scholarly journals Neural models for monitoring the transmembrane flux in the vinasse clarification process by crossflow microfiltration

Author(s):  
André Arthur Bueno da Silva ◽  
Juliana Maria da Silva ◽  
Érica Regina Filletti
1993 ◽  
Vol 69 (05) ◽  
pp. 496-502 ◽  
Author(s):  
Yasuo Ikeda ◽  
Makoto Handa ◽  
Tetsuji Kamata ◽  
Koichi Kawano ◽  
Yohko Kawai ◽  
...  

SummaryWe found that the binding of multimeric vWF to GP Ib under a shear force of 108 dynes/cm2 resulted in the transmembrane flux of Ca2+ ions with a two-to three-fold increase in their intracellular concentration ([Ca2+]i). The blockage of this event, obtained by inhibiting the vWF-GP Ib interaction, suppressed aggregation. In contrast, the blockage of vWF binding to GP IIb-IIIa, as well as the prevention of activation caused by increased intracellular cAMP levels, inhibited aggregation but had no significant effect on [Ca2+]i increase. A monomeric recombinant fragment of vWF containing the GP Ib-binding domain of the molecule (residues 445-733) prevented all effects mediated by multimeric vWF but, by itself, failed to support the increase in [Ca2+]i and aggregation. These results suggest that the binding of multimeric vWF to GP Ib initiates platelets aggregation induced by high shear stress by mediating a transmembrane flux of Ca2+ ions, perhaps through a receptor-dependent calcium channel. The increase in [Ca2+]i may act as an intracellular message and cause the activation of GP IIb-IIIa; the latter receptor then binds vWF and mediates irreversible aggregation.


1996 ◽  
Vol 75 (04) ◽  
pp. 655-660 ◽  
Author(s):  
Mario Mazzucato ◽  
Luigi De Marco ◽  
Paola Pradella ◽  
Adriana Masotti ◽  
Francesco I Pareti

SummaryPorcine von Willebrand factor (P-vWF) binds to human platelet glycoprotein (GP) lb and, upon stirring (1500 rpm/min) at 37° C, induces, in a dose-dependent manner, a transmembrane flux of Ca2+ ions and platelet aggregation with an increase in their intracellular concentration. The inhibition of P-vWF binding to GP lb, obtained with anti GP lb monoclonal antibody (LJ-Ib1), inhibits the increase of intracellular Ca2+ concentration ([Ca2+]i) and platelet aggregation. This effect is not observed with LJ-Ib10, an anti GP lb monoclonal antibody which does not inhibit the vWF binding to GP lb. An anti GP Ilb-IIIa monoclonal antibody (LJ-CP8) shown to inhibit the binding of both vWF and fibrinogen to the GP IIb-IIIa complex, had only a slight effect on the [Ca2+]i rise elicited by the addition of P-vWF. No inhibition was also observed with a different anti GP IIb-IIIa monoclonal antibody (LJ-P5), shown to block the binding of vWF and not that of fibrinogen to the GP IIb-IIIa complex. PGE1, apyrase and indomethacin show a minimal effect on [Ca2+]i rise, while EGTA completely blocks it. The GP lb occupancy by recombinant vWF fragment rvWF445-733 completely inhibits the increase of [Ca2+]i and large aggregates formation. Our results suggest that, in analogy to what is seen with human vWF under high shear stress, the binding of P-vWF to platelet GP lb, at low shear stress and through the formation of aggregates of an appropriate size, induces a transmembrane flux of Ca2+, independently from platelet cyclooxy-genase metabolism, perhaps through a receptor dependent calcium channel. The increase in [Ca2+]i may act as an intracellular message and cause the activation of the GP IIb-IIIa complex.


2021 ◽  
pp. 1-12
Author(s):  
Yingwen Fu ◽  
Nankai Lin ◽  
Xiaotian Lin ◽  
Shengyi Jiang

Named entity recognition (NER) is fundamental to natural language processing (NLP). Most state-of-the-art researches on NER are based on pre-trained language models (PLMs) or classic neural models. However, these researches are mainly oriented to high-resource languages such as English. While for Indonesian, related resources (both in dataset and technology) are not yet well-developed. Besides, affix is an important word composition for Indonesian language, indicating the essentiality of character and token features for token-wise Indonesian NLP tasks. However, features extracted by currently top-performance models are insufficient. Aiming at Indonesian NER task, in this paper, we build an Indonesian NER dataset (IDNER) comprising over 50 thousand sentences (over 670 thousand tokens) to alleviate the shortage of labeled resources in Indonesian. Furthermore, we construct a hierarchical structured-attention-based model (HSA) for Indonesian NER to extract sequence features from different perspectives. Specifically, we use an enhanced convolutional structure as well as an enhanced attention structure to extract deeper features from characters and tokens. Experimental results show that HSA establishes competitive performance on IDNER and three benchmark datasets.


Author(s):  
Yesim Cemek ◽  
Cenk Cidecio ◽  
Asli Umay Ozturk ◽  
Recep Firat Cekinel ◽  
Pinar Karagoz
Keyword(s):  

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 307
Author(s):  
Dawid Wojcieszak ◽  
Maciej Zaborowicz ◽  
Jacek Przybył ◽  
Piotr Boniecki ◽  
Aleksander Jędruś

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.


2019 ◽  
Vol 67 (6) ◽  
pp. 2143-2150 ◽  
Author(s):  
Andres Viveros-Wacher ◽  
Jose Ernesto Rayas-Sanchez ◽  
Zabdiel Brito-Brito

1996 ◽  
Vol 112 (2) ◽  
pp. 287-296 ◽  
Author(s):  
Muhammad H. Al-Malack ◽  
G.K. Anderson

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