Can genetic screening be used to personalize education for students? Genome-wide association studies (GWAS) screen an individual’s DNA for specific variations in their genome, and how said variations relate to specific traits. The variations can then be assigned a corresponding weight and summed to produce polygenic scores (PGS) for given traits. see more Though first developed for disease risk, PGS is now used to predict educational achievement. Using a novel simulation method, this paper examines if PGS could advance screening in schools, a goal of personalized education. Results show limited potential benefits for using PGS to personalize education for individual students. However, further analysis shows PGS can be effectively used alongside progress monitoring measures to screen for learning disability risk. Altogether, PGS is not useful in personalizing education for every child but has potential utility when used simultaneously with additional screening tools to help determine which children may struggle academically.This study aims to investigate the characteristics of the phenotype and management of chronic obstructive pulmonary disease (COPD) patients in the general population in China. We analyzed spirometry-confirmed COPD patients who were identified from a population-based, nationally representative sample in China. All participants were measured with airflow limitation severity based on post-bronchodilator FEV1 percent predicted, bronchodilator responsiveness, exacerbation history, and respiratory symptoms. Among a total of 9134 COPD patients, 90.3% were non-exacerbators, 2.9% were frequent exacerbators without chronic bronchitis, 2.0% were frequent exacerbators with chronic bronchitis, and 4.8% were asthma-COPD overlap. Less than 5% of non-exacerbators ever had pulmonary function testing performed. The utilization rate of inhaled medication in non-exacerbators, exacerbators without chronic bronchitis, exacerbators with chronic bronchitis, and asthma-COPD overlap was 1.4, 23.5, 29.5, and 19.4%, respectively. A comprehensive strategy for the management of COPD patients based on phenotype in primary care is urgently needed.Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer. Although it is a rare subtype, IBC is responsible for roughly 10% of breast cancer deaths. In order to obtain a better understanding of the genomic landscape and intratumor heterogeneity (ITH) in IBC, we conducted whole-exome sequencing of 16 tissue samples (12 tumor and four normal samples) from six hormone-receptor-positive IBC patients, analyzed somatic mutations and copy number aberrations, and inferred subclonal structures to demonstrate ITH. Our results showed that KMT2C was the most frequently mutated gene (42%, 5/12 samples), followed by HECTD1, LAMA3, FLG2, UGT2B4, STK33, BRCA2, ACP4, PIK3CA, and DNAH8 (all nine genes tied at 33% frequency, 4/12 samples). Our data indicated that PTEN and FBXW7 mutations may be considered driver gene mutations for IBC. We identified various subclonal structures and different levels of ITH between IBC patients, and mutations in the genes EIF4G3, IL12RB2, and PDE4B may potentially generate ITH in IBC.Ride-sharing-the combination of multiple trips into one-may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people’s willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and another one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that even a moderate increase in the financial incentives may have a disproportionately large effect on the ride-sharing adoption of individual user groups.The impact of breast cancer-related lymphedema (BCRL) on long-term quality of life is unknown. The aim of this study was to investigate the impact of BCRL on health-related quality of life (HRQoL) up to 10 years after breast cancer treatment. This regional population-based study enrolled patients treated for breast cancer with axillary lymph node dissection between January 1st 2007 and December 31th 2017. Follow up and assessments of the included patients were conducted between January 2019 and May 2020. The study outcome was HRQoL, evaluated with the Lymphedema Functioning, Disability and Health Questionnaire, the Disabilities of the Arm, Shoulder and Hand Questionnaire and the Short Form (36) Health Survey Questionnaire. Multivariate linear logistic regression models adjusted for confounders provided mean score differences (MDs) with 95% confidence intervals in each HRQoL scale and item. This study enrolled 244 patients with BCRL and 823 patients without BCRL. Patients with BCRL had significantly poorer HRQoL than patients without BCRL in 16 out of 18 HRQoL subscales, for example, in physical function (MDs 27, 95%CI 24; 30), mental health (MDs 24, 95%CI 21; 27) and social role functioning (MDs 20, 95%CI 17; 23). Age, BMI, BCRL severity, hand and dominant arm affection had only minor impact on HRQoL (MDs  less then  5), suggesting a high degree of inter-individual variation in coping with lymphedema. This study showed that BCRL is associated with long-term impairments in HRQoL, especially affecting the physical and psychosocial domains. Surprisingly, BCRL diagnosis rather than clinical severity drove the largest impairments in HRQoL.The development of ‘age clocks’, machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson-Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.