Group averaging and the Gini deviation

It is known that partitioning a society into groups with subsequent averaging in each group decreases the Gini coefficient. The resulting Lorenz function is piecewise linear. This study deals with a natural question: by how much the Gini coefficient could decrease when passing to a piecewise linear Lorenz function? Obtained results are quite illustrative (since they are expressed in terms of the geometric parameters of the polygon Lorenz curve, such as the lengths of its segments and the angles between successive segments) upper bound estimates for the maximum possible change in the Gini coefficient with a restriction on the group shares, or on the difference between the averaged values of the attribute for consecutive groups. It is shown that there exist Lorenz curves with the Gini coefficient arbitrarily close to one, and at the same time with the Gini coefficient of the averaged society arbitrarily close to zero.

Texting Older Sisters to Step (TOSS) Using Fitbits to Promote Physical Activity: A Feasibility Study

Black women are disproportionately diagnosed with obesity (BMI ≥ 30 kg/m2). Obesity is a preventable but complex, public health problem that is multifaceted, chronic, and approximately 58% of Black women 60 years and older are classified as obese, compared to 38% of their White counterparts. This 12 week, pre/post, 2-group study aimed to determine if a peer-informed physical activity (PA) intervention with peer support would be feasible among community- dwelling, obese, older Black women to promote regular PA. Forty-eight potential participants were screened, 24 categorized as obese were enrolled and completed the study. The mean age was 64 (SD 3.0) years. Steps were measured by a Fitbit-Inspire with data successfully collected on 98% of days with the treatment group averaging a daily increase of 700-steps more than the control. Evaluation of intervention’s acceptability revealed that 100% enjoyed the study and using the Fitbit device. Text message readability was 100% and 95% said the study was motivational. Additionally, 8.3% said daily prompts were too frequent, 12% indicated that future studies should include additional social support, and 88% did not comment on the Fitbit community option for support, suggesting that this feature was not practical. Findings demonstrated that this intervention meets the criteria of being scalable, low cost, feasible, and acceptable for the older Black women. Using self-monitoring techniques in combination with at least one other behavioral strategy, such as our TOSS messages (cues for motivation) as the delivery channel for health promotion messages are a promising approach to increase PA behaviors.

Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Jumping over Baselines with New Methods to Predict Activation Maps from Resting-state fMRI

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on 'connectome fingerprinting'. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Grasslands on Coastal Headlands in New South Wales, south eastern Australia

Aims: To use unsupervised techniques to produce a hierarchical classification of grasslands on coastal headlands of New South Wales in eastern Australia. Methods: A dataset of 520 vegetation plots scored on cover and placed across grasslands on coastal headlands (ca. 2000 km of coastline). Vegetation assemblages were identified with the aid of a clustering method based on group averaging and tested using similarity profile analysis (SIMPROF) using Bray-Curtis similarity. A hierarchical schema was developed based on EcoVeg hierarchy and was circumscribed using positive and negative diagnostic taxa via similarity percentage analysis (SIMPER) and importance based on summed cover scores and frequency. Mapping the occurrences grasslands was initially constructed using remote sensing which was verified and modified with on ground observations. Results: One group Themeda – Pultenaea – Zoysia – Cynodon grasslands and heathy grasslands was defined to include all coastal headland grassland vegetation of the New South Wales, and within this, three alliances and ten associations. Only one of the circumscribed associations is represented within the current state classification schema. In total 107 ha were mapped of which 68 ha occurred within secure conservation tenure. Conclusions: A number of unique and rare grassland assemblages on coastal headlands have to date gone undescribed. The most common alliance constitutes approximately 87% of extant grassland occurrences but is currently the only type listed as endangered and afforded protection. Although Poa spp. are listed as a threat to Themeda dominated assemblages on headlands data from this study suggest that this is unlikely to be the case. Taxonomic reference: PlantNET (http://plantnet/10rbgsyd.nsw.gov.au/; accessed June 2019). Abbreviations: BC Act = Biodiversity Conservation Act; NMDS = non-metric multidimensional scaling; NSW = New South Wales; PCT = Plant Community Type; SIMPER = similarity percentage analysis; SIMPROF = Similarity profile analysis.

High-Resolution Metabolic Mapping of the Cerebellum Using a Zoomed Magnetic Resonance Spectroscopic Imaging

PurposeThe human cerebellum plays an important role in functional activity cerebrum which is ranging from motor to cognitive activities since due to its relaying role between spinal cord and cerebrum. The cerebellum poses many challenges to magnetic resonance spectroscopic imaging (MRSI) due to the caudal location, the susceptibility to physiological artifacts and partial volume artifact due to its complex anatomical structure. Thus, in present study, we propose a high-resolution MRSI acquisition scheme for the cerebellum.MethodsA zoomed or reduced-field of view (rFOV) metabolite-cycled full-intensity magnetic resonance spectroscopic imaging (MRSI) at 3T with a nominal resolution of 62.5 μL was developed. Single-slice rFOV MRSI data were acquired from the cerebellum of 5 healthy subjects with a nominal resolution of 2.5□×□2.5□mm2 in 9□minutes 36. Spectra were quantified with LCModel. A spatially unbiased atlas template of the cerebellum was used for analyzing metabolite distributions in the cerebellum.ResultsThe high quality of the achieved spectra enabled to generate a high-resolution metabolic map of total N-acetylaspartate, total creatine, total choline, glutamate+glutamine and myo-inositol with Cramér-Rao lower bounds below 50%. A spatially unbiased atlas template of the cerebellum-based region of interest (ROIs) analysis resulted in spatially dependent metabolite distributions in 9 ROIs. The group-averaging across subjects in the Montreal Neurological Institute-152 template space allowed to generate a very high-resolution metabolite maps in the cerebellum.ConclusionThese findings indicate that very high-resolution metabolite probing of cerebellum is feasible using rFOV or zoomed MRSI at 3T.

Within-Subject Consistency of Unimodal and Bimodal Force Application during the Countermovement Jump

Countermovement jump (CMJ) force data are often time-normalized so researchers and practitioners can study the effect that sex, training status, and training intervention have on CMJ strategy: the so-called force–time curve shape. Data are often collected on an individual basis and then averaged across interest-groups. However, little is known about the agreement of the CMJ force–time curve shape within-subject, and this formed the aim of this study. Fifteen men performed 10 CMJs on in-ground force plates. The resulting force–time curves were plotted, with their shape categorized as exhibiting either a single peak (unimodal) or a double peak (bimodal). Percentage-agreement and the kappa-coefficient were used to assess within-subject agreement. Over two and three trials, 13% demonstrated a unimodal shape, 67% exhibited a bimodal shape, and 20% were inconsistent. When five trials were considered, the unimodal shape was not demonstrated consistently; 67% demonstrated a bimodal shape, and 33% were inconsistent. Over 10 trials, none demonstrated a unimodal shape, 60% demonstrated a bimodal shape, and 40% were inconsistent. The results of this study suggest that researchers and practitioners should ensure within-subject consistency before group averaging CMJ force–time data, to avoid errors.