Teaching Appropriate Feedback Reception Skills Using Computer-Based Training

Feedback is a commonly used intervention to address performance issues in a number of settings. Most research on feedback has focused on manipulating parameters surrounding the delivery of feedback. However, the interaction between those delivering the feedback and a feedback recipient may also influence the impact of performance feedback. The current study investigated the efficacy of training individuals to receive feedback in an appropriate manner using a computer-based training format. Following computer-based training, participants exhibited increases in accuracy of appropriate feedback behaviors when compared to baseline. Participants also demonstrated slight increases in performance on primary job tasks. This study extends the application of computer-based trainings to a new and complex set of behaviors. This study also discusses how computer-based training may increase training efficiency when applied to settings where a sizable portion of an organization needs to be trained in certain skills when compared to traditional in-person training formats. This study extends the research line of training appropriate feedback reception skills

Signatures of Tidal Disruption in Ultra-Faint Dwarf Galaxies: A Combined HST, Gaia, and MMT/Hectochelle Study of Leo V

The ultra-faint dwarf galaxy Leo V has shown both photometric overdensities and kinematic members at large radii, along with a tentative kinematic gradient, suggesting that it may have undergone a close encounter with the Milky Way. We investigate these signs of disruption through a combination of i) high-precision photometry obtained with the Hubble Space Telescope (HST), ii) two epochs of stellar spectra obtained with the Hectochelle Spectrograph on the MMT, and iii) measurements from the Gaia mission. Using the HST data, we examine one of the reported stream-like overdensities at large radii, and conclude that it is not a true stellar stream, but instead a clump of foreground stars and background galaxies. Our spectroscopic analysis shows that one known member star is likely a binary, and challenges the membership status of three others, including two distant candidates that had formerly provided evidence for overall stellar mass loss. We also find evidence that the proposed kinematic gradient across Leo V might be due to small number statistics. We update the systemic proper motion of Leo V, finding $(\mu_\alpha \cos\delta, \mu_\delta)= (0.009\pm0.560$, $-0.777\pm0.314)$ mas yr$^{-1}$, which is consistent with its reported orbit that did not put Leo V at risk of being disturbed by the Milky Way. These findings remove most of the observational clues that suggested Leo V was disrupting, however, we also find new plausible member stars, two of which are located >5 half-light radii from the main body. These stars require further investigation. Therefore, the nature of Leo V still remains an open question.Comment: Higher resolution figures are available upon request. Submitted to the Ap

Oxide Heterostructures from a Realistic Many-Body Perspective

Oxide heterostructures are a new class of materials by design, that open the possibility for engineering challenging electronic properties, in particular correlation effects beyond an effective single-particle description. This short review tries to highlight some of the demanding aspects and questions, motivated by the goal to describe the encountered physics from first principles. The state-of-the-art methodology to approach realistic many-body effects in strongly correlated oxides, the combination of density functional theory with dynamical mean-field theory, will be briefly introduced. Discussed examples deal with prominent Mott-band- and band-band-insulating type of oxide heterostructures, where different electronic characteristics may be stabilized within a single architectured oxide material.Comment: 19 pages, 9 figure

AI Driven Analysis of MRI To Measure Health and Disease Progression in FSHD

Facioscapulohumeral muscular dystrophy (FSHD) affects roughly 1 in 7500 individuals. While at the population level there is a general pattern of affected muscles, there is substantial heterogeneity in muscle expression across- and within-patients. There can also be substantial variation in the pattern of fat and water signal intensity within a single muscle. While quantifying individual muscles across their full length using magnetic resonance imaging (MRI) represents the optimal approach to follow disease progression and evaluate therapeutic response, the ability to automate this process has been limited. The goal of this work was to develop and optimize an artificial intelligence-based image segmentation approach to comprehensively measure muscle volume, fat fraction, fat fraction distribution, and elevated short-tau inversion recovery signal in the musculature of patients with FSHD. Intra-rater, inter-rater, and scan-rescan analyses demonstrated that the developed methods are robust and precise. Representative cases and derived metrics of volume, cross-sectional area, and 3D pixel-maps demonstrate unique intramuscular patterns of disease. Future work focuses on leveraging these AI methods to include upper body output and aggregating individual muscle data across studies to determine best-fit models for characterizing progression and monitoring therapeutic modulation of MRI biomarkers

MHCII-mediated dialog between group 2 innate lymphoid cells and CD4+ T cells potentiates type 2 immunity and promotes parasitic helminth expulsion

Group 2 innate lymphoid cells (ILC2s) release interleukin-13 (IL-13) during protective immunity to helminth infection and detrimentally during allergy and asthma. Using two mouse models to deplete ILC2s in vivo, we demonstrate that T helper 2 (Th2) cell responses are impaired in the absence of ILC2s. We show that MHCII-expressing ILC2s interact with antigen-specific T cells to instigate a dialog in which IL-2 production from T cells promotes ILC2 proliferation and IL-13 production. Deletion of MHCII renders IL-13-expressing ILC2s incapable of efficiently inducing Nippostrongylus brasiliensis expulsion. Thus, during transition to adaptive T cell-mediated immunity, the ILC2 and T cell crosstalk contributes to their mutual maintenance, expansion and cytokine production. This interaction appears to augment dendritic-cell-induced T cell activation and identifies a previously unappreciated pathway in the regulation of type-2 immunity

Group 2 Innate Lymphoid Cells Exhibit Tissue-Specific Dynamic Behaviour During Type 2 Immune Responses.

Group 2 innate lymphoid cells (ILC2s) are early effectors of mucosal type 2 immunity, producing cytokines such as interleukin (IL)-13 to mediate responses to helminth infection and allergen-induced inflammation. ILC2s are also present in lymph nodes (LNs) and can express molecules required for antigen presentation, but to date there are limited data on their dynamic behaviour. We used a CD2/IL-13 dual fluorescent reporter mouse for in vivo imaging of ILC2s and Th2 T cells in real time following a type 2 priming helminth infection or egg injection. After helminth challenge, we found that ILC2s were the main source of IL-13 in lymphoid organs (Peyer's patches and peripheral LNs), and were located in T cell areas. Intravital imaging demonstrated an increase in IL-13+ ILC2 size and movement following helminth infection, but reduced duration of interactions with T cells compared with those in homeostasis. In contrast, in the intestinal mucosa, we observed an increase in ILC2-T cell interactions post-infection, including some of prolonged duration, as well as increased IL-13+ ILC2 movement. These data suggest that ILC2 activation enhances cell motility, with the potential to increase the area of distribution of cytokines to optimise the early generation of type 2 responses. The prolonged ILC2 interactions with T cells within the intestinal mucosa are consistent with the conclusion that contact-based T cell activation may occur within inflamed tissues rather than lymphoid organs. Our findings have important implications for our understanding of the in vivo biology of ILC2s and the way in which these cells facilitate adaptive immune responses

Neuropathological consensus criteria for the evaluation of Lewy pathology in post-mortem brains: a multi-centre study

Currently, the neuropathological diagnosis of Lewy body disease (LBD) may be stated according to several staging systems, which include the Braak Lewy body stages (Braak), the consensus criteria by McKeith and colleagues (McKeith), the modified McKeith system by Leverenz and colleagues (Leverenz), and the Unified Staging System by Beach and colleagues (Beach). All of these systems use semi-quantitative scoring (4- or 5-tier scales) of Lewy pathology (LP; i.e., Lewy bodies and Lewy neurites) in defined cortical and subcortical areas. While these systems are widely used, some suffer from low inter-rater reliability and/or an inability to unequivocally classify all cases with LP. To address these limitations, we devised a new system, the LP consensus criteria (LPC), which is based on the McKeith system, but applies a dichotomous approach for the scoring of LP (i.e., “absent” vs. “present”) and includes amygdala-predominant and olfactory-only stages. α-Synuclein-stained slides from brainstem, limbic system, neocortex, and olfactory bulb from a total of 34 cases with LP provided by the Newcastle Brain Tissue Resource (NBTR) and the University of Pennsylvania brain bank (UPBB) were scanned and assessed by 16 raters, who provided diagnostic categories for each case according to Braak, McKeith, Leverenz, Beach, and LPC systems. In addition, using LP scores available from neuropathological reports of LP cases from UPBB (n = 202) and NBTR (n = 134), JT (UPBB) and JA (NBTR) assigned categories according to all staging systems to these cases. McKeith, Leverenz, and LPC systems reached good (Krippendorff’s α ≈ 0.6), while both Braak and Beach systems had lower (Krippendorff’s α ≈ 0.4) inter-rater reliability, respectively. Using the LPC system, all cases could be unequivocally classified by the majority of raters, which was also seen for 97.1% when the Beach system was used. However, a considerable proportion of cases could not be classified when using Leverenz (11.8%), McKeith (26.5%), or Braak (29.4%) systems. The category of neocortical LP according to the LPC system was associated with a 5.9 OR (p < 0.0001) of dementia in the 134 NBTR cases and a 3.14 OR (p = 0.0001) in the 202 UPBB cases. We established that the LPC system has good reproducibility and allows classification of all cases into distinct categories. We expect that it will be reliable and useful in routine diagnostic practice and, therefore, suggest that it should be the standard future approach for the basic post-mortem evaluation of LP

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

Studying the Long-term Impact of COVID-19 in Kids (SLICK). Healthcare use and costs in children and young people following community-acquired SARS-CoV-2 infection:protocol for an observational study using linked primary and secondary routinely collected healthcare data from England, Scotland and Wales

IntroductionSARS-CoV-2 infection rarely causes hospitalisation in children and young people (CYP), but mild or asymptomatic infections are common. Persistent symptoms following infection have been reported in CYP but subsequent healthcare use is unclear. We aim to describe healthcare use in CYP following community-acquired SARS-CoV-2 infection and identify those at risk of ongoing healthcare needs.Methods and analysisWe will use anonymised individual-level, population-scale national data linking demographics, comorbidities, primary and secondary care use and mortality between 1 January 2019 and 1 May 2022. SARS-CoV-2 test data will be linked from 1 January 2020 to 1 May 2022. Analyses will use Trusted Research Environments: OpenSAFELY in England, Secure Anonymised Information Linkage (SAIL) Databank in Wales and Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 in Scotland (EAVE-II). CYP aged ≥4 and &lt;18 years who underwent SARS-CoV-2 reverse transcription PCR (RT-PCR) testing between 1 January 2020 and 1 May 2021 and those untested CYP will be examined.The primary outcome measure is cumulative healthcare cost over 12 months following SARS-CoV-2 testing, stratified into primary or secondary care, and physical or mental healthcare. We will estimate the burden of healthcare use attributable to SARS-CoV-2 infections in the 12 months after testing using a matched cohort study of RT-PCR positive, negative or untested CYP matched on testing date, with adjustment for confounders. We will identify factors associated with higher healthcare needs in the 12 months following SARS-CoV-2 infection using an unmatched cohort of RT-PCR positive CYP. Multivariable logistic regression and machine learning approaches will identify risk factors for high healthcare use and characterise patterns of healthcare use post infection.Ethics and disseminationThis study was approved by the South-Central Oxford C Health Research Authority Ethics Committee (13/SC/0149). Findings will be preprinted and published in peer-reviewed journals. Analysis code and code lists will be available through public GitHub repositories and OpenCodelists with meta-data via HDR-UK Innovation Gateway