Fully Automated Bone Age Assessment On Large-Scale Hand X-Ray Dataset

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance

A Joint Communication and Control System for URLLC in Industrial IoT

Ultra reliable low latency communication (URLLC) is playing an important role in future wireless networks, such as industrial Internet of things (IIoT). Enormous industrial devices are requiring instant and highly-frequent control services, namely delay sensitive control, in order to improve the human safety or the operation accuracy. In this paper, a joint communication and delay sensitive control (JCDSC) system with URLLC is studied, where the control intervals are much shorter than the transmission latency. The control performance, known as the mean square error (MSE) of the plant states, are analysed in theory. The optimal blocklength of the control codewords, the data rate, as well as the minimum required control periods are then obtained, in order to minimize the control latency. Simulation results validate our theoretical analysis, while also demonstrating that an optimal blocklength of codewords should be selected in order to reduce the control latency

Thermal induced spin-polarized current protected by spin-momentum locking in nanowires

Spin-momentum locking arising from strong spin-orbit coupling is one of the key natures of topological materials. Since charge can induce a spin polarization due to spin-momentum locking, the search for materials that exhibit this feature has become one of the top priorities in the field of spintronics. In this paper, we report the electrical detection of the spin-transport properties of nanowires, using a nonlocal geometry measurement. A clear hysteresis voltage signal, which depends on the relative orientations between the magnetization of the ferromagnetic electrodes and the carrier spin polarization, has been observed. The hysteresis voltage states can be reversed by altering the electron movement direction, providing direct evidence of the spin-momentum locking feature of nanowires and revealing its topological nature. Furthermore, the current-dependent measurement suggests that the charge (spin) current is induced by thermal effect, which utilizes the thermoelectric properties of . Using the thermal effect to control the spin-polarized current protected by spin-momentum locking offers possibilities for small-sized devices based on the topological materials

Epigenetic regulation during cancer transitions across 11 tumour types

Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasi

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Variant annotation was supported by software resources provided via the Caché Campus program of the InterSystems GmbH to Alexander Teumer

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Robust Switching Control and Subspace Identification for Flutter of Flexible Wing

Active flutter suppression and subspace identification for a flexible wing model using micro fiber composite actuator were experimentally studied in a low speed wind tunnel. NACA0006 thin airfoil model was used for the experimental object to verify the performance of identification algorithm and designed controller. The equation of the fluid, vibration, and piezoelectric coupled motion was theoretically analyzed and experimentally identified under the open-loop and closed-loop condition by subspace method for controller design. A robust pole placement algorithm in terms of linear matrix inequality that accommodates the model uncertainty caused by identification deviation and flow speed variation was utilized to stabilize the divergent aeroelastic system. For further enlarging the flutter envelope, additional controllers were designed subject to the models beyond the flutter speed. Wind speed was measured online as the decision parameter of switching between the controllers. To ensure the stability of arbitrary switching, Common Lyapunov function method was applied to design the robust pole placement controllers for different models to ensure that the closed-loop system shared a common Lyapunov function. Wind tunnel result showed that the designed controllers could stabilize the time varying aeroelastic system over a wide range under arbitrary switching

A Methodology to Obtain the Accurate RVEs by a Multiscale Numerical Simulation of the 3D Braiding Process

To accurately evaluate the mechanical performance of three-dimensional (3D) braiding composites, it is essential to consider the braiding process and generate realistic representative volume element (RVE) structures. An efficient simulation methodology based on truss elements was used to simulate the 3D four-directional (3D4D) braiding process utilizing the finite element method (FEM) on the macroscale. The goal was to obtain the spatial trajectories of yarns and establish the relationship between the braiding parameters and the preform structure. Based on the initial yarn topology, the yarns were discretized as bundles of virtual sub-yarns. Then, a temperature drop simulation using hybrid elements was implemented to deform the yarn cross-section and obtain the interior, surface, and corner cells on the mesoscale. The simulation results show good agreement with the experiment. A parametric study was deployed to identify the effect of the model input parameters on the computation cost and accuracy. Furthermore, the approach applies to the other braiding processes, such as the cylindrical braiding composite