Evidence for Three Subgroups of Female FMR1 Premutation Carriers Defined by Distinct Neuropsychiatric Features: A Pilot Study

Over 200 Cytosine-guanine-guanine (CGG) trinucleotide repeats in the 5′ untranslated region of the Fragile X mental retardation 1 (FMR1) gene results in a “full mutation,” clinically Fragile X Syndrome (FXS), whereas 55 – 200 repeats result in a “premutation.” FMR1 premutation carriers (PMC) are at an increased risk for a range of psychiatric, neurocognitive, and physical conditions. Few studies have examined the variable expression of neuropsychiatric features in female PMCs, and whether heterogeneous presentation among female PMCs may reflect differential presentation of features in unique subgroups. In the current pilot study, we examined 41 female PMCs (ages 17–78 years) and 15 age-, sex-, and IQ-matched typically developing controls (TDC) across a battery of self-report, eye tracking, expressive language, neurocognitive, and resting state EEG measures to determine the feasibility of identifying discrete clusters. Secondly, we sought to identify the key features that distinguished these clusters of female PMCs. We found a three cluster solution using k-means clustering. Cluster 1 represented a psychiatric feature group (27% of our sample); cluster 2 represented a group with executive dysfunction and elevated high frequency neural oscillatory activity (32%); and cluster 3 represented a relatively unaffected group (41%). Our findings indicate the feasibility of using a data-driven approach to identify naturally occurring clusters in female PMCs using a multi-method assessment battery. CGG repeat count and its association with neuropsychiatric features differ across clusters. Together, our findings provide important insight into potential diverging pathophysiological mechanisms and risk factors for each female PMC cluster, which may ultimately help provide novel and individualized targets for treatment options.

Considerations of Use-Use Interactions between Macroalgae Cultivation and Other Maritime Sectors: An Eastern Baltic MSP Case Study

With the blue economic sectors growing, marine macroalgae cultivation plays an important role in securing food and energy supplies, as well as better water quality in sustainable ways, whether alone or as part of a cluster solution to mitigate the effects of fish farming. While macroalgae cultivation exists in Europe, it is not that widely distributed yet; with increasing marine activities at sea, Maritime Spatial Planning (MSP) needs to ensure social recognition as well as social and spatial representation for such a new marine activity. This comparative case study analysis of MSPs of three eastern Baltic Sea countries explores the levels of support for the development of macroalgae cultivation in MSP and the degree of co-location options for this new and increasingly important sector. It presents new analytical ways of incorporating co-location considerations into the concept of social sustainability. The results of this study support the harmonisation of views on co-location, propose ways of using space to benefit multiple users as well as marine ecosystems, and highlight some of the key social challenges and enablers for this sector.

A cluster analytic examination and validation of adult victim sexual offending subtypes in two Canadian samples

The development and validation of sexual offense perpetrator typologies remains a useful endeavor with implications for theory and correctional/clinical practice. Most such typologies—which rely on factors such as the individual’s motivation for offending—have not been validated empirically. The current study utilized a validated sexual violence risk-needs instrument, the Violence Risk Scale—Sexual Offense version (VRS-SO; Wong, Olver, Nicholaichuk, & Gordon [2003, 2017], Regional Psychiatric Centre and University of Saskatchewan, Saskatoon, Canada), to develop and validate an empirically-derived adult victim sexual offense (AVSO) typology through model-based cluster analysis of dynamic risk-need domains. The study featured two treated samples of men (n = 283 and 169) convicted for contact sexual offenses against adult victims. A three-cluster solution was identified and replicated across the two samples: high antisociality high deviance (HA-HD), high antisociality low deviance (HA-LD), and low antisociality low deviance (LA-LD). External validation analyses demonstrated that HA-HD men had more dense sexual offense histories, were more likely to be diagnosed with a paraphilia, and had the highest rates of sexual recidivism (Sample 2 only). By contrast, the HA-LD men had greater concerns on indexes of nonsexual criminality, particularly high base rates of antisocial personality and substance use disorders, and high rates of general violent recidivism (particularly Sample 1). The findings suggest that the VRS-SO factors may have utility in discriminating between AVSO types to inform sexual offending theory, case formulation, and risk management.

Evaluation of Postural Sway in Post-stroke Patients by Dynamic Time Warping Clustering

Post-stroke complications are the second most frequent cause of death and the third leading cause of disability worldwide. The motor function of post-stroke patients is often assessed by measuring the postural sway in the patients during quiet standing, based on sway measures, such as sway area and velocity, which are obtained from temporal variations of the center of pressure. However, such approaches to establish a relationship between the sway measures and patients' demographic factors have hardly been successful (e.g., days after onset). This study instead evaluates the postural sway features of post-stroke patients using the clustering method of machine learning. First, we collected the stroke patients' multi-variable motion-capture standing-posture data and processed them into t s long data slots. Then, we clustered the t-s data slots into K cluster groups using the dynamic-time-warping partition-around-medoid (DTW-PAM) method. The DTW measures the similarity between two temporal sequences that may vary in speed, whereas PAM identifies the centroids for the DTW clustering method. Finally, we used a post-hoc test and found that the sway amplitudes of markers in the shoulder, hip, knee, and center-of-mass are more important than their sway frequencies. We separately plotted the marker amplitudes and frequencies in the medial-lateral direction during a 5-s data slot and found that the post-stroke patients' postural sway frequency lay within the bandwidth of 0.5–1.5 Hz. Additionally, with an increase in the onset days, the cluster index of cerebral hemorrhage patients gradually transits in a four-cluster solution. However, the cerebral infarction patients did not exhibit such pronounced transitions over time. Moreover, we found that the postural-sway amplitude increased in clusters 1, 3, and 4. However, the amplitude of cluster 2 did not follow this pattern, owing to age effects related to the postural sway changes with age. A rehabilitation doctor can utilize these findings as guidelines to direct the post-stroke patient training.

Modeling the differences in the time-series profiles of new COVID-19 daily confirmed cases in 3,108 contiguous U.S. counties: A retrospective analysis

Objective The COVID-19 pandemic in the U.S. has exhibited a distinct multiwave pattern beginning in March 2020. Paradoxically, most counties do not exhibit this same multiwave pattern. We aim to answer three research questions: (1) How many distinct clusters of counties exhibit similar COVID-19 patterns in the time-series of daily confirmed cases? (2) What is the geographic distribution of the counties within each cluster? and (3) Are county-level demographic, socioeconomic and political variables associated with the COVID-19 case patterns? Materials and methods We analyzed data from counties in the U.S. from March 1, 2020 to January 2, 2021. Time series clustering identified clusters in the daily confirmed cases of COVID-19. An explanatory model was used to identify demographic, socioeconomic and political variables associated with the outbreak patterns. Results Three patterns were identified from the cluster solution including counties in which cases are still increasing, those that peaked in the late fall, and those with low case counts to date. Several county-level demographic, socioeconomic, and political variables showed significant associations with the identified clusters. Discussion The pattern of the outbreak is related both to the geographic location within the U.S. and several variables including population density and government response. Conclusion The reported pattern of cases in the U.S. is observed through aggregation of the daily confirmed COVID-19 cases, suggesting that local trends may be more informative. The pattern of the outbreak varies by county, and is associated with important demographic, socioeconomic, political and geographic factors.

Sensor-Based Categorization of Upper Limb Performance in Daily Life of Persons With and Without Neurological Upper Limb Deficits

Background: The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits.Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

Critical Civic Motivation of Marginalized Youth: An Expectancy-Value Approach

The present study advances a model of critical civic motivation grounded in expectancy-value theory and highlights diverse manifestations of motivation among marginalized adolescents. The participants were 447 high school youth (85.0% Latinx; 62.9% low-income). Two complementary methodological approaches were employed to examine civic motivation, conceptualized as expectancies (individual and collective), and values (interest and attainment). First, regression analyses found that civic expectancies and values differentially predicted behavioral outcomes of service and activism. Expectancies and values interactively predicted activism, whereas only main effects of individual and collective efficacy and interest value positively predicted service. Second, person-centered analyses yielded a five-cluster solution, with one-third of the participants exhibiting discrepancies between expectancies and values. Distinct patterns in the manifestations of civic motivation components may inform practices that support civic engagement of marginalized youth. Overall, the findings illustrate the utility of a holistic, critical framework that integrates established literatures on political efficacy and interest.

Sustainable campus using concept mapping: a bottom-up approach engaging both staff and students

Purpose This paper aims to highlight a multidimensional model of sustainability practices that are considered meaningful and practical for implementation by higher education institutions (HEIs). Design/methodology/approach The Concept System® Global MAX© software facilitated the bottom-up approach of this study, where 32 staff and students of a private HEI were prompted with the question, “What design features or facilities should the university building have for you to consider the building sustainable?” Their opinions were collected as statements via online brainstorming and the process was followed through with the sorting and rating of reduced statements. The software was also used to perform multi-dimensional scaling and cluster analysis to generate the findings of this study. Findings An eight-cluster solution was proposed based on the themes within respective clusters. Respondents rated the importance of sustainability resource usage and sustainability and technology highly. Whilst sustainable campus facilities appeal to students, education for sustainable development were more important to staff. Originality/value The community-based participatory research recommends activities that enhance the adoption of sustainable development in making universities a “living lab” for sustainability.

An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences

The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.

Machine Learning based Pricing Methodology for the Logistic Domain: a Preliminary Approach

In this work, we introduce a new methodology to discover logistic regions for pricing. We use value-based characteristics from different sources, such as demographic, socioeconomic, risk, transportation, among others, to find homogeneous and valuable pricing regions. The problem was formulated as a traditional cluster solution, where well-know metrics, such as BIC and silhouette score, were used for technical validation, and business premises and constraints, operational and sales, where used to enrich feature engineering and refine cluster formation. The results presented here are from a preliminary work that was validated through several sessions with stakeholders of interest, but it is still missing the market validation. Indeed, this work will be deployed soon and a more detailed validation process, including client adherence, will be performed and monitored until the end of this year.