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This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

Supervised analysis using spectral data requires a well-informed characterisation of the response variables and abundant spectral data points. The presented hyperspectral dataset comes from five sets of geometallurgical samples, each characterised by different methods. To provide the spectral data, all mineral samples were scanned with SPECIM VNIR and SWIR hyperspectral cameras. For each subset the following data are provided 1) hyperspectral reflectance images in the VNIR spectral range (400–1000 nm wavelength); 2) hyperspectral reflectance images in the SWIR spectral range (900–2500 nm wavelength); 3) hyperspectral reflectance images in the VNIR-SWIR range (merged to SWIR spatial resolution); 4) RGB images constructed from hyperspectral data using a Bilateral Filter based sensor fusion method; 5) response variables representing mineral sample characterisation results, provided as training and validation data. This dataset is intended for use in general regression and classification research and experiments. All subsets were validated using machine learning models with satisfactory results.

Mineral exploration combined with prospectivity mapping has become the standard process for utilising mineral exploration data. Nowadays, most techniques integrate multiple layers of information and use machine learning for both data-driven and knowledge-driven approaches. This study introduces a novel and generalised methodology for comparing different layers of information by using superpixels instead of pixels to identify similarities. This methodology provides an enhanced statistical representation of regions, facilitating and enabling effective comparisons. Three different statistical distance measures were considered: Kullback–Leibler divergence, Wasserstein distance and total variation distance. We apply the proposed process to data from the Antofagasta region of northern Chile, a well-known area for metallogenic belts, that contain notable copper reserves. Each metric was used and compared, resulting in different similarity maps highlighting interesting mineral exploration areas. The study results lead to the conclusion that the proposed methodology can be applied at different scales and helps in the identification of areas with similar characteristics.

Most mining companies have registered important amounts of drill core composite spectra using different acquisition equipment and by following diverse protocols. These companies have used classic spectrography based on the detection of absorption features to perform semi-quantitative mineralogy. This methodology requires ideal laboratory conditions in order to obtain normalized spectra to compare. However, the inherent variability of spectral features—due to environmental conditions and geological context, among others—is unavoidable and needs to be managed. This work presents a novel methodology for geometallurgical sample characterization consisting of a heterogeneous, multi-pixel processing pipeline which addresses the effects of ambient conditions and geological context variability to estimate critical geological and geometallurgical variables. It relies on the assumptions that the acquisition of hyperspectral images is an inherently stochastic process and that ore sample information is deployed in the whole spectrum. The proposed framework is basically composed of: (a) a new hyperspectral image segmentation algorithm, (b) a preserving-information dimensionality reduction scheme and (c) a stochastic hierarchical regression model. A set of experiments considering white reference spectral characterization and geometallurgical variable estimation is presented to show promising results for the proposed approach.

Despite unprecedented global efforts to rapidly develop SARS-CoV-2 treatments, in order to reduce the burden placed on health systems, the situation remains critical. Effective diagnosis, treatment, and prophylactic measures are urgently required to meet global demand: recombinant antibodies fulfill these requirements and have marked clinical potential. Here, we describe the fast-tracked development of an alpaca Nanobody specific for the receptor-binding-domain (RBD) of the SARS-CoV-2 Spike protein with potential therapeutic applicability. We present a rapid method for nanobody isolation that includes an optimized immunization regimen coupled with VHH library E. coli surface display, which allows single-step selection of Nanobodies using a simple density gradient centrifugation of the bacterial library. The selected single and monomeric Nanobody, W25, binds to the SARS-CoV-2 S RBD with sub-nanomolar affinity and efficiently competes with ACE-2 receptor binding. Furthermore, W25 potently neutralizes SARS-CoV-2 wild type and the D614G variant with IC50 values in the nanomolar range, demonstrating its potential as antiviral agent.

The resinous exudates from Escallonia illinita by products was characterized by FT-IR, NMR and HRMS. Six compounds were isolated and identified as follows: 1,5-diphenylpent-1-en-3-one (1), 4-(5-hydroxy-3,7-dimethoxy-4-oxo-4H-chromen-2-yl)phenyl acetate (2), pinocembrin (3), kaempferol 3-O-methylether (4), (3S,5S)-(E)-1,7-diphenylhept-1-ene-3,5-diol (5) and the new diarylheptanoid (3S,5S)-(E)-5-hydroxy-1,7-diphenylhept-1-en-3-yl acetate (6). The anti-oomycete potential of the resinous exudate, as well as the main compounds, was tested in vitro against Saprolegnia parasitica and Saprolegnia australis. The resinous exudate showed a strong anti-oomycete activity. In addition, the compounds 6, 1 and 3 demonstrated significant inhibition of Saprolegnia strains development. These findings strongly suggest that E. illinita is a potential biomass that could be used as a natural anti-oomycete product.

Neutrophils display an array of biological functions including the formation of neutrophil extracellular traps (NETs), web-like structures specialized in trapping, neutralizing, killing and preventing microbial dissemination within the host. However, NETs contribute to a number of inflammatory pathologies, including severe equine asthma. Tamoxifen (TX) is a selective estrogen receptor modulator which belongs to the triphenylethyllenes group of molecules, and which is used as a treatment in all stages of estrogen-positive human breast cancer. Our previous results suggest that tamoxifen can modulate neutrophil functionality and promote resolution of inflammation; this would partly explain the clinical beneficial effect of this drug in horses with airway inflammation. Enhanced NETs production has been reported with tamoxifen use in humans, but minimal data exists regarding the drug's effect on NETs in horses. The aim of this study is to assess the in vitro effect of TX on NETs formation from peripheral blood of healthy horses. Five clinically healthy mixed-breed adult horses were enrolled in the study. For this, cellular free DNA quantification, immunofluorescence for the visualization of NETs, assessment of different types of NETs, and detection of mitochondrial superoxide. TX induced NETs formation at a concentration of 10 uM. Our results show that only two types of NETs were induced by TX: 95% spread NETs ( sprNETs ) and 5% aggregated NETs ( aggNETs ). Furthermore, induction of these NETs could be influenced by mitochondrial ROS. Future research should involve an In vivo study of horses with severe asthma and TX treatment, to evaluate BALF neutrophil NET formation. In conclusion, this in vitro study suggests that the resolution of inflammation by TX in horses with airway inflammation is due to inhibition of other neutrophilic functions but not to NET formation.

Background Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results We present iNS934, the first GSMM for N. salina , including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.

Abstract Despite unprecedented global efforts to rapidly develop SARS-CoV-2 treatments, in order to reduce the burden placed on health systems, the situation remains critical. Effective diagnosis, treatment, and prophylactic measures are urgently required to meet global demand: recombinant antibodies fulfill these requirements and have marked clinical potential. Here, we describe the fast-tracked development of an alpaca Nanobody specific for the receptor-binding-domain (RBD) of the SARS-CoV-2 Spike protein with therapeutic potential applicability. We present a rapid method for nanobody isolation that includes an optimized immunization regimen coupled with VHH library E. coli surface display, which allows single-step selection of high-affinity nanobodies using a simple density gradient centrifugation of the bacterial library. The selected single and monomeric Nanobody, W25, binds to the SARS-CoV-2 S RBD with sub-nanomolar affinity and efficiently competes with ACE-2 receptor binding. Furthermore, W25 potently neutralizes SARS-CoV-2 wild type and the D614G variant with IC50 values in the nanomolar range, demonstrating its potential as antiviral agent. Competing Interest Statement Conflict of interest statement The Austral University of Chile claiming priority to U.S. Provisional Patent Application No. US Serial No. 63/025534, filed MAY-2020. Footnotes * Developing a rapid method for nanobody isolation which allows single-step selection of high-affinity nanobodies, by using a simple density gradient centrifugation of the bacterial library, allowed us to identify a very high affinity nanobody against the Spike protein of SARS-CoV-2 which we called W25. We determined that W25 binds to the SARS-CoV-2 Spike RBD with sub-nanomolar affinity and efficiently competes with ACE-2 receptor binding. Furthermore, the W25 nanobody potently neutralizes SARS-CoV-2 wild type and the D614G variant with IC50 values in the nanomolar range, demonstrating its potential as an antiviral agent.