Large-scale single-photon imaging

Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing. However, large-scale high-fidelity single-photon imaging remains a big challenge, due to the complex hardware manufacture craft and heavy noise disturbance of SPAD arrays. In this work, we introduce deep learning into SPAD, enabling super-resolution single-photon imaging over an order of magnitude, with significant enhancement of bit depth and imaging quality. We first studied the complex photon flow model of SPAD electronics to accurately characterize multiple physical noise sources, and collected a real SPAD image dataset (64 $\times$ 32 pixels, 90 scenes, 10 different bit depth, 3 different illumination flux, 2790 images in total) to calibrate noise model parameters. With this real-world physical noise model, we for the first time synthesized a large-scale realistic single-photon image dataset (image pairs of 5 different resolutions with maximum megapixels, 17250 scenes, 10 different bit depth, 3 different illumination flux, 2.6 million images in total) for subsequent network training. To tackle the severe super-resolution challenge of SPAD inputs with low bit depth, low resolution, and heavy noise, we further built a deep transformer network with a content-adaptive self-attention mechanism and gated fusion modules, which can dig global contextual features to remove multi-source noise and extract full-frequency details. We applied the technique on a series of experiments including macroscopic and microscopic imaging, microfluidic inspection, and Fourier ptychography. The experiments validate the technique's state-of-the-art super-resolution SPAD imaging performance, with more than 5 dB superiority on PSNR compared to the existing methods

Cumulative Evidence for Relationships Between 8q24 Variants and Prostate Cancer

Multiple independent cancer susceptibility loci at chromosome 8q24 have been identified by GWAS (Genome-wide association studies). Forty six articles including 60,293 cases and 62,971 controls were collected to conduct a meta-analysis to evaluate the associations between 21 variants in 8q24 and prostate cancer risk. Of the 21 variants located in 8q2\5 were significantly associated with the risk of prostate cancer. In particular, both homozygous AA and heterozygous CA genotypes of rs16901979, as well as the AA and CA genotypes of rs1447295, were associated with the risk of prostate cancer. Our study showed that variants in the 8q24 region are associated with prostate cancer risk in this large-scale research synopsis and meta-analysis. Further studies are needed to explore the role of the 8q24 variants involved in the etiology of prostate cancer

The importance of the Indo-Pacific humpback dolphin (<i>Sousa chinesis</i>) population of Sanniang bay, Guangxi Province, PR China: recommendations for habitat protection. Scientific Committee Document SC/58/SM18, International Whaling Commission, May-June 2006, St.Kitts

During the period June 2004 - January 2006, a research team from the Qinzhou Bay Chinese White Dolphins Research Center of Peking University, the Peoples Republic of China, conducted systematic and opportunistic boat surveys of Sanniang Bay, Guangxi Province, in which Indo-Pacific humpback dolphins Sousa chinensis were regularly seen. Ninety eight dolphins were photographically identified. The dolphins appear to inhabit a small, shallow area of core habitat within the greater Sanniang Bay area. They do not appear to travel up the two rivers which are located to each side of the bay. Of the five populations known from the coastal area of China, the one that resides in Sanniang Bay is determined as having the least impact from anthropogenic activities. The area itself has been designated as a nature tourism location and considerable effort and money has been spent on developing appropriate tourist facilities. The dolphin watching industry in the area is strictly monitored and controlled by one local authority. The largest estuary adjacent to Sanniang Bay has been allocated for industrial development and a paper pulp mill will be established there. Considering the investment already made in the nature tourism industry, the natural beauty of the bay and the surrounding area and the likelihood that this is the only population of Indo-Pacific humpback dolphins which remain in uncompromised and relatively pristine habitat in all of China, it is urged that all effort be made to maintain the natural integrity of the bay. It is recommended that all development and operational aspects of the paper pulp be thoroughly scrutinized and all efforts made to minimize impact upon the environment and that all current and future industries and activities in this area must not detrimentally impact the dolphin population or compromise the integrity of the bay ecosystem

Near-Space Communications: the Last Piece of 6G Space-Air-Ground-Sea Integrated Network Puzzle

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space-air-ground-sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics and unique problems of NS-COM network. Afterwards, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite (StratoSat) -to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output (MIMO) and holographic MIMO, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, StratoSat-based radar detection and imaging, NS-COM assisted enhanced global navigation system, NS-COM assisted intelligent unmanned system and free space optical (FSO) communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.Comment: 28 pages, 8 figures, 2 table

Early-start antiplatelet therapy after operation in patients with spontaneous intracerebral hemorrhage and high risk of ischemic events (E-start):Protocol for a multi-centered, prospective, open-label, blinded endpoint randomized controlled trial

BACKGROUND: For severe spontaneous intracerebral hemorrhage (sSICH) patients with high risk of ischemic events, the incidence of postoperative major cardiovascular/cerebrovascular and peripheral vascular events (MACCPE) is notable. Although antiplatelet therapy is a potential way to benefit these patients, the severe hemorrhagic complications, e.g., intracranial re-hemorrhage, is a barrier for early starting antiplatelet therapy. OBJECTIVES: This randomized controlled trial aims to identify the benefit and safety of early starting antiplatelet therapy after operation for sSICH patients with high risk of ischemic events. METHODS: This study is a multicenter, prospective, randomized, open-label, blinded-endpoint trial. We will enroll 250 sSICH patients with a high risk of ischemic events (including cerebral infarcts, transient ischemic attack, myocardial infarction, pulmonary embolism, and deep venous thrombosis). The participants will be randomized in a 1:1 manner to early-start group (start antiplatelet therapy at 3 days after operation) and normal-start group (start antiplatelet therapy at 30 days after operation). The early-start group will receive aspirin 100 mg daily. The control group will not receive antithrombotic therapy until 30 days after operation. The efficacy endpoint is the incidence of MACCPE, and the safety endpoint is the incidence of intracranial re-hemorrhage. DISCUSSION: The Early-Start antiplatelet therapy after operation in patients with spontaneous intracerebral hemorrhage trial (E-start) is the first randomized trial about early start antiplatelet therapy for operated sSICH patients with a high risk of ischemic events. This study will provide a new strategy and evidence for postoperative management in the future. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT04820972; Available at: https://clinicaltrials.gov/ct2/show/NCT04820972?term=NCT04820972&draw=2&rank=1. Chinese Clinical Trial Registry, identifier ChiCTR2100044560; Available at: http://www.chictr.org.cn/showproj.aspx?proj=123277

A Multi-Focal Green Plant Image Fusion Method Based on Stationary Wavelet Transform and Parameter-Adaptation Dual Channel Pulse-Coupled Neural Network

ObjectiveTo construct the 3D point cloud model of green plants a large number of clear images are needed. Due to the limitation of the depth of field of the lens, part of the image would be out of focus when the green plant image with a large depth of field is collected, resulting in problems such as edge blurring and texture detail loss, which greatly affects the accuracy of the 3D point cloud model. However, the existing processing algorithms are difficult to take into account both processing quality and processing speed, and the actual effect is not ideal. The purpose of this research is to improve the quality of the fused image while taking into account the processing speed.MethodsA plant image fusion method based on non-subsampled shearlet transform (NSST) based parameter-adaptive dual channel pulse-coupled neural network (PADC-PCNN) and stationary wavelet transform (SWT) was proposed. Firstly, the RGB image of the plant was separated into three color channels, and the G channel with many features such as texture details was decomposed by NSST in four decomposition layers and 16 directions, which was divided into one group of low frequency subbands and 64 groups of high frequency subbands. The low frequency subband used the gradient energy fusion rule, and the high frequency subband used the PADC-PCNN fusion rule. In addition, the weighting of the eight-neighborhood modified Laplacian operator was used as the link strength of the high-frequency fusion part, which enhanced the fusion effect of the detailed features. At the same time, for the R and B channels with more contour information and background information, a SWT with fast speed and translation invariance was used to suppress the pseudo-Gibbs effect. Through the high-precision and high-stability multi-focal length plant image acquisition system, 480 images of 8 experimental groups were collected. The 8 groups of data were divided into an indoor light group, natural light group, strong light group, distant view group, close view group, overlooking group, red group, and yellow group. Meanwhile, to study the application range of the algorithm, the focus length of the collected clear plant image was used as the reference (18 mm), and the image acquisition was adjusted four times before and after the step of 1.5 mm, forming the multi-focus experimental group. Subjective evaluation and objective evaluation were carried out for each experimental group to verify the performance of the algorithm. Subjective evaluation was analyzed through human eye observation, detail comparison, and other forms, mainly based on the human visual effect. The image fusion effect of the algorithm was evaluated using four commonly used objective indicators, including average gradient (AG), spatial frequency (SF), entropy (EN), and standard deviation (SD).Results and DiscussionsThe proposed PADC-PCNN-SWT algorithm and other five algorithms of common fast guided filtering algorithm (FGF), random walk algorithm (RW), non-subsampled shearlet transform based PCNN (NSST-PCNN) algorithm, SWT algorithm and non-subsampled shearlet transform based parameter-adaptive dual-channel pulse-coupled neural network (NSST-PADC) and were compared. In the objective evaluation data except for the red group and the yellow group, each index of the PADC-PCNN-SWT algorithm was second only to the NSST-PADC algorithm, but the processing speed was 200.0% higher than that of the NSST-PADC algorithm on average. At the same time, compared with the FDF, RW, NSST-PCNN, and SWT algorithms, the PADC-PCN -SWT algorithm improved the clarity index by 5.6%, 8.1%, 6.1%, and 17.6%, respectively, and improved the spatial frequency index by 2.9%, 4.8%, 7.1%, and 15.9%, respectively. However, the difference between the two indicators of information entropy and standard deviation was less than 1%, and the influence was ignored. In the yellow group and the red group, the fusion quality of the non-green part of the algorithm based on PADC-PCNN-SWT was seriously degraded. Compared with other algorithms, the sharpness index of the algorithm based on PADC-PCNN-SWT decreased by an average of 1.1%, and the spatial frequency decreased by an average of 5.1%. However, the indicators of the green part of the fused image were basically consistent with the previous several groups of experiments, and the fusion effect was good. Therefore, the algorithm based on PADC-PCNN-SWT only had a good fusion effect on green plants. Finally, by comparing the quality of four groups of fused images with different focal length ranges, the results showed that the algorithm based on PADC-PCNN-SWT had a better contour and color restoration effect for out-of-focus images in the range of 15-21 mm, and the focusing range based on PADC-PCNN-SWT was about 6 mm.ConclusionsThe multi-focal length image fusion algorithm based on PADC-PCNN-SWT achieved better detail fusion performance and higher image fusion efficiency while ensuring fusion quality, providing high-quality data, and saving a lot of time for building 3D point cloud model of green plants

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies

BackgroundLow birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML models in maternal health; thus, it is critical to establish benchmarks to advance ML use to subsequently improve birth outcomes. ObjectiveThis study aimed to establish several key benchmarking ML models to predict LBW and systematically apply different rebalancing optimization methods to a large-scale and extremely imbalanced all-payer hospital record data set that connects mother and baby data at a state level in the United States. We also performed feature importance analysis to identify the most contributing features in the LBW classification task, which can aid in targeted intervention. MethodsOur large data set consisted of 266,687 birth records across 6 years, and 8.63% (n=23,019) of records were labeled as LBW. To set up benchmarking ML models to predict LBW, we applied 7 classic ML models (ie, logistic regression, naive Bayes, random forest, extreme gradient boosting, adaptive boosting, multilayer perceptron, and sequential artificial neural network) while using 4 different data rebalancing methods: random undersampling, random oversampling, synthetic minority oversampling technique, and weight rebalancing. Owing to ethical considerations, in addition to ML evaluation metrics, we primarily used recall to evaluate model performance, indicating the number of correctly predicted LBW cases out of all actual LBW cases, as false negative health care outcomes could be fatal. We further analyzed feature importance to explore the degree to which each feature contributed to ML model prediction among our best-performing models. ResultsWe found that extreme gradient boosting achieved the highest recall score—0.70—using the weight rebalancing method. Our results showed that various data rebalancing methods improved the prediction performance of the LBW group substantially. From the feature importance analysis, maternal race, age, payment source, sum of predelivery emergency department and inpatient hospitalizations, predelivery disease profile, and different social vulnerability index components were important risk factors associated with LBW. ConclusionsOur findings establish useful ML benchmarks to improve birth outcomes in the maternal health domain. They are informative to identify the minority class (ie, LBW) based on an extremely imbalanced data set, which may guide the development of personalized LBW early prevention, clinical interventions, and statewide maternal and infant health policy changes

Effect of Negative Valve Overlap on Combustion and Emissions of CNG-Fueled HCCI Engine with Hydrogen Addition

In order to study the effect of negative valve overlap on combustion and emission characteristics of a homogeneous charge compression ignition engine fueled with natural gas and hydrogen, the test and the simulation were conducted using an engine cycle model coupling the chemical kinetic reaction mechanism under different valve timing conditions. Results show that the internal EGR formed by using negative valve overlap could heat the inlet mixtures and improve the spontaneous ignition characteristic of the engine. The residual exhaust gas could slow down the heat release rate, decrease the pressure rise rate and the maximum combustion temperature, and reduce the NOx emission simultaneously. Among the three NVO schemes, the strategy of changing the intake valve opening timing individually can create the least power loss, and the symmetric NVO strategy which changes both the exhaust valve closing timing and the intake valve opening timing simultaneously can achieve the best heating effect of inlet mixtures and the satisfactory decrease of combustion temperature, as well as the largest reduction of NOx emission