The multidimensional assessment of interoceptive awareness, version 2 (MAIA-2)

Interoception, the process by which the nervous system senses, interprets, and integrates signals originating from within the body, has become major research topic for mental health and in particular for mind-body interventions. Interoceptive awareness here is defined as the conscious level of interoception with its multiple dimensions potentially accessible to self-report. The Multidimensional Assessment of Interoceptive Awareness (MAIA) is an 8-scale state-trait questionnaire with 32 items to measure multiple dimensions of interoception by self-report and was published in November 2012. Its numerous applications in English and other languages revealed low internal consistency reliability for two of its scales. This study’s objective was to improve these scales and the psychometrics of the MAIA by adding three new items to each of the two scales and evaluate these in a new sample. Data were collected within a larger project that took place as part of the Live Science residency programme at the Science Museum London, UK, where visitors to the museum (N = 1,090) completed the MAIA and the six additional items. Based on exploratory factor analysis in one-half of the adult participants and Cronbach alphas, we discarded one and included five of the six additional items into a Version 2 of the MAIA and conducted confirmatory factor analysis in the other half of the participants. The 8-factor model of the resulting 37-item MAIA-2 was confirmed with appropriate fit indices (RMSEA = 0.055 [95% CI 0.052–0.058]; SRMR = 0.064) and improved internal consistency reliability. The MAIA-2 is public domain and available (www.osher.ucsf.edu/maia) for interoception research and the evaluation of clinical mind-body interventions

Exploring the Multidimensional Assessment of Interoceptive Awareness in youth aged 7–17 years

Objective: This study aimed to adapt the Multidimensional Assessment of Interoceptive Awareness (MAIA) questionnaire for younger respondents. Method: The language of the MAIA was revised and children aged 7–10 years (n = 212) and adolescents aged 11–17 years (n = 217) completed the questionnaire. Results: The original eight-factor model was tested for fit using confirmatory factor analysis. The model had an acceptable fit in the total sample and younger subsample and overall fit in the older subsample was adequate following modification. Internal consistency was good, except for the Noticing, Not-Distracting and Not-Worrying scales. Results also demonstrated a negative linear relationship between the trusting scale and age, suggesting that youths may lose trust in their body as they age. Conclusion: The adapted MAIA can be used with a younger population and, depending on the research question, individual MAIA scales may be selected. The survey is available at https://osher.ucsf.edu/maia

Accelerated multiscale & multiphysics modelling tools for battery cell manufacturing improvement

The recent launch of battery factories in Europe, motivates intense efforts to achieve cost-effective, scalable and sustainable battery manufacturing processes. Within DEFACTO project, multiscale multiphysics modelling tools are developed to increase lithium-ion battery (LIB) cell manufacturing process productivity and performance. A novel workflow framework that mimics the main cell manufacturing steps such as the electrode processing and electrolyte filling and later predicts cell performance and ageing is presented to turbocharge the development of next-generation LIBs. In addition, taking advantage of the characterization and manufacturing data to feed and validate the computational tools, the resulting workflow aims at providing deep understanding and therefore guidance to reduce the production process time and cost while increasing the overall efficiency of battery cells

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

The Multidimensional Assessment of Interoceptive Awareness, Version 2 (MAIA-2).

Interoception, the process by which the nervous system senses, interprets, and integrates signals originating from within the body, has become major research topic for mental health and in particular for mind-body interventions. Interoceptive awareness here is defined as the conscious level of interoception with its multiple dimensions potentially accessible to self-report. The Multidimensional Assessment of Interoceptive Awareness (MAIA) is an 8-scale state-trait questionnaire with 32 items to measure multiple dimensions of interoception by self-report and was published in November 2012. Its numerous applications in English and other languages revealed low internal consistency reliability for two of its scales. This study's objective was to improve these scales and the psychometrics of the MAIA by adding three new items to each of the two scales and evaluate these in a new sample. Data were collected within a larger project that took place as part of the Live Science residency programme at the Science Museum London, UK, where visitors to the museum (N = 1,090) completed the MAIA and the six additional items. Based on exploratory factor analysis in one-half of the adult participants and Cronbach alphas, we discarded one and included five of the six additional items into a Version 2 of the MAIA and conducted confirmatory factor analysis in the other half of the participants. The 8-factor model of the resulting 37-item MAIA-2 was confirmed with appropriate fit indices (RMSEA = 0.055 [95% CI 0.052-0.058]; SRMR = 0.064) and improved internal consistency reliability. The MAIA-2 is public domain and available (www.osher.ucsf.edu/maia) for interoception research and the evaluation of clinical mind-body interventions

Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome

International audienceRNA viruses in aquatic environments remain poorly studied. Here, we analysed the RNA virome from approximately 10 l water from Yangshan Deep-Water Harbour near the Yangtze River estuary in China and identified more than 4,500 distinct RNA viruses, doubling the previously known set of viruses. Phylogenomic analysis identified several major lineages, roughly, at the taxonomic ranks of class, order and family. The 719-member-strong Yangshan virus assemblage is the sister clade to the expansive class Alsuviricetes and consists of viruses with simple genomes that typically encode only RNA-dependent RNA polymerase (RdRP), capping enzyme and capsid protein. Several clades within the Yangshan assemblage independently evolved domain permutation in the RdRP. Another previously unknown clade shares ancestry with Potyviridae, the largest known plant virus family. The 'Aquatic picorna-like viruses/Marnaviridae' clade was greatly expanded, with more than 800 added viruses. Several RdRP-linked protein domains not previously detected in any RNA viruses were identified, such as the small ubiquitin-like modifier (SUMO) domain, phospholipase A2 and PrsW-family protease domain. Multiple viruses utilize alternative genetic codes implying protist (especially ciliate) hosts. The results reveal a vast RNA virome that includes many previously unknown groups. However, phylogenetic analysis of the RdRPs supports the previously established five-branch structure of the RNA virus evolutionary tree, with no additional phyla