Item Response Theory Analysis of Self-Reported Social–Emotional Learning Competencies in an Australian Population Cohort Aged 11 Years

Childhood social and emotional competencies are recognized as teachable skills affecting well-being and developmental outcomes across the life span. This study sought to develop and validate a brief self-report measure of social–emotional competencies in middle childhood. The study used items from the 2015 Middle Childhood Survey, administered to a representative subsample of the New South Wales Child Development Study cohort, comprising sixth grade students (n = 26,837; aged 11–12 years) attending primary school in New South Wales, Australia. Exploratory and confirmatory factor analyses assessed the latent structure of social–emotional competencies, and item response theory and construct validity analyses evaluated the reliability, validity, and psychometric properties of the derived measure. A correlated five-factor model outperformed other latent structures (one-factor, higher order, and bifactor models) and was consistent with the framework developed by the Collaborative for Academic, Social, and Emotional Learning that informs the Australian school-based social–emotional learning curriculum, incorporating the following: Self- Awareness; Self-Management; Social Awareness; Relationship Skills; and Responsible Decision-Making. This brief (20-item), psychometrically sound, self-report measure of social–emotional competencies in middle childhood provides capacity for exploration of these skills as mediators and moderators of developmental outcomes across the life span

On the use of AI for generation of functional music to improve mental health

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reduction of cases of dementia in elderly populations, and improvements in markers of general mental well-being such as stress reduction. Here, we describe short case studies addressing general mental well-being (anxiety, stress-reduction) through AI-driven music generation. Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range. However, access to music-making can be prohibitive in terms of access to expertise, materials, and cost. Furthermore the use of existing music for functional outcomes (such as targeted improvement in physical and mental health markers suggested above) can be hindered by issues of repetition and subsequent over-familiarity with existing material. In this paper, we describe machine learning (ML) approaches which create functional music informed by biophysiological measurement across two case studies, with target emotional states at opposing ends of a Cartesian affective space (a dimensional emotion space with points ranging from descriptors from relaxation, to fear). We use Galvanic skin response (GSR) as a marker of psychological arousal and as an estimate of emotional state to be used as a control signal in the training of the ML algorithm. This algorithm creates a non-linear time series of musical features for sound synthesis ‘on-the-fly’, using a perceptually informed musical feature similarity model. We find an interaction between familiarity (or more generally, the featureset model we have implemented) and perceived emotional response so focus on generating new, emotionally-congruent pieces. We also report on subsequent psychometric evaluation of the generated material, and consider how these - and similar techniques -might be useful for a range of functional music generation tasks, for example in nonlinear sound-tracking such as that found in interactive media or video games

Stereotyping starlings are more 'pessimistic'.

Negative affect in humans and animals is known to cause individuals to interpret ambiguous stimuli pessimistically, a phenomenon termed 'cognitive bias'. Here, we used captive European starlings (Sturnus vulgaris) to test the hypothesis that a reduction in environmental conditions, from enriched to non-enriched cages, would engender negative affect, and hence 'pessimistic' biases. We also explored whether individual differences in stereotypic behaviour (repetitive somersaulting) predicted 'pessimism'. Eight birds were trained on a novel conditional discrimination task with differential rewards, in which background shade (light or dark) determined which of two covered dishes contained a food reward. The reward was small when the background was light, but large when the background was dark. We then presented background shades intermediate between those trained to assess the birds' bias to choose the dish associated with the smaller food reward (a 'pessimistic' judgement) when the discriminative stimulus was ambiguous. Contrary to predictions, changes in the level of cage enrichment had no effect on 'pessimism'. However, changes in the latency to choose and probability of expressing a choice suggested that birds learnt rapidly that trials with ambiguous stimuli were unreinforced. Individual differences in performance of stereotypies did predict 'pessimism'. Specifically, birds that somersaulted were more likely to choose the dish associated with the smaller food reward in the presence of the most ambiguous discriminative stimulus. We propose that somersaulting is part of a wider suite of behavioural traits indicative of a stress response to captive conditions that is symptomatic of a negative affective state

Evaluation of Dynamic Cell Processes and Behavior Using Video Bioinformatics Tools

Just as body language can reveal a person’s state of well-being, dynamic changes in cell behavior and morphology can be used to monitor processes in cultured cells. This chapter discusses how CL-Quant software, a commercially available video bioinformatics tool, can be used to extract quantitative data on: (1) growth/proliferation, (2) cell and colony migration, (3) reactive oxygen species (ROS) production, and (4) neural differentiation. Protocols created using CL-Quant were used to analyze both single cells and colonies. Time-lapse experiments in which different cell types were subjected to various chemical exposures were done using Nikon BioStations. Proliferation rate was measured in human embryonic stem cell colonies by quantifying colony area (pixels) and in single cells by measuring confluency (pixels). Colony and single cell migration were studied by measuring total displacement (distance between the starting and ending points) and total distance traveled by the colonies/cells. To quantify ROS production, cells were pre-loaded with MitoSOX Red™, a mitochondrial ROS (superoxide) indicator, treated with various chemicals, then total intensity of the red fluorescence was measured in each frame. Lastly, neural stem cells were incubated in differentiation medium for 12 days, and time lapse images were collected daily. Differentiation of neural stem cells was quantified using a protocol that detects young neurons. CLQuant software can be used to evaluate biological processes in living cells, and the protocols developed in this project can be applied to basic research and toxicological studies, or to monitor quality control in culture facilities

Relationship of performance on the sensory organization test to landing characteristics

Background: Jump landing tasks have been used to assess landing characteristics and require significant sensorimotor feedback to maintain functional joint stability (FJS) throughout the task. Postural stability (PS) also requires significant sensorimotor feedback and control and would seemingly involve similar sensory feedback pathways. However, previous literature clarifying the relationship between these two processes, maintaining FJS and PS, is limited. Participants: 80 Special Tactics Operators Methods: PS was assessed using the Sensory Organization Test (SOT). SOT variables included: Composite, Somatosensory, Visual, Vestibular, and Preference scores. Landing characteristics were assessed using motion analysis and during a double-legged (DLSJ) and single-legged (SLSJ) stop jump task. Pearson’s correlation coefficients were calculated to assess the relationship between SOT scores and landing characteristics (α<.05) Results: For the DLSJ, significant correlations were found between: Composite and peak posterior ground reaction forces (-.257), Vestibular and peak knee abduction moment (-.237), and Preference and initial contact hip flexion (-.297), peak hip flexion (-.249). For the SLSJ, significant correlations were found between: Somatosensory and peak vertical ground reaction forces (-.246); Preference and initial contact hip flexion (-.295), peak hip flexion (-.262). Conclusions: The results indicate that the SOT may not be a sensitive enough tool to assess sensorimotor control in a healthy, athletic population

Melting of a 2D Quantum Electron Solid in High Magnetic Field

The melting temperature ($T_m$) of a solid is generally determined by the pressure applied to it, or indirectly by its density ($n$) through the equation of state. This remains true even for helium solids\cite{wilk:67}, where quantum effects often lead to unusual properties\cite{ekim:04}. In this letter we present experimental evidence to show that for a two dimensional (2D) solid formed by electrons in a semiconductor sample under a strong perpendicular magnetic field\cite{shay:97} ($B$), the $T_m$ is not controlled by $n$, but effectively by the \textit{quantum correlation} between the electrons through the Landau level filling factor $\nu$=$nh/eB$. Such melting behavior, different from that of all other known solids (including a classical 2D electron solid at zero magnetic field\cite{grim:79}), attests to the quantum nature of the magnetic field induced electron solid. Moreover, we found the $T_m$ to increase with the strength of the sample-dependent disorder that pins the electron solid.Comment: Some typos corrected and 2 references added. Final version with minor editoriol revisions published in Nature Physic