Spatial Linear Mixed Effects Modelling for OCT Images: SLME Model.

Much recent research focuses on how to make disease detection more accurate as well as "slimmer", i.e., allowing analysis with smaller datasets. Explanatory models are a hot research topic because they explain how the data are generated. We propose a spatial explanatory modelling approach that combines Optical Coherence Tomography (OCT) retinal imaging data with clinical information. Our model consists of a spatial linear mixed effects inference framework, which innovatively models the spatial topography of key information via mixed effects and spatial error structures, thus effectively modelling the shape of the thickness map. We show that our spatial linear mixed effects (SLME) model outperforms traditional analysis-of-variance approaches in the analysis of Heidelberg OCT retinal thickness data from a prospective observational study, involving 300 participants with diabetes and 50 age-matched controls. Our SLME model has a higher power for detecting the difference between disease groups, and it shows where the shape of retinal thickness profiles differs between the eyes of participants with diabetes and the eyes of healthy controls. In simulated data, the SLME model demonstrates how incorporating spatial correlations can increase the accuracy of the statistical inferences. This model is crucial in the understanding of the progression of retinal thickness changes in diabetic maculopathy to aid clinicians for early planning of effective treatment. It can be extended to disease monitoring and prognosis in other diseases and with other imaging technologies

Development and external validation of a mixed-effects deep learning model to diagnose COVID-19 from CT imaging

BackgroundThe automatic analysis of medical images has the potential improve diagnostic accuracy while reducing the strain on clinicians. Current methods analyzing 3D-like imaging data, such as computerized tomography imaging, often treat each image slice as individual slices. This may not be able to appropriately model the relationship between slices.MethodsOur proposed method utilizes a mixed-effects model within the deep learning framework to model the relationship between slices. We externally validated this method on a data set taken from a different country and compared our results against other proposed methods. We evaluated the discrimination, calibration, and clinical usefulness of our model using a range of measures. Finally, we carried out a sensitivity analysis to demonstrate our methods robustness to noise and missing data.ResultsIn the external geographic validation set our model showed excellent performance with an AUROC of 0.930 (95%CI: 0.914, 0.947), with a sensitivity and specificity, PPV, and NPV of 0.778 (0.720, 0.828), 0.882 (0.853, 0.908), 0.744 (0.686, 0.797), and 0.900 (0.872, 0.924) at the 0.5 probability cut-off point. Our model also maintained good calibration in the external validation dataset, while other methods showed poor calibration.ConclusionDeep learning can reduce stress on healthcare systems by automatically screening CT imaging for COVID-19. Our method showed improved generalizability in external validation compared to previous published methods. However, deep learning models must be robustly assessed using various performance measures and externally validated in each setting. In addition, best practice guidelines for developing and reporting predictive models are vital for the safe adoption of such models

Inactivation of TRPM2 channels by extracellular divalent copper

Cu2+ is an essential metal ion that plays a critical role in the regulation of a number of ion channels and receptors in addition to acting as a cofactor in a variety of enzymes. Here, we showed that human melastatin transient receptor potential 2 (hTRPM2) channel is sensitive to inhibition by extracellular Cu2+. Cu2 + at concentrations as low as 3 μM inhibited the hTRPM2 channel completely and irreversibly upon washing or using Cu2+ chelators, suggesting channel inactivation. The Cu2+-induced inactivation was similar when the channels conducted inward or outward currents, indicating the permeating ions had little effect on Cu2+-induced inactivation. Furthermore, Cu2+ had no effect on singe channel conductance. Alanine substitution by site-directed mutagenesis of His995 in the pore-forming region strongly attenuated Cu2+-induced channel inactivation, and mutation of several other pore residues to alanine altered the kinetics of channel inactivation by Cu2+. In addition, while introduction of the P1018L mutation is known to result in channel inactivation, exposure to Cu2+ accelerated the inactivation of this mutant channel. In contrast with the hTRPM2, the mouse TRPM2 (mTRPM2) channel, which contains glutamine at the position equivalent to His995, was insensitive to Cu2+. Replacement of His995 with glutamine in the hTRPM2 conferred loss of Cu2+-induced channel inactivation. Taken together, these results suggest that Cu2+ inactivates the hTRPM2 channel by interacting with the outer pore region. Our results also indicate that the amino acid residue difference in this region gives rise to species-dependent effect by Cu2+ on the human and mouse TRPM2 channels

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

Immune Evasion Strategies of Pre-Erythrocytic Malaria Parasites

Malaria is a mosquito-borne infectious disease of humans. It begins with a bite from an infected female Anopheles mosquito and leads to the development of the pre-erythrocytic and blood stages. Blood-stage infection is the exclusive cause of clinical symptoms of malaria. In contrast, the pre-erythrocytic stage is clinically asymptomatic and could be an excellent target for preventive therapies. Although the robust host immune responses limit the development of the liver stage, malaria parasites have also evolved strategies to suppress host defenses at the pre-erythrocytic stage. This paper reviews the immune evasion strategies of malaria parasites at the pre-erythrocytic stage, which could provide us with potential targets to design prophylactic strategies against malaria

An Improved Crucible Spatial Bubble Detection Based on YOLOv5 Fusion Target Tracking

A three-dimensional spatial bubble counting method is proposed to solve the problem of the existing crucible bubble detection only being able to perform two-dimensional statistics. First, spatial video images of the transparent layer of the crucible are acquired by a digital microscope, and a quartz crucible bubble dataset is constructed independently. Secondly, to address the problems of poor real-time and the insufficient small-target detection capability of existing methods for quartz crucible bubble detection, rich detailed feature information is retained by reducing the depth of down-sampling in the YOLOv5 network structure. In the neck, the dilated convolution algorithm is used to increase the feature map perceptual field to achieve the extraction of global semantic features; in front of the detection layer, an effective channel attention network (ECA-Net) mechanism is added to improve the capability of expressing significant channel characteristics. Furthermore, a tracking algorithm based on Kalman filtering and Hungarian matching is presented for bubble counting in crucible space. The experimental results demonstrate that the detector algorithm presented in this paper can effectively reduce the missed detection rate of tiny bubbles and increase the average detection precision from 96.27% to 98.76% while reducing weight by half and reaching a speed of 82 FPS. The excellent detector performance improves the tracker’s accuracy significantly, allowing for real-time and high-precision counting of bubbles in quartz crucibles. It is an effective method for detecting crucible spatial bubbles

Exploration of Vehicle Target Detection Method Based on Lightweight YOLOv5 Fusion Background Modeling

Due to the explosive increase per capita in vehicle ownership in China brought about by the continuous development of the economy and society, many negative impacts have arisen, making it necessary to establish the smart city system that has rapidly developing vehicle detection technology as its data acquisition system. This paper proposes a lightweight detection model based on an improved version of YOLOv5 to address the problem of missed and false detections caused by occlusion during rush hour vehicle detection in surveillance videos. The proposed model replaces the BottleneckCSP structure with the Ghostnet structure and prunes the network model to speed up inference. Additionally, the Coordinate Attention Mechanism is introduced to enhance the network’s feature extraction and improve its detection and recognition ability. Distance-IoU Non-Maximum Suppression replaces Non-Maximum Suppression to address the issue of false detection and omission when detecting congested targets. Lastly, the combination of the five-frame differential method with VIBE and MD-SILBP operators is used to enhance the model’s feature extraction capabilities for vehicle contours. The experimental results show that the proposed model outperforms the original model in terms of the number of parameters, inference ability, and accuracy when applied to both the expanded UA-DETRAC and a self-built dataset. Thus, this method has significant industrial value in intelligent traffic systems and can effectively improve vehicle detection indicators in traffic monitoring scenarios

Safety Risks of Self-driving Vehicle: Identification and Measurement

Self-driving vehicle is a hot application of artificial intelligence, and the identification and measurement of its safety risks has become an urgent research topic in the field of artificial intelligence safety. In this study, we collect qualitative information through case interviews and identify the key elements of safety risks using the qualitative research method and the grounded theory. Further, we propose for the first time a six-element frame for the safety risks of self-driving vehicle. These elements include single vehicle safety, networking safety, technological level, legal policies, public opinion, and industrial risks. Subsequently, we design a questionnaire and conduct two online questionnaires surveys to measure the safety risk elements. To cope with future safety risks of self-driving vehicle, enterprises should strengthen the research and manufacturing of key components, increase investment in information security, participate in the formulation of industry standards and regulations, and maintain a sustainable development. The government should strengthen the supervision over self-driving vehicle tests, improve regulations and standards, and guide talent training. Consumers should keep good driving habits and maintain rational regarding self-driving vehicle