Membership Probabilities Research Articles

Provincial income convergence clubs in Indonesia: Identification and conditioning factors

AbstractThe discussion on regional income per capita convergence in Indonesia continues with an emphasis on whether regional per capita incomes tend to converge or diverge over the long run. This study evaluates the regional convergence hypothesis at the provincial level during the recent deindustrialization period 2001–2017 of Indonesia. First, the study examines the existence of convergence clubs in Indonesia using a recently assembled panel dataset. Results reveal four convergence clubs with particularly large income gaps, mostly arising from the top of the income distribution. Second, using an ordered logit model, the study examines the determinants of the convergence clubs. Result reveals that industrialization increases the probability of membership to a high‐income club.

A Gaia-based Photometric and Kinematic Analysis of the Old Open Cluster King 11

Abstract This paper presents an investigation of an old age open cluster King 11 using Gaia’s Early Data Release 3 data. Considering the stars with membership probability (P μ ) > 90%, we identified 676 most probable cluster members within the cluster’s limiting radius. The mean proper motion for King 11 is determined as: μ x = − 3.391 ± 0.006 and μ y = − 0.660 ± 0.004 mas yr−1. The blue straggler stars of King 11 show a centrally concentrated radial distribution. The values of limiting radius, age, and distance are determined as 18.′51, 3.63 ± 0.42 Gyr, and 3.33 ± 0.15 kpc, respectively. The cluster’s apex coordinates (A = 267.°84 ± 1.°01, D = − 27.°48 ± 1.°03) are determined using the apex diagram method and verified using the (μ U , μ T ) diagram. We also obtained the orbit that the cluster follows in the Galaxy and estimated its tentative birthplace in the disk. The resulting spatial velocity of King 11 is 60.2 ± 2.16 km s−1. A significant oscillation along the Z coordinate up to 0.556 ± 0.022 kpc is determined.

Mountain biogeography coming full circle: a new '3D' floristic approach provides units for reconstructing evolutionary trajectories.

This article is a Commentary on Li et al. (2021), 232: 1424–1435.

The Effect of Weekend Work on Shared Leisure Time

AbstractUsing panel data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, I estimate the effect of weekend work on five measures of shared leisure time. Binary and ordered probit estimates indicate that weekend work has a negative association with all five measures. Fixed effects estimates suggest that weekend work has a negative effect on work–family balance and the probability of club membership, but no significant effect on the frequency of social time, attendance at community events or attendance at religious services.

Patient preferences in the treatment of hemophilia A: A latent class analysis.

To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A. Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences. The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership. The LCM analysis addresses heterogeneity in respondents' choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.

Blood metal trends in the Strong Heart Family Study: 1997-2009

BACKGROUND AND AIM: Although some metal exposures (Pb, Cd) have declined over time in the general US population, it is not known if American Indian (AI) communities experienced similar declines attributable to similar sources. Our objectives were to a) characterize trajectories of blood metal changes over time, and b) to identify characteristics of participants who experienced larger declines in blood metal concentrations in the Strong Heart Family Study (SHFS). METHODS: As, Cd, Pb, Mn, Hg, Ni, Se, and Zn were measured in whole blood samples from 310 SHFS participants at visit 3 (1997-1999) and visit 5 (2006-2009). We estimated the percent reduction in geometric mean of each metal across subgroups, used growth mixture models to identify trajectory classes of blood metal change, and evaluated the probability of trajectory class membership by subgroup via generalized estimating equations and logistic regression. RESULTS:Overall, percent geometric mean declines were 13% (As), 15% (Cd), 39% (Pb), 7% (Mn), 28% (Ni), 3% (Se), and 19% (Zn). Cd declines were largest for former smokers (26%), participants 30 years old (19%), and participants from North/South Dakota (30%). Pb declines were largest for participants from Arizona and Oklahoma (51%), former smokers (45%), and participants 30 years old (44%). The adjusted odds of being in the Cd High-Decreasing trajectory were higher for females vs. males (odds ratio 1.10, 95% CI 0.50, 2.40) and participants from North/South Dakota vs. Arizona (8.15, 95% CI 1.74, 38.0). The odds of being in the Pb High-Decreasing trajectory were lower for females vs. males (odds ratio 0.61, 95% CI 0.32, 1.16) and participants from Oklahoma vs. Arizona (0.57, 95% CI 0.22, 1.48). CONCLUSIONS:Substantial variability in the recent decline in blood metal concentrations exists across subgroups within the SHFS, indicating that declines in metal exposures in recent decades were not uniform across these AI communities. KEYWORDS: metals, American Indian, lead, arsenic, trends

Planets Across Space and Time (PAST). II. Catalog and Analyses of the LAMOST–Gaia–Kepler Stellar Kinematic Properties

Abstract The Kepler telescope has discovered over 4000 planets (candidates) by searching ∼200,000 stars over a wide range of distance (order of kpc) in our Galaxy. Characterizing the kinematic properties (e.g., Galactic component membership and kinematic age) of these Kepler targets (including the planet candidate hosts) is the first step toward studying Kepler planets in the Galactic context, which will reveal fresh insights into planet formation and evolution. In this paper, the second part of the Planets Across the Space and Time (PAST) series, by combining the data from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and Gaia and then applying the revised kinematic methods from PAST I, we present a catalog of kinematic properties (i.e., Galactic positions, velocities, and the relative membership probabilities among the thin disk, thick disk, Hercules stream, and the halo) as well as other basic stellar parameters for 35,835 Kepler stars. Further analyses of the LAMOST–Gaia–Kepler catalog demonstrate that our derived kinematic age reveals the expected stellar activity-age trend. Furthermore, we find that the fraction of thin (thick) disk stars increases (decreases) with the transiting planet multiplicity (N p = 0, 1, 2 and 3+) and the kinematic age decreases with N p, which could be a consequence of the dynamical evolution of planetary architecture with age. The LAMOST–Gaia–Kepler catalog will be useful for future studies on the correlations between the exoplanet distributions and the stellar Galactic environments as well as ages.

Contraceptive use among women through their later reproductive years: Findings from an Australian prospective cohort study.

ObjectiveExamine patterns of contraceptive use and contraceptive transitions over time among an Australian cohort of women through their later reproductive years.Study designLatent Transition Analysis was performed using data on 8,197 women from the Australian Longitudinal Study on Women’s Health’s 1973–78 cohort to identify distinct patterns of contraceptive use across 2006, 2012 and 2018. Women were excluded from the analysis at time points where they were not at risk of an unintended pregnancy. Latent status membership probabilities, item-response probabilities, transitions probabilities and the effect of predictors on latent status membership were estimated and reported.ResultsPatterns of contraceptive use were relatively consistent over time, particularly for high efficacy contraceptive methods with 71% of women using long-acting reversible contraceptives in 2012 also using long-acting reversible contraceptives in 2018. Multiple contraceptive use was highest in 2006 when women were aged 28–33 years (19.3%) but declined over time to 14.3% in 2018 when women were aged 40–45 years. Overall, contraceptive patterns stabilised as the women moved into their late 30s and early 40s.ConclusionsAlthough fertility declines with age, the stability of contraceptive choice and continued use of short-acting contraception among some women suggests that a contraceptive review may be helpful for women during perimenopause so that they are provided with contraceptive options most appropriate to their specific circumstances.

Hamacher heronian mean operators for multi-critria decision-making under multi-valued picture fuzzy uncertain lingsuitic environment

Picture fuzzy set (PFS) and linguistic term set (LTS) are two significant notions in multi-criteria decision-making (MCDM). In practice, decision-makers sometimes need utilize the multiple probable membership degrees for an uncertain linguistic term to express evaluation information. Motivated by these, to better convey the vagueness and uncertainty of cognitive information, multi-valued picture fuzzy uncertain linguistic set combining picture hesitant fuzzy set with uncertain linguistic term set is proposed. We firstly define the concepts of multi-valued picture fuzzy uncertain linguistic set and multi-valued picture fuzzy uncertain linguistic number. Hamacher operations are more general and flexible in information fusion, thus, Hamacher operations and comparison method are developed at the same time. Improved generalized Heronian Mean operator can simultaneously reflect correlations between values and prevent the redundant calculation. Then, two operators of improved generalized weighted Heronian mean and improved generalized geometric weighted Heronian mean in view of Hamacher operations are proposed. Meanwhile, some distinguished properties and instances of two operators are explored as well. Moreover, a novel MCDM approach applying the developed operators is constructed. Ultimately, an illustrative example on vendor selection is performed, and sensitivity analysis and comparison analysis are provided to verify the powerfulness of the proposed method.

GRAVITY K-band spectroscopy of HD 206893 B

Context. Near-infrared interferometry has become a powerful tool for studying the orbital and atmospheric parameters of substellar companions. Aims. We aim to reveal the nature of the reddest known substellar companion HD 206893 B by studying its near-infrared colors and spectral morphology and by investigating its orbital motion. Methods. We fit atmospheric models for giant planets and brown dwarfs and perform spectral retrievals with petitRADTRANS and ATMO on the observed GRAVITY, SPHERE, and GPI spectra of HD 206893 B. To recover its unusual spectral features, first and foremost its extremely red near-infrared color, we include additional extinction by high-altitude dust clouds made of enstatite grains in the atmospheric model fits. However, forsterite, corundum, and iron grains predict similar extinction curves for the grain sizes considered here. We also infer the orbital parameters of HD 206893 B by combining the ~100 μas precision astrometry from GRAVITY with data from the literature and constrain the mass and position of HD 206893 C based on the Gaia proper motion anomaly of the system. Results. The extremely red color and the very shallow 1.4 μm water absorption feature of HD 206893 B can be fit well with the adapted atmospheric models and spectral retrievals. By comparison with AMES-Cond evolutionary tracks, we find that only some atmosphericmodels predict physically plausible objects. Altogether, our analysis suggests an age of ~ 3–300 Myr and a mass of ~ 5–30 MJup for HD 206893 B, which is consistent with previous estimates but extends the parameter space to younger and lower-mass objects. The GRAVITY astrometry points to an eccentric orbit (e = 0.29−0.11+0.06) with a mutual inclination of &lt;34.4 deg with respectto the debris disk of the system. Conclusions. While HD 206893 B could in principle be a planetary-mass companion, this possibility hinges on the unknown influence of the inner companion on the mass estimate of 10−4+5 MJup from radial velocity and Gaia as well as a relatively small but significant Argus moving group membership probability of ~ 61%. However, we find that if the mass of HD 206893 B is &lt;30 MJup, then the inner companion HD 206893 C should have a mass between ~ 8–15 MJup. Finally, further spectroscopic or photometric observations at higher signal-to-noise and longer wavelengths are required to learn more about the composition and dust cloud properties of HD 206893 B.

Cardiovascular damage phenotypes and all-cause and CVD mortality in older adults

PurposeThe association between CVD risk factors and mortality is well established, however, current tools for addressing subgroups have focused on the overall burden of disease. The identification of risky combinations of characteristics may lead to a better understanding of physiologic pathways that underlie morbidity and mortality in older adults. MethodsParticipants included 5067 older adults from the Cardiovascular Health Study, followed for up to 6 years. Using latent class analysis (LCA), we created CV damage phenotypes based on probabilities of abnormal brain infarctions, major echocardiogram abnormalities, N-terminal probrain natriuretic peptide, troponin T, interleukin-6, c reactive-protein, galectin-3, cystatin C. We assigned class descriptions based on the probability of having an abnormality among risk factors, such that a healthy phenotype would have low probabilities in all risk factors. Participants were assigned to phenotypes based on the maximum probability of membership. We used Cox-proportional hazards regression to evaluate the association between the categorical CV damage phenotype and all-cause and CVD-mortality. ResultsThe analysis yielded 5 CV damage phenotypes consistent with the following descriptions: healthy (59%), cardio-renal (11%), cardiac (15%), multisystem morbidity (6%), and inflammatory (9%). All four phenotypes were statistically associated with a greater risk of all-cause mortality when compared with the healthy phenotype. The multisystem morbidity phenotype had the greatest risk of all-cause death (HR: 4.02; 95% CI: 3.44, 4.70), and CVD-mortality (HR: 4.90, 95% CI: 3.95, 6.06). ConclusionsFive CV damage phenotypes emerged from CVD risk factor measures. CV damage across multiple systems confers a greater mortality risk compared to damage in any single domain.

Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury

ABSTRACT Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was ‘healthy-limb’ class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61<AUC<0.62) and FACL group (0.57<AUC<0.59). The ACL injury incidence proportion was highest in athletes with probabilities below 0.20 (9.4%; +2.7% to baseline), while athletes with probabilities above 0.80 had an incidence proportion of 4.1% (−2.6%). While findings that a low probability might represent an increase in injury risk on a group level, it is not sensitive enough for injury screening to predict a future injury on the individual level.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning.

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

A comparative analysis of freeway crash incident clearance time using random parameter and latent class hazard-based duration model

The effects of freeway incident clearance times on the flow of traffic have recently increased interests in understanding what factors influence incident durations. This has particularly become topical due to the financial and economic implications of traffic gridlocks caused by freeway incidents on industries and personal mobility. This paper presents two advanced econometric modeling methods, random parameters duration modeling and latent class duration modeling in understanding the factors that impact freeway incident clearance times in the State of Alabama. These two modeling approaches were further compared to identify which of them provides the best fit for the data with respect to accounting for unobserved heterogeneity. A total of 2206 freeway crash incident data from January 1 to December 31, 2018 were examined in developing the models. The study was based on a unique dataset that involved merging and matching Traffic Incident Management response data from the Alabama Department of Transportation (ALDOT) Traffic Management Center (TMC), freeway crash data from the Center for Advanced Public Safety (CAPS) at the University of Alabama, Alabama Service and Assistance Patrol (ASAP) data from ALDOT and traffic volume from ALDOT’s Highway Performance Management System (HPMS). The model estimation results reveal that a total of nineteen variables were found statistically significant with five random variables (on-road, nighttime, rain, AADT, and ASAP existing coverage area) and fourteen fixed effects variables for the random parameters model. For latent class model, a total of eighteen variables were observed statistically significant within two distinct latent classes (Latent Class 1 with class membership probability of 0.23 and Latent Class 2 with class membership probability of 0.77) at a 0.05 significance level. A comparison of the two models reveals that the latent class model provides the better fit for the incident duration data. The findings of this study are expected to contribute to the body of knowledge on incident duration by employing two advanced econometric modeling methods and to inform statewide efforts in significantly reducing the duration of freeway incident clearance time. Moreover, this is to ensure that policy decisions that may arise from the findings of the study are sound and based on data-driven evidence.

Clustering Longitudinal Life-Course Sequences using Mixtures of Exponential-Distance Models

AbstractSequence analysis is an increasingly popular approach for analysing life courses represented by ordered collections of activities experienced by subjects over time. Here, we analyse a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22. We propose a novel, model-based clustering approach suited to the analysis of such data from a holistic perspective, with the aims of estimating the number of typical career trajectories, identifying the relevant features of these patterns, and assessing the extent to which such patterns are shaped by background characteristics. Several criteria exist for measuring pairwise dissimilarities among categorical sequences. Typically, dissimilarity matrices are employed as input to heuristic clustering algorithms. The family of methods we develop instead clusters sequences directly using mixtures of exponential-distance models. Basing the models on weighted variants of the Hamming distance metric permits closed-form expressions for parameter estimation. Simultaneously allowing the component membership probabilities to depend on fixed covariates and accommodating sampling weights in the clustering process yields new insights on the Northern Irish data. In particular, we find that school examination performance is the single most important predictor of cluster membership.

Membership of stars in open clusters using random forest with gaia data

Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published membership data of nine open star clusters as a training set to find new members from Gaia DR2 data using a supervised random forest model with a precision of around 90%. The number of new members found is often double the published number. Membership probability of a larger sample of stars in clusters is a major benefit in determination of cluster parameters like distance, extinction and mass functions. We also found members in the outer regions of the cluster and found sub-structures in the clusters studied. The color magnitude diagrams are more populated and enriched by the addition of new members making their study more promising.

PLS for classification

Partial Least Squares regression (PLS) is a multivariate technique developed to perform regression in the case of multivariate responses when multicollinearity, redundancy and noise affect the predictors. In spite of several efforts have been made to extend PLS to classification problems, this is still a current field of research. In the present study, a new technique called PLS for classification is introduced to solve the general G-class problem. It is developed within a self-consistent framework based on linear algebra and on the theory of compositional data. After the introduction of the notion of probability-data vector, the space of the predictors and that of the conditional probabilities are linked, and a well-defined least squares problem, whose solution specifies the relationship between probabilities and predictors, is solved by a suitable reformulation of PLS2. The method estimates directly the conditional probability of the class membership given the predictors. The score vectors are introduced only in a second step to improve model interpretation. The main properties of PLS for classification and its relationships with PLS-DA are discussed. One simulated and one real data sets are investigated to show how the method works in practice.

Deep learning-based tree species mapping in a highly diverse tropical urban setting

Spatially explicit information on urban tree species distribution is crucial for green infrastructure management in cities. This information is usually acquired with ground-based surveys, which are time-consuming and usually cover limited spatial extents. The combination of machine learning algorithms and remote sensing images has been hailed as a promising way to map tree species over broad areas. Recently, convolutional neural networks (CNNs), a type of deep learning method, have achieved outstanding results for tree species discrimination in various remote sensing data types. However, there is a lack of studies using CNN-based methods to produce tree species composition maps, particularly for tropical urban settings. Here, we propose a multi-task CNN to map tree species in a highly diverse neighborhood in Rio de Janeiro, Brazil. Our network architecture takes an aerial photograph (RGB bands and pixel size = 0.15 m) and delivers two outputs: a semantically segmented image and a distance map transform. In the former, all pixel positions are labeled, while in the latter, each pixel position contains the Euclidean distance to the crown boundary. We developed a post-processing approach that combines the two outputs, and we classified nine and five tree species with an average F1-score of 79.3 ± 8.6% and 87.6 ± 4.4%, respectively. Moreover, our post-processing approach produced a realistic tree species composition map by labeling only pixels of the target species with high class membership probabilities. Our results show the potential of CNNs and aerial photographs to map tree species in highly diverse tropical urban settings, providing valuable insights for urban forest management and green spaces planning.

A study of the Czernik 2 and NGC 7654 open clusters using CCD UBV photometric and Gaia EDR3 data

We analysed the open clusters Czernik 2 and NGC 7654 using CCD UBV photometric and Gaia Early Data Release 3 (EDR3) photometric and astrometric data. Structural parameters of the two clusters were derived, including the physical sizes of Czernik 2 being r=5 and NGC 7654 as 8 min. We calculated membership probabilities of stars based on their proper motion components as released in the Gaia EDR3. To identify member stars of the clusters, we used these membership probabilities taking into account location and the impact of binarity on main-sequence stars. We used membership probabilities higher than $P=0.5$ to identify 28 member stars for Czernik 2 and 369 for NGC 7654. We estimated colour-excesses and metallicities separately using two-colour diagrams to derive homogeneously determined parameters. The derived $E(B-V)$ colour excess is 0.46(0.02) mag for Czernik 2 and 0.57(0.04) mag for NGC 7654. Metallicities were obtained for the first time for both clusters, -0.08(0.02) dex for Czernik 2 and -0.05(0.01) dex for NGC 7654. Keeping the reddening and metallicity as constant quantities, we fitted PARSEC models using colour-magnitude diagrams, resulting in estimated distance moduli and ages of the two clusters. We obtained the distance modulus for Czernik 2 as 12.80(0.07) mag and for NGC 7654 as 13.20(0.16) mag, which coincide with ages of 1.2(0.2) Gyr and 120(20) Myr, respectively. The distances to the clusters were calculated using the Gaia EDR3 trigonometric parallaxes and compared with the literature. We found good agreement between the distances obtained in this study and the literature. Present day mass function slopes for both clusters are comparable with the value of Salpeter (1955), being X=-1.37(0.24) for Czernik 2 and X=-1.39(0.19) for NGC 7654.

Exploring Decisional Conflict With Measures of Numeracy and Optimism in a Stated Preference Survey.

ObjectivesLow optimism and low numeracy are associated with difficulty or lack of participation in making treatment-related health care decisions. We investigated whether low optimism and low self-reported numeracy scores could help uncover evidence of decisional conflict in a discrete-choice experiment (DCE).MethodsPreferences for a treatment to delay type 1 diabetes were elicited using a DCE among 1501 parents in the United States. Respondents chose between two hypothetical treatments or they could choose no treatment (opt out) in a series of choice questions. The survey included a measure of optimism and a measure of subjective numeracy. We used latent class analyses where membership probability was predicted by optimism and numeracy scores.ResultsRespondents with lower optimism scores had a higher probability of membership in a class with disordered preferences (P value for optimism coefficient = 0.032). Those with lower self-reported numeracy scores were more likely to be in a class with a strong preference for opting out and disordered preferences (P = 0.000) or a class with a preference for opting out and avoiding serious treatment-related risks (P = 0.015).ConclusionsIf respondents with lower optimism and numeracy scores are more likely to choose to opt out or have disordered preferences in a DCE, it may indicate that they have difficulty completing choice tasks.