Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptomic profiles reveal differences between the right and left ventricle in normoxia and hypoxia

Chronic hypoxia from diseases in the lung, such as pulmonary hypertension, pulmonary fibrosis, and chronic obstructive pulmonary disease, can increase pulmonary vascular resistance, resulting in hypertrophy and dysfunction of the right ventricle (RV). In order to obtain insight into RV biology and perhaps uncover potentially novel therapeutic approaches for RV dysfunction, we performed RNA‐sequencing (RNA‐seq) of RV and LV tissue from rats in normal ambient conditions or subjected to hypoxia (10% O2) for 2 weeks. Gene ontology and pathway analysis of the RV and LV revealed multiple transcriptomic differences, in particular increased expression in the RV of genes related to immune function in both normoxia and hypoxia. Immune cell profiling by flow cytometry of cardiac digests revealed that in both conditions, the RV had a larger percentage than the LV of double‐positive CD45+/CD11b/c+ cells (which are predominantly macrophages and dendritic cells). Analysis of gene expression changes under hypoxic conditions identified multiple pathways that may contribute to hypoxia‐induced changes in the RV, including increased expression of genes related to cell mitosis/proliferation and decreased expression of genes related to metabolic processes. Together, the findings indicate that the RV differs from the LV with respect to content of immune cells and expression of certain genes, thus suggesting the two ventricles differ in aspects of pathophysiology and in potential therapeutic targets for RV dysfunction.
Vascular Resections for Pancreatic Ductal Adenocarcinoma: Vascular Resections for PDAC

Background and Aims: It has become clear that vein resection and reconstruction for pancreatic ductal adenocarcinoma (PDAC) is the standard of care as supported by multiple guidelines. However, resection of large peri-pancreatic arteries remains debatable. Materials and Methods: This review examines the current state of vascular resection with curative intent for PDAC in the last 5 years. Herein, we consider venous (superior mesenteric vein, portal vein), as well as arterial (superior mesenteric artery, celiac trunk, hepatic artery) resection or both with or without reconstruction. Results: Improvement of multidrug chemotherapy has revolutionized care for PDAC that should shift traditional surgical thinking from an anatomical classification of resectability to a prognostic and biological classification. Conclusion: The present review gives an overview on the results of pancreatectomy associated with vascular resection, with consideration of new perspectives offered by the availability of better systemic therapies.
Nutrient addition increases grassland sensitivity to droughts.

Grasslands worldwide are expected to experience an increase in extreme events such as drought, along with simultaneous increases in mineral nutrient inputs as a result of human industrial activities. These changes are likely to interact because elevated nutrient inputs may alter plant diversity and increase the sensitivity to droughts. Dividing a system's sensitivity to drought into resistance to change during the drought and rate of recovery after the drought generates insights into different dimensions of the system's resilience in the face of drought. Here, we examine the effects of experimental nutrient fertilization and the resulting diversity loss on the resistance to and recovery from severe regional droughts. We do this at 13 North American sites spanning gradients of aridity, 5 annual grasslands in California and 8 perennial grasslands in the Great Plains. We measured rate of resistance as the change in annual aboveground biomass (ANPP) per unit change in growing season precipitation as conditions declined from normal to drought. We measured recovery as the change in ANPP during the post drought period and the return to normal precipitation. Resistance and recovery did not vary across the 400 mm range of mean growing season precipitation spanned by our sites in the Great Plains. However, chronic nutrient fertilization in the Great Plains reduced drought resistance and increased drought recovery. In the California annual grasslands, arid sites had a greater recovery post-drought than mesic sites, and nutrient addition had no consistent effects on resistance or recovery. Across all study sites, we found that pre-drought species richness in natural grasslands was not consistently associated with rates of resistance to or recovery from the drought, in contrast to earlier findings from experimentally assembled grassland communities. Taken together, these results suggest that human-induced eutrophication may destabilize grassland primary production, but the effects of this may vary across regions and flora, especially between perennial and annual-dominated grasslands.
Natural medicinal resources and their therapeutic applications

Natural medicinal resources are a country’s natural wealth. Natural medicinal waters, medicinal gases, and peloids have many properties that enable their use in the treatment of gastrointestinal, circulatory, respiratory, bone and joint, and skin and soft tissue disorders. Balneotherapy can be also applicable in prevention of many diseases and rehabilitation. At present, because there are several chemicals of synthetic origin, there is a need to search for nonpharmacological approaches and explore natural healing sources, which better fit the human body. Compared to synthetic drugs, these resources rarely show side effects, which increases the comfort of therapy. The use of natural medicinal resources in the form of treatments in health resort medicine centers under the supervision of balneologists, combined with the healing properties of the climate, contributes not only to the reduction of treatment time for many diseases but also to improvement of therapy’s results. The article discusses natural medicinal resources and some of their therapeutic applications.
Three Decades of Coastal Changes in Sindh, Pakistan (1989-2018): A Geospatial Assessment

Coastal erosion endangers millions living near-shore and puts coastal infrastructure at risk, particularly in low-lying deltaic coasts of developing nations. This study focuses on morphological changes along the ~320-km-long Sindh coastline of Pakistan over past three decades. In this study, the Landsat images from 1989 to 2018 at an interval of 10 years are used to analyze the state of coastline erosion. For this purpose, well-known statistical approaches such as end point rate (EPR), least median of squares (LMS), and linear regression rate (LRR) are used to calculate the rates of coastline change. We analyze the erosion trend along with the underlying controlling variables of coastal change. Results show that most areas along the coastline have experienced noteworthy erosion during the study period. It is found that Karachi coastline experienced 2.43 ± 0.45 m/yr of erosion and 8.34 ± 0.45 m/yr of accretion, while erosion on the western and eastern sides of Indus River reached 12.5 ± 0.55 and 19.96 ± 0.65 m/yr on average, respectively. Coastal erosion is widespread along the entire coastline. However, the rate of erosion varies across the study area with a general trend of erosion increasing from west to east in the Indus Delta region (IDR), and the highest average erosion rate is 27.46 m/yr. The interdecadal change during 1989–1999, 1999–2009 and 2009–2018 periods depicted an increasing linear trend (R2 = 0.78) from Karachi to Indus River (IR) East zone. The spatial trend from west to east is positively correlated with mean sea level rise, which has increased from 1.1 to 1.9 mm/year, and negatively correlated with topographic slope, which is found to be decreasing eastward along the coastline. The findings necessitate appropriate actions and have important implications to better manage coastal areas in Pakistan in the wake of global climate change.
Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.
Ontology Enrichment for Aspect-Oriented Sentiment Analysis with Deep Learning Using Logical Concept Analysis

: Aspect - oriented sentiment analysis is a problem that have been attracting much attention from the research community. In the past, using ontologies to represent domain knowledge helped increase the performance of this task. Recently, with the rapid development of deep learning techniques, combination of deep learning with ontology has become very promising. A natural approach is to use aspects represented by ontologies to form input vectors s for deep learning models. However, ontologies often only represent concrete concepts, whereas machine learning techniques often focus on latent features. To address this, we propose using Logical Concept Analysis (LCA), an extension of Formal Concept Analysis (FCA), to enrich the ontology by generating more abstract concepts from existing concrete concepts. We have applied this approach to real data and achieved promising results.
Parameter Optimization for Computer Numerical Controlled Machining Using Fuzzy and Game Theory

Under the strict restrictions of international environmental regulations, how to reduce environmental hazards at the production stage has become an important issue in the practice of automated production. The precision computerized numerical-controlled (CNC) cutting process was chosen as an example of this, while tool wear and cutting noise were chosen as the research objectives of CNC cutting quality. The effects of quality optimizing were verified using the depth of cut, cutting speed, feed rate, and tool nose runoff as control parameters and actual cutting on a CNC lathe was performed. Further, the relationships between Fuzzy theory and control parameters as well as quality objectives were used to define semantic rules to perform fuzzy quantification. The quantified output value was introduced into game theory to carry out the multi-quality bargaining game. Through the statistics of strategic probability, the strategy with the highest total probability was selected to obtain the optimum plan of multi-quality and multi-strategy. Under the multi-quality optimum parameter combination, the tool wear and cutting noise, compared to the parameter combination recommended by the cutting manual, was reduced by 23% and 1%, respectively. This research can indeed ameliorate the multi-quality cutting problem. The results of the research provided the technicians with a set of all-purpose economic prospective parameter analysis methods in the manufacturing process to enhance the international competitiveness of the automated CNC industry.
Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.
Detection of Earthquake-Induced Building Damages Using Polarimetric SAR Data

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.
Bayesian Harmonic Modelling of Sparse and Irregular Satellite Remote Sensing Time Series of Vegetation Indexes: A Story of Clouds and Fires

Vegetation index time series from Landsat and Sentinel-2 have great potential for following the dynamics of ecosystems and are the key to develop essential variables in the realm of biodiversity. Unfortunately, the removal of pixels covered mainly by clouds reduces the temporal resolution, producing irregularity in time series of satellite images. We propose a Bayesian approach based on a harmonic model, fitted on an annual base. To deal with data sparsity, we introduce hierarchical prior distribution that integrate information across the years. From the model, the mean and standard deviation of yearly Ecosystem Functional Attributes (i.e., mean, standard deviation, and peak’s day) plus the inter-year standard deviation are calculated. Accuracy is evaluated with a simulation that uses real cloud patterns found in the Peneda-Gêres National Park, Portugal. Sensitivity to the model’s abrupt change is evaluated against a record of multiple forest fires in the Bosco Difesa Grande Regional Park in Italy and in comparison with the BFAST software output. We evaluated the sensitivity in dealing with mixed patch of land cover by comparing yearly statistics from Landsat at 30m resolution, with a 2m resolution land cover of Murgia Alta National Park (Italy) using FAO Land Cover Classification System 2.
SAR Target Classification Based on Deep Forest Model

Synthetic aperture radar (SAR) has become one of the most important means of information acquisition in today’s society and shows great potential in many fields. Target identification and classification of SAR images are also the focus of research. With the vigorous development of deep learning, many researchers apply this method to SAR target classification to obtain a more automatic process and more accurate results. In this paper, a novel deep forest model constructed by multi-grained cascade forest (gcForest), which is different from the traditional neural network (NN) model, is employed to classify ten types of SAR targets in the moving and stationary target acquisition and recognition (MSTAR) dataset. Considering that the targets of input images may be off-center and of different sizes in practical applications, two improved models based on varying weights by image features have been put forward, and both obtain better results. A series of experiments have been conducted to optimize model parameters, and final results with the MSTAR dataset illustrate that the two improved models are both superior to the original gcForest model. This is the first attempt to classify SAR targets using the non-NN model.
Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.
Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.
Antarctic Supraglacial Lake Detection Using Landsat 8 and Sentinel-2 Imagery: Towards Continental Generation of Lake Volumes

Melt and supraglacial lakes are precursors to ice shelf collapse and subsequent accelerated ice sheet mass loss. We used data from the Landsat 8 and Sentinel-2 satellites to develop a threshold-based method for detection of lakes found on the Antarctic ice shelves, calculate their depths and thus their volumes. To achieve this, we focus on four key areas: the Amery, Roi Baudouin, Nivlisen, and Riiser-Larsen ice shelves, which are all characterized by extensive surface meltwater features. To validate our products, we compare our results against those obtained by an independent method based on a supervised classification scheme (e.g., Random Forest algorithm). Additional verification is provided by manual inspection of results for nearly 1000 Landsat 8 and Sentinel-2 images. Our dual-sensor approach will enable constructing high-resolution time series of lake volumes. Therefore, to ensure interoperability between the two datasets, we evaluate depths from contemporaneous Landsat 8 and Sentinel-2 image pairs. Our assessments point to a high degree of correspondence, producing an average R2 value of 0.85, no bias, and an average RMSE of 0.2 m. We demonstrate our method’s ability to characterize lake evolution by presenting first evidence of drainage events outside of the Antarctic Peninsula on the Amery Ice shelf. The methods presented here pave the way to upscaling throughout the Landsat 8 and Sentinel-2 observational record across Antarctica to produce a first-ever continental dataset of supraglacial lake volumes. Such a dataset will improve our understanding of the influence of surface hydrology on ice shelf stability, and thus, future projections of Antarctica’s contribution to sea level rise.