ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Visualization and Analysis of Air Pollution in US East Coast Cities

Air pollution has negative impact on human health and leads to many chronic diseases. U.S. Environmental Protection Agency (EPA) has been closely monitoring the air pollution using its ground stations in various locations around the nation. The collected data has been included in its air pollution database and made publically available in its website. The detailed daily air pollutant concentrations (e.g. PM2.5, PM10, SO2, CO, Pb, NO2, Ozone) can be downloaded in Excel format. In this poster, we visualize and analyze the air pollution in the US East Coast in the past years using Tableau software. Such visualization allows us to observe the trend of air pollution and its transmission pattern in major cities of the east coast. The correlations between air pollution and various conditions (e.g. traffic, season, location) are discussed. The influence of various terrain conditions to the PM2.5 pollutant diffusion is explored. The visualization and analysis of air pollution data helps better understand its mechanism and distribution pattern

Simulation of PM2.5 Particulate Matter Pollution in US East Coast Using SMAT-CE Software

Nowadays air pollution is becoming a serious global threat to human health and environment. Particulate matter with size of 2.5 micron or less are much more harmful for health because they can be easily absorbed into deep part of the lung. In order to control PM2.5 air pollution, it is important to understand the PM2.5 pollution diffusion mechanism and find out the factors affecting its propagation. In this poster, we use SMAT-CE software by US EPA and TIBCO to simulate the PM2.5 air pollution in US east coast. The data of PM2.5 pollution levels were collected by EPA and available in its website. Through SMAT-CE simulation, we plot the heat map of PM2.5 concentrations of major cities in US east coast in the past years. Based on the data, we tried to find its relation to various weather conditions, wind and other factors. The results will be helpful to further understand the behavior of PM2.5 air pollution and suggest some possible ways to improve the air quality in the area

Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contains noisy samples. This mixture of clean and noisy samples leads to misguidance in DNN training during SSL, resulting in impaired generalization performance due to confirmation bias caused by error accumulation in sample selection. To address this issue, we propose a method called Collaborative Sample Selection (CSS), which leverages the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy samples from the identified clean set. We achieve this by training a 2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities from CLIP with the predictions from the DNN classifier. To further enhance the adaptation of CLIP to LNL, we introduce a co-training mechanism with a contrastive loss in semi-supervised learning. This allows us to jointly train the prompt of CLIP and the DNN classifier, resulting in improved feature representation, boosted classification performance of DNNs, and reciprocal benefits to our Collaborative Sample Selection. By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method in comparison with the state-of-the-art approaches

A Crucial Role of IL-17 and IFN-γ during Acute Rejection of Peripheral Nerve Xenotransplantation in Mice

Nerve injuries causing segmental loss require nerve grafting. However, autografts and allografts have limitations for clinical use. Peripheral nerve xenotransplantation has become an area of great interest in clinical surgery research as an alternative graft strategy. However, xenotransplant rejection is severe with cellular immunity, and Th1 cells play an important role in the process. To better understand the process of rejection, we used peripheral nerve xenografts from rats to mice and found that mononuclear cells expressing IFN-γ and IL-17 infiltrated around the grafts, and IFN-γ and IL-17 producing CD4+ and CD8+ T cells increased during the process of acute rejection. The changes of IL-4 level had no significant difference between xenotransplanted group and sham control group. The rejection of xenograft was significantly prevented after the treatment of IL-17 and IFN-γ neutralizing antibodies. These data suggest that Th17 cells contribute to the acute rejection process of peripheral nerve xenotransplant in addition to Th1 cells

Oxidative stress mediates hippocampal neuronal apoptosis through ROS/JNK/P53 pathway in rats with PTSD triggered by high-voltage electrical burn

Background: The pathogenesis of post-traumatic stress disorder (PTSD) triggered by high-voltage electrical burn (HVEB) remains unclear and the oxidative stress plays a role in this process. The purpose of this study is to investigate the underlying mechanism of oxidative stress mediates hippocampal neuronal apoptosis in rats with PTSD triggered by HVEB. Materials and methods: The PTSD rat model was developed by stimulating with high voltage electricity and screened using behavioral performance including Morris water maze (MWM), elevated plus-maze (EPM) and open-field test (OFT). The reactive oxygen species (ROS) generation was measured by DHE fluorescence staining or flow cytometry. Western blotting assay was used to detect the proteins of p-JNK, JNK, P53, PUMA, Bcl-2 and Bax in hippocampal tissue or HT22 cells treated with electrical stimulation. Results: The serum MDA and 8-OHdG levels were increased (P < 0.001), while the activities of SOD and CAT were decreased (P < 0.001) significantly in patients with HVEB. Behavioral test results showed that high-voltage electric stimulation induced the PTSD-like symptoms and the ROS-JNK-P53 pathway was involved in the neuronal apoptosis in rats with PTSD induced by HVEB. In vitro experiments further confirmed the electrical stimulation induced neuronal apoptosis through ROS/JNK/P53 signaling pathway and the antioxidant NAC could rescued the ROS generation, activation of JNK/P53 proteins and improved the cell apoptosis rate in HT22 cells. Finally, the JNK inhibitor SP600125 could significantly inhibited the percentage of HT22 cell apoptosis induced by electrical stimulation (P < 0.001). Conclusions: These results indicated that oxidative stress mediates hippocampal neuronal apoptosis through ROS/JNK/P53 pathway in rats with PTSD triggered by HVEB

Genome-wide identification and analysis of the invertase gene family in tobacco (Nicotiana tabacum) reveals NtNINV10 participating the sugar metabolism

Sucrose (Suc) is directly associated with plant growth and development as well as tolerance to various stresses. Invertase (INV) enzymes played important role in sucrose metabolism by irreversibly catalyzing Suc degradation. However, genome-wide identification and function of individual members of the INV gene family in Nicotiana tabacum have not been conducted. In this report, 36 non-redundant NtINV family members were identified in Nicotiana tabacum including 20 alkaline/neutral INV genes (NtNINV1-20), 4 vacuolar INV genes (NtVINV1-4), and 12 cell wall INV isoforms (NtCWINV1-12). A comprehensive analysis based on the biochemical characteristics, the exon-intron structures, the chromosomal location and the evolutionary analysis revealed the conservation and the divergence of NtINVs. For the evolution of the NtINV gene, fragment duplication and purification selection were major factors. Besides, our analysis revealed that NtINV could be regulated by miRNAs and cis-regulatory elements of transcription factors associated with multiple stress responses. In addition, 3D structure analysis has provided evidence for the differentiation between the NINV and VINV. The expression patterns in diverse tissues and under various stresses were investigated, and qRT-PCR experiments were conducted to confirm the expression patterns. Results revealed that changes in NtNINV10 expression level were induced by leaf development, drought and salinity stresses. Further examination revealed that the NtNINV10-GFP fusion protein was located in the cell membrane. Furthermore, inhibition of the expression of NtNINV10 gene decreased the glucose and fructose in tobacco leaves. Overall, we have identified possible NtINV genes functioned in leaf development and tolerance to environmental stresses in tobacco. These findings provide a better understanding of the NtINV gene family and establish the basis for future research