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Journal articles on the topic 'Biological framework'

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Author: Grafiati

Published: 11 March 2023

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1

Sciberras, Josette, Raymond Zammit, and Patricia Vella Bonanno. "The European framework for intellectual property rights for biological medicines." Generics and Biosimilars Initiative Journal 10, no. 4 (December 15, 2021): 172–83. http://dx.doi.org/10.5639/gabij.2021.1004.022.

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Introduction: The Pharmaceutical Strategy for Europe (2020) proposes actions related to intellectual property (IP) rights as a means of ensuring patients’ access to medicines. This review aims to describe and discuss the European IP framework and its impact on accessibility of biological medicines and makes some recommendations. Methods: A non-systematic literature review on IP for biological medicines was conducted. Data on authorizations and patent and exclusivity expiry dates of biological medicines obtained from the European Medicines Agency’s (EMA) website and literature was analysed quantitatively and qualitatively. Results: The analysis showed that as at end July 2021, 1,238 medicines were authorized in Europe, of which 332 (26.8%) were biological medicines. There were only 55 biosimilars for 17 unique biologicals. There is an increasing trend in biological authorizations but signifi cant delays in submission of applications for marketing authorization of biosimilars, with no signifi cant diff erences in the time for assessment for marketing authorization between originator biologicals and biosimilars. For some of the more recent biosimilars, applications for authorization were submitted prior to patent and exclusivity expiry. COVID vaccines confi rmed the impact of knowledge transfer on accessibility, especially when linked to joint procurement. Discussion: IP protects originator products and impacts the development of biosimilars. Strategies to improve competition in the EU biological market are discussed. Pricing policies alone do not increase biosimilar uptake since patients are switched to second generation products. Evergreening strategies might be abusing the IP framework, and together with trade secrets and disproportionate prices compared to R & D and manufacturing costs lead to an imbalance between market access and innovation. Conclusion: The European Pharmaceutical Strategy should focus on IP initiatives that support earlier authorization of biosimilars of new biologicals. Recommendations include knowledge sharing, simplifi cation of the regulatory framework and transparency of prices and R & D costs.

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2

Belova, D. A. "Legal Framework for Reproductive Biological Material." Lex Russica, no. 7 (July 19, 2021): 111–21. http://dx.doi.org/10.17803/1729-5920.2021.176.7.111-121.

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The paper is devoted to the study of the legal nature of reproductive biological material and determination of the optimal legal regime of germ cells (oocytes, sperm) and tissues of human reproductive organs intended for reproduction purposes. It is noted that the reproductive biomaterial is not a thing, since it does not have the characteristics inherent in this legal category, and needs a special legal regime. The extension of the regime of ownership of the germ cells and tissues of human reproductive organs is unacceptable neither from the position of the current legislation, nor from the perspective of its development prospects. An analysis of the legal opportunities provided by the legislator in relation to reproductive biomaterial, as well as the procedure for their implementation, led to the conclusion that neither the persons from whom it comes, nor medical organizations can be recognized as its owners. The regime of property rights is not suitable for ensuring and protecting the interests of participants in public relations arising in connection with the use of reproductive biomaterial. It is proved that in relation to the germ cells and tissues of the reproductive organs, the interest of a person is not in acquiring actual and legal domination over them as such, but in acquiring or, on the contrary, not acquiring parental rights and obligations in relation to a child born as a result of their use. It is concluded that the designated interest should be mediated not by a real, but by a reproductive right.

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3

Gutschick, Vincent P., and Hormoz BassiriRad. "Biological Extreme Events: A Research Framework." Eos, Transactions American Geophysical Union 91, no. 9 (2010): 85. http://dx.doi.org/10.1029/2010eo090001.

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4

Song, Cheng Long, Chen Zou, Wen Ke Wang, and Si Kun Li. "An Integrated Framework for Biological Data Visualization." Advanced Materials Research 846-847 (November 2013): 1145–48. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1145.

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In the field of bioinformatics visualization, integrating software and data in different levels is the development trend. This paper presents an integration framework for biomolecular structure and genome sequences visualization. The framework can effectively support the data and software interoperability of biomolecular structure / genome sequences visualization. Based on the framework, we developed an integrated visualization system, which provides some new comprehensive visualization functions. Preliminary trial showed that the framework has a good prospect in the research of bioinformatics.

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5

M. Colombo, Rinaldo, and Elena Rossi. "A modeling framework for biological pest control." Mathematical Biosciences and Engineering 17, no. 2 (2020): 1413–27. http://dx.doi.org/10.3934/mbe.2020072.

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6

WEBB, BARBARA. "A FRAMEWORK FOR MODELS OF BIOLOGICAL BEHAVIOUR." International Journal of Neural Systems 09, no. 05 (October 1999): 375–81. http://dx.doi.org/10.1142/s0129065799000356.

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Modelling is most clearly understood as a adjunct in the process of deriving predictions from hypotheses. By representing a hypothesised mechanism in a model we hope by manipulating the model to understand the hypotheses' consequences. Eight dimensions on which models of biological behaviour can vary are described: the degree of realism with which they apply to biology; the level of biology they represent; the generality or range of systems the model is supposed to cover; the abstraction or amount of biological detail represented; the accuracy of representation of the mechanisms; the medium in which the model is built; the match of the model behaviour to biological behaviour; and the utility of the model in providing biological understanding and/or technical insight. It is hoped this framework will help to clarify debates over different approaches to modelling, particularly by pointing out how the above dimensions are relatively independent and should not be conflated.

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7

Horie, Ryota. "An optimization framework of biological dynamical systems." Journal of Theoretical Biology 253, no. 1 (July 2008): 45–54. http://dx.doi.org/10.1016/j.jtbi.2008.02.029.

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8

Maloney, Laurence T. "A mathematical framework for biological color vision." Behavioral and Brain Sciences 15, no. 1 (March 1992): 45–46. http://dx.doi.org/10.1017/s0140525x00067467.

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9

Schipper, Harvey, Eva A. Turley, and Michael Baum. "A new biological framework for cancer research." Lancet 348, no. 9035 (October 1996): 1149–51. http://dx.doi.org/10.1016/s0140-6736(96)06184-3.

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10

Blackburn, Tim M., Petr Pyšek, Sven Bacher, James T. Carlton, Richard P. Duncan, Vojtěch Jarošík, John R. U. Wilson, and David M. Richardson. "A proposed unified framework for biological invasions." Trends in Ecology & Evolution 26, no. 7 (July 2011): 333–39. http://dx.doi.org/10.1016/j.tree.2011.03.023.

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11

Mishra, Dinesh Kumar, Vinod Dhote, and Pradyumna Kumar Mishra. "Transdermal immunization: biological framework and translational perspectives." Expert Opinion on Drug Delivery 10, no. 2 (December 21, 2012): 183–200. http://dx.doi.org/10.1517/17425247.2013.746660.

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12

Chen, Huajun, Xi Chen, Peiqin Gu, Zhaohui Wu, and Tong Yu. "OWL Reasoning Framework over Big Biological Knowledge Network." BioMed Research International 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/272915.

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Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

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13

Maghrabi, Faheema, Hossam M. Faheem, Taysir Soliman, and Zaki T. Fayed. "A Multiagent-based Framework for Integrating Biological Data." International Journal of Intelligent Information Technologies 4, no. 2 (April 2008): 24–36. http://dx.doi.org/10.4018/jiit.2008040102.

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14

Thomas, Jeffrey D., Taesik Lee, and Nam P. Suh. "A Function-Based Framework for Understanding Biological Systems." Annual Review of Biophysics and Biomolecular Structure 33, no. 1 (June 9, 2004): 75–93. http://dx.doi.org/10.1146/annurev.biophys.33.110502.132654.

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15

Liang, Ping. "Semantic Web Framework for Biological Information Knowledge Base." Journal of Bionanoscience 7, no. 2 (April 1, 2013): 233–36. http://dx.doi.org/10.1166/jbns.2013.1124.

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16

Hoyt, Charles Tapley, Andrej Konotopez, and Christian Ebeling. "PyBEL: a computational framework for Biological Expression Language." Bioinformatics 34, no. 4 (October 18, 2017): 703–4. http://dx.doi.org/10.1093/bioinformatics/btx660.

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17

Cleveland, David A., Soleri Daniela, and Steven E. Smith. "A biological framework for understanding farmers’ plant breeding." Economic Botany 54, no. 3 (July 2000): 377–94. http://dx.doi.org/10.1007/bf02864788.

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18

Jetter, Karen. "Economic framework for decision making in biological control." Biological Control 35, no. 3 (December 2005): 348–57. http://dx.doi.org/10.1016/j.biocontrol.2005.07.007.

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19

Woolcott, Geoff. "Towards a Biological Framework for Learning and Teaching." International Journal of Learning: Annual Review 16, no. 7 (2009): 299–310. http://dx.doi.org/10.18848/1447-9494/cgp/v16i07/46404.

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20

Haigh, Carol A. "Reconstructing nursing altruism using a biological evolutionary framework." Journal of Advanced Nursing 66, no. 6 (April 1, 2010): 1401–8. http://dx.doi.org/10.1111/j.1365-2648.2010.05274.x.

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21

Karr, James R. "Biological monitoring and environmental assessment: a conceptual framework." Environmental Management 11, no. 2 (March 1987): 249–56. http://dx.doi.org/10.1007/bf01867203.

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22

Nahl, Diane. "Social–biological information technology: An integrated conceptual framework." Journal of the American Society for Information Science and Technology 58, no. 13 (2007): 2021–46. http://dx.doi.org/10.1002/asi.20690.

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23

Dumont, Sophie, and Manu Prakash. "Emergent mechanics of biological structures." Molecular Biology of the Cell 25, no. 22 (November 5, 2014): 3461–65. http://dx.doi.org/10.1091/mbc.e14-03-0784.

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Mechanical force organizes life at all scales, from molecules to cells and tissues. Although we have made remarkable progress unraveling the mechanics of life's individual building blocks, our understanding of how they give rise to the mechanics of larger-scale biological structures is still poor. Unlike the engineered macroscopic structures that we commonly build, biological structures are dynamic and self-organize: they sculpt themselves and change their own architecture, and they have structural building blocks that generate force and constantly come on and off. A description of such structures defies current traditional mechanical frameworks. It requires approaches that account for active force-generating parts and for the formation of spatial and temporal patterns utilizing a diverse array of building blocks. In this Perspective, we term this framework “emergent mechanics.” Through examples at molecular, cellular, and tissue scales, we highlight challenges and opportunities in quantitatively understanding the emergent mechanics of biological structures and the need for new conceptual frameworks and experimental tools on the way ahead.

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24

Neidhöfer, Claudio. "On the Evolution of the Biological Framework for Insight." Philosophies 6, no. 2 (May 21, 2021): 43. http://dx.doi.org/10.3390/philosophies6020043.

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The details of abiogenesis, to date, remain a matter of debate and constitute a key mystery in science and philosophy. The prevailing scientific hypothesis implies an evolutionary process of increasing complexity on Earth starting from (self-) replicating polymers. Defining the cut-off point where life begins is another moot point beyond the scope of this article. We will instead walk through the known evolutionary steps that led from these first exceptional polymers to the vast network of living biomatter that spans our world today, focusing in particular on perception, from simple biological feedback mechanisms to the complexity that allows for abstract thought. We will then project from the well-known to the unknown to gain a glimpse into what the universe aims to accomplish with living matter, just to find that if the universe had ever planned to be comprehended, evolution still has a long way to go.

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25

Yarden, Yosef. "The Biological Framework: Translational Research from Bench to Clinic." Oncologist 15, S5 (November 2010): 1–7. http://dx.doi.org/10.1634/theoncologist.2010-s5-01.

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26

Yarden, Yosef. "The Biological Framework: Translational Research from Bench to Clinic." Oncologist 16, S1 (January 2011): 23–29. http://dx.doi.org/10.1634/theoncologist.2011-s1-23.

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27

McCarthy, Sean. "A Biological Framework for Perceptual Video Processing and Compression." SMPTE Motion Imaging Journal 119, no. 8 (November 2010): 24–32. http://dx.doi.org/10.5594/j17294.

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28

M.F.ElHouby, Enas. "A Framework for Extracting Biological Relations from Different Resources." International Journal of Computer Applications 119, no. 3 (June 18, 2015): 1–8. http://dx.doi.org/10.5120/21044-3675.

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29

Gabriel, Clifford J., and R. James Cook. "Biological Control: The Need for a New Scientific Framework." BioScience 40, no. 3 (March 1990): 204–6. http://dx.doi.org/10.2307/1311366.

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30

Aggarwal, Ashwani Kumar. "Biological Tomato Leaf Disease Classification using Deep Learning Framework." International Journal of Biology and Biomedical Engineering 16 (March 27, 2022): 241–44. http://dx.doi.org/10.46300/91011.2022.16.30.

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Biological Tomato leaf classification is very important to decide the pesticide, insecticide, and other treatments needed for the plant to yield good crop. The images captured by handheld cameras or using drones are used by various machine learning algorithms to identify the diseases. Such methods need extraction of features from the images before the machine learning methods can be used for disease identification. In this paper, a deep learning framework is proposed that automatically extracts features in a hierarchical manner. The features are classified using neural networks to classify the leaves into three classes, viz. no disease, bacterial spot, and Septoria leaf spot. The performance of the model is tested using accuracy as the performance metric. The obtained performance metric validates the performance of the method. The method is useful for taking corrective measures to disease management of tomato plants.

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31

Alon, Noga, Vera Asodi, Charles Cantor, Simon Kasif, and John Rachlin. "Multi-Node Graphs: A Framework for Multiplexed Biological Assays." Journal of Computational Biology 13, no. 10 (December 2006): 1659–72. http://dx.doi.org/10.1089/cmb.2006.13.1659.

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32

Barnat, Jiří, Luboš Brim, Ivana Černá, Sven Dražan, Jana Fabriková, Jan Láník, David Šafránek, and Hongwu Ma. "BioDiVinE: A Framework for Parallel Analysis of Biological Models." Electronic Proceedings in Theoretical Computer Science 6 (October 8, 2009): 31–45. http://dx.doi.org/10.4204/eptcs.6.3.

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33

Shelton, Andrew Olaf, James Lawrence O'Donnell, Jameal F. Samhouri, Natalie Lowell, Gregory D. Williams, and Ryan P. Kelly. "A framework for inferring biological communities from environmental DNA." Ecological Applications 26, no. 6 (September 2016): 1645–59. http://dx.doi.org/10.1890/15-1733.1.

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34

Paul, Torsten Johann, and Philip Kollmannsberger. "Biological network growth in complex environments: A computational framework." PLOS Computational Biology 16, no. 11 (November 30, 2020): e1008003. http://dx.doi.org/10.1371/journal.pcbi.1008003.

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Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. These biological functions depend on the architecture of the network, which emerges as the result of a dynamic, feedback-driven developmental process. While cell behavior during growth can be genetically encoded, the resulting network structure depends on spatial constraints and tissue architecture. Since network growth is often difficult to observe experimentally, computer simulations can help to understand how local cell behavior determines the resulting network architecture. We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. Growth is described as a biased correlated random walk where direction and branching depend on the local environmental conditions and constraints, which are presented as 3D multilayer grid. To demonstrate the application of our tool, we perform growth simulations of a dense network between cells and compare the results to experimental data from osteocyte networks in bone. Our generic framework might help to better understand how network patterns depend on spatial constraints, or to identify the biological cause of deviations from healthy network function.

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35

Xu, Li-Li, Hai-Feng Zhang, Mian Li, Seik Weng Ng, Jiang-He Feng, Jiang-Gao Mao, and Dan Li. "Chiroptical Activity from an Achiral Biological Metal–Organic Framework." Journal of the American Chemical Society 140, no. 37 (August 24, 2018): 11569–72. http://dx.doi.org/10.1021/jacs.8b06725.

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36

Bovigny, Christophe, Giorgio Tamò, Thomas Lemmin, Nicolas Maïno, and Matteo Dal Peraro. "LipidBuilder:A Framework To Build Realistic Models for Biological Membranes." Journal of Chemical Information and Modeling 55, no. 12 (December 10, 2015): 2491–99. http://dx.doi.org/10.1021/acs.jcim.5b00501.

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37

Battré, Dominic, and David Sigfredo Angulo. "MPI framework for parallel searching in large biological databases." Journal of Parallel and Distributed Computing 66, no. 12 (December 2006): 1503–11. http://dx.doi.org/10.1016/j.jpdc.2006.08.003.

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38

Kumar, Pawan, Vasudha Bansal, A. K. Paul, Lalit M. Bharadwaj, Akash Deep, and Ki-Hyun Kim. "Biological applications of zinc imidazole framework through protein encapsulation." Applied Nanoscience 6, no. 7 (December 1, 2015): 951–57. http://dx.doi.org/10.1007/s13204-015-0511-x.

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39

Pearson, Dean E., Yvette K. Ortega, Özkan Eren, and José L. Hierro. "Community Assembly Theory as a Framework for Biological Invasions." Trends in Ecology & Evolution 33, no. 5 (May 2018): 313–25. http://dx.doi.org/10.1016/j.tree.2018.03.002.

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40

Shea, K. "Community ecology theory as a framework for biological invasions." Trends in Ecology & Evolution 17, no. 4 (April 1, 2002): 170–76. http://dx.doi.org/10.1016/s0169-5347(02)02495-3.

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41

JABER, Khalid Mohammad, Nesreen A. HAMAD, and Fatima M. QUIAM. "A Framework for Query Optimization Algorithms for Biological Data." International Journal of Computational and Experimental Science and Engineering 5, no. 2 (July 31, 2019): 76–79. http://dx.doi.org/10.22399/ijcesen.508889.

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42

Van Arsdale, Adam P. "A Shifting Theoretical Framework for Biological Anthropology in 2012." American Anthropologist 115, no. 2 (May 17, 2013): 262–72. http://dx.doi.org/10.1111/aman.12008.

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43

Mshvidobadze, Tinatin. "Bioinformatics as emerging tool and pipeline framework." Science Progress and Research 2, no. 1 (January 10, 2022): 491–95. http://dx.doi.org/10.52152/spr/2022.156.

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Biological data are being produced at phenomenal rate and storing, analyzing and interpreting such data in a meaningful way is assuming greater significance. In this article, we will discuss the areas of origin of bioinformatics in the human health care system. Due to the growing network of biological information databases such as human genomes, transcriptomics and proteomics, bioinformatics has become the approach of choosing forensic sciences. High-throughput bioinfor-matic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Here we survey and compare the design philosophies of several current pipeline frameworks.The contributions from the field of biological and medical sciences have facilitated a tremendous increase in the data on various aspects as highlighted above in the text. The results of genomic research will bring a revolution into the field of medicine. The links between various databases of biological and medical significance are important and bioinformatics plays a very vital role in this direction.

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44

Clark, Richard J., Norelle L. Daly, and David J. Craik. "Structural plasticity of the cyclic-cystine-knot framework: implications for biological activity and drug design." Biochemical Journal 394, no. 1 (January 27, 2006): 85–93. http://dx.doi.org/10.1042/bj20051691.

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The cyclotide family of plant proteins is of interest because of their unique topology, which combines a head-to-tail cyclic backbone with an embedded cystine knot, and because their remarkable chemical and biological properties make them ideal candidates as grafting templates for biologically active peptide epitopes. The present study describes the first steps towards exploiting the cyclotide framework by synthesizing and structurally characterizing two grafted analogues of the cyclotide kalata B1. The modified peptides have polar or charged residues substituted for residues that form part of a surface-exposed hydrophobic patch that plays a significant role in the folding and biological activity of kalata B1. Both analogues retain the native cyclotide fold, but lack the undesired haemolytic activity of their parent molecule, kalata B1. This finding confirms the tolerance of the cyclotide framework to residue substitutions and opens up possibilities for the substitution of biologically active peptide epitopes into the framework.

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45

Huskey, Richard, Amelia Couture Bue, Allison Eden, Clare Grall, Dar Meshi, Kelsey Prena, Ralf Schmälzle, Christin Scholz, Benjamin O. Turner, and Shelby Wilcox. "Marr’s Tri-Level Framework Integrates Biological Explanation Across Communication Subfields." Journal of Communication 70, no. 3 (June 1, 2020): 356–78. http://dx.doi.org/10.1093/joc/jqaa007.

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Abstract In this special issue devoted to speaking across communication subfields, we introduce a domain general explanatory framework that integrates biological explanation with communication science and organizes our field around a shared explanatory empirical model. Specifically, we draw on David Marr’s classical framework, which subdivides the explanation of human behavior into three levels: computation (why), algorithm (what), and implementation (how). Prior theorizing and research in communication has primarily addressed Marr’s computational level (why), but has less frequently investigated algorithmic (what) or implementation (how all communication phenomena emerge from and rely on biological processes) explanations. Here, we introduce Marr’s framework and apply it to three research domains in communication science—audience research, persuasion, and social comparisons—to demonstrate what a unifying framework for explaining communication across the levels of why, what, and how can look like, and how Marr’s framework speaks to and receives input from all subfields of communication inquiry.

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46

Mostafa, Hesham, and Gert Cauwenberghs. "A Learning Framework for Winner-Take-All Networks with Stochastic Synapses." Neural Computation 30, no. 6 (June 2018): 1542–72. http://dx.doi.org/10.1162/neco_a_01080.

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Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this letter, we show that a biologically motivated model based on multilayer winner-take-all circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task and a semisupervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

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47

Mindiola, Lácides Pinto, Gelvis Melo Freile, and Carlos Socarras Bertiz. "Biological Inspiration—Theoretical Framework Mitosis Artificial Neural Networks Unsupervised Algorithm." International Journal of Communications, Network and System Sciences 08, no. 09 (2015): 374–98. http://dx.doi.org/10.4236/ijcns.2015.89036.

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48

Garcia, R., L. E. Caltagirone, and A. P. Gutierrez. "Biological Control: The Need for a New Scientific Framework: Reply." BioScience 40, no. 3 (March 1990): 207. http://dx.doi.org/10.2307/1311367.

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49

Arslan, Ferhat, and Handan Ankaralı. "Sars-Cov-2 virus and vaccination; biological and statistical framework." Expert Review of Vaccines 20, no. 9 (August 11, 2021): 1059–63. http://dx.doi.org/10.1080/14760584.2021.1965884.

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50

Aliaj, Erjola. "AN ASSESSMENT OF THE BIOLOGICAL DAMAGE IN ITALIAN LEGAL FRAMEWORK." Revue Européenne du Droit Social 57, no. 4 (August 29, 2022): 106–12. http://dx.doi.org/10.53373/reds.2022.55.2.0090.

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