Constraining the Star Formation Rate using Joint CIB Continuum and [CII] Intensity Mapping

Line intensity mapping (LIM) experiments probing the nearby universe can expect a considerable amount of cosmic infrared background (CIB) contiuum emission coming from near and far-infrared galaxies. For the purpose of using the LIM data to constrain the star formation rate (SFR), we argue that the CIB continuum - traditionally treated as contamination - can be combined with the LIM signal to enhance the SFR constraints achievable. We first present a power spectrum model that is capable of joining continuum and line emissions that assume the same prior SFR model. We subsequently analyze the effectiveness of the joint model in the context of the EXperiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM), which utilizes the [CII] molecular line to study the SFR. We numerically compute the theoretical power spectra according to our model and the EXCLAIM survey specifics, and perform Fisher analysis to obtain SFR parameter constraints. We find that although the joint model has no considerable advantage over LIM alone assuming the current survey level of EXCLAIM, its effects become significant when we consider more optimistic values of survey resolution and angular span that are expected of future LIM experiments. By manipulating the Fisher formalism, we show that the CIB is not only an additional SFR sensitive signal, but also serves to break the SFR parameter degeneracy that naturally emerges from the [CII] Fisher matrix. For this reason, addition of the CIB will allow improvements in the survey parameters to be better reflected in the SFR constraints, and can be effectively utilized by future LIM experiments.Comment: 12 pages, 5 figures, will submit to MNRA

BT2BT^2: Backward-compatible Training with Basis Transformation

Modern retrieval system often requires recomputing the representation of every piece of data in the gallery when updating to a better representation model. This process is known as backfilling and can be especially costly in the real world where the gallery often contains billions of samples. Recently, researchers have proposed the idea of Backward Compatible Training (BCT) where the new representation model can be trained with an auxiliary loss to make it backward compatible with the old representation. In this way, the new representation can be directly compared with the old representation, in principle avoiding the need for any backfilling. However, followup work shows that there is an inherent tradeoff where a backward compatible representation model cannot simultaneously maintain the performance of the new model itself. This paper reports our ``not-so-surprising'' finding that adding extra dimensions to the representation can help here. However, we also found that naively increasing the dimension of the representation did not work. To deal with this, we propose Backward-compatible Training with a novel Basis Transformation ($BT^2$). A basis transformation (BT) is basically a learnable set of parameters that applies an orthonormal transformation. Such a transformation possesses an important property whereby the original information contained in its input is retained in its output. We show in this paper how a BT can be utilized to add only the necessary amount of additional dimensions. We empirically verify the advantage of $BT^2$ over other state-of-the-art methods in a wide range of settings. We then further extend $BT^2$ to other challenging yet more practical settings, including significant change in model architecture (CNN to Transformers), modality change, and even a series of updates in the model architecture mimicking the evolution of deep learning models.Comment: 13 pages, 2 figure

An anoikis-related gene signature for prediction of the prognosis in prostate cancer

PurposeThis study presents a novel approach to predict postoperative biochemical recurrence (BCR) in prostate cancer (PCa) patients which involves constructing a signature based on anoikis-related genes (ARGs).MethodsIn this study, we utilised data from TCGA-PARD and GEO databases to identify specific ARGs in prostate cancer. We established a signature of these ARGs using Cox regression analysis and evaluated their clinical predictive efficacy and immune-related status through various methods such as Kaplan-Meier survival analysis, subject work characteristics analysis, and CIBERSORT method. Our findings suggest that these ARGs may have potential as biomarkers for prostate cancer prognosis and treatment. To investigate the biological pathways of genes associated with anoikis, we utilised GSVA, GO, and KEGG. The expression of ARGs was confirmed by the HPA database. Furthermore, we conducted PPI analysis to identify the core network of ARGs in PCa.ResultsBased on analysis of the TCGA database, a set of eight ARGs were identified as prognostic signature genes for prostate cancer. The reliability and validity of this signature were well verified in both the TCGA and GEO codifications. Using this signature, patients were classified into two groups based on their risk for developing BCR. There was a significant difference in BCR-free time between the high and low risk groups (P < 0.05).This signature serves as a dependable and unbiased prognostic factor for predicting biochemical recurrence (BCR) in prostate cancer (PCa) patients. It outperforms clinicopathological characteristics in terms of accuracy and reliability. PLK1 may play a potential regulatory role as a core gene in the development of prostate cancer.ConclusionThis signature suggests the potential role of ARGs in the development and progression of PCa and can effectively predict the risk of BCR in PCa patients after surgery. It also provides a basis for further research into the mechanism of ARGs in PCa and for the clinical management of patients with PCa

Evaluation of the IP-10 mRNA release assay for diagnosis of TB in HIV-infected individuals

HIV-infected individuals are susceptible to Mycobacterium tuberculosis (M.tb) infection and are at high risk of developing active tuberculosis (TB). Interferon-gamma release assays (IGRAs) are auxiliary tools in the diagnosis of TB. However, the performance of IGRAs in HIV-infected individuals is suboptimal, which limits clinical application. Interferon-inducible protein 10 (IP-10) is an alternative biomarker for identifying M.tb infection due to its high expression after stimulation with M.tb antigens. However, whether IP-10 mRNA constitutes a target for the diagnosis of TB in HIV-infected individuals is unknown. Thus, we prospectively enrolled HIV-infected patients with suspected active TB from five hospitals between May 2021 and May 2022, and performed the IGRA test (QFT-GIT) alongside the IP-10 mRNA release assay on peripheral blood. Of the 216 participants, 152 TB patients and 48 non-TB patients with a conclusive diagnosis were included in the final analysis. The number of indeterminate results of IP-10 mRNA release assay (13/200, 6.5%) was significantly lower than that of the QFT-GIT test (42/200, 21.0%) (P = 0.000026). IP-10 mRNA release assay had a sensitivity of 65.3% (95%CI 55.9% – 73.8%) and a specificity of 74.2% (95%CI 55.4% – 88.1%), respectively; while the QFT-GIT test had a sensitivity of 43.2% (95%CI 34.1% – 52.7%) and a specificity of 87.1% (95%CI 70.2% – 96.4%), respectively. The sensitivity of the IP-10 mRNA release assay was significantly higher than that of QFT-GIT test (P = 0.00062), while no significant difference was detected between the specificities of these two tests (P = 0.198). The IP-10 mRNA release assay showed a lower dependence on CD4+ T cells than that of QFT-GIT test. This was evidenced by the fact that the QFT-GIT test had a higher number of indeterminate results and a lower sensitivity when the CD4+ T cells counts were decreased (P < 0.05), while no significant difference in the number of indeterminate results and sensitivity were observed for the IP-10 mRNA release assay among HIV-infected individuals with varied CD4+T cells counts (P > 0.05). Therefore, our study suggested that M.tb specific IP-10 mRNA is a better biomarker for diagnosis of TB in HIV-infected individuals

Cost-effectiveness of CYP2C19-guided antiplatelet therapy in patients with acute coronary syndrome and percutaneous coronary intervention informed by real-world data

Current guidelines recommend dual antiplatelet therapy (DAPT) consisting of aspirin and a P2Y12 inhibitors following percutaneous coronary intervention (PCI). CYP2C19 genotype can guide DAPT selection, prescribing ticagrelor or prasugrel for loss-of-function (LOF) allele carriers (genotype-guided escalation). Cost-effectiveness analyses (CEA) are traditionally grounded in clinical trial data. We conduct a CEA using real-world data using a 1-year decision-analytic model comparing primary strategies: universal empiric clopidogrel (base case), universal ticagrelor, and genotype-guided escalation. We also explore secondary strategies commonly implemented in practice, wherein all patients are prescribed ticagrelor for 30 days post PCI. After 30 days, all patients are switched to clopidogrel irrespective of genotype (nonguided de-escalation) or to clopidogrel only if patients do not harbor an LOF allele (genotype-guided de-escalation). Compared with universal clopidogrel, both universal ticagrelor and genotype-guided escalation were superior with improvement in quality-adjusted life years (QALY’s). Only genotype-guided escalation was cost-effective ($42,365/QALY) and demonstrated the highest probability of being cost-effective across conventional willingness-to-pay thresholds. In the secondary analysis, compared with the nonguided de-escalation strategy, although genotype-guided de-escalation and universal ticagrelor were more effective, with ICER of$188,680/QALY and $678,215/QALY, respectively, they were not cost-effective. CYP2C19 genotype-guided antiplatelet prescribing is cost-effective compared with either universal clopidogrel or universal ticagrelor using real-world implementation data. The secondary analysis suggests genotype-guided and nonguided de-escalation may be viable strategies, needing further evaluation

Myeloid Cell Hypoxia-Inducible Factors Promote Resolution of Inflammation in Experimental Colitis

Colonic tissues in Inflammatory Bowel Disease (IBD) patients exhibit oxygen deprivation and activation of hypoxia-inducible factor 1α and 2α (HIF-1α and HIF-2α), which mediate cellular adaptation to hypoxic stress. Notably, macrophages and neutrophils accumulate preferentially in hypoxic regions of the inflamed colon, suggesting that myeloid cell functions in colitis are HIF-dependent. By depleting ARNT (the obligate heterodimeric binding partner for both HIFα subunits) in a murine model, we demonstrate here that myeloid HIF signaling promotes the resolution of acute colitis. Specifically, myeloid pan-HIF deficiency exacerbates infiltration of pro-inflammatory neutrophils and Ly6C+ monocytic cells into diseased tissue. Myeloid HIF ablation also hinders macrophage functional conversion to a protective, pro-resolving phenotype, and elevates gut serum amyloid A levels during the resolution phase of colitis. Therefore, myeloid cell HIF signaling is required for efficient resolution of inflammatory damage in colitis, implicating serum amyloid A in this process

Statistical Methods for Multi-Omics Inference from Single Cell Transcriptome

This thesis comprises three sections of research in statistical genomics and computational biology. Chapter 1 and Chapter 2 describe two statistical methods for multi-omics inference from single cell transcriptome, representing the theme of this thesis. Chapter 3 describes a side-project on copy number variation detection in large biobank data base. Part 1: Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that detects genetically distinct subclones, assigns each single cell to a subclone, and reconstructs the phylogenetic tree describing the tumor’s evolutionary history. DENDRO utilizes information from single nucleotide mutations in transcribed regions and accounts for technical noise and expression stochasticity at the single cell level. The accuracy of DENDRO was benchmarked on spike-in datasets and on scRNA-seq data with known subpopulation structure. We applied DENDRO to delineate subclonal expansion in a mouse melanoma model in response to immunotherapy, highlighting the role of neoantigens in treatment response. We also applied DENDRO to primary and lymph-node metastasis samples in breast cancer, where the new approach allowed us to better understand the relationship between genetic and transcriptomic intratumor variation. Part 2: Recent technological advances allow the simultaneous profiling, across many cells in parallel, of multiple omics features in the same cell. In particular, high throughput quantification of the transcriptome and a selected panel of cell surface proteins in the same cell is now feasible through the REAP-seq and CITE-seq protocols. Yet, due to technological barriers and cost considerations, most single cell studies, including Human Cell Atlas (HCA) project, quantify the transcriptome only and do not have cell-matched measurements of relevant surface proteins that can serve as integral markers of cellular function and targets for therapeutic intervention. Here we propose cTP-net (single cell Transcriptome to Protein prediction with deep neural network), a transfer learning approach based on deep neural networks, that imputes surface protein abundances for scRNA-seq data. Through comprehensive benchmark evaluations and applications to HCA and AML data sets, we show that cTP-net outperform existing methods and can transfer information from training data to accurately impute 24 immunophenotype markers, which achieve a more detailed characterization of cellular state and cellular phenotypes than transcriptome measurements alone. cTP-net relies, for model training, on accumulating public data of cells with paired transcriptome and surface protein measurements. Part 3: Copy number variations (CNVs) are gains and losses of DNA segments that are highly associated with multiple diseases. The Penn Medicine BioBank stores SNP-array and NGS data for more than 10000 individuals across ethnicity and conditions, providing a rich resource for CNV discovery and analysis. This type of experiment design fits perfectly for CNV detection tool - Integrated Copy Number Variation caller (iCNV), which I developed as my master thesis. The distinguishing feature of iCNV includes adaptation of platform specific normalization, utilization of allele specific reads from sequencing and integration of matched NGS and SNP-array data by a Hidden Markov Model (HMM). We applied iCNV on Penn Medicine BioBank data set, calling CNV over more than 10000 individuals (~2000 AFR, ~8000 EUR) with different phenotypes. iCNV detected on average 34.1 deletions and 11.3 duplications per EUR sample, and 38 deletions and 10.6 duplications per AFR sample. iCNV calling results show great improvement in detection sensitivity and specificity comparing to single platform detection method. Penn Medicine BioBank CNV sets by iCNV provide a rich database for researchers to study the relationship between diseases phenotypes and CNV across ethnicity and conditions

Statistical Methods for Multi-Omics Inference from Single Cell Transcriptome

This thesis comprises three sections of research in statistical genomics and computational biology. Chapter 1 and Chapter 2 describe two statistical methods for multi-omics inference from single cell transcriptome, representing the theme of this thesis. Chapter 3 describes a side-project on copy number variation detection in large biobank data base. Part 1: Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that detects genetically distinct subclones, assigns each single cell to a subclone, and reconstructs the phylogenetic tree describing the tumor’s evolutionary history. DENDRO utilizes information from single nucleotide mutations in transcribed regions and accounts for technical noise and expression stochasticity at the single cell level. The accuracy of DENDRO was benchmarked on spike-in datasets and on scRNA-seq data with known subpopulation structure. We applied DENDRO to delineate subclonal expansion in a mouse melanoma model in response to immunotherapy, highlighting the role of neoantigens in treatment response. We also applied DENDRO to primary and lymph-node metastasis samples in breast cancer, where the new approach allowed us to better understand the relationship between genetic and transcriptomic intratumor variation. Part 2: Recent technological advances allow the simultaneous profiling, across many cells in parallel, of multiple omics features in the same cell. In particular, high throughput quantification of the transcriptome and a selected panel of cell surface proteins in the same cell is now feasible through the REAP-seq and CITE-seq protocols. Yet, due to technological barriers and cost considerations, most single cell studies, including Human Cell Atlas (HCA) project, quantify the transcriptome only and do not have cell-matched measurements of relevant surface proteins that can serve as integral markers of cellular function and targets for therapeutic intervention. Here we propose cTP-net (single cell Transcriptome to Protein prediction with deep neural network), a transfer learning approach based on deep neural networks, that imputes surface protein abundances for scRNA-seq data. Through comprehensive benchmark evaluations and applications to HCA and AML data sets, we show that cTP-net outperform existing methods and can transfer information from training data to accurately impute 24 immunophenotype markers, which achieve a more detailed characterization of cellular state and cellular phenotypes than transcriptome measurements alone. cTP-net relies, for model training, on accumulating public data of cells with paired transcriptome and surface protein measurements. Part 3: Copy number variations (CNVs) are gains and losses of DNA segments that are highly associated with multiple diseases. The Penn Medicine BioBank stores SNP-array and NGS data for more than 10000 individuals across ethnicity and conditions, providing a rich resource for CNV discovery and analysis. This type of experiment design fits perfectly for CNV detection tool - Integrated Copy Number Variation caller (iCNV), which I developed as my master thesis. The distinguishing feature of iCNV includes adaptation of platform specific normalization, utilization of allele specific reads from sequencing and integration of matched NGS and SNP-array data by a Hidden Markov Model (HMM). We applied iCNV on Penn Medicine BioBank data set, calling CNV over more than 10000 individuals (~2000 AFR, ~8000 EUR) with different phenotypes. iCNV detected on average 34.1 deletions and 11.3 duplications per EUR sample, and 38 deletions and 10.6 duplications per AFR sample. iCNV calling results show great improvement in detection sensitivity and specificity comparing to single platform detection method. Penn Medicine BioBank CNV sets by iCNV provide a rich database for researchers to study the relationship between diseases phenotypes and CNV across ethnicity and conditions