H i study of isolated and paired galaxies: the MIR SFR-M⋆ sequence

ABSTRACT Using mid-infrared star formation rate and stellar mass indicators in WISE (Wide-field Infrared Survey Explorer), we construct and contrast the relation between star formation rate and stellar mass for isolated and paired galaxies. Our samples comprise a selection of AMIGA (Analysis of the interstellar Medium in Isolated GAlaxies; isolated galaxies) and pairs of ALFALFA (Arecibo Legacy Fast ALFA) galaxies with H i detections such that we can examine the relationship between H i content (gas fraction, H i deficiency) and galaxy location on the main sequence (MS) in these two contrasting environments. We derive for the first time an H i scaling relation for isolated galaxies using WISE stellar masses, and thereby establish a baseline predictor of H i content that can be used to assess the impact of environment on H i content when compared with samples of galaxies in different environments. We use this updated relation to determine the H i deficiency of both our paired and isolated galaxies. Across all the quantities examined as a function of environment in this work (MS location, gas fraction, and H i deficiency), the AMIGA sample of isolated galaxies is found to have the lower dispersion: σAMIGA = 0.37 versus σPAIRS = 0.55 on the MS, σAMIGA = 0.44 versus σPAIRS = 0.54 in gas fraction, and σAMIGA = 0.28 versus σPAIRS = 0.34 in H i deficiency. We also note fewer isolated quiescent galaxies, 3 (0.6${{\ \rm per\ cent}}$), compared to 12 (2.3${{\ \rm per\ cent}}$) quiescent pair members. Our results suggest the differences in scatter measured between our samples are environment driven. Galaxies in isolation behave relatively predictably, and galaxies in more densely populated environments adopt a more stochastic behaviour, across a broad range of quantities.

Developing a COVID-19 mortality risk prediction model when individual-level data are not available

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

P368 Tofacitinib induction efficiency and intracellular cytokine dynamics in ulcerative colitis: Results from clinical practice

Background Tofacitinib is an oral JAK inhibitor approved for the treatment of ulcerative colitis (UC). Its efficiency was proven in registration trials; however, data from clinical practice are insufficient. Our aim was to evaluate response to tofacitinib after 8 weeks in UC patients, and to assess potential predictors of response including early cytokine production shifts. Methods Data from consecutive UC patients who started tofacitinib 10 mg b.i.d. were evaluated. Disease activity was assessed by Mayo score at baseline and week 8 together with C-reactive protein (CRP) and faecal calprotectin (FC). Production of IL-4, IL-10, IL-17, TNFα and IFNγ in T-helper cells was determined at baseline and week 4. At week 8, patients with total Mayo 0–5 with endoscopic subscores 0–1 were considered responders. Adverse events were registered at every visit. Results Twenty-four patients (41.7% males, 58.3% females), mean age 35.3 ± 11.8 years were included. Mean disease duration was 8.3 ± 5.2 years. In median, the patients were previously treated with two biologic agents; however, 25% of the patients were naive to any biologic therapy. Systemic corticosteroids were present in 41.7% patients at baseline and no patient had concomitant biologic or other immunosuppressive therapy. At week 8, 52.9% of patients responded to therapy. Total Mayo decreased in responders from mean 5.9 ± 3.5 to 1.1 ± 1.3 (p = 0.01), while in nonresponders it changed from 8.0 ± 2.5 to 8.9 ± 2.1 (p = 0.86). Endoscopic subscore decreased from 2.0 ± 1.0 to 0.6 ± 0.7 (p = 0.02) in responders, however, remained stationary in nonresponders (2.9). CRP and FC dropped significantly in responders (6.7 ± 6.2 vs. 2.0 ± 2.2 mg/l, p = 0.04; 1195 ± 1189 vs. 578 ± 654 μg/g, p = 0.05), but not in nonresponders. The responding and nonresponding groups differed significantly in baseline triglycerides, which were higher in nonresponders. Other baseline parameters were comparable. In responders, there was a significant decrease in IL-4 and no change in IL-10, while in nonresponders, there was no change in IL-4 and a significant decrease in IL-10. Tofacitinib was stopped in 23.5% of patients at week 8 due to insufficient response. Two patients reported headaches after treatment initiation and single events of CMV colitis, C. diff. colitis and oral candidiasis occurred. Conclusion Tofacitinib was efficient in inducing clinical response with mucosal healing in about 50% of UC patients after 8 weeks of therapy. There was no clear baseline predictor of response, however, considering limited sample, there was also no indication of even multiple biologics failure negatively affecting the response. Preliminary results of cytokine dynamics suggest early IL-4 decrease as a potential biomarker of response, warranting further investigation.

Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern Analysis and Prediction

Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.

Study of urinary protein creatinine index in hypertensive patient

Objectives: Proteinuria is a hallmark of glomerular diseases. Uncontrolled high blood pressure increases the risk of glomerular disease leading to proteinuria and high urinary creatinine. So an attempt was made to validate the PCI of a random urine sample as a reliable and a convenient test.Methods: Total of 42 hypertensive patients and 80 controls were selected. Their urinary protein was estimated by urinary dipstick method and colorimetric sulfosalicylic acid method. Urinary creatinine was estimated by modified Jaffe’s method. Protein creatinine index was measured for each patients and controls.Results: It was found that the amount of creatinine in urine in hypertensive patients (0.91 ± 0.29 mmol/dl) was comparable to that in the control subjects (0.86 ± 0.38 mmol/dl).The mean of urinary protein concentration in the hypertensive patients was 13.66 ± 5.77 mg/dl, and in the controls was 8.13 ± 2.82 mg/dl respectively. Highly significant value of PCI were observed in hypertensive patients (153 ±59.08) as compared to the controls where PCI was 114.64 ±47.96 (p<0.001). Conclusion: PCI of a random urine sample can serve as a reliable and convenient test to replace 24 hr urine protein estimation. It can serve as baseline predictor of progression of renal diseases.

Normalizing Item-Based Collaborative Filter Using Context-Aware Scaled Baseline Predictor

Item-based collaborative filter algorithms play an important role in modern commercial recommendation systems (RSs). To improve the recommendation performance, normalization is always used as a basic component for the predictor models. Among a lot of normalizing methods, subtracting the baseline predictor (BLP) is the most popular one. However, the BLP uses a statistical constant without considering the context. We found that slightly scaling the different components of the BLP separately could dramatically improve the performance. This paper proposed some normalization methods based on the scaled baseline predictors according to different context information. The experimental results show that using context-aware scaled baseline predictor for normalization indeed gets better recommendation performance, including RMSE, MAE, precision, recall, and nDCG.