Integrated adaptive approach for reliable multicast transmission over geostationary satellite networks

Multiple retransmission passes, in order to ensure bit-perfect reliability in multicast transmission, results in lower resource utilization and higher session delay. Hence, an integrated adaptive transmission via the use of a cross-layer strategy is proposed in this paper in order to increase forward and return link resource utilization. Specifically, the integration of Channel State Information (CSI) collection policies in the uplink and Channel-Aware Scheduling (CAS) in the downlink is proposed in the face of fluctuating channel conditions observed by multicast terminals. The integration approach can be mathematically represented by suppression error due to the way CSI is collected and suppressed in the return link. Particularly, the suppression error occurs since only a subset of users update their CSI values at any CSI collection instant. In relation to the analytical representation of the suppression error, the performance parameters are then verified via simulation results. From the comparison, it was found that the simulation and analytical results approach agreement at large numbers of terminals. This observation suits the multicast transmission over satellite networks which expect large numbers of terminals over wide coverage

A Study of the Residual 39Ar Content in Argon from Underground Sources

The discovery of argon from underground sources with significantly less 39Ar than atmospheric argon was an important step in the development of direct-detection dark matter experiments using argon as the active target. We report on the design and operation of a low background detector with a single phase liquid argon target that was built to study the 39Ar content of the underground argon. Underground argon from the Kinder Morgan CO2 plant in Cortez, Colorado was determined to have less than 0.65% of the 39Ar activity in atmospheric argon.Comment: 21 pages, 10 figure

Diagnostic potential of structural neuroimaging for depression from a multi-ethnic community sample

Background At present, we do not have any biological tests which can contribute towards a diagnosis of depression. Neuroimaging measures have shown some potential as biomarkers for diagnosis. However, participants have generally been from the same ethnic background while the applicability of a biomarker would require replication in individuals of diverse ethnicities. Aims We sought to examine the diagnostic potential of the structural neuroanatomy of depression in a sample of a wide ethnic diversity. Method Structural magnetic resonance imaging (MRI) scans were obtained from 23 patients with major depressive disorder in an acute depressive episode (mean age: 39.8 years) and 20 matched healthy volunteers (mean age: 38.8 years). Participants were of Asian, African and Caucasian ethnicity recruited from the general community. Results Structural neuroanatomy combining white and grey matter distinguished patients from controls at the highest accuracy of 81% with the most stable pattern being at around 70%. A widespread network encompassing frontal, parietal, occipital and cerebellar regions contributed towards diagnostic classification. Conclusions These findings provide an important step in the development of potential neuroimaging-based tools for diagnosis as they demonstrate that the identification of depression is feasible within a multi-ethnic group from the community. Declaration of interests C.H.Y.F. has held recent research grants from Eli Lilly and Company and GlaxoSmithKline. L.M. is a former employee and stockholder of Eli Lilly and Company

Presence of factors that activate platelet aggregation in mitral stenotic patients' plasma

BACKGROUND: Although the association between mitral stenosis (MS) and increased coagulation activity is well recognized, it is unclear whether enhanced coagulation remains localized in the left atrium or whether this represents a systemic problem. To assess systemic coagulation parameters and changes in platelet aggregation, we measured fibrinogen levels and performed in vitro platelet function tests in plasma obtained from mitral stenotic patients' and from healthy control subjects' peripheral venous blood. METHODS: Sixteen newly diagnosed patients with rheumatic MS (Group P) and 16 healthy subjects (Group N) were enrolled in the study. Platelet-equalized plasma samples were evaluated to determine in vitro platelet function, using adenosine diphosphate (ADP), collagen and epinephrine in an automated aggregometer. In vitro platelet function tests in group N were performed twice, with and without plasma obtained from group P. RESULTS: There were no significant differences between the groups with respect to demographic variables. Peripheral venous fibrinogen levels in Group P were not significantly different from those in Group N. Adenosine diphosphate, epinephrine and collagen-induced platelet aggregation ratios were significantly higher in Group P than in Group N. When plasma obtained from Group P was added to Group N subjects' platelets, ADP and collagen-induced, but not epinephrine-induced, aggregation ratios were significantly increased compared to baseline levels in Group N. CONCLUSION: Platelet aggregation is increased in patients with MS, while fibrinogen levels remain similar to controls. We conclude that mitral stenotic patients exhibit increased systemic coagulation activity and that plasma extracted from these patients may contain some transferable factors that activate platelet aggregation

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission:results from the PHENOM consortium

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal volume’ subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership (‘None’), and mixed SG1 + SG2 subgroups (‘Mixed’). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of ‘lower brain volume’ in SG1 and ‘higher striatal volume’ (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.Supported by NIH grant R01MH112070 and the National Health and Medical Research Council (NHMRC) grant GA126980. Russel Shinohara was supported by the NIH grant R01M123550. RCG was supported by the NIH grant R01MH119219.Open Access funding enabled and organized by CAUL and its Member Institutions.Peer reviewe

Light Yield in DarkSide-10: a Prototype Two-phase Liquid Argon TPC for Dark Matter Searches

As part of the DarkSide program of direct dark matter searches using liquid argon TPCs, a prototype detector with an active volume containing 10 kg of liquid argon, DarkSide-10, was built and operated underground in the Gran Sasso National Laboratory in Italy. A critically important parameter for such devices is the scintillation light yield, as photon statistics limits the rejection of electron-recoil backgrounds by pulse shape discrimination. We have measured the light yield of DarkSide-10 using the readily-identifiable full-absorption peaks from gamma ray sources combined with single-photoelectron calibrations using low-occupancy laser pulses. For gamma lines of energies in the range 122-1275 keV, we get consistent light yields averaging 8.887+-0.003(stat)+-0.444(sys) p.e./keVee. With additional purification, the light yield measured at 511 keV increased to 9.142+-0.006(stat) p.e./keVee.Comment: 10 pages, 7 figures, Accepted for publication in Astroparticle Physic

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Microenvironmental Influences that Drive Progression from Benign Breast Disease to Invasive Breast Cancer

Invasive breast cancer represents the endpoint of a developmental process that originates in the terminal duct lobular units and is believed to progress through stages of increasing proliferation, atypical hyperplasia, and carcinoma in situ before the cancer acquires invasive and metastatic capabilities. By comparison with invasive breast cancer, which has been studied extensively, the preceding stages of benign breast disease are more poorly understood. Much less is known about the molecular changes underlying benign breast disease development and progression, as well as the transition from in situ into invasive disease. Even less focus has been given to the specific role of stroma in this progression. The reasons for lack of knowledge about these lesions often come from their small size and limited sample availability. More challenges are posed by limitations of the models used to investigate the lesions preceding invasive breast cancer. However, recent studies have identified alterations in stromal cell function that may be critical for disease progression from benign disease to invasive cancer: key functions of myoepithelial cells that maintain tissue structure are lost, while tissue fibroblasts become activated to produce proteases that degrade the extracellular matrix and trigger the invasive cellular phenotype. Gene expression profiling of stromal alterations associated with disease progression has also identified key transcriptional changes that occur early in disease development. In this review, we will summarize recent studies showing how stromal factors can facilitate progression of ductal carcinoma in situ to invasive disease. We also suggest approaches to identify processes that control earlier stages of disease progression