Found 10216 matches for
Evaluation of Adaptive Feedback in a Smartphone-Based Game on Health Care Providers' Learning Gain: Randomized Controlled Trial.
BACKGROUND:Although smartphone-based emergency care training is more affordable than traditional avenues of training, it is still in its infancy, remains poorly implemented, and its current implementation modes tend to be invariant to the evolving learning needs of the intended users. In resource-limited settings, the use of such platforms coupled with gamified approaches remains largely unexplored, despite the lack of traditional training opportunities, and high mortality rates in these settings. OBJECTIVE:The primary aim of this randomized experiment is to determine the effectiveness of offering adaptive versus standard feedback, on the learning gains of clinicians, through the use of a smartphone-based game that assessed their management of a simulated medical emergency. A secondary aim is to examine the effects of learner characteristics and learning spacing with repeated use of the game on the secondary outcome of individualized normalized learning gain. METHODS:The experiment is aimed at clinicians who provide bedside neonatal care in low-income settings. Data were captured through an Android app installed on the study participants' personal phones. The intervention, which was based on successful attempts at a learning task, included adaptive feedback provided within the app to the experimental arm, whereas the control arm received standardized feedback. The primary end point was completion of the second learning session. Of the 572 participants enrolled between February 2019 and July 2019, 247 (43.2%) reached the primary end point. The primary outcome was standardized relative change in learning gains between the study arms as measured by the Morris G effect size. The secondary outcomes were the participants individualized normalized learning gains. RESULTS:The effect of adaptive feedback on care providers' learning gain was found to be g=0.09 (95% CI -0.31 to 0.46; P=.47). In exploratory analysis, using normalized learning gains, when subject-treatment interaction and differential time effect was controlled for, this effect increased significantly to 0.644 (95% CI 0.35 to 0.94; P<.001) with immediate repetition, which is a moderate learning effect, but reduced significantly by 0.28 after a week. The overall learning change from the app use in both arms was large and may have obscured a direct effect of feedback. CONCLUSIONS:There is a considerable learning gain between the first two rounds of learning with both forms of feedback and a small added benefit of adaptive feedback after controlling for learner differences. We suggest that linking the adaptive feedback provided to care providers to how they space their repeat learning session(s) may yield higher learning gains. Future work might explore in more depth the feedback content, in particular whether or not explanatory feedback (why answers were wrong) enhances learning more than reflective feedback (information about what the right answers are). TRIAL REGISTRATION:Pan African Clinical Trial Registry (PACTR) 201901783811130; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=5836. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR2-10.2196/13034.
Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments
© 2020 IEEE. Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance.
© 2020 IEEE. Fine-grained object recognition and classification in biomedical images poses a number of challenges. Images typically contain multiple instances (e.g. glands) and the recognition of salient structures is confounded by visually complex backgrounds. Due to the cost of data acquisition or the limited availability of specimens, data sets tend to be small. We propose a simple yet effective attention based deep architecture to address these issues, specially to improve background suppression and recognition of important instances per image. Attention maps per instance are learnt in an end-to-end fashion. Microscopic images of fungi (new data) and a publicly available Breast Cancer Histology benchmark dataset are used to demonstrate the performance of the proposed approach. Experimental results suggest that the proposed approach advances the state-of-the-art.
© 2020 SPIE. Ureteroscopy is a conventional procedure used for localization and removal of kidney stones. Laser is commonly used to fragment the stones until they are small enough to be removed. Often, the surgical team faces tremendous challenge to successfully perform this task, mainly due to poor image quality, presence of floating debris and occlusions in the endoscopy video. Automated localization and segmentation can help to perform stone fragmentation efficiently. However, the automatic segmentation of kidney stones is a complex and challenging procedure due to stone heterogeneity in terms of shape, size, texture, color and position. In addition, dynamic background, motion blur, local deformations, occlusions, varying illumination conditions and visual clutter from the stone debris make the segmentation task even more challenging. In this paper, we present a novel illumination invariant optical flow based segmentation technique. We introduce a multi-frame based dense optical flow estimation in a primal-dual optimization framework embedded with a robust data-term based on normalized correlation transform descriptors. The proposed technique leverages the motion fields between multiple frames reducing the effect of blur, deformations, occlusions and debris; and the proposed descriptor makes the method robust to illumination changes and dynamic background. Both qualitative and quantitative evaluations show the efficacy of the proposed method on ureteroscopy data. Our algorithm shows an improvement of 5-8% over all evaluation metrics as compared to the previous method. Our multi-frame strategy outperforms classically used two-frame model.
Sequence and structural variations determining the recruitment of WNK kinases to the KLHL3 E3 ligase
<jats:title>Abstract</jats:title><jats:p>The BTB-Kelch protein KLHL3 is a Cullin3-dependent E3 ligase that mediates the ubiquitin-dependent degradation of kinases WNK1-4 to control blood pressure and cell volume. A crystal structure of KLHL3 has defined its binding to an acidic degron motif containing a PXXP sequence that is strictly conserved in WNK1, WNK2 and WNK4. Mutations in the second proline abrograte the interaction causing the hypertension syndrome pseudohypoaldosteronism type II. WNK3 shows a diverged degron motif containing 4 amino acid substitutions that remove the PXXP motif raising questions as to the mechanism of its binding. To understand this atypical interaction, we determined the crystal structure of the KLHL3 Kelch domain in complex with a WNK3 peptide. The electron density enabled the complete 11-mer WNK-family degron motif to be traced for the first time revealing several conserved features not captured in previous work, including additional salt bridge and hydrogen bond interactions. Overall, the WNK3 peptide adopted a conserved binding pose except for a subtle shift to accommodate bulkier amino acid substitutions at the binding interface. At the centre, the second proline was substituted by WNK3 Thr541, providing a unique phosphorylatable residue among the WNK-family degrons. Fluorescence polarisation and structural modelling experiments revealed that its phosphorylation would abrogate the KLHL3 interaction similarly to hypertension-causing mutations. Together, these data reveal how the KLHL3 Kelch domain can accommodate the binding of multiple WNK isoforms and highlight a potential regulatory mechanism for the recruitment of WNK3.</jats:p>
Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital
<jats:p>The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered.</jats:p>
The classification of human body motion is a difficult problem. In particular, the automatic segmentation of sequences containing more than one class of motion is challenging. An effective approach is to use mixed discrete/continuous states to couple perception with classification. A spline contour is used to track the outline of the person. We show that for a quasi-periodic human body motion, an autoregressive process is a suitable model for the contour dynamics. This can then be used as a dynamical model for mixed state CONDENSATION filtering, switching automatically between different motion classes. We have developed `Partial Importance Sampling' to enhance the efficiency of the mixed state CONDENSATION filter. It is also shown here that the importance sampling can be done in linear time, in place of the previous quadratic algorithm. `Tying' of discrete states is used to obtain further efficiency improvements. Automatic segmentation is demonstrated on video sequences of aerobic exercises. Performance is promising, but there remains a residual misclassification rate and possible explanations for this are discussed.
© 2020 IEEE. Ureteroscopy has evolved into a routine technique for treatment of kidney stones. Laser lithotripsy is commonly used to fragment the kidney stones until they are small enough to be removed. Poor image quality, presence of floating debris and severe occlusions in the endoscopy video make it difficult to target stones during the ureteroscopy procedure. A potential solution is automated localization and segmentation of the stone fragments. However, the heterogeneity of stones in terms of shape, texture, as well as colour and the presence of moving debris make the task of stone segmentation challenging. Further, dynamic background, motion blur, local deformations, occlusions and varying illumination conditions need to be taken into account during segmentation. To address these issues, we compliment state-of-the-art U-Net based segmentation strategy with the learned motion information. This technique leverages difference in motion between the large stones and surrounding debris and additionally tackles problems due to illumination variability, occlusions and other factors that are present in the frame-of-interest. The proposed motion induced U-Net (MI-UNet) architecture consists of two main components: 1) U-Net and 2) DVFNet. The quantitative results show consistent performance and improvement over most evaluation metrics. The qualitative validation also illustrate that our complimentary DVFNet is able to effectively reduce the effect of surrounding debris in contrast to U-Net.
Extracting Axial Depth and Trajectory Trend Using Astigmatism, Gaussian Fitting, and CNNs for Protein Tracking
© 2020 IEEE. Accurate analysis of vesicle trafficking in live cells is challenging for a number of reasons: varying appearance, complex protein movement patterns, and imaging conditions. To allow fast image acquisition, we study how employing an astigmatism can be utilized for obtaining additional information that could make tracking more robust. We present two approaches for measuring the z position of individual vesicles. Firstly, Gaussian curve fitting with CNN-based denoising is applied to infer the absolute depth around the focal plane of each localized protein. We demonstrate that adding denoising yields more accurate estimation of depth while preserving the overall structure of the localized proteins. Secondly, we investigate if we can predict using a custom CNN architecture the axial trajectory trend. We demonstrate that this method performs well on calibration beads data without the need for denoising. By incorporating the obtained depth information into a trajectory analysis, we demonstrate the potential improvement in vesicle tracking.
© 2020 IEEE. The study of protein transport in living cell requires automated techniques to capture and quantify dynamics of the protein packaged into secretory vesicles. The movement of the vesicles is not consistent along the trajectory, therefore the quantitative study of their dynamics requires trajectories segmentation. This paper explores quantification of such vesicle dynamics and introduces a novel 1D U-Net based trajectory segmentation. Unlike existing mean squared displacement based methods, our proposed framework is not restricted under the requirement of long trajectories for effective segmentation. Moreover, as our approach provides segmentation within each sliding window, it enables effectively capture even short segments. The approach is quantified by the data acquired from spinning disk microscopy imaging of protein trafficking in Drosophila epithelial cells. The extracted trajectories have lengths ranging from 5 (short tracks) to 135 (long tracks) points. The proposed approach achieves 77.7% accuracy for the trajectory segmentation.
Denisovans are members of a hominin group who are currently only known directly from fragmentary fossils, the genomes of which have been studied from a single site, Denisova Cave1-3 in Siberia. They are also known indirectly from their genetic legacy through gene flow into several low-altitude East Asian populations4,5 and high-altitude modern Tibetans6. The lack of morphologically informative Denisovan fossils hinders our ability to connect geographically and temporally dispersed fossil hominins from Asia and to understand in a coherent manner their relation to recent Asian populations. This includes understanding the genetic adaptation of humans to the high-altitude Tibetan Plateau7,8, which was inherited from the Denisovans. Here we report a Denisovan mandible, identified by ancient protein analysis9,10, found on the Tibetan Plateau in Baishiya Karst Cave, Xiahe, Gansu, China. We determine the mandible to be at least 160 thousand years old through U-series dating of an adhering carbonate matrix. The Xiahe specimen provides direct evidence of the Denisovans outside the Altai Mountains and its analysis unique insights into Denisovan mandibular and dental morphology. Our results indicate that archaic hominins occupied the Tibetan Plateau in the Middle Pleistocene epoch and successfully adapted to high-altitude hypoxic environments long before the regional arrival of modern Homo sapiens.
Diabetes is a global health problem caused primarily by the inability of pancreatic β-cells to secrete adequate levels of insulin. The molecular mechanisms underlying the progressive failure of β-cells to respond to glucose in type-2 diabetes remain unresolved. Using a combination of transcriptomics and proteomics, we find significant dysregulation of major metabolic pathways in islets of diabetic βV59M mice, a non-obese, eulipidaemic diabetes model. Multiple genes/proteins involved in glycolysis/gluconeogenesis are upregulated, whereas those involved in oxidative phosphorylation are downregulated. In isolated islets, glucose-induced increases in NADH and ATP are impaired and both oxidative and glycolytic glucose metabolism are reduced. INS-1 β-cells cultured chronically at high glucose show similar changes in protein expression and reduced glucose-stimulated oxygen consumption: targeted metabolomics reveals impaired metabolism. These data indicate hyperglycaemia induces metabolic changes in β-cells that markedly reduce mitochondrial metabolism and ATP synthesis. We propose this underlies the progressive failure of β-cells in diabetes.
Interaction mapping of endoplasmic reticulum ubiquitin ligases identifies modulators of innate immune signalling
<jats:p>Ubiquitin ligases (E3s) embedded in the endoplasmic reticulum (ER) membrane regulate essential cellular activities including protein quality control, calcium flux, and sterol homeostasis. At least 25 different, transmembrane domain (TMD)-containing E3s are predicted to be ER-localised, but for most their organisation and cellular roles remain poorly defined. Using a comparative proteomic workflow, we mapped over 450 protein-protein interactions for 21 stably expressed, full-length E3s. Bioinformatic analysis linked ER-E3s and their interactors to multiple homeostatic, regulatory, and metabolic pathways. Among these were four membrane-embedded interactors of RNF26, a polytopic E3 whose abundance is auto-regulated by ubiquitin-proteasome dependent degradation. RNF26 co-assembles with TMEM43, ENDOD1, TMEM33 and TMED1 to form a complex capable of modulating innate immune signalling through the cGAS-STING pathway. This RNF26 complex represents a new modulatory axis of STING and innate immune signalling at the ER membrane. Collectively, these data reveal the broad scope of regulation and differential functionalities mediated by ER-E3s for both membrane-tethered and cytoplasmic processes.</jats:p>
Male reproductive aging arises via multifaceted mating-dependent sperm and seminal proteome declines, but is postponable in Drosophila
<jats:p>Declining ejaculate performance with male age is taxonomically widespread and has broad fitness consequences. Ejaculate success requires fully functional germline (sperm) and soma (seminal fluid) components. However, some aging theories predict that resources should be preferentially diverted to the germline at the expense of the soma, suggesting differential impacts of aging on sperm and seminal fluid and trade-offs between them or, more broadly, between reproduction and lifespan. While harmful effects of male age on sperm are well known, we do not know how much seminal fluid deteriorates in comparison. Moreover, given the predicted trade-offs, it remains unclear whether systemic lifespan-extending interventions could ameliorate the declining performance of the ejaculate as a whole. Here, we address these problems using <jats:italic>Drosophila melanogaster.</jats:italic> We demonstrate that seminal fluid deterioration contributes to male reproductive decline via mating-dependent mechanisms that include posttranslational modifications to seminal proteins and altered seminal proteome composition and transfer. Additionally, we find that sperm production declines chronologically with age, invariant to mating activity such that older multiply mated males become infertile principally via reduced sperm transfer and viability. Our data, therefore, support the idea that both germline and soma components of the ejaculate contribute to male reproductive aging but reveal a mismatch in their aging patterns. Our data do not generally support the idea that the germline is prioritized over soma, at least, within the ejaculate. Moreover, we find that lifespan-extending systemic down-regulation of insulin signaling results in improved late-life ejaculate performance, indicating simultaneous amelioration of both somatic and reproductive aging.</jats:p>
Broad and strong memory CD4 + and CD8 + T cells induced by SARS-CoV-2 in UK convalescent COVID-19 patients.
COVID-19 is an ongoing global crisis in which the development of effective vaccines and therapeutics will depend critically on understanding the natural immunity to the virus, including the role of SARS-CoV-2-specific T cells. We have conducted a study of 42 patients following recovery from COVID-19, including 28 mild and 14 severe cases, comparing their T cell responses to those of 16 control donors. We assessed the immune memory of T cell responses using IFNγ based assays with overlapping peptides spanning SARS-CoV-2 apart from ORF1. We found the breadth, magnitude and frequency of memory T cell responses from COVID-19 were significantly higher in severe compared to mild COVID-19 cases, and this effect was most marked in response to spike, membrane, and ORF3a proteins. Total and spike-specific T cell responses correlated with the anti-Spike, anti-Receptor Binding Domain (RBD) as well as anti-Nucleoprotein (NP) endpoint antibody titre (p<0.001, <0.001 and =0.002). We identified 39 separate peptides containing CD4 + and/or CD8 + epitopes, which strikingly included six immunodominant epitope clusters targeted by T cells in many donors, including 3 clusters in spike (recognised by 29%, 24%, 18% donors), two in the membrane protein (M, 32%, 47%) and one in the nucleoprotein (Np, 35%). CD8+ responses were further defined for their HLA restriction, including B*4001-restricted T cells showing central memory and effector memory phenotype. In mild cases, higher frequencies of multi-cytokine producing M- and NP-specific CD8 + T cells than spike-specific CD8 + T cells were observed. They furthermore showed a higher ratio of SARS-CoV-2-specific CD8 + to CD4 + T cell responses. Immunodominant epitope clusters and peptides containing T cell epitopes identified in this study will provide critical tools to study the role of virus-specific T cells in control and resolution of SARS-CoV-2 infections. The identification of T cell specificity and functionality associated with milder disease, highlights the potential importance of including non-spike proteins within future COVID-19 vaccine design.