S-Geranylgeranyl-L-glutathione is a ligand for human B cell-confinement receptor P2RY8.

Germinal centres are important sites for antibody diversification and affinity maturation, and are also a common origin of B cell malignancies. Despite being made up of motile cells, germinal centres are tightly confined within B cell follicles. The cues that promote this confinement are incompletely understood. P2RY8 is a Gα13-coupled receptor that mediates the inhibition of migration and regulates the growth of B cells in lymphoid tissues1,2. P2RY8 is frequently mutated in germinal-centre B cell-like diffuse large B cell lymphoma (GCB-DLBCL) and Burkitt lymphoma1,3-6, and the ligand for this receptor has not yet been identified. Here we perform a search for P2RY8 ligands and find P2RY8 bioactivity in bile and in culture supernatants of several mouse and human cell lines. Using a seven-step biochemical fractionation procedure and a drop-out mass spectrometry approach, we show that a previously undescribed biomolecule, S-geranylgeranyl-L-glutathione (GGG), is a potent P2RY8 ligand that is detectable in lymphoid tissues at the nanomolar level. GGG inhibited the chemokine-mediated migration of human germinal-centre B cells and T follicular helper cells, and antagonized the induction of phosphorylated AKT in germinal-centre B cells. We also found that the enzyme gamma-glutamyltransferase-5 (GGT5), which was highly expressed by follicular dendritic cells, metabolized GGG to a form that did not activate the receptor. Overexpression of GGT5 disrupted the ability of P2RY8 to promote B cell confinement to germinal centres, which indicates that GGT5 establishes a GGG gradient in lymphoid tissues. This work defines GGG as an intercellular signalling molecule that is involved in organizing and controlling germinal-centre responses. As the P2RY8 locus is modified in several other types of cancer in addition to GCB-DLBCL and Burkitt lymphoma, we speculate that GGG might have organizing and growth-regulatory roles in multiple human tissues

MULTISCALE GUIDED DEBLURRING: CHROMATIC ABERRATION CORRECTION IN COLOR AND NEAR-INFRARED IMAGING

Chromatic aberration, caused by photographic lens imperfections, results in the image of only one spectral channel being sharp, while the other channels are blurred depending on their wavelengths difference with the sharp channel. We study chromatic aberration for a system that jointly records color and near-infrared (NIR) images on a single sensor. Chromatic aberration in such a system leads to a blurred NIR image when the color image is in-focus and sharp. We propose an algorithm that deblurs the NIR image using the gradients of the sharp color image, as both scene representations are generally similar. However, the details of these images often exhibit significant differences due to varying scene reflection and absorption in the corresponding bands. To account for this, we compute the correlation between color and NIR gradients, and use the gradients of the color image in reconstructing NIR only where the gradients are highly correlated. We propose a multiscale scheme that gradually deblurs NIR and accurately computes similarities between color and NIR gradients. Experimental results show that our algorithm recovers details of NIR without producing visible artifacts

Mayawaves: Python Library for Interacting with the Einstein Toolkit and the MAYA Catalog

Numerical relativity simulations are crucial for studying black holes and have been instrumental in the detection of gravitational waves by the LVK. However, these simulations produce vast amounts of data that must be processed in order to perform studies, create models, and use them with gravitational wave detection pipelines. This paper introduces mayawaves, an open-source python library for processing, studying, and exporting numerical relativity simulations performed using the Einstein Toolkit and MAYA. Mayawaves streamlines the process of analyzing simulations with an intuitive interface, greatly reducing the learning curve for numerical relativity.Comment: 2 page

Oxidation Pond for Municipal Wastewater Treatment

This literature review examines process, design, and cost issues related to using oxidation ponds for wastewater treatment. Many of the topics have applications at either full scale or in isolation for laboratory analysis. Oxidation ponds have many advantages. The oxidation pond treatment process is natural, because it uses microorganisms such as bacteria and algae. This makes the method of treatment cost-effective in terms of its construction, maintenance, and energy requirements. Oxidation ponds are also productive, because it generates effluent that can be used for other applications. Finally, oxidation ponds can be considered a sustainable method for treatment of wastewater

Does publication bias inflate the apparent efficacy of psychological treatment for major depressive disorder? A systematic review and meta-analysis of US national institutes of health-funded trials

Background The efficacy of antidepressant medication has been shown empirically to be overestimated due to publication bias, but this has only been inferred statistically with regard to psychological treatment for depression. We assessed directly the extent of study publication bias in trials examining the efficacy of psychological treatment for depression. Methods and Findings We identified US National Institutes of Health grants awarded to fund randomized clinical trials comparing psychological treatment to control conditions or other treatments in patients diagnosed with major depressive disorder for the period 1972–2008, and we determined whether those grants led to publications. For studies that were not published, data were requested from investigators and included in the meta-analyses. Thirteen (23.6%) of the 55 funded grants that began trials did not result in publications, and two others never started. Among comparisons to control conditions, adding unpublished studies (Hedges’ g = 0.20; CI95% -0.11~0.51; k = 6) to published studies (g = 0.52; 0.37~0.68; k = 20) reduced the psychotherapy effect size point estimate (g = 0.39; 0.08~0.70) by 25%. Moreover, these findings may overestimate the "true" effect of psychological treatment for depression as outcome reporting bias could not be examined quantitatively. Conclusion The efficacy of psychological interventions for depression has been overestimated in the published literature, just as it has been for pharmacotherapy. Both are efficacious but not to the extent that the published literature would suggest. Funding agencies and journals should archive both original protocols and raw data from treatment trials to allow the detection and correction of outcome reporting bias. Clinicians, guidelines developers, and decision makers should be aware that the published literature overestimates the effects of the predominant treatments for depression

Multicloud solutions with massless and massive monopoles

Certain spontaneously broken gauge theories contain massless magnetic monopoles. These are realized classically as clouds of non-Abelian fields surrounding one or more massive monopoles. In order to gain a better understanding of these clouds, we study BPS solutions with four massive and six massless monopoles in an SU(6) gauge theory. We develop an algebraic procedure, based on the Nahm construction, that relates these solutions to previously known examples. Explicit implementation of this procedure for a number of limiting cases reveals that the six massless monopoles condense into four distinct clouds, of two different types. By analyzing these limiting solutions, we clarify the correspondence between clouds and massless monopoles, and infer a set of rules that describe the conditions under which a finite size cloud can be formed. Finally, we identify the parameters entering the general solution and describe their physical significance.Comment: 58 pages, 5 figure

A CREDENCE Trial Substudy

Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.OBJECTIVES: The study compared the performance for detection and grading of coronary stenoses using artificial intelligence-enabled quantitative coronary computed tomography angiography (AI-QCT) analyses to core lab-interpreted coronary computed tomography angiography (CTA), core lab quantitative coronary angiography (QCA), and invasive fractional flow reserve (FFR). BACKGROUND: Clinical reads of coronary CTA, especially by less experienced readers, may result in overestimation of coronary artery disease stenosis severity compared with expert interpretation. AI-based solutions applied to coronary CTA may overcome these limitations. METHODS: Coronary CTA, FFR, and QCA data from 303 stable patients (64 ± 10 years of age, 71% male) from the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia) trial were retrospectively analyzed using an Food and Drug Administration-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. RESULTS: Disease prevalence was high, with 32.0%, 35.0%, 21.0%, and 13.0% demonstrating ≥50% stenosis in 0, 1, 2, and 3 coronary vessel territories, respectively. Average AI-QCT analysis time was 10.3 ± 2.7 minutes. AI-QCT evaluation demonstrated per-patient sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 94%, 68%, 81%, 90%, and 84%, respectively, for ≥50% stenosis, and of 94%, 82%, 69%, 97%, and 86%, respectively, for detection of ≥70% stenosis. There was high correlation between stenosis detected on AI-QCT evaluation vs QCA on a per-vessel and per-patient basis (intraclass correlation coefficient = 0.73 and 0.73, respectively; P < 0.001 for both). False positive AI-QCT findings were noted in in 62 of 848 (7.3%) vessels (stenosis of ≥70% by AI-QCT and QCA of <70%); however, 41 (66.1%) of these had an FFR of <0.8. CONCLUSIONS: A novel AI-based evaluation of coronary CTA enables rapid and accurate identification and exclusion of high-grade stenosis and with close agreement to blinded, core lab-interpreted quantitative coronary angiography. (Computed TomogRaphic Evaluation of Atherosclerotic DEtermiNants of Myocardial IsChEmia [CREDENCE]; NCT02173275).proofepub_ahead_of_prin

The effect of scan and patient parameters on the diagnostic performance of AI for detecting coronary stenosis on coronary CT angiography

Publisher Copyright: © 2022 The AuthorsObjectives: To determine whether coronary computed tomography angiography (CCTA) scanning, scan preparation, contrast, and patient based parameters influence the diagnostic performance of an artificial intelligence (AI) based analysis software for identifying coronary lesions with ≥50% stenosis. Background: CCTA is a noninvasive imaging modality that provides diagnostic and prognostic benefit to patients with coronary artery disease (CAD). The use of AI enabled quantitative CCTA (AI-QCT) analysis software enhances our diagnostic and prognostic ability, however, it is currently unclear whether software performance is influenced by CCTA scanning parameters. Methods: CCTA and quantitative coronary CT (QCT) data from 303 stable patients (64 ± 10 years, 71% male) from the derivation arm of the CREDENCE Trial were retrospectively analyzed using an FDA-cleared cloud-based software that performs AI-enabled coronary segmentation, lumen and vessel wall determination, plaque quantification and characterization, and stenosis determination. The algorithm's diagnostic performance measures (sensitivity, specificity, and accuracy) for detecting coronary lesions of ≥50% stenosis were determined based on concordance with QCA measurements and subsequently compared across scanning parameters (including scanner vendor, model, single vs dual source, tube voltage, dose length product, gating technique, timing method), scan preparation technique (use of beta blocker, use and dose of nitroglycerin), contrast administration parameters (contrast type, infusion rate, iodine concentration, contrast volume) and patient parameters (heart rate and BMI). Results: Within the patient cohort, 13% demonstrated ≥50% stenosis in 3 vessel territories, 21% in 2 vessel territories, 35% in 1 vessel territory while 32% had 400 mg/ml 95.2%; p = 0.0287) in the context of low injection flow rates. On a per patient basis there were no significant differences in AI diagnostic performance measures across all measured scanner, scan technique, patient preparation, contrast, and individual patient parameters. Conclusion: The diagnostic performance of AI-QCT analysis software for detecting moderate to high grade stenosis are unaffected by commonly used CCTA scanning parameters and across a range of common scanning, scanner, contrast and patient variables. Condensed abstract: An AI-enabled quantitative CCTA (AI-QCT) analysis software has been validated as an effective tool for the identification, quantification and characterization of coronary plaque and stenosis through comparison to blinded expert readers and quantitative coronary angiography. However, it is unclear whether CCTA screening parameters related to scanner parameters, scan technique, contrast volume and rate, radiation dose, or a patient's BMI or heart rate at time of scan affect the software's diagnostic measures for detection of moderate to high grade stenosis. AI performance measures were unaffected across a broad range of commonly encountered scanner, patient preparation, scan technique, intravenous contrast and patient parameters.publishersversionpublishe