Therapy Effects of Bone Marrow Stromal Cells on Ischemic Stroke

Stroke is the second most common cause of death and major cause of disability worldwide. Recently, bone marrow stromal cells (BMSCs) have been shown to improve functional outcome after stroke. In this review, we will focus on the protective effects of BMSCs on ischemic brain and the relative molecular mechanisms underlying the protective effects of BMSCs on stroke

Current Progress in CAR-T Cell Therapy for Solid Tumors

Cancer immunotherapy by chimeric antigen receptor-modified T (CAR-T) cells has shown exhilarative clinical efficacy for hematological malignancies. Recently two CAR-T cell based therapeutics, Kymriah (Tisagenlecleucel) and Yescarta (Axicabtagene ciloleucel) approved by US FDA (US Food and Drug Administration) are now used for treatment of B cell acute lymphoblastic leukemia (B-ALL) and diffuse large B-cell lymphoma (DLBCL) respectively in the US. Despite the progresses made in treating hematological malignancies, challenges still remain for use of CAR-T cell therapy to treat solid tumors. In this landscape, most studies have primarily focused on improving CAR-T cells and overcoming the unfavorable effects of tumor microenvironment on solid tumors. To further understand the current status and trend for developing CAR-T cell based therapies for various solid tumors, this review emphasizes on CAR-T techniques, current obstacles, and strategies for application, as well as necessary companion diagnostics for treatment of solid tumors with CAR-T cells

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Evaluation of Agricultural Eco-Efficiency and Its Spatiotemporal Differentiation in China, Considering Green Water Consumption and Carbon Emissions Based on Undesired Dynamic SBM-DEA

Both agricultural output and environmental pollution in China show certain characteristics of spatiotemporal variation due to the evolution and inter-provincial disparities of resource endowment, economic development level, and production mode. This paper considered the green water consumption, carbon emissions in agricultural production, and the persistent role of fixed asset investment, constructed an undesired dynamic SBM-DEA model, and evaluated the agricultural eco-efficiency (AEE) of 31 provinces in China from 2007 to 2018, analyzing the spatiotemporal differentiation. The results show that, during 2007–2018, the following can be concluded: (1) The AEE of 31 provinces in China showed the characteristics of an overall stable rise from 0.64 to 0.70 but uneven development among regions from 2007 to 2018. (2) The averages of either the agricultural resource consumption efficiency index or pollution emission efficiency index in 31 provinces slightly increased over time, while the average value of the agricultural fixed asset investment efficiency index showed a decline in volatility. The spatial discrepancy of any index mainly stems from the index disparity between groups with a high AEE and groups with a low one, with contribution rates of more than 85%. (3) It is recommended to emphasize strengthening communication and cooperation between provinces with high and low AEE and implement distinct regional strategies to improve AEE

Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work

Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work

Opening and Sharing of Large-scale Instruments and Equipment in Agricultural Research Institutes: A Case Study of Environment and Plant Protection Institute of Chinese Academy of Tropical Agricultural Sciences

The article introduces the main practices and achievements of the Environment and Plant Protection Institute of Chinese Academy of Tropical Agricultural Sciences in promoting the sharing of large-scale instruments and equipment in recent years, analyzes the existing problems in the management system, management team, assessment incentives and maintenance guarantee, and proposes improvement measures and suggestions from aspects of improving the sharing management system, strengthening management team building, strengthening sharing assessment and incentives, improving maintenance capabilities and expanding external publicity, to further improve the sharing management of large-scale instruments and equipment

Chemical Characteristics of PM_2.5 and Water-Soluble Organic Nitrogen in Yangzhou, China

Chemical characterization of fine atmospheric particles (PM2.5) is important for effective reduction of air pollution. This work analyzed PM2.5 samples collected in Yangzhou, China, during 2016. Ionic species, organic matter (OM), elemental carbon (EC), and trace metals were determined, and an Aerodyne soot-particle aerosol mass spectrometer (SP-AMS) was introduced to determine the OM mass, rather than only organic carbon mass. We found that inorganic ionic species was dominant (~52%), organics occupied about 1/4, while trace metals (~1%) and EC (~2.1%) contributed insignificantly to the total PM2.5 mass. Water-soluble OM appeared to link closely with secondary OM, while water-insoluble OM correlated well with primary OM. The PM2.5 concentrations were relatively low during summertime, while its compositions varied little among different months. Seasonal variations of water-soluble organic nitrogen (WSON) concentrations were not significant, while the mass contributions of WSON to total nitrogen were remarkably high during summer and autumn. WSON was found to associate better with secondary sources based on both correlation analyses and principle component analyses. Analyses of potential source contributions to WSON showed that regional emissions were dominant during autumn and winter, while the ocean became relatively important during spring and summer

Application Value of SAT-TB Combined with Acid-Fast Staining in the Diagnosis and Treatment of Pulmonary Tuberculosis

Objectives. This study is aimed at evaluating the clinical application value of RNA simultaneous amplification and testing method for Mycobacterium tuberculosis (SAT-TB) combined with acid-fast staining in the diagnosis and treatment of pulmonary tuberculosis (PTB). Methods. This paper included 168 suspected and confirmed PTB sufferers admitted to The Sixth People’s Hospital of Wenzhou from December 2018 to December 2019, whose sputum was collected and tested using SAT-TB, smear acid-fast staining method, and the BACTEC MGIT 960 system. With the MGIT 960 culture test method as the gold standard, the application value of SAT-TB, acid-fast staining, or SAT-TB combined with acid-fast staining in the diagnosis and treatment of PTB was assessed. Results. With the MGIT 960 culture as the gold standard, the sensitivity, specificity, positive predictive value, and negative predictive value of SAT-TB for the diagnosis of PTB were 57.3%, 92.5%, 84.3%, and 73.5%, respectively. The conformity was 76.8%, and the Kappa value was 0.515, suggesting a statistically significant difference (χ2=7.314, p0.05) and a relatively high consistency degree. Conclusion. SAT-TB combined with acid-fast staining had a similar detection rate to that of the MGIT 960 culture test with a high consistency degree, which could be applied in the diagnosis of PTB efficiently and accurately