Optical superluminal motion measurement in the neutron-star merger GW170817.

The afterglow of the binary neutron-star merger GW1708171 gave evidence for a structured relativistic jet2-6 and a link3,7,8 between such mergers and short gamma-ray bursts. Superluminal motion, found using radio very long baseline interferometry3 (VLBI), together with the afterglow light curve provided constraints on the viewing angle (14-28 degrees), the opening angle of the jet core (less than 5 degrees) and a modest limit on the initial Lorentz factor of the jet core (more than 4). Here we report on another superluminal motion measurement, at seven times the speed of light, leveraging Hubble Space Telescope precision astrometry and previous radio VLBI data for GW170817. We thereby obtain a measurement of the Lorentz factor of the wing of the structured jet, as well as substantially improved constraints on the viewing angle (19-25 degrees) and the initial Lorentz factor of the jet core (more than 40).

A very luminous jet from the disruption of a star by a massive black hole.

Tidal disruption events (TDEs) are bursts of electromagnetic energy that are released when supermassive black holes at the centres of galaxies violently disrupt a star that passes too close1. TDEs provide a window through which to study accretion onto supermassive black holes; in some rare cases, this accretion leads to launching of a relativistic jet2-9, but the necessary conditions are not fully understood. The best-studied jetted TDE so far is Swift J1644+57, which was discovered in γ-rays, but was too obscured by dust to be seen at optical wavelengths. Here we report the optical detection of AT2022cmc, a rapidly fading source at cosmological distance (redshift z = 1.19325) the unique light curve of which transitioned into a luminous plateau within days. Observations of a bright counterpart at other wavelengths, including X-ray, submillimetre and radio, supports the interpretation of AT2022cmc as a jetted TDE containing a synchrotron 'afterglow', probably launched by a supermassive black hole with spin greater than approximately 0.3. Using four years of Zwicky Transient Facility10 survey data, we calculate a rate of [Formula: see text] per gigapascals cubed per year for on-axis jetted TDEs on the basis of the luminous, fast-fading red component, thus providing a measurement complementary to the rates derived from X-ray and radio observations11. Correcting for the beaming angle effects, this rate confirms that approximately 1 per cent of TDEs have relativistic jets. Optical surveys can use AT2022cmc as a prototype to unveil a population of jetted TDEs.

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator.

Clinicians and patients must make treatment decisions at a series of key decision points throughout disease progression. A dynamic treatment regime is a set of sequential decision rules that return treatment decisions based on accumulating patient information, like that commonly found in electronic medical record (EMR) data. When applied to a patient population, an optimal treatment regime leads to the most favorable outcome on average. Identifying optimal treatment regimes that maximize residual life is especially desirable for patients with life-threatening diseases such as sepsis, a complex medical condition that involves severe infections with organ dysfunction. We introduce the residual life value estimator (ReLiVE), an estimator for the expected value of cumulative restricted residual life under a fixed treatment regime. Building on ReLiVE, we present a method for estimating an optimal treatment regime that maximizes expected cumulative restricted residual life. Our proposed method, ReLiVE-Q, conducts estimation via the backward induction algorithm Q-learning. We illustrate the utility of ReLiVE-Q in simulation studies, and we apply ReLiVE-Q to estimate an optimal treatment regime for septic patients in the intensive care unit using EMR data from the Multiparameter Intelligent Monitoring Intensive Care database. Ultimately, we demonstrate that ReLiVE-Q leverages accumulating patient information to estimate personalized treatment regimes that optimize a clinically meaningful function of residual life.

SUMOylation of Smad2 mediates TGF-ß-regulated endothelial-mesenchymal transition.

Endothelial-mesenchymal transition (EndoMT) is a complex biological process in which endothelial cells are transformed into mesenchymal cells, and dysregulated EndoMT causes a variety of pathological processes. Transforming growth factor beta (TGF-ß) signaling effectively induces the EndoMT process in endothelial cells, and Smad2 is the critical protein of the TGF-ß signaling pathway. However, whether small ubiquitin-like modifier modification (SUMOylation) is involved in EndoMT remains unclear. Here, we show that Smad2 is predominantly modified by SUMO1 at two major SUMOylation sites with PIAS2α as the primary E3 ligase, whereas SENP1 (sentrin/SUMO-specific protease 1) mediates the deSUMOylation of Smad2. In addition, we identified that SUMOylation significantly enhances the transcriptional activity and protein stability of Smad2, regulating the expression of downstream target genes. SUMOylation increases the phosphorylation of Smad2 and the formation of the Smad2-Smad4 complex, thus promoting the nuclear translocation of Smad2. Ultimately, the wildtype, but not SUMOylation site mutant Smad2 facilitated the EndoMT process. More importantly, TGF-ß enhances the nuclear translocation of Smad2 by enhancing its SUMOylation and promoting the EndoMT process. These results demonstrate that SUMOylation of Smad2 plays a critical role in the TGF-ß-mediated EndoMT process, providing a new theoretical basis for the treatment and potential drug targets of EndoMT-related clinical diseases.

Distributed Cox proportional hazards regression using summary-level information.

Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.

Optical-pulse-based reconfigurable phase control for independent beamforming and band fusion in a dual-band phased array receiver.

This Letter proposes an optical-pulse-based reconfigurable phase control method, enabling a dual-band phased array receiver to operate in two modes: dual-band-independent operation and dual-band fusion. The method utilizes optical pulses and optical delay to compensate for phase differences across frequency bands. An electrical phase shifter is employed to compensate for phase residual in both bands. All phase operations to both bands are processed concurrently in one link, thereby maintaining inter-band phase coherence. Experimental results verify the ability of dual-band-independent beamforming and inter-band phase coherence maintaining. A four-channel dual-band (X- and Ku-band) phased array antenna (PAA) receiver is constructed to measure PAA patterns and demonstrate band fusion. The pulse compression results in all directions reveal a doubled improvement in range resolution, which shows the potential for enhancement of radar performance.

Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials.

Both individually and cluster randomized study designs have been used for vaccine trials to assess the effects of vaccine on reducing the risk of disease or infection. The choice between individually and cluster randomized designs is often driven by the target estimand of interest (eg, direct versus total), statistical power, and, importantly, logistic feasibility. To combat emerging infectious disease threats, especially when the number of events from one single trial may not be adequate to obtain vaccine effect estimates with a desired level of precision, it may be necessary to combine information across multiple trials. In this article, we propose a model formulation to estimate the direct, indirect, total, and overall vaccine effects combining data from trials with two types of study designs: individual-randomization and cluster-randomization, based on a Cox proportional hazards model, where the hazard of infection depends on both vaccine status of the individual as well as the vaccine status of the other individuals in the same cluster. We illustrate the use of the proposed model and assess the potential efficiency gain from combining data from multiple trials, compared to using data from each individual trial alone, through two simulation studies, one of which is designed based on a cholera vaccine trial previously carried out in Matlab, Bangladesh.

Predictive model of acute kidney injury in critically ill patients with acute pancreatitis: a machine learning approach using the MIMIC-IV database.

BACKGROUND: Acute kidney injury (AKI) is a common and serious complication in severe acute pancreatitis (AP), associated with high mortality rate. Early detection of AKI is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models using machine learning (ML) to identify the onset of AKI in patients with AP. METHODS: Patients with AP were extracted from the MIMIC-IV database. We performed feature selection using the random forest method. Model construction involved an ensemble of ML, including random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), naive Bayes (NB), neural network (NNET), generalized linear model (GLM), and gradient boosting machine (GBM). The best-performing model was fine-tuned and evaluated through split-set validation. RESULTS: We analyzed 1,235 critically ill patients with AP, of which 667 cases (54%) experienced AKI during hospitalization. We used 49 variables to construct models, including GBM, GLM, KNN, NB, NNET, RF, and SVM. The AUC for these models was 0.814 (95% CI, 0.763 to 0.865), 0.812 (95% CI, 0.769 to 0.854), 0.671 (95% CI, 0.622 to 0.719), 0.812 (95% CI, 0.780 to 0.864), 0.688 (95% CI, 0.624 to 0.752), 0.809 (95% CI, 0.766 to 0.851), and 0.810 (95% CI, 0.763 to 0.856) respectively. In the test set, the GBM's performance was consistent, with an area of 0.867 (95% CI, 0.831 to 0.903). CONCLUSIONS: The GBM model's precision is crucial, aiding clinicians in identifying high-risk patients and enabling timely interventions to reduce mortality rates in critical care.

Let-7b-5p inhibits colon cancer progression by prohibiting APC ubiquitination degradation and the Wnt pathway by targeting NKD1.

Naked cuticle homolog 1 (NKD1), which is expressed at low levels in many tumors, is considered an inhibitor of the Wnt/ß-catenin pathway, but it is highly expressed in colon cancer and can promote colon cancer cell proliferation. miRNAs are involved in the occurrence and progression of many tumors. However, miRNAs that can regulate NKD1 and the mechanisms by which NKD1 regulates tumor progression remain ambiguous. This research aims to reveal the potential regulatory network of NKD1 in colon cancer. miRNA data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were analyzed by bioinformatics to screen for potential miRNAs targeting NKD1. Let-7b-5p was found to inhibit proliferation, migration, and invasion of colon cancer cells targeting NKD1. Further studies suggested that let-7b-5p can modulate Wnt signaling activity, and the nuclear accumulation of ß-catenin was significantly restrained by let-7b-5p through targeting NKD1. Moreover, NKD1 could prohibit the expression of the APC protein. Further studies manifested that NKD1 bound to APC and promoted the ubiquitination degradation of APC through restraining the expression of the deubiquitinating enzyme USP15 and blocking the combination between USP15 and APC. Functionally, NKD1 enhanced the proliferation and migration of colon cancer cells by inhibiting APC expression. This research revealed a novel mechanism by which the let-7b-5p-NKD1-APC-ß-catenin signaling pathway inhibited colon cancer cell progression.

Immunotherapy combined with chemotherapy in the treatment for rare primary mucoepidermoid carcinoma of the liver: a case report.

Primary mucoepidermoid carcinoma of the liver (PMCL) is rare in the hepatic system, with no standard treatment and poor prognosis with a median overall survival of only 120 days. PMCL with immunotherapy has not been reported yet. Here, we present a case of PMCL treated by immunotherapy and chemotherapy. A 64-year-old male with PMCL underwent partial right hepatectomy and liver lesion resection on 19 June 2020. Two months later, the chest computed tomography indicated the presence of multiple nodules in both lungs with higher tumor markers. Considering the presence of a tumor metastasis, the patient received four courses of immunotherapy plus mGEMOX chemotherapy from 8 September 2020. The patient tolerated the combined therapy well, with red moles on the face and chest which were considered as grade 1 reactive cutaneous capillary endothelial proliferation. He also had grade 2 thrombocytopenia and leucopenia after the first course of chemotherapy, but no neutropenia, fatigue, vomiting or diarrhea. However, his disease progressed. The patient refused further treatment and died on 20 April 2021. The overall survival time after diagnosis was 301 days. We describe here the first case report on immunotherapy treatment for PMCL. That suggested immunotherapy combined with chemotherapy may be an option after a hepatic lobectomy for PMCL.

The Differences of Quantitative Flow Ratio in Coronary Artery Stenosis with or without Atrial Fibrillation.

Quantitative flow ratio (QFR) is a new method for the assessment of the extent of coronary artery stenosis. But it may be obscured by the cardiac remodeling and abnormal blood flow of the coronary artery when encountering atrial fibrillation (AF). The present study aimed to examine the impact of these changed structures and blood flow of coronary arteries on QFR results in AF patients. Methods and Results. We evaluated QFR in 223 patients (112 patients with AF; 111 non-AF patients served as controls) who had undergone percutaneous coronary intervention (PCI) due to severe stenoses in coronary arteries. QFR of the target coronary was determined according to the flow rate of the contrast agent. Results showed that AF patients had significantly higher QFR values than control (0.792 ± 0.118 vs. 0.685 ± 0.167, p < 0.001). We further analyzed local QFR around the stenoses (0.858 ± 0.304 vs. 0.756 ± 0.146, p=0.002), residual QFR (0.958 ± 0.055 vs. 0.929 ± 0.093, p=0.005), and index QFR (0.807 ± 0.108 vs. 0.713 ± 0.152, p < 0.001) in these two groups of patients with and without AF. Further analysis revealed that QFR in AF patients was negatively correlated with coronary flow velocity (R = -0.22, p=0.02) and area of stenosis (R = -0.70, p < 0.001) but positively correlated with the minimum lumen area (MLA) (R = 0.47, p < 0.001). Conclusion. AF patients with coronary artery stenosis have higher QFR values, which are associated with decreased blood flow velocity, smaller stenosis, and larger MLA in AF patients upon cardiac remodeling.

Functional data analysis for longitudinal data with informative observation times.

In functional data analysis for longitudinal data, the observation process is typically assumed to be noninformative, which is often violated in real applications. Thus, methods that fail to account for the dependence between observation times and longitudinal outcomes may result in biased estimation. For longitudinal data with informative observation times, we find that under a general class of shared random effect models, a commonly used functional data method may lead to inconsistent model estimation while another functional data method results in consistent and even rate-optimal estimation. Indeed, we show that the mean function can be estimated appropriately via penalized splines and that the covariance function can be estimated appropriately via penalized tensor-product splines, both with specific choices of parameters. For the proposed method, theoretical results are provided, and simulation studies and a real data analysis are conducted to demonstrate its performance.

A general framework for subgroup detection via one-step value difference estimation.

Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates.

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.

Change-plane analysis for subgroup detection with a continuous treatment.

Detecting and characterizing subgroups with differential effects of a binary treatment has been widely studied and led to improvements in patient outcomes and population risk management. Under the setting of a continuous treatment, however, such investigations remain scarce. We propose a semiparametric change-plane model and consequently a doubly robust test statistic for assessing the existence of two subgroups with differential treatment effects under a continuous treatment. The proposed testing procedure is valid when either the baseline function for the covariate effects or the generalized propensity score function for the continuous treatment is correctly specified. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established. When the null hypothesis of no subgroup is rejected, the change-plane parameters that define the subgroups can be estimated. This paper provides a unified framework of the change-plane method to handle various types of outcomes, including the exponential family of distributions and time-to-event outcomes. Additional extensions with nonparametric estimation approaches are also provided. We evaluate the performance of our proposed methods through extensive simulation studies under various scenarios. An application to the Health Effects of Arsenic Longitudinal Study with a continuous environmental exposure of arsenic is presented.

Inhibition of myocardial remodeling through miR-150/TET3 axis after AMI.

BACKGROUND: Current studies have suggested that miRNA is beneficial in inhibiting myocardial remodeling after myocardial infarction (AMI), however, its underlying mechanism is unclear. OBJECTIVES: We aimed to investigate whether miR-150 can inhibit myocardial remodeling after myocardial infarction and whether this process is regulated by the miR-150/TET3 pathway. METHODS: On the first day, C57BL/6 AMI mice(n = 15) were administrated with miR-150, and another 15 AMI mice were administrated with the same volume of control Agomir. Left ventricular ejection fraction (LVEF%) and myocardial remodeling were compared after one week; TET3 (ten-eleven translocation 3) and VEGF-α (vascular endothelial growth factor-α) were also determined in the infracted heart simultaneously. The neovascularization in the infarcted area at day 21 was compared through CD31 using fluorescence microscopy; Activated monocytes stimulated with LPS were transfected with miR-150. Laser scanning confocal microscopy was used to detect the intracytoplasmic imaging of miR-150 in Ly6Chigh monocytes. Expression of the miR-150 in the monocytes was measured using Q-PCR. After 48 h, the proportion of Ly6Chigh/low monocytes was determined using flow cytometry. Expression of TET3 in Ly6Chigh/low monocytes was measured using Q-PCR and Western blot. After the downregulation of TET3 specifically, the levels of Ly6Chigh/low monocytes were further determined. RESULTS: We first observed an increased trend of mice survival rate in the miR-150 injection group, but it didn't reach a statistical difference (66.7% vs. 40.0%, p = 0.272). However, AMI mice administrated with miR-150 displayed better LVEF% (51.78%±2.90% vs. 40.28%±4.20%, p<0.001) and decreased infarct size% (25.47 ± 7.75 vs. 50.39 ± 16.91, p = 0.002). After miR-150 was transfected into monocytes, the percentage of Ly6Clow monocytes increased significantly after 48 h (48.5%±10.1% vs. 42.5%±8.3%, p < 0.001). Finally, Western blot analysis (0.56 ± 0.10/ß-actin vs. 0.99 ± 0.12/ß-actin, p < 0.001) and real-time PCR (1.09 ± 0.09/GAPDH vs. 2.53 ± 0.15/GAPDH, p < 0.001, p < 0.001) both confirmed decreased expression of TET3 in monocytes after transfection with miR-150. After the downregulation of TET3 specifically, Ly6Clow monocytes showed a significant increase (16.73%±6.45% vs. 6.94%±2.99%, p<0.001, p < 0.001). CONCLUSIONS: miR-150 alleviated myocardial remodeling after AMI. Possible mechanisms are ascribed to the regulating of TET3 and VEGF-α in inflammatory monocytes.

On restricted optimal treatment regime estimation for competing risks data.

It is well accepted that individualized treatment regimes may improve the clinical outcomes of interest. However, positive treatment effects are often accompanied by certain side effects. Therefore, when choosing the optimal treatment regime for a patient, we need to consider both efficacy and safety issues. In this article, we propose to model time to a primary event of interest and time to severe side effects of treatment by a competing risks model and define a restricted optimal treatment regime based on cumulative incidence functions. The estimation approach is derived using a penalized value search method and investigated through extensive simulations. The proposed method is applied to an HIV dataset obtained from Health Sciences South Carolina, where we minimize the risk of treatment or virologic failures while controlling the risk of serious drug-induced side effects.

Gene-set integrative analysis of multi-omics data using tensor-based association test.

MOTIVATION: Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. RESULTS: We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. AVAILABILITY AND IMPLEMENTATION: R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/~jytzeng/Software/TR.omics/TRinstruction.pdf. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime.

When to initiate treatment on patients is an important problem in many medical studies such as AIDS and cancer. In this article, we formulate the treatment initiation time problem for time-to-event data and propose an optimal individualized regime that determines the best treatment initiation time for individual patients based on their characteristics. Different from existing optimal treatment regimes where treatments are undertaken at a pre-specified time, here new challenges arise from the complicated missing mechanisms in treatment initiation time data and the continuous treatment rule in terms of initiation time. To tackle these challenges, we propose to use restricted mean residual lifetime as a value function to evaluate the performance of different treatment initiation regimes, and develop a nonparametric estimator for the value function, which is consistent even when treatment initiation times are not completely observable and their distribution is unknown. We also establish the asymptotic properties of the resulting estimator in the decision rule and its associated value function estimator. In particular, the asymptotic distribution of the estimated value function is nonstandard, which follows a weighted chi-squared distribution. The finite-sample performance of the proposed method is evaluated by simulation studies and is further illustrated with an application to a breast cancer data.