Continuous Detection of pH-responsive Drug Delivery System in Cells in situ by Confocal Laser Scanning Microscopy

Mesoporous silica nanoparticles （MSN） were coated by pH-responsive polymer chitosan-poly （methacrylic acid） （CS-PMAA）. This nano drug delivery system showed good application prospects and the polymer-coated microspheres were promising site-specific anticancer drug delivery carriers in biomedical field. A continuous detection of pH-responsive drug delivery system in cells in situ, utilizing MSN/CS-PMAA composite microspheres, was pro- posed. Two kinds of different cell lines, tumor cell line （Hela） and normal somatic cells （293T）, were used to inves- tigate the behaviours of the drug loaded system in the cells. Conclusions could be drawn from the fluorescent im- ages obtained by confocal laser scanning microscopy （CLSM）, modified drug-loaded microspheres （MSN/CS-PMAA） were ingested into cells more easily, the uptake of DOX@FITC-MSN/CS-PMAA by HeLa/293T cells were performed at pH 7.4/pH 6.8, DOX was released during the ingestion process, fluorescence intensity de- creased with time because of efflux transport and photo-bleaching. Fluoresence detection by flow cytometry was performed as comparison. The continuous fluorescent observation in situ could be widely used in the pH-responsive releasing process of drug delivery system in the cells.

An echo detection algorithm for underwater continuous wave active detection

The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.

Collision-aware interactive simulation using graph neural networks

Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.

Improved iterative convergence method in Q-ary LDPC coded high order PR-CPM

The Q-ary low-density parity-check（LDPC） coded high order partial response continuous phase modulation（PR-CPM） with double iterative loops is investigated. This scheme shows significant improvements in power and bandwidth efficiency, but at the expense of long iterative decoding delay and computational complexity induced by the improper match between the demodulator and the decoder. To address this issue, the convergence behavior of Q-ary LDPC coded CPM is investigated for the Q=2 and Q〉2 cases, and an optimized design method based on the extrinsic information transfer chart is proposed to improve the systematic iterative efficiency. Simulation results demonstrate that the proposed method can achieve a perfect tradeoff between iterative decoding delay and bit error rate performance to satisfy real-time applications.

Automated and refined wetland mapping of Dongting Lake using migrated training samples based on temporally dense Sentinel 1/2 imagery

Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland mapping.Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.

GPU Accelerated Real-Time Collision Handling in Virtual Disassembly

Previous collision detection methods for virtual disassembly mainly detect collisions at discrete time intervals and use oriented bounding boxes to speed up the process. However, these discrete methods cannot guarantee no penetration occurs when the components move. Meanwhile, because some of the components are embedded into each other, these components cannot be separated in the subsequent process. To solve these problems, we propose an approach for real-time collision handling by utilizing the computational power of modern GPUs. First we present a novel GPU-based collision handling framework for virtual disassembly. Second we use a collision-streams based continuous collision detection to guarantee no collision missed. Finally we introduce a triangle intersection detection algorithm to solve the problem that collision cannot be detected when the components are embedded into each other at the initial configuration. The experimental results show that our method can improve the overall performance of collision detection and achieve real-time simulation.

Connection Skeleton Extraction Based on Contour Connectedness

A stable skeleton is very important to some applications such as vehicle navigation, object represent and pattern recognition. The connection skeleton is just one that not only can be computed stably but also can figure the connectivity structure of contour. A new method named continuous connectivity detection and a new model named approximate regular polygon (ARP) were proposed for connection skeleton extraction. Both the method and the model were tested by the real maps of road network including flyovers, interchanges and other common object contours. Satisfactory results were obtained.