Miracle-dz/UniFAR-FTUs huggingface dataset:
Addressing Therapeutic Inertia in 2020 and Beyond: A 3-Year Initiative of the American Diabetes Association

Research has shown that getting to glycemic targets early on leads to better outcomes in people with type 2 diabetes; yet, there has been no improvement in the attainment of A1C targets in the past decade. One reason is therapeutic inertia: the lack of timely adjustment to the treatment regimen when a person’s therapeutic targets are not met. This article describes the scope and priorities of the American Diabetes Association’s 3-year Overcoming Therapeutic Inertia Initiative. Its planned activities include publishing a systematic review and meta-analysis of approaches to reducing therapeutic inertia, developing a registry of effective strategies, launching clinician awareness and education campaigns, leveraging electronic health record and clinical decision-support tools, influencing payer policies, and potentially executing pragmatic research to test promising interventions.
Race Disparities in the Use of Prevention, Screening, and Monitoring Services in Michigan Medicare Beneficiaries With Type 2 Diabetes and Combinations of Multiple Chronic Conditions

People with diabetes need routine health care to prevent potential exacerbations of diabetes and detect or prevent the development of additional chronic conditions that can worsen the course of diabetes. Using 2012 Medicare claims data from the State of Michigan for 443,932 beneficiaries with type 2 diabetes, we determined that there are differences between white and racial/ethnic minority people with diabetes in accessing any preventive care and in the amount of service used once they do access care, even after adjusting for the presence of multiple chronic conditions.
Genomic Assemblies of Members of Burkholderia and Related Genera as a Resource for Natural Product Discovery

The genomes of 450 members of Burkholderiaceae, isolated from clinical and environmental sources, were sequenced and assembled as a resource for genome mining. Genomic analysis of the collection has enabled the identification of multiple metabolites and their biosynthetic gene clusters, including the antibiotics gladiolin, icosalide A, enacyloxin, and cepacin A. ABSTRACT The genomes of 450 members of Burkholderiaceae, isolated from clinical and environmental sources, were sequenced and assembled as a resource for genome mining. Genomic analysis of the collection has enabled the identification of multiple metabolites and their biosynthetic gene clusters, including the antibiotics gladiolin, icosalide A, enacyloxin, and cepacin A.
Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.
Complete Genome Sequences of Streptococcus pyogenes Serotype M3, M28, and M89 Strains Isolated from Human Patients in Japan, 1994 to 2009

Streptococcus pyogenes (group A Streptococcus [GAS]) is a major human pathogen that occasionally causes severe and life-threatening invasive diseases. Here, we report the complete genome sequences of four GAS strains of three M types, which were isolated from patients with severe invasive disease in Japan. ABSTRACT Streptococcus pyogenes (group A Streptococcus [GAS]) is a major human pathogen that occasionally causes severe and life-threatening invasive diseases. Here, we report the complete genome sequences of four GAS strains of three M types, which were isolated from patients with severe invasive disease in Japan.
A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas

Water detection from Synthetic Aperture Radar (SAR) images has been widely utilized in various applications. However, it remains an open challenge due to the high similarity between water and shadow in SAR images. To address this challenge, a new end-to-end framework based on deep learning has been proposed to automatically classify water and shadow areas in SAR images. This end-to-end framework is mainly composed of three parts, namely, Multi-scale Spatial Feature (MSF) extraction, Multi-Level Selective Attention Network (MLSAN) and the Improvement Strategy (IS). Firstly, the dataset is input to MSF for multi-scale low-level feature extraction via three different methods. Then, these low-level features are fed into the MLSAN network, which contains the Encoder and Decoder. The Encoder aims to generate different levels of features using residual network of 101 layers. The Decoder extracts geospatial contextual information and fuses the multi-level features to generate high-level features that are further optimized by the IS. Finally, the classification is implemented with the Softmax function. We name the proposed framework as MSF-MLSAN, which is trained and tested using millimeter wave SAR datasets. The classification accuracy reaches 0.8382 and 0.9278 for water and shadow, respectively; while the overall Intersection over Union (IoU) is 0.9076. MSF-MLSAN demonstrates the success of integrating SAR domain knowledge and state-of-the-art deep learning techniques.
Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan

Rainfall prediction is an important task due to the dependence of many people on it, especially in the agriculture sector. Prediction is difficult and even more complex due to the dynamic nature of rainfalls. In this study, we carry out monthly rainfall prediction over Simtokha a region in the capital of Bhutan, Thimphu. The rainfall data were obtained from the National Center of Hydrology and Meteorology Department (NCHM) of Bhutan. We study the predictive capability with Linear Regression, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Long Short Term Memory (BLSTM) based on the parameters recorded by the automatic weather station in the region. Furthermore, this paper proposes a BLSTM-GRU based model which outperforms the existing machine and deep learning models. From the six different existing models under study, LSTM recorded the best Mean Square Error (MSE) score of 0.0128. The proposed BLSTM-GRU model outperformed LSTM by 41.1% with a MSE score of 0.0075. Experimental results are encouraging and suggest that the proposed model can achieve lower MSE in rainfall prediction systems.
Adaptive baseline fitting for 

 1H MR spectroscopy analysis

Accurate baseline modeling is essential for reliable MRS analysis and interpretation—particularly at short echo‐times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study, a new method is presented to estimate its optimal value.
Integrating Landsat Time Series Observations and Corona Images to Characterize Forest Change Patterns in a Mining Region of Nanjing, Eastern China from 1967 to 2019

Long-term surface mining and subsequent vegetation recovery greatly alter land cover types, reshape landscape patterns and impose several impacts on local ecosystem services. However, studies on the history of forest changes in mining areas from the 1960s to the present have not been reported. This study developed a new idea to investigate the spatial and temporal dynamics of forest cover in a mining area of Mufu Mountain (Mt. Mufu) from 1967 to 2019 by integrating Landsat and Corona data, and to explore the relationships among the forest changes, landscape structures and ecosystem functions. Firstly, we applied the vegetation change tracker (VCT) algorithm and visual interpretation to create annual forest change datasets. Subsequently, the forest loss process was divided into subdivision, shrinkage, perforation and attrition components. An improved forest restoration model in this study extended the recovery process to bridge, branch, infilling and increment components. Finally, remote sensing variables and crown density were coupled to assess the forest aboveground biomass (AGB) to reflect the ecosystem function in the restoration area. Results showed that the combined use of Corona and the dense time series of Landsat can provide more detailed information on forest changes. Forest cover sharply decreased from 343.89 in 1967 to 298.44 ha in 1990, and after 2003, the forest area substantially increased and finally reached a maximum of 434.16 ha in 2019. Subdivision and bridge not only occupied the larger areas in the process of forest loss and restoration, but also they had strong correlations with forest changes and the Pearson correlation coefficients (r) were respectively 0.96 and 0.91. These all revealed that forest changes mainly affected landscape structure connectivity. The total forest AGB of Mt. Mufu increased from 20,173.35 in 2006 to 31,035.77 t in 2017, but the increases in AGB were only 30-40 t/ha in most recovery areas with high structure connectivity (bridge regions), indicating there is room for improving restoration projects in the future. The obtained findings can provide mining site restoration managers with clear, long-term forest change information and mine restoration assessment methods.
Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint

Blind source separation (or unmixing) methods process a set of mixed signals, which are typically linear memoryless combinations of source signals, so as to estimate these unknown source signals and/or combination coefficients. These methods have been extensively applied to hyperspectral images in the field of remote sensing, because the reflectance spectrum of each image pixel is often a mixture of elementary contributions, due to the limited spatial resolution of hyperspectral remote sensing sensors. Each spatial source signal then corresponds to a pure material, and its value in each pixel is equal to the “abundance fraction” of the corresponding Earth surface covered by that pure material. The mixing coefficients then form the pure material spectra. Various unmixing methods have been designed for this data model and the majority of them are either geometrical or statistical, or even based on sparse regressions. Various such unmixing techniques mainly consider assumptions that are related to the presence or absence of pure pixels (i.e., pixels which contain only one pure material). The case when, for each pure material, the image includes at least one pixel or zone which only contains that material yielded attractive unmixing methods, but corresponds to a stringent sparsity condition. We here aim at relaxing that condition, by only requesting a few tiny pixel zones to contain two pure materials. The proposed linear and geometrical sparse-based, blind (or unsupervised) unmixing method first automatically detects these zones. Each such zone defines a line in the data representation space. These lines are then estimated and clustered. The pairs of cluster centers, corresponding to lines, which have an intersection, yield the spectra of pure materials, forming the columns of the mixing matrix. Finally, the proposed method derives all abundance fractions, i.e., source signals, by using a least squares method with a non-negativity constraint. This method is applied to realistic synthetic images and is shown to outperform various methods from the literature. Indeed, and for the conducted experiments, when considering the pure material spectra extraction, the obtained improvements, for the considered spectral angle mapper performance criterion, vary between 0.02∘ and 12.43∘. For the abundance fractions estimation, the proposed technique is able to achieve a normalized mean square error lower than 0.01%, while the tested literature methods yield errors greater than 0.1%.
Automatic question generation

Automatic generation of semantically well‐formed questions from a given text can contribute to various domains, including education, dialogues/interactive question answering systems, search engines, and more. It is well‐known as a challenging task, which involves the common obstacles of other natural language processing (NLP) activities. We start this advanced review with a brief overview of the most common automatic question generation (AQG) applications. Then we describe the main steps of a typical AQG pipeline, namely question construction, ranking, and evaluation. Finally, we discuss the open challenges of the AQG field that still need to be addressed by NLP researchers.
Bacterial Motility

Motility is universally recognized as a significant biological character of certain bacteria, and is used as a fundamental basis of classification in many taxonomic systems. Significance is often attached to motility as an identifying character in the differentiation of species, and independent species have sometimes been established on the basis of lack or possession of motility. Relatively little attention seems to have been paid to variability in motility and the factors that influence it. Discordant statements and opinions about the presence or absence of motility in different species are, however, not difficult to find. Although the overwhelming weight of opinion today is that dysentery bacilli are non-motile (Lentz and Prigge, 1931; Gardner, 1929), both Shiga and Flexner originally described these organisms as motile and several experienced observers have since reported that motility either occurs spontaneously in certain strains or may be induced by the application of special methods.2 As regards other groups of bacteria, such as the so-called Morgan bacilli, quite divergent statements as to the presence or absence of motility are made. It is well known also that non-motile strains of predominantly motile species sometimes occur; Smith and Reagh's classical observations (1903) on the distinction between somatic and flagellar agglutinins were based on the discovery of a strain of the hog-cholera bacillus which was non-motile but in all other respects appeared to be identical with the motile type. The effect of environmental factors on motility seems to have been little studied.
Application of the Msplit Estimation Method in the Detection and Dimensioning of the Displacement of Adjacent Planes

Building constructions are exposed to various forces and natural phenomena. Some of them are sudden and violent, e.g., an earthquake or heavy rains, causing a displacement of the ground. Other phenomena affect objects on a longer-term, e.g., vibrations caused by daily road traffic. Sometimes, building structures may have defects due to incorrect construction. In any case, if an engineering object shows changes in the relation to its correct geometry or position, deformation and displacement measurements are required. Engineering objects are also monitored during their construction. Nowadays, it is important to perform measurements quickly and with high accuracy. The use of a Terrestrial Laser Scanning (TLS) allows for the required measurement speed and accuracy. This measurement technology allows a large dataset, which can be arbitrarily elaborated, to be obtained. The structure of building objects can include vertices, lines, planes, and other shapes and can be described using mathematical functions. This allows data processing to be automated. In this article, we present the Msplit method as an effective approach to the processing of data obtained as a result of TLS measurements. The proposed approach is new because until now, the Msplit estimation method has not been used to detect adjacent planes in one-point cloud obtained from TLS. The Msplit estimation method allows a functional model to be split into two or more competitive models and thus two or more entities in a point cloud to be estimated simultaneously. Four different objects measured using TLS are presented: two objects representing vertical displacements and two objects representing horizontal displacements. The test results and analysis confirm that the Msplit estimation method can be successfully applied in the detection of adjacent planes.
Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.
Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.
Comparative and functional genomics of wheat

Wheat is the heterogenous polyploid plant with large chromosomes and high level of repetitive sequences. These characters have limited the progress of wheat genomics studies. Comparative genomics provided a platform for study of wheat genomics by using model plants Functional genomics focused on the expressed portion of the wheat genome. Its aim was determining the expression and function of genes. This paper reviews the application of comparative genomics in wheat genomic studies and the contents and methods of functional genomics of wheat.
Additions and refinements to the molecular phylogeny of the Calopteryginae sl (Zygoptera: Calopterygidae)

Of 8 previously unstudied calopterygine taxa, the ITS 1 and 2 was sequenced and inserted into a pre-existing phylogenetic tree of all Eurasian and American genera. ITS is mainly appropriate for looking at shallow phylogenetic relationships, and resolved the relationship within and between genera best, with weak support for relationships at the subfamily level. Thus, Atrocalopreryx-Matrona was found to be a complex but very robust clade, while Vestal is s.l. was confirmed to consist of 2 distinct genera. The generic versus specific or subspecific status of few other taxa is discussed. Within Mnais and Vestalis, the position was tested of 2 suspected "aberrant" members, M. gregoryi and V. beryllae. Both were confirmed to belong to the genera to which they had been traditionally assigned.
A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.