to be A. baumannii while one was A. nosocomialis. Most A. baumannii genomes fell under three clonal complexes (CCs): the globally dominant CC1 and CC2, and CC10; one strain had a novel sequence type (ST). AMR phenotype-genotype agreement was observed and the genomes contained various beta-lactamase genes including blaOXA-23 (n = 12), blaOXA-66 (n = 6), and blaNDM-1 (n = 3). All genomes displayed roughly similar virulomes, however some virulence genes such as the Acinetobactin bauA and the type IV pilus gene pilA displayed high genetic variability. CC2 strains carried highest levels of plasmidic gene content and possessed conjugative elements carrying AMR genes, virulence factors and insertion sequences. Conclusion: This study presents the first comparative genomic analysis of XDR clinical Acinetobacter spp. from Bangladesh. It highlights the prevalence of different classes of beta-lactamases, mobilome-derived heterogeneity in genetic architecture and virulence gene variability in prominent Acinetobacter clonal complexes in the country. The findings of this study would be valuable in understanding the genomic epidemiology of A. baumannii clones and their association with closely related pathogenic species like A. nosocomialis in Bangladesh. 2022, The Author(s).This work was funded by North South University Conference Travel and Research Grants (NSU CTRG) Committee under the grant number: NSU-RP-18-042.Scopu

Bioinformatic analysis of Streptococcus uberis genes and genomes

Streptococcus uberis is a Gram-positive, catalase-negative member of the family Streptococcaceae and is an important environmental pathogen primarily responsible for a significant amount of bovine intramammary infections. This thesis describes the sequencing and comparison of multiple strains from clinical and sub-clinical infections. Following de novo assembly, these are compared to the single reference strain (0140J). The assemblies of strains sequenced with two technologies (Illumina and SOLiD) were compared. From these assemblies, annotation allowed the comparison of gene content, the pan and core genomes and gene gain/loss of coding sequences associated with clustered regularly interspaced short palindromic repeats (CRISPRs), prophage and bacteriocin production. Identification of sequence variants allowed identification of highly conserved and highly variant genes. Inferred intraspecies and interspecies (host-S. uberis) protein-protein interaction networks revealed pathways of bovine proteins enriched with potentially interacting pathogen proteins. These identified known and predicted pathways and also novel interaction partners. This was the first “whole-genome” comparison of multiple S. uberis strains isolated from clinical vs non-clinical intramammary infections including the type virulent vs non-virulent strains. These data allowed the first insight into potential evolutionary forces behind virulence differences

Analysis of COVID-19 M Protein for Possible Clues Regarding Virion Stability, Longevity and Spreading

The Severe Acute Respiratory Syndrome Coronavirus 2 or COVID-19 has been the cause of a global pandemic in 2020. With the numbers infected rising well above a 100,000 and confirmed deaths above 4000, it has become the paramount health concern for the global community at present. The COVID-19 genome has since been sequenced and its predicted proteome identified. In this study, we looked at the expected COVID-19 proteins and compare them to its close relative, the Severe Acute Respiratory Syndrome-Related Coronavirus. In particular we focussed on the M protein which is known to play a significant role in the virion structure of Coronaviruses. The rationale here was that since the major risk factor associated with COVID-19 was its ease of spread, we wished to focus on the viral structure and architecture to look for clues that may indicate structural stability, thus prolonging the time span for which it can survive free of a host. As a result of the study, we found some rather interesting differences between the M protein for COVID-19 and the SARS-CoV virus M protein. This included amino acid changes from non-polar to polar residues in regions important for anchoring the protein in the envelope membrane

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.Open Access funding provided by the Qatar National Library. This work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem. 2022 Elsevier LtdThis work was supported by the Qatar National Research Grant: UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu

Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

The human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.This research was funded by Qatar University High Impact grant QUHI-CENG-23/24-216 and student grant QUST-1-CENG-2023-796 and is also supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1444). The open-access publication cost is covered by the Qatar National Library

Table_1_Diversity of Streptococcus spp. and genomic characteristics of Streptococcus uberis isolated from clinical mastitis of cattle in Bangladesh.docx

IntroductionStreptococci are the major etiology in mastitis in dairy cattle, a cause of huge economic losses in the dairy industries. This study was aimed to determine the diversity of Streptococcus spp. isolated from clinical mastitis of cattle reared in Bangladesh.MethodsA total of 843 lactating cattle reared in four prominent dairy farms and one dairy community were purposively included in this study where 80 cattle were positive to clinical mastitis (CM) based on gross changes in the udder (redness, swelling, and sensitive udder) and/or milk (flakes and/or clots). Milk samples were collected from all the eighty cattle with clinical mastitis (CCM) and twenty five apparently healthy cattle (AHC). Samples were enriched in Luria Bertani broth (LB) and one hundred microliter of the enrichment culture was spread onto selective media for the isolation of Staphylococcus spp., Streptococcus spp., Enterococcus spp., Escherichia coli and Corynebacterium spp., the major pathogen associated with mastitis. Isolates recovered from culture were further confirmed by species specific PCR.Results and DiscussionOut of 105 samples examined 56.2% (59/105), 17.14% (18/105), 9.52% (10/105) and 22.9% (24/105) samples were positive for Staphylococcus, Streptococcus, Enterococcus faecalis and E. coli, respectively. This study was then directed to the determination of diversity of Streptococcus spp. through the sequencing of 16S rRNA. A total of eighteen of the samples from CCM (22.5%) but none from the AHC were positive for Streptococcus spp. by cultural and molecular examination. Sequencing and phylogenetic analysis of 16S rRNA identified 55.6, 33.3, 5.6 and 5.6% of the Streptococcus isolates as Streptococcus uberis, Streptococcus agalactiae, Streptococcus hyovaginalis and Streptococcus urinalis, respectively. Considering the high prevalence and worldwide increasing trend of S. uberis in mastitis, in-depth molecular characterization of S. uberis was performed through whole genome sequencing. Five of the S. uberis strain isolated in this study were subjected to WGS and on analysis two novel ST types of S. uberis were identified, indicating the presence of at least two different genotypes of S. uberis in the study areas. On virulence profiling, all the isolates harbored at least 35 virulence and putative virulence genes probably associated with intramammary infection (IMI) indicating all the S. uberis isolated in this study are potential mastitis pathogen. Overall findings suggest that Streptococcus encountered in bovine mastitis is diverse and S. uberis might be predominantly associated with CM in the study areas. The S. uberis genome carries an array of putative virulence factors that need to be investigated genotypically and phenotypically to identify a specific trait governing the virulence and fitness of this bacterium. Moreover, the genomic information could be used for the development of new genomic tools for virulence gene profiling of S. uberis.</p