Linking satellites to genes with machine learning to estimate phytoplankton community structure from space

Ocean color remote sensing has been used for more than 2 decades to estimate primary productivity. Approaches have also been developed to disentangle phytoplankton community structure based on spectral data from space, in particular when combined with in situ measurements of photosynthetic pigments. Here, we propose a new ocean color algorithm to derive the relative cell abundance of seven phytoplankton groups, as well as their contribution to total chlorophyll a (Chl a) at the global scale. Our algorithm is based on machine learning and has been trained using remotely sensed parameters (reflectance, backscattering, and attenuation coefficients at different wavelengths, plus temperature and Chl a) combined with an omics-based biomarker developed using Tara Oceans data representing a single-copy gene encoding a component of the photosynthetic machinery that is present across all phytoplankton, including both prokaryotes and eukaryotes. It differs from previous methods which rely on diagnostic pigments to derive phytoplankton groups. Our methodology provides robust estimates of the phytoplankton community structure in terms of relative cell abundance and contribution to total Chl a concentration. The newly generated datasets yield complementary information about different aspects of phytoplankton that are valuable for assessing the contributions of different phytoplankton groups to primary productivity and inferring community assembly processes. This makes remote sensing observations excellent tools to collect essential biodiversity variables (EBVs) and provide a foundation for developing marine biodiversity forecasts.</p

Prediction of transient tumor enlargement using MRI tumor texture after radiosurgery on vestibular schwannoma

Purpose: Vestibular schwannomas (VSs) are uncommon benign brain tumors, generally treated using Gamma Knife radiosurgery (GKRS). However, due to the possible adverse effect of transient tumor enlargement (TTE), large VS tumors are often surgically removed instead of treated radiosurgically. Since microsurgery is highly invasive and results in a significant increased risk of complications, GKRS is generally preferred. Therefore, prediction of TTE for large VS tumors can improve overall VS treatment and enable physicians to select the most optimal treatment strategy on an individual basis. Currently, there are no clinical factors known to be predictive for TTE. In this research, we aim at predicting TTE following GKRS using texture features extracted from MRI scans. Methods: We analyzed clinical data of patients with VSs treated at our

Preventing Pseudomonas aeruginosa and Chromobacterium violaceum infections by anti-adhesion-active components of edible seeds

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas aeruginosa </it>adhesion to animal/human cells for infection establishment involves adhesive proteins, including its galactose- and fucose-binding lectins PA-IL (LecA) and PA-IIL (LecB). The lectin binding to the target-cell receptors may be blocked by compatible glycans that compete with those of the receptors, functioning as anti-adhesion glycodecoys. The anti-adhesion treatment is of the utmost importance for abrogating devastating antibiotic-resistant <it>P. aeruginosa </it>infections in immunodeficient and cystic fibrosis (CF) patients. This strategy functions in nature in protecting embryos and neonates. We have shown that PA-IL, PA-IIL, and also CV-IIL (a PA-IIL homolog produced in the related pathogen <it>Chromobacterium violaceum</it>) are highly useful for revealing natural glycodecoys that surround embryos in diverse avian eggs and are supplied to neonates in milks and royal jelly. In the present study, these lectins were used as probes to search for seed embryo-protecting glycodecoys.</p> <p>Methods</p> <p>The lectin-blocking glycodecoy activities were shown by the hemagglutination-inhibition test. Lectin-binding glycoproteins were detected by Western blotting with peroxidase-labeled lectins.</p> <p>Results</p> <p>The present work reports the finding - by using PA-IL, PA-IIL, and CV-IIL - of rich glycodecoy activities of low (< 10 KDa) and high MW (> 10 kDa) compounds (including glycoproteins) in extracts of cashew, cocoa, coffee, pumpkin, and tomato seeds, resembling those of avian egg whites, mammal milks, and royal jelly.</p> <p>Conclusions</p> <p>Edible seed extracts possess lectin-blocking glycodecoys that might protect their embryos from infections and also might be useful for hampering human and animal infections.</p

The universe formation by a space reduction cascade with random initial parameters

In this paper we discuss the creation of our universe using the idea of extra dimensions. The initial, multidimensional Lagrangian contains only metric tensor. We have found many sets of the numerical values of the Lagrangian parameters corresponding to the observed low-energy physics of our universe. Different initial parameters can lead to the same values of fundamental constants by the appropriate choice of a dimensional reduction cascade. This result diminishes the significance of the search for the 'unique' initial Lagrangian. We also have obtained a large number of low-energy vacua, which is known as a 'landscape' in the string theory.Comment: 17 pages, 1 figur

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio