Image segmentation in marine environments using convolutional LSTM for temporal context

Unmanned surface vehicles (USVs) carry a wealth of possible applications, many of which are limited by the vehicle's level of autonomy. The development of efficient and robust computer vision algorithms is a key factor in improving this, as they permit autonomous detection and thereby avoidance of obstacles. Recent developments in convolutional neural networks (CNNs), and the collection of increasingly diverse datasets, present opportunities for improved computer vision algorithms requiring less data and computational power. One area of potential improvement is the utilisation of temporal context from USV camera feeds in the form of sequential video frames to consistently identify obstacles in diverse marine environments under challenging conditions. This paper documents the implementation of this through long short-term memory (LSTM) cells in existing CNN structures and the exploration of parameters affecting their efficacy. It is found that LSTM cells are promising for achieving improved performance; however, there are weaknesses associated with network training procedures and datasets. Several novel network architectures are presented and compared using a state-of-the-art benchmarking method. It is shown that LSTM cells allow for better model performance with fewer training iterations, but that this advantage diminishes with additional training

Dynamic location model for designated COVID-19 hospitals in China

In order to effectively cope with the situation caused by the COVID-19 pandemic, cases should be concentrated in designated medical institutions with full capability to deal with patients infected by this virus. We studied the location of such hospitals dividing the patients into two categories: ordinary and severe. Genetic algorithms were constructed to achieve a three-phase dynamic approach for the location of hospitals designated to receive and treat COVID-19 cases based on the goal of minimizing the cost of construction and operation isolation wards as well as the transportation costs involved. A dynamic location model was established with the decision variables of the corresponding ‘chromosome’ of the genetic algorithms designed so that this goal could be reached. In the static location model, 15 hospitals were required throughout the treatment cycle, whereas the dynamic location model found a requirement of only 11 hospitals. It further showed that hospital construction costs can be reduced by approximately 13.7% and operational costs by approximately 26.7%. A comparison of the genetic algorithm and the Gurobi optimizer gave the genetic algorithm several advantages, such as great convergence and high operational efficiency

Cohort size required for prognostic genes analysis of stage II/III esophageal squamous cell carcinoma

Background: Few overlaps between prognostic biomarkers are observed among different independently performed genomic studies of esophageal squamous cell carcinoma (ESCC). One of the reasons for this is the insufficient cohort size. How many cases are needed to prognostic genes analysis in ESCC?Methods: Here, based on 387 stage II/III ESCC cases analyzed by whole-genome sequencing from one single center, effects of cohort size on prognostic genes analysis were investigated. Prognostic genes analysis was performed in 100 replicates at each cohort size level using a random resampling method.Results: The number of prognostic genes followed a power-law increase with cohort size in ESCC patients with stage II and stage III, with exponents of 2.27 and 2.25, respectively. Power-law curves with increasing events number were also observed in stage II and III ESCC, respectively, and they almost overlapped. The probability of obtaining statistically significant prognostic genes shows a logistic cumulative distribution function with respect to cohort size. To achieve a 100% probability of obtaining statistically significant prognostic genes, the minimum cohort sizes required in stage II and III ESCC were approximately 95 and 60, respectively, corresponding to a number of outcome events of 33 and 36, respectively.Conclusion: In summary, the number of prognostic genes follows a power-law growth with the cohort size or events number in ESCC. The minimum events number required to achieve a 100% probability of obtaining a statistically significant prognostic gene is approximately 35

PND-Net: Physics based Non-local Dual-domain Network for Metal Artifact Reduction

Metal artifacts caused by the presence of metallic implants tremendously degrade the reconstructed computed tomography (CT) image quality, affecting clinical diagnosis or reducing the accuracy of organ delineation and dose calculation in radiotherapy. Recently, deep learning methods in sinogram and image domains have been rapidly applied on metal artifact reduction (MAR) task. The supervised dual-domain methods perform well on synthesized data, while unsupervised methods with unpaired data are more generalized on clinical data. However, most existing methods intend to restore the corrupted sinogram within metal trace, which essentially remove beam hardening artifacts but ignore other components of metal artifacts, such as scatter, non-linear partial volume effect and noise. In this paper, we mathematically derive a physical property of metal artifacts which is verified via Monte Carlo (MC) simulation and propose a novel physics based non-local dual-domain network (PND-Net) for MAR in CT imaging. Specifically, we design a novel non-local sinogram decomposition network (NSD-Net) to acquire the weighted artifact component, and an image restoration network (IR-Net) is proposed to reduce the residual and secondary artifacts in the image domain. To facilitate the generalization and robustness of our method on clinical CT images, we employ a trainable fusion network (F-Net) in the artifact synthesis path to achieve unpaired learning. Furthermore, we design an internal consistency loss to ensure the integrity of anatomical structures in the image domain, and introduce the linear interpolation sinogram as prior knowledge to guide sinogram decomposition. Extensive experiments on simulation and clinical data demonstrate that our method outperforms the state-of-the-art MAR methods.Comment: 19 pages, 8 figure

The functional impact on donor vessel following transcatheter closure of coronary artery fistulas—a retrospective study using QFR analysis

BackgroundThe impact of transcatheter closure of coronary artery fistula (CAF) and residual shunt after occlusion on improving blood flow in the donor vessel remains uncertain.ObjectivesTo evaluate the functional impact on the donor vessel following CAFs closure using QFR (Quantitative Flow Ratio) analysis.MethodsA total of 46 patients with 48 CAFs who underwent transcatheter closure at Shanghai Chest Hospital and Shuguang Hospital between March 2015 and August 2023 were included in the review. The clinical, angiographic details, and QFR data were subjected to analysis. The size of the fistulae was defined according to the ratio between the diameters of the fistulae and the largest diameter of the coronary vessel not feeding the coronary fistula.ResultsAmong 48 CAFs, the average diameter of the fistulae ostium was 3.19 ± 1.04 mm, while the mean diameter of the donor vessel segment following fistulae was 3.45 ± 1.01 mm. The mean QFR value of the donor vessels with medium CAFs was found to be significantly lower than those with small CAFs (0.93 ± 0.10 vs. 0.98 ± 0.03; p < 0.05). Furthermore, the mean QFR value of donor vessels with medium CAFs was observed to be significantly improved after occlusion (0.99 ± 0.01 vs. 0.93 ± 0.10; p = 0.01). However, there was no statistical difference in the mean QFR value of donor vessels with small CAFs before and after occlusion (0.98 ± 0.03 vs. 0.98 ± 0.02; p > 0.05). Moreover, the changes in QFR were more pronounced in donor vessels with medium CAFs compared to those with small CAFs after occlusion (0.06 ± 0.10 vs. 0.005 ± 0.012; p = 0.01). There is no statistical difference in the mean QFR variation and QFR variation rate between donor vessels with CAFs that occurred residual shunt and those without residual shunt after occlusion (p > 0.05).ConclusionsThe presence of medium CAFs has a significant impact on the blood flow of the donor vessel, as compared to small CAFs, and may benefit from occlusion. A small residual shunt has no significant impact on the effectiveness of CAFs occlusion in enhancing donor blood flow

Novel Viroid‐Like RNAs Naturally Infect a Filamentous Fungus

© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License. https://creativecommons.org/licenses/by/4.0/To date, viroids have been found to naturally infect only plants, resulting in substantial losses for some crops. Whether viroids or viroid‐like RNAs naturally infect non‐plant hosts remains unknown. Here the existence of a set of exogenous, single‐stranded circular RNAs, ranging in size from 157 to 450 nucleotides, isolated from the fungus Botryosphaeria dothidea and nominated B. dothidea RNAs (BdcRNAs) is reported. BdcRNAs replicate autonomously in the nucleus via a rolling‐circle mechanism following a symmetric pathway. BdcRNA infection induces symptoms, because BdcRNAs can apparently modulate, to different degrees, specific biological traits (e.g., alter morphology, decrease growth rate, attenuate virulence, and increase or decrease tolerance to osmotic and oxidative stress) of the host fungus. Overall, BdcRNAs have genome characteristics similar to those of viroids and exhibit pathogenic effects on fungal hosts. It is proposed that these novel fungus infecting RNAs should be termed mycoviroids. BdcRNA(s) may be considered additional inhabitants at the frontier of life in terms of genomic complexity, and represent a new class of acellular entities endowed with regulatory functions, and novel epigenomic carriers of biological information.Peer reviewe

Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation

Purpose: Scatter artifacts severely degrade image quality of cone-beam CT (CBCT). We present an ultrafast scatter correction framework by using GPU-based Monte Carlo (MC) simulation and prior patient CT image, aiming at automatically finish the whole process including both scatter correction and reconstruction within 30 seconds.Methods: The method consists of six steps: 1) FDK reconstruction using raw projection data; 2) Rigid Registration of planning CT to the FDK results; 3) MC scatter calculation at sparse view angles using the planning CT; 4) Interpolation of the calculated scatter signals to other angles; 5) Removal of scatter from the raw projections; 6) FDK reconstruction using the scatter-corrected projections. In addition to using GPU to accelerate MC photon simulations, we also use a small number of photons and a down-sampled CT image in simulation to further reduce computation time. A novel denoising algorithm is used to eliminate MC noise from the simulated scatter images caused by low photon numbers. The method is validated on one simulated head-and-neck case with 364 projection angles.Results: We have examined variation of the scatter signal among projection angles using Fourier analysis. It is found that scatter images at 31 angles are sufficient to restore those at all angles with < 0.1% error. For the simulated patient case with a resolution of 512 × 512 × 100, we simulated 5 × 106 photons per angle. The total computation time is 20.52 seconds on a Nvidia GTX Titan GPU, and the time at each step is 2.53, 0.64, 14.78, 0.13, 0.19, and 2.25 seconds, respectively. The scatter-induced shading/cupping artifacts are substantially reduced, and the average HU error of a region-of-interest is reduced from 75.9 to 19.0 HU.Conclusion: A practical ultrafast MC-based CBCT scatter correction scheme is developed. It accomplished the whole procedure of scatter correction and reconstruction within 30 seconds.----------------------------Cite this article as: Xu Y, Bai T, Yan H, Ouyang L, Wang J, Pompos A, Zhou L, Jiang SB, Jia X. Ultrafast cone-beam CT scatter correction with GPU-based Monte Carlo simulation. Int J Cancer Ther Oncol 2014; 2(2):020245. DOI: 10.14319/ijcto.0202.4

Study on Resistance Switching Properties of Na0.5Bi0.5TiO3Thin Films Using Impedance Spectroscopy

The Na0.5Bi0.5TiO3(NBT) thin films sandwiched between Au electrodes and fluorine-doped tin oxide (FTO) conducting glass were deposited using a sol–gel method. Based on electrochemical workstation measurements, reproducible resistance switching characteristics and negative differential resistances were obtained at room temperature. A local impedance spectroscopy measurement of Au/NBT was performed to reveal the interface-related electrical characteristics. The DC-bias-dependent impedance spectra suggested the occurrence of charge and mass transfer at the interface of the Au/NBT/FTO device. It was proposed that the first and the second ionization of oxygen vacancies are responsible for the conduction in the low- and high-resistance states, respectively. The experimental results showed high potential for nonvolatile memory applications in NBT thin films