Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. View Full-Text Keywords: neuropsychiatric systemic lupus erythematosus; rs-fMRI; graph theory; functional connectivity; surrogate data; machine learning; visuomotor ability; mental flexibilit

Expanded national database collection and data coverage in the FINDbase worldwide database for clinically relevant genomic variation allele frequencies

FINDbase (http://www.findbase.org) is a comprehensive data repository that records the prevalence of clinically relevant genomic variants in various populations worldwide, such as pathogenic variants leading mostly to monogenic disorders and pharmacogenomics biomarkers. The database also records the incidence of rare genetic diseases in various populations, all in well-distinct data modules. Here, we report extensive data content updates in all data modules, with direct implications to clinical pharmacogenomics. Also, we report significant new developments in FINDbase, namely (i) the release of a new version of the ETHNOS software that catalyzes development curation of national/ethnic genetic databases, (ii) the migration of all FINDbase data content into 90 distinct national/ethnic mutation databases, all built around Microsoft’s PivotViewer (http://www.getpivot.com) software (iii) new data visualization tools and (iv) the interrelation of FINDbase with DruGeVar database with direct implications in clinical pharmacogenomics. The above mentioned updates further enhance the impact of FINDbase, as a key resource for Genomic Medicine applications

Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19

Bright-Moon sky as a wide-field linear Polarimetric flat source for calibration

Context. Next-generation wide-field optical polarimeters such as Wide-Area Linear Optical Polarimeters (WALOPs) have a field of view (FoV) of tens of arcminutes. Wide-field polarimetric flat sources are essential to the efficient and accurate calibration of these instruments. However, no established wide-field polarimetric standard or flat sources exist at present. Aims. This study tests the feasibility of using the polarized sky patches of the size of around 10 × 10 arcminutes2, at a distance of up to 20 from the Moon, on bright-Moon nights as a wide-field linear polarimetric flat source. Methods. We observed 19 patches of the sky adjacent to the bright-Moon with the RoboPol instrument in the SDSS-r broadband filter. These patches were observed on five nights within two days of the full-Moon across two RoboPol observing seasons. Results. We find that for 18 of the 19 patches, the uniformity in the measured normalized Stokes parameters q and u is within 0.2%, with 12 patches exhibiting uniformity within 0.07% or better for both q and u simultaneously, making them reliable and stable wide-field linear polarization flats. Conclusions. We demonstrate that the sky on bright-Moon nights is an excellent wide-field linear polarization flat source. Various combinations of the normalized Stokes parameters q and u can be obtained by choosing suitable locations of the sky patch with respect to the Moon

Bright-Moon Sky as a Wide-Field Linear Polarimetric Flat Source for Calibration

Next-generation wide-field optical polarimeters like the Wide-Area Linear Optical Polarimeters (WALOPs) have a field of view (FoV) of tens of arcminutes. For efficient and accurate calibration of these instruments, wide-field polarimetric flat sources will be essential. Currently, no established wide-field polarimetric standard or flat sources exist. This paper tests the feasibility of using the polarized sky patches of the size of around ten-by-ten arcminutes, at a distance of up to 20 degrees from the Moon, on bright-Moon nights as a wide-field linear polarimetric flat source. We observed 19 patches of the sky adjacent to the bright-Moon with the RoboPol instrument in the SDSS-r broadband filter. These were observed on five nights within two days of the full-Moon across two RoboPol observing seasons. We find that for 18 of the 19 patches, the uniformity in the measured normalized Stokes parameters $q$ and $u$ is within 0.2 %, with 12 patches exhibiting uniformity within 0.07 % or better for both $q$ and $u$ simultaneously, making them reliable and stable wide-field linear polarization flats. We demonstrate that the sky on bright-Moon nights is an excellent wide-field linear polarization flat source. Various combinations of the normalized Stokes parameters $q$ and $u$ can be obtained by choosing suitable locations of the sky patch with respect to the MoonComment: 8 pages including appendix, 6 figures and 3 tables. Submitted to Astronomy and Astrophysics for review. Comments are welcom

Starlight-polarization-based tomography of the magnetized ISM: Pasiphae's line-of-sight inversion method

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in 3D within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data taking into account realistic uncertainties from $Gaia$ and as expected for the optical polarization survey PASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective in recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than $\sim 0.1\%$, for the adopted survey exposure time and level of systematic uncertainty. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus opening ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing dataset of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties.Comment: 28 pages, including 2 appendices, submitted to A&

Starlight-polarization-based tomography of the magnetized ISM: P ASIPHAE - s line-of-sight inversion method

We present the first Bayesian method for tomographic decomposition of the plane-of-sky orientation of the magnetic field with the use of stellar polarimetry and distance. This standalone tomographic inversion method presents an important step forward in reconstructing the magnetized interstellar medium (ISM) in three dimensions within dusty regions. We develop a model in which the polarization signal from the magnetized and dusty ISM is described by thin layers at various distances, a working assumption which should be satisfied in small-angular circular apertures. Our modeling makes it possible to infer the mean polarization (amplitude and orientation) induced by individual dusty clouds and to account for the turbulence-induced scatter in a generic way. We present a likelihood function that explicitly accounts for uncertainties in polarization and parallax. We develop a framework for reconstructing the magnetized ISM through the maximization of the log-likelihood using a nested sampling method. We test our Bayesian inversion method on mock data, representative of the high Galactic latitude sky, taking into account realistic uncertainties from Gaia and as expected for the optical polarization survey PASIPHAE according to the currently planned observing strategy. We demonstrate that our method is effective at recovering the cloud properties as soon as the polarization induced by a cloud to its background stars is higher than -0.1% for the adopted survey exposure time and level of systematic uncertainty. The larger the induced polarization is, the better the method s performance, and the lower the number of required stars. Our method makes it possible to recover not only the mean polarization properties but also to characterize the intrinsic scatter, thus creating new ways to characterize ISM turbulence and the magnetic field strength. Finally, we apply our method to an existing data set of starlight polarization with known line-of-sight decomposition, demonstrating agreement with previous results and an improved quantification of uncertainties in cloud properties

Two Secreted Proteoglycans, Activators of Urothelial Cell–Cell Adhesion, Negatively Contribute to Bladder Cancer Initiation and Progression

Osteomodulin (OMD) and proline/arginine-rich end leucine repeat protein (PRELP) are secreted extracellular matrix proteins belonging to the small leucine-rich proteoglycans family. We found that OMD and PRELP were specifically expressed in umbrella cells in bladder epithelia, and their expression levels were dramatically downregulated in all bladder cancers from very early stages and various epithelial cancers. Our in vitro studies including gene expression profiling using bladder cancer cell lines revealed that OMD or PRELP application suppressed the cancer progression by inhibiting TGF-β and EGF pathways, which reversed epithelial–mesenchymal transition (EMT), activated cell–cell adhesion, and inhibited various oncogenic pathways. Furthermore, the overexpression of OMD in bladder cancer cells strongly inhibited the anchorage-independent growth and tumorigenicity in mouse xenograft studies. On the other hand, we found that in the bladder epithelia, the knockout mice of OMD and/or PRELP gene caused partial EMT and a loss of tight junctions of the umbrella cells and resulted in formation of a bladder carcinoma in situ-like structure by spontaneous breakdowns of the umbrella cell layer. Furthermore, the ontological analysis of the expression profiling of an OMD knockout mouse bladder demonstrated very high similarity with those obtained from human bladder cancers. Our data indicate that OMD and PRELP are endogenous inhibitors of cancer initiation and progression by controlling EMT. OMD and/or PRELP may have potential for the treatment of bladder cancer

Post-common envelope binaries from SDSS - XVI. Long orbital period systems and the energy budget of CE evolution

Virtually all close compact binary stars are formed through common-envelope (CE) evolution. It is generally accepted that during this crucial evolutionary phase a fraction of the orbital energy is used to expel the envelope. However, it is unclear whether additional sources of energy, such as the recombination energy of the envelope, play an important role. Here we report the discovery of the second and third longest orbital period post-common envelope binaries (PCEBs) containing white dwarf (WD) primaries, i.e. SDSSJ121130.94-024954.4 (Porb = 7.818 +- 0.002 days) and SDSSJ222108.45+002927.7 (Porb = 9.588 +- 0.002 days), reconstruct their evolutionary history, and discuss the implications for the energy budget of CE evolution. We find that, despite their long orbital periods, the evolution of both systems can still be understood without incorporating recombination energy, although at least small contributions of this additional energy seem to be likely. If recombination energy significantly contributes to the ejection of the envelope, more PCEBs with relatively long orbital periods (Porb >~ 1-3 day) harboring massive WDs (Mwd >~ 0.8 Msun) should exist.Comment: Accepted for publication in MNRAS. 8 pages, 6 figures and 4 table

Diets with high or low protein content and glycemic index for weight-loss maintenance

BACKGROUND: Studies of weight-control diets that are high in protein or low in glycemic index have reached varied conclusions, probably owing to the fact that the studies had insufficient power. METHODS: We enrolled overweight adults from eight European countries who had lost at least 8% of their initial body weight with a 3.3-MJ (800-kcal) low-calorie diet. Participants were randomly assigned, in a two-by-two factorial design, to one of five ad libitum diets to prevent weight regain over a 26-week period: a low-protein and low-glycemic-index diet, a low-protein and high-glycemic-index diet, a high-protein and low-glycemic-index diet, a high-protein and high-glycemic-index diet, or a control diet. RESULTS: A total of 1209 adults were screened (mean age, 41 years; body-mass index [the weight in kilograms divided by the square of the height in meters], 34), of whom 938 entered the low-calorie-diet phase of the study. A total of 773 participants who completed that phase were randomly assigned to one of the five maintenance diets; 548 completed the intervention (71%). Fewer participants in the high-protein and the low-glycemic-index groups than in the low-protein-high-glycemic-index group dropped out of the study (26.4% and 25.6%, respectively, vs. 37.4%; P=0.02 and P=0.01 for the respective comparisons). The mean initial weight loss with the low-calorie diet was 11.0 kg. In the analysis of participants who completed the study, only the low-protein-high-glycemic-index diet was associated with subsequent significant weight regain (1.67 kg; 95% confidence interval [CI], 0.48 to 2.87). In an intention-to-treat analysis, the weight regain was 0.93 kg less (95% CI, 0.31 to 1.55) in the groups assigned to a high-protein diet than in those assigned to a low-protein diet (P=0.003) and 0.95 kg less (95% CI, 0.33 to 1.57) in the groups assigned to a low-glycemic-index diet than in those assigned to a high-glycemic-index diet (P=0.003). The analysis involving participants who completed the intervention produced similar results. The groups did not differ significantly with respect to diet-related adverse events. CONCLUSIONS: In this large European study, a modest increase in protein content and a modest reduction in the glycemic index led to an improvement in study completion and maintenance of weight loss