The Significance of IDH1 Mutations in Tumor-Associated Seizure in 60 Chinese Patients with Low-Grade Gliomas

Background. Seizure is a common clinical presentation in patients suffering from primary brain tumors, especially from low-grade gliomas (LGGs). However, the genetic factors of tumor-associated seizure, at present, are still very poorly understood. The aim of this study was to investigate the potential correlation between tumor-associated epilepsy and IDH1 mutations in a Chinese population with LGGs. Materials and Methods. This study reviewed 60 patients with histologically confirmed low-grade gliomas, and the status of IDH1 was detected after the operation at our institution. Univariate and multivariate logistic regression analysis were used to explore the potential risk factors for tumor-related seizures. Results. IDH1 mutation was detected in 46 (76.7%) patients, among which 14 patients had no epilepsies and 32 patients had epilepsies (, chi-square test). Multivariate logistic regression analysis demonstrated that the mutation of IDH1 seems to be the strongest predictor for preoperative seizure (OR, 6.130; 95% CI, 1.523–24.669; ). Conclusions. IDH1 mutation was frequently detected in LGGs, and it may result in tumor-related seizures

IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role for diverse real-world applications. Nevertheless, most of existing works focus on designing advanced network architectures to map the feature difference to the final change map while ignoring the influence of the quality of the feature difference. In this paper, we study the CD from a new perspective, i.e., how to optimize the feature difference to highlight changes and suppress unchanged regions, and propose a novel module denoted as iterative difference-enhanced transformers (IDET). IDET contains three transformers: two transformers for extracting the long-range information of the two images and one transformer for enhancing the feature difference. In contrast to the previous transformers, the third transformer takes the outputs of the first two transformers to guide the enhancement of the feature difference iteratively. To achieve more effective refinement, we further propose the multi-scale IDET-based change detection that uses multi-scale representations of the images for multiple feature difference refinements and proposes a coarse-to-fine fusion strategy to combine all refinements. Our final CD method outperforms seven state-of-the-art methods on six large-scale datasets under diverse application scenarios, which demonstrates the importance of feature difference enhancements and the effectiveness of IDET.Comment: conferenc

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas

Background-Mixed Augmentation for Weakly Supervised Change Detection

Change detection (CD) is to decouple object changes (i.e., object missing or appearing) from background changes (i.e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc. In particular, the endless patterns of background changes require detectors to have a high generalization against unseen environment variations, making this task significantly challenging. Recent deep learning-based methods develop novel network architectures or optimization strategies with paired-training examples, which do not handle the generalization issue explicitly and require huge manual pixel-level annotation efforts. In this work, for the first attempt in the CD community, we study the generalization issue of CD from the perspective of data augmentation and develop a novel weakly supervised training algorithm that only needs image-level labels. Different from general augmentation techniques for classification, we propose the background-mixed augmentation that is specifically designed for change detection by augmenting examples under the guidance of a set of background-changing images and letting deep CD models see diverse environment variations. Moreover, we propose the augmented & real data consistency loss that encourages the generalization increase significantly. Our method as a general framework can enhance a wide range of existing deep learning-based detectors. We conduct extensive experiments in two public datasets and enhance four state-of-the-art methods, demonstrating the advantages of our method. We release the code at https://github.com/tsingqguo/bgmix.Comment: AAAI 2023 Accepte

Degradable composite aerogel with excellent water-absorption for trace water removal in oil and oil-in-water emulsion filtration

In this study, using chitosan (CS) and carboxymethyl cellulose (CMC) as backbone and introducing citric acid (CA)to enhance the electrostatic interaction of the system, citric acid/chitosan/carboxymethyl cellulose (CA/CS/CMC) aerogel is obtained by simple freeze-drying. CA/CS/CMC composite aerogel exhibits light weight, low density, high porosity, outstanding hydrophilic and water retention properties, and satisfactory underwater oleophobicity. The water adsorption capacity of the obtained aerogels can reach 43.87–80.28 g/g, which are far more than that of carboxymethyl cellulose and chitosan aerogels (14.27–20.08 g/g). In addition, with strong hydrophilicity, underwater oleophobicity and water retention endowed by the rough internal microstructure and the rich hydroxyl, amino, and carboxyl groups, the fabricated aerogel can also be used as a filter to achieve effective separation of oil-in-water emulsions and oil/water mixtures. The separation efficiency of aerogel for oil/water mixtures are higher than 90.7%. Because the developed preparation method is green, simple and mild and the raw materials are readily available and environmentally friendly, the obtained CA/CS/CMC aerogel with strong water absorption capacity and good separation efficiency displays a promising application in water-oil separation

Designing a Microfluidic Chip Driven by Carbon Dioxide for Separation and Detection of Particulate Matter

Atmospheric particulate pollution poses a great danger to the environment and human health, and there is a strong need to develop equipment for collecting and separating particulate matter of different particle sizes to study the effects of particulate matter on human health. A virtual impactor is a particle separation device based on the principle of inertial separation which provides scientific guidance for identifying the composition characteristics of particles. Much existing virtual impactor research focuses on the design of structural dimensions with little exploration of the effect of fluid properties on performance. In this paper, a microfluidic chip with a cutoff diameter of 1.85 µm was designed based on computational fluid dynamics and numerically simulated via finite element analysis to analyze important parameters such as inlet flow rate, splitting ratio and fluid properties. By numerical simulation of the split ratio, we found that the obtained collection efficiency curves could not be combined into one characteristic curve by the Stk0.5 scaling method. We therefore propose a modified Stokes number equation for predicting the cutoff diameter at different splitting ratios. The collection efficiency curves of different fluids as microfluidic chip media were plotted, and the results show that the cut particle size was reduced from 2.5 µm to 1.85 µm after replacing conventional fluid air with CO2 formed by dry ice sublimation. This is a decrease of approximately 26%, which is superior to other existing methods for reducing the cutoff diameter

Computational Fluid Dynamics Study of the Effects of Temperature and Geometry Parameters on a Virtual Impactor

The virtual impactor, as an atmospheric particle classification chip, provides scientific guidance for identifying the characteristics of particle composition. Most of the studies related to virtual impactors focus on their size structure design, and the effect of temperature in relation to the dynamic viscosity on the cut−off diameter is rarely considered. In this paper, a new method that can reduce the cut−off particle size without increasing the pressure drop is proposed. Based on COMSOL numerical simulations, a new ultra−low temperature virtual impactor with a cut−off diameter of 2.5 μm was designed. A theoretical analysis and numerical simulation of the relationship between temperature and the performance of the virtual impactor were carried out based on the relationship between temperature and dynamic viscosity. The effects of inlet flow rate (Q), major flow channel width (S), minor flow channel width (L) and split ratio (r) on the performance of the virtual impactor were analyzed. The collection efficiency curves were plotted based on the separation effect of the new virtual impactor on different particle sizes. It was found that the new ultra−low temperature approach reduced the PM2.5 cut−off diameter by 19% compared to the conventional virtual impactor, slightly better than the effect of passing in sheath gas. Meanwhile, the low temperature weakens Brownian motion of the particles, thus reducing the wall loss. In the future, this approach can be applied to nanoparticle virtual impactors to solve the problem of their large pressure drop

A Method for the Destriping of an Orbita Hyperspectral Image with Adaptive Moment Matching and Unidirectional Total Variation

The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal–oxide–semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm

A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains