Automatic cell segmentation by adaptive thresholding (ACSAT) for large-scale calcium imaging datasets

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.DP2 NS082126 - NINDS NIH HHSPublished versio

The incidence, monitoring coverage and clinical characteristics of hydroxychloroquine retinopathy in the United Kingdom

Background: Retinal monitoring is recommended for hydroxychloroquine users to detect pre-symptomatic retinopathy and preserve visual function. However, the incidence of hydroxychloroquine retinopathy and monitoring coverage in the U.K. are incompletely characterised. Moreover, the visual benefits of monitoring for retinopathy – recommended for over 70,000 long-term hydroxychloroquine users in the U.K. - remain unproven. Methods: A national, prospective observational study was undertaken with the British Ophthalmological Surveillance Unit (BOSU). Newly diagnosed cases of hydroxychloroquine retinopathy in the U.K. were reported and data captured using a standardised questionnaire over 3.5 years (July 2018–Dec 2021). The frequency of retinopathy and coverage of monitoring amongst long-term users was estimated. Visual function was compared between asymptomatic individuals detected on monitoring and those presenting with visual symptoms. The clinical characteristics, dosing and management of reported cases were captured. Results: The annualised number of incident cases of hydroxychloroquine retinopathy was 29–57, with an annualised frequency of 0.04–0.08% amongst long-term users (~1 in 1247–2625). The coverage of monitoring was approximately 2.6–5.5%. Visual acuity (0.1 vs. 0.22 logMAR; p = 0.007) and visual field mean deviation (−3.73 dB vs. −8.69 dB; p = 0.017) were better preserved in asymptomatic individuals compared to those presenting with visual symptoms. Conclusion: These data support the efficacy of monitoring in the preservation of visual function in patients with hydroxychloroquine retinopathy at diagnosis. The overall population coverage of monitoring was low, consistent with the high proportion of symptomatic patients at diagnosis. This study presents a method for evaluating the yield of monitoring for hydroxychloroquine retinopathy in the U.K

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page: https://zparquet.github.io/Fast-Learning . Code: https://github.com/zParquet/Fast-Learnin

Is subretinal AAV gene replacement still the only viable treatment option for choroideremia?

Introduction: Choroideremia is an X-linked inherited retinal degeneration resulting from mutations in the CHM gene, encoding Rab escort protein-1 (REP1), a protein regulating intracellular vesicular transport. Loss-of-function mutations in CHM lead to progressive loss of retinal pigment epithelium (RPE) with photoreceptor and choriocapillaris degeneration, leading to progressive visual field constriction and loss of visual acuity. Three hundred and fifty-four unique mutations have been reported in CHM. While gene augmentation remains an ideal therapeutic option for choroideremia, other potential future clinical strategies may exist. Areas covered: The authors examine the pathophysiology and genetic basis of choroideremia. They summarize the status of ongoing gene therapy trials and discuss CHM mutations amenable to other therapeutic approaches including CRISPR/Cas-based DNA and RNA editing, nonsense suppression of premature termination codons, and antisense oligonucleosides for splice modification. The authors undertook a literature search in PubMed and NIH Clinical Trials in October 2020. Expert opinion: The authors conclude that AAV-mediated gene augmentation remains the most effective approach for choroideremia. Given the heterogeneity of CHM mutations and potential risks and benefits, genome-editing approaches currently do not offer significant advantages. Nonsense suppression strategies and antisense oligonucleotides are exciting novel therapeutic options; however, their clinical viability remains to be determined

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Active Flutter Suppression and Aeroelastic Response of Functionally Graded Multilayer Graphene Nanoplatelet Reinforced Plates with Piezoelectric Patch

This paper investigates the aeroelastic flutter and vibration reduction of functionally graded (FG) multilayer graphene nanoplatelets (GPLs) reinforced composite plates with piezoelectric patch subjected to supersonic flow. Activated by the control voltage, the piezoelectric patch can generate the active mass and active stiffness that can accordingly increase the base plate’s stiffness and mass. As a result, it changes the GPLs reinforced plate’s dynamic characteristics. The motion equation of the plate-piezoelectric system is derived through the Hamilton principle. Based on the modified Halpin–Tsai model, the effects of graphene nanoplatelets weight fraction and distribution pattern on the dynamic behaviors of the plate are numerically studied in detail. The result illustrates that adding a few amounts of grapheme nanoplatelets can effectually enhance the aeroelastic properties of the plates. Two kinds of control strategies, including the displacement and acceleration feedback control, are applied to suppress the occurrence of the flutter of the plate. It shows that the displacement and acceleration feedback control can improve the critical flutter Mach number of the plate by attaching active stiffness and active mass, respectively. Furthermore, the combined displacement and acceleration feedback control has a better control effect than that of considering only one of them

Active Flutter Suppression and Aeroelastic Response of Functionally Graded Multilayer Graphene Nanoplatelet Reinforced Plates with Piezoelectric Patch

This paper investigates the aeroelastic flutter and vibration reduction of functionally graded (FG) multilayer graphene nanoplatelets (GPLs) reinforced composite plates with piezoelectric patch subjected to supersonic flow. Activated by the control voltage, the piezoelectric patch can generate the active mass and active stiffness that can accordingly increase the base plate’s stiffness and mass. As a result, it changes the GPLs reinforced plate’s dynamic characteristics. The motion equation of the plate-piezoelectric system is derived through the Hamilton principle. Based on the modified Halpin–Tsai model, the effects of graphene nanoplatelets weight fraction and distribution pattern on the dynamic behaviors of the plate are numerically studied in detail. The result illustrates that adding a few amounts of grapheme nanoplatelets can effectually enhance the aeroelastic properties of the plates. Two kinds of control strategies, including the displacement and acceleration feedback control, are applied to suppress the occurrence of the flutter of the plate. It shows that the displacement and acceleration feedback control can improve the critical flutter Mach number of the plate by attaching active stiffness and active mass, respectively. Furthermore, the combined displacement and acceleration feedback control has a better control effect than that of considering only one of them

The stability of tin silicon oxide thin-film transistors with different annealing temperatures

The influence of annealing temperature on the electrical properties of tin silicon oxide (TSO) thin-film transistors (TFTs) and the corresponding bias stress stability have been investigated. With increasing annealing temperature, the TSO films present a structure which is closer to crystallization, and it is conducive to the improvement of the mobility of TSO TFTs. Meanwhile, the positive bias stress (PBS) stability of TSO TFTs is ameliorated due to the decreasing traps at the interface of dielectric layer and channel layer. The threshold voltage shifts in opposite direction after being stressed under negative bias stress (NBS), which is due to the competition between electrons captured by defects related to oxygen vacancies in the channel layer and water molecule adsorption on the back channel

Intelligent ship anti-rolling control system based on a deep deterministic policy gradient algorithm and the Magnus effect

Anti-rolling devices are widely used in modern shipboard components. In particular, ship anti-rolling control systems are developed to achieve a wide range of ship speeds and efficient anti-rolling capabilities. However, factors that are challenging to solve accurately, such as strong nonlinearities, a complex working environment, and hydrodynamic system parameters, limit the investigation of the rolling motion of ships at sea. Moreover, current anti-rolling control systems still face several challenges, such as poor nonlinear adaptability and manual parameter adjustment. In this regard, this study developed a dynamic model for a ship anti-rolling system. In addition, based on deep reinforcement learning (DRL), an efficient anti-rolling controller was developed using a deep deterministic policy gradient (DDPG) algorithm. Finally, the developed system was applied to a ship anti-rolling device based on the Magnus effect. The advantages of reinforcement learning adaptive control enable controlling an anti-rolling system under various wave angles, ship speeds, and wavelengths. The results revealed that the anti-rolling efficiency of the intelligent ship anti-rolling control method using the DDPG algorithm surpassed 95% and had fast convergence. This study lays the foundation for developing a DRL anti-rolling controller for full-scale ships