Covariance approximation for large multivariate spatial data sets with an application to multiple climate model errors

This paper investigates the cross-correlations across multiple climate model errors. We build a Bayesian hierarchical model that accounts for the spatial dependence of individual models as well as cross-covariances across different climate models. Our method allows for a nonseparable and nonstationary cross-covariance structure. We also present a covariance approximation approach to facilitate the computation in the modeling and analysis of very large multivariate spatial data sets. The covariance approximation consists of two parts: a reduced-rank part to capture the large-scale spatial dependence, and a sparse covariance matrix to correct the small-scale dependence error induced by the reduced rank approximation. We pay special attention to the case that the second part of the approximation has a block-diagonal structure. Simulation results of model fitting and prediction show substantial improvement of the proposed approximation over the predictive process approximation and the independent blocks analysis. We then apply our computational approach to the joint statistical modeling of multiple climate model errors.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS478 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Genome-wide screen for genes involved in Caenorhabditis elegans developmentally timed sleep

In Caenorhabditis elegans, Notch signaling regulates developmentally timed sleep during the transition from L4 larval stage to adulthood (L4/A) . To identify core sleep pathways and to find genes acting downstream of Notch signaling, we undertook the first genome-wide, classical genetic screen focused on C. elegans developmentally timed sleep. To increase screen efficiency, we first looked for mutations that suppressed inappropriate anachronistic sleep in adult hsp::osm-11 animals overexpressing the Notch coligand OSM-11 after heat shock. We retained suppressor lines that also had defects in L4/A developmentally timed sleep, without heat shock overexpression of the Notch coligand. Sixteen suppressor lines with defects in developmentally timed sleep were identified. One line carried a new allele of goa-1; loss of GOA-1 Gαo decreased C. elegans sleep. Another line carried a new allele of gpb-2, encoding a Gβ5 protein; Gβ5 proteins have not been previously implicated in sleep. In other scenarios, Gβ5 GPB-2 acts with regulators of G protein signaling (RGS proteins) EAT-16 and EGL-10 to terminate either EGL-30 Gαq signaling or GOA-1 Gαo signaling, respectively. We found that loss of Gβ5 GPB-2 or RGS EAT-16 decreased L4/A sleep. By contrast, EGL-10 loss had no impact. Instead, loss of RGS-1 and RGS-2 increased sleep. Combined, our results suggest that, in the context of L4/A sleep, GPB-2 predominantly acts with EAT-16 RGS to inhibit EGL-30 Gαq signaling. These results confirm the importance of G protein signaling in sleep and demonstrate that these core sleep pathways function genetically downstream of the Notch signaling events promoting sleep

Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?

The Potential Outcome Framework (POF) plays a prominent role in the field of causal inference. Most causal inference models based on the POF (CIMs-POF) are designed for eliminating confounding bias and default to an underlying assumption of Confounding Covariates. This assumption posits that the covariates consist solely of confounders. However, the assumption of Confounding Covariates is challenging to maintain in practice, particularly when dealing with high-dimensional covariates. While certain methods have been proposed to differentiate the distinct components of covariates prior to conducting causal inference, the consequences of treating non-confounding covariates as confounders remain unclear. This ambiguity poses a potential risk when conducting causal inference in practical scenarios. In this paper, we present a unified graphical framework for the CIMs-POF, which greatly enhances the comprehension of these models' underlying principles. Using this graphical framework, we quantitatively analyze the extent to which the inference performance of CIMs-POF is influenced when incorporating various types of non-confounding covariates, such as instrumental variables, mediators, colliders, and adjustment variables. The key findings are: in the task of eliminating confounding bias, the optimal scenario is for the covariates to exclusively encompass confounders; in the subsequent task of inferring counterfactual outcomes, the adjustment variables contribute to more accurate inferences. Furthermore, extensive experiments conducted on synthetic datasets consistently validate these theoretical conclusions.Comment: 12 pages, 4 figure

Normal sleep bouts are not essential for C. elegans survival and FoxO is important for compensatory changes in sleep

Additional file 6: Decreased lag-2 function does not slow vulval development. The progeny of wild type and lag-2(q420) animals raised at 25.5 °C were selected at the L4 stage, prior to lethargus entry. Vulval eversion was scored after 3 h; the percentage of animals completing vulval eversion was recorded. Significance was assessed by student’s two-tailed t-test p value < 0.5; error bars represents SEM from 3 trials. Total number of animals: wild type n = 45 and lag-2(q420) n = 42

VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference

Causal inference plays a vital role in diverse domains like epidemiology, healthcare, and economics. De-confounding and counterfactual prediction in observational data has emerged as a prominent concern in causal inference research. While existing models tackle observed confounders, the presence of unobserved confounders remains a significant challenge, distorting causal inference and impacting counterfactual outcome accuracy. To address this, we propose a novel variational learning model of unobserved confounders for counterfactual inference (VLUCI), which generates the posterior distribution of unobserved confounders. VLUCI relaxes the unconfoundedness assumption often overlooked by most causal inference methods. By disentangling observed and unobserved confounders, VLUCI constructs a doubly variational inference model to approximate the distribution of unobserved confounders, which are used for inferring more accurate counterfactual outcomes. Extensive experiments on synthetic and semi-synthetic datasets demonstrate VLUCI's superior performance in inferring unobserved confounders. It is compatible with state-of-the-art counterfactual inference models, significantly improving inference accuracy at both group and individual levels. Additionally, VLUCI provides confidence intervals for counterfactual outcomes, aiding decision-making in risk-sensitive domains. We further clarify the considerations when applying VLUCI to cases where unobserved confounders don't strictly conform to our model assumptions using the public IHDP dataset as an example, highlighting the practical advantages of VLUCI.Comment: 15 pages, 8 figure

De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network

Counterfactual inference for continuous rather than binary treatment variables is more common in real-world causal inference tasks. While there are already some sample reweighting methods based on Marginal Structural Model for eliminating the confounding bias, they generally focus on removing the treatment's linear dependence on confounders and rely on the accuracy of the assumed parametric models, which are usually unverifiable. In this paper, we propose a de-confounding representation learning (DRL) framework for counterfactual outcome estimation of continuous treatment by generating the representations of covariates disentangled with the treatment variables. The DRL is a non-parametric model that eliminates both linear and nonlinear dependence between treatment and covariates. Specifically, we train the correlations between the de-confounded representations and the treatment variables against the correlations between the covariate representations and the treatment variables to eliminate confounding bias. Further, a counterfactual inference network is embedded into the framework to make the learned representations serve both de-confounding and trusted inference. Extensive experiments on synthetic datasets show that the DRL model performs superiorly in learning de-confounding representations and outperforms state-of-the-art counterfactual inference models for continuous treatment variables. In addition, we apply the DRL model to a real-world medical dataset MIMIC and demonstrate a detailed causal relationship between red cell width distribution and mortality.Comment: 15 pages,4 figure

2-Selenouridine Triphosphate Synthesis and Se-RNA Transcription

2-Selenouridine (SeU) is one of the naturally occurring modifications of Se-tRNAs (SeU-RNA) at the wobble position of the anticodon loop. Its role in the RNA-RNA interaction, especially during the mRNA decoding, is elusive. To assist the research exploration, herein we report the enzymatic synthesis of the SeU-RNA via 2-selenouridine triphosphate (SeUTP) synthesis and RNA transcription. Moreover, we have demonstrated that the synthesized SeUTP is stable and recognizable by T7 RNA polymerase. Under the optimized conditions, the transcription yield of SeU-RNA can reach up to 85% of the corresponding native RNA. Furthermore, the transcribed SeU-hammerhead ribozyme has the similar activity as the corresponding native, which suggests usefulness of SeU-RNAs in function and structure studies of noncoding RNAs, including the Se-tRNAs

High-level expression and purification of soluble recombinant FGF21 protein by SUMO fusion in Escherichia coli

<p>Abstract</p> <p>Background</p> <p>Fibroblast growth factor 21 (FGF21) is a promising drug candidate to combat metabolic diseases. However, high-level expression and purification of recombinant FGF21 (rFGF21) in <it>Escherichia coli (E. coli) </it>is difficult because rFGF21 forms inclusion bodies in the bacteria making it difficult to purify and obtain high concentrations of bioactive rFGF21. To overcome this problem, we fused the <it>FGF21 </it>with <it>SUMO </it>(Small ubiquitin-related modifier) by polymerase chain reaction (PCR), and expressed the fused gene in <it>E. coli </it>BL21(DE3).</p> <p>Results</p> <p>By inducing with IPTG, SUMO-FGF21 was expressed at a high level. Its concentration reached 30% of total protein, and exceeded 95% of all soluble proteins. The fused protein was purified by DEAE sepharose FF and Ni-NTA affinity chromatography. Once cleaved by the SUMO protease, the purity of rFGF21 by high performance liquid chromatography (HPLC) was shown to be higher than 96% with low endotoxin level (<1.0 EU/ml). The results of <it>in vivo </it>animal experiments showed that rFGF21 produced by using this method, could decrease the concentration of plasma glucose in diabetic rats by streptozotocin (STZ) injection.</p> <p>Conclusions</p> <p>This study demonstrated that SUMO, when fused with FGF21, was able to promote its soluble expression of the latter in <it>E. coli</it>, making it more convenient to purify rFGF21 than previously. This may be a better method to produce rFGF21 for pharmaceutical research and development.</p

CemOrange2 fusions facilitate multifluorophore subcellular imaging in C. elegans

Due to its ease of genetic manipulation and transparency, Caenorhabditis elegans (C. elegans) has become a preferred model system to study gene function by microscopy. The use of Aequorea victoria green fluorescent protein (GFP) fused to proteins or targeting sequences of interest, further expanded upon the utility of C. elegans by labeling subcellular structures, which enables following their disposition during development or in the presence of genetic mutations. Fluorescent proteins with excitation and emission spectra different from that of GFP accelerated the use of multifluorophore imaging in real time. We have expanded the repertoire of fluorescent proteins for use in C. elegans by developing a codon-optimized version of Orange2 (CemOrange2). Proteins or targeting motifs fused to CemOrange2 were distinguishable from the more common fluorophores used in the nematode; such as GFP, YFP, and mKate2. We generated a panel of CemOrange2 fusion constructs, and confirmed they were targeted to their correct subcellular addresses by colocalization with independent markers. To demonstrate the potential usefulness of this new panel of fluorescent protein markers, we showed that CemOrange2 fusion proteins could be used to: 1) monitor biological pathways, 2) multiplex with other fluorescent proteins to determine colocalization and 3) gain phenotypic knowledge of a human ABCA3 orthologue, ABT-4, trafficking variant in the C. elegans model organism

A novel cell-free mitochondrial fusion assay amenable for high-throughput screenings of fusion modulators

Abstract Background Mitochondria are highly dynamic organelles whose morphology and position within the cell is tightly coupled to metabolic function. There is a limited list of essential proteins that regulate mitochondrial morphology and the mechanisms that govern mitochondrial dynamics are poorly understood. However, recent evidence indicates that the core machinery that governs mitochondrial dynamics is linked within complex intracellular signalling cascades, including apoptotic pathways, cell cycle transitions and nuclear factor kappa B activation. Given the emerging importance of mitochondrial plasticity in cell signalling pathways and metabolism, it is essential that we develop tools to quantitatively analyse the processes of fission and fusion. In terms of mitochondrial fusion, the field currently relies upon on semi-quantitative assays which, even under optimal conditions, are labour-intensive, low-throughput and require complex imaging techniques. Results In order to overcome these technical limitations, we have developed a new, highly quantitative cell-free assay for mitochondrial fusion in mammalian cells. This assay system has allowed us to establish the energetic requirements for mitochondrial fusion. In addition, our data reveal a dependence on active protein phosphorylation for mitochondrial fusion, confirming emerging evidence that mitochondrial fusion is tightly integrated within the global cellular response to signaling events. Indeed, we have shown that cytosol derived from cells stimulated with different triggers either enhance or inhibit the cell-free fusion reaction. Conclusions The adaptation of this system to high-throughput analysis will provide an unprecedented opportunity to identify and characterize novel regulatory factors. In addition, it provides a framework for a detailed mechanistic analysis of the process of mitochondrial fusion and the various axis of regulation that impinge upon this process in a wide range of cellular conditions. See Commentary: http://www.biomedcentral.com/1741-7007/8/9