Adaptive variable-grid least-squares reverse-time migration

Variable-grid methods have the potential to save computing costs and memory requirements in forward modeling and least-squares reverse-time migration (LSRTM). However, due to the inherent difficulty of automatic grid discretization, conventional variable-grid methods have not been widely used in industrial production. We propose a variable-grid LSRTM (VG-LSRTM) method based on an adaptive sampling strategy to improve computing efficiency and reduce memory requirements. Based on the mapping relation of two coordinate systems, we derive variable-grid acoustic wave equation and its corresponding Born forward modeling equation. On this basis, we develop a complete VG-LSRTM framework. Numerical experiments on a layered model validate the feasibility of the proposed VG-LSRTM algorithm. LSRTM tests on a modified Marmousi model demonstrate that our method can save computational costs and memory requirements with little accuracy loss

Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS

Non-maximum suppression (NMS) is an essential post-processing module used in many 3D object detection frameworks to remove overlapping candidate bounding boxes. However, an overreliance on classification scores and difficulties in determining appropriate thresholds can affect the resulting accuracy directly. To address these issues, we introduce fuzzy learning into NMS and propose a novel generalized Fuzzy-NMS module to achieve finer candidate bounding box filtering. The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process. Adequate validation experiments are conducted using the mainstream KITTI and large-scale Waymo 3D object detection benchmarks. The results of these tests demonstrate the proposed Fuzzy-NMS module can improve the accuracy of numerous recently NMS-based detectors significantly, including PointPillars, PV-RCNN, and IA-SSD, etc. This effect is particularly evident for small objects such as pedestrians and bicycles. As a plug-and-play module, Fuzzy-NMS does not need to be retrained and produces no obvious increases in inference time

Salivary and fecal microbiota: potential new biomarkers for early screening of colorectal polyps

ObjectiveGut microbiota plays an important role in colorectal cancer (CRC) pathogenesis through microbes and their metabolites, while oral pathogens are the major components of CRC-associated microbes. Multiple studies have identified gut and fecal microbiome-derived biomarkers for precursors lesions of CRC detection. However, few studies have used salivary samples to predict colorectal polyps. Therefore, in order to find new noninvasive colorectal polyp biomarkers, we searched into the differences in fecal and salivary microbiota between patients with colorectal polyps and healthy controls.MethodsIn this case–control study, we collected salivary and fecal samples from 33 patients with colorectal polyps (CP) and 22 healthy controls (HC) between May 2021 and November 2022. All samples were sequenced using full-length 16S rRNA sequencing and compared with the Nucleotide Sequence Database. The salivary and fecal microbiota signature of colorectal polyps was established by alpha and beta diversity, Linear discriminant analysis Effect Size (LEfSe) and random forest model analysis. In addition, the possibility of microbiota in identifying colorectal polyps was assessed by Receiver Operating Characteristic Curve (ROC).ResultsIn comparison to the HC group, the CP group’s microbial diversity increased in saliva and decreased in feces (p < 0.05), but there was no significantly difference in microbiota richness (p > 0.05). The principal coordinate analysis revealed significant differences in β-diversity of salivary and fecal microbiota between the CP and HC groups. Moreover, LEfSe analysis at the species level identified Porphyromonas gingivalis, Fusobacterium nucleatum, Leptotrichia wadei, Prevotella intermedia, and Megasphaera micronuciformis as the major contributors to the salivary microbiota, and Ruminococcus gnavus, Bacteroides ovatus, Parabacteroides distasonis, Citrobacter freundii, and Clostridium symbiosum to the fecal microbiota of patients with polyps. Salivary and fecal bacterial biomarkers showed Area Under ROC Curve of 0.8167 and 0.8051, respectively, which determined the potential of diagnostic markers in distinguishing patients with colorectal polyps from controls, and it increased to 0.8217 when salivary and fecal biomarkers were combined.ConclusionThe composition and diversity of the salivary and fecal microbiota were significantly different in colorectal polyp patients compared to healthy controls, with an increased abundance of harmful bacteria and a decreased abundance of beneficial bacteria. A promising non-invasive tool for the detection of colorectal polyps can be provided by potential biomarkers based on the microbiota of the saliva and feces

Role of lncRNAs in acute pancreatitis: pathogenesis, diagnosis, and therapy

Acute pancreatitis (AP) is one of the most common acute abdominal diseases characterized by an injury and inflammatory disorder of the pancreas with complicated pathological mechanisms. Long non-coding RNAs (lncRNAs) have been shown to play an important role in various physiological and pathological processes in humans, and they have emerged as potential biomarkers of diagnosis and therapeutic targets in various diseases. Recently, accumulating evidence has shown significant alterations in the expression of lncRNAs, which are involved in the pathogenesis of AP, such as premature trypsinogen activation, impaired autophagy, inflammatory response, and acinar cell death. Moreover, lncRNAs can be the direct target of AP treatment and show potential as biomarkers for the diagnosis. Thus, in this review, we focus on the role of lncRNAs in the pathogenesis, diagnosis, and therapy of AP and emphasize the future directions to study lncRNAs in AP, providing new insight into understanding the cellular and molecular mechanisms of AP and seeking novel biomarkers for the diagnosis and therapeutic targets to improve clinical management in the future

Mechanism of Structural Colors in Binary Mixtures of Nanoparticle-based Supraballs

Inspired by structural colors in avian species, various synthetic strategies have been developed to produce non-iridescent, saturated colors using nanoparticle assemblies. Mixtures of nanoparticles varying in particle chemistry (or complex refractive indices) and particle size have additional emergent properties that impact the color produced. For such complex multi-component systems, an understanding of assembled structure along with a robust optical modeling tool can empower scientists to perform intensive structure-color relationship studies and fabricate designer materials with tailored color. Here, we demonstrate how we can reconstruct the assembled structure from small-angle scattering measurements using the computational reverse-engineering analysis for scattering experiments (CREASE) method and then use the reconstructed structure in finite-difference time-domain (FDTD) calculations to predict color. We successfully, quantitatively predict experimentally observed color in mixtures containing strongly absorbing melanin nanoparticles and demonstrate the influence of a single layer of segregated nanoparticles on color produced. The versatile computational approach presented in this work is useful for engineering synthetic materials with desired colors without laborious trial and error experiments.Comment: 23 Pages, 5 Figures, 1 ToC Figur

Phenolic compounds weaken the impact of drought on soil enzyme activity in global wetlands

Soil enzymes play a central role in carbon and nutrient cycling, and their activities can be affected by drought-induced oxygen exposure. However, a systematic global estimate of enzyme sensitivity to drought in wetlands is still lacking. Through a meta-analysis of 55 studies comprising 761 paired observations, this study found that phosphorus-related enzyme activity increased by 38% as result of drought in wetlands, while the majority of other soil enzyme activities remained stable. The expansion of vascular plants under long-term drought significantly promoted the accumulation of phenolic compounds. Using a 2-week incubation experiment with phenol supplementation, we found that phosphorus-related enzyme could tolerate higher biotoxicity of phenolic compounds than other enzymes. Moreover, a long-term (35 years) drainage experiment in a northern peatland in China confirmed that the increased phenolic concentration in surface layer resulting from a shift in vegetation composition inhibited the increase in enzyme activities caused by rising oxygen availability, except for phosphorus-related enzyme. Overall, these results demonstrate the complex and resilient nature of wetland ecosystems, with soil enzymes showing a high degree of adaptation to drought conditions. These new insights could help evaluate the impact of drought on future wetland ecosystem services and provide a theoretical foundation for the remediation of degraded wetlands

A high-throughput and sensitive method to measure Global DNA Methylation: Application in Lung Cancer

<p>Abstract</p> <p>Background</p> <p>Genome-wide changes in DNA methylation are an epigenetic phenomenon that can lead to the development of disease. The study of global DNA methylation utilizes technology that requires both expensive equipment and highly specialized skill sets.</p> <p>Methods</p> <p>We have designed and developed an assay, <it>CpG</it>lobal, which is easy-to-use, does not utilize PCR, radioactivity and expensive equipment. <it>CpG</it>lobal utilizes methyl-sensitive restriction enzymes, HRP Neutravidin to detect the biotinylated nucleotides incorporated in an end-fill reaction and a luminometer to measure the chemiluminescence. The assay shows high accuracy and reproducibility in measuring global DNA methylation. Furthermore, <it>CpG</it>lobal correlates significantly with High Performance Capillary Electrophoresis (HPCE), a gold standard technology. We have applied the technology to understand the role of global DNA methylation in the natural history of lung cancer. World-wide, it is the leading cause of death attributed to any cancer. The survival rate is 15% over 5 years due to the lack of any clinical symptoms until the disease has progressed to a stage where cure is limited.</p> <p>Results</p> <p>Through the use of cell lines and paired normal/tumor samples from patients with non-small cell lung cancer (NSCLC) we show that global DNA hypomethylation is highly associated with the progression of the tumor. In addition, the results provide the first indication that the normal part of the lung from a cancer patient has already experienced a loss of methylation compared to a normal individual.</p> <p>Conclusion</p> <p>By detecting these changes in global DNA methylation, <it>CpG</it>lobal may have a role as a barometer for the onset and development of lung cancer.</p

Birds Detection in Natural Scenes Based on Improved Faster RCNN

To realize the accurate detection of small-scale birds in natural scenes, this paper proposes an improved Faster RCNN model to detect bird species. Firstly, the model uses a depth residual network to extract convolution features and performs multi-scale fusion for feature maps of different convolutional layers. Secondly, the K-means clustering algorithm is used to cluster the bounding boxes. We improve the anchoring according to the clustering results. The improved anchor frame tends toward the real bounding box of the dataset. Finally, the Soft Non-Maximum Suppression method is used to reduce the missed detection of overlapping birds. Compared with the original model, the improved model has faster effect and higher accuracy