Analysis Of Datasets Research Articles

Cervical cancer screening efficacy using SurePath, ThinPrep and conventional cytology: A large data set analysis from the Japan Cancer Society.

Over the past decade, liquid-based cytology has replaced conventional cytology for cervical cancer screening in many countries, including Japan. We aimed to evaluate the efficacy of liquid-based cytology using a large database and compare two major liquid-based cytology platforms, SurePath and ThinPrep, to conventional cytology. Cervical cancer screening data were collected from the Japan Cancer Society between 2015 and 2019. The efficacy of liquid-based and conventional cytology in detecting cervical intraepithelial neoplasia (CIN) was evaluated. Detection rates and positive predictive values were compared using a Poisson regression model. We collected data of 3,918,149 participants, including 2,248,202 conventional cytology, 874,807 SurePath and 795,140 ThinPrep smears. The detection rate of CIN2 or more was 1.14 times higher using SurePath than that using conventional cytology (95% confidence interval [CI], 1.09-1.20; p < 0.001). Contrastingly, the detection rate of CIN2 or more was 0.91 times lower using ThinPrep (95% CI, 0.86-0.96; p < 0.001). The detection rates of CIN3 or more did not differ significantly between SurePath and conventional cytology (detection rate ratio, 1.04; 95% CI, 0.97-1.12; p = 0.224). The positive predictive value ratios of CIN2 or more were 0.80 using SurePath (95% CI, 0.76-0.84; p < 0.001) and 0.83 using ThinPrep (95% CI, 0.79-0.87; p < 0.001) compared with conventional cytology. Liquid-based cytology, particularly SurePath, was useful for detecting CIN2 or higher in population-based cervical cancer screening. Further widespread use of liquid-based cytology methods would lead to efficient detection of cervical precancerous lesions.

“I JUST WANTED TO GET THE PAIN OUT OF THE WAY”: WHY PEOPLE WITH SIGNS AND SYMPTOMS OF SUSPECTED CAUDA EQUINA SYNDROME ATTEND THE EMERGENCY DEPARTMENT

Purpose and backgroundCauda Equina Syndrome (CES), a rare (&lt;1 per 100,000) and potentially devasting condition, involves compression of the lumbosacral nerve roots. If not quickly identified and treated, it can lead to lasting disability, and high medicolegal costs (&gt;£186 million in the decade to 2018). This study identified why people with suspected CES attend the emergency department (ED) and explored any delays in attending.Methods and ResultsThe design was a secondary analysis of a qualitative dataset comprising patients with back pain who attended the ED, undertaken using an interpretivist approach. Fourteen patients (8M:6F, aged 23–63 years) with suspected CES were purposively sampled from 4 EDs (2 Northern and 2 Southern) in England between August and December 2021. Semi-structured interviews were conducted online, audio-recorded, transcribed verbatim and analysed thematically.Acopia with pain was the biggest factor in a participant's decision to attend the ED, along with the need for a diagnosis. This pain was the worst ever experienced and debilitating, leaving people unable to cope and desperate for relief. 12/14 were advised to attend the ED following identification of red flags by: GPs (n=9); physiotherapists (n=2); surgical colleague (n=1); and 111 (n=1). Factors such as guilt, previous experience of being disregarded, and symptom misattribution were seen to cause delays in seeking care.ConclusionThis paper revealed a disconnect between the priorities of patients and clinicians prior to attending the ED. Clinicians need to validate the pain experience, communicate clearly why signs and symptoms are concerning, and convey the urgency and potential impact of CES.Conflicts of interestNoneSources of fundingFunding for primary data: Health Education England &amp; NationalInstitute of Health and Care Research (ICA-CDRF-2018-04-ST2-040)

Novel metagenomics analysis of stony coral tissue loss disease.

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from 4 stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio, was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared with a prior metagenome analysis of the same dataset.

Actin networks modulate heterogeneous NF-κB dynamics in response to TNFα.

The canonical NF-κB transcription factor RELA is a master regulator of immune and stress responses and is upregulated in pancreatic ductal adenocardinoma (PDAC) tumours. In this study, we characterised previously unexplored endogenous RELA-GFP dynamics in PDAC cell lines through live single-cell imaging. Our observations revealed that TNFα stimulation induces rapid, sustained, and non-oscillatory nuclear translocation of RELA. Through Bayesian analysis of single-cell datasets with variation in nuclear RELA, we predicted that RELA heterogeneity in PDAC cell lines is dependent on F-actin dynamics. RNA-seq analysis identified distinct clusters of RELA-regulated gene expression in PDAC cells, including TNFα-induced RELA upregulation of the actin regulators NUAK2 and ARHGAP31. Further, siRNA-mediated depletion of ARHGAP31 and NUAK2 altered TNFα-stimulated nuclear RELA dynamics in PDAC cells, establishing a novel negative feedback loop that regulates RELA activation by TNFα. Additionally, we characterised the NF-κB pathway in PDAC cells, identifying how NF-κB/IκB proteins genetically and physically interact with RELA in the absence or presence of TNFα. Taken together, we provide computational and experimental support for interdependence between the F-actin network and the NF-κB pathway with RELA translocation dynamics in PDAC.

Multi-Omics Analysis of Primary Prostate Cancer Datasets Reveals Novel Biomarkers.

Prostate cancer (PCa) ranks second in cancer-related deaths in men. Current screenings used in the diagnosis are not sufficient enough in the early stages therefore, more diagnostic biomarker studies are needed. We performed a meta-analysis on the biomarker potential of miRNAs, mRNAs, and methylation for the early stages of PCa by searching available microarrays from the GEO dataset for PCa tissue and benign prostatic hyperplasia (BPH) or normal adjacent to PCa. Target genes of miRNAs were determined using the miRWalk and miRDB datasets. The results were visualized using network analysis. qPCR quantification of potential miRNA and genes was performed in human prostate epithelial cell line (RWPE-1) and human prostate carcinoma epithelial cell line (22RV1). Our meta-analysis of potential biomarkers for the diagnosis of PCa identified several candidates. It was shown that miR-7-5p is overexpressed. CAMKK2, TMEM97 expression were upregulated and CLIP1 expression was downregulated and these genes were shown to be targets of miR-7-5p. CAMKK2, TMEM97, and CLIP1 genes were found to be hypermethylated. Although the changes in the expression levels of miR-7-5p and CAMKK2, TMEM97, and CLIP1 in the two cell lines used in our study were not consistent with the significant expression differences observed in the meta-analysis, our meta-analysis results would be promising in human prostate tissue or different human tumor cell line studies. This highlights the importance of our meta-analysis results in prostate cancer biomarker research, given the difficulty of experimental validation of our large-scale data meta-analysis results using a specific cell line.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

JmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data

We have introduced the R package jmBIG to facilitate the analysis of large healthcare datasets and the development of predictive models. This package provides a comprehensive set of tools and functions specifically designed for the joint modelling of longitudinal and survival data in the context of big data analytics. The jmBIG package offers efficient and scalable implementations of joint modelling algorithms, allowing for integrating large-scale healthcare datasets.By utilizing the capabilities of jmBIG, researchers and analysts can effectively handle the challenges associated with big healthcare data, such as high dimensionality and complex relationships between multiple outcomes.With the support of jmBIG, analysts can seamlessly fit Bayesian joint models, generate predictions, and evaluate the performance of the models. The package incorporates cutting-edge methodologies and harnesses the computational capabilities of parallel computing to accelerate the analysis of large-scale healthcare datasets significantly. In summary, jmBIG empowers researchers to gain deeper insights into disease progression and treatment response, fostering evidence-based decision-making and paving the way for personalized healthcare interventions that can positively impact patient outcomes on a larger scale.

A framework for in-field and out-of-field patient specific secondary cancer risk estimates from treatment plans using the TOPAS Monte Carlo system

Objective. To allow the estimation of secondary cancer risks from radiation therapy treatment plans in a comprehensive and user-friendly Monte Carlo (MC) framework. Method. Patient planning computed tomography scans were extended superior-inferior using the International Commission on Radiological Protection’s Publication 145 computational mesh phantoms and skeletal matching. Dose distributions were calculated with the TOPAS MC system using novel mesh capabilities and the digital imaging and communications in medicine radiotherapy extension interface. Finally, in-field and out-of-field cancer risk was calculated using both sarcoma and carcinoma risk models with two alternative parameter sets. Result. The TOPAS MC framework was extended to facilitate epidemiological studies on radiation-induced cancer risk. The framework is efficient and allows automated analysis of large datasets. Out-of-field organ dose was small compared to in-field dose, but the risk estimates indicate a non-negligible contribution to the total radiation induced cancer risk. Significance. This work equips the TOPAS MC system with anatomical extension, mesh geometry, and cancer risk model capabilities that make state-of-the-art out-of-field dose calculation and risk estimation accessible to a large pool of users. Furthermore, these capabilities will facilitate further refinement of risk models and sensitivity analysis of patient specific treatment options.

Finding love in algorithms: deciphering the emotional contexts of close encounters with AI chatbots

Abstract AI chatbots are permeating the socio-emotional realms of human life, presenting both benefits and challenges to interpersonal dynamics and well-being. Despite burgeoning interest in human–AI relationships, the conversational and emotional nuances of real-world, in situ human–AI social interactions remain underexplored. Through computational analysis of a multimodal dataset with over 35,000 screenshots and posts from r/replika, we identified seven prevalent types of human–AI social interactions: intimate behavior, mundane interaction, self-disclosure, play and fantasy, customization, transgression, and communication breakdown, and examined their associations with six basic human emotions. Our findings suggest the paradox of emotional connection with AI, indicated by the bittersweet emotion in intimate encounters with AI chatbots, and the elevated fear in uncanny valley moments when AI exhibits semblances of mind in deep self-disclosure. Customization characterizes the distinctiveness of AI companionship, positively elevating user experiences, whereas transgression and communication breakdown elicit fear or sadness.

A framework to improve causal inferences from visualizations using counterfactual operators

Exploratory data analysis of high-dimensional datasets is a crucial task for which visual analytics can be especially useful. However, the ad hoc nature of exploratory analysis can also lead users to draw incorrect causal inferences. Previous studies have demonstrated this risk and shown that integrating counterfactual concepts within visual analytics systems can improve users’ understanding of visualized data. However, effectively leveraging counterfactual concepts can be challenging, with only bespoke implementations found in prior work. Moreover, it can require expertise in both counterfactual subset analysis and visualization to implement the functionalities practically. This paper aims to help address these challenges in two ways. First, we propose an operator-based conceptual model for the use of counterfactuals that is informed by prior work in visualization research. Second, we contribute the Co-op library, an open and extensible reference implementation of this model that can support the integration of counterfactual-based subset computation with visualization systems. To evaluate the effectiveness and generalizability of Co-op, the library was used to construct two different visual analytics systems each supporting a distinct user workflow. In addition, expert interviews were conducted with professional visual analytics researchers and engineers to gain more insights regarding how Co-op could be leveraged. Finally, informed in part by these evaluation results, we distil a set of key design implications for effectively leveraging counterfactuals in future visualization systems.

60 Dissecting the Cellular and Epigenetic Landscape of Clear Cell Renal Cell Carcinoma Through Paired snRNA/ATAC-Seq Analysis

Abstract Background Clear cell renal cell carcinoma (ccRCC) stands as one of the most prevalent and aggressive forms of renal malignancies, accounting for a substantial portion of kidney cancer-related mortality worldwide. The heterogeneity within ccRCC poses a significant challenge to understanding its molecular mechanisms and devising effective therapeutic strategies. [1] Previous studies utilizing single nucleus RNA sequencing (snRNA-seq) have revealed transcriptional contributions to cell-type specificity in both mature human and mouse kidneys. Moreover, recent advancements have expanded this approach to single-cell profiling of chromatin accessibility. Single-nuclei assay for transposase-accessible chromatin using sequencing (snATAC-seq) has enabled the measurement of chromatin accessibility in thousands of individual cells, providing insights into the dynamic process that drives nephron development and differentiation. Integration and analysis of multimodal single-cell datasets, such as snRNA-seq and snATAC-seq, allows for the prediction of cell-type-specific cis-regulatory DNA interactions and transcription factor activity, complementing the transcriptional information obtained by snRNA-seq.[2] [3] [1] Yang, Wang, and Yang, “Treatment Strategies for Clear Cell Renal Cell Carcinoma.” [2] Muto et al., “Single Cell Transcriptional and Chromatin Accessibility Profiling Redefine Cellular Heterogeneity in the Adult Human Kidney.” [3] Monteagudo-Sánchez, Noordermeer, and Greenberg, “The Impact of DNA Methylation on CTCF-Mediated 3D Genome Organization.” Methods This study leveraged paired snRNA/ATAC-seq in tumor and normal-adjacent samples from the Renal Tumor Biobank at Dartmouth (n= 63) to explore the ccRCC landscape at single-cell resolution using 10x multiome technology. The snRNA-seq dataset was processed with Seurat v5.0 to remove low-quality nuclei. Doublets were removed with DoubletFinder v2.02, and ambient DNA was removed using decontX from celda. Normalization was conducted via SCTransform, and missing data was imputed with ALRA. Clustering was performed by constructing a KNN graph and running the Louvain algorithm. Cell type assignments were predicted using Azimuth with the human kidney reference dataset. The snATAC-seq data was processed with ArchR v1.0.1, which performed dimensionality reduction and clustering. Cluster identities defined in the Seurat workflow were cross-annotated to the ATAT-seq data, and open peaks were identified within these cell-type clusters using MACS3 v3.0.1. Identification of marker peaks, motif enrichment, and motif enrichment deviations were inferred utilizing ArchR’s getmarkerFeatures, peakAnnoEnrichment, and addMotifAnnotations functions, respectively. Results Motif enrichment analysis revealed canonical cancer-associated transcription factor (TF) dysregulation in tumor cells (nephron loop-like), including enrichment of HNF1B, SMARCC1, FOSL1, and JUND. Interestingly, HNF and SMARCC were also enriched in the lymphoid compartment, indicating possible crosstalk between these two cell types. Previous work has investigated the involvement of the HNF1 in ccRCC and in chromophobe RCC for prognosis,and we plan on investigating the relationship of these enrichments to tumor grade, stage and outcome.[1] Additional TF enrichments included BCL families and ELF within the myeloid compartment, and vascularization related TF families in endothelial cell types including enrichment of various ETS TFs. Investigation of cell-cell interactions between immune and endothelial cell populations as well as between tumor and normal adjacent tissues will be performed to further disentangle the role differentially accessable chromatin plays in ccRCC. [1] Buchner et al., “Downregulation of HNF-1B in Renal Cell Carcinoma Is Associated With Tumor Progression and Poor Prognosis.” Conclusions Our multi-omic approach has several advantages. First, it enables the characterization of cellular heterogeneity within the tumor microenvironment, identifying cell populations and their contributions to disease progression. Secondly, by integrating transcriptomic and epigenomic data, we gain insights into the dynamic interplay between gene expression and chromatin accessibility, uncovering potential therapeutic vulnerabilities and biomarkers for personalized treatment strategies. DOD CDMRP Funding: yes

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

The other side of platform politics. Law‐making and online participatory democracy in the Five Star Movement

AbstractBetween 2013 and 2021, the Italian Five Star Movement implemented an advanced tool, Lex Eletti, to enhance citizens' online participation in law‐making. Elected representatives had to present their draft bills on the party's platform before introducing them in Parliament, allowing ordinary party members to discuss them and suggest modifications. This paper investigates the impact of the Lex Eletti platform on participatory democracy in law‐making using text analysis and sentence embeddings to compare draft bills and their parliamentary counterparts. Our analysis of a comprehensive dataset, also including statistics about MPs' and party members' online interactions and MPs' legislative activity in Parliament, reveals that the platform had minimal impact: only a few MPs engaged with it, and user attention for bills on Lex Eletti decreased over time. Interaction between platform users and MPs was also limited. Most bills reached Parliament with little to no modifications, irrespective of platform engagement levels. The Five Star Movement's attempt to foster democratic participation through this platform did not significantly affect law‐making activity.

Diagnostic utility of sortilin & other biomarkers in the diagnosis of carotid & coronary atherosclerosis in individuals with arterial hypertension.

Background & objectives Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. The aim of this investigation was to study the role of biological markers in predicting the risk of carotid and coronary artery atherosclerosis. Methods A total of 161 males in the age group of 30-65 yr were included in this study. All participants underwent biochemical analyses [cholesterol, low density lipoprotein cholesterol (LDL-C), triglycerides, glucose, (interleukin) IL-8, IL-10, (proprotein convertase inhibitors subtilisin/kexin type 9) PCSK9, sortilin, creatinine]; ECG; echocardiography; coronary angiography; ultrasound doppler of brachiocephalic arteries. Based on PCSK9 levels, participants were divided into four groups: group 1, n=41 individuals with PCSK9 level of 100-250 ng/ml; group 2, n=37 individuals with PCSK9 level of 251-400 ng/ml; group 3, n=51 individuals with PCSK9 level of 401-600 ng/ml and group 4, n=32 individuals with PCSK9 level of 601-900 ng/ml. Results Sortilin level was the highest in group 2. Group 3 individuals had the highest level of IL-8. Correlation analysis of the entire data set revealed the relationship of relative left ventricular thickness index with age, cardiovascular risk, body mass index, intima-media thickness and left ventricular mass index; sortilin had a negative relationship of weak strength with age and smoking, a direct relationship between the risk of cardiovascular complications and with IL-10. Interpretation & conclusions Sortilin is the innovative marker of CVDs. In the present investigation, we demonstrated the clear increase in the inflammatory markers (IL-8) in individuals with subclinical atherosclerosis. This fact can be explained by the oxygen stress activation. In individuals with coronary artery stenosis (50% and more), the increase in IL-10 levels demonstrates, to our opinion, the activation of antioxidant protection activation.

Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism.

Carbon-carbon (C-C) coupling is essential in the electrocatalytic reduction of CO2 for the production of green chemicals. However, due to the complexity of the reaction network, there remains controversy regarding the underlying reaction mechanisms and the optimal direction for catalyst material design. Here, we present a global perspective to establish a comprehensive data set encompassing all C-C coupling precursors and catalytic active site compositions to explore the reaction mechanisms and screen catalysts via big data set analysis. The 2D-3D ensemble machine learning strategy, developed to target a variety of adsorption configurations, can quickly and accurately expand quantum chemical calculation data, enabling the rapid acquisition of this extensive big data set. Analyses of the big data set establish that (1) asymmetric coupling mechanisms exhibit greater potential efficiency compared to symmetric coupling, with the optimal path involving the coupling CHO with CH or CH2, and (2) C-C coupling selectivity of Cu-based catalysts can be enhanced through bimetallic doping including CuAgNb sites. Importantly, we experimentally substantiate the CuAgNb catalyst to demonstrate actual boosted performance in C-C coupling. Our finding evidence the practicality of our big data set generated from machine learning-accelerated quantum chemical computations. We conclude that combining big data with complex catalytic reaction mechanisms and catalyst compositions will set a new paradigm for accelerating optimal catalyst design.

Quantifying local lattice distortions in refractory high-entropy alloys

Severe local lattice distortions (LLDs), originating from the size mismatch among atoms, have been proposed as one of the key mechanisms responsible for the excellent mechanical properties of bcc-structured high-entropy alloys (HEAs). They have also been connected to phase stability, as well as physical properties such as electrical conductivity. Experimental measurements of LLDs are, however, difficult and often ambiguous. Analysis of total scattering data in real space has been proposed to provide a uniquely suitable probe of LLDs, but its widespread application and validation are still limited. We conduct a thorough study of LLD measurements in refractory high-entropy alloys (RHEAs) using small-box pair distribution function (PDF) analysis. We start by reexamining existing literature data using a recently proposed coherent theoretical framework to demonstrate that LLDs in RHEAs can indeed be considered as severe and can be reliably measured even in the absence of known thermal components. We perform total scattering experiments of a typical RHEA (HfNbTaTiZr) using both x-rays and neutrons, and show that real-space PDF analysis of data from different types of radiation gives consistent values of LLDs. The results are also in good agreement with the values derived from reciprocal-space data. Finally, through simulation and analysis of theoretical two-phase PDFs, we demonstrate that the effect of the chemical segregation in the investigated RHEA on the measured LLDs is limited when dealing with comparatively large LLDs. The results show that PDF analysis using small-box modeling provides a fast and reliable tool for measuring LLDs in RHEAs, which makes it ideal for analysis of large data sets from time-resolved measurements. Published by the American Physical Society 2024

Abstract We141: Leveraging Cardiac Gene Reprogramming at Single-Cell Resolution to Understand Heart Failure

Background: A defining characteristic of heart failure is cardiac gene reprogramming. Heart failure-induced pathological stressors activate transcription factors, including Gata4 , Tbx5 , and Mef2, required for cardiogenesis. These transcription factors activate fetal cardiac genes in the failing adult heart. The mechanism and role of such cardiac gene reprogramming in response to heart failure remain not fully characterized. Here, we leverage transcriptional reprogramming induced by thyroid hormone deficiency as a model system to understand adult heart failure at the single-cell level. Methods: We engineered a novel Myh6 / Myh7 reporter mouse line. Hypothyroidism was induced in mice with propylthiouracil (PTU). PTU treatment led to global cardiac gene reprogramming. To profile gene expression and chromatin accessibility during cardiac gene reprogramming, single nuclei multiomic sequencing was conducted. Echocardiographs on control and PTU-treated mice evaluated cardiac function changes associated with reprogramming. Cardiac tissues were collected to validate both known and newly identified candidates involved in cardiac gene reprogramming. Results: Characterization of hypothyroidic mice as a model for cardiac reprogramming revealed that the ventricles of PTU-treated hearts significantly switch from Myh6 to dominantly express Myh7 . Evaluation of echocardiographs showed that PTU-treated hearts are significantly atrophic, have chamber dilatation, and reduced cardiac function. Analysis of multiome datasets revealed a unique gene set that is differentially expressed and accessible during cardiac gene reprogramming. Upregulated and more accessible genes in PTU-treated cardiomyocytes (CM) are involved in muscle cell differentiation, muscle hypertrophy, and mitochondrion disassembly ( Tbx5 , Sox4 , Wnt2 , Rgs2 , Hey2 , Mfn2 ). Downregulated and less accessible genes in PTU-treated CMs are related to heart contraction, regulation of heart rate, and ion membrane transport ( Kcnd3 , Jarid2 , Epas1, Atp2a2 , Scn5a , Slc25a4 ). Conclusion: Induction of cardiac gene reprogramming by thyroid inhibition can be applied to understand adult heart failure mechanisms. Hypothyroidism effectively induces global remodeling in the adult heart. Single nuclei multiomic analysis reveals new mechanistic insights into cardiac gene reprogramming that alters the expression and accessibility of genes essential for heart development and maturation.

TransTEx: novel tissue-specificity scoring method for grouping human transcriptome into different expression groups.

Although human tissues carry out common molecular processes, gene expression patterns can distinguish different tissues. Traditional informatics methods, primarily at the gene level, overlook the complexity of alternative transcript variants and protein isoforms produced by most genes, changes in which are linked to disease prognosis and drug resistance. We developed TransTEx (Transcript-level Tissue Expression), a novel tissue-specificity scoring method, for grouping transcripts into four expression groups. TransTEx applies sequential cut-offs to tissue-wise transcript probability estimates, subsampling-based P-values and fold-change estimates. Application of TransTEx on GTEx mRNA-seq data divided 199166 human transcripts into different groups as 17999 tissue-specific (TSp), 7436 tissue-enhanced, 36783 widely expressed (Wide), 79191 lowly expressed (Low), and 57757 no expression (Null) transcripts. Testis has the most (13466) TSp isoforms followed by liver (890), brain (701), pituitary (435), and muscle (420). We found that the tissue specificity of alternative transcripts of a gene is predominantly influenced by alternate promoter usage. By overlapping brain-specific transcripts with the cell-type gene-markers in scBrainMap database, we found that 63% of the brain-specific transcripts were enriched in nonneuronal cell types, predominantly astrocytes followed by endothelial cells and oligodendrocytes. In addition, we found 61 brain cell-type marker genes encoding a total of 176 alternative transcripts as brain-specific and 22 alternative transcripts as testis-specific, highlighting the complex TSp and cell-type specific gene regulation and expression at isoform-level. TransTEx can be adopted to the analysis of bulk RNA-seq or scRNA-seq datasets to find tissue- and/or cell-type specific isoform-level gene markers. TransTEx database: https://bmi.cewit.stonybrook.edu/transtexdb/ and the R package is available via GitHub: https://github.com/pallavisurana1/TransTEx.

Abstract We032: FOXK1-Gli2 network regulates cardiomyocyte proliferation and heart regeneration

Unlike adult mammals, the neonatal mouse has an exceptional regenerative capacity following myocardial injury. Our goal was to decipher and define novel factors and mechanisms that have a role in regeneration of the injured adult mouse heart. FOXK1 is a transcription factor that regulates cell cycle kinetics, myogenic stem cell proliferation, and skeletal muscle regeneration. During development, its expression is restricted to progenitors, somites and the heart. In these studies, we identified a novel role for FOXK1 as a regulator of cardiomyocyte (CM) proliferation and regeneration through the regulation of Gli2 . Analysis of single nucleus RNAseq datasets of neonatal hearts revealed that FOXK1 was expressed in neonatal CMs. qPCR analysis from P1 hearts of control and Foxk1 null mice confirmed this finding and demonstrated dysregulation of the cell cycle regulators, Ccne1 and p21 , in the absence of Foxk1 . Control and Foxk1 null mice were pulsed with EdU from P3 to P7 which demonstrated that the absence of Foxk1 led to a decrease in CM proliferation in-vivo. Then, using a transgenic mouse model that we engineered to overexpress FOXK1 in muscle ( MCK-Foxk1 ), we observed CM proliferation in adult MCK-Foxk1 mice, which showed increased incorporation of EdU and increased pH3 expression compared to control. Bulk RNA-seq analysis of P1, P7, and P14 hearts indicated increased CM proliferation in the MCK-Foxk1 mouse and revealed Gli2 as a potential downstream target of FOXK1. A gel shift assay was performed, which demonstrated that FOXK1 was able to bind to a FOXK1 motif upstream of the Gli2 gene. ChIP-PCR further verified that Gli2 was a direct downstream target of FOXK1 in the embryonic stem cell-embryoid body (ES-EB) system. We then performed ligation of the left anterior descending coronary artery (LAD) in adult control and MCK-Foxk1 mice. Echocardiography following the LAD-ligation injuries showed improved cardiac function in MCK-Foxk1 transgenic mice compared with controls. These results identify FOXK1 as a regulator of CM proliferation and heart regeneration and suggest that this function is mediated through the regulation of Gli2 gene expression.

Estimating the mean in the space of ranked phylogenetic trees.

Reconstructing evolutionary histories of biological entities, such as genes, cells, organisms, populations, and species, from phenotypic and molecular sequencing data is central to many biological, palaeontological, and biomedical disciplines. Typically, due to uncertainties and incompleteness in data, the true evolutionary history (phylogeny) is challenging to estimate. Statistical modelling approaches address this problem by introducing and studying probability distributions over all possible evolutionary histories, but can also introduce uncertainties due to misspecification. In practice, computational methods are deployed to learn those distributions typically by sampling them. This approach, however, is fundamentally challenging as it requires designing and implementing various statistical methods over a space of phylogenetic trees (or treespace). Although the problem of developing statistics over a treespace has received substantial attention in the literature and numerous breakthroughs have been made, it remains largely unsolved. The challenge of solving this problem is 2-fold: a treespace has nontrivial often counter-intuitive geometry implying that much of classical Euclidean statistics does not immediately apply; many parametrizations of treespace with promising statistical properties are computationally hard, so they cannot be used in data analyses. As a result, there is no single conventional method for estimating even the most fundamental statistics over any treespace, such as mean and variance, and various heuristics are used in practice. Despite the existence of numerous tree summary methods to approximate means of probability distributions over a treespace based on its geometry, and the theoretical promise of this idea, none of the attempts resulted in a practical method for summarizing tree samples. In this paper, we present a tree summary method along with useful properties of our chosen treespace while focusing on its impact on phylogenetic analyses of real datasets. We perform an extensive benchmark study and demonstrate that our method outperforms currently most popular methods with respect to a number of important 'quality' statistics. Further, we apply our method to three empirical datasets ranging from cancer evolution to linguistics and find novel insights into corresponding evolutionary problems in all of them. We hence conclude that this treespace is a promising candidate to serve as a foundation for developing statistics over phylogenetic trees analytically, as well as new computational tools for evolutionary data analyses. An implementation is available at https://github.com/bioDS/Centroid-Code.