The implications of changing education distributions for life expectancy gradients

Recent research has proposed that shifting education distributions across cohorts are influencing estimates of educational gradients in mortality. We use data from the United States and Finland covering four decades to explore this assertion. We base our analysis around our new finding: a negative logarithmic relationship between relative education and relative mortality. This relationship holds across multiple age groups, both sexes, two very different countries, and time periods spanning four decades. The inequality parameters from this model indicate increasing relative mortality differentials over time. We use these findings to develop a method that allows us to compute life expectancy for any given segment of the education distribution (e.g., education quintiles). We apply this method to Finnish and American data to compute life expectancy gradients that are adjusted for changes in the education distribution. In Finland, these distribution-adjusted education differentials in life expectancy between the top and bottom education quintiles have increased by two years for men, and remained stable for women between 1971 and 2010. Similar distribution-adjusted estimates for the U.S. suggest that educational disparities in life expectancy increased by 3.3 years for non-Hispanic white men and 3.0 years for non-Hispanic white women between the 1980s and 2000s. For men and women, respectively, these differentials between the top and bottom education quintiles are smaller than the differentials between the top and bottom education categories by 18% and 39% in the U.S. and by 39% and 100% in Finland. Had the relative inequality parameters of mortality governing the Finnish and U.S. populations remained constant at their earliest period values, the difference in life expectancy between the top and bottom education quintiles would - because of overall mortality reductions - have declined moderately. The findings suggest that educational expansion may bias estimates of trends in educational differences in life expectancy upwards.Peer reviewe

Intralake heterogeneity of thermal responses to climate change: a study of large northern hemisphere lakes

Lake surface water temperature (LSWT) measurements from various sources illustrate that lakes are warming in response to climate change. Most previous studies of geographical distributions of lake warming have tended to utilize data with limited spatial resolution of LSWTs, including single-point time series. Spatially resolved LSWT time-series are now available from satellite observations and some studies have investigated previously the intra-lake warming patterns in specific lakes (e.g., North American Great Lakes). However, across-lake comparisons of intra-lake warming differences have not yet been investigated at a large, across-continental scale, thus limiting our understanding of how intra-lake warming patterns differ more broadly. In this study, we analyze up to 20 years of satellite data from 19 lakes situated across the Northern Hemisphere, to investigate how LSWT changes vary across different lake surfaces. We find considerable intra-lake variability in warming trends across many lakes. The deepest areas of large lakes are characterized by a later onset of thermal stratification, a shorter stratified warming season and exhibit longer correlation timescales of LSWT anomalies. We show that deep areas of large lakes across the Northern Hemisphere as a result tend to display higher rates of warming of summer LSWT, arising from a greater temporal persistence in deep areas of the temperature anomalies associated with an earlier onset of thermal stratification. Utilization of single-point LSWT trends to represent changes in large lakes therefore suppresses important aspects of lake responses to climate change, whereas spatially resolved LSWT measurements can be exploited to provide more comprehensive understanding

3-D density model of the crust of southern and central Finland obtained from joint interpretation of the SVEKALAPKO crustal P-wave velocity models and gravity data

Peer reviewe

Lateralization of face processing in the human brain

Are visual face processing mechanisms the same in the left and right cerebral hemispheres? The possibility of such ‘duplicated processing’ seems puzzling in terms of neural resource usage, and we currently lack a precise characterization of the lateral differences in face processing. To address this need, we have undertaken a three-pronged approach. Using functional magnetic resonance imaging, we assessed cortical sensitivity to facial semblance, the modulatory effects of context and temporal response dynamics. Results on all three fronts revealed systematic hemispheric differences. We found that: (i) activation patterns in the left fusiform gyrus correlate with image-level face-semblance, while those in the right correlate with categorical face/non-face judgements. (ii) Context exerts significant excitatory/inhibitory influence in the left, but has limited effect on the right. (iii) Face-selectivity persists in the right even after activity on the left has returned to baseline. These results provide important clues regarding the functional architecture of face processing, suggesting that the left hemisphere is involved in processing ‘low-level’ face semblance, and perhaps is a precursor to categorical ‘deep’ analyses on the right.John Merck FundSimons FoundationJames S. McDonnell FoundationNational Eye Institute (NIH, grant number R21-EY015521

A Match in Time Saves Nine: Deterministic Online Matching With Delays

We consider the problem of online Min-cost Perfect Matching with Delays (MPMD) introduced by Emek et al. (STOC 2016). In this problem, an even number of requests appear in a metric space at different times and the goal of an online algorithm is to match them in pairs. In contrast to traditional online matching problems, in MPMD all requests appear online and an algorithm can match any pair of requests, but such decision may be delayed (e.g., to find a better match). The cost is the sum of matching distances and the introduced delays. We present the first deterministic online algorithm for this problem. Its competitive ratio is $O(m^{\log_2 5.5})$ $= O(m^{2.46})$, where $2 m$ is the number of requests. This is polynomial in the number of metric space points if all requests are given at different points. In particular, the bound does not depend on other parameters of the metric, such as its aspect ratio. Unlike previous (randomized) solutions for the MPMD problem, our algorithm does not need to know the metric space in advance

An empirical cognitive model of the development of shared understanding of requirements

It is well documented that customers and software development teams need to share and refine understanding of the requirements throughout the software development lifecycle. The development of this shared understand- ing is complex and error-prone however. Techniques and tools to support the development of a shared understanding of requirements (SUR) should be based on a clear conceptualization of the phenomenon, with a basis on relevant theory and analysis of observed practice. This study contributes to this with a detailed conceptualization of SUR development as sequence of group-level state transi- tions based on specializing the Team Mental Model construct. Furthermore it proposes a novel group-level cognitive model as the main result of an analysis of data collected from the observation of an Agile software development team over a period of several months. The initial high-level application of the model shows it has promise for providing new insights into supporting SUR development

Soccer Team Vectors

In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.Comment: 11 pages, 1 figure; This paper was presented at the 6th Workshop on Machine Learning and Data Mining for Sports Analytics at ECML/PKDD 2019, W\"urzburg, Germany, 201

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments

Background: Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIPseq)is increasingly being applied to study transcriptional regulation on a genome-wide scale. Whilenumerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, theirrelative merits and potential limitations remain unclear in practical applications.Results: The present study compares the state-of-the-art algorithms for detecting transcriptionfactor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings.First, we demonstrate how the biological conclusions may change dramatically when the differentalgorithms are applied. The reproducibility across biological replicates is then investigated as aninternal validation of the detections. Finally, the predicted binding sites with each method arecompared to high-scoring binding motifs as well as binding regions confirmed in independent qPCRexperiments.Conclusions: In general, our results indicate that the optimal choice of the computationalapproach depends heavily on the dataset under analysis. In addition to revealing valuableinformation to the users of this technology about the characteristics of the binding site detectionapproaches, the systematic evaluation framework provides also a useful reference to thedevelopers of improved algorithms for ChIP-seq data

ILoReg: a tool for high-resolution cell population identification from single-cell RNA-seq data

Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging.\nWe introduce ILoReg, an R package implementing a new cell population identification method that improves identification of cell populations with subtle differences through a probabilistic feature extraction step that is applied before clustering and visualization. The feature extraction is performed using a novel machine learning algorithm, called iterative clustering projection (ICP), that uses logistic regression and clustering similarity comparison to iteratively cluster data. Remarkably, ICP also manages to integrate feature selection with the clustering through L1-regularization, enabling the identification of genes that are differentially expressed between cell populations. By combining solutions of multiple ICP runs into a single consensus solution, ILoReg creates a representation that enables investigating cell populations with a high resolution. In particular, we show that the visualization of ILoReg allows segregation of immune and pancreatic cell populations in a more pronounced manner compared with current state-of-the-art methods.\nILoReg is available as an R package at https://bioconductor.org/packages/ILoReg.\nSupplementary data are available at Supplementary Information and Supplementary Files 1 and 2.\nMOTIVATION\nRESULTS\nAVAILABILITY\nSUPPLEMENTARY INFORMATIO

Weight Loss Trajectories in Healthy Weight Coaching : Cohort Study

Background: As global obesity prevalence continues to increase, there is a need for accessible and affordable weight management interventions, such as web-based programs. Objective: This paper aims to assess the outcomes of healthy weight coaching (HWC), a web-based obesity management program integrated into standard Finnish clinical care. Methods: HWC is an ongoing, structured digital 12-month program based on acceptance and commitment therapy. It includes weekly training sessions focused on lifestyle, general health, and psychological factors. Participants received remote one-on-one support from a personal coach. In this real-life, single-arm, prospective cohort study, we examined the total weight loss, weight loss profiles, and variables associated with weight loss success and program retention in 1189 adults (963 women) with a BMI >25 kg/m(2) among participants of the program between October 2016 and March 2019. Absolute (kg) and relative (%) weight loss from the baseline were the primary outcomes. We also examined the weight loss profiles, clustered based on the dynamic time-warping distance, and the possible variables associated with greater weight loss success and program retention. We compared different groups using the Mann-Whitney test or Kruskal-Wallis test for continuous variables and the chi-squared test for categorical variables. We analyzed changes in medication using the McNemar test. Results: Among those having reached the 12-month time point (n=173), the mean weight loss was 4.6% (SE 0.5%), with 43% (n=75) achieving clinically relevant weight loss (>= 5%). Baseline BMI >= 40 kg/m(2) was associated with a greater weight loss than a lower BMI (mean 6.6%, SE 0.9%, vs mean 3.2%, SE 0.6%; P=.02). In addition, more frequent weight reporting was associated with greater weight loss. No significant differences in weight loss were observed according to sex, age, baseline disease, or medication use. The total dropout rate was 29.1%. Dropouts were slightly younger than continuers (47.2, SE 0.6 years vs 49.2, SE 0.4 years; P=.01) and reported their weight less frequently (3.0, SE 0.1 entries per month vs 3.3, SE 0.1 entries per month; P Conclusions: A comprehensive web-based program such as HWC is a potential addition to the repertoire of obesity management in a clinical setting. Heavier patients lost more weight, but weight loss success was otherwise independent of baseline characteristics.Peer reviewe