Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.
Mixed Neuroendocrine Carcinoma and Hepatocellular Carcinoma: A Case Report and Literature Review

Background: Neuroendocrine tumors are heterogeneous malignancies that originate from the neuroendocrine system. Previous studies show that this cancer type mainly localizes in the gastrointestinal tract and often metastasizes to the liver. Primary liver neuroendocrine tumors are very rare and primary hepatic neuroendocrine tumors (PHNET) with concurrent hepatocellular carcinoma (HCC) are extremely rare. To the best of our knowledge, only few PHNET cases have been identified, making their diagnosis difficult. Here, we report the biggest ever reported and “deceiving” lesion of a mixed neuroendocrine-non-neuroendocrine neoplasm in the liver, aiming to raise awareness and improve treatment of the disease. Case Presentation: Here, we report a preoperative misdiagnosed case that presented with hepatocellular carcinoma clinical features and no extrahepatic tumors. Postoperative pathology confirmed that it was a mixed neuroendocrine-non-neuroendocrine neoplasm. The patient was then referred for etoposide and cisplatin-based chemotherapy. No disease recurrence was observed at the 6-month follow-up. Conclusion: We report a very rare and easily misdiagnosed case and we speculate that there were “undifferentiated cells” undergoing neuroendocrine and hepatocellular carcinoma differentiation, during which some hepatocellular carcinoma cells express neuroendocrine features. We recommend proper surgery and postoperative platinum-based chemotherapy in the management of this disease.
Amadis: A Comprehensive Database for Association Between Microbiota and Disease

The human gastrointestinal tract represents a symbiotic bioreactor that can mediate the interaction of the human host. The deployment and integration of multi-omics technologies have depicted a more complete image of the functions performed by microbial organisms. In addition, a large amount of data has been generated in a short time. However, researchers struggling to keep track of these mountains of information need a way to conveniently gain a comprehensive understanding of the relationship between microbiota and human diseases. To tackle this issue, we developed Amadis (http://gift2disease.net/GIFTED), a manually curated database that provides experimentally supported microbiota-disease associations and a dynamic network construction method. The current version of the Amadis database documents 20167 associations between 221 human diseases and 774 gut microbes across 17 species, curated from more than 1000 articles. By using the curated data, users can freely select and combine modules to obtain a specific microbe-based human disease network. Additionally, Amadis provides a user-friendly interface for browsing, searching and downloading. We hope it can serve as a useful and valuable resource for researchers exploring the associations between gastrointestinal microbiota and human diseases.
Plandemic Revisited: A Product of Planned Disinformation Amplifying the COVID-19 “infodemic”

During the first few months of the COVID-19 pandemic, a 26-min documentary entitled Plandemic was released online and fanatically shared via Twitter and other major social media platforms. The producers of the documentary sought to spread misinformation and conspiracy theories and to discredit scientific experts using a sophisticated disinformation campaign. They set out to accomplish this by coaching citizens toward activism to maximize the speed at which the documentary propagated and decrease positive sentiments toward public health interventions. Applying techniques from social network analysis in conjunction with a formative content analysis of Twitter data, we examined the effectiveness of the Plandemic disinformation campaign as a case study of social engineering during the COVID-19 pandemic. By comparing the Twitter network's community structure and communication patterns before and after the release of the film, we demonstrate the Plandemic campaign to have been effective for two reasons. First, the campaign established a decentralized information sharing network on Twitter by coaching low-reach social media users to mass share the documentary, effectively subverting efforts to gatekeep its misinformation. Second, the campaign amplified negative sentiments regarding vaccination and containment measures among conspiracy theorists. These effects possibly have an indirect impact on the public's willingness to comply with public health measures. Our results show the necessity of further research about sophisticated social experiments such as the Plandemic disinformation campaign and provide important insights for policy-making to combat the spread of health misinformation during public health crises.
Differences in Facial Expression Recognition Between Unipolar and Bipolar Depression

Purpose To explore the differences in facial emotion recognition among patients with unipolar depression (UD), bipolar depression (BD), and normal controls. Methods Thirty patients with UD and 30 patients with BD, respectively, were recruited in Zhumadian Second People’s Hospital from July 2018 to August 2019. Fifteen groups of facial expressions including happiness, sadness, anger, surprise, fear, and disgust were identified. Results A single-factor ANOVA was used to analyze the facial expression recognition results of the three groups, and the differences were found in the happy-sad (P = 0.009), happy-angry (P = 0.001), happy-surprised (P = 0.034), and disgust-surprised (P = 0.038) facial expression groups. The independent sample T-test analysis showed that compared with the normal control group, there were differences in the happy-sad (P = 0.009) and happy-angry (P = 0.009) groups in patients with BD, and the accuracy of facial expression recognition was lower than the normal control group. Compared with patients with UD, there were differences between the happy-sad (P = 0.005) and happy-angry (P = 0.002) groups, and the identification accuracy of patients with UD was higher than that of patients with BD. The time of facial expression recognition in the normal control group was shorter than that in the patient group. Using happiness-sadness to distinguish unipolar and BDs, the area under the ROC curve (AUC) is 0.933, the specificity is 0.889, and the sensitivity is 0.667. Using happiness-anger to distinguish unipolar and BD, the AUC was 0.733, the specificity was 0.778, and the sensitivity was 0.600. Conclusion Patients with UD had lower performance in recognizing negative expressions and had longer recognition times. Those with BD had lower accuracy in recognizing positive expressions and longer recognition times. Rapid facial expression recognition performance may be as a potential endophenotype for early identification of unipolar and BD.
Differential Expression of microRNAs Correlates With the Severity of Experimental Autoimmune Cystitis

Interstitial cystitis (IC)/bladder pain syndrome (BPS) primarily affects women. It varies in its severity and currently has no effective treatment. The symptoms of IC include pelvic pain, urgency and frequency of urination, and discomfort or pain in the bladder and lower abdomen. The bladders of IC patients exhibit infiltration by immune cells, which lends credence to the hypothesis that immune mechanisms also play a role in the etiology and pathophysiology of IC. The Differentially expressed microRNAs (miRs) in immune cells may serve as crucial immunoregulators in the IC. Therefore, we sought to determine whether miRs might play a regulatory role in the progression and pathogenesis of IC, using experimental autoimmune cystitis (EAC) model. In the present study, we observed differential expression of a specific subset of miRs in iliac lymph nodes (ILNs) and urinary bladders (UB) of IC mice compared to that in control mice. Microarray analysis of 96 miRs from the bladder and 135 miRs from ILNs allowed us to identify 50 that exhibited at least a 1.5-fold greater difference in expression in EAC mice compared to control mice. Hierarchical cluster analysis of the microarray data was used to search available databases to predict molecular pathways with which the miRs might interact. Four miRs from each organ that exhibited altered expression in EAC mice and that were predicted to have roles in inflammation (miR-146a, -181, -1931, and -5112) were selected for further analysis by reverse transcription-polymerase chain reaction (RT-PCR). All were confirmed to be elevated in EAC mice. Histological inflammatory scores, systemic chemokines, and cytokines expressed by T helper type 1 (Th1) lymphocytes were also elevated in EAC mice as compared to control animals. We hypothesize that the mechanism of EAC induction might involve the modulation of specific miRs that increase local and systemic levels of chemokines and cytokines. The present study identifies novel miRs expressed in UB and ILNs that will allow us to highlight mechanisms of EAC pathogenesis and may provide potential biomarkers and/or serve as the basis of new therapies for the treatment of IC.
Evolution in the Practice of Pediatric Endoscopy and Sedation

The fields of pediatric gastrointestinal endoscopy and sedation are critically important to the diagnosis and treatment of gastrointestinal (GI) disease in children. Since its inception in the 1970s, pediatric endoscopy has benefitted from tremendous technological innovation related to the design of the endoscope and its associated equipment. Not only that, but expertise among pediatric gastroenterologists has moved the field forward to include a full complement of diagnostic and therapeutic endoscopic procedures in children. In this review, we discuss the remarkable history of pediatric endoscopy and highlight current limitations and future advances in the practice and technology of pediatric endoscopy and sedation.
Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion

The presence and thickness of snow overlying lake ice affects both the timing of melt and ice-free conditions, can contribute to overall ice thickness through its insulative capacity, and fosters the development of variable ice types. The use of UAVs to retrieve snow depths with high spatial resolution is necessary for the next generation of ultra-fine hydrological models, as the direct contribution of water from snow on lake ice is unknown. Such information is critical to the understanding of the physical processes of snow redistribution and capture in catchments on small lakes in the Arctic, which has been historically estimated from its relationship to terrestrial snowpack properties. In this study, we use a quad-copter UAV and SfM principles to retrieve and map snow depth at the winter maximum at high resolution over a the freshwater West Twin Lake on the Arctic Coastal Plain of northern Alaska. The accuracy of the snow depth retrievals is assessed using in-situ observations (n = 1,044), applying corrections to account for the freeboard of floating ice. The average snow depth from in-situ observations was used calculate a correction factor based on the freeboard of the ice to retrieve snow depth from UAV acquisitions (RMSE = 0.06 and 0.07 m for two transects on the lake. The retrieved snow depth map exhibits drift structures that have height deviations with a root mean square (RMS) of 0.08 m (correlation length = 13.8 m) for a transect on the west side of the lake, and an RMS of 0.07 m (correlation length = 18.7 m) on the east. Snow drifts present on the lake also correspond to previous investigations regarding the variability of snow on lakes, with a periodicity (separation) of 20 and 16 m for the west and east side of the lake, respectively. This study represents the first retrieval of snow depth on a frozen lake surface from a UAV using photogrammetry, and promotes the potential for high-resolution snow depth retrieval on small ponds and lakes that comprise a significant portion of landcover in Arctic environments.
Cortical Thickness and Hippocampal Volume in Vascular and Non-vascular Depressed Patients

Background: Reduced cortical thickness and hippocampal volume are prevalent markers of late life depression as well as mild cognitive impairment (MCI) but are conspicuously absent in the vascular depression (VD) literature. The present study aimed to determine differences in cortical thickness and hippocampal volume between VD and non-VD patients. Methods: Participants were enrolled in an 8-week open treatment antidepressant trial. Forty-one depressed individuals aged 50 and older underwent brain magnetic resonance imaging at baseline and were classified as VD or non-VD. Cortical thickness values for the left and right entorhinal, parahippocampal, and precuneal cortices, as well as left and right hippocampal volume, were linearly regressed on VD status to determine mean differences between VD and non-VD. Covariates included site, age, sex, and mean thickness or intracranial volume. Results: No statistical differences were found between VD and non-VD patients in cortical thickness of the bilateral precuneal, entorhinal, or parahippocampal cortices, or hippocampal volume (p > 0.001). Conclusions: The absence of statistical differences in gray matter between VD and non-VD patients raises several diagnostic, etiological, and developmental possibilities, namely that VD may not be connected with other late-life psychiatric illnesses such as MCI or dementia and that vascular disease may not be a common etiological risk factor for depression and dementia. Larger datasets, prospective longitudinal studies, and cognitively intact controls are needed to further address these types of questions.
How to Assess the Moment Tensor Inversion Resolution for Mining Induced Seismicity: A Case Study for the Rudna Mine, Poland

Underground exploitation of georesources can be highly correlated with induced seismic activity. In order to reduce the risk and improve the mining operations safety, the mining activity is monitored by a dedicated seismic network. Moment tensor inversion is a powerful method to investigate the rupture process of earthquakes in mines, providing information on the geometry of the earthquake source and the moment release. Different approaches have been proposed to estimate the source mechanisms, with some advantages and limitations. One of the simplest and most used methods rely on the fit of the polarity and amplitude of first P wave onsets. More advanced techniques fit the full waveforms and their spectra. Here, we test and compare moment tensor and focal mechanism estimations for both inversion techniques. In order to assess the inversion resolution, we built realistic synthetic data, accounting for real seismic noise conditions and network geometry for the Rudna copper mine, SW Poland. The Rudna mine pertains to the Legnica-Glógow Copper District, where thousands of mining induced earthquakes are detected yearly, representing a serious hazard for miners and mining infrastructures. We simulate a range of different processes and locations, considering pure double couple, deviatoric and full moment tensors with different magnitudes and located in different mining panels. Results show that the P-wave first onset inversion is very sensitive to the geometry of the seismic network, which is limited by the existing underground infrastructure. On the other hand, the quality of the moment tensor solutions for the full waveform inversion is mainly determined by the strength of mining tremor and the signal-to-noize ratio. We discuss the performance of both inversion techniques and provide recommendations toward a reliable moment tensor analysis in mines.
BML: An Efficient and Versatile Tool for BGP Dataset Collection

The Border Gateway Protocol (BGP) is in charge of the route exchange at the Internet scale. Anomalies in BGP’s behaviour can have several causes (e.g. mis-configuration, outage and attacks) and despite being rare, their consequences can threaten the Internet stability and reliability. The study of such anomalies requires the extraction of specific features and internet topology from BGP data. The literature shows that adhoc procedures and tools have been developed to extract specific features to train machine learning models for anomaly detection. In this paper we propose BML, a BGP dataset generation tool that extracts the majority of known features in the literature, the internet topology and that allows the user to build specific features from BGP data. We illustrate the use of BML on a BGP anomaly by extracting 32 synthetic features and 14 BGP’s graphs features which allow a comprehensive understanding of the Border Gateway Protocol.
Mapping Urban Green Spaces at the Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic Segmentation

Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers.
Gene Segment Interactions Can Drive the Emergence of Dominant Yet Suboptimal Gene Constellations During Influenza Virus Reassortment

A segmented genome enables influenza virus to undergo reassortment when two viruses infect the same cell. Although reassortment is involved in the creation of pandemic influenza strains and is routinely used to produce influenza vaccines, our understanding of the factors that drive the emergence of dominant gene constellations during this process is incomplete. Recently, we defined a spectrum of interactions between the gene segments of the A/Udorn/307/72 (H3N2) (Udorn) strain that occur within virus particles, a major interaction being between the NA and PB1 gene segments. In addition, we showed that the Udorn PB1 is preferentially incorporated into reassortant viruses that express the Udorn NA. Here we use an influenza vaccine seed production model where eggs are coinfected with Udorn and the high yielding A/Puerto Rico/8/34 (H1N1) (PR8) virus and track viral genotypes through the reassortment process under antibody selective pressure to determine the impact of Udorn NA-PB1 co-selection. We discovered that 86% of the reassortants contained the PB1 from the Udorn parent after the initial co-infection and this bias towards Udorn PB1 was maintained after two further passages. Included in these were certain gene constellations containing Udorn HA, NA, and PB1 that confered low replicative fitness yet rapidly became dominant at the expense of more fit progeny, even when co-infection ratios of the two viruses favoured PR8. Fitness was not compromised, however, in the corresponding reassortants that also contained Udorn NP. Of particular note is the observation that relatively unfit reassortants could still fulfil the role of vaccine seed candidates as they provided high haemagglutinin (HA) antigen yields through co-production of non-infectious particles and/or by more HA molecules per virion. Our data illustrate the dynamics and complexity of reassortment and highlight how major gene segment interactions formed during packaging, in addition to antibody pressure, initially restrict the reassortant viruses that are formed.