The alchemy of tendon repair: A primer for the (S)mad scientist

During vertebrate development, mesenchymal progenitors capable of forming bone, cartilage, muscle, fat, or tendon arise from either neural crest or somitic mesoderm. Transcriptional programs that specify mesenchymal cell fates are initiated and modified by paracrine cues provided by TGF-β superfamily members and mediated in part via the regulated assembly of Smad-containing multiprotein transcription factor complexes. In this issue of the JCI, Hoffmann and colleagues have identified that Smad8 activation drives tendon formation from C3H10T1/2 cells, a murine cell line that recapitulates many features of normal multipotent mesenchymal cells (see the related article beginning on page 940). Cells programmed to the tenocyte cell fate in vitro formed tenogenic grafts in vivo. These results add to the accumulating evidence that proliferating, multipotent mesenchymal progenitor cells can be programmed to yield multiple cell types — e.g., osteoblasts, myocytes, chondrocytes, and tenocytes — that may be useful in cell-based therapeutic approaches to musculoskeletal diseases

Variational and Diffusion Quantum Monte Carlo Calculations with the CASINO Code

We present an overview of the variational and diffusion quantum Monte Carlo methods as implemented in the CASINO program. We particularly focus on developments made in the last decade, describing state-of-the-art quantum Monte Carlo algorithms and software and discussing their strengths and their weaknesses. We review a range of recent applications of CASINO

Do professional facial image comparison training courses work?

Facial image comparison practitioners compare images of unfamiliar faces and decide whether or not they show the same person. Given the importance of these decisions for national security and criminal investigations, practitioners attend training courses to improve their face identification ability. However, these courses have not been empirically validated so it is unknown if they improve accuracy. Here, we review the content of eleven professional training courses offered to staff at national security, police, intelligence, passport issuance, immigration and border control agencies around the world. All reviewed courses include basic training in facial anatomy and prescribe facial feature (or 'morphological') comparison. Next, we evaluate the effectiveness of four representative courses by comparing face identification accuracy before and after training in novices (n = 152) and practitioners (n = 236). We find very strong evidence that short (1-hour and half-day) professional training courses do not improve identification accuracy, despite 93% of trainees believing their performance had improved. We find some evidence of improvement in a 3-day training course designed to introduce trainees to the unique feature-by-feature comparison strategy used by facial examiners in forensic settings. However, observed improvements are small, inconsistent across tests, and training did not produce the qualitative changes associated with examiners' expertise. Future research should test the benefits of longer examination-focussed training courses and incorporate longitudinal approaches to track improvements caused by mentoring and deliberate practice. In the absence of evidence that training is effective, we advise agencies to explore alternative evidence-based strategies for improving the accuracy of face identification decisions

Multiple Fish Tracking via Viterbi Data Association for Low-Frame-Rate Underwater Camera Systems †

Abstract-Non-extractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Low frame rate and variable illumination in the underwater environment, however, makes conventional tracking methods unreliable. In this paper, a robust multiple fish tracking system for low-framerate underwater stereo cameras is proposed. With the result of fish segmentation, a computationally efficient block-matching method is applied to perform successful stereo matching. A multiple-feature matching cost function is utilized to give a simple but effective metric for finding the temporal match of each target. Built upon reliable stereo matching, a multipletarget tracking algorithm via the Viterbi data association is developed to overcome the poor motion continuity of targets. Experimental results show that an accurate underwater live fish tracking result with stereo cameras is achieved

EAGLE-like simulation models do not solve the entropy core problem in groups and clusters of galaxies

Recent high-resolution cosmological hydrodynamic simulations run with a variety of codes systematically predict large amounts of entropy in the intra-cluster medium at low redshift, leading to flat entropy profiles and a suppressed cool-core population. This prediction is at odds with X-ray observations of groups and clusters. We use a new implementation of the EAGLE galaxy formation model to investigate the sensitivity of the central entropy and the shape of the profiles to changes in the sub-grid model applied to a suite of zoom-in cosmological simulations of a group of mass M500 = 8.8 × 1012 M⊙ and a cluster of mass 2.9 × 1014 M⊙. Using our reference model, calibrated to match the stellar mass function of field galaxies, we confirm that our simulated groups and clusters contain hot gas with too high entropy in their cores. Additional simulations run without artificial conduction, metal cooling or active galactic nuclei (AGN) feedback produce lower entropy levels but still fail to reproduce observed profiles. Conversely, the two objects run without supernova feedback show a significant entropy increase which can be attributed to excessive cooling and star formation. Varying the AGN heating temperature does not greatly affect the profile shape, but only the overall normalization. Finally, we compared runs with four AGN heating schemes and obtained similar profiles, with the exception of bipolar AGN heating, which produces a higher and more uniform entropy distribution. Our study leaves open the question of whether the entropy core problem in simulations, and particularly the lack of power-law cool-core profiles, arise from incorrect physical assumptions, missing physical processes, or insufficient numerical resolution

Applying machine-learning to rapidly analyze large qualitative text datasets to inform the COVID-19 pandemic response: comparing human and machine-assisted topic analysis techniques

IntroductionMachine-assisted topic analysis (MATA) uses artificial intelligence methods to help qualitative researchers analyze large datasets. This is useful for researchers to rapidly update healthcare interventions during changing healthcare contexts, such as a pandemic. We examined the potential to support healthcare interventions by comparing MATA with “human-only” thematic analysis techniques on the same dataset (1,472 user responses from a COVID-19 behavioral intervention).MethodsIn MATA, an unsupervised topic-modeling approach identified latent topics in the text, from which researchers identified broad themes. In human-only codebook analysis, researchers developed an initial codebook based on previous research that was applied to the dataset by the team, who met regularly to discuss and refine the codes. Formal triangulation using a “convergence coding matrix” compared findings between methods, categorizing them as “agreement”, “complementary”, “dissonant”, or “silent”.ResultsHuman analysis took much longer than MATA (147.5 vs. 40 h). Both methods identified key themes about what users found helpful and unhelpful. Formal triangulation showed both sets of findings were highly similar. The formal triangulation showed high similarity between the findings. All MATA codes were classified as in agreement or complementary to the human themes. When findings differed slightly, this was due to human researcher interpretations or nuance from human-only analysis.DiscussionResults produced by MATA were similar to human-only thematic analysis, with substantial time savings. For simple analyses that do not require an in-depth or subtle understanding of the data, MATA is a useful tool that can support qualitative researchers to interpret and analyze large datasets quickly. This approach can support intervention development and implementation, such as enabling rapid optimization during public health emergencies

Raman Spectroscopic Analysis of Fingernail Clippings Can Help Differentiate between Postmenopausal Women Who Have and Have Not Suffered a Fracture

Raman spectroscopy was applied to nail clippings from 633 postmenopausal British and Irish women, from six clinical sites, of whom 42% had experienced a fragility fracture. The objective was to build a prediction algorithm for fracture using data from four sites (known as the calibration set) and test its performance using data from the other two sites (known as the validation set). Results from the validation set showed that a novel algorithm, combining spectroscopy data with clinical data, provided area under the curve (AUC) of 74% compared to an AUC of 60% from a reduced QFracture score (a clinically accepted risk calculator) and 61% from the dual-energy X-ray absorptiometry T-score, which is in current use for the diagnosis of osteoporosis. Raman spectroscopy should be investigated further as a noninvasive tool for the early detection of enhanced risk of fragility fracture

Effectiveness of digital interventions to improve household and community infection prevention and control behaviours and to reduce incidence of respiratory and/or gastro-intestinal infections: a rapid systematic review

BACKGROUND: Digital interventions have potential to efficiently support improved hygiene practices to reduce transmission of COVID-19. OBJECTIVE: To evaluate the evidence for digital interventions to improve hygiene practices within the community. METHODS: We reviewed articles published between 01 January 2000 and 26 May 2019 that presented a controlled trial of a digital intervention to improve hygiene behaviours in the community. We searched MEDLINE, Embase, PsycINFO, Cochrane Controlled Register of Trials (CENTRAL), China National Knowledge Infrastructure and grey literature. Trials in hospitals were excluded, as were trials aiming at prevention of sexually transmitted infections; only target diseases with transmission mechanisms similar to COVID-19 (e.g. respiratory and gastrointestinal infections) were included. Trials had to evaluate a uniquely digital component of an intervention. Study designs were limited to randomised controlled trials, controlled before-and-after trials, and interrupted time series analyses. Outcomes could be either incidence of infections or change in hygiene behaviours. The Risk of Bias 2 tool was used to assess study quality. RESULTS: We found seven studies that met the inclusion criteria. Six studies reported successfully improving self-reported hygiene behaviour or health outcomes, but only one of these six trials, Germ Defence, confirmed improvements using objective measures (reduced consultations and antibiotic prescriptions). Settings included kindergartens, workplaces, and service station restrooms. Modes of delivery were diverse: WeChat, website, text messages, audio messages to mobiles, electronic billboards, and electronic personal care records. Four interventions targeted parents of young children with educational materials. Two targeted the general population; these also used behaviour change techniques or theory to inform the intervention. Only one trial had low risk of bias, Germ Defence; the most common concerns were lack of information about the randomisation, possible bias in reporting of behavioural outcomes, and lack of an analysis plan and possible selective reporting of results. CONCLUSION: There was only one trial that was judged to be at low risk of bias, Germ Defence, which reduced incidence and severity of illness, as confirmed by objective measures. Further evaluation is required to determine the effectiveness of the other interventions reviewed. TRIAL REGISTRATION: PROSPERO CRD42020189919