Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects

Three-Dimensional Virtual Optical Clearing With Cycle-Consistent Generative Adversarial Network

High-throughput deep tissue imaging and chemical tissue clearing protocols have brought out great promotion in biological research. However, due to uneven transparency introduced by tissue anisotropy in imperfectly cleared tissues, fluorescence imaging based on direct chemical tissue clearing still encounters great challenges, such as image blurring, low contrast, artifacts and so on. Here we reported a three-dimensional virtual optical clearing method based on unsupervised cycle-consistent generative adversarial network, termed 3D-VoCycleGAN, to digitally improve image quality and tissue transparency of biological samples. We demonstrated the good image deblurring and denoising capability of our method on imperfectly cleared mouse brain and kidney tissues. With 3D-VoCycleGAN prediction, the signal-to-background ratio (SBR) of images in imperfectly cleared brain tissue areas also showed above 40% improvement. Compared to other deconvolution methods, our method could evidently eliminate the tissue opaqueness and restore the image quality of the larger 3D images deep inside the imperfect cleared biological tissues with higher efficiency. And after virtually cleared, the transparency and clearing depth of mouse kidney tissues were increased by up to 30%. To our knowledge, it is the first interdisciplinary application of the CycleGAN deep learning model in the 3D fluorescence imaging and tissue clearing fields, promoting the development of high-throughput volumetric fluorescence imaging and deep learning techniques

Autotransplantation of Inferior Parathyroid glands during central neck dissection for papillary thyroid carcinoma: A retrospective cohort study

AbstractIntroduction: The management of inferior parathyroid glands during central neck dissection (CND) for papillary thyroid carcinoma (PTC) remains controversial. Most surgeons preserve inferior parathyroid glands in situ. Autotransplantation is not routinely performed unless devascularization or inadvertent parathyroidectomy occurs. This retrospective study aimed to compare the incidence of postoperative hypoparathyroidism and central neck lymph node (CNLN) recurrence in patients with PTC who underwent inferior parathyroid glands autotransplantation vs preservation in situ. Methods: This is a retrospective study which was conducted in a tertiary referral hospital. A total of 477 patients with PTC (pN1) who underwent total thyroidectomy (TT) and bilateral CND with/without lateral neck dissection were included. Patients' demographical characteristics, tumor stage, incidence of hypoparathyroidism, CNLN recurrence and the number of resected CNLN were analyzed. Results: Three hundred and twenty-one patients underwent inferior parathyroid glands autotransplantation (autotransplantation group). Inferior parathyroid glands were preserved in situ among 156 patients (preservation group). Permanent hypoparathyroidism rate was 0.9% (3/321) versus 3.8% (6/156) respectively (p = 0.028). Mean numbers of resected CNLN were 15 ± 3 (6–23) (autotransplantation group) versus 11 ± 3 (7–21) (preservation group) (p < 0.001). CNLN recurrence rate was 0.3% (1/321) versus 3.8% (6/156) respectively (p = 0.003). Conclusion: Inferior parathyroid glands autotransplantation during CND of PTC (pN1) might reduce permanent hypoparathyroidism and CNLN recurrence. Further study enrolling more patients with long-term follow-up is needed to support this conclusion

Reconstruction of dissolved oxygen in the Indian Ocean from 1980 to 2019 based on machine learning techniques

Oceanic dissolved oxygen (DO) decline in the Indian Ocean has profound implications for Earth’s climate and human habitation in Eurasia and Africa. Owing to sparse observations, there is little research on DO variations, regional comparisons, and its relationship with marine environmental changes in the entire Indian Ocean. In this study, we applied different machine learning algorithms to fit regression models between measured DO, ocean reanalysis physical variables, and spatiotemporal variables. We utilized the Extremely Randomized Trees (ERT) model with the best performance, inputting complete reanalysis data and spatiotemporal information to reconstruct a four-dimensional DO dataset of the Indian Ocean during 1980–2019. The evaluation results showed that the ERT-based DO dataset was superior to the DO simulations in Earth System Models across different time and space. Furthermore, we assessed the spatiotemporal variations in reconstructed DO dataset. DO decline and oxygen-minimum zone (OMZ) expansion were prominent in the Arabian Sea, Bay of Bengal, and Equatorial Indian Ocean. Through correlation analysis, we found that temperature and salinity changes related to solubility primarily control the oxygen decrease in the middle and deep sea. However, the complicated factors with solubility change, vertical mixing, and circulation govern the oxygen increase in the upper and middle sea. Finally, we conducted a volume integral to estimate the oxygen content in the Indian Ocean. Overall, a deoxygenation trend of −141.5 ± 15.1 Tmol dec−1 was estimated over four decades, with a slowdown trend of −68.9 ± 31.3 Tmol dec−1 after 2000. Under global warming and climate change, OMZ expanding and deoxygenation in the Indian Ocean are gradually mitigating. This study enhances our understanding of DO dynamics of the Indian Ocean in response to deoxygenation

Typhoon cloud image prediction based on enhanced multi-scale deep neural network

Typhoons threaten individuals’ lives and property. The accurate prediction of typhoon activity is crucial for reducing those threats and for risk assessment. Satellite images are widely used in typhoon research because of their wide coverage, timeliness, and relatively convenient acquisition. They are also important data sources for typhoon cloud image prediction. Studies on typhoon cloud image prediction have rarely used multi-scale features, which cause significant information loss and lead to fuzzy predictions with insufficient detail. Therefore, we developed an enhanced multi-scale deep neural network (EMSN) to predict a 3-hour-advance typhoon cloud image, which has two parts: a feature enhancement module and a feature encode-decode module. The inputs of the EMSN were eight consecutive images, and a feature enhancement module was applied to extract features from the historical inputs. To consider that the images of different time steps had different contributions to the output result, we used channel attention in this module to enhance important features. Because of the spatially correlated and spatially heterogeneous information at different scales, the feature encode-decode module used ConvLSTMs to capture spatiotemporal features at different scales. In addition, to reduce information loss during downsampling, skip connections were implemented to maintain more low-level information. To verify the effectiveness and applicability of our proposed EMSN, we compared various algorithms and explored the strengths and limitations of the model. The experimental results demonstrated that the EMSN efficiently and accurately predicted typhoon cloud images with higher quality than in the literature

Unified Spatial Intersection Algorithms Based on Conformal Geometric Algebra

Conformal Geometric Algebra has been introduced into geographic information science as a mathematical theory because of its advantages in terms of uniform multidimensional representation and computation. The traditional intersection computation between two geometric objects of different types is not unified. In this study, we propose algorithms based on Conformal Geometric Algebra to determine the spatial relationships between geographic objects in a unified manner. The unified representation and intersection computation can be realized for geometric objects of different dimensions. Different basic judgment rules are provided for different simple geometries. The algorithms are designed and implemented using MapReduce to improve the efficiency of the algorithms. From the results of several experiments we provide, the correctness and effectiveness of the algorithms can be verified

From design to clinic: Engineered peptide nanomaterials for cancer immunotherapy

Immunotherapy has revolutionized the field of cancer therapy. Nanomaterials can further improve the efficacy and safety of immunotherapy because of their tunability and multifunctionality. Owing to their natural biocompatibility, diverse designs, and dynamic self-assembly, peptide-based nanomaterials hold great potential as immunotherapeutic agents for many malignant cancers, with good immune response and safety. Over the past several decades, peptides have been developed as tumor antigens, effective antigen delivery carriers, and self-assembling adjuvants for cancer immunotherapy. In this review, we give a brief introduction to the use of peptide-based nanomaterials for cancer immunotherapy as antigens, carriers, and adjuvants, and to their current clinical applications. Overall, this review can facilitate further understanding of peptide-based nanomaterials for cancer immunotherapy and may pave the way for designing safe and efficient methods for future vaccines or immunotherapies

Exploring Multi-Scale Spatiotemporal Twitter User Mobility Patterns with a Visual-Analytics Approach

Understanding human mobility patterns is of great importance for urban planning, traffic management, and even marketing campaign. However, the capability of capturing detailed human movements with fine-grained spatial and temporal granularity is still limited. In this study, we extracted high-resolution mobility data from a collection of over 1.3 billion geo-located Twitter messages. Regarding the concerns of infringement on individual privacy, such as the mobile phone call records with restricted access, the dataset is collected from publicly accessible Twitter data streams. In this paper, we employed a visual-analytics approach to studying multi-scale spatiotemporal Twitter user mobility patterns in the contiguous United States during the year 2014. Our approach included a scalable visual-analytics framework to deliver efficiency and scalability in filtering large volume of geo-located tweets, modeling and extracting Twitter user movements, generating space-time user trajectories, and summarizing multi-scale spatiotemporal user mobility patterns. We performed a set of statistical analysis to understand Twitter user mobility patterns across multi-level spatial scales and temporal ranges. In particular, Twitter user mobility patterns measured by the displacements and radius of gyrations of individuals revealed multi-scale or multi-modal Twitter user mobility patterns. By further studying such mobility patterns in different temporal ranges, we identified both consistency and seasonal fluctuations regarding the distance decay effects in the corresponding mobility patterns. At the same time, our approach provides a geo-visualization unit with an interactive 3D virtual globe web mapping interface for exploratory geo-visual analytics of the multi-level spatiotemporal Twitter user movements

Re-aware Global Ocean Deoxygenation During the Past 140 Years

&lt;p&gt;The intensifying global ocean deoxygenation has significant impacts on the oceanic ecological equilibrium. Gaps persist in understanding the dynamic evolution of global ocean oxygen throughout the 20th century due to the spatiotemporal sparsity of measurements and systematic biases within Earth system models. Here, we propose a novel spatiotemporal embedded artificial intelligence model that integrates diverse ocean datasets to construct a high-precision, monthly-resolved global ocean oxygen dataset spanning the past 140 years. Our quantitative analyses revealed a decline in global ocean oxygen content from 234.5 ± 0.4 Pmol in 1871 to 230.6 ± 0.4 Pmol in 2010, with a sharp drop of −1402.2 ± 173.6 Tmol per decade post-1980, attributing 98.5% of total oxygen loss in the past three decades alone. Our investigation further underscored that the critical temperature at the sea surface beyond which ocean oxygen begins to rapidly decrease is approximately 18°C. Forward-looking projections underlined the potential for a decline exceeding 10% in global ocean oxygen content relative to pre-1900 levels, under uncontrolled anthropogenic carbon dioxide emissions by the end of the 21st century. These findings provide a comprehensive understanding of historical dynamics and future trajectories in global ocean oxygen, as well as a deeper knowledge of ocean deoxygenation and its underlying mechanisms.&lt;/p&gt