Ultrasound-assisted extraction of R-phycoerythrin from Grateloupia turuturu with and without enzyme addition

The aim of this study was to compare two processes for the extraction of R-phycoerythrin (R-PE) from the red seaweed Grateloupia turuturu: ultrasound-assisted extraction (UAE) and ultrasound-assisted enzymatic hydrolysis (UAEH). Process efficiencies were both evaluated by the yield of R-PE extraction and by the level of liquefaction. Experiments were conducted at 40 and 22 °C, for 6 h, using an enzymatic cocktail and an original ultrasonic flow-through reactor. R-PE appeared very sensitive to temperature, thus 22 °C is strongly recommended for its extraction by UAEH or UAE. However, the higher processing temperature (40 °C) clearly increased the extraction of water-soluble compounds (up to 91% of liquefaction). These two new processes are thus promising alternatives for the extraction of water-soluble components including R-PE, from wet seaweeds, with extraction yields at least similar to conventional solid–liquid extraction.

Automatic dynamic mask extraction for PIV images containing an unsteady interface, bubbles, and a moving structure

When performing Particle Image Velocimetry (PIV) measurements in complex fluid flows with moving interfaces and a two-phase flow, it is necessary to develop a mask to remove non-physical measurements. This is the case when studying, for example, the complex bubble sweep-down phenomenon observed in oceanographic research vessels. Indeed, in such a configuration, the presence of an unsteady free surface, of a solid–liquid interface and of bubbles in the PIV frame, leads to generate numerous laser reflections and therefore spurious velocity vectors. In this note, an image masking process is developed to successively identify the boundaries of the ship and the free surface interface. As the presence of the solid hull surface induces laser reflections, the hull edge contours are simply detected in the first PIV frame and dynamically estimated for consecutive ones. As for the unsteady surface determination, a specific process is implemented like the following: i) the edge detection of the gradient magnitude in the PIV frame, ii) the extraction of the particles by filtering high-intensity large areas related to the bubbles and/or hull reflections, iii) the extraction of the rough region containing these particles and their reflections, iv) the removal of these reflections. The unsteady surface is finally obtained with a fifth-order polynomial interpolation. The resulted free surface is successfully validated from the Fourier analysis and by visualizing selected PIV images containing numerous spurious high intensity areas. This paper demonstrates how this data analysis process leads to PIV images database without reflections and an automatic detection of both the free surface and the rigid body. An application of this new mask is finally detailed, allowing a preliminary analysis of the hydrodynamic flow.

Active Foreground Region Extraction and Tracking for Sports Video Annotation

Automatic video segmentation plays a vital role in sports videos annotation. This paper presents a fully automatic and computationally efficient algorithm for analysis of sports videos. Various methods of automatic shot boundary detection have been proposed to perform automatic video segmentation. These investigations mainly concentrate on detecting fades and dissolves for fast processing of the entire video scene without providing any additional feedback on object relativity within the shots. The goal of the proposed method is to identify regions that perform certain activities in a scene. The model uses some low-level feature video processing algorithms to extract the shot boundaries from a video scene and to identify dominant colours within these boundaries. An object classification method is used for clustering the seed distributions of the dominant colours to homogeneous regions. Using a simple tracking method a classification of these regions to active or static is performed. The efficiency of the proposed framework is demonstrated over a standard video benchmark with numerous types of sport events and the experimental results show that our algorithm can be used with high accuracy for automatic annotation of active regions for sport videos.

Benefits of using a spectral hindcast database for wave power extraction assessment

When considering deployment of wave energy converters at a given site, it is of prime importance from both a technical and an economical point of view to accurately assess the total yearly energy that can be extracted by the given device. Especially, to be considered is the assessment of the efficiency of the device over the widest span of the sea-states spectral bandwidth. Hence, the aim of this study is to assess the biases and errors introduced on extracted power classically computed using spectral data derived from analytical functions such as a JONSWAP spectrum, compared to the power derived using actual wave spectra obtained from a spectral hindcast database.

Valorization of the Macroalgae Sargassum Muticum by Enzymatic Hydrolysis, Interest of Surfactants to Improve the Extraction of Phlorotannins and Polysaccharides

The use of surfactants to improve enzymatic hydrolysis of the macroalgae Sargassum muticum has been investigated. Visible absorption spectroscopy has been used to quantify the solubilization of both polysaccharides and phlorotannins in the hydrolysates.   After total extraction, results showed that Sargassum muticum contained 2.74% (expressed in percent of the dry weight of the algae) of phlorotannins whose 32 % were in the cell wall. This result shows that it is important to access to the parietal phlorotannins. To reach this objective, we chose the enzymatic approach for destructurating the cell wall of the algae. The use of 5% dry weight (DW - 5% by weight of hydrolyzed algae) of an enzymatic mix containing a commercial beta-glucanase, a commercial protease and an alginate lyase extracted from Pseudomonas alginovora led after 3 hours of hydrolysis to the solubilization of 2.43% DW polysaccharides and 0.52% DW phlorotannins. The use of 0.5% volume of the surfactant Triton® X-100 with 10% DW of the enzymatic mix has allowed to reaching the value of 2.63% DW of solubilized phlorotannins, that is 96% of the total phenolic content.   The use of non-ionic surfactant, combined to enzymatic hydrolysis, showed an increased efficiency in disrupting cell wall and solubilizing phlorotannins in Sargassum muticum.

IONIC LIQUID EXTRACTION UNVEILS PREVIOUSLY OCCLUDED HUMICBOUND IRON IN PEAT POREWATER

Extracellular iron reduction has been suggested as a candidate metabolic pathway that may explain a large proportion of carbon respiration in temperate peatlands. However, the o-phenanthroline colorimetric method commonly employed to quantitate iron and partition between redox species is known to be unreliable in the presence of humic and fulvic acids, both of which represent a considerable proportion of peatland dissolved organic matter. We propose ionic liquid extraction as a more accurate iron quantitation and redox speciation method in humic-rich peat porewater. We evaluated both o-phenanthroline and ionic liquid extraction in four distinct peatland systems spanning a gradient of physico-chemical conditions to compare total iron recovery and Fe2+:Fe3+ ratios determined by each method. Ionic liquid extraction was found to provide more accurate iron quantitation and speciation in the presence of dissolved organic matter. A multivariate approach utilizing fluorescence- and UV-Vis spectroscopy was used to identify dissolved organic matter characteristics in peat porewater that lead to poor performance of the o-phenanthroline method. Where these interferences are present, we offer an empirical correction factor for total iron quantitation by o-phenanthroline, as verified by ionic liquid extraction. The written work presented in this thesis is in preparation for submission to Soil Biology and Biochemisrty by T.J. Veverica, E.S. Kane, A.M. Marcarelli, and S.A. Green.

Preparation of an Immunosorbent and its use in the Solid Phase Extraction of Benzodiazepines

The use of capillary electrophoresis (CE) has been restricted to applications having high sample concentrations because of its low sensitivity caused by small injection volumes and, when ultraviolet (UV) detection is used, the short optical path length. Sensitivity in CE can be improved by using more sensitive detection systems, or by preconcentration techniques which are based on chromatographic and/or electrophoretic principles. One of the promising strategies to improve sensitivity is solid phase extraction (SPE). Solid Phase Extraction utilizes high sample volumes and a variety of complex matrixes to facilitate trace detection. To increase the specificity of the SPE a selective solid phase must be chosen. Immunosorbents, which are a combination of an antibody and a solid support, have proven to be an excellent option because of high selectivity of the antibody. This thesis is an exploratory study of the application of immunosorbent-SPE combined with CE for trace concentration of benzodiazepines. This research describes the immobilization and performance evaluation of an immunosorbent prepared by immobilizing a benzodiazepine-specific antibody on aminopropyl silica. The binding capacity of the immunosorbent, measured as µg of benzodiazepine/ gram of immunosorbent, was 39 ± 10. The long term stability of the prepared immunosorbent has been improved by capping the remaining aminopropyl groups by reaction with acetic anhydride. The capped immunosorbent retained its binding capacity after several uses.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Verbalizzazione di eventi biomedici espressi nella letteratura scientifica: generazione controllata di linguaggio naturale da grafi di conoscenza mediante transformer text-to-text

Il periodo in cui viviamo rappresenta la cuspide di una forte e rapida evoluzione nella comprensione del linguaggio naturale, raggiuntasi prevalentemente grazie allo sviluppo di modelli neurali. Nell'ambito dell'information extraction, tali progressi hanno recentemente consentito di riconoscere efficacemente relazioni semantiche complesse tra entità menzionate nel testo, quali proteine, sintomi e farmaci. Tale task -- reso possibile dalla modellazione ad eventi -- è fondamentale in biomedicina, dove la crescita esponenziale del numero di pubblicazioni scientifiche accresce ulteriormente il bisogno di sistemi per l'estrazione automatica delle interazioni racchiuse nei documenti testuali. La combinazione di AI simbolica e sub-simbolica può consentire l'introduzione di conoscenza strutturata nota all'interno di language model, rendendo quest'ultimi più robusti, fattuali e interpretabili. In tale contesto, la verbalizzazione di grafi è uno dei task su cui si riversano maggiori aspettative. Nonostante l'importanza di tali contributi (dallo sviluppo di chatbot alla formulazione di nuove ipotesi di ricerca), ad oggi, risultano assenti contributi capaci di verbalizzare gli eventi biomedici espressi in letteratura, apprendendo il legame tra le interazioni espresse in forma a grafo e la loro controparte testuale. La tesi propone il primo dataset altamente comprensivo su coppie evento-testo, includendo diverse sotto-aree biomediche, quali malattie infettive, ricerca oncologica e biologia molecolare. Il dataset introdotto viene usato come base per l'addestramento di modelli generativi allo stato dell'arte sul task di verbalizzazione, adottando un approccio text-to-text e illustrando una tecnica formale per la codifica di grafi evento mediante testo aumentato. Infine, si dimostra la validità degli eventi per il miglioramento delle capacità di comprensione dei modelli neurali su altri task NLP, focalizzandosi su single-document summarization e multi-task learning.

Dna Extraction From Filter-paper Spots Of Vaginal Samples Collected After Sexual Violence.

To detect the presence of male DNA in vaginal samples collected from survivors of sexual violence and stored on filter paper. A pilot study was conducted to evaluate 10 vaginal samples spotted on sterile filter paper: 6 collected at random in April 2009 and 4 in October 2010. Time between sexual assault and sample collection was 4-48hours. After drying at room temperature, the samples were placed in a sterile envelope and stored for 2-3years until processing. DNA extraction was confirmed by polymerase chain reaction for human β-globin, and the presence of prostate-specific antigen (PSA) was quantified. The presence of the Y chromosome was detected using primers for sequences in the TSPY (Y7/Y8 and DYS14) and SRY genes. β-Globin was detected in all 10 samples, while 2 samples were positive for PSA. Half of the samples amplified the Y7/Y8 and DYS14 sequences of the TSPY gene and 30% amplified the SRY gene sequence of the Y chromosome. Four male samples and 1 female sample served as controls. Filter-paper spots stored for periods of up to 3years proved adequate for preserving genetic material from vaginal samples collected following sexual violence.

Advancing Bag-of-visual-words Representations For Lesion Classification In Retinal Images.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.

A New Extraction Approach To Correct The Effect Of Apparent Increase In The Secoiridoid Content After Filtration Of Virgin Olive Oil.

In the current study, a new approach has been developed for correcting the effect that moisture reduction after virgin olive oil (VOO) filtration exerts on the apparent increase of the secoiridoid content by using an internal standard during extraction. Firstly, two main Spanish varieties (Picual and Hojiblanca) were submitted to industrial filtration of VOOs. Afterwards, the moisture content was determined in unfiltered and filtered VOOs, and liquid-liquid extraction of phenolic compounds was performed using different internal standards. The resulting extracts were analyzed by HPLC-ESI-TOF/MS, in order to gain maximum information concerning the phenolic profiles of the samples under study. The reduction effect of filtration on the moisture content, phenolic alcohols, and flavones was confirmed at the industrial scale. Oleuropein was chosen as internal standard and, for the first time, the apparent increase of secoiridoids in filtered VOO was corrected, using a correction coefficient (Cc) calculated from the variation of internal standard area in filtered and unfiltered VOO during extraction. This approach gave the real concentration of secoiridoids in filtered VOO, and clarified the effect of the filtration step on the phenolic fraction. This finding is of great importance for future studies that seek to quantify phenolic compounds in VOOs.