Ego-Lane Index Estimation Based on Lane-Level Map and LiDAR Road Boundary Detection

Correct ego-lane index estimation is essential for lane change and decision making for intelligent vehicles, especially in global navigation satellite system (GNSS)-challenged environments. To achieve this, we propose an ego-lane index estimation approach in an urban scenario based on particle filter (PF). The particles are initialized and propagated by dead reckoning with inertial measurement unit (IMU) and odometry. A lane-level map is used to navigate the particles taking advantage of topologic and geometric information of lanes. GNSS single-point positioning (SPP) can provide position estimation with meter-level accuracy in urban environments, which can limit drift introduced by dead reckoning for updating the weight of each particle. Light detection and ranging (LiDAR) is a common sensor in an intelligent vehicle. A LiDAR-based road boundary detection method provides distance measurements from the vehicle to the left/right road boundaries, which provides a measurement for importance weighting. However, the high precision of the LiDAR measurements may put a tight constraint on the distribution of particles, which can lead to particle degeneration with sparse particle sets. To deal with this problem, we propose a novel step that shifts particles laterally based on LiDAR measurements instead of importance weighting in the traditional PF scheme. We tested our methods on an urban expressway at a low traffic volume period to ensure road boundaries can be detected by LiDAR measurements at most time steps. Experimental results prove that our improved PF scheme can correctly estimate ego-lane index at all time steps, while the traditional PF scheme produces wrong estimations at some time steps.

High trait variability in optimal polygenic prediction strategy within multiple-ancestry cohorts

Polygenic scores (PGS) are individual-level measures that quantify the genetic contribution to a given trait. PGS have predominantly been developed using European ancestry samples and recent studies have shown that the predictive performance of European ancestry-derived PGS is lower in non-European ancestry samples, reflecting differences in linkage disequilibrium, variant frequencies, and variant effects across populations. However, the problem of how best to maximize performance within any one ancestry group given the data available, and the extent to which this varies between traits, are largely unexplored. Here, we investigate the effect of sample size and ancestry composition on the predictive performance of PGS for fifteen traits in UK Biobank and evaluate an importance reweighting approach that aims to counteract the under-representation of certain groups within training data. We find that, for a minority of the traits, PGS estimated using a relatively small number of Black/Black British individuals outperformed, on a Black/Black British test set, scores estimated using a much larger number of White individuals. For example, a PGS for mean corpuscular volume trained on only Black individuals achieved a 4-fold improvement on a corresponding PGS trained on only White individuals. For the remainder of the traits, the reverse was true; a PGS for height trained on only Black/Black British individuals explained less than 0.5% of the variance in height in a Black/Black British test set, compared to 3.9% for a PGS trained on a much larger training set consisting of only White individuals. We find that while importance weighting provides moderate benefit for some traits (for example, 40% improvement for mean corpuscular volume compared to no reweighting), the improvement is modest in most cases, arguing that only targeted collection of data from underrepresented groups can address differences in PGS performance.

Landslide Risk Mapping in an Urban Area of the City of Natal, Brazil

Landslides are part of the natural processes of Earth’s surface dynamic, which could be accelerated or triggered by anthropic interference. Inadequate occupation of areas highly susceptible to landslide processes is the principal cause of accidents on Brazilian urban slopes, especially those occupied by settlements and slums. In Natal, Rio Grande do Norte state, Brazil, the existence of areas with steep and densely occupied slopes makes the municipality susceptible to landslides. In this context, the present study aimed to map the risk of landslides in an urban area located in the city of Natal. Using the quali-quantitative model proposed by Faria (2011), adapted for the conditions of the study area, which applies a multicriteria analytical hierarchy process (AHP) to a Geographic Information System (GIS), 11 risk indicators were submitted to pairwise comparisons by 10 risk management specialists in order to determine the relative importance (weighting) for each of these factors as a function of their contribution to the risk. The weightings obtained were combined to produce the final risk map of the study area, using a map algebra framework. The results show the existence of a critical risk for the resident population, primarily related to the possibility of a landslide, with potentially negative economic, environmental, and mainly social impacts.

Validity of the older people quality of life-7 domains (OQoL-7) scale

Background The Older people Quality of Life-7 domains (OQoL-7) is a 28-item multidimensional questionnaire developed to measure community-dwelling older people’s QoL. The OQoL-7 assesses both importance of and satisfaction in seven QoL domains (Material resources; Close entourage; Social and cultural life; Esteem and recognition; Health and mobility; Feeling of safety; and Autonomy). This study aimed to investigate concurrent and construct validity of the OQoL-7. A secondary aim was to compare different methods of weighting participants’ ratings of satisfaction according to their individual ratings of importance, as compared to the OQoL-7 total score (unweighted). Methods Data came from the first and second samples of the Lausanne cohort 65+ study, assessed at the same age of 72–77 years in 2011 (N = 1117) and 2016 (N = 1091), respectively. To assess concurrent validity, the OQoL-7 was compared to other measures of the same concept (single QoL item) or related concepts (self-rated health, SF-12). Construct validity was tested by comparing subscores in the seven QoL domains in the presence and absence of two stressful events during the preceding year (financial difficulties and relationship difficulties). The effect of importance weighting was assessed using moderated regression analysis. Results The OQoL-7 total score was significantly associated with the single QoL item (Spearman’s rho 0.46), self-rated health (Spearman’s rho 0.34), SF-12 physical (Spearman’s rho 0.22) and mental (Spearman’s rho 0.28) component scores. Large differences (Cohen’s d > 0.8) were observed in the presence or absence of stressful events in the expected QoL domains: “Material resources” in the presence or absence of “Financial difficulties” (Cohen’s d 1.34), and “Close entourage” in the presence or absence of “Relationship difficulties” (Cohen’s d 0.84). Importance weighting resulted in a very small improvement in the prediction of the single QoL item (ΔR2 0.018). All results were highly consistent across 2011 and 2016 samples. Conclusions The OQoL-7 showed adequate concurrent and construct validity in two samples of older people. In future studies, the decision to use weighted or unweighted scores will depend on the priority given to either optimizing the prediction of QoL or limiting the burden on respondents and the amount of missing data.

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. α-helices and β-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.

Validity of Importance Weighting in Subjective Well-Being Research

The practice of giving certain life domains (such as health, family life) more weight in calculating an overall score, known as importance weighting, has been a subject of debate in subjective well-being (SWB) research for decades. In this paper, we present evidence by analyzing data from 513 Chinese adults to caution readers that findings of importance weighting in the SWB studies should be interpreted carefully. Given the many unsettled issues in assessing importance weighting, findings are often not clear-cut or definitive and overgeneralization can mislead our understanding of well-being. Future research on the topic of importance weighting should acknowledge study assumptions and limitations to avoid potential overgeneralization of findings.