Workload model for video decoding and its applications

Ph.DDOCTOR OF PHILOSOPH

Clinical analysis of 82 cases of acute promyelocytic leukemia with PML-RARα short isoform in children and adults

BackgroundAcute promyelocytic leukemia (APL) with PML/RARα fusion gene is a distinct variant of acute myeloid leukemia. According to the different break sites of the PML gene, there are three transcripts: Long (bcr1), Variant (bcr2) and Short (bcr3).MethodsWe retrospectively analyzed 82 APL cases with PML-RARα short isoform.ResultsA total of 384 patients with APL were seen, of which 85(22.14%) had PML/RARα short isoform (bcr3) and 82 met the inclusion criteria. The median age was 33.5 years (range, 2-72 years). The incidences of hemorrhage in the intermediate- and high-risk group were higher, but only the incidence between medium and low risk differed statistically (P=0.006), and the incidences of fever, fatigue, splenomegaly, and lymph node enlargement and differentiation syndrome (DS) in those groups were not statistically significant (P>0.05). FLT3 gene mutation rate and the mortality rate of the high-risk group were significantly higher than that of other groups (P=0.040 and P=0.004, P=0.041 and P=0.037, respectively). The mortality rate was lowest (4.26%) in the group treated with ATRA combined with arsenic and anthracycline. The 3-year OS and the 3-year DFS of the low and intermediate-risk group were better (P=0.019 and P=0.017, respectively).ConclusionsATRA combined with arsenic and anthracycline had significant impact on outcomes in APL with PML-RARα short isoform

Artemisinin Directly Targets Malarial Mitochondria through Its Specific Mitochondrial Activation

The biological mode of action of artemisinin, a potent antimalarial, has long been controversial. Previously we established a yeast model addressing its mechanism of action and found mitochondria the key in executing artemisinin's action. Here we present data showing that artemisinin directly acts on mitochondria and it inhibits malaria in a similar way as yeast. Specifically, artemisinin and its homologues exhibit correlated activities against malaria and yeast, with the peroxide bridge playing a key role for their inhibitory action in both organisms. In addition, we showed that artemisinins are distributed to malarial mitochondria and directly impair their functions when isolated mitochondria were tested. In efforts to explore how the action specificity of artemisinin is achieved, we found strikingly rapid and dramatic reactive oxygen species (ROS) production is induced with artemisinin in isolated yeast and malarial but not mammalian mitochondria, and ROS scavengers can ameliorate the effects of artemisinin. Deoxyartemisinin, which lacks an endoperoxide bridge, has no effect on membrane potential or ROS production in malarial mitochondria. OZ209, a distantly related antimalarial endoperoxide, also causes ROS production and depolarization in isolated malarial mitochondria. Finally, interference of mitochondrial electron transport chain (ETC) can alter the sensitivity of the parasite towards artemisinin. Addition of iron chelator desferrioxamine drastically reduces ETC activity as well as mitigates artemisinin-induced ROS production. Taken together, our results indicate that mitochondrion is an important direct target, if not the sole one, in the antimalarial action of artemisinins. We suggest that fundamental differences among mitochondria from different species delineate the action specificity of this class of drugs, and differing from many other drugs, the action specificity of artemisinins originates from their activation mechanism

Национальный стиль современного искусства в Китае

В статье анализируется китайский стиль современного искусства, который становится все более популярным не только в КНР, но и во всем мире. Исследуется традиционное искусство китайских этносов, к которому в последнее время наблюдается повышенный интерес не только старшего поколения, но и молодежи. В то же время отмечается, что актуализируется как народная, так и классическая музыка. Национальный стиль содержит характеристики инструментального и вокального традиционного искусства некоторых этнических меньшинств Китая, классической оперы китайского народа.The article analyzes the Chinese style of contemporary art, which is becoming increasingly popular not only in the PRC, but also all over the world. The traditional art of Chinese ethnic groups, to which recently there is an increased interest not only of the older generation, but also of the youth, is investigated. At the same time, it is noted that both folk and classical music is actualized. It contains characteristics of instrumental and vocal traditional art of some ethnic minorities of China, classical opera of Chinese people

AST-GIN: Attribute-Augmented Spatial-Temporal Graph Informer Network for Electric Vehicle Charging Station Availability Forecasting

Electric Vehicle (EV) charging demand and charging station availability forecasting is one of the challenges in the intelligent transportation system. With the accurate EV station situation prediction, suitable charging behaviors could be scheduled in advance to relieve range anxiety. Many existing deep learning methods are proposed to address this issue, however, due to the complex road network structure and comprehensive external factors, such as point of interests (POIs) and weather effects, many commonly used algorithms could just extract the historical usage information without considering comprehensive influence of external factors. To enhance the prediction accuracy and interpretability, the Attribute-Augmented Spatial-Temporal Graph Informer (AST-GIN) structure is proposed in this study by combining the Graph Convolutional Network (GCN) layer and the Informer layer to extract both external and internal spatial-temporal dependence of relevant transportation data. And the external factors are modeled as dynamic attributes by the attribute-augmented encoder for training. AST-GIN model is tested on the data collected in Dundee City and experimental results show the effectiveness of our model considering external factors influence over various horizon settings compared with other baselines.Comment: 10 pages; 17 figures; Under review for IEEE Transaction on Vehicular Technolog

Aircraft Landing Time Prediction with Deep Learning on Trajectory Images

Aircraft landing time (ALT) prediction is crucial for air traffic management, especially for arrival aircraft sequencing on the runway. In this study, a trajectory image-based deep learning method is proposed to predict ALTs for the aircraft entering the research airspace that covers the Terminal Maneuvering Area (TMA). Specifically, the trajectories of all airborne arrival aircraft within the temporal capture window are used to generate an image with the target aircraft trajectory labeled as red and all background aircraft trajectory labeled as blue. The trajectory images contain various information, including the aircraft position, speed, heading, relative distances, and arrival traffic flows. It enables us to use state-of-the-art deep convolution neural networks for ALT modeling. We also use real-time runway usage obtained from the trajectory data and the external information such as aircraft types and weather conditions as additional inputs. Moreover, a convolution neural network (CNN) based module is designed for automatic holding-related featurizing, which takes the trajectory images, the leading aircraft holding status, and their time and speed gap at the research airspace boundary as its inputs. Its output is further fed into the final end-to-end ALT prediction. The proposed ALT prediction approach is applied to Singapore Changi Airport (ICAO Code: WSSS) using one-month Automatic Dependent Surveillance-Broadcast (ADS-B) data from November 1 to November 30, 2022. Experimental results show that by integrating the holding featurization, we can reduce the mean absolute error (MAE) from 82.23 seconds to 43.96 seconds, and achieve an average accuracy of 96.1\%, with 79.4\% of the predictions errors being less than 60 seconds.Comment: In 2023 13th SESAR Innovation Days (SIDS2023

Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery

In surgical oncology, it is challenging for surgeons to identify lymph nodes and completely resect cancer even with pre-operative imaging systems like PET and CT, because of the lack of reliable intraoperative visualization tools. Endoscopic radio-guided cancer detection and resection has recently been evaluated whereby a novel tethered laparoscopic gamma detector is used to localize a preoperatively injected radiotracer. This can both enhance the endoscopic imaging and complement preoperative nuclear imaging data. However, gamma activity visualization is challenging to present to the operator because the probe is non-imaging and it does not visibly indicate the activity origination on the tissue surface. Initial failed attempts used segmentation or geometric methods, but led to the discovery that it could be resolved by leveraging high-dimensional image features and probe position information. To demonstrate the effectiveness of this solution, we designed and implemented a simple regression network that successfully addressed the problem. To further validate the proposed solution, we acquired and publicly released two datasets captured using a custom-designed, portable stereo laparoscope system. Through intensive experimentation, we demonstrated that our method can successfully and effectively detect the sensing area, establishing a new performance benchmark. Code and data are available at https://github.com/br0202/Sensing_area_detection.gitComment: Accepted by MICCAI 202

Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation

Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limited in the scope of assessed tasks and offer no overarching framework to perform them holistically. We present a new comprehensive benchmark, General-purpose Visual Understanding Evaluation (G-VUE), covering the full spectrum of visual cognitive abilities with four functional domains \unicode{x2014} Perceive, Ground, Reason, and Act. The four domains are embodied in 11 carefully curated tasks, from 3D reconstruction to visual reasoning and manipulation. Along with the benchmark, we provide a general encoder-decoder framework to allow for the evaluation of arbitrary visual representation on all 11 tasks. We evaluate various pre-trained visual representations with our framework and observe that (1) Transformer-based visual backbone generally outperforms CNN-based backbone on G-VUE, (2) visual representations from vision-language pre-training are superior to those with vision-only pre-training across visual tasks. With G-VUE, we provide a holistic evaluation standard to motivate research toward building general-purpose visual systems via obtaining more general-purpose visual representations

Impact of intraoperative ocular lubricants on corneal debridement rate during vitreoretinal surgery

Purpose: To compare surgical parameters among patients receiving Viscoat (sodium chondroitin sulfate 4%-sodium hyaluronate 3%) or Goniosol (hydroxypropyl methylcellulose 2.5%) as topical lubricants for retinal surgery. Methods: This was a retrospective analysis of patients undergoing retinal surgery between March 2013 and March 2018 using Goniosol or Viscoat as adjuvants. Primary outcome measures were rate of corneal debridement and operative time between groups, compared using Results: Compared to Viscoat (n=319), the Goniosol group (n=210) had more frequent intraoperative corneal debridement (21.4% vs 0, Conclusion: These findings suggest potential advantages of using Viscoat over Goniosol for corneal lubrication to aid visualization during vitreoretinal surgery