Affective norms for italian words in older adults: Age differences in ratings of valence, arousal and dominance

In line with the dimensional theory of emotional space, we developed affective norms for words rated in terms of valence, arousal and dominance in a group of older adults to complete the adaptation of the Affective Norms for English Words (ANEW) for Italian and to aid research on aging. Here, as in the original Italian ANEW database, participants evaluated valence, arousal, and dominance by means of the Self-Assessment Manikin (SAM) in a paper-and-pencil procedure. We observed high split-half reliabilities within the older sample and high correlations with the affective ratings of previous research, especially for valence, suggesting that there is large agreement among older adults within and across-languages. More importantly, we found high correlations between younger and older adults, showing that our data are generalizable across different ages. However, despite this across-ages accord, we obtained age-related differences on three affective dimensions for a great number of words. In particular, older adults rated as more arousing and more unpleasant a number of words that younger adults rated as moderately unpleasant and arousing in our previous affective norms. Moreover, older participants rated negative stimuli as more arousing and positive stimuli as less arousing than younger participants, thus leading to a less-curved distribution of ratings in the valence by arousal space. We also found more extreme ratings for older adults for the relationship between dominance and arousal: older adults gave lower dominance and higher arousal ratings for words rated by younger adults with middle dominance and arousal values. Together, these results suggest that our affective norms are reliable and can be confidently used to select words matched for the affective dimensions of valence, arousal and dominance across younger and older participants for future research in aging. Figure

Group guided low intensity self-help for community dwelling older adults experiencing low mood : a dissertation presented in partial fulfillment of the requirements for the degree of Doctor of Clinical Psychology, Massey University, Albany, New Zealand

Depression is amongst the most common health issues affecting older adults, however, access to evidence-based psychological treatments remains low amongst this age group. This is due, in part, to numerous barriers that surround current mental health treatment and delivery, which has contributed to discrepancies between treatment needs, availability, and uptake. To address such barriers, low intensity Cognitive Behavioural Therapies (LI-CBT) and in particular guided self-help interventions have emerged as promising, brief, cost-effective, and evidence-based alternatives to traditional high intensity therapies. Recently, interventions have begun to utilise the advantages of guided LI-CBT selfhelp within a group or class setting, thus providing both a cost-effective and time-efficient form of treatment delivery. Of these group guided approaches, Living Life to the Full (LLTTF) is the only intervention that primarily targets depression and has undergone randomised effectiveness testing. While early evidence lends support for the efficacy of LLTTF, further research is needed to extend the findings to different populations and age groups, particularly older adults. The current study examined the effect of the group guided version of LLTTF on community dwelling older adults’ ratings of depression, anxiety, and quality of life. Additionally, the relationship between older adults’ engagement with LLTTF and improvements in their reported ratings on all primary outcome measures was evaluated. Twenty-four older adult participants with symptoms of depression were recruited from a New Zealand community setting. Participants completed the intervention over eight sessions and data was collected at baseline, during each session, and at 1- and 6-week follow-up. Data was analysed using Multilevel Modelling, implementing a multilevel (2 level), repeated measure (11 waves), single group design. Results indicated significant improvements in participants’ symptoms of depression, anxiety, and quality of life over time. There was no evidence of an interaction between participants’ engagement and depression or anxiety ratings. Unexpectedly, engagement did however interact with quality of life, demonstrating that higher levels of out-of-class engagement with self-help content was related to significantly lower improvements in quality of life. Finally, supplementary analyses indicated greater reductions in anxiety symptoms amongst participants who lived with others compared to those who lived alone. These results endorse LLTTF as a viable and effective low intensity treatment option for depression in older adults, with additional benefits for symptoms of anxiety and quality of life. When delivered to older adults, LLTTF could increase treatment access and choice, contribute to the reduction of secondary mental health service load, minimise treatment barriers, and importantly support older adults’ to manage symptoms of depression and anxiety while remaining in communities of their choosing

Parameter estimation of a land surface scheme using multicriteria methods

Attempts to create models of surface-atmosphere interactions with greater physical realism have resulted in land surface schemes (LSS) with large numbers of parameters. The hope has been that these parameters can be assigned typical values by inspecting the literature. The potential for using the various observational data sets that are now available to extract plot-scale estimates for the parameters of a complex LSS via advanced parameter estimation methods developed for hydrological models is explored in this paper. Results are reported for two case studies using data sets of typical quality but very different location and climatological regime (ARM-CART and Tucson). The traditional single-criterion methods were found to be of limited value. However, a multicriteria approach was found to be effective in constraining the parameter estimates into physically plausible ranges when observations on at least one appropriate heat flux and one properly selected state variable are available. Copyright 1999 by the American Geophysical Union

A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information

Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activations, which is designed to help to better understand how these correlations may contribute to generating appropriate motor behavior. The algorithm we propose first divides correlations between muscle synergies into types (noise correlations, quantifying the trial-to-trial covariations of synergy activations at fixed task, and signal correlations, quantifying the similarity of task tuning of the trial-averaged activation coefficients of different synergies), and then uses single-trial methods (task-decoding and information theory) to quantify their overall effect on the task-discriminating information carried by muscle synergy activations. We apply the method to both synchronous and time-varying synergies and exemplify it on electromyographic data recorded during performance of reaching movements in different directions. Our method reveals the robust presence of information-enhancing patterns of signal and noise correlations among pairs of synchronous synergies, and shows that they enhance by 9–15% (depending on the set of tasks) the task-discriminating information provided by the synergy decompositions. We suggest that the proposed methodology could be useful for assessing whether single-trial activations of one synergy depend on activations of other synergies and quantifying the effect of such dependences on the task-to-task differences in muscle activation patterns

A computational pipeline for the diagnosis of CVID patients

Common variable immunodeficiency (CVID) is one of the most frequently diagnosed primary antibody deficiencies (PADs), a group of disorders characterized by a decrease in one or more immunoglobulin (sub) classes and/or impaired antibody responses caused by inborn defects in B cells in the absence of other major immune defects. CVID patients suffer from recurrent infections and disease-related, non-infectious, complications such as autoimmune manifestations, lymphoproliferation, and malignancies. A timely diagnosis is essential for optimal follow-up and treatment. However, CVID is by definition a diagnosis of exclusion, thereby covering a heterogeneous patient population and making it difficult to establish a definite diagnosis. To aid the diagnosis of CVID patients, and distinguish them from other PADs, we developed an automated machine learning pipeline which performs automated diagnosis based on flow cytometric immunophenotyping. Using this pipeline, we analyzed the immunophenotypic profile in a pediatric and adult cohort of 28 patients with CVID, 23 patients with idiopathic primary hypogammaglobulinemia, 21 patients with IgG subclass deficiency, six patients with isolated IgA deficiency, one patient with isolated IgM deficiency, and 100 unrelated healthy controls. Flow cytometry analysis is traditionally done by manual identification of the cell populations of interest. Yet, this approach has severe limitations including subjectivity of the manual gating and bias toward known populations. To overcome these limitations, we here propose an automated computational flow cytometry pipeline that successfully distinguishes CVID phenotypes from other PADs and healthy controls. Compared to the traditional, manual analysis, our pipeline is fully automated, performing automated quality control and data pre-processing, automated population identification (gating) and deriving features from these populations to build a machine learning classifier to distinguish CVID from other PADs and healthy controls. This results in a more reproducible flow cytometry analysis, and improves the diagnosis compared to manual analysis: our pipelines achieve on average a balanced accuracy score of 0.93 (+/- 0.07), whereas using the manually extracted populations, an averaged balanced accuracy score of 0.72 (+/- 0.23) is achieved