The purpose of this article is to provide simple but accurate methods for comparing correlation coefficients between a dependent variable and a set of independent variables. The methods are simple extensions of Dunn & Clark's (1969) work using the Fisher z transformation and include a test and confidence interval for comparing two correlated correlations, a test for heterogeneity, and a test and confidence interval for a contrast among k (>2) correlated correlations. Also briefly discussed is why the traditional Hotelling's t test for comparing correlated correlations is generally not appropriate in practice.We determined the order of authorship alphabetically. The work was partially supported by National Science Foundation Grant SES-88-05433 and by the Spencer Foundation, although the views expressed are our responsibility.We wish to thank three anonymous reviewers for very helpful comments.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY Two major reasons for the popularity of the EM algorithm are that its maximum step involves only complete-data maximum likelihood estimation, which is often computationally simple, and that its convergence is stable, with each iteration increasing the likelihood. When the associated complete-data maximum likelihood estimation itself is complicated, EM is less attractive because the M-step is computationally unattractive. In many cases, however, complete-data maximum likelihood estimation is relatively simple when conditional on some function of the parameters being estimated. We introduce a class of generalized EM algorithms, which we call the ECM algorithm, for Expectation/Conditional Maximization (CM), that takes advantage of the simplicity of completedata conditional maximum likelihood estimation by replacing a complicated M-step of EM with several computationally simpler cM-steps. We show that the ECM algorithm shares all the appealing convergence properties of EM, such as always increasing the likelihood, and present several illustrative examples.

Cytokine release syndrome (CRS) is a major cause of the multi-organ injury and fatal outcome induced by SARS-CoV-2 infection in severe COVID-19 patients. Metabolism can modulate the immune responses against infectious diseases, yet our understanding remains limited on how host metabolism correlates with inflammatory responses and affects cytokine release in COVID-19 patients. Here we perform both metabolomics and cytokine/chemokine profiling on serum samples from healthy controls, mild and severe COVID-19 patients, and delineate their global metabolic and immune response landscape. Correlation analyses show tight associations between metabolites and proinflammatory cytokines/chemokines, such as IL-6, M-CSF, IL-1α, IL-1β, and imply a potential regulatory crosstalk between arginine, tryptophan, purine metabolism and hyperinflammation. Importantly, we also demonstrate that targeting metabolism markedly modulates the proinflammatory cytokines release by peripheral blood mononuclear cells isolated from SARS-CoV-2-infected rhesus macaques ex vivo, hinting that exploiting metabolic alterations may be a potential strategy for treating fatal CRS in COVID-19.

Summary:Pixel-by-pixel spatiotemporal progression of focal ischemia (permanent occlusion) in rats was investigated using quantitative perfusion and diffusion magnetic resonance imaging every 30 minutes for 3 hours. The normal left-hemisphere apparent diffusion coefficient (ADC) was 0.76 ± 0.03 × 10 −3 mm 2 /s and CBF was 0.7 ± 0.3 mL · g −1 · min −1 (mean ± SD, n‫.)5ס‬ The ADC and CBF viability thresholds yielding the lesion volumes (LV) at 3 hours that best approximated the 2,3,5-triphenyltetrazolium chloride (TTC) infarct volumes (200 ± 30 mm 3 ) at 24 hours were 0.53 ± 0.02 × 10 −3 mm 2 /s (30% ± 2% reduction) and 0.30 ± 0.09 mL · g −1 · min −1 (57% ± 11% reduction), respectively. Temporal evolution of the ADC-and CBF-defined LV showed a significant "perfusion-diffusion mismatch" up to 2 hours (P < 0.05, n ‫ס‬ 11), a potential therapeutic window. Based on the viability thresholds, three pixel clusters were identified on the CBF-ADC scatterplots: (1) a "normal" cluster with normal CBF and ADC, (2) an "ischemic core" cluster with markedly reduced CBF and ADC, and (3) a "mismatch" cluster with reduced CBF but slightly reduced ADC. These clusters were color-coded and mapped onto the image and CBF-ADC spaces. Lesions grew peripheral and medial to the initial ADC abnormality. In contrast to the CBF distribution, the ADC distribution in the ischemic hemisphere was bimodal; the relatively time-invariant bimodal-ADC minima were 0.57 ± 0.02 × 10 ), surprisingly similar to the TTCderived thresholds. Together, these results illustrate an analysis approach to systemically track the pixel-by-pixel spatiotemporal progression of acute ischemic brain injury.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARYExisting procedures for obtaining significance levels from multiply-imputed data either (i) require access to the completed-data point estimates and variance-covariance matrices, which may not be available in practice when the dimensionality of the estimand is high, or (ii) directly combine p-values with less satisfactory results. Taking advantage of the well-known relationship between the Wald and log likelihood ratio test statistics, we propose a complete-data log likelihood ratio based procedure. It is shown that, for any number of multiple imputations, the proposed procedure is equivalent in large samples to the existing procedure based on the point estimates and the variance-covariance matrices, yet it only requires the point estimates and evaluations of the complete-data log likelihood ratio statistic as a function of these estimates and the completed data. The proposed procedure, therefore, is especially attractive with highly multiparameter incomplete-data problems since it does not involve the computation of any matrices.

Autophagy is a genetically well-controlled cellular process that is tightly controlled by a set of core genes, including the family of autophagy-related genes (ATG). Autophagy is a “double-edged sword” in tumors. It can promote or suppress tumor development, which depends on the cell and tissue types and the stages of tumor. At present, tumor immunotherapy is a promising treatment strategy against tumors. Recent studies have shown that autophagy significantly controls immune responses by modulating the functions of immune cells and the production of cytokines. Conversely, some cytokines and immune cells have a great effect on the function of autophagy. Therapies aiming at autophagy to enhance the immune responses and anti-tumor effects of immunotherapy have become the prospective strategy, with enhanced antigen presentation and higher sensitivity to CTLs. However, the induction of autophagy may also benefit tumor cells escape from immune surveillance and result in intrinsic resistance against anti-tumor immunotherapy. Increasing studies have proven the optimal use of either ATG inducers or inhibitors can restrain tumor growth and progression by enhancing anti-tumor immune responses and overcoming the anti-tumor immune resistance in combination with several immunotherapeutic strategies, indicating that induction or inhibition of autophagy might show us a prospective therapeutic strategy when combined with immunotherapy. In this article, the possible mechanisms of autophagy regulating immune system, and the potential applications of autophagy in tumor immunotherapy will be discussed.

Autophagy is a highly conserved catabolic process that mediates degradation of pernicious or dysfunctional cellular components, such as invasive pathogens, senescent proteins, and organelles. It can promote or suppress tumor development, so it is a “double-edged sword” in tumors that depends on the cell and tissue types and the stages of tumor. The epithelial-mesenchymal transition (EMT) is a complex biological trans-differentiation process that allows epithelial cells to transiently obtain mesenchymal features, including motility and metastatic potential. EMT is considered as an important contributor to the invasion and metastasis of cancers. Thus, clarifying the crosstalk between autophagy and EMT will provide novel targets for cancer therapy. It was reported that EMT-related signal pathways have an impact on autophagy; conversely, autophagy activation can suppress or strengthen EMT by regulating various signaling pathways. On one hand, autophagy activation provides energy and basic nutrients for EMT during metastatic spreading, which assists cells to survive in stressful environmental and intracellular conditions. On the other hand, autophagy, acting as a cancer-suppressive function, is inclined to hinder metastasis by selectively down-regulating critical transcription factors of EMT in the early phases. Therefore, the inhibition of EMT by autophagy inhibitors or activators might be a novel strategy that provides thought and enlightenment for the treatment of cancer. In this article, we discuss in detail the role of autophagy and EMT in the development of cancers, the regulatory mechanisms between autophagy and EMT, the effects of autophagy inhibition or activation on EMT, and the potential applications in anticancer therapy.

Diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) can rapidly detect lesions in acute ischemic stroke patients. The PWI volume is typically substantially larger than the DWI volume shortly after onset, that is, a diffusion/ perfusion mismatch. The aims of this study were to follow the evolution of the diffusion/ perfusion mismatch in permanent and 60- minute temporary focal experimental ischemia models in Sprague-Dawley rats using the intraluminal middle cerebral artery occlusion (MCAO) method. DWI and arterial spin-labeled PWI were performed at 30, 60, 90, 120, and 180 minutes after occlusion and lesion volumes (mm(3)) calculated At 24 hours after MCAO, and infarct volume was determined using triphenyltetrazolium chloride staining. In the permanent MCAO group, the lesion volume on the ADC maps was significantly smaller than that on the cerebral blood flow maps through the first 60 minutes after MCAO; but not after 90 minutes of occlusion. With 60 minutes of transient ischemia, the diffusion/perfusion mismatch was similar, but after reperfusion, the lesion volumes on ADC and cerebral blood flow maps became much smaller. There was a significant difference in 24- hour infarct volumes between the permanent and temporary occlusion groups.