Annotation Visualization Research Articles

Data-driven approach for automatic detection of aortic valve opening: B point detection from impedance cardiogram.

Pre-ejection period (PEP), an indicator of sympathetic nervous system activity, is useful in psychophysiology and cardiovascular studies. Accurate PEP measurement is challenging and relies on robust identification of the timing of aortic valve opening, marked as the B point on impedance cardiogram (ICG) signals. The ICG sensitivity to noise and its waveform's morphological variability makes automated B point detection difficult, requiring inefficient and cumbersome expert visual annotation. In this article, we propose a machine learning-based automated algorithm to detect the aortic valve opening for PEP measurement, which is robust against noise and ICG morphological variations. We analyzed over 60 hr of synchronized ECG and ICG records from 189 subjects. A total of 3657 averaged beats were formed using our recently developed ICG noise removal algorithm. Features such as the averaged ICG waveform, its first and second derivatives, as well as high-level morphological and critical hemodynamic parameters were extracted and fed into the regression algorithms to estimate the B point location. The morphological features were extracted from our proposed "variable" physiologically valid search-window related to diverse B point shapes. A subject-wise nested cross-validation procedure was performed for parameter tuning and model assessment. After examining multiple regression models, Adaboost was selected, which demonstrated superior performance and higher robustness to five state-of-the-art algorithms that were evaluated in terms of low mean absolute error of 3.5ms, low median absolute error of 0.0ms, high correlation with experts' estimates (Pearson coefficient=0.9), and low standard deviation of errors of 9.2ms. For reproducibility, an open-source toolbox is provided.

Abstract LB149: Luna Pathology: An integrated open source platform for computational pathology research

Abstract Pathologic review of tissue samples is a crucial step in cancer diagnosis and treatment planning. In recent years, quantitative analysis, including artificial intelligence (AI) techniques, have been applied to facilitate the evaluation of histopathologic images. Research in computational pathology comes with numerous engineering challenges: from management of whole slide images (WSIs) and their metadata, collection of expert annotations, data manipulation and feature extraction, to AI model training and evaluation of the results. These challenges span multiple departments and sets of expertise; there is a lack of an efficient system that helps manage and process histologic images and supports AI analysis. In particular, computational oncology requires significant compute resources. The need for an integrated platform with systematic processes to manage the job parameters and complex workflows that interface with multidisciplinary teams is critical for successful experiments and reproducible research. To address these challenges, we developed Luna Pathology, an open-source library and platform for computational pathology research. With flexibility, scalability, and FAIR data principles in mind, Luna Pathology was designed to provide 1) reusable workflows and modular tools for end-to-end pathology analysis 2) FAIR datasets for reproducible research and 3) a collaborative open-source research platform. Namely, Luna Pathology features support data management, annotation support, image processing, image and annotation visualization, feature extraction, and human-in-the-loop machine learning. A description of the Luna Pathology platform, its supporting computational infrastructure, and its integration within the standard computational pathology will be presented. Luna Pathology meets data processing and management needs, as well data analysis experiments and reproducible AI research on a flexible data platform. Its workflows have been deployed in colorectal, neuroblastoma and ovarian cancer projects at Memorial Sloan Kettering Cancer Center (MSK) to derive new insights from ~10,000 WSIs. We additionally present a view of the Luna Pathology platform as part of the institutional effort to bridge computational research and clinical care at MSK. The integrated open platform brings together a multidisciplinary team of pathologists, computational biologists, and bioinformatics engineers to more effectively collaborate on iterative large-scale computational pathology research. Citation Format: Doori Rose, Andrew Aukerman, Druv Patel, Arfath Pasha, Armaan Kohli, Ignacio Vázquez-García, Kevin Boehm, Rami Vanguri, Matthew Shabet, Vassiliki Mancoridis, Elizabeth Zakszewski, Benjamin Gross, Christopher Fong, Pegah Khosravi, Sohrab Shah, JianJiong Gao. Luna Pathology: An integrated open source platform for computational pathology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB149.

Abstract 6246: WNT signaling-mediated DKC1 expression drives stemness associated with colorectal cancer

Abstract The emergence of subclonal mutations in high grade colorectal (CRC) tumors driving resistance against EGFR targeting is a major challenge, thus necessitating the discovery of alternative therapeutic strategies. Targeting aberrantly active WNT signaling provides an alternate route for CRC treatment. Here we show that the high DKC1 expression in CRC patients associates with advanced-stage disease regardless of RAS/RAF mutation status, indicating the potential of DKC1 as a pan-CRC therapeutic target. We show a direct regulation of DKC1 downstream of canonical WNT signaling and confirm it as one of the WNT targets. Mechanistically, DKC1 regulates several critical cellular processes such as telomere maintenance, pseudouridylation of ribosomal RNAs, ribosomal biogenesis, etc. However, its plausible association with cancer stemness remains largely unexplored. We generated stable DKC1 knockdown (DKC1-KD) cell lines, namely WiDr and Colo320HSR to investigate its role in cancer stemness and CRC progression. DKC1-KD cells show a significant reduction in the tumor sphere forming ability along with diminished cell surface expression of stemness markers. Using multiple publicly available gene expression datasets for CRC patients, we performed Gene Set Variation Analysis to characterize the association of DKC1 with upregulated gene signature of consensus molecular subtype 2 (CMS2) of CRC, which has been associated with the highest incidence rate and active WNT signaling. Further, Database for Annotation Visualization and Integrated Discovery tool-based analysis across these CRC patient samples striated based on DKC1 expression shows positive enrichment of biological processes related to cell cycle regulation. Consistently, we observed a G2/M phase cell cycle arrest upon silencing DKC1 in CRC cells. We also show that silencing DKC1 results in enhanced chemosensitivity with a remarkable reduction in lipid accumulation. Moreover, upon DKC1 silencing, an increase in epithelial marker E-cadherin and a decrease in mesenchymal marker N-cadherin expression was observed, suggesting its role in epithelial-to-mesenchymal transition (EMT) in CRC. Our findings demonstrate the role of DKC1 in regulating stemness, drug resistance, and EMT to drive CRC oncogenicity. Taken together, we highlight the role of DKC1 as the most suitable pan-CRC drug target by employing the potential of pharmacological inhibitors against WNT signaling. Citation Format: Shivansh Nigam, Ayush Praveen, Bushra Ateeq. WNT signaling-mediated DKC1 expression drives stemness associated with colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6246.

VISUALLY ANNOTATED RESPONSIVE DIGITAL TWINS FOR REMOTE COLLABORATION IN MIXED REALITY ENVIRONMENTS

Abstract. Various forms of extended reality might empower remote collaboration in ways that the current de facto standards cannot facilitate. Especially when combined by a digital twin of the remote physical object, mixed reality (MR) opens up interesting new ways to support spatial communication. In this study, we explore the use of a digital twin to facilitate visuospatial communication in an expert-guided repair and maintenance operation scenario, supported by visual annotations. We developed two MR prototypes, one with a digital twin of the object of interest, and another where a first-person camera view was shown additionally. We tested these prototypes in a study with 19 participants (9 pairs) against a state-of-the art solution as a baseline and measured their usability, and obtained qualitative user feedback. Our findings suggest that digital twin supported mixed reality enriched with real time visual annotations can potentially improve remote collaboration tasks.

Audio–visual annotation graphs for guiding lens-based scene exploration

We introduce a novel approach for guiding users in the exploration of annotated 2D models using interactive visualization lenses. Information on the interesting areas of the model is encoded in an annotation graph generated at authoring time. Each graph node contains an annotation, in the form of a visual and audio markup of the area of interest, as well as the optimal lens parameters that should be used to explore the annotated area and a scalar representing the annotation importance. Directed graph edges are used, instead, to represent preferred ordering relations in the presentation of annotations, by having each node point to the set of nodes that should be seen before presenting its associated annotation. A scalar associated to each edge determines the strength of this constraint. At run-time, users explore the scene with the lens, and the graph is exploited to select the annotations that have to be presented at a given time. The selection is based on the current view and lens parameters, the graph content and structure, and the navigation history. The best annotation under the lens is presented by playing the associated audio clip and showing the visual markup in overlay. When the user releases control, requests guidance, opts for automatic touring, or when no available annotations are under the lens, the system guides the user towards the next best annotation using glyphs, and potentially moves the lens towards it if the user remains inactive. This approach supports the seamless blending of an automatic tour of the data with interactive lens-based exploration. The approach is tested and discussed in the context of the exploration of multi-layer relightable models.

Changes to the Skeletal Muscle Gene Expression Signature in Response to Nutrient and/or Mechanical Stimuli

Nutrient consumption and mechanical overload influence skeletal muscle metabolism and function. These metabolic and functional outcomes are mediated at least in part by changes to the types of genes that are expressed in skeletal muscle. The extent to which nutrient consumption and mechanical overload act synergistically or independent of each other to change the skeletal muscle gene expression signature remains ill defined. Therefore, the purpose of this study was to define the differential and conserved gene expression responses to either nutrient consumption or mechanical overload as well as the combination of these stimuli. Published RNA‐Seq data sets generated from the muscle of humans subjected to acute resistance exercise were uploaded into the Database for Annotation Visualization and Integrated Discovery (DAVID) to define important functional gene categories related to muscle metabolism and/or function. Genes from the functional categories of ‘cell cycle’, ‘FoxO pathway’, and ‘transcription’ were marked for analysis in a mouse model of acute feeding and mechanical overload. For this model, C57BL/6 mice were initially fasted beginning at 1700 hr. The next morning (0800‐1000 hr), mice were randomized to remain fasted or allowed access to food for 30 min. Then, a mechanical overload was placed upon the left tibialis anterior (TA) muscle of all mice by subjecting the muscle to 10 sets of 6 eccentric muscle contractions by unilateral electrical stimulation of the sciatic nerve. The contralateral TA served as the non‐overloaded control muscle. All mice were deprived of food following the contractions protocol until euthanasia 4 hr later. The relative mRNA expression of Gadd45a, Usp2, Foxo1, Foxo3, and Myod1 were determined by RT‐PCR. Feeding and contractions each decreased the mRNA expression of Gadd45a (cell cycle), Foxo1, and Foxo3 (FoxO pathway), with the combination of feeding and contractions acting to synergistically decrease the expression of each gene. Conversely, the mRNA expression of Usp2 (cell cycle) was decreased in response to muscle contractions, with no effect of feeding noted. Finally, the mRNA expression of Myod1 (transcription) was significantly increased in response to feeding, with contractions causing a differential change in direction of Myod1 mRNA expression depending upon the fasting/feeding state. These data show that nutrient consumption and mechanical overload have shared and differential effects on the skeletal muscle gene expression signature. A more thorough understanding of the genetic changes regulated by each stimulus alone, as well as the combination of stimuli, will provide novel insight for how nutrients and mechanical overload alter metabolism and function in muscle.

A machine learning algorithm for electrocardiographic fQRS quantification validated on multi-center data

Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.

A Study on the Level of Effectiveness of Multimedia Content as Instructional Methodologies to Improve the Quality of Students Learning Experience during COVID-19 Pandemic

With our current situation we became all content creator and through the advancement of technology, schools are also advancing their teaching strategy by utilizing multimedia presentations that would benefit the students. Interactive Multimedia Learning is now a teaching strategy that utilizes visual and audio annotations for the students to comprehend the lessons in an easy and fast manner. The study aims to examine and analyse the effects of interactive multimedia learning on student’s performance in class. The research utilized a quantitative research design that focused more on numerical data. A survey questionnaire administered and distributed to the participants. A total of 30 students from the different Higher Educational Institution the province of Ilocos Norte were involved in the study. Cluster sampling was utilized to give more opportunities for different students to share their insights regarding the topic. Results of the study showed that most of the students stated that multimedia learning contributed to their creativity in performance tasks and they became more focused and motivated due to the presence of creative and aesthetic graphics provided by multimedia. It also made them more independent and participative because the graphics are engaging and appealing to them. However, some students revealed that not all of their classes utilize multimedia learning tool. It was also revealed that there are students who are still engaged in board discussions and plain-text modules.

Automatic annotation correction for wearable EEG based epileptic seizure detection

Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms. Approach. This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with (a) original vEEG seizure annotations, (b) visually corrected seizure annotations, and (c) automatically corrected seizure annotations. Main results. The automated seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG. Significance. The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.

Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis.

Metastasis is the leading cause of melanoma-related mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen the core genes and molecular mechanisms related to melanoma metastasis. A gene expression profile, GSE8401, including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between melanoma metastases and primary melanoma were screened using GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools were utilized to detect the protein–protein interaction (PPI) network among DEGs. The top 10 genes with the highest degrees of the PPI network were defined as hub genes. In the results, 425 DEGs, including 60 upregulated genes and 365 downregulated genes, were identified. The upregulated genes were enriched in ECM–receptor interactions and the regulation of actin cytoskeleton, while 365 downregulated genes were enriched in amoebiasis, melanogenesis, and ECM–receptor interactions. The defined hub genes included CDK1, COL17A1, EGFR, DSG1, KRT14, FLG, CDH1, DSP, IVL, and KRT5. In addition, the mRNA and protein levels of the hub genes during melanoma metastasis were verified in the TCGA database and paired post- and premetastatic melanoma cells, respectively. Finally, KRT5-specific siRNAs were utilized to reduce the KRT5 expression in melanoma A375 cells. An MTT assay and a colony formation assay showed that KRT5 knockdown significantly promoted the proliferation of A375 cells. A Transwell assay further suggested that KRT5 knockdown significantly increased the cell migration and cell invasion of A375 cells. This bioinformatics study provided a deeper understanding of the molecular mechanisms of melanoma metastasis. The in vitro experiments showed that KRT5 played the inhibitory effects on melanoma metastasis. Therefore, KRT5 may serve important roles in melanoma metastasis.

ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes

BackgroundThe recent advancements in high-throughput sequencing have resulted in the availability of annotated genomes, as well as of multi-omics data for many living organisms. This has increased the need for graphic tools that allow the concurrent visualization of genomes and feature-associated multi-omics data on single publication-ready plots.ResultsWe present chromoMap, an R package, developed for the construction of interactive visualizations of chromosomes/chromosomal regions, mapping of any chromosomal feature with known coordinates (i.e., protein coding genes, transposable elements, non-coding RNAs, microsatellites, etc.), and chromosomal regional characteristics (i.e. genomic feature density, gene expression, DNA methylation, chromatin modifications, etc.) of organisms with a genome assembly. ChromoMap can also integrate multi-omics data (genomics, transcriptomics and epigenomics) in relation to their occurrence across chromosomes. ChromoMap takes tab-delimited files (BED like) or alternatively R objects to specify the genomic co-ordinates of the chromosomes and elements to annotate. Rendered chromosomes are composed of continuous windows of a given range, which, on hover, display detailed information about the elements annotated within that range. By adjusting parameters of a single function, users can generate a variety of plots that can either be saved as static image or as HTML documents.ConclusionsChromoMap’s flexibility allows for concurrent visualization of genomic data in each strand of a given chromosome, or of more than one homologous chromosome; allowing the comparison of multi-omic data between genotypes (e.g. species, varieties, etc.) or between homologous chromosomes of phased diploid/polyploid genomes. chromoMap is an extensive tool that can be potentially used in various bioinformatics analysis pipelines for genomic visualization of multi-omics data.

Maternal embryonic leucine zipper kinase serves as a potential prognostic marker and leads to sorafenib chemoresistance modified by miR-142-5p in hepatocellular carcinoma.

Chemotherapy is an important treatment strategy for advanced hepatocellular carcinoma (HCC). Sorafenib is a first-line systemic drug that has been commonly used clinically for patients with advanced HCC. However, the high resistance rate of sorafenib in HCC patients often hinders its long-term efficacy. Therefore, it is vital to reveal the molecular mechanisms of sorafenib resistance in patients with HCC. In current study, we screened out fourteen genes that over-expressed in HCC specimens through integrative bioinformatics analysis. Here, maternal embryonic leucine zipper kinase (MELK) was highlighted as one of the most probable molecules. The Database for Annotation Visualization and Integrated Discovery (DAVID) program was utilized for functional pathway enrichment analysis. Real-time PCR (RT-PCR) and western blot were used to examine the expression levels of MELK. CCK-8, transwell, colony formation assays and flow cytometry were used to detect cell proliferation, the cell cycle. The dual luciferase assays were performed to study the targeting relationship between MELK and miR-142-5p. MELK expressions were correlated significantly with cell proliferation by regulating cell cycle and DNA replication. High MELK expression in patients with HCC indicated a poor prognosis both the overall and diseases free survival rates. MELK knockdown suppresses cell proliferation, migration and invasion in vitro. miR-142-5p regulates MELK expression through binding to the complementary sequence in the 3'-UTR regions. MELK knockdown enhances sensitivity of sorafenib in HCC sorafenib-resistant (HCC/SR) cells. MELK may serve as a potential prognostic marker in HCC and MELK knockdown enhanced sensitivity of HepG2/SR cells to sorafenib treatment. Our findings suggest that MELK/miR-142-5p axis could be a potentially therapeutic target for reversing the sorafenib resistance in HCC treatment.

EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

Sleep stage classification can provide important information regarding neonatal brain development and maturation. Visual annotation, using polysomnography (PSG), is considered as a gold standard for neonatal sleep stage classification. However, visual annotation is time consuming and needs professional neurologists. For this reason, an internet of things and ensemble-based automatic sleep stage classification has been proposed in this study. 12 EEG features, from 9 bipolar channels, were used to train and test the base classifiers including convolutional neural network, support vector machine, and multilayer perceptron. Bagging and stacking ensembles are then used to combine the outputs for final classification. The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage (wake, active sleep, and quiet sleep) classification, respectively. The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min. The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.

The general design of the automation for multiple fields using reinforcement learning algorithm

<p>Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.</p>

Identification of Hub Diagnostic Biomarkers and Candidate Therapeutic Drugs in Heart Failure.

PurposeThe objective of this study was to identify the potential regulatory mechanisms, diagnostic biomarkers, and therapeutic drugs for heart failure (HF).MethodsDifferentially expressed genes (DEGs) between HF and non-failing donors were screened from the GSE57345, GSE5406, and GSE3586 datasets. Database for Annotation Visualization and Integrated Discovery and Metascape were used for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses respectively. The GSE57345 dataset was used for weighted gene co-expression network analysis (WGCNA). The intersecting hub genes from the DEGs and WGCNA were identified and verified with the GSE5406 and GSE3586 datasets. The diagnostic value of the hub genes was calculated through receiver operating characteristic analysis and net reclassification index (NRI). Gene set enrichment analysis (GSEA) was used to filter out the signaling pathways associated with the hub genes. SYBYL 2.1 was used for molecular docking of hub targets and potential HF drugs obtained from the connection map.ResultsFunctional annotation of the DEGs showed enrichment of negative regulation of angiogenesis, endoplasmic reticulum stress response, and heart development. PTN, LUM, ISLR, and ASPN were identified as the hub genes of HF. GSEA showed that the key genes were related to the transforming growth factor-β (TGF-β) and Wnt signaling pathways. Sirolimus, LY-294002, and wortmannin have been confirmed as potential drugs for HF.ConclusionWe identified new hub genes and candidate therapeutic drugs for HF, which are potential diagnostic, therapeutic and prognostic targets and warrant further investigation.

Comparing common and unique disruptions in transposable elements and gene networks in two humanized mouse models of AD.

Alzheimer's Disease (AD) is an widespread neurodegenerative disorder, most common in elderly and Veteran populations, culminating in dementia with additional non-cognitive behavioral and metabolic symptomatology, such as hyperactivity and disrupted sleep/wake patterns, that promote weight loss. Weight loss In demented patients is associated with adverse outcomes like accelerated AD progression and institutionalization. Mouse models of tau deposition, such as Tg4510 mice, and both tau and amyloidosis, such as 3X Tg, develop both cognitive and non-cognitive symptoms. To identify early common and unique factors affecting gene networks in these different molecular models of AD, we analyzed RNA-seq data from young Tg4510 and 3X Tg mice to compare the common and unique transcripts and transposable elements (TE) expression changes that may initiate the behavioral and metabolic derangements in AD. We analyzed RNA-seq from two month old Tg4510 mice (n=5) vs. controls (n=6) and 3X Tg (n=3) and controls (n=3). Using our established bioinformatics TTESA pipeline, reads were aligned to reference genome and measured gene expression or TEs with featureCounts. Pathway enrichment analysis using Visual Annotation Display was used to discover common functional and disease pathways using Gene Ontology, Mammalian Phenotype Ontology, and Protein Ontology. TTESA results detected significantly differentially expressed TEs in both models (p<0.001) albeit 3X Tg expressed four times more TEs. The majority of significantly differentially expressed TEs were retrotransposons (68-85%), mainly of long terminal repeats (LTRs; 50-70%), and some LINE retroelements (10-30%) and a few SINEs (5%) in either transgenic model. VLaD pathway analysis revealed many biological processes upregulated in transgenic mice, including non-cognitive behavioral pathways were downregulated, such as the regulation of circadian and sleep/wake behavior, however with different genes altered within these networks (e.g., Tg3x p<0.032; Ncor1, Rasd1, Rgs16; Tg4510 Ciart, Dbp, Per2, Per3 p<0.0165). There were also unique pathways to each model, e.g., 3X Tg: subcellular organelle structure, cytoskeleton, tubulin and actin binding, whereas Tg4510 dopamine receptor binding and bHLH transcription factor binding. Based on our data, early dysregulation of TEs may be involved altered neural gene expression networks underlying neurogenesis, behavioral changes, and molecular function in two different mouse models of AD.

Novel Insight Into the Association Between Obesity and Hepatocellular Carcinoma Occurrence and Recurrence: High-Throughput Microarray Data Set Analysis of Differentially Expressed Genes.

This study aims to identify potential biomarkers of hepatocellular carcinoma (HCC) occurrence/recurrence and obesity, along with the molecular mechanisms that involve these biomarkers. Three microarray data sets, namely GSE18897, GSE25097, and GSE36376 (genetic suppressor elements associated with obesity, tumor, and recurrence, respectively), were downloaded from Gene Expression Omnibus database to be investigated for their expression as differentially expressed genes (DEGs) in HCC and obesity. The functional and pathway enrichment analysis of these DEGs were identified by the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction network analysis was performed with STRING online tool and Cytoscape software. One hundred sixty common DEGs were screened. We found that these genes were associated with certain pathways such as metabolic pathways, terpenoid backbone biosynthesis, and adipocytokine signaling pathway. The involvements of 10 genes, including RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, EGR1, FDPS, and MCM4, were identified in the subnetwork. HNRNPA2B1 and RPS7 in the GSE18897 data set, RPS16, RPS7, CCT3, HNRNPA2B1, PSMC4, NHP2, FDPS, and MCM4 in the GSE25097 data set, and RPS16, RPS7, CCT3, HNRNPA2B1, EIF4G1, PSMC4, NHP2, FDPS, and MCM4 in the GSE36376 data set exhibited positive fold changes. These DEGs and pathways could be of diagnostic value as potential biomarkers involved in the pathogenesis of HCC, pertaining to both obesity and HCC occurrence/recurrence.

EWAS Open Platform: integrated data, knowledge and toolkit for epigenome-wide association study.

Epigenome-Wide Association Study (EWAS) has become a standard strategy to discover DNA methylation variation of different phenotypes. Since 2018, we have developed EWAS Atlas and EWAS Data Hub to integrate a growing volume of EWAS knowledge and data, respectively. Here, we present EWAS Open Platform (https://ngdc.cncb.ac.cn/ewas) that includes EWAS Atlas, EWAS Data Hub and the newly developed EWAS Toolkit. In the current implementation, EWAS Open Platform integrates 617 018 high-quality EWAS associations from 910 publications, covering 51 phenotypes, 275 diseases and 104 environmental factors. It also provides well-normalized DNA methylation array data and the corresponding metadata from 115 852 samples, which involve 707 tissues, 218 cell lines and 528 diseases. Taking advantage of integrated knowledge and data in EWAS Atlas and EWAS Data Hub, EWAS Open Platform equips with EWAS Toolkit, a powerful one-stop site for EWAS enrichment, annotation, and knowledge network construction and visualization. Collectively, EWAS Open Platform provides open access to EWAS knowledge, data and toolkit and thus bears great utility for a broader range of relevant research.

Multichannel esophageal signals to monitor respiratory rate in preterm infants

BackgroundApnea of prematurity cannot be reliably measured with current monitoring techniques. Instead, indirect parameters such as oxygen desaturation or bradycardia are captured. We propose a Kalman filter-based detection of respiration activity and hence apnea using multichannel esophageal signals in neonatal intensive care unit patients.MethodsWe performed a single-center observational study with moderately preterm infants. Commercially available nasogastric feeding tubes containing multiple electrodes were used to capture signals with customized software. Multichannel esophageal raw signals were manually annotated, processed using extended Kalman filter, and compared with standard monitoring data including chest impedance to measure respiration activity.ResultsOut of a total of 405.4 h captured signals in 13 infants, 100 episodes of drop in oxygen saturation or heart rate were examined. Median (interquartile range) difference in respiratory rate was 0.04 (−2.45 to 1.48)/min between esophageal measurements annotated manually and with Kalman filter and −3.51 (−7.05 to −1.33)/min when compared to standard monitoring, suggesting an underestimation of respiratory rate when using the latter.ConclusionsKalman filter-based estimation of respiratory activity using multichannel esophageal signals is safe and feasible and results in respiratory rate closer to visual annotation than that derived from chest impedance of standard monitoring.

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy

Objective. Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, ‘Epi-AI’, which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results. Epi-AI demonstrated a superior sensitivity, 91.4%–98.8%, and specificity, 93.1%–98.8%, in Models I–III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I–III ranged from 66.9%–91.3% sensitivity and 60.8%–97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I–III at 73.2%–80.1% and 75.8%–98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.