Galactic Foreground Constraints on Primordial B-mode Detection for Ground-based Experiments

Contamination by polarized foregrounds is one of the biggest challenges for future polarized cosmic microwave background (CMB) surveys and the potential detection of primordial B-modes. Future experiments, such as Simons Observatory (SO) and CMB-S4, will aim at very deep observations in relatively small (f sky ∼ 0.1) areas of the sky. In this work, we investigate the forecasted performance, as a function of the survey field location on the sky, for regions over the full sky, balancing between polarized foreground avoidance and foreground component separation modeling needs. To do this, we simulate observations by an SO-like experiment and measure the error bar on the detection of the tensor-to-scalar ratio, σ(r), with a pipeline that includes a parametric component separation method, the Correlated Component Analysis, and the use of the Fisher information matrix. We forecast the performance over 192 survey areas covering the full sky and also for optimized low-foreground regions. We find that modeling the spectral energy distribution of foregrounds is the most important factor, and any mismatch will result in residuals and bias in the primordial B-modes. At these noise levels, σ(r) is not especially sensitive to the level of foreground contamination, provided the survey targets the least-contaminated regions of the sky close to the Galactic poles.

Evaluation of Variations in the Various Blood Indices in COVID-19 Recovered Patients

Introduction: In December 2019, cases of pneumonia with an unknown cause were reported in Wuhan, Hubei Province, China. Novel coronavirus infectious disease (COVID-19) has been spreading worldwide and tracking laboratory indexes during the diagnosis and treatment of patients with severe COVID-19 can provide a reference for patients in other countries and regions. The disease is caused by the Severe Acute Respiratory Syndrome Coronavirus according to studies, and the World Health Organization just dubbed it coronavirus disease 2019. Aim: The aim of this analysis was to evaluate COVID-19 patients' blood parameters changes in comparison with healthy controlled patients. Methods: Blood samples were taken from 10 patients in which 5 are COVID-19 recovered patients and 5 are healthy controls. For these blood samples TBC (Total Blood Count) was taken and the readings of RBC, hemoglobin, WBC, lymphocyte, granulocyte and platelets count was recorded. Independent t-test was done to obtain the results. SPSS software Version 23 was used to give the output comparison as error bar charts. Results: The patients have increased RBC count, increased hemoglobin and reduced WBC count with reduced lymphocytes and Granulocytes counts. Here it can be concluded that COVID-19 recovered patients should take care of themselves by having proper care, doctor consultation and follow up. Conclusion: From this study it can be understood that COVID-19 recovered patients have increased RBC count and hemoglobin percentage. The recovered patients have reduced WBC, lymphocytes and Granulocytes percentage.

RubricOE: a learning framework for genetic epidemiology

Genetic epidemiology is a growing area of interest in the past years due to the availability of genetic data with the decreasing cost of sequencing. Machine learning (ML) algorithms can be a very useful tool to study the genetic factors on disease incidence or on different traits characterizing a population. There are many challenges that plagues the field of genetic epidemiology including the unbalanced case-control data sets, fallibility of standard genome wide association studies with single marker analysis, heavily underdetermined systems with millions of markers in contrast of a few thousands of samples, to name a few. Ensemble ML methods can be a very useful tool to tackle many of these challenges and thus we propose RubricOE, a pipeline of ML algorithms with error bar computations to obtain interpretable genetic and non-genetic features from genomic or transcriptomic data combined with clinical factors in the form of electronic health records. RubricOE is shown to be robust in simulation studies, detecting true associations with traits of interest in arbitrarily structured multi-ethnic populations.

Rodingitization of mafic and ultramafic rocks in ophiolites from Northern Greece, seen by non-traditional stable isotopes, such as Cu, Fe and Zn.

<p>Rodingites are metasomatic rocks, frequently found in ophiolitic complexes. They offer important information about the interaction between ultramafic-mafic rocks and metasomatizing fluids, as well as about the post-magmatic evolution of ophiolitic suites (Tsikouras et al., 2009; Hu & Santosh, 2017; Surour, 2019; Laborda-Lopez et al., 2020). Metasomatism, such as rodingitization, is a very intricate process, which depends on the mineralogy of the initial rock, the nature of the metasomatic agent, the fluid/rock ratio, the duration of metasomatism and the chemical disequilibrium at the time of metasomatism between the host rock and the metasomatic medium (Poitrasson et al., 2013). Rodingites from the Veria-Naousa and Edessa ophiolites, in Northern Greece, were geochemically analyzed and characterized by substantial overprint of primary textures. Their field observation, their neoblastic mineral assemblages and metasomatic textures reveal that they derived from ultramafic and mafic protoliths. The mineral phases in the ultramafic derived rodingites (UDR) include mainly diopside, garnet, chlorite, epidote, tremolite and Fe-Ti oxides whereas mafic derived rodingites (MDR) consist of diopside, garnet, vesuvianite, chlorite, quartz, prehnite and actinolite. The studied rodingites present δ<sup>65</sup>Cu values varying from -0.17‰ to 0.62‰ and for ultramafic and mafic parent-rocks from -0.49‰ to +0.50‰. The UDR and MDR from both ophiolites display δ<sup>66</sup>Zn range from -0.06‰ to 0.74‰ and their photoliths present a narrower range from +0.04‰ to +0.41‰. Rodingitization affects in different way UDR and MDR samples. On one hand, Cu isotope ratios are systematically heavier in rodingites compared to their respective protoliths, except for one rodingite sample that requires confirmation due to large error bar. On the other hand, Zn isotopes show enrichment in light isotopes (group 1: comprising all UDR and some MDR samples), or in heavy isotopes (group 2, only MDR samples). Intriguingly, the same protolith can lead to both group 1 and 2 rodingites, as defined here.  No mineralogical or geochemical trend can be found to understand the dual behavior of Zn stable isotopes during rodingitization so far. Fe isotopes do not show any significant fractionation of δ<sup>56</sup>Fe, ranging from +0.07‰ to +0.19‰ for the rodingites and from +0.12‰ to +0.23‰ for their protoliths, indicating that Fe isotopes are highly resistant to rodingitization. Our study shows that rodingitization enriches metasomatized samples in heavy Cu isotopes and has no impact on Fe isotopes. It remains unclear why Zn isotopes can be affected both ways.</p>

Revisiting the HD 21749 Planetary System with Stellar Activity Modeling*

HD 21749 is a bright (V = 8.1 mag) K dwarf at 16 pc known to host an inner terrestrial planet HD 21749c as well as an outer sub-Neptune HD 21749b, both delivered by TESS. Follow-up spectroscopic observations measured the mass of HD 21749b to be 22.7 ± 2.2 M⊕ with a density of $7.0^{+1.6}_{-1.3}$ g cm−3, making it one of the densest sub-Neptunes. However, the mass measurement was suspected to be influenced by stellar rotation. Here we present new high-cadence PFS RV data to disentangle the stellar activity signal from the planetary signal. We find that HD 21749 has a similar rotational timescale as the planet’s orbital period, and the amplitude of the planetary orbital RV signal is estimated to be similar to that of the stellar activity signal. We perform Gaussian Process (GP) regression on the photometry and RVs from HARPS and PFS to model the stellar activity signal. Our new models reveal that HD 21749b has a radius of 2.86 ± 0.20 R⊕, an orbital period of 35.6133 ± 0.0005 d with a mass of Mb = 20.0 ± 2.7 M⊕ and a density of $4.8^{+2.0}_{-1.4}$ g cm−3 on an eccentric orbit with e = 0.16 ± 0.06, which is consistent with the most recent values published for this system. HD 21749c has an orbital period of 7.7902 ± 0.0006 d, a radius of 1.13 ± 0.10 R⊕, and a 3σ mass upper limit of 3.5 M⊕. Our Monte Carlo simulations confirm that without properly taking stellar activity signals into account, the mass measurement of HD 21749b is likely to arrive at a significantly underestimated error bar.

Influence of Carbon Cap on Self-Diffusion in Silicon Carbide

Self-diffusion of carbon (12C and 13C) and silicon (28Si and 30Si) in 4H silicon carbide has been investigated by utilizing a structure containing an isotope purified 4H-28Si12C epitaxial layer grown on an n-type (0001) 4H-SiC substrate, and finally covered by a carbon capping layer (C-cap). The 13C and 30Si isotope profiles were monitored using secondary ion mass spectrometry (SIMS) following successive heat treatments performed at 2300–2450∘C in Ar atmosphere using an inductively heated furnace. The 30Si profiles show little redistribution within the studied temperature range, with the extracted diffusion lengths for Si being within the error bar for surface roughening during annealing, as determined by profilometer measurements. On the other hand, a significant diffusion of 13C was observed into the isotope purified layer from both the substrate and the C-cap. A diffusivity of D=8.3×106e−10.4/kBT cm2/s for 13C was extracted, in contrast to previous findings that yielded lower both pre-factors and activation energies for C self-diffusion in SiC. The discrepancy between the present measurements and previous theoretical and experimental works is ascribed to the presence of the C-cap, which is responsible for continuous injection of C interstitials during annealing, and thereby suppressing the vacancy mediated diffusion.

Variable Biases: A Study of Scientists’ Interpretation of Plot Types Commonly Used in Scientific Communication

In scientific communication, there are visualization conventions that are widely used to convey uncertainty, such as representing the variability of a dataset with error bars. Yet prior research indicates that scientists frequently misinterpret error bars. In this study, we compared bar charts with error bars to four alternative visualizations: dot, box, violin, and density plots. Our goal was to determine whether these other plot types would produce fewer biases in interpretation relative to bar plots. Scientists who have experience generating and interpreting statistical graphs used plots to assess whether the difference between two datasets was statistically significant. Our results replicated the patterns of biases that have been observed in prior studies of error bar interpretation. However, we found that our participants still had the best overall performance for bar plots with error bars, because they were most familiar with this type of plot.

THU0507 ASSOCIATION BETWEEN JUVENILE IDIOPATHIC ARTHRITIS AND AUTISM

Background:Juvenile Idiopathic Arthritis (JIA) is a heterogenous group of autoimmune disorders characterised by chronic joint inflammation, diagnosed in around 1 in 1,000 children and young people (CYP) under the age of 16. Autistic Spectrum Condition (ASC) is a neurodevelopmental condition characterised by differences in social communication and sensory perception, as well as restricted interests and repetitive behaviours. Recent estimates from the Centers for Disease Control and Prevention (CDC) suggest that 1.68% of CYP are diagnosed with ASC, with males being more likely to be diagnosed (sex ratio of 4:1) [1]. The causes of both JIA and ASC are complex interactions between genetic and environmental factors. There appears to be some evidence that ASC may be associated with certain parental autoimmune conditions [2], although research into any association between JIA and ASC is sparse with the exception of a review of clinical database information [3].Objectives:In this parent-led study, the association between JIA and ASC was explored in order to determine if children with JIA, or children who do not themselves have JIA but have at least one first-degree relative with JIA (FDR), are more likely to be diagnosed with ASC.Methods:Parents of CYP with JIA were invited to complete an online survey, giving details of each member of their family including diagnosis status for JIA and ASC, and age of diagnoses. A total of 247 responses were collated, representing 558 CYP. Overall, 202 CYP were diagnosed with JIA from 197 families. The eldest child with JIA from each family was selected (total 197; 66 male and 131 female) and the rate of ASC was compared against the general population using Fisher’s exact tests.Results:Children with JIA themselves and FDR children were significantly more likely to be diagnosed with ASC.GroupOdds Ratio (95% CI)p-valueJIA children overall6.107 (1.760, 21.190)0.0020**FDR children overall7.009 (2.033, 24.160)0.0006***Figure 1.Proportion of children diagnosed with ASC in the general population (CDC estimates), JIA group and FDR group. Error bar indicates 95% CI. Significance indicated compared to population.Conclusion:Individuals with JIA and family members of individuals with JIA are more likely to be diagnosed with ASC. The results remained unchanged in a sensitivity analysis in which JIA children who had another sibling with JIA were excluded in order to minimise the risk that these results were affected by selecting the eldest child with JIA.It is possible that we are underestimating the association between JIA and ASC in this study. The majority of children sampled were from the United Kingdom and Ireland; however, we chose to utilise the most recent CDC estimates for ASC prevalence, as the most recent estimates from the UK were from 2006 and longitudinal data suggests that ASC prevalence continues to increase, likely due to changes in diagnostic criteria and improved recognition of the condition. When using the UK prevalence estimates, JIA children and FDR children remain significantly more likely to be diagnosed with ASC than the general population as a whole.Future research should focus on confirming these findings in larger, population-based samples.References:[1]Christensen DL, Braun KV, Baio J, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years — Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012.MMWR Surveill Summ(2018); 65 (No. SS-13):1–23.[2]Hughes, H. K., Mills Ko, E., Rose, D. & Ashwood, P. Immune Dysfunction and Autoimmunity as Pathological Mechanisms in Autism Spectrum Disorders.Frontiers in Cellular Neuroscience(2018); 12[3]Haslam, K. P16 Is there an association between paediatric rheumatological disease and autism?Rheumatology2019; 58Disclosure of Interests:None declared

Co-seismic deformation and preliminary fault model of the M6.4 Durres (Albania) Nov. 26, 2019 earthquake, based on space geodesy observations

<p>We report the mapping the co-seismic deformation in the coastal region of Durres (Albania) following the M<sub>w</sub>=6.4 shallow earthquake on Nov. 26, 2019, 02:54 UTC. The tectonics of western and northern Albania is characterised by on-going compression due to collision between Eurasian and Adriatic plates. Crustal deformation is characterised by shortening directed at NNE-SSW to E-W orientation. We analysed co-seismic interferograms of the Sentinel-1 (ESA) satellites (ascending orbit; relative orbit 175, slice numbers 14 & 15) and GPS observations (30-s interval) recorded at two stations (DUR2 and TIR2). The raw GPS data were processed with the GIPSY-OASIS II software, using the Precise Point Positioning (PPP) methodology with Final JPL products, to obtain daily static solutions defined in ITRF14. The coseismic offsets were computed as differences between the mean positions, respectively 5 days before and after the earthquake day. Uncertainties associated with the displacements were calculated by propagating the errors in GPS solutions. For DUR2 the displacement is significant in all three components (East=-1.3 cm, North=-2.1 cm, Up= +1.4 cm), while for TIR2 seems reasonable (0.4 cm on the horizontal components) but within the error bar. The SAR images were processed by the open-source SNAP software and they were obtained on Nov. 14, 2019 20:59 UTC (master scene) and on Nov. 26, 2019 16:31 UTC (slave scene). Each frame (slice) was processed independently and the wrapped phase was mosaicked in order to reveal the full deformation extent. The InSAR fringe pattern shows a 45-km long, NW-SE arrangement of three (3) fringes with a maximum LOS displacement of about +8.4 cm near the village Hamallaj (15 km NE of Durres). Assuming a half-space elastic model with uniform slip along a rectangular fault surface, the source of the ground deformation was inverted using the available geodetic data (GNSS and InSAR). The mean scatter value between data and the model is 2.4 mm.  The inversion modelling indicates that the 2019 Durres (Albania) earthquakes ruptured a low-angle fault (24 km long by 9 km wide) dipping 23° towards east with the fault plane top at 16 km. The geodetic fault-model is in agreement with published moment tensor solutions showing a NNW-SSE fault plane (for example the USGS solution has attributes 337°/27°/91°; strike/dip-angle/rake angle). This geometry is compatible with a blind thrust fault that may root on the main basal thrust i.e. along the main Ionian thrust front that separates Adria-Apulia from Eurasia.</p><p> </p><p>Acknowledgement: This research is supported by HELPOS (“Hellenic Plate Observing System” - MIS 5002697) which is implemented under the Action “Reinforcement of the Research and Innovation Infrastructure”, funded by the Operational Programme "Competitiveness, Entrepreneurship and Innovation" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund). We also thank The Institute of GeoSciences, Energy, Water and Environment of Albania for providing GNSS data.</p>