CosmosDSR -- a methodology for automated detection and tracking of orbital debris using the Unscented Kalman Filter

The Kessler syndrome refers to the escalating space debris from frequent space activities, threatening future space exploration. Addressing this issue is vital. Several AI models, including Convolutional Neural Networks, Kernel Principal Component Analysis, and Model-Agnostic Meta- Learning have been assessed with various data types. Earlier studies highlighted the combination of the YOLO object detector and a linear Kalman filter (LKF) for object detection and tracking. Advancing this, the current paper introduces a novel methodology for the Comprehensive Orbital Surveillance and Monitoring Of Space by Detecting Satellite Residuals (CosmosDSR) by combining YOLOv3 with an Unscented Kalman Filter (UKF) for tracking satellites in sequential images. Using the Spacecraft Recognition Leveraging Knowledge of Space Environment (SPARK) dataset for training and testing, the YOLOv3 precisely detected and classified all satellite categories (Mean Average Precision=97.18%, F1=0.95) with few errors (TP=4163, FP=209, FN=237). Both CosmosDSR and an implemented LKF used for comparison tracked satellites accurately for a mean squared error (MSE) and root mean squared error (RME) of MSE=2.83/RMSE=1.66 for UKF and MSE=2.84/RMSE=1.66 for LKF. The current study is limited to images generated in a space simulation environment, but the CosmosDSR methodology shows great potential in detecting and tracking satellites, paving the way for solutions to the Kessler syndrome.Comment: 7 figures, 15 pages inc ref

Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life

Is the use of deep learning an appropriate means to locate debris in the ocean without harming aquatic wildlife?

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems.Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1,644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life

A Pilot Study on the Impacts of Lung-Strengthening Qigong on Wellbeing

Background Qigong embraces a range of self-care exercises originating from China. Lung-Strengthening Qigong (LSQ) is a specific technique for maintaining and improving physical and mental wellbeing. Methods We recruited 170 practitioners and 42 non-practitioner/control samples to investigate the impacts of LSQ practice on body, mind, thoughts, and feelings. This is a pilot study pursued to plan for an adequately powered, non-clinical randomized controlled trials (RCT) on overall wellbeing and health and to evaluate the adequacy of delivering the physical activity intervention with fidelity. Self-evaluation-based data collection schemes were developed by regularly requesting completion of a questionnaire from both practitioner and control group, and an online diary and end of study survey (EOS) completion only from the practitioners. Diverse types of analyses were conducted, including statistical tests, machine learning, and qualitative thematic models. Results We evaluated all different data resources together and observed that (a)the impacts are diverse, including improvements in physical (e.g., elevated sleep quality, physical energy, reduced fatigue), mental (e.g., increased positivity, reduced stress), and relational (e.g., enhanced connections to self and nature) wellbeing, which were not observed in control group; (b)measured by the level-of-effectiveness, four distinct clusters were identified, from no-effect to a high-level of effect; (c)a majority (84 %) of the LSQ practitioners experienced an improvement in wellbeing; (d)qualitative and quantitative analyses of the diary entries, questionnaires, and EOS were all found to be consistent, (e)majority of the positively impacted practitioners had no or some little prior experience with LSQ. Conclusions Novel features of this study include (i)an increased sample size vis-à-vis other related studies; (ii)provision of weekly live-streamed LSQ sessions; (iii)integration of quantitative and qualitative type of analyses. The pilot study indicated that the proportion of practitioners who continued to engage in completing the regular-interval questionnaires over time was higher for practitioners compared to the control group. The engagement of practitioners may have been sustained by participation in the regular live LSQ sessions. To fully understand the impacts of LSQ on clinical/physiological outcomes, especially for specific patient groups, more objective biomarkers (e.g. respiratory rate, heart rate variation) could be tracked in future studies

Mergeomics : multidimensional data integration to identify pathogenic perturbations to biological systems

Background: Complex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies. Results: We developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package. Conclusion: Mergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.Peer reviewe

Transcription Factor MAFF (MAF Basic Leucine Zipper Transcription Factor F) Regulates an Atherosclerosis Relevant Network Connecting Inflammation and Cholesterol Metabolism

BACKGROUND: Coronary artery disease (CAD) is a multifactorial condition with both genetic and exogenous causes. The contribution of tissue-specific functional networks to the development of atherosclerosis remains largely unclear. The aim of this study was to identify and characterize central regulators and networks leading to atherosclerosis. METHODS: Based on several hundred genes known to affect atherosclerosis risk in mouse (as demonstrated in knockout models) and human (as shown by genome-wide association studies), liver gene regulatory networks were modeled. The hierarchical order and regulatory directions of genes within the network were based on Bayesian prediction models, as well as experimental studies including chromatin immunoprecipitation DNA-sequencing, chromatin immunoprecipitation mass spectrometry, overexpression, small interfering RNA knockdown in mouse and human liver cells, and knockout mouse experiments. Bioinformatics and correlation analyses were used to clarify associations between central genes and CAD phenotypes in both human and mouse. RESULTS: The transcription factor MAFF (MAF basic leucine zipper transcription factor F) interacted as a key driver of a liver network with 3 human genes at CAD genome-wide association studies loci and 11 atherosclerotic murine genes. Most importantly, expression levels of the low-density lipoprotein receptor (LDLR) gene correlated with MAFF in 600 CAD patients undergoing bypass surgery (STARNET [Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task]) and a hybrid mouse diversity panel involving 105 different inbred mouse strains. Molecular mechanisms of MAFF were tested in noninflammatory conditions and showed positive correlation between MAFF and LDLR in vitro and in vivo. Interestingly, after lipopolysaccharide stimulation (inflammatory conditions), an inverse correlation between MAFF and LDLR in vitro and in vivo was observed. Chromatin immunoprecipitation mass spectrometry revealed that the human CAD genome-wide association studies candidate BACH1 (BTB domain and CNC homolog 1) assists MAFF in the presence of lipopolysaccharide stimulation with respective heterodimers binding at the MAF recognition element of the LDLR promoter to transcriptionally downregulate LDLR expression. CONCLUSIONS: The transcription factor MAFF was identified as a novel central regulator of an atherosclerosis/CAD-relevant liver network. MAFF triggered context-specific expression of LDLR and other genes known to affect CAD risk. Our results suggest that MAFF is a missing link between inflammation, lipid and lipoprotein metabolism, and a possible treatment target

Integrative Multiomics to Dissect the Lung Transcriptional Landscape of Pulmonary Arterial Hypertension

Pulmonary arterial hypertension (PAH) remains an incurable and often fatal disease despite currently available therapies. Multiomics systems biology analysis can shed new light on PAH pathobiology and inform translational research efforts. Using RNA sequencing on the largest PAH lung biobank to date (96 disease and 52 control), we aim to identify gene co-expression network modules associated with PAH and potential therapeutic targets. Co-expression network analysis was performed to identify modules of co-expressed genes which were then assessed for and prioritized by importance in PAH, regulatory role, and therapeutic potential via integration with clinicopathologic data, human genome-wide association studies (GWAS) of PAH, lung Bayesian regulatory networks, single-cell RNA-sequencing data, and pharmacotranscriptomic profiles. We identified a co-expression module of 266 genes, called the pink module, which may be a response to the underlying disease process to counteract disease progression in PAH. This module was associated not only with PAH severity such as increased PVR and intimal thickness, but also with compensated PAH such as lower number of hospitalizations, WHO functional class and NT-proBNP. GWAS integration demonstrated the pink module is enriched for PAH-associated genetic variation in multiple cohorts. Regulatory network analysis revealed that BMPR2 regulates the main target of FDA-approved riociguat, GUCY1A2, in the pink module. Analysis of pathway enrichment and pink hub genes (i.e. ANTXR1 and SFRP4) suggests the pink module inhibits Wnt signaling and epithelial-mesenchymal transition. Cell type deconvolution showed the pink module correlates with higher vascular cell fractions (i.e. myofibroblasts). A pharmacotranscriptomic screen discovered ubiquitin-specific peptidases (USPs) as potential therapeutic targets to mimic the pink module signature. Our multiomics integrative study uncovered a novel gene subnetwork associated with clinicopathologic severity, genetic risk, specific vascular cell types, and new therapeutic targets in PAH. Future studies are warranted to investigate the role and therapeutic potential of the pink module and targeting USPs in PAH

Refining colorectal cancer classification and clinical stratification through a single-cell atlas

Background Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells. Results Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance. Conclusions Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patient