Academic literature on the topic 'BIOLOGICAL LANGUAGE MODEL'
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Journal articles on the topic "BIOLOGICAL LANGUAGE MODEL"
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Price, Carolyn. "Review: Language: A Biological Model." Mind 116, no. 463 (July 1, 2007): 766–69. http://dx.doi.org/10.1093/mind/fzm766.
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Duhau, Laura. "Ruth Garrett Millikan, Language: A Biological Model." Crítica (México D. F. En línea) 40, no. 118 (January 8, 2008): 109–15. http://dx.doi.org/10.22201/iifs.18704905e.2008.1026.
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Collins, John. "Language: a Biological Model ? Ruth Garrett Millikan." Philosophical Quarterly 57, no. 226 (January 2007): 142–45. http://dx.doi.org/10.1111/j.1467-9213.2007.476_5.x.
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Cameron, William. "Ruth Garrett Millikan, Language: A Biological Model." Minds and Machines 18, no. 1 (January 17, 2008): 127–31. http://dx.doi.org/10.1007/s11023-008-9088-4.
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woodfield, andrew. "Language: A Biological Model - by Ruth Garrett Millikan." Philosophical Books 48, no. 3 (July 2007): 279–81. http://dx.doi.org/10.1111/j.1468-0149.2007.00449_8.x.
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Wang, Yanbin, Zhu-Hong You, Shan Yang, Xiao Li, Tong-Hai Jiang, and Xi Zhou. "A High Efficient Biological Language Model for Predicting Protein–Protein Interactions." Cells 8, no. 2 (February 3, 2019): 122. http://dx.doi.org/10.3390/cells8020122.
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Many life activities and key functions in organisms are maintained by different types of protein–protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein–protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a “bio-word” segmentation system and a word representation model used for learning the distributed representation for each “bio-word”. The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of “bio language processing technology,” which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.
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Fitch, W. Tecumseh. "Unity and diversity in human language." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1563 (February 12, 2011): 376–88. http://dx.doi.org/10.1098/rstb.2010.0223.
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Human language is both highly diverse—different languages have different ways of achieving the same functional goals—and easily learnable. Any language allows its users to express virtually any thought they can conceptualize. These traits render human language unique in the biological world. Understanding the biological basis of language is thus both extremely challenging and fundamentally interesting. I review the literature on linguistic diversity and language universals, suggesting that an adequate notion of ‘formal universals’ provides a promising way to understand the facts of language acquisition, offering order in the face of the diversity of human languages. Formal universals are cross-linguistic generalizations, often of an abstract or implicational nature. They derive from cognitive capacities to perceive and process particular types of structures and biological constraints upon integration of the multiple systems involved in language. Such formal universals can be understood on the model of a general solution to a set of differential equations; each language is one particular solution. An explicit formal conception of human language that embraces both considerable diversity and underlying biological unity is possible, and fully compatible with modern evolutionary theory.
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Dickinson, Sandra. "Recursion in Development: Support for a Biological Model of Language." Language and Speech 30, no. 3 (July 1987): 239–49. http://dx.doi.org/10.1177/002383098703000304.
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Cuellar, Autumn A., Catherine M. Lloyd, Poul F. Nielsen, David P. Bullivant, David P. Nickerson, and Peter J. Hunter. "An Overview of CellML 1.1, a Biological Model Description Language." SIMULATION 79, no. 12 (December 2003): 740–47. http://dx.doi.org/10.1177/0037549703040939.
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Cardelli, Luca, Marta Kwiatkowska, and Luca Laurenti. "A Language for Modeling and Optimizing Experimental Biological Protocols." Computation 9, no. 10 (October 16, 2021): 107. http://dx.doi.org/10.3390/computation9100107.
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Automation is becoming ubiquitous in all laboratory activities, moving towards precisely defined and codified laboratory protocols. However, the integration between laboratory protocols and mathematical models is still lacking. Models describe physical processes, while protocols define the steps carried out during an experiment: neither cover the domain of the other, although they both attempt to characterize the same phenomena. We should ideally start from an integrated description of both the model and the steps carried out to test it, to concurrently analyze uncertainties in model parameters, equipment tolerances, and data collection. To this end, we present a language to model and optimize experimental biochemical protocols that facilitates such an integrated description, and that can be combined with experimental data. We provide probabilistic semantics for our language in terms of Gaussian processes (GPs) based on the linear noise approximation (LNA) that formally characterizes the uncertainties in the data collection, the underlying model, and the protocol operations. In a set of case studies, we illustrate how the resulting framework allows for automated analysis and optimization of experimental protocols, including Gibson assembly protocols.
Dissertations / Theses on the topic "BIOLOGICAL LANGUAGE MODEL"
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Smith, Kenneth. "The transmission of language : models of biological and cultural evolution." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/27427.
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Theories of language evolution typically attribute its unique structure to pressures acting on the genetic transmission of a language faculty and on the cultural transmission of language itself. In strongly biological accounts, natural selection acting on the genetic transmission of the language faculty is seen as the key determinant of linguistic structure, with culture relegated to a relatively minor role. Strongly cultural accounts place greater emphasis on the role of learning in shaping language, with little or no biological adaptation. Formal modelling of the transmission of language, using mathematical or computational techniques, allows rigorous study of the impact of these two modes of transmission on the structure of language. In this thesis, computational models are used to investigate the evolution of symbolic vocabulary and compositional structure. To what extent can these aspects of language be explained in terms of purely biological or cultural evolution? Should we expect to see a fruitful interaction between these two adaptive processes in a dual transmission model? As a first step towards addressing these questions, models which focus on the cultural transmission of language are developed. These models suggest that the symbolic vocabulary and compositional structure of language can emerge through the adaptation of language itself in response to pressure to be learnable. This pressure arises during cultural transmission as a result of 1) the inductive bias of learners and 2) the poverty of the stimulus available to learners. Language-like systems emerge only when learners acquire their linguistic competence on the basis of sparse input and do so using learning procedures which are biased in favour of one-to-one mappings between meanings and signals. Children acquire language under precisely such circumstances. As the second stage of inquiry, dual transmission models are developed to ascertain whether this cultural evolution of language interacts with the biological evolution of the language faculty. In these models an individual’s learning bias is assumed to be genetically determined. Surprisingly, natural selection during the genetic transmission of this innate endowment does not result in the development of learning biases which lead, through cultural processes, to language-like communication - there is no synergistic interaction between biological and cultural evolution. The evolution of language may therefore best be explained in terms of cultural evolution on a domain-general or exapted innate substrate.
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Gill, Mandeep Singh. "Application of software engineering methodologies to the development of mathematical biological models." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:35178f3a-7951-4f1c-aeab-390cdd622b05.
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Mathematical models have been used to capture the behaviour of biological systems, from low-level biochemical reactions to multi-scale whole-organ models. Models are typically based on experimentally-derived data, attempting to reproduce the observed behaviour through mathematical constructs, e.g. using Ordinary Differential Equations (ODEs) for spatially-homogeneous systems. These models are developed and published as mathematical equations, yet are of such complexity that they necessitate computational simulation. This computational model development is often performed in an ad hoc fashion by modellers who lack extensive software engineering experience, resulting in brittle, inefficient model code that is hard to extend and reuse. Several Domain Specific Languages (DSLs) exist to aid capturing such biological models, including CellML and SBML; however these DSLs are designed to facilitate model curation rather than simplify model development. We present research into the application of techniques from software engineering to this domain; starting with the design, development and implementation of a DSL, termed Ode, to aid the creation of ODE-based biological models. This introduces features beneficial to model development, such as model verification and reproducible results. We compare and contrast model development to large-scale software development, focussing on extensibility and reuse. This work results in a module system that enables the independent construction and combination of model components. We further investigate the use of software engineering processes and patterns to develop complex modular cardiac models. Model simulation is increasingly computationally demanding, thus models are often created in complex low-level languages such as C/C++. We introduce a highly-efficient, optimising native-code compiler for Ode that generates custom, model-specific simulation code and allows use of our structured modelling features without degrading performance. Finally, in certain contexts the stochastic nature of biological systems becomes relevant. We introduce stochastic constructs to the Ode DSL that enable models to use Stochastic Differential Equations (SDEs), the Stochastic Simulation Algorithm (SSA), and hybrid methods. These use our native-code implementation and demonstrate highly-efficient stochastic simulation, beneficial as stochastic simulation is highly computationally intensive. We introduce a further DSL to model ion channels declaratively, demonstrating the benefits of DSLs in the biological domain. This thesis demonstrates the application of software engineering methodologies, and in particular DSLs, to facilitate the development of both deterministic and stochastic biological models. We demonstrate their benefits with several features that enable the construction of large-scale, reusable and extensible models. This is accomplished whilst providing efficient simulation, creating new opportunities for biological model development, investigation and experimentation.
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Ivanov, Sergiu. "On the Power and Universality of Biologically-inspired Models of Computation." Thesis, Paris Est, 2015. http://www.theses.fr/2015PEST1012/document.
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Cette thèse adresse les problèmes d'universalité et de complétude computationelle pour plusieurs modèles de calcul inspirés par la biologie. Il s'agit principalement des systèmes d'insertion/effacement, réseaux de processeurs évolutionnaires, ainsi que des systèmes de réécriture de multi-ensembles. Les résultats décrits se classent dans deux catégories majeures : l'étude de la puissance de calcul des opérations d'insertion et d'effacement avec ou sans mécanismes de contrôle, et la construction des systèmes de réécriture de multi-ensembles universels de petite taille. Les opérations d'insertion et d'effacement consistent à rajouter ou supprimer une sous-chaîne dans une chaîne de caractères dans un contexte donné. La motivation pour l'étude de ces opérations vient de la biologie, ainsi que de la linguistique et de la théorie des langages formels. Dans la première partie de ce manuscrit nous examinons des systèmes d'insertion/effacement correspondant à l'édition de l'ARN, un processus qui insère ou supprime des fragments de ces molécules. Une particularité importante de l'édition de l'ARN est que le endroit auquel se font les modifications est déterminé par des séquences de nucléotides se trouvant toujours du même côté du site de modification. En termes d'insertion et d'effacement, ce phénomène se modéliserait par des règles possédant le contexte uniquement d'un seul côté. Nous montrons qu'avec un contexte gauche de deux caractères il est possible d'engendrer tous les langages rationnels. D'autre part, nous prouvons que des contextes plus longs n'augmentent pas la puissance de calcul du modèle. Nous examinons aussi les systèmes d’insertion/effacement utilisant des mécanismes de contrôle d’application des règles et nous montrons l'augmentation de la puissance d'expression. Les opérations d'insertion et d'effacement apparaissent naturellement dans le domaine de la sécurité informatique. Comme exemple on peut donner le modèle des grammaires gauchistes (leftist grammar), qui ont été introduites pour l'étude des systèmes critiques. Dans cette thèse nous proposons un nouvel instrument graphique d'analyse du comportement dynamique de ces grammaires. La deuxième partie du manuscrit s'intéresse au problème d'universalité qui consiste à trouver un élément concret capable de simuler le travail de n'importe quel autre dispositif de calcul. Nous commençons par le modèle de réseaux de processeurs évolutionnaires, qui abstrait le traitement de l'information génétique. Nous construisons des réseaux universels ayant un petit nombre de règles. Nous nous concentrons ensuite sur les systèmes de réécriture des multi-ensembles, un modèle qui peut être vu comme une abstraction des réactions biochimiques. Pour des raisons historiques, nous formulons nos résultats en termes de réseaux de Petri. Nous construisons des réseaux de Petri universels et décrivons des techniques de réduction du nombre de places, de transitions et d'arcs inhibiteurs, ainsi que du degré maximal des transitions. Une bonne partie de ces techniques repose sur une généralisation des machines à registres introduite dans cette thèse et qui permet d'effectuer plusieurs tests et opérations en un seul changement d'étatThe present thesis considers the problems of computational completeness and universality for several biologically-inspired models of computation: insertion-deletion systems, networks of evolutionary processors, and multiset rewriting systems. The presented results fall into two major categories: study of expressive power of the operations of insertion and deletion with and without control, and construction of universal multiset rewriting systems of low descriptional complexity. Insertion and deletion operations consist in adding or removing a subword from a given string if this subword is surrounded by some given contexts. The motivation for studying these operations comes from biology, as well as from linguistics and the theory of formal languages. In the first part of the present work we focus on insertion-deletion systems closely related to RNA editing, which essentially consists in inserting or deleting fragments of RNA molecules. An important feature of RNA editing is the fact that the locus the operations are carried at is determined by certain sequences of nucleotides, which are always situated to the same side of the editing site. In terms of formal insertion and deletion, this phenomenon is modelled by rules which can only check their context on one side and not on the other. We show that allowing one-symbol insertion and deletion rules to check a two-symbol left context enables them to generate all regular languages. Moreover, we prove that allowing longer insertion and deletion contexts does not increase the computational power. We further consider insertion-deletion systems with additional control over rule applications and show that the computational completeness can be achieved by systems with very small rules. The motivation for studying insertion-deletion systems also comes from the domain of computer security, for the purposes of which a special kind of insertion-deletion systems called leftist grammars was introduced. In this work we propose a novel graphical instrument for visual analysis of the dynamics of such systems. The second part of the present thesis is concerned with the universality problem, which consists in finding a fixed element able to simulate the work any other computing device. We start by considering networks of evolutionary processors (NEPs), a computational model inspired by the way genetic information is processed in the living cell, and construct universal NEPs with very few rules. We then focus on multiset rewriting systems, which model the chemical processes running in the biological cell. For historical reasons, we formulate our results in terms of Petri nets. We construct a series of universal Petri nets and give several techniques for reducing the numbers of places, transitions, inhibitor arcs, and the maximal transition degree. Some of these techniques rely on a generalisation of conventional register machines, proposed in this thesis, which allows multiple register checks and operations to be performed in a single state transition
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Vymazal, Aleš. "Biologicky inspirovaní roboti - brouk." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-416669.
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Pronto, Lindon N. "Exploring German and American Modes of Pedagogical and Institutional Sustainability: Forging a Way into the Future." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/pitzer_theses/21.
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Rooted deep in Germany's past is its modern socio-political grounding for environmental respect and sustainability. This translates into individual and collective action and extends equally to the economic and policy realm as it does to educational institutions. This thesis evaluates research conducted in Germany with a view to what best approaches are transferable to the United States liberal arts setting. Furthermore, exemplary American models of institutional sustainability and environmental education are explored and combined with those from abroad to produce a blueprint and action plan fitting for the American college and university.
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Dresch, Andrea Alves Guimarães. "Método para reconhecimento de vogais e extração de parâmetros acústicos para analises forenses." Universidade Tecnológica Federal do Paraná, 2015. http://repositorio.utfpr.edu.br/jspui/handle/1/1799.
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Exames de Comparação Forense de Locutores apresentam características complexas, demandando análises demoradas quando realizadas manualmente. Propõe-se um método para reconhecimento automático de vogais com extração de características para análises acústicas, objetivando-se contribuir com uma ferramenta de apoio nesses exames. A proposta baseia-se na medição dos formantes através de LPC (Linear Predictive Coding), seletivamente por detecção da frequência fundamental, taxa de passagem por zero, largura de banda e continuidade, sendo o agrupamento das amostras realizado por meio do método k-means. Experimentos realizados com amostras de três diferentes bases de dados trouxeram resultados promissores, com localização das regiões correspondentes a cinco das vogais do Português Brasileiro, propiciando a visualização do comportamento do trato vocal de um falante, assim como detecção de trechos correspondentes as vogais-alvo.Forensic speaker comparison exams have complex characteristics, demanding a long time for manual analysis. A method for automatic recognition of vowels, providing feature extraction for acoustic analysis is proposed, aiming to contribute as a support tool in these exams. The proposal is based in formant measurements by LPC (Linear Predictive Coding), selectively by fundamental frequency detection, zero crossing rate, bandwidth and continuity, with the clustering being done by the k-means method. Experiments using samples from three different databases have shown promising results, in which the regions corresponding to five of the Brasilian Portuguese vowels were successfully located, providing visualization of a speaker’s vocal tract behavior, as well as the detection of segments corresponding to target vowels.
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CHAKRABORTY, RAJKUMAR. "GENOMIC LANGUAGE PROCESSING USING MACHINE LEARNING." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20063.
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The purpose of developing biological language models (BLMs) is to enhance our capacity to
comprehend and analyse biological sequences, such as DNA, RNA, and protein sequences.
These sequences contain crucial information about the structure and function of living
organisms and are involved in virtually every biological process. Nonetheless, analysing
biological sequences can be difficult due to their complexity and enormous potential.
Specifically, the functions and properties of a large number of coding and non-coding DNA
and RNA sequences remain poorly understood. This thesis presents three objectives related to
the application of natural language processing techniques in the field of bio-molecule sciences.
The first objective involves using a combination of a Convolutional Neural Network (CNN)
and a Long Short-Term Memory (LSTM) network, stacked in a sequence-to-sequence
(Seq2Seq) architecture, to predict microRNA sequences from mRNA sequences. The
microRNA are small, generally 28 bp long, non-coding RNAs that play a role in various
physiological and disease processes. Identifying mRNA targeted by microRNAs is a challenge,
and researchers often rely on computational programs to initially identify target candidates for
subsequent validation. In this work, a neural network was trained to predict microRNA from
the bound target segment in mRNA using a dataset of experimentally validated and cleaned
microRNA-mRNA sequence pairs from TarBase v8. Convolutional neural networks (CNNs)
were used to recognize patterns in mRNA segments and extract features, while long short-term
memory (LSTM) networks in a seq2seq architecture were used to predict microRNA
sequences. The model achieved an accuracy of 80% and was validated using experimentally
verified microRNA-RNA pairs involved in skin diseases from an in-house database called
miDerma, correctly predicting an average of 72% of the microRNAs from mRNA in each case.
The package, called "model: A MicroRNA sequence prediction tool from RNA sequence based
on CNNs, LSTMs, and seq2seq architecture," allows users to input a gene symbol and retrieves
the protein coding transcript's sequence from the Ensemble REST API to predict a list of
microRNAs that may bind to potential target segments in the mRNA.
The second objective involves using natural language processing techniques, including an
embedding layer, a CNN layer, and a bidirectional LSTM layer, to predict disordered regions
in proteins. Intrinsically disordered regions (IDRs) are important for various physiological
processes and diseases and play a complementary role to the functions of structured proteins.
They can be identified through multiple experimental techniques, but these methods can be
costly and time-consuming. As a result, researchers rely on computational strategies to predict
probable IDRs/IDPs before conducting further validation through experimental studies. While
there have been significant advancements in predicting long and short IDRs in recent years,
there is still scope for algorithmic improvement. This study aims to improve the prediction of
IDRs by using neural networks, specifically convolutional neural networks (CNNs), recurrent
neural networks (RNNs), and long short-term memory (LSTM) networks, as well as natural
language processing (NLP) techniques. The study also explores the use of different input
sequence lengths and various embedding sizes for the CNN and LSTM models. The results
show that the CNN and LSTM models outperform state-of-the-art techniques for predicting
IDRs, with the LSTM model achieving the highest accuracy of 85.7%. The study also
demonstrates the effectiveness of using NLP techniques for analyzing protein sequences and
the importance of carefully selecting model architectures and hyperparameters to achieve good
performance.
The third objective involves using an autoencoder, a type of deep learning architecture, to
generate drug analogues by reconstructing chemical SMILES (Simplified Molecular-Input
Line-Entry System) representations of molecules and varying the batch size and latent space
dimensionality of the autoencoder. The design of drug analogues involves the creation of
modified versions of existing drugs to improve their efficacy, stability, and safety. Deep
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learning techniques, such as autoencoders, can be used to generate new drug analogues through
a process of chemical structure reproduction. In this study, an autoencoder was trained on
chemical SMILES data from the ChEMBL database and used to generate 157 variants of the
drug Vandetanib by adding noise to its latent representation and reconstructing the resulting
compounds using a decoder. Molecular docking and dynamics simulations were then
performed to determine which of these analogues had a higher binding affinity than
Vandetanib. At least two of the analogues had a higher binding affinity than the control
compound. While this model has the potential to generate a wide range of molecules, it may
have difficulty generating molecules with SMILES strings longer than 80 characters due to a
lack of training data of SMILES string length above 80 characters. The synthesis and laboratory
testing of the generated molecules to determine their potential as drugs also presents a
challenge. However, this study has the potential to make significant contributions to the field
of automatic drug analogue prediction and could be a valuable addition to the current scientific
literature.
The study presents several potential applications for its microRNA, protein disorder region
finding, and drug analogue generation models. The microRNA prediction model could aid in
the development of therapies for diseases by identifying microRNA sequences that regulate
gene expression. The protein disorder prediction model could be used in drug design and
protein engineering by identifying disordered regions in proteins that play a role in various
protein functions. The drug analogue generation model has the potential to generate new drug
analogues with desired properties and could be used in drug discovery and the optimization of
existing drugs. Overall, this research has the potential to make significant contributions to
biomedical research and could lead to the development of new therapies and drugs for diseases,
as well as new bio-molecular language models for other tasks.
Books on the topic "BIOLOGICAL LANGUAGE MODEL"
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Language: A biological model. Oxford: Clarendon Press, 2005.
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Complexity, language, and life: Mathematical approaches. Berlin: Springer-Verlag, 1986.
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L, Casti J., and Karlqvist Anders, eds. Complexity, language, and life: Mathematical approaches. Berlin: Springer-Verlag, 1986.
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Templets and the explanation of complex patterns. Cambridge [Cambridgeshire]: Cambridge University Press, 1986.
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Kupcova, Oksana. The basics of the Latin language with medical terminology. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058964.
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The textbook on the discipline "Fundamentals of the Latin language with medical terminology" contains lexical and grammatical exercises, control and measurement exercises, a Glossary and appendices aimed at developing grammatical, lexical and terminological knowledge and skills, and mastering the basic word-forming models of chemical, pharmaceutical and clinical terminology to the extent necessary for further educational activities. The materials of the manual are suitable both for classroom work under the guidance of a teacher, and for independent work of students during extracurricular time. Meets the requirements of the Federal state educational standards of higher education of the latest generation. For first-year students on the basis of secondary General education and second-year students on the basis of basic General education of secondary medical professional educational organizations studying in the specialties "Nursing", "Medical care", "Midwifery", "Laboratory diagnostics". It can also be used for organizing and conducting classes in clubs or elective courses for students of the 10th and 11th grades of medical and biological-chemical profile in schools, lyceums, gymnasiums.
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1949-, Lumsden Charles J., and Woolridge Nicholas, eds. In silico: Cell biology art and science with MAYA and MEL. Amsterdam: Morgan Kaufmann, 2008.
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Millikan, Ruth Garrett. Language: A Biological Model. Oxford University Press, 2005.
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Language: A Biological Model. Oxford University Press, Incorporated, 2005.
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Millikan, Ruth Garrett. Language: A Biological Model. Oxford University Press, Incorporated, 2005.
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Millikan, Ruth Garrett. Language: A Biological Model. Oxford University Press, USA, 2005.
Find full textBook chapters on the topic "BIOLOGICAL LANGUAGE MODEL"
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Okanoya, Kazuo. "The biopsychology of birdsong: Birdsong as a biological model for human language." In APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior., 539–55. Washington: American Psychological Association, 2017. http://dx.doi.org/10.1037/0000011-026.
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Jan, Steven. "6 Computer Simulation of Musical Evolution." In Music in Evolution and Evolution in Music, 473–564. Cambridge, UK: Open Book Publishers, 2022. http://dx.doi.org/10.11647/obp.0301.06.
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Chapter 6: surveys a number of computer systems developed over the last 20 years or so that attempt to generate music, many of which use evolutionary algorithms. As examples of the radical AI field of computational creativity, or musical metacreation, evolutionary-generative systems relate closely to systems for simulating/emulating language evolution (such as those developed by Simon Kirby), in that they generate music by means of the recombination of discrete patterns occurring in some selective environment. The most sophisticated of these systems, as represented by the work of Eduardo Miranda, emulate societies of virtual musical agents (some use robots as physical manifestations of those virtual agents) and allow for model-ling of both biological (via quasi-genetic inheritance between ‘generations’ of virtual agents) and cultural (via memetic transmission of musical patterns) coevolution. They suggest, as does the consideration of animal ‘musics’ in Chapter 5, that creativity, and the associated issue of taste-judgment, is not exclusively the preserve of our own species.
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Gibet, Sylvie, Marteau Pierre-François, and Frédéric Julliard. "Models with Biological Relevance to Control Anthropomorphic Limbs: A Survey." In Gesture and Sign Language in Human-Computer Interaction, 105–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-47873-6_12.
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da Silveira Coêlho, Marília Lira, Tanja S. H. Wingenbach, and Paulo Sérgio Boggio. "Social and Affective Neuroscience of Embodiment." In Social and Affective Neuroscience of Everyday Human Interaction, 37–51. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08651-9_3.
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AbstractEmbodiment has been discussed in the context of social, affective, and cognitive psychology, and also in the investigations of neuroscience in order to understand the relationship between biological mechanisms, body and cognitive, and social and affective processes. New theoretical models have been presented by researchers considering not only the sensory–motor interaction and the environment but also biological mechanisms regulating homeostasis and neural processes (Tsakiris M, Q J Exp Psychol 70(4):597–609, 2017). Historically, the body and the mind were comprehended as separate entities. The body was considered to function as a machine, responsible for providing sensory information to the mind and executing its commands. The mind, however, would process information in an isolated way, similar to a computer (Pecher D, Zwaan RA, Grounding cognition: the role of perception and action in memory, language, and thinking. Cambridge University Press, 2005). This mind and body perspective (Marmeleira J, Duarte Santos G, Percept Motor Skills 126, 2019; Marshall PJ, Child Dev Perspect 10(4):245–250, 2016), for many years, was the basis for studies in social and cognitive areas, in neuroscience, and clinical psychology.
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"Introduction." In Biological Language Model, 1–7. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0001.
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"Linguistic Feature Analysis of Protein Sequences." In Biological Language Model, 9–19. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0002.
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"Amino Acid Encoding for Protein Sequence." In Biological Language Model, 21–54. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0003.
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"Remote Homology Detection." In Biological Language Model, 55–82. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0004.
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"Structure Prediction." In Biological Language Model, 83–165. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0005.
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"Function Prediction." In Biological Language Model, 167–241. WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811212956_0006.
Full textConference papers on the topic "BIOLOGICAL LANGUAGE MODEL"
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Meghanathan, Natarajan, Nataliya Kostyuk, Raphael D. Isokpehi, and Hari H. P. Cohly. "Clustering model to identify biological signatures for English language anxiety." In 2010 Biomedical Sciences and Engineering Conference (BSEC). IEEE, 2010. http://dx.doi.org/10.1109/bsec.2010.5510829.
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Cheong, Hyunmin, and L. H. Shu. "Automatic Extraction of Causally Related Functions From Natural-Language Text for Biomimetic Design." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70732.
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Identifying relevant analogies from biology is a significant challenge in biomimetic design. Our natural-language approach addresses this challenge by developing techniques to search biological information in natural-language format, such as books or papers. This paper presents the application of natural-language processing techniques, such as part-of-speech tags, typed-dependency parsing, and syntactic patterns, to automatically extract and categorize causally related functions from text with biological information. Causally related functions, which specify how one action is enabled by another action, are considered important for both knowledge representation used to model biological information and analogical transfer of biological information performed by designers. An extraction algorithm was developed and scored F-measures of 0.78–0.85 in an initial development test. Because this research approach uses inexpensive and domain-independent techniques, the extraction algorithm has the potential to automatically identify patterns of causally related functions from a large amount of text that contains either biological or design information.
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Alonzo, Paola, and Rosalba Roccolini. "EVOLUTION, STRUCTURE AND LEARNING OF HUMAN LANGUAGE." In 10th SWS International Scientific Conferences on ART and HUMANITIES - ISCAH 2023. SGEM WORLD SCIENCE, 2023. http://dx.doi.org/10.35603/sws.iscah.2023/fs11.20.
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The conceptual basis of the article includes descriptions, characteristics and relationships between all aspects of reflection. The aim of the work is to present the evolution, structure and learning of language since the evolution of the human species, already of a genetic order, then became cultural, therefore teachable. Culture, through the changes offered by education to intentional learning, is the engine of man's changes and evolution. From language as a biological moment of adaptation we have moved on to an intentional and specific form of language, the structure of which is to be placed in relation to language learning and behaviour. The relationship between language and its physiological bases has been indicated with the support of psycholinguistics: mental, linguistic processes, human cognitive abilities, involved in the conception, elaboration, description and definition of narratives. The investigation of language learning has been referred to classical theories such as the magic number 7 theory (Miller), the universal language theory (Chomsky) and the cognitivist theory (Piaget). The results of the study led to hypothesize and formulate a new teaching/learning model for languages in suitable laboratories and inserted in educational environments. The laboratory is stimulating and is the one generated as CLIL (Content and Language Integrated Learning), to respond to the connotation of multiculturalism and plurilingualism.
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Cohly, Hari H. P. "7.1: Presentation session: Poster session and reception: “Clustering model to identify biological signatures for English language anxiety”." In 2010 Biomedical Sciences and Engineering Conference (BSEC). IEEE, 2010. http://dx.doi.org/10.1109/bsec.2010.5510830.
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Zhu, Qihao, Xinyu Zhang, and Jianxi Luo. "Generative Pre-Trained Transformers for Biologically Inspired Design." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90366.
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Abstract Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a computational approach to bridge the gap. This paper proposes a generative design approach based on the pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the correlation between the domains within the generated BID concepts. The approach is then tested via a case study in which the fine-tuned models are applied to generate and evaluate light-weighted flying car concepts inspired by nature. The results show our approach can generate BID concepts with good performance.
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PALMISANO, A., I. MURA, and C. PRIAMI. "FROM ODES TO LANGUAGE-BASED, EXECUTABLE MODELS OF BIOLOGICAL SYSTEMS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812836939_0023.
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Venables, Anne, and Grace Tan. "A 'Hands on' Strategy for Teaching Genetic Algorithms to Undergraduates." In InSITE 2007: Informing Science + IT Education Conference. Informing Science Institute, 2007. http://dx.doi.org/10.28945/3132.
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Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are ‘intractable’ using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary literature, such as population, chromosome, crossover, cloning, mutation, genes and generations. For beginners studying genetic algorithms, there is quite an overhead in gaining comfort with these terms and an understanding of their parallel meanings in the unfamiliar computing milieu of an evolutionary algorithm. This paper describes a ‘hands on’ strategy to introduce and teach genetic algorithms to undergraduate computing students. By borrowing an analogical model from senior biology classes, poppet beads are used to represent individuals in a population (Harrison, 2001). Described are several introductory exercises that transport students from an illustration of natural selection in Biston betula moths, onto the representation and solution of differing mathematical and computing problems. Through student manipulation and interactions with poppet beads, the exercises cover terms such as population, generation, chromosome, gene, mutation and crossover in both their biological and computing contexts. Importantly, the tasks underline the two key design issues of genetic algorithms: the choice of an appropriate chromosome representation, and a suitable fitness function for each specific instance. Finally, students are introduced to the notion of schema upon which genetic algorithms operate. The constructivist model of learning advocates the use of such contextual problems to create an environment where students become active participants in their own learning (Ben-Ari, 1998; Greening, 2000; Kolb, 1984). Initial student feedback about these “hands on” exercises has been enthusiastic. As well, several students have made reference to the lessons learnt as the basis for GA coding in subsequent open-ended assignments. It seems that once the hurdle of becoming familiar with GA terminology has been surmounted, students find genetic algorithms to be particularly intriguing for their uncanny ability to solve incredibly complex problems quickly and proficiently (Moore, 2001).
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Salogni, A., and D. Alberti. "Dynamic Analysis of a Planar SOFC Stack Fuelled by Biogas." In ASME 2010 8th International Conference on Fuel Cell Science, Engineering and Technology. ASMEDC, 2010. http://dx.doi.org/10.1115/fuelcell2010-33255.
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This paper analyzes the dynamic behaviour of a 50 kW stack using planar co-flow solid oxide fuel cells with direct internal reforming fuelled by a biologically derived gaseous mixture of methane and carbon dioxide. The system modelled is composed by the SOFC stack, a catalytic burner, the heat recovery system and the control device aimed to keep the air temperature at the stack exit and the fuel utilization near to the set values. The model has been implemented using standard and user-defined components of an a-causal software based on the open-source Modelica modelling language. After a brief introduction to the production of the gaseous fuel derived from the anaerobic digestion of pig manure, data obtained from a case study on a pig farm situated in Lombardia (Italy) are presented, focusing on the yield of methane which can be exploited. The steady-state performance of the SOFC system fuelled by pure methane are compared with those obtained for the biogas working conditions, showing that the stack voltage is affected by greater concentration losses. Then, starting from a steady-state delivered current of 750 mA cm−2, the dynamic behaviour of the system when a load change of −150 mA cm−2 occurs is investigated for both pure methane and biogas fuelling hypothesis. The results of the simulations show that the transient phase is only marginally affected by the composition of the fuel, which causes a delay of about 50 s in the voltage transient. Finally, the effect obtained by imposing a linear variation in the fuel composition, which can be representative of a modification in the biological degradation of the organic substrate within the anaerobic digester, is discussed. After an initial transient, which is comparable with that obtained for a variation in the load current, the SOFC system is capable to restore the initial delivered power, provided that the required amount of fuel can be supplied to the anode.
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Dworschak, Steve, Torsten Grote, Arne König, Torsten Schaub, and Philippe Veber. "The System BioC for Reasoning about Biological Models in Action Language C." In 2008 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2008. http://dx.doi.org/10.1109/ictai.2008.69.
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Unknown, Unknown, Sarapuu Tago, and Castera Jeremy. "DEVELOPING STUDENTS' VISUAL LITERACY IN A VIRTUAL MODELING ENVIRONMENT." In eLSE 2012. Editura Universitara, 2012. http://dx.doi.org/10.12753/2066-026x-12-085.
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This study addresses how secondary school students’ visual literacy can be improved by virtual modeling activity in a web-based learning environment. Visual literacy can be defined as the ability to interpret visual messages accurately and to create such visualisations (Avgerinou 2011; Heinich et al. 1982; Pettersson 1993). The ways of how students perceive and understand visual images is one of the crucial issues for the field of visual literacy (Avgerinou & Ericson, 1997). Therefore, the present study concentrates on students’ skills of analysing visual information and the ability of translating information between different visual representations. According to the theoretical framework for designing multimedia proposed by Reed (2009), manipulation of objects, rather than just perception of objects, has the crucial role in learning process. This proposition is also supported by empirical studies about the effect of interactivity in computer-based learning (Evans & Gibbons, 2007). Therefore, the design of the learning environment “Cell World” (http://bio.edu.ee/models/en/) implemented in this study emphasizes the role of object manipulation in instruction. The virtual modeling environment “Cell World” enables learners to explore complex microscopic processes that take place in cells, such as molecular genetics, respiration, photosynthesis, etc. Students’ main activity in the environment is to virtually construct biological processes by adding necessary molecules or changing parameters so that the process can continue. Each model consists of two types of learning objects: the objects that are manipulative by students, and the non-manipulative objects that students cannot move, but only observe how these objects take part in animation of the process. The second goal of the study is to clarify the effect of object manipulation on the learning process. The following research questions were set to the study: 1) How does the virtual modeling activity influence students’ visual literacy?; 2) What is the effect of learning object manipulation on students’ visual literacy? METHODS In order to fulfil the aims of the study, an experiment was conducted in which 190 students (aged 17-18) from 11th grades of nine Estonian secondary schools participated. The students applied the learning environment “Cell World” for 2x45 minutes. In the first lesson, students used the model of protein synthesis, and in the second one, the model of genetic code was applied. For assessing students’ visual literacy skills, pre- and post-test were filled out before and after the intervention. The test consisted of picture-based questions that measured students’ understanding of different representations and the ability to translate information between the different visual representations. For comparison of pre- and post test scores, the Paired samples t-test was implemented with SPSS software. MAIN FINDINGS Students’ visual literacy skills were assessed on the basis of three different representations in the pre- and post-tests. The first representation was a screenshot of the model that was applied during the study and indicated students’ understanding of the representational language of the model. According to the post-test results, students could identify better the objects that were manipulative on the models. Understanding of non-manipulative objects, on the other hand, was significantly lower. This result demonstrates that object manipulation had a positive effect on students’ understanding of visual representations as manipulative objects were more effectively integrated into students’ individual mental models. Students’ skills of translating information between different visual representations were analysed on the basis of two representations. One of the representations was familiar to students from textbook that they had previously used for learning the topic of protein synthesis in classroom settings. The other representation, on the other hand, was unfamiliar to students. Students had to compare the different representations and translate the information between them. Comparison of pre- and post-test data with Paired samples t-test indicated that after model application, students got higher scores in translating information between different representations than before the intervention. Again, in translating tasks, students had marked more often the objects that were manipulative and missed the non-manipulative objects. Therefore, this study provides additional evidence that introducing manipulative learning objects in instructional settings can significantly improve learning outcomes.
Reports on the topic "BIOLOGICAL LANGUAGE MODEL"
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Hillestad, Torgeir Martin. The Metapsychology of Evil: Main Theoretical Perspectives Causes, Consequences and Critique. University of Stavanger, 2014. http://dx.doi.org/10.31265/usps.224.
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The purpose of this text or dissertation is to throw some basic light on a fundamental problem concerning manhood, namely the question of evil, its main sources, dynamics and importance for human attitudes and behaviour. The perspective behind the analysis itself is that of psychology. Somebody, or many, may feel at bit nervous by the word “evil” itself. It may very well be seen as too connected to religion, myth and even superstition. Yet those who are motivated to lose oneself in the subject retain a deep interest in human destructiveness, malevolence and hate, significant themes pointing at threatening prospects for mankind. The text is organized or divided into four main ordinary chapters, the three first of them organized or divided into continuous and numbered sections. A crucial point or question is of cause how to define evil itself. It can of cause be done both intentional, instrumental and by consequence. Other theorists however have stated that the concept of evil exclusively rests on a myth originated in the Judean-Christian conception of Satan and ultimate evil. This last argument presupposes evil itself as non-existent in the real rational world. It seems however a fact that most people attach certain basic meaning to the concept, mainly that it represents ultimately bad and terrible actions and behaviour directed toward common people for the purpose of bringing upon them ultimate pain and suffer. However, there is no room for essentialism here, meaning that we simply can look “inside” some original matter to get to know what it “really” is. Rather, a phenomenon gets its identity from the constituted meaning operating within a certain human communities and contexts loaded with intentionality and inter-subjective meaning. As mentioned above, the concept of evil can be interpreted both instrumental and intentional, the first being the broadest of them. Here evil stands for behaviour and human deeds having terrifying or fatal consequences for subjects and people or in general, regardless of the intentions behind. The intentional interpretation however, links the concept to certain predispositions, characteristics and even strong motives in subjects, groups and sometimes political systems and nations. I will keep in mind and clear the way for both these perspectives for the discussion in prospect. This essay represents a psychological perspective on evil, but makes it clear that a more or less complete account of such a psychological view also should include a thorough understanding or integration of some basic social and even biological assumptions. However, I consider a social psychological position of significant importance, especially because in my opinion it represents some sort of coordination of knowledge and theoretical perspectives inherent in the subject or problem itself, the main task here being to integrate perspectives of a psychological as well as social and biological kind. Since humans are essential social creatures, the way itself to present knowledge concerning the human condition, must be social of some sort and kind, however not referring to some kind of reductionism where social models of explanation possess or holds monopoly. Social and social psychological perspectives itself represents parts of the whole matter regarding understanding and explanation of human evil. The fact that humans present, or has to represent themselves as humans among other humans, means that basically a social language is required both to explain and describe human manners and ways of being. This then truly represents its own way or, more correctly, level or standard of explanation, which makes social psychology some sort of significant, though not sufficient. More substantial, the vision itself of integrating different ontological and theoretical levels and objects of science for the purpose of manifesting or make real a full-fledged psychological perspective on evil, should be considered or characterized a meta-psychological perspective. The text is partially constructed as a review of existing theories and theorists concerning the matter of evil and logically associated themes such as violence, mass murder, genocide, antisocial behaviour in general, aggression, hate and cruelty. However, the demands of making a theoretical distinction between these themes, although connected, is stressed. Above all, an integral perspective combining different scientific disciplines is aimed at.
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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.
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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.