scholarly journals Data Assimilation for Bathymetry Estimation at a Tidal Inlet

2016 ◽  
Vol 33 (10) ◽  
pp. 2145-2163 ◽  
Author(s):  
S. Moghimi ◽  
H. T. Özkan-Haller ◽  
G. W. Wilson ◽  
A. Kurapov
Keyword(s):  
Data Assimilation ◽  
Synthetic Data ◽  
Tidal Inlet ◽  
Current Data ◽  
Radar System ◽  
Surface Velocity ◽  
Tidal Circulation ◽  
X Band ◽  
Different Types ◽  
Synthetic Aperture Radar System

AbstractThis study involved developing and testing a data assimilation framework that accommodates different types of geophysical ocean data (i.e., surface velocity and wave information) and provides an estimation of the bathymetry of a mixed-energy tidal inlet. This framework was successfully applied to a highly variable tidal environment using synthetic data (twin test). The synthetic data consisted of surface velocity components associated with the tidal circulation and wavenumber–frequency pairs of incoming surface gravity waves that mimic data that could be derived from an airborne synthetic aperture radar system and a tower-mounted X-band radar system, respectively. The present ensemble-based assimilation framework has previously been applied in both wave-dominated coastal and current-dominated riverine environments. In contrast, the inlet environment is neither wave nor current dominated. The assimilation of wave and current data together was most useful to obtain a skillful estimate of the spatial map of bathymetry.

Nanoparticles Functionalized with Venom-Derived Peptides and Toxins for Pharmaceutical Applications

2020 ◽  
Vol 21 (2) ◽  
pp. 97-109 ◽  
Author(s):  
Ana P. dos Santos ◽  
Tamara G. de Araújo ◽  
Gandhi Rádis-Baptista
Keyword(s):  
Oral Absorption ◽  
Current Data ◽  
Pharmacological Activities ◽  
Venom Peptides ◽  
Essential Components ◽  
Wide Range ◽  
Different Types ◽  
Direct Use ◽  
Animal Venoms ◽  
Pharmaceutical Biotechnology

Venom-derived peptides display diverse biological and pharmacological activities, making them useful in drug discovery platforms and for a wide range of applications in medicine and pharmaceutical biotechnology. Due to their target specificities, venom peptides have the potential to be developed into biopharmaceuticals to treat various health conditions such as diabetes mellitus, hypertension, and chronic pain. Despite the high potential for drug development, several limitations preclude the direct use of peptides as therapeutics and hamper the process of converting venom peptides into pharmaceuticals. These limitations include, for instance, chemical instability, poor oral absorption, short halflife, and off-target cytotoxicity. One strategy to overcome these disadvantages relies on the formulation of bioactive peptides with nanocarriers. A range of biocompatible materials are now available that can serve as nanocarriers and can improve the bioavailability of therapeutic and venom-derived peptides for clinical and diagnostic application. Examples of isolated venom peptides and crude animal venoms that have been encapsulated and formulated with different types of nanomaterials with promising results are increasingly reported. Based on the current data, a wealth of information can be collected regarding the utilization of nanocarriers to encapsulate venom peptides and render them bioavailable for pharmaceutical use. Overall, nanomaterials arise as essential components in the preparation of biopharmaceuticals that are based on biological and pharmacological active venom-derived peptides.

scholarly journals Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach

2016 ◽  
Vol 9 (9) ◽  
pp. 4425-4445 ◽  
Author(s):  
Nikola Besic ◽  
Jordi Figueras i Ventura ◽  
Jacopo Grazioli ◽  
Marco Gabella ◽  
Urs Germann ◽  
...  
Keyword(s):  
Polarimetric Radar ◽  
Statistical Framework ◽  
X Band ◽  
Different Types ◽  
Unsupervised Approach ◽  
Radar Measurements ◽  
Supervised Classification Methods ◽  
Operational Potential ◽  
Statistical Clustering

Abstract. Polarimetric radar-based hydrometeor classification is the procedure of identifying different types of hydrometeors by exploiting polarimetric radar observations. The main drawback of the existing supervised classification methods, mostly based on fuzzy logic, is a significant dependency on a presumed electromagnetic behaviour of different hydrometeor types. Namely, the results of the classification largely rely upon the quality of scattering simulations. When it comes to the unsupervised approach, it lacks the constraints related to the hydrometeor microphysics. The idea of the proposed method is to compensate for these drawbacks by combining the two approaches in a way that microphysical hypotheses can, to a degree, adjust the content of the classes obtained statistically from the observations. This is done by means of an iterative approach, performed offline, which, in a statistical framework, examines clustered representative polarimetric observations by comparing them to the presumed polarimetric properties of each hydrometeor class. Aside from comparing, a routine alters the content of clusters by encouraging further statistical clustering in case of non-identification. By merging all identified clusters, the multi-dimensional polarimetric signatures of various hydrometeor types are obtained for each of the studied representative datasets, i.e. for each radar system of interest. These are depicted by sets of centroids which are then employed in operational labelling of different hydrometeors. The method has been applied on three C-band datasets, each acquired by different operational radar from the MeteoSwiss Rad4Alp network, as well as on two X-band datasets acquired by two research mobile radars. The results are discussed through a comparative analysis which includes a corresponding supervised and unsupervised approach, emphasising the operational potential of the proposed method.

scholarly journals The Role of Non-Coding RNAs in the Regulation of the Proto-Oncogene MYC in Different Types of Cancer

Biomedicines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 921
Author(s):  
Ekaterina Mikhailovna Stasevich ◽  
Matvey Mikhailovich Murashko ◽  
Lyudmila Sergeevna Zinevich ◽  
Denis Eriksonovich Demin ◽  
Anton Markovich Schwartz
Keyword(s):  
Malignant Tumors ◽  
Current Data ◽  
Cellular Processes ◽  
Cell Divisions ◽  
Specific Tumor ◽  
Different Types ◽  
Myc Gene ◽  
Non Coding Rnas ◽  
Tumor Types

Alterations in the expression level of the MYC gene are often found in the cells of various malignant tumors. Overexpressed MYC has been shown to stimulate the main processes of oncogenesis: uncontrolled growth, unlimited cell divisions, avoidance of apoptosis and immune response, changes in cellular metabolism, genomic instability, metastasis, and angiogenesis. Thus, controlling the expression of MYC is considered as an approach for targeted cancer treatment. Since c-Myc is also a crucial regulator of many cellular processes in healthy cells, it is necessary to find ways for selective regulation of MYC expression in tumor cells. Many recent studies have demonstrated that non-coding RNAs play an important role in the regulation of the transcription and translation of this gene and some RNAs directly interact with the c-Myc protein, affecting its stability. In this review, we summarize current data on the regulation of MYC by various non-coding RNAs that can potentially be targeted in specific tumor types.

scholarly journals Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

2016 ◽  
Author(s):  
Jean M. Bergeron ◽  
Mélanie Trudel ◽  
Robert Leconte
Keyword(s):  
Data Assimilation ◽  
State Vector ◽  
Snow Water Equivalent ◽  
Synthetic Data ◽  
Hydrologic Model ◽  
Open Loop ◽  
The State ◽  
Wide Range ◽  
Snow Water ◽  
The Impact

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the Ensemble Kalman Filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than its individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

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