Journal articles: 'PSPNet'

1

Zhou, Jingchun, Mingliang Hao, Dehuan Zhang, Peiyu Zou, and Weishi Zhang. "Fusion PSPnet Image Segmentation Based Method for Multi-Focus Image Fusion." IEEE Photonics Journal 11, no. 6 (December 2019): 1–12. http://dx.doi.org/10.1109/jphot.2019.2950949.

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Huang, Liang, Xuequn Wu, Qiuzhi Peng, and Xueqin Yu. "Depth Semantic Segmentation of Tobacco Planting Areas from Unmanned Aerial Vehicle Remote Sensing Images in Plateau Mountains." Journal of Spectroscopy 2021 (March 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/6687799.

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The tobacco in plateau mountains has the characteristics of fragmented planting, uneven growth, and mixed/interplanting of crops. It is difficult to extract effective features using an object-oriented image analysis method to accurately extract tobacco planting areas. To this end, the advantage of deep learning features self-learning is relied on in this paper. An accurate extraction method of tobacco planting areas based on a deep semantic segmentation model from the unmanned aerial vehicle (UAV) remote sensing images in plateau mountains is proposed in this paper. Firstly, the tobacco semantic segmentation dataset is established using Labelme. Four deep semantic segmentation models of DeeplabV3+, PSPNet, SegNet, and U-Net are used to train the sample data in the dataset. Among them, in order to reduce the model training time, the MobileNet series of lightweight networks are used to replace the original backbone networks of the four network models. Finally, the predictive images are semantically segmented by trained networks, and the mean Intersection over Union (mIoU) is used to evaluate the accuracy. The experimental results show that, using DeeplabV3+, PSPNet, SegNet, and U-Net to perform semantic segmentation on 71 scene prediction images, the mIoU obtained is 0.9436, 0.9118, 0.9392, and 0.9473, respectively, and the accuracy of semantic segmentation is high. The feasibility of the deep semantic segmentation method for extracting tobacco planting surface from UAV remote sensing images has been verified, and the research method can provide a reference for subsequent automatic extraction of tobacco planting areas.

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Long, Xudong, Weiwei Zhang, and Bo Zhao. "PSPNet-SLAM: A Semantic SLAM Detect Dynamic Object by Pyramid Scene Parsing Network." IEEE Access 8 (2020): 214685–95. http://dx.doi.org/10.1109/access.2020.3041038.

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S.R.F, Natzina Juanita, Nadine Suzanne S.R.F, Shojaa Ayed Aljasar, Yubin Xu, and Muhammad Saqib. "ANAYLSIS AND DETECTION OF COMMUNITY-ACQUIRED PNEUMONIA USING PSPNET WITH COMPLEX DAUBECHIES WAVELETS." Indian Journal of Computer Science and Engineering 11, no. 3 (June 30, 2020): 217–25. http://dx.doi.org/10.21817/indjcse/2020/v11i3/201103076.

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McCall, Hugh, Janine Beahm, Caeleigh Landry, Ziyin Huang, R. Nicholas Carleton, and Heather Hadjistavropoulos. "How Have Public Safety Personnel Seeking Digital Mental Healthcare Been Affected by the COVID-19 Pandemic? An Exploratory Mixed Methods Study." International Journal of Environmental Research and Public Health 17, no. 24 (December 13, 2020): 9319. http://dx.doi.org/10.3390/ijerph17249319.

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Public safety personnel (PSP) experience unique occupational stressors and suffer from high rates of mental health problems. The COVID-19 pandemic has impacted virtually all aspects of human life around the world and has introduced additional occupational stressors for PSP. The objective of this study was to explore how PSP, especially those seeking digital mental health services, have been affected by the pandemic. Our research unit, PSPNET, provides internet-delivered cognitive behavioral therapy to PSP in the Canadian province of Saskatchewan. When the pandemic spread to Saskatchewan, PSPNET began inquiring about the impact of the pandemic on prospective clients during the eligibility screening process. We used content analysis to analyze data from telephone screening interviews (n = 56) and descriptive statistics to analyze data from a questionnaire concerning the impacts of COVID-19 (n = 41). The results showed that most PSP reported facing several novel emotional challenges (e.g., social isolation, boredom, anger, and fear) and logistical challenges (e.g., related to childcare, finances, work, and access to mental healthcare). Most participants indicated they felt at least somewhat afraid of contracting COVID-19 but felt more afraid of their families contracting the virus than themselves. However, few participants reported severe challenges of any kind, and many (40%) indicated that they had not been significantly negatively impacted by the pandemic. Overall, the results suggest that PSP are not expressing significant concern at this time in meeting the novel challenges posed by COVID-19. Continued research will be required to monitor how diverse PSP populations and treatment outcomes are affected by the pandemic as the situation evolves.

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Zhang, Yan, Weihong Li, Weiguo Gong, Zixu Wang, and Jingxi Sun. "An Improved Boundary-Aware Perceptual Loss for Building Extraction from VHR Images." Remote Sensing 12, no. 7 (April 8, 2020): 1195. http://dx.doi.org/10.3390/rs12071195.

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With the development of deep learning technology, an enormous number of convolutional neural network (CNN) models have been proposed to address the challenging building extraction task from very high-resolution (VHR) remote sensing images. However, searching for better CNN architectures is time-consuming, and the robustness of a new CNN model cannot be guaranteed. In this paper, an improved boundary-aware perceptual (BP) loss is proposed to enhance the building extraction ability of CNN models. The proposed BP loss consists of a loss network and transfer loss functions. The usage of the boundary-aware perceptual loss has two stages. In the training stage, the loss network learns the structural information from circularly transferring between the building mask and the corresponding building boundary. In the refining stage, the learned structural information is embedded into the building extraction models via the transfer loss functions without additional parameters or postprocessing. We verify the effectiveness and efficiency of the proposed BP loss both on the challenging WHU aerial dataset and the INRIA dataset. Substantial performance improvements are observed within two representative CNN architectures: PSPNet and UNet, which are widely used on pixel-wise labelling tasks. With BP loss, UNet with ResNet101 achieves 90.78% and 76.62% on IoU (intersection over union) scores on the WHU aerial dataset and the INRIA dataset, respectively, which are 1.47% and 1.04% higher than those simply trained with the cross-entropy loss function. Additionally, similar improvements (0.64% on the WHU aerial dataset and 1.69% on the INRIA dataset) are also observed on PSPNet, which strongly supports the robustness of the proposed BP loss.

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Francis, N. J., N. S. Francis, S. V. Axyonov, S. A. Aljasar, Y. Xu, and M. Saqib. "Diagnostic of Cystic Fibrosis in Lung Computer Tomographic Images using Image Annotation and Improved PSPNet Modelling." Journal of Physics: Conference Series 1611 (August 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1611/1/012062.

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Zhao, Shida, Guangzhao Hao, Yichi Zhang, and Shucai Wang. "A Real-Time Semantic Segmentation Method of Sheep Carcass Images Based on ICNet." Journal of Robotics 2021 (April 19, 2021): 1–12. http://dx.doi.org/10.1155/2021/8847984.

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How to realize the accurate recognition of 3 parts of sheep carcass is the key to the research of mutton cutting robots. The characteristics of each part of the sheep carcass are connected to each other and have similar features, which make it difficult to identify and detect, but with the development of image semantic segmentation technology based on deep learning, it is possible to explore this technology for real-time recognition of the 3 parts of the sheep carcass. Based on the ICNet, we propose a real-time semantic segmentation method for sheep carcass images. We first acquire images of the sheep carcass and use augmentation technology to expand the image data, after normalization, using LabelMe to annotate the image and build the sheep carcass image dataset. After that, we establish the ICNet model and train it with transfer learning. The segmentation accuracy, MIoU, and the average processing time of single image are then obtained and used as the evaluation standard of the segmentation effect. In addition, we verify the generalization ability of the ICNet for the sheep carcass image dataset by setting different brightness image segmentation experiments. Finally, the U-Net, DeepLabv3, PSPNet, and Fast-SCNN are introduced for comparative experiments to further verify the segmentation performance of the ICNet. The experimental results show that for the sheep carcass image datasets, the segmentation accuracy and MIoU of our method are 97.68% and 88.47%, respectively. The single image processing time is 83 ms. Besides, the MIoU of U-Net and DeepLabv3 is 0.22% and 0.03% higher than the ICNet, but the processing time of a single image is longer by 186 ms and 430 ms. Besides, compared with the PSPNet and Fast-SCNN, the MIoU of the ICNet model is increased by 1.25% and 4.49%, respectively. However, the processing time of a single image is shorter by 469 ms and expands by 7 ms, respectively.

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Zhou, Keyang, Kaiwei Wang, and Kailun Yang. "A Robust Monocular Depth Estimation Framework Based on Light-Weight ERF-Pspnet for Day-Night Driving Scenes." Journal of Physics: Conference Series 1518 (April 2020): 012051. http://dx.doi.org/10.1088/1742-6596/1518/1/012051.

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Xu, Guangjun, Cheng Cheng, Wenxian Yang, Wenhong Xie, Lingmei Kong, Renlong Hang, Furong Ma, Changming Dong, and Jingsong Yang. "Oceanic Eddy Identification Using an AI Scheme." Remote Sensing 11, no. 11 (June 5, 2019): 1349. http://dx.doi.org/10.3390/rs11111349.

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Oceanic eddies play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic eddies from sea surface height anomalies data. In order to adapt to segmentation problems for multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods. The results of eddies identified from this artificial intelligence (AI) method are well compared with those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of efforts to bridge AI techniques and oceanography research.

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Rubio, Yoshio, and Oscar Montiel. "Multicriteria Evaluation of Deep Neural Networks for Semantic Segmentation of Mammographies." Axioms 10, no. 3 (August 5, 2021): 180. http://dx.doi.org/10.3390/axioms10030180.

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Breast segmentation plays a vital role in the automatic analysis of mammograms. Accurate segmentation of the breast region increments the probability of a correct diagnostic and minimizes computational cost. Traditionally, model-based approaches dominated the landscape for breast segmentation, but recent studies seem to benefit from using robust deep learning models for this task. In this work, we present an extensive evaluation of deep learning architectures for semantic segmentation of mammograms, including segmentation metrics, memory requirements, and average inference time. We used several combinations of two-stage segmentation architectures composed of a feature extraction net (VGG16 and ResNet50) and a segmentation net (FCN-8, U-Net, and PSPNet). The training examples were taken from the mini Mammographic Image Analysis Society (MIAS) database. Experimental results using the mini-MIAS database show that the best net scored a Dice similarity coefficient of 99.37% for breast boundary segmentation and 95.45% for pectoral muscle segmentation.

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Li, Liming, Rui Sun, Shuguang Zhao, Xiaodong Chai, Shubin Zheng, and Ruichao Shen. "Semantic-Segmentation-Based Rail Fastener State Recognition Algorithm." Mathematical Problems in Engineering 2021 (March 2, 2021): 1–15. http://dx.doi.org/10.1155/2021/8956164.

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Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.

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Gao, Xin Wen, ShuaiQing Li, Bang Yang Jin, Min Hu, and Wei Ding. "Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 4453–69. http://dx.doi.org/10.3233/jifs-201296.

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With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.

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Hu, Feng, Wei Liu, Junyu Lu, Chengpeng Song, Yuan Meng, Jun Wang, and Hanfa Xing. "Urban Function as a New Perspective for Adaptive Street Quality Assessment." Sustainability 12, no. 4 (February 11, 2020): 1296. http://dx.doi.org/10.3390/su12041296.

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Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.

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Huang, Shenjin, Wenting Han, Haipeng Chen, Guang Li, and Jiandong Tang. "Recognizing Zucchinis Intercropped with Sunflowers in UAV Visible Images Using an Improved Method Based on OCRNet." Remote Sensing 13, no. 14 (July 9, 2021): 2706. http://dx.doi.org/10.3390/rs13142706.

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An improved semantic segmentation method based on object contextual representations network (OCRNet) is proposed to accurately identify zucchinis intercropped with sunflowers from unmanned aerial vehicle (UAV) visible images taken over Hetao Irrigation District, Inner Mongolia, China. The proposed method improves on the performance of OCRNet in two respects. First, based on the object region context extraction structure of the OCRNet, a branch that uses the channel attention module was added in parallel to rationally use channel feature maps with different weights and reduce the noise of invalid channel features. Secondly, Lovász-Softmax loss was introduced to improve the accuracy of the object region representation in the OCRNet and optimize the final segmentation result at the object level. We compared the proposed method with extant advanced semantic segmentation methods (PSPNet, DeepLabV3+, DNLNet, and OCRNet) in two test areas to test its effectiveness. The results showed that the proposed method achieved the best semantic segmentation effect in the two test areas. More specifically, our method performed better in processing image details, segmenting field edges, and identifying intercropping fields. The proposed method has significant advantages for crop classification and intercropping recognition based on UAV visible images, and these advantages are more substantive in object-level evaluation metrics (mIoU and intercropping IoU).

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Li, Wangbin, Kaimin Sun, Zhuotong Du, Xiuqing Hu, Wenzhuo Li, Jinjiang Wei, and Song Gao. "PCNet: Cloud Detection in FY-3D True-Color Imagery Using Multi-Scale Pyramid Contextual Information." Remote Sensing 13, no. 18 (September 14, 2021): 3670. http://dx.doi.org/10.3390/rs13183670.

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Cloud, one of the poor atmospheric conditions, significantly reduces the usability of optical remote-sensing data and hampers follow-up applications. Thus, the identification of cloud remains a priority for various remote-sensing activities, such as product retrieval, land-use/cover classification, object detection, and especially for change detection. However, the complexity of clouds themselves make it difficult to detect thin clouds and small isolated clouds. To accurately detect clouds in satellite imagery, we propose a novel neural network named the Pyramid Contextual Network (PCNet). Considering the limited applicability of a regular convolution kernel, we employed a Dilated Residual Block (DRB) to extend the receptive field of the network, which contains a dilated convolution and residual connection. To improve the detection ability for thin clouds, the proposed new model, pyramid contextual block (PCB), was used to generate global information at different scales. FengYun-3D MERSI-II remote-sensing images covering China with 14,165 × 24,659 pixels, acquired on 17 July 2019, are processed to conduct cloud-detection experiments. Experimental results show that the overall precision rates of the trained network reach 97.1% and the overall recall rates reach 93.2%, which performs better both in quantity and quality than U-Net, UNet++, UNet3+, PSPNet and DeepLabV3+.

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Niu, Zijie, Juntao Deng, Xu Zhang, Jun Zhang, Shijia Pan, and Haotian Mu. "Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method." Sensors 21, no. 13 (June 29, 2021): 4442. http://dx.doi.org/10.3390/s21134442.

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It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, numerous challenges exist in the acquisition of accurate data for kiwifruit vines. In this paper, a kiwifruit canopy distribution prediction model is proposed on the basis of low-altitude unmanned aerial vehicle (UAV) images and deep learning techniques. First, the location of the kiwifruit plants and vine distribution are extracted from high-precision images collected by UAV. The canopy gradient distribution maps with different noise reduction and distribution effects are generated by modifying the threshold and sampling size using the resampling normalization method. The results showed that the accuracies of the vine segmentation using PSPnet, support vector machine, and random forest classification were 71.2%, 85.8%, and 75.26%, respectively. However, the segmentation image obtained using depth semantic segmentation had a higher signal-to-noise ratio and was closer to the real situation. The average intersection over union of the deep semantic segmentation was more than or equal to 80% in distribution maps, whereas, in traditional machine learning, the average intersection was between 20% and 60%. This indicates the proposed model can quickly extract the vine distribution and plant position, and is thus able to perform dynamic monitoring of orchards to provide real-time operation guidance.

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Kerkech, Mohamed, Adel Hafiane, and Raphael Canals. "VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map." Remote Sensing 12, no. 20 (October 11, 2020): 3305. http://dx.doi.org/10.3390/rs12203305.

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Vine pathologies generate several economic and environmental problems, causing serious difficulties for the viticultural activity. The early detection of vine disease can significantly improve the control of vine diseases and avoid spread of virus or fungi. Currently, remote sensing and artificial intelligence technologies are emerging in the field of precision agriculture. They offer interesting potential for crop disease management. However, despite the advances in these technologies, particularly deep learning technologies, many problems still present considerable challenges, such as semantic segmentation of images for disease mapping. In this paper, we present a new deep learning architecture called Vine Disease Detection Network (VddNet). It is based on three parallel auto-encoders integrating different information (i.e., visible, infrared and depth). Then, the decoder reconstructs and retrieves the features, and assigns a class to each output pixel. An orthophotos registration method is also proposed to align the three types of images and enable the processing by VddNet. The proposed architecture is assessed by comparing it with the most known architectures: SegNet, U-Net, DeepLabv3+ and PSPNet. The deep learning architectures were trained on multispectral data from an unmanned aerial vehicle (UAV) and depth map information extracted from 3D processing. The results of the proposed architecture show that the VddNet architecture achieves higher scores than the baseline methods. Moreover, this study demonstrates that the proposed method has many advantages compared to methods that directly use the UAV images.

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Huang, Xiaodong, Hui Zhang, Li Zhuo, Xiaoguang Li, and Jing Zhang. "TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine." Computational and Mathematical Methods in Medicine 2020 (August 7, 2020): 1–13. http://dx.doi.org/10.1155/2020/6029258.

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Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

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Xia, Lang, Ruirui Zhang, Liping Chen, Longlong Li, Tongchuan Yi, Yao Wen, Chenchen Ding, and Chunchun Xie. "Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images." Remote Sensing 13, no. 18 (September 9, 2021): 3594. http://dx.doi.org/10.3390/rs13183594.

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Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.

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Flores-Kim, Josué, and Andrew J. Darwin. "Interactions between the Cytoplasmic Domains of PspB and PspC Silence the Yersinia enterocolitica Phage Shock Protein Response." Journal of Bacteriology 198, no. 24 (October 3, 2016): 3367–78. http://dx.doi.org/10.1128/jb.00655-16.

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ABSTRACTThe phage shock protein (Psp) system is a widely conserved cell envelope stress response that is essential for the virulence of some bacteria, includingYersinia enterocolitica. Recruitment of PspA by the inner membrane PspB-PspC complex characterizes the activated state of this response. The PspB-PspC complex has been proposed to be a stress-responsive switch, changing from an OFF to an ON state in response to an inducing stimulus. In the OFF state, PspA cannot access its binding site in the C-terminal cytoplasmic domain of PspC (PspCCT), because this site is bound to PspB. PspC has another cytoplasmic domain at its N-terminal end (PspCNT), which has been thought to play a role in maintaining the OFF state, because its removal causes constitutive activation. However, until now, this role has proved recalcitrant to experimental investigation. Here, we developed a combination of approaches to investigate the role of PspCNTinY. enterocolitica. Pulldown assays provided evidence that PspCNTmediates the interaction of PspC with the C-terminal cytoplasmic domain of PspB (PspBCT)in vitro. Furthermore, site-specific oxidative cross-linking suggested that a PspCNT-PspBCTinteraction occurs only under noninducing conditionsin vivo. Additional experiments indicated that mutations inpspCmight cause constitutive activation by compromising this PspCNTbinding site or by causing a conformational disturbance that repositions PspCNTin vivo. These findings have provided the first insight into the regulatory function of the N-terminal cytoplasmic domain of PspC, revealing that its ability to participate in an inhibitory complex is essential to silencing the Psp response.IMPORTANCEThe phage shock protein (Psp) response has generated widespread interest because it is linked to important phenotypes, including antibiotic resistance, biofilm formation, and virulence in a diverse group of bacteria. Therefore, achieving a comprehensive understanding of how this response is controlled at the molecular level has obvious significance. An integral inner membrane protein complex is believed to be a critical regulatory component that acts as a stress-responsive switch, but some essential characteristics of the switch states are poorly understood. This study provides an important advance by uncovering a new protein interaction domain within this membrane protein complex that is essential to silencing the Psp response in the absence of an inducing stimulus.

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Chen, Xu, Qingyan Meng, Die Hu, Linlin Zhang, and Jian Yang. "Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index." Forests 10, no. 12 (December 4, 2019): 1109. http://dx.doi.org/10.3390/f10121109.

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Urban street-side greenery, as an indispensable element of urban green spaces, is beneficial to residents’ physical and mental health. As readily available internet data, street view images have been widely used in urban green spaces research. While the relevant research using multiple images from different directions at a sampling point, researchers need to calculate the index of visible vegetation cover for many times. However, one Baidu panoramic street view image can cover the 360° view similar to that of a pedestrian. In this study, we selected 9644 points at 50-m intervals along the street lines in the central district of Sanya city, China, and acquired panoramic images via the Baidu application programming interface (API). The sky pixels were detected within the Baidu panoramic street view images using a proposed reflectance indicator. The green vegetation was extracted according to the Back Propagation (BP) neural-network method. Our proposed method was validated by comparing the results of the manual recognition and PSPNet method, and the accuracy met the requirements of the study. The Panoramic Green View Index (PGVI) was proposed to quantitatively evaluate greenery around streets. The authors found that the highest frequency value in the distribution was 0.075, which accounted for 32% of the total sample points, and the average PGVI value in this study area was low; the PGVI values between different roads varied greatly, and primary roads tended to have higher PGVI values than other roads. This case study proved that the PGVI is well suited for evaluating greenery around streets. We suggest that the PGVI derived from Baidu panoramic street view images may be a useful tool for city managers to support urban green spaces planning and management.

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Du, Zhenrong, Jianyu Yang, Cong Ou, and Tingting Zhang. "Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method." Remote Sensing 11, no. 7 (April 11, 2019): 888. http://dx.doi.org/10.3390/rs11070888.

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The growing population in China has led to an increasing importance of crop area (CA) protection. A powerful tool for acquiring accurate and up-to-date CA maps is automatic mapping using information extracted from high spatial resolution remote sensing (RS) images. RS image information extraction includes feature classification, which is a long-standing research issue in the RS community. Emerging deep learning techniques, such as the deep semantic segmentation network technique, are effective methods to automatically discover relevant contextual features and get better image classification results. In this study, we exploited deep semantic segmentation networks to classify and extract CA from high-resolution RS images. WorldView-2 (WV-2) images with only Red-Green-Blue (RGB) bands were used to confirm the effectiveness of the proposed semantic classification framework for information extraction and the CA mapping task. Specifically, we used the deep learning framework TensorFlow to construct a platform for sampling, training, testing, and classifying to extract and map CA on the basis of DeepLabv3+. By leveraging per-pixel and random sample point accuracy evaluation methods, we conclude that the proposed approach can efficiently obtain acceptable accuracy (Overall Accuracy = 95%, Kappa = 0.90) of CA classification in the study area, and the approach performs better than other deep semantic segmentation networks (U-Net/PspNet/SegNet/DeepLabv2) and traditional machine learning methods, such as Maximum Likelihood (ML), Support Vector Machine (SVM), and RF (Random Forest). Furthermore, the proposed approach is highly scalable for the variety of crop types in a crop area. Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.

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Guo, Shichen, Qizhao Jin, Hongzhen Wang, Xuezhi Wang, Yangang Wang, and Shiming Xiang. "Learnable Gated Convolutional Neural Network for Semantic Segmentation in Remote-Sensing Images." Remote Sensing 11, no. 16 (August 17, 2019): 1922. http://dx.doi.org/10.3390/rs11161922.

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Semantic segmentation in high-resolution remote-sensing (RS) images is a fundamental task for RS-based urban understanding and planning. However, various types of artificial objects in urban areas make this task quite challenging. Recently, the use of Deep Convolutional Neural Networks (DCNNs) with multiscale information fusion has demonstrated great potential in enhancing performance. Technically, however, existing fusions are usually implemented by summing or concatenating feature maps in a straightforward way. Seldom do works consider the spatial importance for global-to-local context-information aggregation. This paper proposes a Learnable-Gated CNN (L-GCNN) to address this issue. Methodologically, the Taylor expression of the information-entropy function is first parameterized to design the gate function, which is employed to generate pixelwise weights for coarse-to-fine refinement in the L-GCNN. Accordingly, a Parameterized Gate Module (PGM) was designed to achieve this goal. Then, the single PGM and its densely connected extension were embedded into different levels of the encoder in the L-GCNN to help identify the discriminative feature maps at different scales. With the above designs, the L-GCNN is finally organized as a self-cascaded end-to-end architecture that is able to sequentially aggregate context information for fine segmentation. The proposed model was evaluated on two public challenging benchmarks, the ISPRS 2Dsemantic segmentation challenge Potsdam dataset and the Massachusetts building dataset. The experiment results demonstrate that the proposed method exhibited significant improvement compared with several related segmentation networks, including the FCN, SegNet, RefineNet, PSPNet, DeepLab and GSN.For example, on the Potsdam dataset, our method achieved a 93.65% F 1 score and 88.06% I o U score for the segmentation of tiny cars in high-resolution RS images. As a conclusion, the proposed model showed potential for object segmentation from the RS images of buildings, impervious surfaces, low vegetation, trees and cars in urban settings, which largely varies in size and have confusing appearances.

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Lee, Shou-Lun, Hsien-Kuang Lee, Ting-Yu Chin, Ssu-Chieh Tu, Ming-Hsun Kuo, Ming-Ching Kao, and Yang-Chang Wu. "Inhibitory Effects of Purple Sweet Potato Leaf Extract on the Proliferation and Lipogenesis of the 3T3-L1 Preadipocytes." American Journal of Chinese Medicine 43, no. 05 (January 2015): 915–25. http://dx.doi.org/10.1142/s0192415x15500536.

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Purple sweet potato leaves (PSPLs) are healthy vegetable that is rich in anti-oxidants. A solution of boiling water extract of PSPL (PSPLE) is believed to be able to prevent obesity and metabolic syndrome in the countryside of Taiwan, but its efficacy has not yet been verified. The purpose of this study was to investigate the possible anti-adipogenesis effect of PSPLE in vitro. PSPLE was used to treat the 3T3-L1 cells, and the effects on cell proliferation and adipogenesis were investigated. The results showed that PSPLE caused a dose-dependent decrease in the cell proliferation of 3T3-L1 preadipocytes, but did not alter the cell viability. In addition, PSPLE induced ERK inactivation in the 3T3-L1 preadipocytes. Furthermore, pre-treatment of confluent 3T3-L1 cells with PSPLE led to reduced lipid accumulation in differentiated 3T3-L1 cells. The inhibition of lipogenesis could result from the PSPLE-induced down-regulation of the expression of the C/EBPα and SREBP-1 transcription factors during 3T3-L1 adipocyte differentiation. These results suggest that PSPLE not only inhibits cell proliferation at an early stage but also inhibits adipogenesis at a later stage of the differentiation program.

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Lee, Shou-Lun, Ting-Yu Chin, Ssu-Chieh Tu, Yu-Jie Wang, Ya-Ting Hsu, Ming-Ching Kao, and Yang-Chang Wu. "Purple Sweet Potato Leaf Extract Induces Apoptosis and Reduces Inflammatory Adipokine Expression in 3T3-L1 Differentiated Adipocytes." Evidence-Based Complementary and Alternative Medicine 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/126302.

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Background. Purple sweet potato leaves (PSPL) are widely grown and are considered a healthy vegetable in Taiwan. PSPL contain a high content of flavonoids, and the boiling water-extracted PSPL (PSPLE) is believed to prevent metabolic syndrome. However, its efficacy has not yet been verified. Therefore, we investigated the effect of PSPLE on adipocytes.Methods. The differentiated 3T3-L1 cells used in this study were derived from preadipocytes that were differentiated into adipocytes using an adipogenic agent (insulin, dexamethasone, and 3-isobutyl-1-methylxanthine); approximately 90% of the cells were differentiated using this method.Results. Treating the differentiated 3T3-L1 cells with PSPLE caused a dose-dependent decrease in the number of adipocytes rather than preadipocytes. In addition, treatment with PSPLE resulted in apoptosis of the differentiated 3T3-L1 cells as determined by DAPI analysis and flow cytometry. PSPLE also increased the expression of cleaved caspase-3 and poly ADP-ribose polymerase (PARP). Furthermore, PSPLE induced downregulation of interleukin-6 (IL-6) and tumor necrosis factor-α(TNF-α) gene expression in the differentiated 3T3-L1 cells.Conclusions. These results suggest that PSPLE not only induced apoptosis but also downregulated inflammation-associated genes in the differentiated 3T3-L1 cells.

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Pani, R., A. Soluri, R. Scafe, A. Pergola, R. Pellegrini, G. De Vincentis, G. Trotta, and F. Scopinaro. "Multi-PSPMT scintillation camera." IEEE Transactions on Nuclear Science 46, no. 3 (June 1999): 702–8. http://dx.doi.org/10.1109/23.775602.

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Rajendrakumar, Santhosh, Adityanarayan Mohapatra, Bijay Singh, Vishnu Revuri, Yong-Kyu Lee, Chang Kim, Chong-Su Cho, and In-Kyu Park. "Self-Assembled, Adjuvant/Antigen-Based Nanovaccine Mediates Anti-Tumor Immune Response against Melanoma Tumor." Polymers 10, no. 10 (September 25, 2018): 1063. http://dx.doi.org/10.3390/polym10101063.

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Malignant melanoma is a highly aggressive type of cancer that requires radical treatment strategies to inhibit the cancer cell progression and metastasis. In recent years, preclinical research and clinical trials on melanoma treatment have been considerably focused on the adjuvant-based immunotherapy for enhancing the immune response of innate immune cells against cancer cells. However, the clinical outcome of these adjuvant-based treatments is inadequate due to an improper delivery system for these immune activators to reach the target site. Hence, we developed a vaccine formulation containing tumor lysate protein (TL) and poly I:C (PIC) complexed with positively charged poly (sorbitol-co-polyethylenimine (PEI) (PSPEI). The resulting ionic PSPEI-polyplexed antigen/adjuvant (PAA) (PSPEI-PAA) nanocomplexes were stable at the physiological condition, are non-toxic, and have enhanced intracellular uptake of antigen and adjuvant in immature dendritic cells leading to dendritic cell maturation. In the murine B16F10 tumor xenograft model, PSPEI-PAA nanocomplexes significantly suppressed tumor growth and did not exhibit any noticeable sign of toxicity. The level of matured dendritic cells (CD80+/CD86+ cells) in the tumor draining lymph node of PSPEI-PAA treated tumor mice were enhanced and therefore CD8+ T cells infiltration in the tumor were enriched. Additionally, the cytotoxic T lymphocytes (CTLs) assay involving co-culturing of splenocytes isolated from the PSPEI-PAA-treated mice with that of B16F10 cells significantly revealed enhanced cancer killing by the TL-reactivated CTLs compared to untreated control mice bearing tumor. Therefore, we strongly believe that PSPEI-PAA nanocomplexes could be an efficient antigen/adjuvant delivery system and enhance the antitumor immune response against melanoma tumor in the future clinical trials.

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Calzavarini, Sara, Raja Prince-Eladnani, François Saller, Luca Bologna, Laurent Burnier, Anne C. Brisset, Claudia Quarroz, et al. "Platelet protein S limits venous but not arterial thrombosis propensity by controlling coagulation in the thrombus." Blood 135, no. 22 (May 28, 2020): 1969–82. http://dx.doi.org/10.1182/blood.2019003630.

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Abstract Anticoagulant protein S (PS) in platelets (PSplt) resembles plasma PS and is released on platelet activation, but its role in thrombosis has not been elucidated. Here we report that inactivation of PSplt expression using the Platelet factor 4 (Pf4)-Cre transgene (Pros1lox/loxPf4-Cre+) in mice promotes thrombus propensity in the vena cava, where shear rates are low, but not in the carotid artery, where shear rates are high. At a low shear rate, PSplt functions as a cofactor for both activated protein C and tissue factor pathway inhibitor, thereby limiting factor X activation and thrombin generation within the growing thrombus and ensuring that highly activated platelets and fibrin remain localized at the injury site. In the presence of high thrombin concentrations, clots from Pros1lox/loxPf4-Cre− mice contract, but not clots from Pros1lox/loxPf4-Cre+ mice, because of highly dense fibrin networks. Thus, PSplt controls platelet activation as well as coagulation in thrombi in large veins, but not in large arteries.

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Arun Srivatsan, Rangaprasad, Nicolas Zevallos, Prasad Vagdargi, and Howie Choset. "Registration with a small number of sparse measurements." International Journal of Robotics Research 38, no. 12-13 (April 9, 2019): 1403–19. http://dx.doi.org/10.1177/0278364919842324.

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This work introduces a method for performing robust registration given the geometric model of an object and a small number (less than 20) of sparse point and surface normal measurements of the object’s surface. Such a method is of critical importance in applications such as probing-based surgical registration, contact-based localization, manipulating objects devoid of visual features, etc. Our approach for sparse point and normal registration (SPNR) is iterative in nature. In each iteration, the current best pose estimate is perturbed to generate several candidate poses. Among the generated poses, one pose is selected as the best, by evaluating an inexpensive cost function. This pose is used as the initial condition to estimate the locally optimum registration. This process is repeated until the registration estimate converges within a tolerance bound. Two variants are developed: deterministic (dSPNR) and probabilistic (pSPNR). The dSPNR is faster than pSPNR in converging to the local optimum, but the pSPNR requires fewer parameters to be tuned. The pSPNR also provides pose-uncertainty information in addition to the registration estimate. Both approaches were evaluated in simulation using various standard datasets and then compared with results obtained using state-of-the-art methods. Upon comparison with other methods, both dSPNR and pSPNR were found to be robust to initial pose errors as well as noise in measurements. The effectiveness of the approaches are also demonstrated with robot experiments for the application of probing-based registration.

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Bustamante, A. V., A. M. Sanso, D. O. Segura, A. E. Parma, and P. M. A. Lucchesi. "Dynamic of Mutational Events in Variable Number Tandem Repeats ofEscherichia coliO157:H7." BioMed Research International 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/390354.

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VNTRs regions have been successfully used for bacterial subtyping; however, the hypervariability in VNTR loci is problematic when trying to predict the relationships among isolates. Since few studies have examined the mutation rate of these markers, our aim was to estimate mutation rates of VNTRs specific for verotoxigenicE. coliO157:H7. The knowledge of VNTR mutational rates and the factors affecting them would make MLVA more effective for epidemiological or microbial forensic investigations. For this purpose, we analyzed nine loci performing parallel, serial passage experiments (PSPEs) on 9 O157:H7 strains. The combined 9 PSPE population rates for the 8 mutating loci ranged from 4.4 × 10−05to 1.8 × 10−03mutations/generation, and the combined 8-loci mutation rate was of 2.5 × 10−03mutations/generation. Mutations involved complete repeat units, with only one point mutation detected. A similar proportion between single and multiple repeat changes was detected. Of the 56 repeat mutations, 59% were insertions and 41% were deletions, and 72% of the mutation events corresponded to O157-10 locus. For alleles with up to 13 UR, a constant and low mutation rate was observed; meanwhile longer alleles were associated with higher and variable mutation rates. Our results are useful to interpret data from microevolution and population epidemiology studies and particularly point out that the inclusion or not of O157-10 locus or, alternatively, a differential weighting data according to the mutation rates of loci must be evaluated in relation with the objectives of the proposed study.

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Truman, A., M. J. Palmer, P. T. Durrant, A. J. Bird, D. Ramsden, and J. Stadsnes. "A PSPMT based auroral X-ray imager." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 368, no. 2 (January 1996): 492–97. http://dx.doi.org/10.1016/0168-9002(95)00804-7.

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Rizzo, Luigi, Paolo Valente, Giuseppe Lettieri, and Vincenzo Maffione. "PSPAT: Software packet scheduling at hardware speed." Computer Communications 120 (May 2018): 32–45. http://dx.doi.org/10.1016/j.comcom.2018.02.018.

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Jiang, Yu, Xuemin Yan, Zhaofei Ma, Ping Mei, Wei Xiao, Qinliang You, and Yan Zhang. "Development of the PEO Based Solid Polymer Electrolytes for All-Solid State Lithium Ion Batteries." Polymers 10, no. 11 (November 7, 2018): 1237. http://dx.doi.org/10.3390/polym10111237.

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Solid polymer electrolytes (SPEs) have attracted considerable attention due to the rapid development of the need for more safety and powerful lithium ion batteries. The prime requirements of solid polymer electrolytes are high ion conductivity, low glass transition temperature, excellent solubility to the conductive lithium salt, and good interface stability against Li anode, which makes PEO and its derivatives potential candidate polymer matrixes. This review mainly encompasses on the synthetic development of PEO-based SPEs (PSPEs), and the potential application of the resulting PSPEs for high performance, all-solid-state lithium ion batteries.

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Liu, Liu, Fei Yan, Fangyuan Gai, Linghan Xiao, Lei Shang, Ming Li, and Yuhui Ao. "Enhanced tribological performance of PEEK/SCF/PTFE hybrid composites by graphene." RSC Advances 7, no. 53 (2017): 33450–58. http://dx.doi.org/10.1039/c7ra04969b.

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Baliozian, Puzant, Elmar Lohmüller, Tobias Fellmeth, Nico Wöhrle, Alexander Krieg, and Ralf Preu. "Bifacial p-Type Silicon Shingle Solar Cells − the “pSPEER” Concept." Solar RRL 2, no. 3 (January 10, 2018): 1700171. http://dx.doi.org/10.1002/solr.201700171.

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Kato, Takahiro, Nobukazu Fuwa, and Masao Murakami. "Dose-Volume Comparison of IMRT and PSPT Treatment Plans for Early-Stage Glottic Cancer." International Journal of Particle Therapy 7, no. 2 (September 1, 2020): 42–50. http://dx.doi.org/10.14338/ijpt-20-00008.1.

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Abstract Purpose To clarify the dose distribution characteristics for early-stage glottic cancer by comparing the dose distribution between intensity-modulated radiation therapy (IMRT) and passive scattering proton therapy (PSPT) and to examine the usefulness of PSPT for early-stage glottic cancer. Materials and Methods Computed tomography datasets of 8 patients with T1-2 glottic cancer who had been treated by PSPT were used to create an IMRT plan in Eclipse with 7 fields and a PSPT plan in XiO-M with 2 fields. Organs at risk (OARs) included the carotid arteries, arytenoids, inferior constrictor muscles, strap muscles, thyroid cartilage, cricoid cartilage, and spinal cord. The prescription dose was 66 GyRBE in 33 fractions to the planning target volume (PTV). All plans were optimized such that 95% of the PTV received 90% of the prescription dose considering that the skin was slightly spared. Results The superiority of the PSPT was confirmed in all OARs. In the PSPT, the dose to the contralateral carotid artery and the spinal cord, which is slightly distant from the PTV, was dramatically reduced while maintaining the dose distribution uniformity of the PTV by comparison with IMRT. Conclusion PSPT for early-stage glottic cancer resulted in good target dose homogeneity and significantly spared the OARs as compared with the IMRT. PSPT is expected to be effective in reducing late effects and particularly useful for young people.

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Miura, Takako, Mamoru Baba, Naoki Kawata, Takao Aoki, Masayuki Hagiwara, Tsutomu Hiroishi, Toshiya Sanami, Takeo Nishitani, and Jun-ichi Hori. "Development of position-sensitive proton recoil telescope (PSPRT)." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 493, no. 1-2 (November 2002): 99–105. http://dx.doi.org/10.1016/s0168-9002(02)01459-6.

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Williams, Mark B., Allen R. Goode, Victor Galbis-Reig, Stan Majewski, Andrew G. Weisenberger, and Randolph Wojcik. "Performance of a PSPMT based detector for scintimammography." Physics in Medicine and Biology 45, no. 3 (February 18, 2000): 781–800. http://dx.doi.org/10.1088/0031-9155/45/3/315.

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Liao, Zhongxing, J. Jack Lee, Ritsuko Komaki, Daniel R. Gomez, Michael S. O’Reilly, Frank V. Fossella, George R. Blumenschein, et al. "Bayesian Adaptive Randomization Trial of Passive Scattering Proton Therapy and Intensity-Modulated Photon Radiotherapy for Locally Advanced Non–Small-Cell Lung Cancer." Journal of Clinical Oncology 36, no. 18 (June 20, 2018): 1813–22. http://dx.doi.org/10.1200/jco.2017.74.0720.

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Purpose This randomized trial compared outcomes of passive scattering proton therapy (PSPT) versus intensity-modulated (photon) radiotherapy (IMRT), both with concurrent chemotherapy, for inoperable non–small-cell lung cancer (NSCLC). We hypothesized that PSPT exposes less lung tissue to radiation than IMRT and thereby reduces toxicity without compromising tumor control. The primary end points were grade ≥ 3 radiation pneumonitis (RP) and local failure (LF). Patients and Methods Eligible patients had stage IIB to IIIB NSCLC (or stage IV NSCLC with a single brain metastasis or recurrent lung or mediastinal disease after surgery) who were candidates for concurrent chemoradiation therapy. Pairs of treatment plans for IMRT and PSPT were created for each patient. Patients were eligible for random assignment only if both plans satisfied the same prespecified dose-volume constraints for at-risk organs at the same tumor dose. Results Compared with IMRT (n = 92), PSPT (n = 57) exposed less lung tissue to doses of 5 to 10 Gy(RBE), which is the absorbed Gy dose multiplied by the relative biologic effectiveness (RBE) factor for protons; exposed more lung tissue to ≥ 20 Gy(RBE), but exposed less heart tissue at all dose levels between 5 and 80 Gy(RBE). The grade ≥ 3 RP rate for all patients was 8.1% (IMRT, 6.5%; PSPT, 10.5%); corresponding LF rates were 10.7% (all), 10.9% (IMRT), and 10.5% (PSPT). The posterior probability of IMRT being better than PSPT was 0.54. Exploratory analysis showed that the RP and LF rates at 12 months for patients enrolled before versus after the trial midpoint were 21.1% (before) versus 18.2% (after) for the IMRT group (P = .047) and 31.0% (before) versus 13.1% (after) for the PSPT group (P = .027). Conclusion PSPT did not improve dose-volume indices for lung but did for heart. No benefit was noted in RP or LF after PSPT. Improvements in both end points were observed over the course of the trial.

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Gómez, María Dolores, Begoña Renau-Morata, Edelín Roque, Julio Polaina, José Pío Beltrán, and Luis A. Cañas. "PsPMEP, a pollen-specific pectin methylesterase of pea (Pisum sativum L.)." Plant Reproduction 26, no. 3 (July 10, 2013): 245–54. http://dx.doi.org/10.1007/s00497-013-0220-0.

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Wastl, Clemens, Yong Wang, Aitor Atencia, and Christoph Wittmann. "Independent perturbations for physics parametrization tendencies in a convection-permitting ensemble (pSPPT)." Geoscientific Model Development 12, no. 1 (January 16, 2019): 261–73. http://dx.doi.org/10.5194/gmd-12-261-2019.

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Abstract. A modification of the widely used SPPT (Stochastically Perturbed Parametrisation Tendencies) scheme is proposed and tested in a Convection-permitting – Limited Area Ensemble Forecasting system (C-LAEF) developed at ZAMG (Zentralanstalt für Meteorologie und Geodynamik). The tendencies from four physical parametrization schemes are perturbed: radiation, shallow convection, turbulence, and microphysics. Whereas in SPPT the total model tendencies are perturbed, in the present approach (pSPPT hereinafter) the partial tendencies of the physics parametrization schemes are sequentially perturbed. Thus, in pSPPT an interaction between the uncertainties of the different physics parametrization schemes is sustained and a more physically consistent relationship between the processes is kept. Two configurations of pSPPT are evaluated over two separate months (one in summer and another in winter). Both schemes increase the stability of the model and lead to statistically significant improvements in the probabilistic performance compared to a reference run without stochastic physics. An evaluation of selected test cases shows that the positive effect of stochastic physics is much more pronounced on days with high convective activity. Small discrepancies in the humidity analysis can be dedicated to the use of a very simple supersaturation adjustment. This and other adjustments are discussed to provide some suggestions for future investigations.

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Orlando, Gabriele, Daniele Raimondi, Francesco Tabaro, Francesco Codicè, Yves Moreau, and Wim F. Vranken. "Computational identification of prion-like RNA-binding proteins that form liquid phase-separated condensates." Bioinformatics 35, no. 22 (April 17, 2019): 4617–23. http://dx.doi.org/10.1093/bioinformatics/btz274.

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Abstract Motivation Eukaryotic cells contain different membrane-delimited compartments, which are crucial for the biochemical reactions necessary to sustain cell life. Recent studies showed that cells can also trigger the formation of membraneless organelles composed by phase-separated proteins to respond to various stimuli. These condensates provide new ways to control the reactions and phase-separation proteins (PSPs) are thus revolutionizing how cellular organization is conceived. The small number of experimentally validated proteins, and the difficulty in discovering them, remain bottlenecks in PSPs research. Results Here we present PSPer, the first in-silico screening tool for prion-like RNA-binding PSPs. We show that it can prioritize PSPs among proteins containing similar RNA-binding domains, intrinsically disordered regions and prions. PSPer is thus suitable to screen proteomes, identifying the most likely PSPs for further experimental investigation. Moreover, its predictions are fully interpretable in the sense that it assigns specific functional regions to the predicted proteins, providing valuable information for experimental investigation of targeted mutations on these regions. Finally, we show that it can estimate the ability of artificially designed proteins to form condensates (r=−0.87), thus providing an in-silico screening tool for protein design experiments. Availability and implementation PSPer is available at bio2byte.com/psp. Supplementary information Supplementary data are available at Bioinformatics online.

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Wang, Yaoli, Yujun Duan, Wenxia Di, Qing Chang, and Lipo Wang. "Optimization of Submodularity and BBO-Based Routing Protocol for Wireless Sensor Deployment." Sensors 20, no. 5 (February 27, 2020): 1286. http://dx.doi.org/10.3390/s20051286.

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Wireless sensors are limited by node costs, communication efficiency, and energy consumption when wireless sensors are deployed on a large scale. The use of submodular optimization can reduce the deployment cost. This paper proposes a sensor deployment method based on the Improved Heuristic Ant Colony Algorithm-Chaos Optimization of Padded Sensor Placements at Informative and cost-Effective Locations (IHACA-COpSPIEL) algorithm and a routing protocol based on an improved Biogeography-Based Optimization (BBO) algorithm. First, a mathematical model with submodularity is established. Second, the IHACA is combined with pSPIEL-based on chaos optimization to determine the shortest path. Finally, the selected sensors are used in the biogeography of the improved BBO routing protocols to transmit data. The experimental results show that the IHACA-COpSPIEL algorithm can go beyond the local optimal solutions, and the communication cost of IHACA-COpSPIEL is 38.42%, 24.19% and 8.31%, respectively, lower than that of the greedy algorithm, the pSPIEL algorithm and the IHACA algorithm. It uses fewer sensors and has a longer life cycle. Compared with the LEACH protocol, the routing protocol based on the improved BBO extends the life cycle by 30.74% and has lower energy consumption.

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Girish, B. S., K. S. Srivani, Ravi Subrahmanyan, N. Udaya Shankar, Saurabh Singh, T. Jishnu Nambissan, Mayuri Sathyanarayana Rao, R. Somashekar, and A. Raghunathan. "SARAS CD/EoR Radiometer: Design and Performance of the Digital Correlation Spectrometer." Journal of Astronomical Instrumentation 09, no. 02 (June 2020): 2050006. http://dx.doi.org/10.1142/s2251171720500063.

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In the currently accepted model for cosmic baryon evolution, Cosmic Dawn (CD) and the Epoch of Reionization (EoR) are significant times when first light from the first luminous objects emerged, transformed and subsequently ionized the primordial gas. The 21[Formula: see text]cm (1420[Formula: see text]MHz) hyperfine transition of neutral hydrogen, redshifted from these cosmic times to a frequency range of 40[Formula: see text]MHz to 200[Formula: see text]MHz, has been recognized as an important probe of the physics of CD/EoR. The global 21[Formula: see text]cm signal is predicted to be a spectral distortion of a few 10’s to a few 100’s of mK, which is expected to be present in the cosmic radio background as a trace additive component. Shaped Antenna measurement of the background RAdio Spectrum (SARAS) is a spectral radiometer purpose designed to detect the weak 21[Formula: see text]cm signal from CD/EoR. An important subsystem of the radiometer, the digital correlation spectrometer, is developed around a high-speed digital signal processing platform called pSPEC. pSPEC is built around two quad 10-bit analog-to-digital converters (EV10AQ190) and a Virtex 6 (XC6VLX240T) field programmable gate array, with provision for multiple Gigabit Ethernet and 4.5[Formula: see text]Gbps fiber-optic interfaces. Here, we describe the system design of the digital spectrometer, the pSPEC board, and the adaptation of pSPEC to implement a high spectral resolution (61[Formula: see text]kHz), high dynamic range ([Formula: see text]:1) correlation spectrometer covering the entire CD/EoR band. As the SARAS radiometer is required to be deployed in remote locations where terrestrial radio frequency interference (RFI) is a minimum, the spectrometer is designed to be compact, portable and operating off internal batteries. The paper includes an evaluation of the spectrometer’s susceptibility to RFI and capability to detect signals from CD/EoR.

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Orsolini Cencelli, Valentino, Francesco de Notaristefani, and Enrico D’Abramo. "High-speed readout for the H8500 flat panel PSPMT." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 571, no. 1-2 (February 2007): 389–91. http://dx.doi.org/10.1016/j.nima.2006.10.116.

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Trotta, C., R. Massari, G. Trinci, N. Palermo, S. Boccalini, F. Scopinaro, and A. Soluri. "High-Resolution Imaging System (HiRIS) based on H9500 PSPMT." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 593, no. 3 (August 2008): 454–58. http://dx.doi.org/10.1016/j.nima.2008.05.052.

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Polamraju, Praveen, Alexander F. Bagley, Tyler Williamson, X. Ronald Zhu, and Steven J. Frank. "Hydrogel Spacer Reduces Rectal Dose during Proton Therapy for Prostate Cancer: A Dosimetric Analysis." International Journal of Particle Therapy 5, no. 4 (March 1, 2019): 23–31. http://dx.doi.org/10.14338/ijpt-18-00041.1.

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Abstract Purpose: Proton therapy for prostate cancer may reduce bowel dose and risk of bowel symptoms relative to photon-based methods. Here, we determined the effect of using a biodegradable, injectable hydrogel spacer on rectal dose on plans for treating prostate cancer with intensity-modulated proton therapy (IMPT) or passive scattering proton therapy (PSPT). Materials and Methods: Pairs of IMPT and PSPT plans for 9 patients were created from fused computed tomography/magnetic resonance imaging scans obtained before and after spacer injection. Calculated values of rectal V40, V60, V70, V80, and maximum dose (Dmax) were compared with Wilcoxon signed rank tests. Displacements at the base (BP), midgland (MP), and apex (AP) of the prostate relative to the anterior rectal wall with the spacer in place were averaged for each patient and correlated with V70 by using linear regression models. Results: The presence of a spacer reduced all dosimetric parameters for both PSPT and IMPT, with the greatest difference in V70, which was 81.1% lower for PSPT-with-spacer than for IMPT-without-spacer. Median displacements at BP, MP, and AP were 12 mm (range 7-19), 2 mm (range 0-4), and 1 mm (range 0-5) without the spacer and 19 mm (range 12-23), 10 mm (range 8-16), and 7 mm (range 2-12) with the spacer. Modest linear trends were noted between rectal V70 and displacement for IMPT-with-spacer and PSPT-with-spacer. When displacement was ≥8 mm, V70 was ≤5.1% for IMPT-with-spacer and PSPT-with-spacer. Conclusion: Use of biodegradable hydrogel spacers for prostate cancer treatment provides a significant reduction of radiation dose to the rectum with proton therapy. Significant reductions in rectal dose occurred in both PSPT and IMPT plans, with the greatest reduction for IMPT-with-spacer relative to PSPT alone. Prospective studies are ongoing to assess the clinical impact of reducing rectal dose with hydrogel spacers.

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Komolafe, Blessing F., Moses O. Ogunniran, Fen Y. Zhang, and Xu S. Qian. "A COMPARATIVE PERSPECTIVE OF TEACHING SKILL ACQUISITION IN PRE-SERVICE PHYSICS TEACHER (PsPT) TRAINING PROGRAM IN CHINA AND NIGERIA." Journal of Baltic Science Education 19, no. 3 (June 10, 2020): 356–73. http://dx.doi.org/10.33225/jbse/20.19.356.

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Microteaching practice is an important part of Pre-Service Physics Teacher (PsPT) Training Program adopting different approaches to inspire the acquisition of teaching skills by prospective teachers. Using psychomotor domain aspect of revised Bloom’s taxonomy to explore microteaching practice as it relates to physics teaching, this research examined the significant influences of the two approaches (Nigeria and China) identified on the teaching skill of PsPT and suggests the best ways of improving the teaching skill of PsPT through micro-teaching practices. Data were collected using the mixed-method research design of administering descriptive survey questionnaire on final year PsPT while a structured interview question was used to interview the teachers. It was found that the two approaches had significant influences on the teaching skill acquisition of PsPT through microteaching practices with respect to physics as a subject that requires motor skill, and that this can be improved through micro-teaching by a combination of theory with practice. Also, physics teacher educators need to focus on developing PsPTs’ psychomotor domain in line with time reform in microteaching practices to accommodate more time for PsPTs’ to master the subject concept of physics as a psycho motive subject. Keywords: micro-teaching practices, pre-service physics teacher, teaching skill acquisition, training program.

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Moustafa, Rania, Taissir Albouni, and Ghassan Aziz. "The role of procalcitonin and presepsin in the septic febrile neutropenia in acute leukemia patients." PLOS ONE 16, no. 7 (July 29, 2021): e0253842. http://dx.doi.org/10.1371/journal.pone.0253842.

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Background The source of bacterial infection in neutropenic acute leukemia patients is detected in about 20–30% of cases. Bacterial cultures may require a long incubation period and risk false-positive and false- negative results. Therefore, biomarkers distinguishing septic febrile neutropenia from other etiologies in acute leukemia patients play the important role in patient assessment and treatment planning. This study aims to determine the role of procalcitonin (PCT) and presepsin (PSPN) in infectious complication in comparison to C-reactive protein (CRP) on the first and third day at the onset of febrile neutropenia in patients with acute leukemia. Methods Between June 2018 and February 2019, 60 acute leukemia patients with febrile neutropenia receiving chemotherapy. The 41 acute myeloid leukemia patients and 19 acute lymphoblastic leukemia patients were recruited in this study. Their ages ranged from 14 to 65 years. PCT and PSPN were measured and were compared to CRP at the onset of febrile neutropenia and after 48 hours. 20 patients had a fever of unknown origin (FUO) and 40 patients had a bacterial infection. Findings Our results showed that the values of these markers were higher in patients with infection than patients without. The area under the curve (AUC) of PCT were 0.931 and 0.813 on day one and three respectively, which was the best in determination of infection. The cut-off values of PCT were 1.27 and 1.23 ng/mL and the cut off values of PSPN were 1.75 and 2.9 μg/L in the successive days, their clinical sensitivities were high. PCT and PSPN were capable of distinguishing the cause of febrile neutropenia from the onset of infection and predicting its complications (p<0.05). The PSPN level couldn’t differentiate gram-positive or gram-negative bacterial infection. Significant differences were found between the mean values of the PSPN during the successive days in all patients and patients with bacteremia. This study illustrated a weak positive correlation between PCT and Sequential Organ Failure Assessment (SOFA) score, the negligible correlation between CRP and SOFA score and no significant correlation between PSPN and SOFA score. Interpretation PCT is an accurate biomarker in identifying infection in acute leukemia patients, its concentration is associated with the severity of bacterial sepsis. PSPN is superior to PCT for follow-up of patients.

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Journal articles on the topic 'PSPNet'

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