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Summary: Background: Endovascular therapy (EVT) was demonstrated effective in acute large vessel occlusion (LVO) with large infarction. Revealing subgroups of patients who would or would not benefit from EVT will further inform patient selection for EVT. Methods: This post-hoc analysis of the ANGEL-ASPECT trial, a randomised controlled trial of 456 adult patients with acute anterior-circulation LVO and large infarction, defined by ASPECTS 3–5 or infarct core volume 70–100 mL, enrolled from 46 centres across China, between October 2, 2020 and May 18, 2022. Patients were randomly assigned (1:1) to receiving EVT and medical management or medical management alone. One patient withdrew consent, 455 patients were included in this post-hoc analysis and categorised into 4 subgroups by lower or higher NIHSS (< or ≥16) and smaller or larger infarct core (< or ≥70 mL). Those with lower NIHSS & smaller core, and higher NIHSS & larger core were considered clinical-radiological matched subgroups; otherwise clinical-radiological mismatched subgroups. Primary outcome was 90-day modified Rankin Scale (mRS). ANGEL-ASPECT is registered with ClinicalTrials.gov, NCT04551664. Findings: Overall, 139 (30.5%) patients had lower NIHSS & smaller core, 106 (23.3%) higher NIHSS & larger core, 130 (28.6%) higher NIHSS & smaller core, and 80 (17.6%) lower NIHSS & larger core. There was significant ordinal shift in the 90-day mRS toward a better outcome with EVT in clinical-radiological matched subgroups: lower NIHSS & smaller core (generalised OR, 1.76; 95% CI, 1.18–2.62; p = 0.01) and higher NIHSS & larger core (1.64; 1.06–2.54; 0.01); but not in the two clinical-radiological mismatched subgroups. Interpretation: Our findings suggested that in patients with anterior-circulation LVO and large infarction, EVT was associated with improved 90-day functional outcomes in those with matched clinical and radiological severities, but not in those with mismatched clinical and radiological severities. Simultaneous consideration of stroke severity and infarct core volume may inform patient selection for EVT. Funding: Unrestricted grants from industry [Covidien Healthcare International Trading (Shanghai), Johnson & Johnson MedTech, Genesis MedTech (Shanghai), and Shanghai HeartCare Medical Technology].

In this study, civil gas energy accidents reported by the China Gas Network and related organizations from 2012 to 2021 were collected, and a comprehensive multidimensional correlation analysis was conducted considering factors such as accident timing, geography, causes, and casualties. The results identified July and August, Mondays and Sundays, and the morning, mid-day, and evening cooking times as the high-incidence months, days, and times for gas accidents, respectively. Gas accidents were found to occur more frequently in eastern coastal areas, provincial capitals, and larger cities, while residential and construction sites were identified as high-risk areas for gas accidents. Explosions were the most prevalent type of gas accident, followed by leaks, fires, and poisoning. Third-party construction and valve issues were identified as the primary factors contributing to gas leakage, whereas cooking was identified as the most common ignition source. An analysis of the Pearson correlation coefficient indicated a significant correlation among the gas accident factors. Moreover, a time-series prediction model was developed to forecast gas accidents in China, with the results demonstrating fluctuating gas accidents. This study proposes targeted preventive measures in terms of publicity, education, equipment, and facilities to provide scientific support to government units to improve civil gas energy security measures.

Abstract Background Thyroid hormones have been indicated to be associated with depression, but their relationship with poststroke depression (PSD) remains controversial. Therefore, we performed a meta‐analysis to explore the correlation between thyroid hormone levels in acute stroke and PSD. Methods We searched databases for eligible studies. Standard mean differences (SMD) and 95% confidence intervals (CI) were applied to evaluate the association among levels of thyroid hormones, including thyroid‐stimulating hormone (TSH), free triiodothyronine (FT3), and free thyroxine (FT4), in acute stroke patients and the risk of PSD. Results A total of 13 studies were included in the analysis. Compared to non‐PSD patients, PSD patients had remarkably lower serum TSH and FT3 levels (TSH: SMD = −0.59, 95%CI = −1.04 to −.15, p = .009; FT3: SMD = −0.40, 95%CI = −.51 to −.30, p = .000) and higher serum FT4 levels (SMD = 0.33, 95%CI = .07–.59, p = .013). Subgroup analysis showed that there may be a more statistically significant association between FT3 and the risk of PSD compared to TSH and FT4. Conclusions Our results suggested that patients with lower serum TSH and FT3 levels as well as higher serum FT4 levels in the acute stage of stroke may be more susceptible to PSD.

Purpose: The most widely used classifications of age-related macular degeneration (AMD) and its severity stages still rely on color fundus photographs (CFPs). However, AMD has a wide phenotypic variability that remains poorly understood and is better characterized by OCT. We and others have shown that patients with AMD have a distinct plasma metabolomic profile compared with controls. However, all studies to date have been performed solely based on CFP classifications. This study aimed to assess if plasma metabolomic profiles are associated with OCT features commonly seen in AMD. Design: Prospectively designed, cross-sectional study. Participants: Subjects with a diagnosis of AMD and a control group (> 50 years old) from Boston, United States, and Coimbra, Portugal. Methods: All participants were imaged with CFP, used for AMD staging (Age-Related Eye Disease Study 2 classification scheme), and with spectral domain OCT (Spectralis, Heidelberg). OCT images were graded by 2 independent graders for the presence of characteristic AMD features, according to a predefined protocol. Fasting blood samples were collected for metabolomic profiling (using nontargeted high-resolution mass spectrometry by Metabolon Inc). Analyses were conducted using logistic regression models including the worst eye of each patient (AREDS2 classification) and adjusting for confounding factors. Each cohort (United States and Portugal) was analyzed separately and then results were combined by meta-analyses. False discovery rate (FDR) was used to account for multiple comparisons. Main Outcome Measures: Plasma metabolite levels associated with OCT features. Results: We included data on 468 patients, 374 with AMD and 94 controls, and on 725 named endogenous metabolites. Meta-analysis identified significant associations (FDR < 0.05) between plasma metabolites and 3 OCT features: hyperreflective foci (6), atrophy (6), and ellipsoid zone disruption (3). Most associations were seen with amino acids, and all but 1 metabolite presented specific associations with the OCT features assessed. Conclusions: To our knowledge, we show for the first time that plasma metabolites have associations with specific OCT features seen in AMD. Our results support that the wide spectrum of presentations of AMD likely include different pathophysiologic mechanisms by identifying specific pathways associated with each OCT feature. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

(1) Background: Generally, in nuclear medicine and nuclear power plants, energy spectrum measurements and radioactive nuclide identification are required for evaluation of strong radiation fields to ensure nuclear safety and security; thereby, damage is prevented to nuclear facilities caused by natural disasters or the criminal smuggling of nuclear materials. High count rates can lead to signal accumulation, negatively affecting the performance of gamma spectrometers, and in severe cases, even damaging the detectors. Higher pulse throughput with better energy resolution is the ultimate goal of a gamma-ray spectrometer. Traditionally, pileup pulses, which cause dead time and affect throughput, are rejected to maintain good energy resolution. (2) Method: In this paper, an ultra-throughput boost (UTB) off-line processing method was used to improve the throughput and reduce the pileup effect of the spectrometer. Firstly, by fitting the impulse signal of the detector, the response matrix was built by the functional model of a dual exponential tail convolved with the Gaussian kernel; then, a quadratic programming method based on a non-negative least squares (NNLS) algorithm was adopted to solve the constrained optimization problem for the inversion. (3) Results: Both the simulated and experimental results of the UTB method show that most of the impulses in the pulse sequence from the scintillator detector were restored to δ-like pulses, and the throughput of the UTB method for the NaI(Tl) spectrometer reached 207 kcps with a resolution of 7.71% @661.7 keV. A reduction was also seen in the high energy pileup phenomenon. (4) Conclusions: We conclude that the UTB method can restore individual and piled-up pulses to δ-like sequences, effectively boosting pulse throughput and suppressing high-energy tailing and sum peaks caused by the pileup effect at the cost of a slight loss in energy resolution.

The role of land surface temperature (LST) is of the utmost importance in multiple academic disciplines, such as climatology, hydrology, ecology, and meteorology. To date, many methods have been proposed to estimate LST from satellite thermal infrared data. The single-channel (SC) algorithm can provide an accurate result in retrieving LST based on prior knowledge of known land surface emissivity (LSE). The SC algorithm is extensively employed for retrieving LST from Landsat series data due to its simplicity and its reliance on just one thermal infrared channel. The Thermal Infrared Sensor (IRS) on the Chinese ZY1-02E satellite is a pivotal instrument employed for gathering thermal infrared (TIR) data of land surfaces. The objective of this research is to evaluate the feasibility of a single-channel approach based on water vapor scaling (WVS) for deriving LST from ZY1-02E IRS data because of its wide spectrum range, i.e., 7~12 μm, which is affected strongly by both atmospheric water vapor and ozone. Three study areas, namely the Baotou, Heihe River Basin, and Yantai Sea sites, were selected as validation sites to evaluate the LST inversion accuracy. This evaluation was also conducted via cross-comparison between the retrieved LST and MODIS LST products. The results revealed that the WVS-based method exhibited an average bias of 0.63 K and an RMSE of 1.62 K compared to the in situ LSTs. The WVS-based method demonstrated reasonable accuracy through cross-validation with the MODIS LST product, with an average bias of 0.77 K and an RMSE of 2.0 K. These findings indicate that the WVS-based method is effective in estimating LST from ZY1-02E IRS data.

(1) Background: The imaging energy range of a typical Compton camera is limited due to the fact that scattered gamma photons are seldom fully absorbed when the incident energies are above 3 MeV. Further improving the upper energy limit of gamma-ray imaging has important application significance in the active interrogation of special nuclear materials and chemical warfare agents, as well as range verification of proton therapy. (2) Methods: To realize gamma-ray imaging in a wide energy range of 0.3~7 MeV, a principle prototype, named a portable three-layer Compton camera, is developed using the scintillation detector that consists of an silicon photomultiplier array coupled with a Gd3Al2Ga3O12:Ce pixelated scintillator array. Implemented in a list-mode maximum likelihood expectation maximization algorithm, a far-field energy-domain imaging method based on the two interaction events is applied to estimate the initial energy and spatial distribution of gamma-ray sources. The simulation model of the detectors is established based on the Monte Carlo simulation toolkit Geant4. The reconstructed images of a 133Ba, a 137Cs and a 60Co point-like sources have been successfully obtained with our prototype in laboratory tests and compared with simulation studies. (3) Results: The proportion of effective imaging events accounts for about 2%, which allows our prototype to realize the reconstruction of the distribution of a 0.05 μSv/h 137Cs source in 10 s. The angular resolution for resolving two 137Cs point-like sources is 15°. Additional simulated imaging of the 6.13 MeV gamma-rays from 14.1 MeV neutron scattering with water preliminarily demonstrates the imaging capability for high incident energy. (4) Conclusions: We conclude that the prototype has a good imaging performance in a wide energy range (0.3~7 MeV), which shows potential in several MeV gamma-ray imaging applications.

An artificial neural network (ANN) that identifies radionuclides from low-count gamma spectra of a NaI scintillator is proposed. The ANN was trained and tested using simulated spectra. 14 target nuclides were considered corresponding to the requisite radionuclide library of a radionuclide identification device mentioned in IEC 62327-2017. The network shows an average identification accuracy of 98.63% on the validation dataset, with the gross counts in each spectrum Nc = 100~10000 and the signal to noise ratio SNR = 0.05–1. Most of the false predictions come from nuclides with low branching ratio and/or similar decay energies. If the Nc>1000 and SNR>0.3, which is defined as the minimum identifiable condition, the averaged identification accuracy is 99.87%. Even when the source and the detector are covered with lead bricks and the response function of the detector thus varies, the ANN which was trained using non-shielding spectra still shows high accuracy as long as the minimum identifiable condition is satisfied. Among all the considered nuclides, only the identification accuracy of 235U is seriously affected by the shielding. Identification of other nuclides shows high accuracy even the shielding condition is changed, which indicates that the ANN has good generalization performance.

Potential field data are of great significance to the study of geological characteristics. Downward continuation of the potential field converts potential field data from a high plane to a low plane. Since this method is mathematically an inverse problem solution, it is unstable. The Tikhonov regularization strategy is an effective means of the downward continuation of the potential field. However, achieving high-precision requirements in the stage of precise geophysical exploration is still challenging. Deep learning can effectively solve unstable problems with excellent nonlinear mapping capabilities. Inspired by this, for the downward continuation of the potential field, we propose a new neural network architecture for downward continuation named D-Unet. This study uses the potential field data of a high horizontal plane and the initial model as the network’s input, with the corresponding low-level data serving as the output for supervised learning. Moreover, we add noise to 10% of the data in the training dataset. Model testing shows that our D-Unet has higher accuracy, validity, and stability. In addition, adding noise to the training data can further improve the robustness of the model. Finally, we use the actual potential data of a particular place in northeast China to test our model and satisfactory results have been obtained.

Silicon-based kinetic energy converters employing variable capacitors, also known as electrostatic vibration energy harvesters, hold promise as power sources for Internet of Things devices. However, for most wireless applications, such as wearable technology or environmental and structural monitoring, the ambient vibration is often at relatively low frequencies (1–100 Hz). Since the power output of electrostatic harvesters is positively correlated to the frequency of capacitance oscillation, typical electrostatic energy harvesters, designed to match the natural frequency of ambient vibrations, do not produce sufficient power output. Moreover, energy conversion is limited to a narrow range of input frequencies. To address these shortcomings, an impacted-based electrostatic energy harvester is explored experimentally. The impact refers to electrode collision and it triggers frequency upconversion, namely a secondary high-frequency free oscillation of the electrodes overlapping with primary device oscillation tuned to input vibration frequency. The main purpose of high-frequency oscillation is to enable additional energy conversion cycles since this will increase the energy output. The devices investigated were fabricated using a commercial microfabrication foundry process and were experimentally studied. These devices exhibit non-uniform cross-section electrodes and a springless mass. The non-uniform width electrodes were used to prevent pull-in following electrode collision. Springless masses from different materials and sizes, such as 0.5 mm diameter Tungsten carbide, 0.8 mm diameter Tungsten carbide, zirconium dioxide, and silicon nitride, were added in an attempt to force collisions over a range of applied frequencies that would not otherwise result in collisions. The results show that the system operates over a relatively wide frequency range (up to 700 Hz frequency range), with the lower limit far below the natural frequency of the device. The addition of the springless mass successfully increased the device bandwidth. For example, at a low peak-to-peak vibration acceleration of 0.5 g (peak-to-peak), the addition of a zirconium dioxide ball doubled the device’s bandwidth. Testing with different balls indicates that the different sizes and material properties have different effects on the device’s performance, altering its mechanical and electrical damping.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at various temperatures from −20 °C to 50 °C. Pulse processing and spectrum synthesis showed that the position drift of the 137Cs 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranged from 6.91% to 10.60% with the trapezoidal width set from 1000 ns to 100 ns. The DTSAC method corrects the temperature effect via pulse processing, and needs no reference peak, reference spectrum or additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at a high counting rate.

Three-body recombination is a chemical reaction where the collision of three atoms leads to the formation of a diatomic molecule. In the ultracold regime it is expected that the production rate of a molecule generally decreases with its binding energy E_{b}, however, its precise dependence and the physics governing it have been left unclear so far. Here we present a comprehensive experimental and theoretical study of the energy dependency for three-body recombination of ultracold Rb. For this, we determine production rates for molecules in a state-to-state resolved manner, with the binding energies E_{b} ranging from 0.02 to 77 GHz×h. We find that the formation rate approximately scales as E_{b}^{−α}, where α is in the vicinity of 1. The formation rate typically varies only within a factor of two for different rotational angular momenta of the molecular product, apart from a possible centrifugal barrier suppression for low binding energies. In addition to numerical three-body calculations we present a perturbative model which reveals the physical origin of the energy scaling of the formation rate. Furthermore, we show that the scaling law potentially holds universally for a broad range of interaction potentials.

Cerebral small vessel disease (cSVD)—a common cause of stroke and vascular dementia—is a group of clinical syndromes that affects the brain's small vessels, including arterioles, capillaries, and venules. Its pathogenesis is not fully understood, and effective treatments are limited. Increasing evidence indicates that an elevated total serum homocysteine level is directly and indirectly associated with cSVD, and endothelial dysfunction plays an active role in this association. Hyperhomocysteinemia affects endothelial function through oxidative stress, inflammatory pathways, and epigenetic alterations at an early stage, even before the onset of small vessel injuries and the disease. Therefore, hyperhomocysteinemia is potentially an important therapeutic target for cSVD. However, decreasing the homocysteine level is not sufficiently effective, possibly due to delayed treatment, which underlying reason remains unclear. In this review, we examined endothelial dysfunction to understand the close relationship between hyperhomocysteinemia and cSVD and identify the optimal timing for the therapy.

Epigallocatechin Gallate (EGCG) is the main component of green tea which can strongly bind to proteins, small molecular drugs, and DNA. Therefore, EGCG can be used to deliver biomacromolecules and drugs. In this study, a doxorubicin (DOX) delivery system (FPE/DOX NPs) based on the folic acid-functionalized EGCG was developed for the targeting therapy of ovarian cancer. Cellular uptake studies showed that FPE/DOX NPs enhanced DOX uptake by SKOV3 cells compared with PE/DOX NPs that without folic acid modification. In vitro, the toxicity of FPE/DOX NPs to SKOV3 cells was stronger than nanoparticles without folic acid modification. In vivo, the SKOV3 tumor growth inhibition effect of FPE/DOX NPs was substantially more effective than other groups. Together, the results of this study suggested that FPE/DOX NPs may have promising applications in ovarian cancer therapy.

The silver acetylene silver nitrate loading technology of the light initiated high explosive, as one of important means to simulate the structural response of powerful pulsed X-ray, adopts the pulse laser initiation. It has advantages of improvement of practical control, heterogenous loading realization and simultaneous loading timeliness. In this paper, the physical and mathematical models of hot spot initiation and photochemical initiation of energetic materials under the action of laser are firstly established, and then the laser initiation mechanism of the light initiated high explosive is specifically analyzed, and the laser initiation experiment is conducted based on the optical adsorption property of the light initiated high explosive. From this study, the laser initiation thresholds of 193 nm, 266 nm, 532 nm, 1064 nm wavelengths are given, and they are 5.07 mJ/mm2, 6.77 mJ/mm2, 7.21 mJ/mm2 and 10.61 mJ/mm2, respectively, and the complete detonation process is verified by detonation velocity. This work technically supports the study of pulse laser initiation process, mechanism and explosion loading rule as well as the loading technology of the light initiated high explosive to simulate the structural response of X ray.

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

Epilepsies are a diverse group of neurological disorders, which are characterized by spontaneous recurrent seizures. Although a wide range of pathogenic mechanisms such as alterations in ion channels, inflammation and neuronal loss have been reported to be implicated in the epileptogenesis, the underlying pathogenesis of epilepsy remains unclear currently. Endoplasmic reticulum (ER) stress is regarded as a condition that unfolded or misfolded proteins accumulate in the ER lumen. Excessive or prolonged ER stress causes the activation of the unfolded protein response (UPR) to buffer ER stress and restore ER homeostasis. Increasing evidence has indicated dysregulated ER stress during epileptogenesis, which may participate in various pathological processes associated with epilepsy. In this present review, we summarized recent advances in the involvement of ER stress in the pathogenesis of epilepsy. Additionally, the antiepileptic and neuroprotective effects of interventions targeting ER stress were also discussed.

Neurodegenerative diseases are chronic and progressive disorders which are not effectively treated through adopting conventional therapies. For this unmet medical need, alternative therapeutic methods including gene-based therapies are emphasized. MicroRNAs (miRNAs) are small non-coding RNAs which can regulate gene expression at the post-transcriptional level. In recent years, dysregulated miRNAs have been indicated to be implicated in the occurrence and development of neurodegenerative diseases. They are investigated as candidates for diagnostic and prognostic biomarkers, as well as therapeutic targets. The miR-200 family consists of miR-200a, -200b, -200c, -141, and -429. Numerous studies have found that miR-200 family members are associated with the pathogenesis of neurodegenerative diseases. It is reported that miR-200 family members are aberrantly expressed in several neurodegenerative diseases, participating in various cellular processes including beta-amyloid (Aβ) secretion, alpha-synuclein aggregation and DNA repair, etc. In the present review, we summarize the recent progress in the roles of miR-200 family in neurodegenerative diseases.

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.