So Where Do You See This Going? The Effects of Commitment Asymmetry and Asynchrony on Relationship Satisfaction and Breakup

We test the contribution of multiple types of romantic partners’ commitment asymmetry (discrepancies between partners’ commitment at a single time point) and asynchrony (discrepancies in the progression of commitment over time) to later relationship satisfaction and breakup. In three dyadic studies (N = 6,960 couples) over months (Study 1), days (Study 2), and years (Study 3), commitment asymmetry and asynchrony consistently did not predict satisfaction or breakup when controlling for commitment scores of individuals and their partners. Only one’s own commitment and proportion of downturns in commitment (when participants reported lower commitment than the previous time point) consistently predicted satisfaction across all three studies. For breakup, women’s (but not men’s) commitment was consistently negatively associated with breakup and proportion of downturns was consistently positively associated with breakup. Our findings indicate that, contrary to some significant findings in prior research, commitment asymmetry and asynchrony are not indicative of future relationship outcomes.

Theoretical aspects on the use of single-time-point dosimetry for radionuclide therapy

Objective: This study considers the error distributions for time-integrated activity (TIA) of single-time-point (STP) methods for patient-specific dosimetry in radionuclide therapy. Approach: The general case with the same pharmaceutical labelled with different radionuclides for imaging and therapy are considered for a mono-exponential time-activity curve. Two methods for STP dosimetry, both based on the combination of one activity estimate with the population-mean effective decay constant, are investigated. The cumulative distribution functions (CDFs) and the probability density functions for the two methods are analytically derived for arbitrary distributions of the biological decay constant. The CDFs are used for determining 95 % coverage intervals of the relative errors for different combinations of imaging time points, physical decay constants, and relative standard deviations of the biological decay constant. Two examples, in the form of kidney dosimetry in [177Lu]Lu-DOTA-TATE therapy and tumour dosimetry for Na[131I]I therapy for thyroid cancer with dosimetry based on imaging of Na[124I]I, are also studied in more detail with analysis of the sensitivity with respect to errors in the mean biological decay constant and to higher moments of the distribution. Main results: The distributions of the relative errors are negatively skewed, potentially leading to the situation that some TIA estimates are highly underestimated even if the majority of estimates are close to the true value. Significance: The main limitation of the studied STP dosimetry methods is thereby the risk of large underestimations of the TIA.

Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

Achieving high-performance numerical weather prediction (NWP) is important for people’s livelihoods and for socioeconomic development. However, NWP is obtained by solving differential equations with globally observed data without capturing enough local and spatial information at the observed station. To improve the forecasting performance, we propose a novel spatial lightGBM (Light Gradient Boosting Machine) model to correct the numerical forecast results at each observation station. By capturing the local spatial information of stations and using a single-station single-time strategy, the proposed method can incorporate the observed data and model data to achieve high-performance correction of medium-range predictions. Experimental results for temperature and wind prediction in Hainan Province show that the proposed correction method performs well compared with the ECWMF model and outperforms other competing methods.

The Concept of Using LSTM to Detect Moisture in Brick Walls by Means of Electrical Impedance Tomography

This paper refers to an original concept of tomographic measurement of brick wall humidity using an algorithm based on long short-term memory (LSTM) neural networks. The measurement vector was treated as a data sequence with a single time step in the presented study. This approach enabled the use of an algorithm utilising a recurrent deep neural network of the LSTM type as a system for converting the measurement vector into output images. A prototype electrical impedance tomograph was used in the research. The LSTM network, which is often employed for time series classification, was used to tackle the inverse problem. The task of the LSTM network was to convert 448 voltage measurements into spatial images of a selected section of a historical building’s brick wall. The 3D tomographic image mesh consisted of 11,297 finite elements. A novelty is using the measurement vector as a single time step sequence consisting of 448 features (channels). Through the appropriate selection of network parameters and the training algorithm, it was possible to obtain an LSTM network that reconstructs images of damp brick walls with high accuracy. Additionally, the reconstruction times are very short.

Trajectories of Depressive Symptoms and Incident Diabetes: A Prospective Study

Background Elevated depressive symptoms are associated with an increased risk for diabetes. Depression is a heterogeneous and chronic condition in which symptoms may remit, emerge, lessen, or intensify over time. Purpose The purpose of this study was to determine if trajectories of depressive symptoms measured at five time points over 8 years predicted incident diabetes over an 8-year follow-up in middle-aged and older adults. A secondary aim was to determine if trajectories of depressive symptoms predict incident diabetes, above and beyond depressive symptoms measured at a single time point. Methods Data came from the Health and Retirement Study (n = 9,233). Depressive symptoms were measured biennially from 1998 to 2006. Self-reported incident diabetes was measured during an 8-year follow-up. Results Five trajectories of depressive symptoms were identified (no depressive symptoms, low depressive symptoms, low-moderate depressive symptoms, moderate depressive symptoms, elevated and increasing depressive symptoms). Compared to the no depressive symptoms trajectory group (referent), all other trajectory groups were at higher risk of developing diabetes after adjusting for covariates. In most cases, trajectory group membership was associated with incident diabetes after controlling for depressive symptoms at a single time point. Conclusions Patterns of depressive symptoms over time were associated with incident diabetes. Patterns of depressive symptoms may be more predictive of diabetes incidence than depressive symptoms measured at a single time point.

Phylogeny of Amathusiini butterflies based on adult morphology (Lepidoptera, Nymphalidae, Satyrinae)

Based on comparative morphology of adults, a phylogeny is proposed for the butterfly tribe Amathusiini (Nymphalidae, Satyrinae). The dataset includes 92 characters scored for 45 species in 12 genera, representing the most comprehensive phylogenetic analysis for this group. Parsimony analyses produced a well-resolved strict consensus tree where genera were divided in three main groups: (clade 1) Stichophthalma; (clade 2) Aemona, Faunis, Melanocyma and Taenaris; (clade 3) Enispe, Discophora, Thaumantis, Thauria, Amathusia, Amathuxidia, and Zeuxidia. While genera in clades 1 and 2 were found to be morphologically homogeneous, clade 3 showed remarkable morphological divergence between and within genera. The monophyly of most genera was recovered with variable levels of support, but Melanocyma and Taenaris nested within Faunis. Therefore, here Melanocyma NEW SYN. is subsumed within Faunis, and Taenaris STAT. REV. is regarded as a subgenus of Faunis. Mimicry likely evolved a single time within the Faunis-Taenaris assemblage, as species of Taenaris formed a monophyletic group. Results are compared to early classifications and recent DNA-based analyses, and points of agreement and conflicts are discussed.

Trajectories of high-sensitivity cardiac troponin I in the two decades before cardiovascular death in Whitehall II

Background High-sensitivity cardiac troponin may be a promising biomarker that could be used for personalised cardiovascular risk prediction and monitoring in the general population. Temporal changes in high-sensitivity cardiac troponin before cardiovascular death are largely unexplored. Purpose Using the longitudinal Whitehall II cohort, we evaluated whether three serial high-sensitivity cardiac troponin I measurements over 15 years improved prediction of cardiovascular death when compared to a single time point at baseline. Methods Whitehall II is an ongoing longitudinal observation cohort study of 10,308 civil servants, and we included participants who had at least one cardiac troponin measurement and outcome data available. We constructed time trajectories to evaluate the temporal pattern of cardiac troponin I in those who died from cardiovascular disease as compared to those who did not. Cox regression and joint models were used to investigate the association of cardiac troponin I in relation to cardiovascular death using single time point (at baseline) and repeated measurements (at baseline, 10 and 15 years), respectively. The discriminative ability was assessed by the concordance index. Results In total, we included 7,293 individuals (mean age of 58 years [SD 7] at baseline, 29.4% women). Of these, 5,818 (79.8%) and 4,045 (55.5%) individuals had a second and third cardiac troponin I concentration measured, respectively. Cardiovascular death occurred in 281 (3.9%) individuals during a median follow-up of 21.4 [IQR, 15.8 to 21.8] years. In the 21-year trajectories of cardiac troponin I, we observed higher baseline concentrations in those who died due to cardiovascular disease as compared to those who did not (median 5 [IQR, 2 to 9] ng/L versus 3 [IQR, 2 to 5] ng/L respectively, Figure). Cardiac troponin I was an independent predictor of cardiovascular death, and the hazard ratio (HR) derived from the joint model that included serial cardiac troponin measurements was higher than the HR derived from the single time point model (single time point model: adjusted HR 1.53, 95% Confidence Interval [CI] 1.37 to 1.70 per naturally log transformed unit of cardiac troponin I, versus repeated measurements model: adjusted HR 1.79, 95% CI 1.58 to 2.02). The discriminative ability of the cardiac troponin model improved when using repeated measurements (concordance index of unadjusted cardiac troponin models, single time point: 0.668 versus repeated measurements: 0.724). Conclusions Our study shows that cardiac troponin I trajectories were persistently higher among individuals who died from cardiovascular disease. Cardiac troponin I is a strong independent predictor of cardiovascular death, and incorporating repeated measurements of cardiac troponin improves cardiovascular risk prediction in the general population. FUNDunding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Cardiac troponin I measurements and analysis were supported by were supported by Siemens Healthineers. The study was supported by Health Data Research UK which receives its funding from HDR UK Ltd (HDR-5012) funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and the Wellcome Trust. NLM is supported by the British Heart Foundation through a Senior Clinical Research Fellowship (FS/16/14/32023), Programme Grant (RG/20/10/34966) and a Research Excellence Award (RE/18/5/34216). The funders had no role in the study and the decision to submit this work to be considered for publication.

Digital Medical Record System using Blockchain Technology: A Survey

In these modern times where diseases, viral flu, and infections are too common for the human being, to tackle this problem sometimes it gets hard, as the patient has incomplete documents of their diagnostics in case the document get lost for certain reason or patient left some important reports at home, and carrying a file every single time when patients visit a doctor is quite a burden, and some doctors give fake medication to the patients, and as patient migrate to a new place and visits a doctor then that particular doctor would need all patients previous medication and hereditary diseases information if any, doctor has to go through the whole check-up in order to get any allergies for certain medication or any lab reports if there are missing any. Keywords: Blockchain, Hyperledger Fabric, Medical Records, Orderers, Radiologist, Tester, Patient, Doctor