The present article describes a one-pot and cascade mode process using biocompatible/biodegradable reagents, for simply obtaining surfactant compositions comprising mixtures of d-mannuronic acid and ...l-guluronic acid directly from oligoalginates or semi-refined alginates (mixtures of alginate, cellulose, hemicellulose, laminaran, and fucan). Simple treatments of partial purification of the reaction crudes (elimination of the salts and/or the residual fatty alcohols) or isolation of the surfactant compositions result in sugar-based compounds having performance levels appropriate to applications in detergency. In addition, the challenging extension of this cascading one-pot synthesis technology to crude milled brown seaweeds was successfully carried out to provide promising surface-active compositions made up of alkyl uronate and alkyl glycoside monosaccharides.
Marine polysaccharides are part of the huge seaweeds resources and present many applications for several industries. In order to widen their potential as additives or bioactive compounds, some ...structural modifications have been studied. Among them, simple hydrophobization reactions have been developed in order to yield to grafted polysaccharides bearing acyl-, aryl-, alkyl-, and alkenyl-groups or fatty acid chains. The resulting polymers are able to present modified physicochemical and/or biological properties of interest in the current pharmaceutical, cosmetics, or food fields. This review covers the chemical structures of the main marine polysaccharides, and then focuses on their structural modifications, and especially on hydrophobization reactions mainly esterification, acylation, alkylation, amidation, or even cross-linking reaction on native hydroxyl-, amine, or carboxylic acid functions. Finally, the question of the necessary requirement for more sustainable processes around these structural modulations of marine polysaccharides is addressed, considering the development of greener technologies applied to traditional polysaccharides.
The present article describes a one-pot and cascade mode process using biocompatible/biodegradable reagents, for directly and simply obtaining surfactant compositions of mixtures of D-mannuronic acid ...and L-guluronic acid directly from oligoalginates or semi-refined alginates (mixtures of alginate, cellulose, hemicellulose, laminaran and fucan). Simple treatments of partial purification of the reaction crudes (elimination of the salts and/or the residual fatty alcohols) or isolation of the surfactant compositions result in derived compounds and in compositions having performance levels appropriate to applications in detergency. In addition, the challenging extension of this cascading one-pot synthesis technology to crude milled brown seaweeds was successfully carried out to provide promising surface-active compositions made up of alkyl uronate and alkyl glycoside monosaccharides.
Mesenchymal stem cell (MSC) therapies demonstrate particular promise in ameliorating diseases of immune dysregulation but are hampered by short in vivo cell persistence and inconsistencies in ...phenotype. Here, we demonstrate that biomaterial encapsulation into alginate using a microfluidic device could substantially increase in vivo MSC persistence after intravenous (i.v.) injection. A combination of cell cluster formation and subsequent cross-linking with polylysine led to an increase in injected MSC half-life by more than an order of magnitude. These modifications extended persistence even in the presence of innate and adaptive immunity-mediated clearance. Licensing of encapsulated MSCs with inflammatory cytokine pretransplantation increased expression of immunomodulatory-associated genes, and licensed encapsulates promoted repopulation of recipient blood and bone marrow with allogeneic donor cells after sublethal irradiation by a ∼2-fold increase. The ability ofmicrogel encapsulation to sustain MSC survival and increase overall immunomodulatory capacity may be applicable for improving MSC therapies in general.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is constantly evolving. Prior studies focused on high-case-density locations, such as the northern and western metropolitan areas of the ...United States. This study demonstrates continued SARS-CoV-2 evolution in a suburban southern region of the United States by high-density amplicon sequencing of symptomatic cases. 57% of strains carry the spike D614G variant, which is associated with higher genome copy numbers, and its prevalence expands with time. Four strains carry a deletion in a predicted stem loop of the 3′ UTR. The data are consistent with community spread within local populations and the larger continental United States. The data instill confidence in current testing sensitivity and validate “testing by sequencing” as an option to uncover cases, particularly nonstandard coronavirus disease 2019 (COVID-19) clinical presentations. This study contributes to the understanding of COVID-19 through an extensive set of genomes from a non-urban setting and informs vaccine design by defining D614G as a dominant and emergent SARS-CoV-2 isolate in the United States.
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•NGS of SARS-CoV-2 from a rural/suburban area shows local spread as an epidemic driver•The D614G spike mutant is observed in >50% of cases•Deletion in the 3′ UTR of SARS-CoV-2 is identified•Targeted NGS has 100% specificity and is as sensitive as qPCR
McNamara et al. use next-generation sequencing (NGS) with a high-density tiling array across SARS-CoV-2 to find a deletion and document how the D614G spike protein mutation rapidly swept through a rural/suburban population. D614G is associated with slightly higher viral loads.
Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers ...have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions.
Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of ...bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1.
The JAMMIT algorithm jointly approximates an arbitrary number of data matrices by rank-1 outer-products composed of "sparse" left-singular vectors (eigen-arrays) that are unique to each matrix and a right-singular vector (eigen-signal) that is common to all the matrices. The non-zero coefficients of the eigen-arrays identify small subsets of variables for each data type (i.e., signatures) that in aggregate, or individually, best explain a dominant eigen-signal defined on the columns of the data matrices. The approximation is specified by a single "sparsity" parameter that is selected based on false discovery rate estimated by permutation testing. Multiple signals of interest in a given MDDS are sequentially detected and modeled by iterating JAMMIT on "residual" data matrices that result from a given sparse approximation.
We show that JAMMIT outperforms other joint analysis algorithms in the detection of multiple signatures embedded in simulated MDDS. On real multimodal data for ovarian and liver cancer we show that JAMMIT identified multi-modal signatures that were clinically informative and enriched for cancer-related biology.
Sparse matrix approximations of rank-1 provide a simple yet effective means of jointly reducing multiple, big data types to a small subset of variables that characterize important clinical and/or biological attributes of the bio-samples from which the data were acquired.
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