Neurofunctional Segmentation Shifts in the Hippocampus

The hippocampus is one of the most phylogenetically preserved structures in the mammalian brain. Engaged in a host of diverse cognitive processes, there has been increasing interest in understanding how the hippocampus dynamically supports these functions. One of the lingering questions is how to reconcile the seemingly disparate cytoarchitectonic organization, which favors a dorsal-ventral layering, with the neurofunctional topography, which has strong support for longitudinal axis (anterior-posterior) and medial-lateral orientation. More recently, meta-analytically driven (e.g., big data) approaches have been employed, however, the question remains whether they are sensitive to important task-specific features such as context, cognitive processes recruited, or the type of stimulus being presented. Here, we used hierarchical clustering on functional magnetic resonance imaging (fMRI) data acquired from healthy individuals at 7T using a battery of tasks that engage the hippocampus to determine whether stimulus or task features influence cluster profiles in the left and right hippocampus. Our data suggest that resting state clustering appears to favor the cytoarchitectonic organization, while task-based clustering favors the neurofunctional clustering. Furthermore, encoding tasks were more sensitive to stimulus type than were recognition tasks. Interestingly, a face-name paired associate task had nearly identical clustering profiles for both the encoding and recognition conditions of the task, which were qualitatively morphometrically different than simple encoding of words or faces. Finally, corroborating previous research, the left hippocampus had more stable cluster profiles compared to the right hippocampus. Together, our data suggest that task-based and resting state cluster profiles are different and may account for the disparity or inconsistency in results across studies.

ALF-Score+ - Personalization of a Predictive Network-Based Walkability Scoring System

Measuring environments around us (cities, roads, social environments) is crucial to understand human behaviour and help predict how aspects of environment influence behaviour and health. Walkability is one measure of environment used to predict health. Walkability combines aspects of environment (population, roads, amenities) into a single score. Existing measures are often one-size-fits-all with very limited personalization. In our previous work, we defined Active Living Feature Score, ALF-Score, a novel approach to measure network-based walkability. ALF-Score uses road network structures and points of interest to generate models capable of estimating walkability for any point on map. One of ALF-Score's contributions was the inclusion of user opinions to partially address the different perception among individuals and help derive a more personalized walkability score. Here, we take this personalization much further by introducing ALF-Score+ which uses individual user demographics (age, gender, ...) grouped using k-means and t-distributed stochastic neighbor embedding to create clusters based on individuals’ demographic characteristics. Each cluster is treated as a single profile representing a subset of users. Cluster profiles are added into our pipelines to generate profile-specific network-based walkability models. Results show strong variability among scores generated for each cluster profile with a clear variation in walkability generated for different users within same clusters. ALF-Score+ maintains an accuracy of 90.48% on average showing improvement compared to ALF-Score. We found strong association between cluster profiles' demographics and their scores. ALF-Score+ shows promising results providing personalized walkability based on cluster profiles, instead of a one-size-fits-all approach used by other walkability measures.

Associations among physical activity tracking, physical activity motivation and level of physical activity in young adults

Whether physical activity (PA) tracking devices are associated with PA motivation in young adults is largely unknown. We compared total PA minutes per week, total minutes walking/week, meeting moderate-to vigorous PA guidelines, and past-year activity tracking across motivation cluster profiles among 799 young adults. Participants with “self-determined” profiles reported the highest total PA minutes/week followed by participants with “low intrinsic,” “controlled self-determined,” and “high external” profiles. A behavior regulation profile X activity tracking frequency interaction was not significant. Behavior regulation profiles may need to be considered in PA interventions using activity trackers.

Examining the Evolution of E-Government Development of Nations Through Machine Learning Techniques

Several initiatives have tried to measure the efforts nations have made towards developing e-government. The UN E-Government Development Index (EGDI) is the only global report that ranks and classifies the UN Member States into four categories based on a weighted average of normalized scores on online service, telecom infrastructure, and human capital. The authors argue that the EGDI fails in showing the efforts of nations over time and in informing nations and policymakers as to what and from whom to draw policy lessons. Using the UN EGDI data from 2008 to 2020, they profile the UN Member States and show the relevance of machine learning techniques in addressing these issues. They examine the resulting cluster profiles in terms of theoretical perspectives in the literature and derive policy insights from the different groupings of nations and their evolution over time. Finally, they discuss the policy implications of the proposed methodology and the insights obtained.

Clustering of Physical Activity and Sedentary Behavior Associated With Body Composition in Brazilian Older Adults

This study described the clustering patterns of moderate to vigorous physical activity and sedentary time (ST) according to handgrip strength and investigated the association between identified clusters of fat and lean mass in older adults from southern Brazil. Objective measures were used for moderate to vigorous physical activity, ST, and body composition outcomes. Two-step cluster and linear regression analyses were conducted according to handgrip strength. Three clusters were identified: all-day sitters, sitters, and active sitters. The prevalence of clusters in the low-strength group was 58.2%, 22.8%, and 19.0%, respectively, while the prevalence of clusters in the high-strength group was 42.1%, 34.8%, and 23.1%, respectively. All-day sitters had 2.6% more fat mass than active sitters with low strength. High levels of ST characterized all cluster profiles; low strength, lack of moderate to vigorous physical activity, and high ST levels among older adults may indicate a subpopulation at a greater risk of overweight and obesity-related diseases.

CODEX Weak Lensing Mass Catalogue and implications on the mass-richness relation

The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at 0.35 < z < 0.65. It was selected from ROSAT data in the 10,000 square degrees of overlap with BOSS, mapping a total number of 2770 high-z galaxy clusters. We present here the full results of the CFHT CODEX program on cluster mass measurement, including a reanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster masses. We employ lensfit shape measurement and perform a conservative colour-space selection and weighting of background galaxies. Using the combination of shape noise and an analytic covariance for intrinsic variations of cluster profiles at fixed mass due to large scale structure, miscentring, and variations in concentration and ellipticity, we determine the likelihood of the observed shear signal as a function of true mass for each cluster. We combine 25 individual cluster mass likelihoods in a Bayesian hierarchical scheme with the inclusion of optical and X-ray selection functions to derive constraints on the slope α, normalization β, and scatter σln λ|μ of our richness–mass scaling relation model in log-space: <ln λ|μ > =αμ + β, with μ = ln (M200c/Mpiv), and Mpiv = 1014.81M⊙. We find a slope $\alpha = 0.49^{+0.20}_{-0.15}$, normalization $\exp (\beta ) = 84.0^{+9.2}_{-14.8}$ and $\sigma _{\ln \lambda | \mu } = 0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other weak lensing richness-mass relations, we find the normalization of the richness statistically agreeing with the normalization of other scaling relations from a broad redshift range (0.0 < z < 0.65) and with different cluster selection (X-ray, Sunyaev-Zeldovich, and optical).

Assessing the Multidimensionality of Students’ Learning Orientations: The Use of LO-COMPASS for the Well-being and Scholastic Success

The twofold aim of the present study is to identify specific cluster-profiles of the learning orientations measured by «LO-COMPASS: Learning Orientation-Cognition Metacognition Participation Assessment»; and to create a psychometric rule to cluster the raw scores obtained by the student at the LO-COMPASS factorial dimensions into a specific cluster-profile. 183 middle-school students (91 males and 92 females) validly completed the original version of the LO-COMPASS Questionnaire. Confirmatory factor analysis and cluster analysis were conducted. LO-COMPASS measures four factors of students’ learning orientations. Furthermore, the instrument has been furnished with a psychometric rule to cluster the raw scores obtained by the student at the LO-COMPASS factorial dimensions into two profiles. The application of LO-COMPASS will allow educational psychologists and teachers to analyze middle-school students’ difficulties and problems, as well as strengths in their motivation to learn. The instrument will be useful at multiple levels: prevention, intervention, evaluation.

Insights on the taxonomy and ecogenomics of the Synechococcus collective

ABSTRACTThe genus Synechococcus (also named Synechococcus collective, SC) is a major contributor to global primary productivity. It is found in a wide range of aquatic ecosystems. Synechococcus is metabolically diverse, with some lineages thriving in polar and nutrient-rich locations, and other in tropical riverine waters. Although many studies have discussed the ecology and evolution of Synechococcus, there is a paucity of knowledge on the taxonomic structure of SC. Only a few studies have addressed the taxonomy of SC, and this issue still remains largely ignored. Our aim was to establish a new classification system for SC. Our analyses included comparing GC% content, genome size, pairwise Average Amino acid Identity (AAI) values, phylogenomics and gene cluster profiles of 170 publicly available SC genomes. All analyses were consistent with the discrimination of 11 genera, from which 2 are newly proposed (Lacustricoccus and Synechospongium). The new classification is also consistent with the habitat distribution (seawater, freshwater and thermal environments) and reflects the ecological and evolutionary relationships of SC. We provide a practical and consistent classification scheme for the entire Synechococcus collective.