475 research outputs found
A GIS approach towards estimating tourist's off-road use in a mountainous protected area of Northwest Yunnan, China
To address the environmental impacts of tourism in protected areas, park managers need to understand the spatial distribution of tourist use. Standard monitoring measures (tourist surveys and counting and tracking techniques) are not sufficient to accomplish this task, in particular for off-road travel. This article predicts tourists' spatial use patterns through an alternative approach: park accessibility measurement. Naismith's rule and geographical information system's anisotropic cost analysis are integrated into the modeling process, which results in a more realistic measure of off-road accessibility than that provided by other measures. The method is applied to a mountainous United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage Site in northwest Yunnan Province, China, where there is increasing concern about potential impacts of unregulated tourist use. Based on the assumption that accessibility tends to attract more tourists, a spatial pattern of predicted off-road use by tourists is derived. This pattern provides information that can help park managers develop strategies that are effective for both tourism management and species conservation
Development of high-frequency magnetic probe for plasma diagnostics of XuanLong-50
A high-frequency magnetic probe has been designed and developed on
XuanLong-50 (EXL-50) spherical torus to measure high-frequency magnetic field
fluctuations caused by energetic ions and electrons in the plasma. The magnetic
loop, radio filters, radio-frequency (RF) limiter, and data acquisition system
of the probe are described in detail. The results of the preliminary test show
that the probe can have a frequency response within the 1- 180 MHz range. The
fluctuation data from the EXL-50 plasma were analyzed in the time-frequency
domain using fast Fourier transforms. Using this diagnostic system, distinct
high-frequency instabilities were detected. In particular, significant
frequency chirping was observed, which is consistent with the bump on tail
drive instability predicted by the Berk-Breizman model
Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
During the process of computed tomography (CT), metallic implants often cause
disruptive artifacts in the reconstructed images, impeding accurate diagnosis.
Several supervised deep learning-based approaches have been proposed for
reducing metal artifacts (MAR). However, these methods heavily rely on training
with simulated data, as obtaining paired metal artifact CT and clean CT data in
clinical settings is challenging. This limitation can lead to decreased
performance when applying these methods in clinical practice. Existing
unsupervised MAR methods, whether based on learning or not, typically operate
within a single domain, either in the image domain or the sinogram domain. In
this paper, we propose an unsupervised MAR method based on the diffusion model,
a generative model with a high capacity to represent data distributions.
Specifically, we first train a diffusion model using CT images without metal
artifacts. Subsequently, we iteratively utilize the priors embedded within the
pre-trained diffusion model in both the sinogram and image domains to restore
the degraded portions caused by metal artifacts. This dual-domain processing
empowers our approach to outperform existing unsupervised MAR methods,
including another MAR method based on the diffusion model, which we have
qualitatively and quantitatively validated using synthetic datasets. Moreover,
our method demonstrates superior visual results compared to both supervised and
unsupervised methods on clinical datasets
TBFormer: Two-Branch Transformer for Image Forgery Localization
Image forgery localization aims to identify forged regions by capturing
subtle traces from high-quality discriminative features. In this paper, we
propose a Transformer-style network with two feature extraction branches for
image forgery localization, and it is named as Two-Branch Transformer
(TBFormer). Firstly, two feature extraction branches are elaborately designed,
taking advantage of the discriminative stacked Transformer layers, for both RGB
and noise domain features. Secondly, an Attention-aware Hierarchical-feature
Fusion Module (AHFM) is proposed to effectively fuse hierarchical features from
two different domains. Although the two feature extraction branches have the
same architecture, their features have significant differences since they are
extracted from different domains. We adopt position attention to embed them
into a unified feature domain for hierarchical feature investigation. Finally,
a Transformer decoder is constructed for feature reconstruction to generate the
predicted mask. Extensive experiments on publicly available datasets
demonstrate the effectiveness of the proposed model.Comment: 5 pages, 3 figure
Does epigenetic polymorphism contribute to phenotypic variances in Jatropha curcas L.?
<p>Abstract</p> <p>Background</p> <p>There is a growing interest in <it>Jatropha curcas </it>L. (jatropha) as a biodiesel feedstock plant. Variations in its morphology and seed productivity have been well documented. However, there is the lack of systematic comparative evaluation of distinct collections under same climate and agronomic practices. With the several reports on low genetic diversity in jatropha collections, there is uncertainty on genetic contribution to jatropha morphology.</p> <p>Result</p> <p>In this study, five populations of jatropha plants collected from China (CN), Indonesia (MD), Suriname (SU), Tanzania (AF) and India (TN) were planted in one farm under the same agronomic practices. Their agronomic traits (branching pattern, height, diameter of canopy, time to first flowering, dormancy, accumulated seed yield and oil content) were observed and tracked for two years. Significant variations were found for all the agronomic traits studied. Genetic diversity and epigenetic diversity were evaluated using florescence Amplified Fragment Length Polymorphism (fAFLP) and methylation sensitive florescence AFLP (MfAFLP) methods. Very low level of genetic diversity was detected (polymorphic band <0.1%) within and among populations. In contrast, intermediate but significant epigenetic diversity was detected (25.3% of bands were polymorphic) within and among populations. More than half of CCGG sites surveyed by MfAFLP were methylated with significant difference in inner cytosine and double cytosine methylation among populations. Principal coordinates analysis (PCoA) based on Nei's epigenetic distance showed Tanzania/India group distinct from China/Indonesia/Suriname group. Inheritance of epigenetic markers was assessed in one F1 hybrid population between two morphologically distinct parent plants and one selfed population. 30 out of 39 polymorphic markers (77%) were found heritable and followed Mendelian segregation. One epiallele was further confirmed by bisulphite sequencing of its corresponding genomic region.</p> <p>Conclusion</p> <p>Our study confirmed climate and practice independent differences in agronomic performance among jatropha collections. Such agronomic trait variations, however, were matched by very low genetic diversity and medium level but significant epigenetic diversity. Significant difference in inner cytosine and double cytosine methylation at CCGG sites was also found among populations. Most epigenetic differential markers can be inherited as epialleles following Mendelian segregation. These results suggest possible involvement of epigenetics in jatropha development.</p
Chitin perception in plasmodesmata characterizes submembrane immune-signaling specificity in plants
The plasma membrane (PM) is composed of heterogeneous subdomains, characterized by differences in protein and lipid composition. PM receptors can be dynamically sorted into membrane domains to underpin signaling in response to extracellular stimuli. In plants, the plasmodesmal PM is a discrete microdomain that hosts specific receptors and responses. We exploited the independence of this PM domain to investigate how membrane domains can independently integrate a signal that triggers responses across the cell. Focusing on chitin signaling, we found that responses in the plasmodesmal PM require the LysM receptor kinases LYK4 and LYK5 in addition to LYM2. Chitin induces dynamic changes in the localization, association, or mobility of these receptors, but only LYM2 and LYK4 are detected in the plasmodesmal PM. We further uncovered that chitin-induced production of reactive oxygen species and callose depends on specific signaling events that lead to plasmodesmata closure. Our results demonstrate that distinct membrane domains can integrate a common signal with specific machinery that initiates discrete signaling cascades to produce a localized response
Sentiment Analysis of Long-term Social Data during the COVID-19 Pandemic
The COVID-19 pandemic has bringing the āinfodemicā in the social media worlds. Various social platforms play a significant role in instantly acquiring the latest updates of the pandemic. Social media such as Twitter and Facebook produce vast amounts of posts related to the virus, vaccines, economics, and politics. In order to figure out how public opinion and sentiments are expressed during the pandemic, this work analyzes the long-term social posts from social media and conducts sentiment analysis on tweets within 12 months. Our findings show the trend topics of long-term social communities during the pandemic and express peopleās attitudes towards progress of major actions during the pandemic. We explore the main topics during the prolonged pandemic, including information surrounding economics, vaccines, and politics. Besides, we show the differences in gender-based attitudes and propose future research questions refer to the āinfodemicā. We believe that our work contributes to attracting public attention to the āinfodemicā of the social crisis
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Denoising low-dose computed tomography (CT) images is a critical task in
medical image computing. Supervised deep learning-based approaches have made
significant advancements in this area in recent years. However, these methods
typically require pairs of low-dose and normal-dose CT images for training,
which are challenging to obtain in clinical settings. Existing unsupervised
deep learning-based methods often require training with a large number of
low-dose CT images or rely on specially designed data acquisition processes to
obtain training data. To address these limitations, we propose a novel
unsupervised method that only utilizes normal-dose CT images during training,
enabling zero-shot denoising of low-dose CT images. Our method leverages the
diffusion model, a powerful generative model. We begin by training a cascaded
unconditional diffusion model capable of generating high-quality normal-dose CT
images from low-resolution to high-resolution. The cascaded architecture makes
the training of high-resolution diffusion models more feasible. Subsequently,
we introduce low-dose CT images into the reverse process of the diffusion model
as likelihood, combined with the priors provided by the diffusion model and
iteratively solve multiple maximum a posteriori (MAP) problems to achieve
denoising. Additionally, we propose methods to adaptively adjust the
coefficients that balance the likelihood and prior in MAP estimations, allowing
for adaptation to different noise levels in low-dose CT images. We test our
method on low-dose CT datasets of different regions with varying dose levels.
The results demonstrate that our method outperforms the state-of-the-art
unsupervised method and surpasses several supervised deep learning-based
methods. Codes are available in https://github.com/DeepXuan/Dn-Dp
Three-Dimensional Medical Image Fusion with Deformable Cross-Attention
Multimodal medical image fusion plays an instrumental role in several areas
of medical image processing, particularly in disease recognition and tumor
detection. Traditional fusion methods tend to process each modality
independently before combining the features and reconstructing the fusion
image. However, this approach often neglects the fundamental commonalities and
disparities between multimodal information. Furthermore, the prevailing
methodologies are largely confined to fusing two-dimensional (2D) medical image
slices, leading to a lack of contextual supervision in the fusion images and
subsequently, a decreased information yield for physicians relative to
three-dimensional (3D) images. In this study, we introduce an innovative
unsupervised feature mutual learning fusion network designed to rectify these
limitations. Our approach incorporates a Deformable Cross Feature Blend (DCFB)
module that facilitates the dual modalities in discerning their respective
similarities and differences. We have applied our model to the fusion of 3D MRI
and PET images obtained from 660 patients in the Alzheimer's Disease
Neuroimaging Initiative (ADNI) dataset. Through the application of the DCFB
module, our network generates high-quality MRI-PET fusion images. Experimental
results demonstrate that our method surpasses traditional 2D image fusion
methods in performance metrics such as Peak Signal to Noise Ratio (PSNR) and
Structural Similarity Index Measure (SSIM). Importantly, the capacity of our
method to fuse 3D images enhances the information available to physicians and
researchers, thus marking a significant step forward in the field. The code
will soon be available online
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