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Relevant bibliographies by topics / Biology Classification

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Academic literature on the topic 'Biology Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Biology Classification"

1

Kunimoto, Kayo, Yuki Yamamoto, and Masatoshi Jinnin. "ISSVA Classification of Vascular Anomalies and Molecular Biology." International Journal of Molecular Sciences 23, no. 4 (February 21, 2022): 2358. http://dx.doi.org/10.3390/ijms23042358.

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Vascular anomalies include various diseases, which are classified into two types according to the International Society for the Study of Vascular Anomalies (ISSVA) classification: vascular tumors with proliferative changes of endothelial cells, and vascular malformations primarily consisting of structural vascular abnormalities. The most recent ISSVA classifications, published in 2018, detail the causative genes involved in many lesions. Here, we summarize the latest findings on genetic abnormalities, with the presentation of the molecular pathology of vascular anomalies.

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Tsikitis, Vassiliki L., and Maureen A. Chung. "Biology of Ductal Carcinoma in Situ Classification Based on Biologic Potential." American Journal of Clinical Oncology 29, no. 3 (June 2006): 305–10. http://dx.doi.org/10.1097/01.coc.0000198740.33617.2f.

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Rindi, G. "Classification and Tumor Biology of Nets." Annals of Oncology 23 (September 2012): ix47. http://dx.doi.org/10.1093/annonc/mds384.

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Carey, Francis A. "Pulmonary adenocarcinoma: classification and molecular biology." Journal of Pathology 184, no. 3 (March 1998): 229–30. http://dx.doi.org/10.1002/(sici)1096-9896(199803)184:3<229::aid-path18>3.0.co;2-0.

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Wang, Yunxiang, and Yupei Wang. "BIOLOGY AND CLASSIFICATION OF PRYMNESIUM SALTANS." Acta Hydrobiologica Sinica 16, no. 3 (July 1, 1992): 193–99. http://dx.doi.org/10.3724/issn1000-3207-1992-3-193-j.

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Kerr, Keith M. "Classification of Lung Cancer: Proposals for Change?" Archives of Pathology & Laboratory Medicine 136, no. 10 (October 1, 2012): 1190–93. http://dx.doi.org/10.5858/arpa.2012-0240-sa.

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Clinical need and developments in pathology and molecular biology require our cancer classifications to be constantly updated to keep them relevant and useful. A review of lung cancer classification is due and has been initiated with new proposals on classification of lung adenocarcinoma. Other major lung cancer types also deserve a similar consideration. As well as addressing the categories of tumor, as signed out in surgical resection specimens, recent proposals on small diagnostic-sample reporting would be an important addition to any new classification. The huge increase in data on the molecular biology of lung cancer has improved our understanding of these diseases, has driven improved therapy for some patients, and must be reflected in the way lung cancer is classified.

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Kluge, Arnold G., and Alec L. Panchen. "Classification, Evolution and the Nature of Biology." Systematic Biology 42, no. 4 (December 1993): 591. http://dx.doi.org/10.2307/2992493.

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Lewall, D. B. "Hydatid disease: Biology, pathology, imaging and classification." Clinical Radiology 53, no. 12 (December 1998): 863–74. http://dx.doi.org/10.1016/s0009-9260(98)80212-2.

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Harrison, Terry. "Classification, evolution, and the nature of biology." International Journal of Primatology 15, no. 3 (June 1994): 503–5. http://dx.doi.org/10.1007/bf02696108.

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Stéphanou, A., and V. Volpert. "Hybrid Modelling in Biology: a Classification Review." Mathematical Modelling of Natural Phenomena 11, no. 1 (December 3, 2015): 37–48. http://dx.doi.org/10.1051/mmnp/201611103.

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Dissertations / Theses on the topic "Biology Classification"

1

Lantz, Henrik. "Phylogeny and classification of the tribe Vanguerieae (Rubiaceae)." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.bibl. [distributör], 2003. http://publications.uu.se/theses/91-554-5529-8/.

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Martin, Traykovski Linda V. (Linda Victoria) 1966. "Acoustic classification of zooplankton." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/49620.

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Wang, Min-Hui. "Classification using phylogenetic trees /." The Ohio State University, 1999. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488190595939375.

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Currie, Sheila. "Data classification for choropleth mapping." Thesis, University of Ottawa (Canada), 1989. http://hdl.handle.net/10393/5725.

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Sanassy, Daven. "Meta-stochastic simulation for systems and synthetic biology using classification." Thesis, University of Newcastle upon Tyne, 2015. http://hdl.handle.net/10443/2993.

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To comprehend the immense complexity that drives biological systems, it is necessary to generate hypotheses of system behaviour. This is because one can observe the results of a biological process and have knowledge of the molecular/genetic components, but not directly witness biochemical interaction mechanisms. Hypotheses can be tested in silico which is considerably cheaper and faster than “wet” lab trialand- error experimentation. Bio-systems are traditionally modelled using ordinary differential equations (ODEs). ODEs are generally suitable for the approximation of a (test tube sized) in vitro system trajectory, but cannot account for inherent system noise or discrete event behaviour. Most in vivo biochemical interactions occur within small spatially compartmentalised units commonly known as cells, which are prone to stochastic noise due to relatively low intracellular molecular populations. Stochastic simulation algorithms (SSAs) provide an exact mechanistic account of the temporal evolution of a bio-system, and can account for noise and discrete cellular transcription and signalling behaviour. Whilst this reaction-by-reaction account of system trajectory elucidates biological mechanisms more comprehensively than ODE execution, it comes at increased computational expense. Scaling to the demands of modern biology requires ever larger and more detailed models to be executed. Scientists evaluating and engineering tissue-scale and bacterial colony sized biosystems can be limited by the tractability of their computational hypothesis testing techniques. This thesis evaluates a hypothesised relationship between SSA computational performance and biochemical model characteristics. This relationship leads to the possibility of predicting the fastest SSA for an arbitrary model - a method that can provide computational headroom for more complex models to be executed. The research output of this thesis is realised as a software package for meta-stochastic simulation called ssapredict. Ssapredict uses statistical classification to predict SSA performance, and also provides high performance stochastic simulation implementations to the wider community.

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Qiao, Mu. "Morphological, Physiological and Molecular Classification of Mouse Retinal Ganglion Cells." Thesis, Harvard University, 2016. http://nrs.harvard.edu/urn-3:HUL.InstRepos:26718723.

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Visual information is conveyed from the retina to the brain through axons of retinal ganglion cells (RGCs). There are >20 different subtypes of RGCs, each of which detects specific features. Classification of RGC subtypes is thus essential for us to understand how visual information is processed and delivered to the brain. Here I reported my efforts in classifying different subtypes of RGCs, using morphological, physiological and molecular criteria. A combination of these criteria allowed me to successfully identify subtypes from alpha RGCs, Foxp2-positive RGCs (F-RGCs) and RGCs labeled in a transgenic mouse line W3. First, I presented studies of classifying subtypes of alpha RGCs. Cell attached recording followed by morphology reconstruction revealed four subtypes of alpha-like RGCs: Off-sustained, Off-transient, On-sustained, On-transient subtypes, each of which has distinct morphological properties. In addition, we found osteopontin (OPN) as a molecular marker for all alpha RGCs. Following this discovery, we studied the role of OPN in alpha RGCs, Analysis showed that alpha RGCs preferentially survive and regenerate compared with other RGCs, leading us to test whether OPN can promote axon regeneration. Indeed, by combining OPN with growth factors, we were able to promote axon regenerations of RGCs. Second, I presented work in classifying subtypes of F-RGCs, which are recognized by expressing a transcription factor, Foxp2. Combinatory expression of Foxp2 with other transcriptional factors divides F-RGCs into four subtypes, which form two pairs differing in their dendritic field sizes. Cell attached recording showed that one pair, F-minion and F-minioff RGCs, are direction-selective, while the other pair, F-midion and F-midioff RGCs, are not. Thus, we identified four new subtypes of RGCs labeled by transcriptional factor Foxp2. Third, I described initial efforts in classifying subtypes of RGCs labeled in the transgenic mouse line W3. W3 RGCs can be separated into two group based on their expression levels of fluorescent proteins, with the dimly labeled RGCs (W3D) remained uncharacterized. Initial analysis showed W3D RGCs include at least five subtypes of RGCs, which are different in their structures and physiological properties. Lastly, I described my work in developing a molecular tool for mapping electrical synaptic connections from genetically defined neurons or neuronal subtypes, making use of a dipeptide transporter, Pept2. Cells expressing Pept2 (in a Cre-dependent way) take up a gap junction permeable fluorescent dipeptide, which then diffuses and labels the coupled cells. We tested this method in cultured cells and validated it in mouse retina using AAV carrying Cre-dependent Pept2. I applied this method to one subtype of RGCs, J-RGCs, to label their coupling partners.
Biology, Molecular and Cellular

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Liang, Jiarong. "Federated Learning for Bioimage Classification." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420615.

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Graham, Martin. "Visualising multiple overlapping classification hierarchies." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/2430.

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The revision or reorganisation of hierarchical data sets can result in many possible hierarchical classifications composed of the same or overlapping data sets existing in parallel with each other. These data sets are difficult for people to handle and conceptualise, as they try to reconcile the different perspectives and structures that such data represents. One area where this situation occurs is the study of botanical taxonomy, essentially the classification and naming of plants. Revisions, new discoveries and new dimensions for classifying plants lead to a proliferation of classifications over the same set of plant data. Taxonomists would like a method of exploring these multiple overlapping hierarchies for interesting information, correlations, or anomalies. The application and extension of Information Visualisation (IV) techniques, the graphical display of abstract information, is put forward as a solution to this problem. Displaying the multiple classification hierarchies in a visually appealing manner along with powerful interaction mechanisms for examination and exploration of the data allows taxonomists to unearth previously hidden information. This visualisation gives detail that previous visualisations and statistical overviews cannot offer. This thesis work has extended previous IV work in several respects to achieve this goal. Compact, yet full and unambiguous, hierarchy visualisations have been developed. Linking and brushing techniques have been extended to work on a higher class of structure, namely overlapping trees and hierarchies. Focus and context techniques have been pushed to achieve new effects across the visually distinct representations of these multiple hierarchies. Other data types, such as multidimensional data and large cluster hierarchies have also been displayed using the final version of the visualisation.

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Civallero, Edgardo. "UDC Biology Revision Project: First Stage: Class 59 Vertebrates." UDC Consortium, 2010. http://hdl.handle.net/10150/200632.

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The paper presents and describes the work on the revision of the zoology of vertebrates, which is published in E&C 32 and introduced in UDC MRF 2010. This is the first stage of a larger project of revision, correction and update affecting all tables related to systematics (zoology, botany, microbiology and virology) to be undertaken from 2011-2013. The first part of the paper briefly introduces the current systems of classification of living and extinct beings, and explains how different perspectives with respect to the arrangement of biological entities have been reflected (or not) in the UDC schedules. The second part gives an overview of problems detected in UDC prior to this revision and explains solutions that were implemented in UDC MRF 2010 indicating tools and methods used in this work.

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Xiong, Kuangnan. "Roughened Random Forests for Binary Classification." Thesis, State University of New York at Albany, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3624962.

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Binary classification plays an important role in many decision-making processes. Random forests can build a strong ensemble classifier by combining weaker classification trees that are de-correlated. The strength and correlation among individual classification trees are the key factors that contribute to the ensemble performance of random forests. We propose roughened random forests, a new set of tools which show further improvement over random forests in binary classification. Roughened random forests modify the original dataset for each classification tree and further reduce the correlation among individual classification trees. This data modification process is composed of artificially imposing missing data that are missing completely at random and subsequent missing data imputation.

Through this dissertation we aim to answer a few important questions in building roughened random forests: (1) What is the ideal rate of missing data to impose on the original dataset? (2) Should we impose missing data on both the training and testing datasets, or only on the training dataset? (3) What are the best missing data imputation methods to use in roughened random forests? (4) Do roughened random forests share the same ideal number of covariates selected at each tree node as the original random forests? (5) Can roughened random forests be used in medium- to high- dimensional datasets?

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Books on the topic "Biology Classification"

1

Monger, Grace. Systematics and classification. Harlow: Longman for the Nuffield-Chelsea Curriculum Trust, 1988.

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Minelli, Alessandro. Biological systematics: The state of the art. London: Chapman & Hall, 1993.

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Margulis, Lynn. Kingdoms & domains: An illustrated guide to the phyla of life on Earth. 4th ed. Amsterdam: Academic Press/Elsevier, 2009.

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Sebastiá, María Teresa. Phytoplankton: Biology, classification, and environmental impacts. Hauppauge, New York: Nova Biomedical, 2014.

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Richard, Moore, and Cambridge Educational (Firm), eds. The classification of living things. Lawrenceville, NJ: Cambridge Educational, 2007.

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Ereshefsky, Marc. The poverty of Linnaean hierarchy: A philosophical study of biological taxonomy. Cambridge, U.K: Cambridge University Press, 2001.

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Joel, Cracraft, and Donoghue Michael J, eds. Assembling the tree of life. New York, N.Y: Oxford University Press, 2004.

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L, Hawksworth D., and Systematics Association, eds. Prospects in systematics. Oxford: Published for the Systematics Association by Clarendon Press, 1988.

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Japan) International Symposium of Biological Shape Analysis (1st 2009 Tsukuba-shi. Biological shape analysis: Proceedings of the 1st International Symposium, Tsukuba, Japan, 3-6 June 2009. Edited by Lestrel Pete E. New Jersey: World Scientific, 2011.

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Biologists, Society of Systematic. Systematic biology. Washington, D.C., USA: Society of Systematic Biologists, 1992.

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Book chapters on the topic "Biology Classification"

1

Davies, R. G. "Classification and biology." In Outlines of Entomology, 115–81. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1189-5_4.

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Davies, R. G. "Classification and biology." In Outlines of Entomology, 115–81. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-017-0508-0_4.

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Hüllermeier, Eyke, Thomas Fober, and Marco Mernberger. "Classification." In Encyclopedia of Systems Biology, 407–10. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_606.

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Koch, Friedrich, and Gebhard Koch. "Classification." In The Molecular Biology of Poliovirus, 15–27. Vienna: Springer Vienna, 1985. http://dx.doi.org/10.1007/978-3-7091-7000-7_2.

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Ali-Ahmed, Syed Toufeeq. "Text Classification." In Encyclopedia of Systems Biology, 2156. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_174.

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McDowall, Jennifer, and Sarah Hunter. "InterPro Protein Classification." In Methods in Molecular Biology, 37–47. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-977-2_3.

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Radovich, Theodore J. K. "Biology and Classification of Vegetables." In Handbook of Vegetables and Vegetable Processing, 1–22. Oxford, UK: Wiley-Blackwell, 2011. http://dx.doi.org/10.1002/9780470958346.ch1.

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Radovich, Theodore J. K. "Biology and Classification of Vegetables." In Handbook of Vegetables and Vegetable Processing, 1–23. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119098935.ch1.

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Chun, Hoon Jai, Seun Ja Park, Yun Jeong Lim, and Si Young Song. "Classification, Pathology, and Tumor Biology." In Gastrointestinal Cancer, 423–29. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0815-8_58.

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Matsuura, Makoto, and Seiki Yamane. "Phylogeny and Classification." In Biology of the Vespine Wasps, 219–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75230-8_10.

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Conference papers on the topic "Biology Classification"

1

Sztojanov, Istvan, Vasilica Voinea, and Catalina Popescu-Mina. "Image Processing for Classification in Biology Systems." In 2007 2nd International Workshop on Soft Computing Applications. IEEE, 2007. http://dx.doi.org/10.1109/sofa.2007.4318301.

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Dumitrescu, Adina, Dumitru Popescu, Catalina Mina Popescu, and Livia Petrescu. "Classification based on the anatomo-fractal dimension in biology." In 2017 21st International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2017. http://dx.doi.org/10.1109/icstcc.2017.8107078.

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Lundström, Johanna. "Blue target classification and Heureka." In 5th European Congress of Conservation Biology. Jyväskylä: Jyvaskyla University Open Science Centre, 2018. http://dx.doi.org/10.17011/conference/eccb2018/108011.

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Kaufman, James H., Ed Seabolt, Mark Kunitomi, Akshay Agarwal, Kristen Beck, Harsha Krishnareddy, and Bart C. Weimer. "Exploiting Functional Context in Biology: Reconsidering Classification of Bacterial Life." In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). IEEE, 2018. http://dx.doi.org/10.1109/icdew.2018.00009.

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FLESIA, A. G. "UNSUPERVISED CLASSIFICATION OF TREE STRUCTURED OBJECTS." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814271820_0018.

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Akrami, A., S. Solhjoo, A. Motie-Nasrabadi, and M. R. Hashemi-Golpayegani. "EEG-Based Mental Task Classification: Linear and Nonlinear Classification of Movement Imagery." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615501.

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HERNÁNDEZ, ISAIAS CHAIREZ, J. NATIVIDAD GURROLA REYES, CIPRIANO GARCIA GUTIERREZ, and FRANCISCO ECHAVARRIA CHAIREZ. "GRASSHOPPER DENSITY POPULATION CLASSIFICATION WITH NEURAL NETWORKS." In International Symposium on Mathematical and Computational Biology. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812812339_0016.

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Garamendi, J. F., N. Malpica, E. Schiavi, Alberto Cabada, Eduardo Liz, and Juan J. Nieto. "Multiphase Systems for Medical Image Region Classification." In MATHEMATICAL MODELS IN ENGINEERING, BIOLOGY AND MEDICINE: International Conference on Boundary Value Problems: Mathematical Models in Engineering, Biology and Medicine. AIP, 2009. http://dx.doi.org/10.1063/1.3142929.

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Rhee, Sungmin, Seokjun Seo, and Sun Kim. "Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/490.

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Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown an ability to address the problem like this. However, it conventionally used grid-like structured data, thus application of deep learning technologies to the human disease subtypes is yet to be explored. To overcome the issue, we propose a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). Experimental results on synthetic data and breast cancer data demonstrate that our proposed method shows better performances than existing methods.

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Xia, Victoria, Natasha Jaques, Sara Taylor, Szymon Fedor, and Rosalind Picard. "Active learning for electrodermal activity classification." In 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). IEEE, 2015. http://dx.doi.org/10.1109/spmb.2015.7405467.

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Reports on the topic "Biology Classification"

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Swanson, David K., Craig L. Schmitt, Diane M. Shirley, Vicky Erickson, Kenneth J. Schuetz, Michael L. Tatum, and David C. Powell. Aspen biology, community classification, and management in the Blue Mountains. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2010. http://dx.doi.org/10.2737/pnw-gtr-806.

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Savosko, V., I. Komarova, Yu Lykholat, E. Yevtushenko, and T. Lykholat. Predictive model of heavy metals inputs to soil at Kryvyi Rih District and its use in the training for specialists in the field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4511.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values (mg ⋅ m2/year): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline "Computer modelling in biology").

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Савосько, Василь Миколайович, Ірина Олександрівна Комарова, Юрій Васильович Лихолат, Едуард Олексійович Євтушенко,, and Тетяна Юріївна Лихолат. Predictive Model of Heavy Metals Inputs to Soil at Kryvyi Rih District and its Use in the Training for Specialists in the Field of Biology. IOP Publishing, 2021. http://dx.doi.org/10.31812/123456789/4266.

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The importance of our research is due to the need to introduce into modern biological education methods of predictive modeling which are based on relevant factual material. Such an actual material may be the entry of natural and anthropic heavy metals into the soil at industrial areas. The object of this work: (i) to work out a predictive model of the total heavy metals inputs to soil at the Kryvyi Rih ore-mining & metallurgical District and (ii) to identify ways to use this model in biological education. Our study areas are located in the Kryvyi Rih District (Dnipropetrovsk region, Central Ukraine). In this work, classical scientific methods (such as analysis and synthesis, induction and deduction, analogy and formalization, abstraction and concretization, classification and modelling) were used. By summary the own research results and available scientific publications, the heavy metals total inputs to soils at Kryvyi Rih District was predicted. It is suggested that the current heavy metals content in soils of this region due to 1) natural and 2) anthropogenic flows, which are segmented into global and local levels. Predictive calculations show that heavy metals inputs to the soil of this region have the following values ( mg ∙ m ଶ year ⁄ ): Fe – 800-80 000, Mn – 125-520, Zn – 75-360, Ni – 20-30, Cu – 15-50, Pb – 7.5-120, Cd – 0.30-0.70. It is established that anthropogenic flows predominate in Fe and Pb inputs (60-99 %), natural flows predominate in Ni and Cd inputs (55-95 %). While, for Mn, Zn, and Cu inputs the alternate dominance of natural and anthropogenic flows are characterized. It is shown that the predictive model development for heavy metals inputs to soils of the industrial region can be used for efficient biological education (for example in bachelors of biologists training, discipline “Computer modelling in biology”).

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4

Bilyk, Zhanna I., Yevhenii B. Shapovalov, Viktor B. Shapovalov, Anna P. Megalinska, Fabian Andruszkiewicz, and Agnieszka Dołhańczuk-Śródka. Assessment of mobile phone applications feasibility on plant recognition: comparison with Google Lens AR-app. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4403.

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The paper is devoted to systemizing all mobile applications used during the STEM-classes and can be used to identify plants. There are 10 mobile applications that are plant identifiers worldwide. These applications can be divided into three groups, such as plant identifiers that can analyze photos, plant classification provides the possibility to identify plants manually, plants-care apps that remind water of the plant, or change the soil. In this work, mobile apps such as Flora Incognita, PlantNet, PlantSnap, PictureThis, LeafSnap, Seek, PlantNet were analyzed for usability parameters and accuracy of identification. To provide usability analysis, a survey of experts of digital education on installation simplicity, level of friendliness of the interface, and correctness of picture processing. It is proved that Flora Incognita and PlantNet are the most usable and the most informative interface from plant identification apps. However, they were characterized by significantly lower accuracy compared to Google Lens results. Further comparison of the usability of applications that have been tested in the article with Google Lens, proves that Google Lens characterize by better usability and therefore, Google Lens is the most recommended app to use to provide plant identification during biology classes.

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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, September 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.

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The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.

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