Inviscid analysis of the plume created by multiple rocket engines. Part II - Description of the computer programs

Computer program for calculation of single jet and five jet interaction regimes associated with multiple rocket engine

Development of bio-based chitosan films with incorporated chestnut extract

Hydrolysable tannins have prominent biological activity and thus their industrial application is gaining importance in many fields. This study explored the possibility for the utilization of a commercially available chestnut extract (CE) as an active component in chitosan-based films intended for food packaging. Therefore, a set of chitosan-based films with incorporated CE was prepared and evaluated regarding physicochemical properties. The estimated total phenolic content (TPC) has revealed a maximal value of 19.5 mgGAE gfilm-1. Moreover, the moisture content (MC) in the films has decreased (from 29.6% to 18.6%), while tensile strength (TS) has increased (from 13.5 MPa to 48.5 MPa) after the incorporation of CE. The observed coherence between TPC and evaluated properties has been confirmed by the existence of strong negative and positive linear correlations in the case of MC and TS, respectively. In large, CE extract was found to be a promising candidate as an active component in chitosan-based films

Acute lower limb ischemia due to thrombo-embolic arterial occlusions in two previously healthy men with markedly elevated Lp(a)

Lipoprotein (a) (Lp(a)) is a well-documented risk factor for atherosclerotic cardiovascular disease. Its role in acute thrombo-embolic occlusions of peripheral arteries is not known. We describe two cases of multiple, acute, peripheral arterial occlusions in two previously healthy men with markedly elevated Lp(a). Both cases had unsatisfactory results after percutaneous and surgical revascularization procedures. Experience yielded in these two cases suggests that when an unfavorable outcome occurs in a peripheral artery disease patient in the absence of the regular risk factors, Lp(a) should be determined and its role investigated

Diagnosis of Burkitt's lymphoma in due time: a practical approach

Aims: The quick diagnosis of Burkitt's lymphoma (BL) and its clear-cut differentiation from diffuse large B-cell lymphoma (DLBCL) is of great clinical importance since treatment for these two disease entities differ markedly and should promptly be initiated in BL. However, these two tumours are difficult to distinguish using the current WHO classification, particularly in regard to BL variants, i.e., BL with plasmacytoid differentiation and atypical Burkitt's/Burkitt's-like lymphomas. Methods: We studied 39 cases of highly proliferative blastic B-cell lymphoma (HPBCL) to establish a practical differential-diagnostic algorithm. Characteristics set for BL were a typical morphology, a mature B-cell phenotype of CD10+, Bcl-6+ and Bcl-2- tumour cells, a proliferation rate of >95%, and the presence of C-MYC rearrangements in the absence of t(14;18)(q32;q21). All cases were selectively negative for cyclin D-1, CD5, CD23, LMP-EBV, CD34 and TdT, and there were no cases of endemic or immunodeficiency-associated Burkitt's lymphoma. Results: Altogether the set BL characteristics were found in only 5/39 cases (12.8%), whereas the majority of tumours revealed mosaic features (87.2%). In a second attempt, we followed a pragmatic stepwise approach for a classification algorithm that includes the assessment of C-MYC status to stratify HPBCL into four predefined diagnostic categories (DC), namely DC I (5/39, 12.8%): "classical BL", corresponding to the classical variant of sporadic BL in the WHO classification; DC II (11/39, 28.2%): "atypical BL", corresponding to the atypical Burkitt's/Burkitt's-like variants of sporadic BL in the WHO classification; DC III (9/39, 23.1%): "C-MYC+ DLBCL"; and DC IV (14/39, 35.9%): "C-MYC- HPBCL". Conclusion: This proposal may serve as a robust and objective operational basis for therapeutic decisions for HPBCL within one week and is applicable to be evaluated for its prognostic relevance in prospective clinical trials

Reduction in Spoilage Microbiota and Cyclopiazonic Acid Mycotoxin with Chestnut Extract Enriched Chitosan Packaging: Stability of Inoculated Gouda Cheese

Active chitosan-based films, blended with fibrous chestnut (Castanea sativa Mill.) tannin-rich extract were used to pack Gouda cheese that has been contaminated with spoilage microflora Pseudomonas fluorescens, Escherichia coli, and fungi Penicillium commune. A comprehensive experimental plan including active chitosan-based films with (i) chestnut extract (CE), (ii) tannic acid (TA), and (iii) without additives was applied to evaluate the film′s effect on induced microbiological spoilage reduction and chemical indices of commercial Gouda cheese during 37 days while stored at 4 °C and 25 °C, respectively. The cheese underwent microbiology analysis and chemical assessments of ultra-high-performance liquid chromatography (UHPLC) (cyclopiazonic acid), pH, and moisture content. The biopackaging used for packing cheese was characterized by mechanical properties before food packaging and analyzed with the same chemical analysis. The cheese microbiology showed that the bacterial counts were most efficiently decreased by the film without additives. However, active films with CE and TA were more effective as they did not break down around the cheese and showed protective properties against mycotoxin, moisture loss, and pH changes. Films themselves, when next to high-fat content food, changed their pH to less acidic, acted as absorbers, and degraded without plant-derived additives

Biodegradability study of active chitosan biopolymer films enriched with Quercus polyphenol extract in different soil types

One of the recent trends within the circular economy is the development of materials derived from food processing waste and their utility as an alternative to plastic packaging. In this context, the study aims to evaluate biological causes of deterioration or degradation of chitosan-based films with and without incorporated natural Quercus polyphenol extract in three different types of soils (industrial compost, commercial garden soil, and soil from a vineyard). Degradation and active properties deterioration was followed by measurement of the loss of mass of tested active films for 14 days, and it was accompanied by other analytical techniques such as measurement of polyphenolic content, FT-IR analysis, and SEM examination of the packaging morphology. The results showed that chitosan-based film properties deteriorate in less than 3 days followed by biodegradation in all tested soils after 14 days. Films with incorporated Quercus polyphenol extract undergo deterioration of active properties in compost and garden soil in 6 days, while the fractionation and degradation process has not been complete in the vineyard soil during the 14 days. Furthermore, it has also been revealed that the addition of water to the soil decreased the rate of active chitosan film biodegradation in the terrestrial environment

Identifying dynamical systems with bifurcations from noisy partial observation

Dynamical systems are used to model a variety of phenomena in which the bifurcation structure is a fundamental characteristic. Here we propose a statistical machine-learning approach to derive lowdimensional models that automatically integrate information in noisy time-series data from partial observations. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation structure.Comment: 16 pages, 6 figure

Multimodal Emotion Classification

Most NLP and Computer Vision tasks are limited to scarcity of labelled data. In social media emotion classification and other related tasks, hashtags have been used as indicators to label data. With the rapid increase in emoji usage of social media, emojis are used as an additional feature for major social NLP tasks. However, this is less explored in case of multimedia posts on social media where posts are composed of both image and text. At the same time, w.e have seen a surge in the interest to incorporate domain knowledge to improve machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emotion classification task. We exploit the importance of different modalities from social media post for emotion classification task using state-of-the-art deep learning architectures. Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other. Our results also demonstrate that emoji sense depends on the textual context, and emoji combined with text encodes better information than considered separately. The highest accuracy of 71.98\% is achieved with a training data of 550k posts.Comment: Accepted at the 2nd Emoji Workshop co-located with The Web Conference 201