Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.
Impacts of Urban Expansion Forms on Ecosystem Services in Urban Agglomerations: A Case Study of Shanghai-Hangzhou Bay Urban Agglomeration

Exploring impacts of urban expansion on ecosystem services has become a hot topic for regional sustainable development, while analyzing the ecological effects of urban expansion forms under different expansion intensities and city sizes is relatively rare. Therefore, taking a typical urban agglomeration, Shanghai-Hangzhou Bay Urban Agglomeration, as a case study, this study first analyzed the dynamics of urban expansion forms (leapfrogging, edge-expansion, and infilling) and four critical ecosystem services (carbon sequestration, food supply, habitat quality, and soil retention) in three periods from 1990 to 2019. The multiple linear regression model and zonal statistics analysis model were used to quantitatively identify the impacts of urban expansion forms on ecosystem services, taking into account different expansion intensities and city sizes. The results showed that the urban expansion trend in the study area experienced a morphological change from integration to diffusion and then to integration in 1990–2019; edge-expansion was the dominant expansion form. Food supply decreased continuously while other ecosystem services had fluctuating changes, and they all had spatial heterogeneity. The leapfrogging, edge-expansion, and infilling all had negative impacts on ecosystem services, and among them, the edge-expansion intensity had the highest influence degree in the early expansion, and the leapfrogging intensity occupied the dominant position in all influences with the expansion of urban scales. For different city sizes, the impact of edge-expansion in large-scale cities was greater than in small-scale cities in the early expansion, and the impact of leapfrogging in large-scale cities exceeded the edge-expansion in the subsequent expansion. These findings will help further understand the influential mechanisms between urban expansion and ecosystem services and provide a scientific basis for formulating reasonable urban planning.
Investigating the Role of Green Infrastructure on Urban WaterLogging: Evidence from Metropolitan Coastal Cities

Urban green infrastructures (UGI) can effectively reduce surface runoff, thereby alleviating the pressure of urban waterlogging. Due to the shortage of land resources in metropolitan areas, it is necessary to understand how to utilize the limited UGI area to maximize the waterlogging mitigation function. Less attention, however, has been paid to investigating the threshold level of waterlogging mitigation capacity. Additionally, various studies mainly focused on the individual effects of UGI factors on waterlogging but neglected the interactive effects between these factors. To overcome this limitation, two waterlogging high-risk coastal cities—Guangzhou and Shenzhen, are selected to examine the effectiveness and stability of UGI in alleviating urban waterlogging. The results indicate that the impact of green infrastructure on urban waterlogging largely depends on its area and biophysical parameter. Healthier or denser vegetation (superior ecological environment) can more effectively intercept and store rainwater runoff. This suggests that while increasing the area of UGI, more attention should be paid to the biophysical parameter of vegetation. Hence, the mitigation effect of green infrastructure would be improved from the “size” and “health”. The interaction of composition and spatial configuration greatly enhances their individual effects on waterlogging. This result underscores the importance of the interactive enhancement effect between UGI composition and spatial configuration. Therefore, it is particularly important to optimize the UGI composition and spatial pattern under limited land resource conditions. Lastly, the effect of green infrastructure on waterlogging presents a threshold phenomenon. The excessive area proportions of UGI within the watershed unit or an oversized UGI patch may lead to a waste of its mitigation effect. Therefore, the area proportion of UGI and its mitigation effect should be considered comprehensively when planning UGI. It is recommended to control the proportion of green infrastructure at the watershed scale (24.4% and 72.1% for Guangzhou and Shenzhen) as well as the area of green infrastructure patches (1.9 ha and 2.8 ha for Guangzhou and Shenzhen) within the threshold level to maximize its mitigation effect. Given the growing concerns of global warming and continued rapid urbanization, these findings provide practical urban waterlogging prevention strategies toward practical implementations.
Inflammatory Skin Disorders: Monocyte-Derived Cells Take Center Stage

Current biologics targeting pro-inflammatory cytokines in psoriasis and atopic dermatitis (AD) show excellent clinical efficacy but are not curative, underscoring the need to study the skin immune and non-immune landscape. A recent article published in Science employed single-cell RNA sequencing (scRNA-seq) to generate an atlas of healthy human skin during early prenatal life and adulthood, and further dissect changes occurring in inflammatory skin diseases (1). Developing skin is enriched in innate lymphoid cells and macrophages (Mf) while healthy adult skin is predominantly populated with T cells, Langerhans cells (LCs), and dendritic cells (DCs). Specifically, adult skin Mf were subdivided into Mac1 and Mac2 based on distinct molecular signatures. In AD and psoriasis, the percentage of Mac2 is increased resulting in an altered Mac1/ Mac2 ratio in lesions. Fibroblasts predominate in fetal non-immune cell compartment while keratinocytes, melanocytes, and endothelial cells represent the major cell types in healthy adult skin. Among the three vascular endothelial (VE) cell subsets identified, only the proportion of VE3 is elevated in adult inflamed skin. Interestingly, Mac2 and VE3 in inflamed skin share a common molecular signature with Mf and VE in fetal skin. Overall, the authors conclude that skin developmental programs are recalled during inflammatory skin diseases and proposed Mf as potential targets of treatment. The concept of re-emergence of fetal skin program was recently reported for fetal Tregs that express an effector memory phenotype and closely align with adult Tregs in healthy skin (2). The authors further highlight the advantage of this molecular approach that identified 14 mononuclear phagocyte (MNP) cell states, and state that flow cytometry (FCM) has limitations to uncover diversity of rare cells. However, high dimensional single cell FCM is a complementary approach that has been proven suitable to reveal heterogeneity of skin MNP cell states in blood and skin (3, 4).
Fibromyalgia and Associated Disorders: From Pain to Chronic Suffering, From Subjective Hypersensitivity to Hypersensitivity Syndrome

The concept of fibromyalgia has progressed to achieve a certain consensus regarding the definition of the condition. We summarize what is known in 2020, be it in terms of diagnosis, with the criteria that have changed over the years, or at the level of the psychological profile, via the notions of “catastrophizing” and “coping” and post-traumatic syndrome. The importance of fatigue and sleep disorders is underlined, with the chronological sequence of post-traumatic syndrome, chronic fatigue, and then amplification of the pain and the onset of multiple associated symptoms. The etiopathogenic debate has been enriched thanks to neuro-imaging data to discover the start of the central neurological signature. The many associated symptoms are reanalyzed in the context of so-called sister conditions which form sometimes more or less separate entities, such as chronic fatigue syndrome or restless legs syndrome for example. What these conditions have in common is hypersensitivity, not just to pain, but also to all exteroceptive stimuli, from deep sensitivity in the neuro-vegetative system, the sense organs and certain functions of the central nervous system, to the psychological aspects and sleep control. In summary, it is possible to define fibromyalgia as a cognitive disorder of cortical integration of chronic pain, with amplification of painful and sensory nociception, decrease in the threshold for the perception of pain, and persistence of a stimulus that maintains the process in chronicity. Fibromyalgia is part of a group of chronic hypersensitivity syndromes of central origin, with a very wide range of means of expression.
Whole Genome Sequencing Reveals Signatures for Artificial Selection for Different Sizes in Japanese Primitive Dog Breeds

Body size is an important trait in companion animals. Recently, a primitive Japanese dog breed, the Shiba Inu, has experienced artificial selection for smaller body size, resulting in the “Mame Shiba Inu” breed. To identify loci and genes that might explain the difference in the body size of these Shiba Inu dogs, we applied whole genome sequencing of pooled samples (pool-seq) on both Shiba Inu and Mame Shiba Inu. We identified a total of 13,618,261 unique SNPs in the genomes of these two breeds of dog. Using selective sweep approaches, including FST, Hp and XP-CLR with sliding windows, we identified a total of 12 genomic windows that show signatures of selection that overlap with nine genes (PRDM16, ZNF382, ZNF461, ERGIC2, ENSCAFG00000033351, CCDC61, ALDH3A2, ENSCAFG00000011141, and ENSCAFG00000018533). These results provide candidate genes and specific sites that might be associated with body size in dogs. Some of these genes are associated with body size in other mammals, but 8 of the 9 genes are novel candidate genes that need further study.
MBFFNet: Multi-Branch Feature Fusion Network for Colonoscopy

Colonoscopy is currently one of the main methods for the detection of rectal polyps, rectal cancer, and other diseases. With the rapid development of computer vision, deep learning–based semantic segmentation methods can be applied to the detection of medical lesions. However, it is challenging for current methods to detect polyps with high accuracy and real-time performance. To solve this problem, we propose a multi-branch feature fusion network (MBFFNet), which is an accurate real-time segmentation method for detecting colonoscopy. First, we use UNet as the basis of our model architecture and adopt stepwise sampling with channel multiplication to integrate features, which decreases the number of flops caused by stacking channels in UNet. Second, to improve model accuracy, we extract features from multiple layers and resize feature maps to the same size in different ways, such as up-sampling and pooling, to supplement information lost in multiplication-based up-sampling. Based on mIOU and Dice loss with cross entropy (CE), we conduct experiments in both CPU and GPU environments to verify the effectiveness of our model. The experimental results show that our proposed MBFFNet is superior to the selected baselines in terms of accuracy, model size, and flops. mIOU, F score, and Dice loss with CE reached 0.8952, 0.9450, and 0.1602, respectively, which were better than those of UNet, UNet++, and other networks. Compared with UNet, the flop count decreased by 73.2%, and the number of participants also decreased. The actual segmentation effect of MBFFNet is only lower than that of PraNet, the number of parameters is 78.27% of that of PraNet, and the flop count is 0.23% that of PraNet. In addition, experiments on other types of medical tasks show that MBFFNet has good potential for general application in medical image segmentation.
Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

Objectives To differentiate Glioblastomas (GBM) and Brain Metastases (BM) using a radiomic features-based Machine Learning (ML) classifier trained from post-contrast three-dimensional T1-weighted (post-contrast 3DT1) MR imaging, and compare its performance in medical diagnosis versus human experts, on a testing cohort. Methods We enrolled 143 patients (71 GBM and 72 BM) in a retrospective bicentric study from January 2010 to May 2019 to train the classifier. Post-contrast 3DT1 MR images were performed on a 3-Tesla MR unit and 100 radiomic features were extracted. Selection and optimization of the Machine Learning (ML) classifier was performed using a nested cross-validation. Sensitivity, specificity, balanced accuracy, and area under the receiver operating characteristic curve (AUC) were calculated as performance metrics. The model final performance was cross-validated, then evaluated on a test set of 37 patients, and compared to human blind reading using a McNemar’s test. Results The ML classifier had a mean [95% confidence interval] sensitivity of 85% [77; 94], a specificity of 87% [78; 97], a balanced accuracy of 86% [80; 92], and an AUC of 92% [87; 97] with cross-validation. Sensitivity, specificity, balanced accuracy and AUC were equal to 75, 86, 80 and 85% on the test set. Sphericity 3D radiomic index highlighted the highest coefficient in the logistic regression model. There were no statistical significant differences observed between the performance of the classifier and the experts’ blinded examination. Conclusions The proposed diagnostic support system based on radiomic features extracted from post-contrast 3DT1 MR images helps in differentiating solitary BM from GBM with high diagnosis performance and generalizability.
Coronavirus, the King Who Wanted More Than a Crown: From Common to the Highly Pathogenic SARS-CoV-2, Is the Key in the Accessory Genes?

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that emerged in late 2019, is the etiologic agent of the current “coronavirus disease 2019” (COVID-19) pandemic, which has serious health implications and a significant global economic impact. Of the seven human coronaviruses, all of which have a zoonotic origin, the pandemic SARS-CoV-2, is the third emerging coronavirus, in the 21st century, highly pathogenic to the human population. Previous human coronavirus outbreaks (SARS-CoV-1 and MERS-CoV) have already provided several valuable information on some of the common molecular and cellular mechanisms of coronavirus infections as well as their origin. However, to meet the new challenge caused by the SARS-CoV-2, a detailed understanding of the biological specificities, as well as knowledge of the origin are crucial to provide information on viral pathogenicity, transmission and epidemiology, and to enable strategies for therapeutic interventions and drug discovery. Therefore, in this review, we summarize the current advances in SARS-CoV-2 knowledges, in light of pre-existing information of other recently emerging coronaviruses. We depict the specificity of the immune response of wild bats and discuss current knowledge of the genetic diversity of bat-hosted coronaviruses that promotes viral genome expansion (accessory gene acquisition). In addition, we describe the basic virology of coronaviruses with a special focus SARS-CoV-2. Finally, we highlight, in detail, the current knowledge of genes and accessory proteins which we postulate to be the major keys to promote virus adaptation to specific hosts (bat and human), to contribute to the suppression of immune responses, as well as to pathogenicity.
Identification and Confirmation of the miR-30 Family as a Potential Central Player in Tobacco-Related Head and Neck Squamous Cell Carcinoma

Constituents of tobacco that can cause DNA adduct formation and oxidative stress are implicated in the development of head and neck squamous cell carcinoma (HNSCC). However, there are few studies on the mechanism(s) that underlie tobacco-associated HNSCC. Here, we used a model in which tumors were induced in rats using 4-nitroquinoline 1-oxide (4NQO), which mimicked tobacco-related HNSCC, and analyzed the expression profiles of microRNAs and mRNAs. Our results indicated that 57 miRNAs and 474 mRNA/EST transcripts exhibited differential expression profiles between tumor and normal tongue tissues. In tumor tissue, the expression levels of rno-miR-30 family members (rno-miR-30a, rno-miR-30a-3p, rno-miR-30b-5p, rno-miR-30c, rno-miR-30d, rno-miR-30e and rno-miR-30e-3p) were only 8% to 37% of those in the control group. The GO terms enrichment analysis of the differentially expressed miRNAs indicated that oxidation reduction was the most enriched process. Low expression of miR-30 family members in human HNSCC cell lines and tissues was validated by qPCR. The results revealed that the expression of miR-30b-5p and miR-30e-5p was significantly decreased in the TCGA HNSCC dataset and validation datasets, and this decrease in expression further distinguishes HNSCC associated with tobacco use from other subtypes of HNSCC. CCK8, colony formation, transwell migration and HNSCC xenograft tumor assays indicated that miR-30b-5p or miR-30e-5p inhibited proliferation, migration and invasion in vitro, and miR-30b-5p suppressed tumor growth in vivo. Moreover, we uncovered that KRAS might be the potential target gene of miR-30e-5p or miR-30b-5p. Thus, our data clearly showed that decreased expression of miR-30e-5p or miR-30b-5p may play a crucial role in cancer development, especially that of tobacco-induced HNSCC, and may be a novel candidate biomarker and target for this HNSCC subtype.
Tanshinol Alleviates Microcirculation Disturbance and Impaired Bone Formation by Attenuating TXNIP Signaling in GIO Rats

Impaired bone formation is the main characteristics of glucocorticoid (GC)-induced osteoporosis (GIO), which can be ameliorated by tanshinol, an aqueous polyphenol isolated from Salvia miltiorrhiza Bunge. However, the underlying mechanism is still not entirely clear. In the present study, we determined the parameters related to microstructure and function of bone tissue, bone microcirculation, and TXNIP signaling to investigate the beneficial effects of tanshinol on skeleton and its molecular mechanism in GIO rats. Male Sprague-Dawley rats aged 4 months were administrated orally with distilled water (Con), tanshinol (Tan, 25 mg kg−1 d−1), prednisone (GC, 5 mg kg−1 d−1) and GC plus tanshinol (GC + Tan) for 14 weeks. The results demonstrated that tanshinol played a significant preventive role in bone loss, impaired microstructure, dysfunction of bone metabolism and poor bone quality, based on analysis of correlative parameters acquired from the measurement by using Micro-CT, histomorphometry, ELISA and biomechanical assay. Tanshinol also showed a significant protective effect in bone microcirculation according to the evidence of microvascular perfusion imaging of cancellous bone in GIO rats, as well as the migration ability of human endothelial cells (EA.hy926, EA cells). Moreover, tanshinol also attenuated GC-elicited the activation of TXNIP signaling pathway, and simultaneously reversed the down-regulation of Wnt and VEGF pathway as manifested by using Western-blot method in GIO rats, EA cells, and human osteoblast-like MG63 cells (MG cells). Collectively, our data highlighted that tanshinol ameliorated poor bone health mediated by activation of TXNIP signaling via inhibiting microcirculation disturbance and the following impaired bone formation in GIO rats.
Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.