Drawing inspiration from biological optical systems

NASA Astrophysics Data System (ADS)

Wolpert, H. D.

2009-08-01

Bio-Mimicking/Bio-Inspiration: How can we not be inspired by Nature? Life has evolved on earth over the last 3.5 to 4 billion years. Materials formed during this time were not toxic; they were created at low temperatures and low pressures unlike many of the materials developed today. The natural materials formed are self-assembled, multifunctional, nonlinear, complex, adaptive, self-repairing and biodegradable. The designs that failed are fossils. Those that survived are the success stories. Natural materials are mostly formed from organics, inorganic crystals and amorphous phases. The materials make economic sense by optimizing the design of the structures or systems to meet multiple needs. We constantly "see" many similar strategies in approaches, between man and nature, but we seldom look at the details of natures approaches. The power of image processing, in many of natures creatures, is a detail that is often overlooked. Seldon does the engineer interact with the biologist and learn what nature has to teach us. The variety and complexity of biological materials and the optical systems formed should inspire us.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Somayaji, Anil B.; Amai, Wendy A.; Walther, Eleanor A.

This reports describes the successful extension of artificial immune systems from the domain of computer security to the domain of real time control systems for robotic vehicles. A biologically-inspired computer immune system was added to the control system of two different mobile robots. As an additional layer in a multi-layered approach, the immune system is complementary to traditional error detection and error handling techniques. This can be thought of as biologically-inspired defense in depth. We demonstrated an immune system can be added with very little application developer effort, resulting in little to no performance impact. The methods described here aremore » extensible to any system that processes a sequence of data through a software interface.« less

Self-organization, embodiment, and biologically inspired robotics.

PubMed

Pfeifer, Rolf; Lungarella, Max; Iida, Fumiya

2007-11-16

Robotics researchers increasingly agree that ideas from biology and self-organization can strongly benefit the design of autonomous robots. Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, indefinite richness, and limited availability of information. Industrial robots, in contrast, operate in highly controlled environments with no or very little uncertainty. Although many challenges remain, concepts from biologically inspired (bio-inspired) robotics will eventually enable researchers to engineer machines for the real world that possess at least some of the desirable properties of biological organisms, such as adaptivity, robustness, versatility, and agility.

Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics.

PubMed

List, Johann-Mattis; Pathmanathan, Jananan Sylvestre; Lopez, Philippe; Bapteste, Eric

2016-08-20

For a long time biologists and linguists have been noticing surprising similarities between the evolution of life forms and languages. Most of the proposed analogies have been rejected. Some, however, have persisted, and some even turned out to be fruitful, inspiring the transfer of methods and models between biology and linguistics up to today. Most proposed analogies were based on a comparison of the research objects rather than the processes that shaped their evolution. Focusing on process-based analogies, however, has the advantage of minimizing the risk of overstating similarities, while at the same time reflecting the common strategy to use processes to explain the evolution of complexity in both fields. We compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir's drift). Apart from new analogies between evolutionary processes, we also identified processes which are specific to either biology or linguistics. This shows that general evolution cannot be studied from within one discipline alone. In order to get a full picture of evolution, biologists and linguists need to complement their studies, trying to identify cross-disciplinary and discipline-specific evolutionary processes. The fact that we found many process-based analogies favoring transfer from biology to linguistics further shows that certain biological methods and models have a broader scope than previously recognized. This opens fruitful paths for collaboration between the two disciplines. This article was reviewed by W. Ford Doolittle and Eugene V. Koonin.

Probing biological redox chemistry with large amplitude Fourier transformed ac voltammetry

PubMed Central

Adamson, Hope

2017-01-01

Biological electron-exchange reactions are fundamental to life on earth. Redox reactions underpin respiration, photosynthesis, molecular biosynthesis, cell signalling and protein folding. Chemical, biomedical and future energy technology developments are also inspired by these natural electron transfer processes. Further developments in techniques and data analysis are required to gain a deeper understanding of the redox biochemistry processes that power Nature. This review outlines the new insights gained from developing Fourier transformed ac voltammetry as a tool for protein film electrochemistry. PMID:28804798

Biology and Architecture: Two Buildings Inspired by the Anatomy of the Visual System.

PubMed

Maro Kiris, Irem

2018-05-04

Architectural production has been influenced by a variety of sources. Forms derived from nature, biology and live organisms, had often been utilised in art and architecture. Certain features of the human anatomy had been reflected in design process in various ways, as imitations, abstractions, interpretations of the reality. The correlation of ideal proportions had been investigated throughout centuries. Scholars, art historians starting with Vitruvius from the world of ancient Roman architecture, described the human figure as being the principal source of proportion among the classical orders of architecture. This study aims to investigate two contemporary buildings, namely Kiasma Museum in Helsinki and Eye Museum in Amsterdam, inspired directly from the anatomy of visual system. Morover the author discussed the relationship of biology and architecture through these two special buildings by viewing the eye and chiasma as metaphors for elements of architecture.

Adaptation of sensor morphology: an integrative view of perception from biologically inspired robotics perspective

PubMed Central

Nurzaman, Surya G.

2016-01-01

Sensor morphology, the morphology of a sensing mechanism which plays a role of shaping the desired response from physical stimuli from surroundings to generate signals usable as sensory information, is one of the key common aspects of sensing processes. This paper presents a structured review of researches on bioinspired sensor morphology implemented in robotic systems, and discusses the fundamental design principles. Based on literature review, we propose two key arguments: first, owing to its synthetic nature, biologically inspired robotics approach is a unique and powerful methodology to understand the role of sensor morphology and how it can evolve and adapt to its task and environment. Second, a consideration of an integrative view of perception by looking into multidisciplinary and overarching mechanisms of sensor morphology adaptation across biology and engineering enables us to extract relevant design principles that are important to extend our understanding of the unfinished concepts in sensing and perception. PMID:27499843

Biologically inspired toys using artificial muscles

NASA Technical Reports Server (NTRS)

Bar-Cohen, Y.

2001-01-01

Recent developments in electroactive polymers, so-called artificial muscles, could one day be used to make bionics possible. Meanwhile, as this technology evolves novel mechanisms are expected to emerge that are biologically inspired.

Biologically inspired LED lens from cuticular nanostructures of firefly lantern

PubMed Central

Kim, Jae-Jun; Lee, Youngseop; Kim, Ha Gon; Choi, Ki-Ju; Kweon, Hee-Seok; Park, Seongchong; Jeong, Ki-Hun

2012-01-01

Cuticular nanostructures found in insects effectively manage light for light polarization, structural color, or optical index matching within an ultrathin natural scale. These nanostructures are mainly dedicated to manage incoming light and recently inspired many imaging and display applications. A bioluminescent organ, such as a firefly lantern, helps to out-couple light from the body in a highly efficient fashion for delivering strong optical signals in sexual communication. However, the cuticular nanostructures, except the light-producing reactions, have not been well investigated for physical principles and engineering biomimetics. Here we report a unique observation of high-transmission nanostructures on a firefly lantern and its biological inspiration for highly efficient LED illumination. Both numerical and experimental results clearly reveal high transmission through the nanostructures inspired from the lantern cuticle. The nanostructures on an LED lens surface were fabricated by using a large-area nanotemplating and reconfigurable nanomolding with heat-induced shear thinning. The biologically inspired LED lens, distinct from a smooth surface lens, substantially increases light transmission over visible ranges, comparable to conventional antireflection coating. This biological inspiration can offer new opportunities for increasing the light extraction efficiency of high-power LED packages. PMID:23112185

Using parallel evolutionary development for a biologically-inspired computer vision system for mobile robots.

PubMed

Wright, Cameron H G; Barrett, Steven F; Pack, Daniel J

2005-01-01

We describe a new approach to attacking the problem of robust computer vision for mobile robots. The overall strategy is to mimic the biological evolution of animal vision systems. Our basic imaging sensor is based upon the eye of the common house fly, Musca domestica. The computational algorithms are a mix of traditional image processing, subspace techniques, and multilayer neural networks.

Biology-Inspired Autonomous Control

DTIC Science & Technology

2011-08-31

from load sensing in a turbulent flow field with high levels of plant uncertainty and optical feedback latency. The results of this paper suggest... Mimicry of biological systems, in the form of precise mathematical or physical dynamical modeling, is yielding impressive insight into the underlying...processing and plants , the aerospace industry has been slow to accept adaptive control. In the past decade however, newer methods for design of adaptive

Microgravity Fluids for Biology, Workshop

NASA Technical Reports Server (NTRS)

Griffin, DeVon; Kohl, Fred; Massa, Gioia D.; Motil, Brian; Parsons-Wingerter, Patricia; Quincy, Charles; Sato, Kevin; Singh, Bhim; Smith, Jeffrey D.; Wheeler, Raymond M.

2013-01-01

Microgravity Fluids for Biology represents an intersection of biology and fluid physics that present exciting research challenges to the Space Life and Physical Sciences Division. Solving and managing the transport processes and fluid mechanics in physiological and biological systems and processes are essential for future space exploration and colonization of space by humans. Adequate understanding of the underlying fluid physics and transport mechanisms will provide new, necessary insights and technologies for analyzing and designing biological systems critical to NASAs mission. To enable this mission, the fluid physics discipline needs to work to enhance the understanding of the influence of gravity on the scales and types of fluids (i.e., non-Newtonian) important to biology and life sciences. In turn, biomimetic, bio-inspired and synthetic biology applications based on physiology and biology can enrich the fluid mechanics and transport phenomena capabilities of the microgravity fluid physics community.

Biological issues in materials science and engineering: Interdisciplinarity and the bio-materials paradigm

NASA Astrophysics Data System (ADS)

Murr, L. E.

2006-07-01

Biological systems and processes have had, and continue to have, important implications and applications in materials extraction, processing, and performance. This paper illustrates some interdisciplinary, biological issues in materials science and engineering. These include metal extraction involving bacterial catalysis, galvanic couples, bacterial-assisted corrosion and degradation of materials, biosorption and bioremediation of toxic and other heavy metals, metal and material implants and prostheses and related dental and medical biomaterials developments and applications, nanomaterials health benefits and toxicity issue, and biomimetics and biologically inspired materials developments. These and other examples provide compelling evidence and arguments for emphasizing biological sicences in materials science and engineering curricula and the implementation of a bio-materials paradigm to facilitate the emergence of innovative interdisciplinarity involving the biological sciences and materials sciences and engineering.

Digital and biological computing in organizations.

PubMed

Kampfner, Roberto R

2002-01-01

Michael Conrad unveiled many of the fundamental characteristics of biological computing. Underlying the behavioral variability and the adaptability of biological systems are these characteristics, including the ability of biological information processing to exploit quantum features at the atomic level, the powerful 3-D pattern recognition capabilities of macromolecules, the computational efficiency, and the ability to support biological function. Among many other things, Conrad formalized and explicated the underlying principles of biological adaptability, characterized the differences between biological and digital computing in terms of a fundamental tradeoff between adaptability and programmability of information processing, and discussed the challenges of interfacing digital computers and human society. This paper is about the encounter of biological and digital computing. The focus is on the nature of the biological information processing infrastructure of organizations and how it can be extended effectively with digital computing. In order to achieve this goal effectively, however, we need to embed properly digital computing into the information processing aspects of human and social behavior and intelligence, which are fundamentally biological. Conrad's legacy provides a firm, strong, and inspiring foundation for this endeavor.

Biologically Inspired Micro-Flight Research

NASA Technical Reports Server (NTRS)

Raney, David L.; Waszak, Martin R.

2003-01-01

Natural fliers demonstrate a diverse array of flight capabilities, many of which are poorly understood. NASA has established a research project to explore and exploit flight technologies inspired by biological systems. One part of this project focuses on dynamic modeling and control of micro aerial vehicles that incorporate flexible wing structures inspired by natural fliers such as insects, hummingbirds and bats. With a vast number of potential civil and military applications, micro aerial vehicles represent an emerging sector of the aerospace market. This paper describes an ongoing research activity in which mechanization and control concepts for biologically inspired micro aerial vehicles are being explored. Research activities focusing on a flexible fixed- wing micro aerial vehicle design and a flapping-based micro aerial vehicle concept are presented.

Algorithms in nature: the convergence of systems biology and computational thinking

PubMed Central

Navlakha, Saket; Bar-Joseph, Ziv

2011-01-01

Computer science and biology have enjoyed a long and fruitful relationship for decades. Biologists rely on computational methods to analyze and integrate large data sets, while several computational methods were inspired by the high-level design principles of biological systems. Recently, these two directions have been converging. In this review, we argue that thinking computationally about biological processes may lead to more accurate models, which in turn can be used to improve the design of algorithms. We discuss the similar mechanisms and requirements shared by computational and biological processes and then present several recent studies that apply this joint analysis strategy to problems related to coordination, network analysis, and tracking and vision. We also discuss additional biological processes that can be studied in a similar manner and link them to potential computational problems. With the rapid accumulation of data detailing the inner workings of biological systems, we expect this direction of coupling biological and computational studies to greatly expand in the future. PMID:22068329

Endogenous Biologically Inspired Art of Complex Systems.

PubMed

Ji, Haru; Wakefield, Graham

2016-01-01

Since 2007, Graham Wakefield and Haru Ji have looked to nature for inspiration as they have created a series of "artificial natures," or interactive visualizations of biologically inspired complex systems that can evoke nature-like aesthetic experiences within mixed-reality art installations. This article describes how they have applied visualization, sonification, and interaction design in their work with artificial ecosystems and organisms using specific examples from their exhibited installations.

Intelligent detectors modelled from the cat's eye

NASA Astrophysics Data System (ADS)

Lindblad, Th.; Becanovic, V.; Lindsey, C. S.; Szekely, G.

1997-02-01

Biologically inspired image/signal processing, in particular neural networks like the Pulse-Coupled Neural Network (PCNN), are revisited. Their use with high granularity high-energy physics detectors, as well as optical sensing devices, for filtering, de-noising, segmentation, object isolation and edge detection is discussed.

Developing an active artificial hair cell using nonlinear feedback control

NASA Astrophysics Data System (ADS)

Joyce, Bryan S.; Tarazaga, Pablo A.

2015-09-01

The hair cells in the mammalian cochlea convert sound-induced vibrations into electrical signals. These cells have inspired a variety of artificial hair cells (AHCs) to serve as biologically inspired sound, fluid flow, and acceleration sensors and could one day replace damaged hair cells in humans. Most of these AHCs rely on passive transduction of stimulus while it is known that the biological cochlea employs active processes to amplify sound-induced vibrations and improve sound detection. In this work, an active AHC mimics the active, nonlinear behavior of the cochlea. The AHC consists of a piezoelectric bimorph beam subjected to a base excitation. A feedback control law is used to reduce the linear damping of the beam and introduce a cubic damping term which gives the AHC the desired nonlinear behavior. Model and experimental results show the AHC amplifies the response due to small base accelerations, has a higher frequency sensitivity than the passive system, and exhibits a compressive nonlinearity like that of the mammalian cochlea. This bio-inspired accelerometer could lead to new sensors with lower thresholds of detection, improved frequency sensitivities, and wider dynamic ranges.

Biologically-Inspired Concepts for Self-Management of Complexity

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, G.

2006-01-01

Inherent complexity in large-scale applications may be impossible to eliminate or even ameliorate despite a number of promising advances. In such cases, the complexity must be tolerated and managed. Such management may be beyond the abilities of humans, or require such overhead as to make management by humans unrealistic. A number of initiatives inspired by concepts in biology have arisen for self-management of complex systems. We present some ideas and techniques we have been experimenting with, inspired by lesser-known concepts in biology that show promise in protecting complex systems and represent a step towards self-management of complexity.

Introducing memory and association mechanism into a biologically inspired visual model.

PubMed

Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng

2014-09-01

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

Fabrication of Nanochannels

PubMed Central

Zhang, Yuqi; Kong, Xiang-Yu; Gao, Loujun; Tian, Ye; Wen, Liping; Jiang, Lei

2015-01-01

Nature has inspired the fabrication of intelligent devices to meet the needs of the advanced community and better understand the imitation of biology. As a biomimetic nanodevice, nanochannels/nanopores aroused increasing interest because of their potential applications in nanofluidic fields. In this review, we have summarized some recent results mainly focused on the design and fabrication of one-dimensional nanochannels, which can be made of many materials, including polymers, inorganics, biotic materials, and composite materials. These nanochannels have some properties similar to biological channels, such as selectivity, voltage-dependent current fluctuations, ionic rectification current and ionic gating, etc. Therefore, they show great potential for the fields of biosensing, filtration, and energy conversions. These advances can not only help people to understand the living processes in nature, but also inspire scientists to develop novel nanodevices with better performance for mankind. PMID:28793564

Biologically inspired artificial compound eyes.

PubMed

Jeong, Ki-Hun; Kim, Jaeyoun; Lee, Luke P

2006-04-28

This work presents the fabrication of biologically inspired artificial compound eyes. The artificial ommatidium, like that of an insect's compound eyes, consists of a refractive polymer microlens, a light-guiding polymer cone, and a self-aligned waveguide to collect light with a small angular acceptance. The ommatidia are omnidirectionally arranged along a hemispherical polymer dome such that they provide a wide field of view similar to that of a natural compound eye. The spherical configuration of the microlenses is accomplished by reconfigurable microtemplating, that is, polymer replication using the deformed elastomer membrane with microlens patterns. The formation of polymer waveguides self-aligned with microlenses is also realized by a self-writing process in a photosensitive polymer resin. The angular acceptance is directly measured by three-dimensional optical sectioning with a confocal microscope, and the detailed optical characteristics are studied in comparison with a natural compound eye.

Biomimetics--using nature to inspire human innovation.

PubMed

Bar-Cohen, Yoseph

2006-03-01

Evolution has resolved many of nature's challenges leading to lasting solutions. Nature has always inspired human achievements and has led to effective materials, structures, tools, mechanisms, processes, algorithms, methods, systems, and many other benefits (Bar-Cohen Y (ed) 2005 Biomimetics-Biologically Inspired Technologies (Boca Raton, FL: CRC Press) pp 1-552). This field, which is known as biomimetics, offers enormous potential for inspiring new capabilities for exciting future technologies. There are numerous examples of biomimetic successes that involve making simple copies, such as the use of fins for swimming. Others examples involved greater mimicking complexity including the mastery of flying that became possible only after the principles of aerodynamics were better understood. Some commercial implementations of biomimetics, including robotic toys and movie subjects, are increasingly appearing and behaving like living creatures. More substantial benefits of biomimetics include the development of prosthetics that closely mimic real limbs and sensory-enhancing microchips that are interfaced with the brain to assist in hearing, seeing and controlling instruments. A review is given of selected areas that were inspired by nature, and an outlook for potential development in biomimetics is presented.

A Survey of Memristive Threshold Logic Circuits.

PubMed

Maan, Akshay Kumar; Jayadevi, Deepthi Anirudhan; James, Alex Pappachen

2017-08-01

In this paper, we review different memristive threshold logic (MTL) circuits that are inspired from the synaptic action of the flow of neurotransmitters in the biological brain. The brainlike generalization ability and the area minimization of these threshold logic circuits aim toward crossing Moore's law boundaries at device, circuits, and systems levels. Fast switching memory, signal processing, control systems, programmable logic, image processing, reconfigurable computing, and pattern recognition are identified as some of the potential applications of MTL systems. The physical realization of nanoscale devices with memristive behavior from materials, such as TiO 2 , ferroelectrics, silicon, and polymers, has accelerated research effort in these application areas, inspiring the scientific community to pursue the design of high-speed, low-cost, low-power, and high-density neuromorphic architectures.

A biologically inspired model of bat echolocation in a cluttered environment with inputs designed from field Recordings

NASA Astrophysics Data System (ADS)

Loncich, Kristen Teczar

Bat echolocation strategies and neural processing of acoustic information, with a focus on cluttered environments, is investigated in this study. How a bat processes the dense field of echoes received while navigating and foraging in the dark is not well understood. While several models have been developed to describe the mechanisms behind bat echolocation, most are based in mathematics rather than biology, and focus on either peripheral or neural processing---not exploring how these two levels of processing are vitally connected. Current echolocation models also do not use habitat specific acoustic input, or account for field observations of echolocation strategies. Here, a new approach to echolocation modeling is described capturing the full picture of echolocation from signal generation to a neural picture of the acoustic scene. A biologically inspired echolocation model is developed using field research measurements of the interpulse interval timing used by a frequency modulating (FM) bat in the wild, with a whole method approach to modeling echolocation including habitat specific acoustic inputs, a biologically accurate peripheral model of sound processing by the outer, middle, and inner ear, and finally a neural model incorporating established auditory pathways and neuron types with echolocation adaptations. Field recordings analyzed underscore bat sonar design differences observed in the laboratory and wild, and suggest a correlation between interpulse interval groupings and increased clutter. The scenario model provides habitat and behavior specific echoes and is a useful tool for both modeling and behavioral studies, and the peripheral and neural model show that spike-time information and echolocation specific neuron types can produce target localization in the midbrain.

Introductory Biology Textbooks Under-Represent Scientific Process

PubMed Central

Duncan, Dara B.; Lubman, Alexandra; Hoskins, Sally G.

2011-01-01

Attrition of undergraduates from Biology majors is a long-standing problem. Introductory courses that fail to engage students or spark their curiosity by emphasizing the open-ended and creative nature of biological investigation and discovery could contribute to student detachment from the field. Our hypothesis was that introductory biology books devote relatively few figures to illustration of the design and interpretation of experiments or field studies, thereby de-emphasizing the scientific process. To investigate this possibility, we examined figures in six Introductory Biology textbooks published in 2008. On average, multistep scientific investigations were presented in fewer than 5% of the hundreds of figures in each book. Devoting such a small percentage of figures to the processes by which discoveries are made discourages an emphasis on scientific thinking. We suggest that by increasing significantly the illustration of scientific investigations, textbooks could support undergraduates’ early interest in biology, stimulate the development of design and analytical skills, and inspire some students to participate in investigations of their own. PMID:23653758

Hierarchical biointerfaces assembled by leukocyte-inspired particles for specifically recognizing cancer cells.

PubMed

Meng, Jingxin; Liu, Hongliang; Liu, Xueli; Yang, Gao; Zhang, Pengchao; Wang, Shutao; Jiang, Lei

2014-09-24

By mimicking certain biochemical and physical attributes of biological cells, bio-inspired particles have attracted great attention for potential biomedical applications based on cell-like biological functions. Inspired by leukocytes, hierarchical biointerfaces are designed and prepared based on specific molecules-modified leukocyte-inspired particles. These biointerfaces can efficiently recognize cancer cells from whole blood samples through the synergistic effect of molecular recognition and topographical interaction. Compared to flat, mono-micro or nano-biointerfaces, these micro/nano hierarchical biointerfaces are better able to promote specific recognition interactions, resulting in an enhanced cell-capture efficiency. It is anticipated that this study may provide promising guidance to develop new bio-inspired hierarchical biointerfaces for biomedical applications. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis

PubMed Central

Fu, Hongping; Niu, Zhendong; Zhang, Chunxia; Ma, Jing; Chen, Jie

2016-01-01

Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention modulation and memory processing of primate visual cortex. In this paper, we employ the above properties of primate visual cortex to improve CNN and propose a biological-mechanism-driven-feature-construction based answer recommendation method (BMFC-ARM), which is used to recommend the best answer for the corresponding given questions in community question answering. BMFC-ARM is an improved CNN with four channels respectively representing questions, answers, asker information and answerer information, and mainly contains two stages: biological mechanism driven feature construction (BMFC) and answer ranking. BMFC imitates the attention modulation property by introducing the asker information and answerer information of given questions and the similarity between them, and imitates the memory processing property through bringing in the user reputation information for answerers. Then the feature vector for answer ranking is constructed by fusing the asker-answerer similarities, answerer's reputation and the corresponding vectors of question, answer, asker, and answerer. Finally, the Softmax is used at the stage of answer ranking to get best answers by the feature vector. The experimental results of answer recommendation on the Stackexchange dataset show that BMFC-ARM exhibits better performance. PMID:27471460

Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis.

PubMed

Fu, Hongping; Niu, Zhendong; Zhang, Chunxia; Ma, Jing; Chen, Jie

2016-01-01

Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention modulation and memory processing of primate visual cortex. In this paper, we employ the above properties of primate visual cortex to improve CNN and propose a biological-mechanism-driven-feature-construction based answer recommendation method (BMFC-ARM), which is used to recommend the best answer for the corresponding given questions in community question answering. BMFC-ARM is an improved CNN with four channels respectively representing questions, answers, asker information and answerer information, and mainly contains two stages: biological mechanism driven feature construction (BMFC) and answer ranking. BMFC imitates the attention modulation property by introducing the asker information and answerer information of given questions and the similarity between them, and imitates the memory processing property through bringing in the user reputation information for answerers. Then the feature vector for answer ranking is constructed by fusing the asker-answerer similarities, answerer's reputation and the corresponding vectors of question, answer, asker, and answerer. Finally, the Softmax is used at the stage of answer ranking to get best answers by the feature vector. The experimental results of answer recommendation on the Stackexchange dataset show that BMFC-ARM exhibits better performance.

Cell-free biology: exploiting the interface between synthetic biology and synthetic chemistry

PubMed Central

Harris, D. Calvin; Jewett, Michael C.

2014-01-01

Just as synthetic organic chemistry once revolutionized the ability of chemists to build molecules (including those that did not exist in nature) following a basic set of design rules, cell-free synthetic biology is beginning to provide an improved toolbox and faster process for not only harnessing but also expanding the chemistry of life. At the interface between chemistry and biology, research in cell-free synthetic systems is proceeding in two different directions: using synthetic biology for synthetic chemistry and using synthetic chemistry to reprogram or mimic biology. In the coming years, the impact of advances inspired by these approaches will make possible the synthesis of non-biological polymers having new backbone compositions, new chemical properties, new structures, and new functions. PMID:22483202

Biomimetics as a Model for Inspiring Human Innovation

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph

2006-01-01

Electroactive polymers (EAP) are human made actuators that are the closest to mimic biological muscles. Technology was advanced to the level that biologically inspired robots are taking increasing roles in the world around us and making science fiction ideas a closer engineering reality. Artificial technologies (AI, AM, and others) are increasingly becoming practical tools for making biologically inspired devices and instruments with enormous potential for space applications. Polymer materials are used to produce figures that resemble human and animals. These materials are widely employed by the movie industry for making acting figures and by the orthopedic industry to construct cyborg components. There are still many challenges ahead that are critical to making such possibilities practical. The annual armwrestling contest is providing an exciting measure of how well advances in EAP are implemented to address the field challenges. There is a need to document natures inventions in an engineering form to possibly inspire new capabilities.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

Winging It: Using Digital Imaging To Investigate Butterfly Metamorphosis

ERIC Educational Resources Information Center

Bowen, Anne; Bell, Randy L.

2004-01-01

One of the best ways to inspire interest in biology is through observations of living things. Unfortunately, this important component of science methodology is often left out because of the difficulty of including it in the classroom. Additionally, amazing processes occur in nature that few have the chance to observe. This article reviews a…

Cry, Baby, Cry: A Dialogic Response to Emotion

ERIC Educational Resources Information Center

White, E. Jayne

2013-01-01

This article challenges traditional approaches to emotion as a discreet biological or dialectic process in the early years. In doing so the proposition is made that emotion is an answerable social act of meaning-making and self-hood. Inspired by Bakhtinian philosophy, which resists separating emotion from cognition or the individual from their…

Biologically inspired collision avoidance system for unmanned vehicles

NASA Astrophysics Data System (ADS)

Ortiz, Fernando E.; Graham, Brett; Spagnoli, Kyle; Kelmelis, Eric J.

2009-05-01

In this project, we collaborate with researchers in the neuroscience department at the University of Delaware to develop an Field Programmable Gate Array (FPGA)-based embedded computer, inspired by the brains of small vertebrates (fish). The mechanisms of object detection and avoidance in fish have been extensively studied by our Delaware collaborators. The midbrain optic tectum is a biological multimodal navigation controller capable of processing input from all senses that convey spatial information, including vision, audition, touch, and lateral-line (water current sensing in fish). Unfortunately, computational complexity makes these models too slow for use in real-time applications. These simulations are run offline on state-of-the-art desktop computers, presenting a gap between the application and the target platform: a low-power embedded device. EM Photonics has expertise in developing of high-performance computers based on commodity platforms such as graphic cards (GPUs) and FPGAs. FPGAs offer (1) high computational power, low power consumption and small footprint (in line with typical autonomous vehicle constraints), and (2) the ability to implement massively-parallel computational architectures, which can be leveraged to closely emulate biological systems. Combining UD's brain modeling algorithms and the power of FPGAs, this computer enables autonomous navigation in complex environments, and further types of onboard neural processing in future applications.

How simple autonomous decisions evolve into robust behaviours? A review from neurorobotics, cognitive, self-organized and artificial immune systems fields.

PubMed

Fernandez-Leon, Jose A; Acosta, Gerardo G; Rozenfeld, Alejandro

2014-10-01

Researchers in diverse fields, such as in neuroscience, systems biology and autonomous robotics, have been intrigued by the origin and mechanisms for biological robustness. Darwinian evolution, in general, has suggested that adaptive mechanisms as a way of reaching robustness, could evolve by natural selection acting successively on numerous heritable variations. However, is this understanding enough for realizing how biological systems remain robust during their interactions with the surroundings? Here, we describe selected studies of bio-inspired systems that show behavioral robustness. From neurorobotics, cognitive, self-organizing and artificial immune system perspectives, our discussions focus mainly on how robust behaviors evolve or emerge in these systems, having the capacity of interacting with their surroundings. These descriptions are twofold. Initially, we introduce examples from autonomous robotics to illustrate how the process of designing robust control can be idealized in complex environments for autonomous navigation in terrain and underwater vehicles. We also include descriptions of bio-inspired self-organizing systems. Then, we introduce other studies that contextualize experimental evolution with simulated organisms and physical robots to exemplify how the process of natural selection can lead to the evolution of robustness by means of adaptive behaviors. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

A Project-Based Biologically-Inspired Robotics Module

ERIC Educational Resources Information Center

Crowder, R. M.; Zauner, K.-P.

2013-01-01

The design of any robotic system requires input from engineers from a variety of technical fields. This paper describes a project-based module, "Biologically-Inspired Robotics," that is offered to Electronics and Computer Science students at the University of Southampton, U.K. The overall objective of the module is for student groups to…

Natural and bio-inspired underwater adhesives: Current progress and new perspectives

NASA Astrophysics Data System (ADS)

Cui, Mengkui; Ren, Susu; Wei, Shicao; Sun, Chengjun; Zhong, Chao

2017-11-01

Many marine organisms harness diverse protein molecules as underwater adhesives to achieve strong and robust interfacial adhesion under dynamic and turbulent environments. Natural underwater adhesion phenomena thus provide inspiration for engineering adhesive materials that can perform in water or high-moisture settings for biomedical and industrial applications. Here we review examples of biological adhesives to show the molecular features of natural adhesives and discuss how such knowledge serves as a heuristic guideline for the rational design of biologically inspired underwater adhesives. In view of future bio-inspired research, we propose several potential opportunities, either in improving upon current L-3, 4-dihydroxyphenylalanine-based and coacervates-enabled adhesives with new features or engineering conceptually new types of adhesives that recapitulate important characteristics of biological adhesives. We underline the importance of viewing natural adhesives as dynamic materials, which owe their outstanding performance to the cellular coordination of protein expression, delivery, deposition, assembly, and curing of corresponding components with spatiotemporal control. We envision that the emerging synthetic biology techniques will provide great opportunities for advancing both fundamental and application aspects of underwater adhesives.

Diversification and enrichment of clinical biomaterials inspired by Darwinian evolution.

PubMed

Green, D W; Watson, G S; Watson, J A; Lee, D-J; Lee, J-M; Jung, H-S

2016-09-15

Regenerative medicine and biomaterials design are driven by biomimicry. There is the essential requirement to emulate human cell, tissue, organ and physiological complexity to ensure long-lasting clinical success. Biomimicry projects for biomaterials innovation can be re-invigorated with evolutionary insights and perspectives, since Darwinian evolution is the original dynamic process for biological organisation and complexity. Many existing human inspired regenerative biomaterials (defined as a nature generated, nature derived and nature mimicking structure, produced within a biological system, which can deputise for, or replace human tissues for which it closely matches) are without important elements of biological complexity such as, hierarchy and autonomous actions. It is possible to engineer these essential elements into clinical biomaterials via bioinspired implementation of concepts, processes and mechanisms played out during Darwinian evolution; mechanisms such as, directed, computational, accelerated evolutions and artificial selection contrived in the laboratory. These dynamos for innovation can be used during biomaterials fabrication, but also to choose optimal designs in the regeneration process. Further evolutionary information can help at the design stage; gleaned from the historical evolution of material adaptations compared across phylogenies to changes in their environment and habitats. Taken together, harnessing evolutionary mechanisms and evolutionary pathways, leading to ideal adaptations, will eventually provide a new class of Darwinian and evolutionary biomaterials. This will provide bioengineers with a more diversified and more efficient innovation tool for biomaterial design, synthesis and function than currently achieved with synthetic materials chemistry programmes and rational based materials design approach, which require reasoned logic. It will also inject further creativity, diversity and richness into the biomedical technologies that we make. All of which are based on biological principles. Such evolution-inspired biomaterials have the potential to generate innovative solutions, which match with existing bioengineering problems, in vital areas of clinical materials translation that include tissue engineering, gene delivery, drug delivery, immunity modulation, and scar-less wound healing. Evolution by natural selection is a powerful generator of innovations in molecular, materials and structures. Man has influenced evolution for thousands of years, to create new breeds of farm animals and crop plants, but now molecular and materials can be molded in the same way. Biological molecules and simple structures can be evolved, literally in the laboratory. Furthermore, they are re-designed via lessons learnt from evolutionary history. Through a 3-step process to (1) create variants in material building blocks, (2) screen the variants with beneficial traits/properties and (3) select and support their self-assembly into usable materials, improvements in design and performance can emerge. By introducing biological molecules and small organisms into this process, it is possible to make increasingly diversified, sophisticated and clinically relevant materials for multiple roles in biomedicine. Copyright © 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Biomimicry and the culinary arts.

PubMed

Burton, Lisa J; Cheng, Nadia; Vega, César; Andrés, José; Bush, John W M

2013-12-01

We present the results of a recent collaboration between scientists, engineers and chefs. Two particular devices are developed, both inspired by natural phenomena reliant on surface tension. The cocktail boat is a drink accessory, a self-propelled edible boat powered by alcohol-induced surface tension gradients, whose propulsion mechanism is analogous to that employed by a class of water-walking insects. The floral pipette is a novel means of serving small volumes of fluid in an elegant fashion, an example of capillary origami modeled after a class of floating flowers. The biological inspiration and mechanics of these two devices are detailed, along with the process that led to their development and deployment.

From Here to Autonomicity: Self-Managing Agents and the Biological Metaphors that Inspire Them

NASA Technical Reports Server (NTRS)

Sterritt, Roy; Hinchey, Mike

2005-01-01

We seek inspiration for self-managing systems from (obviously, pre-existing) biological mechanisms. Autonomic Computing (AC), a self-managing systems initiative based on the biological metaphor of the autonomic nervous system, is increasingly gaining momentum as the way forward for integrating and designing reliable systems, while agent technologies have been identified as a key enabler for engineering autonomicity in systems. This paper looks at other biological metaphors such as reflex and healing, heart- beat monitors, pulse monitors and apoptosis for assisting in the realization of autonomicity.

Biologically-inspired hexapod robot design and simulation

NASA Technical Reports Server (NTRS)

Espenschied, Kenneth S.; Quinn, Roger D.

1994-01-01

The design and construction of a biologically-inspired hexapod robot is presented. A previously developed simulation is modified to include models of the DC drive motors, the motor driver circuits and their transmissions. The application of this simulation to the design and development of the robot is discussed. The mechanisms thought to be responsible for the leg coordination of the walking stick insect were previously applied to control the straight-line locomotion of a robot. We generalized these rules for a robot walking on a plane. This biologically-inspired control strategy is used to control the robot in simulation. Numerical results show that the general body motion and performance of the simulated robot is similar to that of the robot based on our preliminary experimental results.

Advanced biologically plausible algorithms for low-level image processing

NASA Astrophysics Data System (ADS)

Gusakova, Valentina I.; Podladchikova, Lubov N.; Shaposhnikov, Dmitry G.; Markin, Sergey N.; Golovan, Alexander V.; Lee, Seong-Whan

1999-08-01

At present, in computer vision, the approach based on modeling the biological vision mechanisms is extensively developed. However, up to now, real world image processing has no effective solution in frameworks of both biologically inspired and conventional approaches. Evidently, new algorithms and system architectures based on advanced biological motivation should be developed for solution of computational problems related to this visual task. Basic problems that should be solved for creation of effective artificial visual system to process real world imags are a search for new algorithms of low-level image processing that, in a great extent, determine system performance. In the present paper, the result of psychophysical experiments and several advanced biologically motivated algorithms for low-level processing are presented. These algorithms are based on local space-variant filter, context encoding visual information presented in the center of input window, and automatic detection of perceptually important image fragments. The core of latter algorithm are using local feature conjunctions such as noncolinear oriented segment and composite feature map formation. Developed algorithms were integrated into foveal active vision model, the MARR. It is supposed that proposed algorithms may significantly improve model performance while real world image processing during memorizing, search, and recognition.

Trusted computation through biologically inspired processes

NASA Astrophysics Data System (ADS)

Anderson, Gustave W.

2013-05-01

Due to supply chain threats it is no longer a reasonable assumption that traditional protections alone will provide sufficient security for enterprise systems. The proposed cognitive trust model architecture extends the state-of-the-art in enterprise anti-exploitation technologies by providing collective immunity through backup and cross-checking, proactive health monitoring and adaptive/autonomic threat response, and network resource diversity.

Additive manufacturing of biologically-inspired materials.

PubMed

Studart, André R

2016-01-21

Additive manufacturing (AM) technologies offer an attractive pathway towards the fabrication of functional materials featuring complex heterogeneous architectures inspired by biological systems. In this paper, recent research on the use of AM approaches to program the local chemical composition, structure and properties of biologically-inspired materials is reviewed. A variety of structural motifs found in biological composites have been successfully emulated in synthetic systems using inkjet-based, direct-writing, stereolithography and slip casting technologies. The replication in synthetic systems of design principles underlying such structural motifs has enabled the fabrication of lightweight cellular materials, strong and tough composites, soft robots and autonomously shaping structures with unprecedented properties and functionalities. Pushing the current limits of AM technologies in future research should bring us closer to the manufacturing capabilities of living organisms, opening the way for the digital fabrication of advanced materials with superior performance, lower environmental impact and new functionalities.

Cochlea-inspired sensing node for compressive sensing

NASA Astrophysics Data System (ADS)

Peckens, Courtney A.; Lynch, Jerome P.

2013-04-01

While sensing technologies for structural monitoring applications have made significant advances over the last several decades, there is still room for improvement in terms of computational efficiency, as well as overall energy consumption. The biological nervous system can offer a potential solution to address these current deficiencies. The nervous system is capable of sensing and aggregating information about the external environment through very crude processing units known as neurons. Neurons effectively communicate in an extremely condensed format by encoding information into binary electrical spike trains, thereby reducing the amount of raw information sent throughout a neural network. Due to its unique signal processing capabilities, the mammalian cochlea and its interaction with the biological nervous system is of particular interest for devising compressive sensing strategies for dynamic engineered systems. The cochlea uses a novel method of place theory and frequency decomposition, thereby allowing for rapid signal processing within the nervous system. In this study, a low-power sensing node is proposed that draws inspiration from the mechanisms employed by the cochlea and the biological nervous system. As such, the sensor is able to perceive and transmit a compressed representation of the external stimulus with minimal distortion. Each sensor represents a basic building block, with function similar to the neuron, and can form a network with other sensors, thus enabling a system that can convey input stimulus in an extremely condensed format. The proposed sensor is validated through a structural monitoring application of a single degree of freedom structure excited by seismic ground motion.

Designing Networks that are Capable of Self-Healing and Adapting

DTIC Science & Technology

2017-04-01

from statistical mechanics, combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we... principles for self-healing networks, and applications, and construct an all-possible-paths model for network adaptation. 2015-11-16 UNIT CONVERSION...combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we will undertake the fol

Biologically inspired dynamic material systems.

PubMed

Studart, André R

2015-03-09

Numerous examples of material systems that dynamically interact with and adapt to the surrounding environment are found in nature, from hair-based mechanoreceptors in animals to self-shaping seed dispersal units in plants to remodeling bone in vertebrates. Inspired by such fascinating biological structures, a wide range of synthetic material systems have been created to replicate the design concepts of dynamic natural architectures. Examples of biological structures and their man-made counterparts are herein revisited to illustrate how dynamic and adaptive responses emerge from the intimate microscale combination of building blocks with intrinsic nanoscale properties. By using top-down photolithographic methods and bottom-up assembly approaches, biologically inspired dynamic material systems have been created 1) to sense liquid flow with hair-inspired microelectromechanical systems, 2) to autonomously change shape by utilizing plantlike heterogeneous architectures, 3) to homeostatically influence the surrounding environment through self-regulating adaptive surfaces, and 4) to spatially concentrate chemical species by using synthetic microcompartments. The ever-increasing complexity and remarkable functionalities of such synthetic systems offer an encouraging perspective to the rich set of dynamic and adaptive properties that can potentially be implemented in future man-made material systems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Material requirements for bio-inspired sensing systems

NASA Astrophysics Data System (ADS)

Biggins, Peter; Lloyd, Peter; Salmond, David; Kusterbeck, Anne

2008-10-01

The aim of developing bio-inspired sensing systems is to try and emulate the amazing sensitivity and specificity observed in the natural world. These capabilities have evolved, often for specific tasks, which provide the organism with an advantage in its fight to survive and prosper. Capabilities cover a wide range of sensing functions including vision, temperature, hearing, touch, taste and smell. For some functions, the capabilities of natural systems are still greater than that achieved by traditional engineering solutions; a good example being a dog's sense of smell. Furthermore, attempting to emulate aspects of biological optics, processing and guidance may lead to more simple and effective devices. A bio-inspired sensing system is much more than the sensory mechanism. A system will need to collect samples, especially if pathogens or chemicals are of interest. Other functions could include the provision of power, surfaces and receptors, structure, locomotion and control. In fact it is possible to conceive of a complete bio-inspired system concept which is likely to be radically different from more conventional approaches. This concept will be described and individual component technologies considered.

Flexible Ionic-Electronic Hybrid Oxide Synaptic TFTs with Programmable Dynamic Plasticity for Brain-Inspired Neuromorphic Computing.

PubMed

John, Rohit Abraham; Ko, Jieun; Kulkarni, Mohit R; Tiwari, Naveen; Chien, Nguyen Anh; Ing, Ng Geok; Leong, Wei Lin; Mathews, Nripan

2017-08-01

Emulation of biological synapses is necessary for future brain-inspired neuromorphic computational systems that could look beyond the standard von Neuman architecture. Here, artificial synapses based on ionic-electronic hybrid oxide-based transistors on rigid and flexible substrates are demonstrated. The flexible transistors reported here depict a high field-effect mobility of ≈9 cm 2 V -1 s -1 with good mechanical performance. Comprehensive learning abilities/synaptic rules like paired-pulse facilitation, excitatory and inhibitory postsynaptic currents, spike-time-dependent plasticity, consolidation, superlinear amplification, and dynamic logic are successfully established depicting concurrent processing and memory functionalities with spatiotemporal correlation. The results present a fully solution processable approach to fabricate artificial synapses for next-generation transparent neural circuits. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nature inspires sensors to do more with less.

PubMed

Mulvaney, Shawn P; Sheehan, Paul E

2014-10-28

The world is filled with widely varying chemical, physical, and biological stimuli. Over millennia, organisms have refined their senses to cope with these diverse stimuli, becoming virtuosos in differentiating closely related antigens, handling extremes in concentration, resetting the spent sensing mechanisms, and processing the multiple data streams being generated. Nature successfully deals with both repeating and new stimuli, demonstrating great adaptability when confronted with the latter. Interestingly, nature accomplishes these feats using a fairly simple toolbox. The sensors community continues to draw inspiration from nature's example: just look at the antibodies used as biosensor capture agents or the neural networks that process multivariate data streams. Indeed, many successful sensors have been built by simply mimicking natural systems. However, some of the most exciting breakthroughs occur when the community moves beyond mimicking nature and learns to use nature's tools in innovative ways.

Bioinspired Infrared Sensing Materials and Systems.

PubMed

Shen, Qingchen; Luo, Zhen; Ma, Shuai; Tao, Peng; Song, Chengyi; Wu, Jianbo; Shang, Wen; Deng, Tao

2018-05-11

Bioinspired engineering offers a promising alternative approach in accelerating the development of many man-made systems. Next-generation infrared (IR) sensing systems can also benefit from such nature-inspired approach. The inherent compact and uncooled operation of biological IR sensing systems provides ample inspiration for the engineering of portable and high-performance artificial IR sensing systems. This review overviews the current understanding of the biological IR sensing systems, most of which are thermal-based IR sensors that rely on either bolometer-like or photomechanic sensing mechanism. The existing efforts inspired by the biological IR sensing systems and possible future bioinspired approaches in the development of new IR sensing systems are also discussed in the review. Besides these biological IR sensing systems, other biological systems that do not have IR sensing capabilities but can help advance the development of engineered IR sensing systems are also discussed, and the related engineering efforts are overviewed as well. Further efforts in understanding the biological IR sensing systems, the learning from the integration of multifunction in biological systems, and the reduction of barriers to maximize the multidiscipline collaborations are needed to move this research field forward. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yusufoglu, Yusuf

Nature offers many exciting ideas and inspiration for the development of new materials and processes. The toughness of spider silk, the strength and lightweight of bone, and the adhesion abilities of the gecko's feet are some of the many examples of highperformance natural materials, which have attracted the interest of scientist to duplicate their properties in man-made materials. Materials found in nature combine many inspiring properties such as miniaturization, sophistication, hierarchical organization, hybridization, and adaptability. In all biological systems, whether very basic or highly complex, nature provides a multiplicity of materials, architectures, systems and functions. Generally, the architectural configurations andmore » material characteristics are the important features that have been duplicated from nature for building synthetic structural composites.« less

Material science lesson from the biological photosystem.

PubMed

Kim, Younghye; Lee, Jun Ho; Ha, Heonjin; Im, Sang Won; Nam, Ki Tae

2016-01-01

Inspired by photosynthesis, artificial systems for a sustainable energy supply are being designed. Each sequential energy conversion process from light to biomass in natural photosynthesis is a valuable model for an energy collection, transport and conversion system. Notwithstanding the numerous lessons of nature that provide inspiration for new developments, the features of natural photosynthesis need to be reengineered to meet man's demands. This review describes recent strategies toward adapting key lessons from natural photosynthesis to artificial systems. We focus on the underlying material science in photosynthesis that combines photosystems as pivotal functional materials and a range of materials into an integrated system. Finally, a perspective on the future development of photosynthesis mimetic energy systems is proposed.

Development of new smart materials and spinning systems inspired by natural silks and their applications

NASA Astrophysics Data System (ADS)

Cheng, Jie; Lee, Sang-Hoon

2015-12-01

Silks produced by spiders and silkworms are charming natural biological materials with highly optimized hierarchical structures and outstanding physicomechanical properties. The superior performance of silks relies on the integration of a unique protein sequence, a distinctive spinning process, and complex hierarchical structures. Silks have been prepared to form a variety of morphologies and are widely used in diverse applications, for example, in the textile industry, as drug delivery vehicles, and as tissue engineering scaffolds. This review presents an overview of the organization of natural silks, in which chemical and physical functions are optimized, as well as a range of new materials inspired by the desire to mimic natural silk structure and synthesis.

Biomimetics inspired surfaces for drag reduction and oleophobicity/philicity.

PubMed

Bhushan, Bharat

2011-01-01

The emerging field of biomimetics allows one to mimic biology or nature to develop nanomaterials, nanodevices, and processes which provide desirable properties. Hierarchical structures with dimensions of features ranging from the macroscale to the nanoscale are extremely common in nature and possess properties of interest. There are a large number of objects including bacteria, plants, land and aquatic animals, and seashells with properties of commercial interest. Certain plant leaves, such as lotus (Nelumbo nucifera) leaves, are known to be superhydrophobic and self-cleaning due to the hierarchical surface roughness and presence of a wax layer. In addition to a self-cleaning effect, these surfaces with a high contact angle and low contact angle hysteresis also exhibit low adhesion and drag reduction for fluid flow. An aquatic animal, such as a shark, is another model from nature for the reduction of drag in fluid flow. The artificial surfaces inspired from the shark skin and lotus leaf have been created, and in this article the influence of structure on drag reduction efficiency is reviewed. Biomimetic-inspired oleophobic surfaces can be used to prevent contamination of the underwater parts of ships by biological and organic contaminants, including oil. The article also reviews the wetting behavior of oil droplets on various superoleophobic surfaces created in the lab.

Biomimetics inspired surfaces for drag reduction and oleophobicity/philicity

PubMed Central

2011-01-01

Summary The emerging field of biomimetics allows one to mimic biology or nature to develop nanomaterials, nanodevices, and processes which provide desirable properties. Hierarchical structures with dimensions of features ranging from the macroscale to the nanoscale are extremely common in nature and possess properties of interest. There are a large number of objects including bacteria, plants, land and aquatic animals, and seashells with properties of commercial interest. Certain plant leaves, such as lotus (Nelumbo nucifera) leaves, are known to be superhydrophobic and self-cleaning due to the hierarchical surface roughness and presence of a wax layer. In addition to a self-cleaning effect, these surfaces with a high contact angle and low contact angle hysteresis also exhibit low adhesion and drag reduction for fluid flow. An aquatic animal, such as a shark, is another model from nature for the reduction of drag in fluid flow. The artificial surfaces inspired from the shark skin and lotus leaf have been created, and in this article the influence of structure on drag reduction efficiency is reviewed. Biomimetic-inspired oleophobic surfaces can be used to prevent contamination of the underwater parts of ships by biological and organic contaminants, including oil. The article also reviews the wetting behavior of oil droplets on various superoleophobic surfaces created in the lab. PMID:21977417

Bio-inspired synthesis and biological evaluation of a colchicine-related compound library.

PubMed

Nicolaou, K C; Valiulin, Roman A; Pokorski, Jonathan K; Chang, Vicki; Chen, Jason S

2012-06-01

A bio-inspired investigation of the reactions of substrates of type 1 with VOF(3) and PIFA [phenyliodine(III) bis(trifluoroacetate)] led to a collection of colchicine-like compounds 2-5 and related systems. Biological evaluation revealed that some of the synthesized products had significant cytotoxic properties against the colon cancer cell line HT-29. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Biologically-Inspired Neural Network Architecture for Image Processing

DTIC Science & Technology

1990-12-01

was organized into twelve groups of 8-by-8 node arrays. Weights were con- strained for each group of nodes, with each node "viewing" a 5-by-5 pixel...single wIndow * smk 0; for(J-0; j< 6 4 ; J++)( sum -sum +t %borfi][J]*rfarray[j]; ) /* Finished calculating ore block, one j:,,sition (first layer

Biologically inspired flexible quasi-single-mode random laser: an integration of Pieris canidia butterfly wing and semiconductors.

PubMed

Wang, Cih-Su; Chang, Tsung-Yuan; Lin, Tai-Yuan; Chen, Yang-Fang

2014-10-23

Quasi-periodic structures of natural biomaterial membranes have great potentials to serve as resonance cavities to generate ecological friendly optoelectronic devices with low cost. To achieve the first attempt for the illustration of the underlying principle, the Pieris canidia butterfly wing was embedded with ZnO nanoparticles. Quite interestingly, it is found that the bio-inspired quasi-single-mode random laser can be achieved by the assistance of the skeleton of the membrane, in which ZnO nanoparticles act as emitting gain media. Such unique characteristics can be interpreted well by the Fabry-Perot resonance existing in the window-like quasi-periodic structure of butterfly wing. Due to the inherently promising flexibility of butterfly wing membrane, the laser action can still be maintained during the bending process. Our demonstrated approach not only indicates that the natural biological structures can provide effective scattering feedbacks but also pave a new avenue towards designing bio-controlled photonic devices.

Biologically inspired flexible quasi-single-mode random laser: An integration of Pieris canidia butterfly wing and semiconductors

PubMed Central

Wang, Cih-Su; Chang, Tsung-Yuan; Lin, Tai-Yuan; Chen, Yang-Fang

2014-01-01

Quasi-periodic structures of natural biomaterial membranes have great potentials to serve as resonance cavities to generate ecological friendly optoelectronic devices with low cost. To achieve the first attempt for the illustration of the underlying principle, the Pieris canidia butterfly wing was embedded with ZnO nanoparticles. Quite interestingly, it is found that the bio-inspired quasi-single-mode random laser can be achieved by the assistance of the skeleton of the membrane, in which ZnO nanoparticles act as emitting gain media. Such unique characteristics can be interpreted well by the Fabry-Perot resonance existing in the window-like quasi-periodic structure of butterfly wing. Due to the inherently promising flexibility of butterfly wing membrane, the laser action can still be maintained during the bending process. Our demonstrated approach not only indicates that the natural biological structures can provide effective scattering feedbacks but also pave a new avenue towards designing bio-controlled photonic devices. PMID:25338507

Novel High Integrity Bio-Inspired Systems with On-Line Self-Test and Self-Repair Properties

NASA Astrophysics Data System (ADS)

Samie, Mohammad; Dragffy, Gabriel; Pipe, Tony

2011-08-01

Since the beginning of life nature has been developing some remarkable solutions to the problem of creating reliable systems that can operate under difficult environmental and fault conditions. Yet, no matter how sophisticated our systems are, we are still unable to match the high degree of reliability that biological organisms posses. Since the early '90s attempts have been made to adapt biological properties and processes to the design of electronic systems but the results have always been unduly complex.This paper, proposes a novel model using a radically new approach to construct highly reliable electronic systems with online fault repair properties. It uses the characteristics and behaviour of unicellular bacteria and bacterial communities to achieve this. The result is a configurable bio-inspired cellular array architecture that, with built-in self-diagnostic and self-repair properties, can implement any application specific electronic system but is particularly suited for safety critical environments, such as space.

Biologically inspired flexible quasi-single-mode random laser: An integration of Pieris canidia butterfly wing and semiconductors

NASA Astrophysics Data System (ADS)

Wang, Cih-Su; Chang, Tsung-Yuan; Lin, Tai-Yuan; Chen, Yang-Fang

2014-10-01

Quasi-periodic structures of natural biomaterial membranes have great potentials to serve as resonance cavities to generate ecological friendly optoelectronic devices with low cost. To achieve the first attempt for the illustration of the underlying principle, the Pieris canidia butterfly wing was embedded with ZnO nanoparticles. Quite interestingly, it is found that the bio-inspired quasi-single-mode random laser can be achieved by the assistance of the skeleton of the membrane, in which ZnO nanoparticles act as emitting gain media. Such unique characteristics can be interpreted well by the Fabry-Perot resonance existing in the window-like quasi-periodic structure of butterfly wing. Due to the inherently promising flexibility of butterfly wing membrane, the laser action can still be maintained during the bending process. Our demonstrated approach not only indicates that the natural biological structures can provide effective scattering feedbacks but also pave a new avenue towards designing bio-controlled photonic devices.

Metal-coordination: Using one of nature’s tricks to control soft material mechanics

PubMed Central

Holten-Andersen, Niels; Jaishankar, Aditya; Harrington, Matthew; Fullenkamp, Dominic E.; DiMarco, Genevieve; He, Lihong; McKinley, Gareth H.; Messersmith, Phillip B.; Lee, Ka Yee C.

2015-01-01

Growing evidence supports a critical role of dynamic metal-coordination crosslinking in soft biological material properties such as self-healing and underwater adhesion1. Using bio-inspired metal-coordinating polymers, initial efforts to mimic these properties have shown promise2. Here we demonstrate how bio-inspired aqueous polymer network mechanics can be easily controlled via metal-coordination crosslink dynamics; metal ion-based crosslink stability control allows aqueous polymer network relaxation times to be finely tuned over several orders of magnitude. In addition to further biological material insights, our demonstration of this compositional scaling mechanism should provide inspiration for new polymer material property-control designs. PMID:26413297

Bio-inspired algorithms applied to molecular docking simulations.

PubMed

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

BIOCOMPUTATION: some history and prospects.

PubMed

Cull, Paul

2013-06-01

At first glance, biology and computer science are diametrically opposed sciences. Biology deals with carbon based life forms shaped by evolution and natural selection. Computer Science deals with electronic machines designed by engineers and guided by mathematical algorithms. In this brief paper, we review biologically inspired computing. We discuss several models of computation which have arisen from various biological studies. We show what these have in common, and conjecture how biology can still suggest answers and models for the next generation of computing problems. We discuss computation and argue that these biologically inspired models do not extend the theoretical limits on computation. We suggest that, in practice, biological models may give more succinct representations of various problems, and we mention a few cases in which biological models have proved useful. We also discuss the reciprocal impact of computer science on biology and cite a few significant contributions to biological science. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Bionic Manufacturing: Towards Cyborg Cells and Sentient Microbots.

PubMed

Srivastava, Sarvesh Kumar; Yadav, Vikramaditya G

2018-05-01

Bio-inspired engineering applies biological design principles towards developing engineering solutions but is not practical as a manufacturing paradigm. We advocate 'bionic manufacturing', a synergistic fusion of biotic and abiotic components, to transition away from bio-inspiration toward bio-augmentation to address current limitations in bio-inspired manufacturing. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Inspiration and application in the evolution of biomaterials.

PubMed

Huebsch, Nathaniel; Mooney, David J

2009-11-26

Biomaterials, traditionally defined as materials used in medical devices, have been used since antiquity, but recently their degree of sophistication has increased significantly. Biomaterials made today are routinely information rich and incorporate biologically active components derived from nature. In the future, biomaterials will assume an even greater role in medicine and will find use in a wide variety of non-medical applications through biologically inspired design and incorporation of dynamic behaviour.

SABRE: a bio-inspired fault-tolerant electronic architecture.

PubMed

Bremner, P; Liu, Y; Samie, M; Dragffy, G; Pipe, A G; Tempesti, G; Timmis, J; Tyrrell, A M

2013-03-01

As electronic devices become increasingly complex, ensuring their reliable, fault-free operation is becoming correspondingly more challenging. It can be observed that, in spite of their complexity, biological systems are highly reliable and fault tolerant. Hence, we are motivated to take inspiration for biological systems in the design of electronic ones. In SABRE (self-healing cellular architectures for biologically inspired highly reliable electronic systems), we have designed a bio-inspired fault-tolerant hierarchical architecture for this purpose. As in biology, the foundation for the whole system is cellular in nature, with each cell able to detect faults in its operation and trigger intra-cellular or extra-cellular repair as required. At the next level in the hierarchy, arrays of cells are configured and controlled as function units in a transport triggered architecture (TTA), which is able to perform partial-dynamic reconfiguration to rectify problems that cannot be solved at the cellular level. Each TTA is, in turn, part of a larger multi-processor system which employs coarser grain reconfiguration to tolerate faults that cause a processor to fail. In this paper, we describe the details of operation of each layer of the SABRE hierarchy, and how these layers interact to provide a high systemic level of fault tolerance.

Methods and Apparatus for Autonomous Robotic Control

NASA Technical Reports Server (NTRS)

Gorshechnikov, Anatoly (Inventor); Livitz, Gennady (Inventor); Versace, Massimiliano (Inventor); Palma, Jesse (Inventor)

2017-01-01

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements.

Neuro-inspired smart image sensor: analog Hmax implementation

NASA Astrophysics Data System (ADS)

Paindavoine, Michel; Dubois, Jérôme; Musa, Purnawarman

2015-03-01

Neuro-Inspired Vision approach, based on models from biology, allows to reduce the computational complexity. One of these models - The Hmax model - shows that the recognition of an object in the visual cortex mobilizes V1, V2 and V4 areas. From the computational point of view, V1 corresponds to the area of the directional filters (for example Sobel filters, Gabor filters or wavelet filters). This information is then processed in the area V2 in order to obtain local maxima. This new information is then sent to an artificial neural network. This neural processing module corresponds to area V4 of the visual cortex and is intended to categorize objects present in the scene. In order to realize autonomous vision systems (consumption of a few milliwatts) with such treatments inside, we studied and realized in 0.35μm CMOS technology prototypes of two image sensors in order to achieve the V1 and V2 processing of Hmax model.

Controlled biological and biomimetic systems for landmine detection.

PubMed

Habib, Maki K

2007-08-30

Humanitarian demining requires to accurately detect, locate and deactivate every single landmine and other buried mine-like objects as safely and as quickly as possible, and in the most non-invasive manner. The quality of landmine detection affects directly the efficiency and safety of this process. Most of the available methods to detect explosives and landmines are limited by their sensitivity and/or operational complexities. All landmines leak with time small amounts of their explosives that can be found on surrounding ground and plant life. Hence, explosive signatures represent the robust primary indicator of landmines. Accordingly, developing innovative technologies and efficient techniques to identify in real-time explosives residue in mined areas represents an attractive and promising approach. Biological and biologically inspired detection technology has the potential to compete with or be used in conjunction with other artificial technology to complement performance strengths. Biological systems are sensitive to many different scents concurrently, a property that has proven difficult to replicate artificially. Understanding biological systems presents unique opportunities for developing new capabilities through direct use of trained bio-systems, integration of living and non-living components, or inspiring new design by mimicking biological capabilities. It is expected that controlled bio-systems, biotechnology and microbial techniques will contribute to the advancement of mine detection and other application domains. This paper provides directions, evaluation and analysis on the progress of controlled biological and biomimetic systems for landmine detection. It introduces and discusses different approaches developed, underlining their relative advantages and limitations, and highlighting trends, safety and ecology concern, and possible future directions.

Energy-efficient neuron, synapse and STDP integrated circuits.

PubMed

Cruz-Albrecht, Jose M; Yung, Michael W; Srinivasa, Narayan

2012-06-01

Ultra-low energy biologically-inspired neuron and synapse integrated circuits are presented. The synapse includes a spike timing dependent plasticity (STDP) learning rule circuit. These circuits have been designed, fabricated and tested using a 90 nm CMOS process. Experimental measurements demonstrate proper operation. The neuron and the synapse with STDP circuits have an energy consumption of around 0.4 pJ per spike and synaptic operation respectively.

Synthetic biology, inspired by synthetic chemistry.

PubMed

Malinova, V; Nallani, M; Meier, W P; Sinner, E K

2012-07-16

The topic synthetic biology appears still as an 'empty basket to be filled'. However, there is already plenty of claims and visions, as well as convincing research strategies about the theme of synthetic biology. First of all, synthetic biology seems to be about the engineering of biology - about bottom-up and top-down approaches, compromising complexity versus stability of artificial architectures, relevant in biology. Synthetic biology accounts for heterogeneous approaches towards minimal and even artificial life, the engineering of biochemical pathways on the organismic level, the modelling of molecular processes and finally, the combination of synthetic with nature-derived materials and architectural concepts, such as a cellular membrane. Still, synthetic biology is a discipline, which embraces interdisciplinary attempts in order to have a profound, scientific base to enable the re-design of nature and to compose architectures and processes with man-made matter. We like to give an overview about the developments in the field of synthetic biology, regarding polymer-based analogs of cellular membranes and what questions can be answered by applying synthetic polymer science towards the smallest unit in life, namely a cell. Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

How can we improve problem solving in undergraduate biology? Applying lessons from 30 years of physics education research.

PubMed

Hoskinson, A-M; Caballero, M D; Knight, J K

2013-06-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research.

An Exploration of Design Students' Inspiration Process

ERIC Educational Resources Information Center

Dazkir, Sibel S.; Mower, Jennifer M.; Reddy-Best, Kelly L.; Pedersen, Elaine L.

2013-01-01

Our purpose was to explore how different sources of inspiration influenced two groups of students' inspiration process and their attitudes toward their design projects. Assigned sources of inspiration and instructor's assistance in the search for inspiration varied for two groups of students completing a small culture inspired product design…

Biomimetics - using nature as an inspiring model for innovation

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph

2006-01-01

In this presentation, various aspects of the field of biomimetics will be reviewed, examples of inspiring biological models and practical applications will be described, and challenges and potential direction of the field will be discussed.

Natural product-inspired cascade synthesis yields modulators of centrosome integrity.

PubMed

Dückert, Heiko; Pries, Verena; Khedkar, Vivek; Menninger, Sascha; Bruss, Hanna; Bird, Alexander W; Maliga, Zoltan; Brockmeyer, Andreas; Janning, Petra; Hyman, Anthony; Grimme, Stefan; Schürmann, Markus; Preut, Hans; Hübel, Katja; Ziegler, Slava; Kumar, Kamal; Waldmann, Herbert

2011-12-25

In biology-oriented synthesis, the scaffolds of biologically relevant compound classes inspire the synthesis of focused compound collections enriched in bioactivity. This criterion is, in particular, met by the scaffolds of natural products selected in evolution. The synthesis of natural product-inspired compound collections calls for efficient reaction sequences that preferably combine multiple individual transformations in one operation. Here we report the development of a one-pot, twelve-step cascade reaction sequence that includes nine different reactions and two opposing kinds of organocatalysis. The cascade sequence proceeds within 10-30 min and transforms readily available substrates into complex indoloquinolizines that resemble the core tetracyclic scaffold of numerous polycyclic indole alkaloids. Biological investigation of a corresponding focused compound collection revealed modulators of centrosome integrity, termed centrocountins, which caused fragmented and supernumerary centrosomes, chromosome congression defects, multipolar mitotic spindles, acentrosomal spindle poles and multipolar cell division by targeting the centrosome-associated proteins nucleophosmin and Crm1.

Biophysics and Thermodynamics: The Scientific Building Blocks of Bio-inspired Drug Delivery Nano Systems.

PubMed

Demetzos, Costas

2015-06-01

Biophysics and thermodynamics are considered as the scientific milestones for investigating the properties of materials. The relationship between the changes of temperature with the biophysical variables of biomaterials is important in the process of the development of drug delivery systems. Biophysics is a challenge sector of physics and should be used complementary with the biochemistry in order to discover new and promising technological platforms (i.e., drug delivery systems) and to disclose the 'silence functionality' of bio-inspired biological and artificial membranes. Thermal analysis and biophysical approaches in pharmaceuticals present reliable and versatile tools for their characterization and for the successful development of pharmaceutical products. The metastable phases of self-assembled nanostructures such as liposomes should be taken into consideration because they represent the thermal events can affect the functionality of advanced drug delivery nano systems. In conclusion, biophysics and thermodynamics are characterized as the building blocks for design and development of bio-inspired drug delivery systems.

Mixing enhancement by biologically inspired convection in a micro-chamber using alternating current galvanotactic control of the Tetrahymena pyriformis

NASA Astrophysics Data System (ADS)

Kim, Jihoon; Jang, Yonghee; Byun, Doyoung; Hyung Kim, Dal; Jun Kim, Min

2013-09-01

Recently, there has been increasing interest in the swimming behavior of microorganisms and biologically inspired micro-robots. In this study, we investigated biologically induced convection flow with living microorganism using galvanotaxis. We fabricated and evaluated our micro-mixer with motile cells. For the cell based active micro-mixers, two miscible fluids were used to measure the mixing index. Under alternating current (AC) electric fields with varying frequency, a group of motile Tetrahymena pyriformis cells generated reciprocal motion with circulating flows around their pathline, enhancing the mixing ratio.

The Biological Relevance of Artificial Life: Lessons from Artificial Intelligence

NASA Technical Reports Server (NTRS)

Colombano, Silvano

2000-01-01

There is no fundamental reason why A-life couldn't simply be a branch of computer science that deals with algorithms that are inspired by, or emulate biological phenomena. However, if these are the limits we place on this field, we miss the opportunity to help advance Theoretical Biology and to contribute to a deeper understanding of the nature of life. The history of Artificial Intelligence provides a good example, in that early interest in the nature of cognition quickly was lost to the process of building tools, such as "expert systems" that, were certainly useful, but provided little insight in the nature of cognition. Based on this lesson, I will discuss criteria for increasing the biological relevance of A-life and the probability that this field may provide a theoretical foundation for Biology.

Overview of the HFM-181 Symposium Programme, Medical Technology Repurposed to Enhance Human Performance

DTIC Science & Technology

2009-10-01

BIO -INSPIRED HUMAN PERFORMANCE ENHANCEMENT 3.1 Biological performance currently outside of the bounds of the human species HPE opportunities may...strategies to preferentially burn fat in weight reduction (85). 3.2 Bio -inspired opportunities for human performance There are many interesting...solutions to assist human performance Nonmedical applications of bio -inspired engineering and computing technologies are a recognized priority in

A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

PubMed

Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin

2017-10-01

Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary to fixed window length conventional filters. Copyright © 2017 Elsevier B.V. All rights reserved.

Design and Fabrication of Tunable Nanoparticles for Biomedical Applications

NASA Astrophysics Data System (ADS)

Sun, Leming

In this dissertation, we first reviewed the naturally occurring nanoparticles and their limitations (Chapter 1). We then discussed the need and the parameters to design and fabricate bio-inspired tunable nanoparticles for wound healing, Alzheimer's disease (AD) diagnosis and progression monitoring. Tunable nanoparticles enhanced adhesive was inspired from the self-assembly processes, nanocomposite and chemical structures. Fluorescent peptide nanoparticles were inspired from the biological peptide self-assembly and naturally occurring fluorescent proteins. Then we reported the development of an in situ synthesis approach for fabricating tunable nanoparticle enhanced adhesives inspired from the strong adhesive produced by English ivy in Chapter 2. Special attention was given to tunable features of the adhesive produced by the biological process. Parameters that may be used to tune properties of the adhesive were proposed. To illustrate and validate the proposed approach, an experimental platform was presented for fabricating tunable chitosan adhesive enhanced by Au nanoparticles synthesized in situ. This study contributes to a bio-inspired approach for in situ synthesis of tunable nanocomposite adhesives by mimicking the natural biological processes of ivy adhesive synthesis. Using a bio-inspired approach, we synthesized adhesive hydrogels comprised of sodium alginate, gum arabic, and calcium ions to mimic the properties of the natural sundew-derived adhesive hydrogels in Chapter 3. We then characterized and showed that these sundew-inspired hydrogels promote wound healing through their superior adhesive strength, nanostructure, and resistance to shearing; when compared to other hydrogels in vitro. In vivo, sundew-inspired hydrogels promoted a "suturing" effect to wound sites; which was demonstrated by enhanced wound closure following topical application of the hydrogels. In combination with mouse adipose derived stem cells (ADSCs), and compared to other therapeutic biomaterials, the sundew-inspired hydrogels demonstrated superior wound healing capabilities. Collectively, our studies show that sundew-inspired hydrogels contain ideal properties that promote wound healing and suggest that sundew-inspired-ADSCs combination therapy is an efficacious approach for treating wounds without eliciting noticeable toxicity or inflammation. While tremendous efforts have been spent in investigating scalable approaches for fabricating nanoparticles, less progress has been made in scalable synthesizing cyclic peptide nanoparticles and nanotubes, despite their great potential for broader biomedical applications. In Chapter 4, tunable synthesis of self-assembled cyclic peptide nanotubes and nanoparticles using three different methods, phase equilibrium, pH-driven, and pH-sensitive methods were proposed and investigated. The goal is for scalable nano-manufacturing of cyclic peptide nanoparticles and nanotubes with different sizes in large quality by controlling multiple process parameters. The dimensions of self-assembled nanostructures were found to be strongly influenced by the cyclic peptides concentration, side chains modification, pH value, reaction time, stirring intensity, and sonication time. This study proposed an overall strategy to integrate all the parameters to achieve optimal synthesis outputs. AD is associated with the accumulation of insoluble forms of amyloid-beta (Abeta) in plaques in extracellular spaces, as well as in the walls of blood vessels, and aggregation of microtubule protein tau in neurofibrillary tangles in neurons. In Chapter 5, we designed and synthesized a series of fluorescent cyclic peptide nanoparticles that can be used to detect Abeta aggregates in both the cerebrospinal fluid (CSF) and serum, which were obtained from healthy people and AD patients in different disease stages. Our experimental studies indicate that the fluorescence intensities and wavelengths generated from the interactions between the negatively charged fluorescent cyclic peptide nanoparticles and Abeta aggregates in both the CSF and serum changed with disease status, as compared to healthy individuals. The morphological and cytotoxicity studies demonstrated a potential inhibitory effect of the negatively charged nanoparticles on amyloid fibril growth. The underlying mechanisms leading to these changes are interpreted based on the aromatic, hydrophobic, and electrostatic interactions between c-PNPs and Abeta peptides. There is a critical need to diagnose and monitor the progression of AD using blood-based biomarkers. At present, it is believed that no single biomarker can be utilized to reliably detect AD. Combined biomarkers using multimodal techniques are highly sought after for AD diagnosis and progression monitoring. For this purpose, we developed a fluorescent peptide nanoparticles arrayed microfluidic chip that is capable of detecting multiple blood-based AD biomarkers simultaneously in Chapter 6. The concentration, aggregation stages, and Young's modulus of biomarkers could be analyzed by monitoring the changes of multimodal fluorescence intensity, nano-morphological, and nano-mechanical properties of the f-PNPs array. In this study, Abeta polypeptides and tau proteins were used to verify the proposed idea. To conclude, we demonstrate that how to design and fabrication of tunable nanoparticles for biomedical applications. Inspired from English ivy and sundew nanoadhesive, tunable nanoparticles enhanced adhesive hydrogels were prepared and validated for wound healing applications. Moreover, fluorescent peptide nanoparticles were designed, synthesized, characterized, and validated for AD diagnosis and progression monitoring. (Abstract shortened by ProQuest.).

Remotely controlled fusion of selected vesicles and living cells: a key issue review

NASA Astrophysics Data System (ADS)

Bahadori, Azra; Moreno-Pescador, Guillermo; Oddershede, Lene B.; Bendix, Poul M.

2018-03-01

Remote control over fusion of single cells and vesicles has a great potential in biological and chemical research allowing both transfer of genetic material between cells and transfer of molecular content between vesicles. Membrane fusion is a critical process in biology that facilitates molecular transport and mixing of cellular cytoplasms with potential formation of hybrid cells. Cells precisely regulate internal membrane fusions with the aid of specialized fusion complexes that physically provide the energy necessary for mediating fusion. Physical factors like membrane curvature, tension and temperature, affect biological membrane fusion by lowering the associated energy barrier. This has inspired the development of physical approaches to harness the fusion process at a single cell level by using remotely controlled electromagnetic fields to trigger membrane fusion. Here, we critically review various approaches, based on lasers or electric pulses, to control fusion between individual cells or between individual lipid vesicles and discuss their potential and limitations for present and future applications within biochemistry, biology and soft matter.

The new science of mind and the future of knowledge.

PubMed

Kandel, Eric

2013-10-30

Understanding mental processes in biological terms makes available insights from the new science of the mind to explore connections between philosophy, psychology, the social sciences, the humanities, and studies of disorders of mind. In this Perspective we examine how these linkages might be forged and how the new science of the mind might serve as an inspiration for further exploration. Copyright © 2013 Elsevier Inc. All rights reserved.

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

PubMed Central

Brinkworth, Russell S. A.; O'Carroll, David C.

2009-01-01

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors. PMID:19893631

On the Cultivation of Students' Interests in Biology Teaching

ERIC Educational Resources Information Center

Li, Yan

2011-01-01

This paper introduces the importance of middle school students' interests in learning biology. Considering the psychological characteristics of middle school students, this paper suggests several practical ways for inspiring students' interests in learning biology.

Writing Inspired

ERIC Educational Resources Information Center

Tischhauser, Karen

2015-01-01

Students need inspiration to write. Assigning is not teaching. In order to inspire students to write fiction worth reading, teachers must take them through the process of writing. Physical objects inspire good writing with depth. In this article, the reader will be taken through the process of inspiring young writers through the use of boxes.…

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Artificial intelligence and synthetic biology: A tri-temporal contribution.

PubMed

Bianchini, Francesco

2016-10-01

Artificial intelligence can make numerous contributions to synthetic biology. I would like to suggest three that are related to the past, present and future of artificial intelligence. From the past, works in biology and artificial systems by Turing and von Neumann prove highly interesting to explore within the new framework of synthetic biology, especially with regard to the notions of self-modification and self-replication and their links to emergence and the bottom-up approach. The current epistemological inquiry into emergence and research on swarm intelligence, superorganisms and biologically inspired cognitive architecture may lead to new achievements on the possibilities of synthetic biology in explaining cognitive processes. Finally, the present-day discussion on the future of artificial intelligence and the rise of superintelligence may point to some research trends for the future of synthetic biology and help to better define the boundary of notions such as "life", "cognition", "artificial" and "natural", as well as their interconnections in theoretical synthetic biology. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The "Biologically-Inspired Computing" Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2007-01-01

Self-managing systems, whether viewed from the perspective of Autonomic Computing, or from that of another initiative, offers a holistic vision for the development and evolution of biologically-inspired computer-based systems. It aims to bring new levels of automation and dependability to systems, while simultaneously hiding their complexity and reducing costs. A case can certainly be made that all computer-based systems should exhibit autonomic properties [6], and we envisage greater interest in, and uptake of, autonomic principles in future system development.

Review: bioprinting: a beginning.

PubMed

Mironov, Vladimir; Reis, Nuno; Derby, Brian

2006-04-01

An increasing demand for directed assembly of biologically relevant materials, with prescribed three-dimensional hierarchical organizations, is stimulating technology developments with the ultimate goal of re-creating multicellular tissues and organs de novo. Existing techniques, mostly adapted from other applications or fields of research, are capable of independently meeting partial requirements for engineering biological or biomimetic structures, but their integration toward organ engineering is proving difficult. Inspired by recent developments in material transfer processes operating at all relevant length scales--from nano to macro--which are amenable to biological elements, a new research field of bioprinting and biopatterning has emerged. Here we present a short review regarding the framework, state of the art, and perspectives of this new field, based on the findings presented at a recent international workshop.

Nitrate removal from drinking water with a focus on biological methods: a review.

PubMed

Rezvani, Fariba; Sarrafzadeh, Mohammad-Hossein; Ebrahimi, Sirous; Oh, Hee-Mock

2017-05-31

This article summarizes several developed and industrial technologies for nitrate removal from drinking water, including physicochemical and biological techniques, with a focus on autotrophic nitrate removal. Approaches are primarily classified into separation-based and elimination-based methods according to the fate of the nitrate in water treatment. Biological denitrification as a cost-effective and promising method of biological nitrate elimination is reviewed in terms of its removal process, applicability, efficiency, and associated disadvantages. The various pathways during biological nitrate removal, including assimilatory and dissimilatory nitrate reduction, are also explained. A comparative study was carried out to provide a better understanding of the advantages and disadvantages of autotrophic and heterotrophic denitrification. Sulfur-based and hydrogen-based denitrifications, which are the most common autotrophic processes of nitrate removal, are reviewed with the aim of presenting the salient features of hydrogenotrophic denitrification along with some drawbacks of the technology and research areas in which it could be used but currently is not. The application of algae-based water treatment is also introduced as a nature-inspired approach that may broaden future horizons of nitrate removal technology.

Structure-function relationship of skeletal muscle provides inspiration for design of new artificial muscle

NASA Astrophysics Data System (ADS)

Gao, Yingxin; Zhang, Chi

2015-03-01

A variety of actuator technologies have been developed to mimic biological skeletal muscle that generates force in a controlled manner. Force generation process of skeletal muscle involves complicated biophysical and biochemical mechanisms; therefore, it is impossible to replace biological muscle. In biological skeletal muscle tissue, the force generation of a muscle depends not only on the force generation capacity of the muscle fiber, but also on many other important factors, including muscle fiber type, motor unit recruitment, architecture, structure and morphology of skeletal muscle, all of which have significant impact on the force generation of the whole muscle or force transmission from muscle fibers to the tendon. Such factors have often been overlooked, but can be incorporated in artificial muscle design, especially with the discovery of new smart materials and the development of innovative fabrication and manufacturing technologies. A better understanding of the physiology and structure-function relationship of skeletal muscle will therefore benefit the artificial muscle design. In this paper, factors that affect muscle force generation are reviewed. Mathematical models used to model the structure-function relationship of skeletal muscle are reviewed and discussed. We hope the review will provide inspiration for the design of a new generation of artificial muscle by incorporating the structure-function relationship of skeletal muscle into the design of artificial muscle.

Zebrafish response to a robotic replica in three dimensions

PubMed Central

Ruberto, Tommaso; Mwaffo, Violet; Singh, Sukhgewanpreet; Neri, Daniele

2016-01-01

As zebrafish emerge as a species of choice for the investigation of biological processes, a number of experimental protocols are being developed to study their social behaviour. While live stimuli may elicit varying response in focal subjects owing to idiosyncrasies, tiredness and circadian rhythms, video stimuli suffer from the absence of physical input and rely only on two-dimensional projections. Robotics has been recently proposed as an alternative approach to generate physical, customizable, effective and consistent stimuli for behavioural phenotyping. Here, we contribute to this field of investigation through a novel four-degree-of-freedom robotics-based platform to manoeuvre a biologically inspired three-dimensionally printed replica. The platform enables three-dimensional motions as well as body oscillations to mimic zebrafish locomotion. In a series of experiments, we demonstrate the differential role of the visual stimuli associated with the biologically inspired replica and its three-dimensional motion. Three-dimensional tracking and information-theoretic tools are complemented to quantify the interaction between zebrafish and the robotic stimulus. Live subjects displayed a robust attraction towards the moving replica, and such attraction was lost when controlling for its visual appearance or motion. This effort is expected to aid zebrafish behavioural phenotyping, by offering a novel approach to generate physical stimuli moving in three dimensions. PMID:27853566

Inspiring Integration in College Students Reading Multiple Biology Texts

ERIC Educational Resources Information Center

Firetto, Carla

2013-01-01

Introductory biology courses typically present topics on related biological systems across separate chapters and lectures. A complete foundational understanding requires that students understand how these biological systems are related. Unfortunately, spontaneous generation of these connections is rare for novice learners. These experiments focus…

Recent Advances in Skin-Inspired Sensors Enabled by Nanotechnology

NASA Astrophysics Data System (ADS)

Loh, Kenneth J.; Azhari, Faezeh

2012-07-01

The highly optimized performance of nature's creations and biological assemblies has inspired the development of their bio-inspired artificial counterparts that can potentially outperform conventional systems. In particular, the skin of humans, animals, and insects exhibits unique functionalities and properties and has subsequently led to active research in developing skin-inspired sensors. This paper presents a summary of selected work related to skin-inspired tactile, distributed strain, and artificial hair cell flow sensors, with a particular focus on technologies enabled by recent advancements in the nanotechnology domain. The purpose is not to present a comprehensive review on this broad subject matter but rather to use selected work to outline the diversity of current research activities.

How Can We Improve Problem Solving in Undergraduate Biology? Applying Lessons from 30 Years of Physics Education Research

PubMed Central

Hoskinson, A.-M.; Caballero, M. D.; Knight, J. K.

2013-01-01

If students are to successfully grapple with authentic, complex biological problems as scientists and citizens, they need practice solving such problems during their undergraduate years. Physics education researchers have investigated student problem solving for the past three decades. Although physics and biology problems differ in structure and content, the instructional purposes align closely: explaining patterns and processes in the natural world and making predictions about physical and biological systems. In this paper, we discuss how research-supported approaches developed by physics education researchers can be adopted by biologists to enhance student problem-solving skills. First, we compare the problems that biology students are typically asked to solve with authentic, complex problems. We then describe the development of research-validated physics curricula emphasizing process skills in problem solving. We show that solving authentic, complex biology problems requires many of the same skills that practicing physicists and biologists use in representing problems, seeking relationships, making predictions, and verifying or checking solutions. We assert that acquiring these skills can help biology students become competent problem solvers. Finally, we propose how biology scholars can apply lessons from physics education in their classrooms and inspire new studies in biology education research. PMID:23737623

Kramers problem in evolutionary strategies

NASA Astrophysics Data System (ADS)

Dunkel, J.; Ebeling, W.; Schimansky-Geier, L.; Hänggi, P.

2003-06-01

We calculate the escape rates of different dynamical processes for the case of a one-dimensional symmetric double-well potential. In particular, we compare the escape rates of a Smoluchowski process, i.e., a corresponding overdamped Brownian motion dynamics in a metastable potential landscape, with the escape rates obtained for a biologically motivated model known as the Fisher-Eigen process. The main difference between the two models is that the dynamics of the Smoluchowski process is determined by local quantities, whereas the Fisher-Eigen process is based on a global coupling (nonlocal interaction). If considered in the context of numerical optimization algorithms, both processes can be interpreted as archetypes of physically or biologically inspired evolutionary strategies. In this sense, the results discussed in this work are utile in order to evaluate the efficiency of such strategies with regard to the problem of surmounting various barriers. We find that a combination of both scenarios, starting with the Fisher-Eigen strategy, provides a most effective evolutionary strategy.

Biomimetic cellular metals-using hierarchical structuring for energy absorption.

PubMed

Bührig-Polaczek, A; Fleck, C; Speck, T; Schüler, P; Fischer, S F; Caliaro, M; Thielen, M

2016-07-19

Fruit walls as well as nut and seed shells typically perform a multitude of functions. One of the biologically most important functions consists in the direct or indirect protection of the seeds from mechanical damage or other negative environmental influences. This qualifies such biological structures as role models for the development of new materials and components that protect commodities and/or persons from damage caused for example by impacts due to rough handling or crashes. We were able to show how the mechanical properties of metal foam based components can be improved by altering their structure on various hierarchical levels inspired by features and principles important for the impact and/or puncture resistance of the biological role models, rather than by tuning the properties of the bulk material. For this various investigation methods have been established which combine mechanical testing with different imaging methods, as well as with in situ and ex situ mechanical testing methods. Different structural hierarchies especially important for the mechanical deformation and failure behaviour of the biological role models, pomelo fruit (Citrus maxima) and Macadamia integrifolia, were identified. They were abstracted and transferred into corresponding structural principles and thus hierarchically structured bio-inspired metal foams have been designed. A production route for metal based bio-inspired structures by investment casting was successfully established. This allows the production of complex and reliable structures, by implementing and combining different hierarchical structural elements found in the biological concept generators, such as strut design and integration of fibres, as well as by minimising casting defects. To evaluate the structural effects, similar investigation methods and mechanical tests were applied to both the biological role models and the metallic foams. As a result an even deeper quantitative understanding of the form-structure-function relationship of the biological concept generators as well as the bio-inspired metal foams was achieved, on deeper hierarchical levels and overarching different levels.

Photochemical mineralization of europium, titanium, and iron oxyhydroxide nanoparticles in the ferritin protein cage.

PubMed

Klem, Michael T; Mosolf, Jesse; Young, Mark; Douglas, Trevor

2008-04-07

The Fe storage protein ferritin was used as a size-constrained reaction vessel for the photoreduction and reoxidation of complexed Eu, Fe, and Ti precursors for the formation of oxyhydroxide nanoparticles. The resultant materials were characterized by dynamic light scattering, gel electrophoresis, UV-vis spectroscopy, and transmission electron microscopy. The photoreduction and reoxidation process is inspired by biological sequestration mechanisms observed in some marine siderophore systems.

Biological Investigation of the Stimulated Flapping Motions of the Moth, Manduca sexta

DTIC Science & Technology

2011-03-01

the sites of toxic chemical spills or damaged nuclear reactors (Alexander, 2002). Dr. Mark Reeder, Editor-in-Chief of the International Journal...Process The overall objective of research, using bio -inspired technology and techniques, is to develop clear scientific understanding about some of...it in a small container and submerging it in a bucket of ice for approximately 15 minutes, until the moth was unresponsive to stimulation.  Under

New bioactive bone-like microspheres with intrinsic magnetic properties obtained by bio-inspired mineralisation process.

PubMed

Fernandes Patrício, Tatiana Marisa; Panseri, Silvia; Sandri, Monica; Tampieri, Anna; Sprio, Simone

2017-08-01

A bio-inspired mineralisation process was investigated and applied to develop novel hybrid magnetic materials by heterogeneous nucleation of Fe 2+ /Fe 3+ -doped hydroxyapatite nanocrystals onto a biopolymeric matrix made of a Type I collagen-based recombinant peptide (RCP). The effect of the synthesis temperature on the phase composition, crystallinity and magnetic properties of the nucleated inorganic phase was studied. The as-obtained magnetic materials were then engineered, by using a water-in-oil emulsification process, into hybrid magnetic microspheres, which were stabilized by de-hydrothermal treatment yielding cross-linking of the macromolecular matrix. Thorough investigation of the physicochemical, morphological and biological properties of the new hybrid microspheres, as induced by the presence of the inorganic nanophase and controlled iron substitution into hydroxyapatite lattice, revealed bone-like composition, good cytocompatibility, designed shape and size, and tailored magnetization. Such features are interesting and promising for application as new biomaterials with ability of remote activation and control by using external magnetic fields, for smart and personalized applications in medicine, particularly in bone tissue regeneration. Copyright © 2017 Elsevier B.V. All rights reserved.

Nature as an engineer: one simple concept of a bio-inspired functional artificial muscle.

PubMed

Schmitt, S; Haeufle, D F B; Blickhan, R; Günther, M

2012-09-01

The biological muscle is a powerful, flexible and versatile actuator. Its intrinsic characteristics determine the way how movements are generated and controlled. Robotic and prosthetic applications expect to profit from relying on bio-inspired actuators which exhibit natural (muscle-like) characteristics. As of today, when constructing a technical actuator, it is not possible to copy the exact molecular structure of a biological muscle. Alternatively, the question may be put how its characteristics can be realized with known mechanical components. Recently, a mechanical construct for an artificial muscle was proposed, which exhibits hyperbolic force-velocity characteristics. In this paper, we promote the constructing concept which is made by substantiating the mechanical design of biological muscle by a simple model, proving the feasibility of its real-world implementation, and checking their output both for mutual consistency and agreement with biological measurements. In particular, the relations of force, enthalpy rate and mechanical efficiency versus contraction velocity of both the construct's technical implementation and its numerical model were determined in quick-release experiments. All model predictions for these relations and the hardware results are now in good agreement with the biological literature. We conclude that the construct represents a mechanical concept of natural actuation, which is suitable for laying down some useful suggestions when designing bio-inspired actuators.

Near-Infrared Trigged Stimulus-Responsive Photonic Crystals with Hierarchical Structures.

PubMed

Lu, Tao; Pan, Hui; Ma, Jun; Li, Yao; Zhu, Shenmin; Zhang, Di

2017-10-04

Stimuli-responsive photonic crystals (PCs) trigged by light would provide a novel intuitive and quantitative method for noninvasive detection. Inspired by the flame-detecting aptitude of fire beetles and the hierarchical photonic structures of butterfly wings, we herein developed near-infrared stimuli-responsive PCs through coupling photothermal Fe 3 O 4 nanoparticles with thermoresponsive poly(N-isopropylacrylamide) (PNIPAM), with hierarchical photonic structured butterfly wing scales as the template. The nanoparticles within 10 s transferred near-infrared radiation into heat that triggered the phase transition of PNIPAM; this almost immediately posed an anticipated effect on the PNIPAM refractive index and resulted in a composite spectrum change of ∼26 nm, leading to the direct visual readout. It is noteworthy that the whole process is durable and stable mainly owing to the chemical bonding formed between PNIPAM and the biotemplate. We envision that this biologically inspired approach could be utilized in a broad range of applications and would have a great impact on various monitoring processes and medical sensing.

Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

PubMed

Manning, Timmy; Sleator, Roy D; Walsh, Paul

2013-01-01

For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

Biological materials: a materials science approach.

PubMed

Meyers, Marc A; Chen, Po-Yu; Lopez, Maria I; Seki, Yasuaki; Lin, Albert Y M

2011-07-01

The approach used by Materials Science and Engineering is revealing new aspects in the structure and properties of biological materials. The integration of advanced characterization, mechanical testing, and modeling methods can rationalize heretofore unexplained aspects of these structures. As an illustration of the power of this methodology, we apply it to biomineralized shells, avian beaks and feathers, and fish scales. We also present a few selected bioinspired applications: Velcro, an Al2O3-PMMA composite inspired by the abalone shell, and synthetic attachment devices inspired by gecko. Copyright © 2010 Elsevier Ltd. All rights reserved.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Artificially Engineered Protein Polymers.

PubMed

Yang, Yun Jung; Holmberg, Angela L; Olsen, Bradley D

2017-06-07

Modern polymer science increasingly requires precise control over macromolecular structure and properties for engineering advanced materials and biomedical systems. The application of biological processes to design and synthesize artificial protein polymers offers a means for furthering macromolecular tunability, enabling polymers with dispersities of ∼1.0 and monomer-level sequence control. Taking inspiration from materials evolved in nature, scientists have created modular building blocks with simplified monomer sequences that replicate the function of natural systems. The corresponding protein engineering toolbox has enabled the systematic development of complex functional polymeric materials across areas as diverse as adhesives, responsive polymers, and medical materials. This review discusses the natural proteins that have inspired the development of key building blocks for protein polymer engineering and the function of these elements in material design. The prospects and progress for scalable commercialization of protein polymers are reviewed, discussing both technology needs and opportunities.

A novel pipeline based FPGA implementation of a genetic algorithm

NASA Astrophysics Data System (ADS)

Thirer, Nonel

2014-05-01

To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.

Finite Dimensional Approximations for Continuum Multiscale Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Berlyand, Leonid

2017-01-24

The completed research project concerns the development of novel computational techniques for modeling nonlinear multiscale physical and biological phenomena. Specifically, it addresses the theoretical development and applications of the homogenization theory (coarse graining) approach to calculation of the effective properties of highly heterogenous biological and bio-inspired materials with many spatial scales and nonlinear behavior. This theory studies properties of strongly heterogeneous media in problems arising in materials science, geoscience, biology, etc. Modeling of such media raises fundamental mathematical questions, primarily in partial differential equations (PDEs) and calculus of variations, the subject of the PI’s research. The focus of completed researchmore » was on mathematical models of biological and bio-inspired materials with the common theme of multiscale analysis and coarse grain computational techniques. Biological and bio-inspired materials offer the unique ability to create environmentally clean functional materials used for energy conversion and storage. These materials are intrinsically complex, with hierarchical organization occurring on many nested length and time scales. The potential to rationally design and tailor the properties of these materials for broad energy applications has been hampered by the lack of computational techniques, which are able to bridge from the molecular to the macroscopic scale. The project addressed the challenge of computational treatments of such complex materials by the development of a synergistic approach that combines innovative multiscale modeling/analysis techniques with high performance computing.« less

The 'Biologically-Inspired Computing' Column

NASA Technical Reports Server (NTRS)

Hinchey, Mike

2006-01-01

The field of Biology changed dramatically in 1953, with the determination by Francis Crick and James Dewey Watson of the double helix structure of DNA. This discovery changed Biology for ever, allowing the sequencing of the human genome, and the emergence of a "new Biology" focused on DNA, genes, proteins, data, and search. Computational Biology and Bioinformatics heavily rely on computing to facilitate research into life and development. Simultaneously, an understanding of the biology of living organisms indicates a parallel with computing systems: molecules in living cells interact, grow, and transform according to the "program" dictated by DNA. Moreover, paradigms of Computing are emerging based on modelling and developing computer-based systems exploiting ideas that are observed in nature. This includes building into computer systems self-management and self-governance mechanisms that are inspired by the human body's autonomic nervous system, modelling evolutionary systems analogous to colonies of ants or other insects, and developing highly-efficient and highly-complex distributed systems from large numbers of (often quite simple) largely homogeneous components to reflect the behaviour of flocks of birds, swarms of bees, herds of animals, or schools of fish. This new field of "Biologically-Inspired Computing", often known in other incarnations by other names, such as: Autonomic Computing, Pervasive Computing, Organic Computing, Biomimetics, and Artificial Life, amongst others, is poised at the intersection of Computer Science, Engineering, Mathematics, and the Life Sciences. Successes have been reported in the fields of drug discovery, data communications, computer animation, control and command, exploration systems for space, undersea, and harsh environments, to name but a few, and augur much promise for future progress.

Biology-inspired Architecture for Situation Management

NASA Technical Reports Server (NTRS)

Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

2006-01-01

Situation Management is a rapidly developing science combining new techniques for data collection with advanced methods of data fusion to facilitate the process leading to correct decisions prescribing action. Current research focuses on reducing increasing amounts of diverse data to knowledge used by decision makers and on reducing time between observations, decisions and actions. No new technology is more promising for increasing the diversity and fidelity of observations than sensor networks. However, current research on sensor networks concentrates on a centralized network architecture. We believe this trend will not realize the full potential of situation management. We propose a new architecture modeled after biological ecosystems where motes are autonomous and intelligent, yet cooperate with local neighborhoods. Providing a layered approach, they sense and act independently when possible, and cooperate with neighborhoods when necessary. The combination of their local actions results in global effects. While situation management research is currently dominated by military applications, advances envisioned for industrial and business applications have similar requirements. NASA has requirements for intelligent and autonomous systems in future missions that can benefit from advances in situation management. We describe requirements for the Integrated Vehicle Health Management program where our biology-inspired architecture provides a layered approach and decisions can be made at the proper level to improve safety, reduce costs, and improve efficiency in making diagnostic and prognostic assessments of the structural integrity, aerodynamic characteristics, and operation of aircraft.

Climbing with adhesion: from bioinspiration to biounderstanding

PubMed Central

Cutkosky, Mark R.

2015-01-01

Bioinspiration is an increasingly popular design paradigm, especially as robots venture out of the laboratory and into the world. Animals are adept at coping with the variability that the world imposes. With advances in scientific tools for understanding biological structures in detail, we are increasingly able to identify design features that account for animals' robust performance. In parallel, advances in fabrication methods and materials are allowing us to engineer artificial structures with similar properties. The resulting robots become useful platforms for testing hypotheses about which principles are most important. Taking gecko-inspired climbing as an example, we show that the process of extracting principles from animals and adapting them to robots provides insights for both robotics and biology. PMID:26464786

Using cell structures to develop functional nanomaterials and nanostructures--case studies of actin filaments and microtubules.

PubMed

Wu, Kevin Chia-Wen; Yang, Chung-Yao; Cheng, Chao-Min

2014-04-25

This article is based on the continued development of biologically relevant elements (i.e., actin filaments and microtubules in living cells) as building blocks to create functional nanomaterials and nanostructures that can then be used to manufacture nature-inspired small-scale devices or systems. Here, we summarize current progress in the field and focus specifically on processes characterized by (1) robustness and ease of use, (2) inexpensiveness, and (3) potential expandability to mass production. This article, we believe, will provide scientists and engineers with a more comprehensive understanding of how to mine biological materials and natural design features to construct functional materials and devices.

Biomimetic and bioinspired nanoparticles for targeted drug delivery.

PubMed

Gagliardi, Mariacristina

2017-03-01

In drug targeting, the urgent need for more effective and less iatrogenic therapies is pushing toward a complete revision of carrier setup. After the era of 'articles used as homing systems', novel prototypes are now emerging. Newly conceived carriers are endowed with better biocompatibility, biodistribution and targeting properties. The biomimetic approach bestows such improved functional properties. Exploiting biological molecules, organisms and cells, or taking inspiration from them, drug vector performances are now rapidly progressing toward the perfect carrier. Following this direction, researchers have refined carrier properties, achieving significant results. The present review summarizes recent advances in biomimetic and bioinspired drug vectors, derived from biologicals or obtained by processing synthetic materials with a biomimetic approach.

Training mechanical engineering students to utilize biological inspiration during product development.

PubMed

Bruck, Hugh A; Gershon, Alan L; Golden, Ira; Gupta, Satyandra K; Gyger, Lawrence S; Magrab, Edward B; Spranklin, Brent W

2007-12-01

The use of bio-inspiration for the development of new products and devices requires new educational tools for students consisting of appropriate design and manufacturing technologies, as well as curriculum. At the University of Maryland, new educational tools have been developed that introduce bio-inspired product realization to undergraduate mechanical engineering students. These tools include the development of a bio-inspired design repository, a concurrent fabrication and assembly manufacturing technology, a series of undergraduate curriculum modules and a new senior elective in the bio-inspired robotics area. This paper first presents an overview of the two new design and manufacturing technologies that enable students to realize bio-inspired products, and describes how these technologies are integrated into the undergraduate educational experience. Then, the undergraduate curriculum modules are presented, which provide students with the fundamental design and manufacturing principles needed to support bio-inspired product and device development. Finally, an elective bio-inspired robotics project course is present, which provides undergraduates with the opportunity to demonstrate the application of the knowledge acquired through the curriculum modules in their senior year using the new design and manufacturing technologies.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System

PubMed Central

Milde, Moritz B.; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware. PMID:28747883

Biotemplated synthesis of inorganic materials: An emerging paradigm for nanomaterial synthesis inspired by nature

DOE PAGES

Krajina, Brad A.; Proctor, Amy C.; Schoen, Alia P.; ...

2017-08-08

Biomineralization, the process by which biological systems direct the synthesis of inorganic structures from organic templates, is an exquisite example of nanomaterial self-assembly in nature. Its products include the shells of mollusks and the bones and teeth of vertebrates. By comparison, conventional inorganic synthesis techniques provide limited control over inorganic nanomaterial architecture. Inspired by biomineralization in nature, over the last two decades, the field of biotemplating has emerged as a new paradigm for inorganic nanomaterial assembly, wherein researchers seek to design novel nano-structures in which inorganic nanomaterial synthesis is directed from an underlying biomolecular template. Here, we review the motivation,more » mechanistic understanding, progress, and challenges for the field of biotemplating. We highlight the interdisciplinary nature of this field, and survey a broad range of examples of bio-templated engineering: ranging from strategies that exploit the inherent capabilities of proteins in nature, to genetically-engineered systems that unlock new capabilities for self-assembly with biomolecules. Here, we illustrate that the use of biological materials as templates for inorganic self-assembly holds tremendous potential for nanomaterial engineering, with applications that range from electronics and energy to medicine.« less

Magnesium-aspartate-based crystallization switch inspired from shell molt of crustacean

PubMed Central

Tao, Jinhui; Zhou, Dongming; Zhang, Zhisen; Xu, Xurong; Tang, Ruikang

2009-01-01

Many animals such as crustacean periodically undergo cyclic molt of the exoskeleton. During this process, amorphous calcium mineral phases are biologically stabilized by magnesium and are reserved for the subsequent rapid formation of new shell tissue. However, it is a mystery how living organisms can regulate the transition of the precursor phases precisely. We reveal that the shell mineralization from the magnesium stabilized precursors is associated with the presence of Asp-rich proteins. It is suggested that a cooperative effect of magnesium and Asp-rich compound can result into a crystallization switch in biomineralization. Our in vitro experiments confirm that magnesium increases the lifetime of amorphous calcium carbonate and calcium phosphate in solution so that the crystallization can be temporarily switched off. Although Asp monomer alone inhibits the crystallization of pure amorphous calcium minerals, it actually reduces the stability of the magnesium-stabilized precursors to switch on the transformation from the amorphous to crystallized phases. These modification effects on crystallization kinetics can be understood by an Asp-enhanced magnesium desolvation model. The interesting magnesium-Asp-based switch is a biologically inspired lesson from nature, which can be developed into an advanced strategy to control material fabrications. PMID:20007788

Magnesium-aspartate-based crystallization switch inspired from shell molt of crustacean.

PubMed

Tao, Jinhui; Zhou, Dongming; Zhang, Zhisen; Xu, Xurong; Tang, Ruikang

2009-12-29

Many animals such as crustacean periodically undergo cyclic molt of the exoskeleton. During this process, amorphous calcium mineral phases are biologically stabilized by magnesium and are reserved for the subsequent rapid formation of new shell tissue. However, it is a mystery how living organisms can regulate the transition of the precursor phases precisely. We reveal that the shell mineralization from the magnesium stabilized precursors is associated with the presence of Asp-rich proteins. It is suggested that a cooperative effect of magnesium and Asp-rich compound can result into a crystallization switch in biomineralization. Our in vitro experiments confirm that magnesium increases the lifetime of amorphous calcium carbonate and calcium phosphate in solution so that the crystallization can be temporarily switched off. Although Asp monomer alone inhibits the crystallization of pure amorphous calcium minerals, it actually reduces the stability of the magnesium-stabilized precursors to switch on the transformation from the amorphous to crystallized phases. These modification effects on crystallization kinetics can be understood by an Asp-enhanced magnesium desolvation model. The interesting magnesium-Asp-based switch is a biologically inspired lesson from nature, which can be developed into an advanced strategy to control material fabrications.

Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System.

PubMed

Milde, Moritz B; Blum, Hermann; Dietmüller, Alexander; Sumislawska, Dora; Conradt, Jörg; Indiveri, Giacomo; Sandamirskaya, Yulia

2017-01-01

Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.

Biomimetics in the design of a robotic exoskeleton for upper limb therapy

NASA Astrophysics Data System (ADS)

Baniqued, Paul Dominick E.; Dungao, Jade R.; Manguerra, Michael V.; Baldovino, Renann G.; Abad, Alexander C.; Bugtai, Nilo T.

2018-02-01

Current methodologies in designing robotic exoskeletons for upper limb therapy simplify the complex requirements of the human anatomy. As a result, such devices tend to compromise safety and biocompatibility with the intended user. However, a new design methodology uses biological analogues as inspiration to address these technical issues. This approach follows that of biomimetics, a design principle that uses the extraction and transfer of useful information from natural morphologies and processes to solve technical design issues. In this study, a biomimetic approach in the design of a 5-degree-of-freedom robotic exoskeleton for upper limb therapy was performed. A review of biomimetics was first discussed along with its current contribution to the design of rehabilitation robots. With a proposed methodological framework, the design for an upper limb robotic exoskeleton was generated using CATIA software. The design was inspired by the morphology of the bones and the muscle force transmission of the upper limbs. Finally, a full design assembly presented had integrated features extracted from the biological analogue. The successful execution of a biomimetic design methodology made a case in providing safer and more biocompatible robots for rehabilitation.

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

PubMed

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

Biotemplated synthesis of inorganic materials: An emerging paradigm for nanomaterial synthesis inspired by nature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krajina, Brad A.; Proctor, Amy C.; Schoen, Alia P.

Biomineralization, the process by which biological systems direct the synthesis of inorganic structures from organic templates, is an exquisite example of nanomaterial self-assembly in nature. Its products include the shells of mollusks and the bones and teeth of vertebrates. By comparison, conventional inorganic synthesis techniques provide limited control over inorganic nanomaterial architecture. Inspired by biomineralization in nature, over the last two decades, the field of biotemplating has emerged as a new paradigm for inorganic nanomaterial assembly, wherein researchers seek to design novel nano-structures in which inorganic nanomaterial synthesis is directed from an underlying biomolecular template. Here, we review the motivation,more » mechanistic understanding, progress, and challenges for the field of biotemplating. We highlight the interdisciplinary nature of this field, and survey a broad range of examples of bio-templated engineering: ranging from strategies that exploit the inherent capabilities of proteins in nature, to genetically-engineered systems that unlock new capabilities for self-assembly with biomolecules. Here, we illustrate that the use of biological materials as templates for inorganic self-assembly holds tremendous potential for nanomaterial engineering, with applications that range from electronics and energy to medicine.« less

On a biologically inspired topology optimization method

NASA Astrophysics Data System (ADS)

Kobayashi, Marcelo H.

2010-03-01

This work concerns the development of a biologically inspired methodology for the study of topology optimization in engineering and natural systems. The methodology is based on L systems and its turtle interpretation for the genotype-phenotype modeling of the topology development. The topology is analyzed using the finite element method, and optimized using an evolutionary algorithm with the genetic encoding of the L system and its turtle interpretation, as well as, body shape and physical characteristics. The test cases considered in this work clearly show the suitability of the proposed method for the study of engineering and natural complex systems.

Planar maneuvering control of underwater snake robots using virtual holonomic constraints.

PubMed

Kohl, Anna M; Kelasidi, Eleni; Mohammadi, Alireza; Maggiore, Manfredi; Pettersen, Kristin Y

2016-11-24

This paper investigates the problem of planar maneuvering control for bio-inspired underwater snake robots that are exposed to unknown ocean currents. The control objective is to make a neutrally buoyant snake robot which is subject to hydrodynamic forces and ocean currents converge to a desired planar path and traverse the path with a desired velocity. The proposed feedback control strategy enforces virtual constraints which encode biologically inspired gaits on the snake robot configuration. The virtual constraints, parametrized by states of dynamic compensators, are used to regulate the orientation and forward speed of the snake robot. A two-state ocean current observer based on relative velocity sensors is proposed. It enables the robot to follow the path in the presence of unknown constant ocean currents. The efficacy of the proposed control algorithm for several biologically inspired gaits is verified both in simulations for different path geometries and in experiments.

Texture and art with deep neural networks.

PubMed

Gatys, Leon A; Ecker, Alexander S; Bethge, Matthias

2017-10-01

Although the study of biological vision and computer vision attempt to understand powerful visual information processing from different angles, they have a long history of informing each other. Recent advances in texture synthesis that were motivated by visual neuroscience have led to a substantial advance in image synthesis and manipulation in computer vision using convolutional neural networks (CNNs). Here, we review these recent advances and discuss how they can in turn inspire new research in visual perception and computational neuroscience. Copyright © 2017. Published by Elsevier Ltd.

Network science of biological systems at different scales: A review

NASA Astrophysics Data System (ADS)

Gosak, Marko; Markovič, Rene; Dolenšek, Jurij; Slak Rupnik, Marjan; Marhl, Marko; Stožer, Andraž; Perc, Matjaž

2018-03-01

Network science is today established as a backbone for description of structure and function of various physical, chemical, biological, technological, and social systems. Here we review recent advances in the study of complex biological systems that were inspired and enabled by methods of network science. First, we present

Engaging Non-Science Majors in Biology, One Disease at a Time

ERIC Educational Resources Information Center

Garcia, Rebecca; Rahman, Alvina; Klein, Janette Gomos

2015-01-01

We designed a human biology course that interests nonmajors while improving science literacy through student engagement, using a constructivist-inspired, topic-centered approach. This way of learning highlights common diseases that provide a basis to incorporate specific biological concepts. The topic-centered approach triggers interest and…

Amoeba-inspired nanoarchitectonic computing: solving intractable computational problems using nanoscale photoexcitation transfer dynamics.

PubMed

Aono, Masashi; Naruse, Makoto; Kim, Song-Ju; Wakabayashi, Masamitsu; Hori, Hirokazu; Ohtsu, Motoichi; Hara, Masahiko

2013-06-18

Biologically inspired computing devices and architectures are expected to overcome the limitations of conventional technologies in terms of solving computationally demanding problems, adapting to complex environments, reducing energy consumption, and so on. We previously demonstrated that a primitive single-celled amoeba (a plasmodial slime mold), which exhibits complex spatiotemporal oscillatory dynamics and sophisticated computing capabilities, can be used to search for a solution to a very hard combinatorial optimization problem. We successfully extracted the essential spatiotemporal dynamics by which the amoeba solves the problem. This amoeba-inspired computing paradigm can be implemented by various physical systems that exhibit suitable spatiotemporal dynamics resembling the amoeba's problem-solving process. In this Article, we demonstrate that photoexcitation transfer phenomena in certain quantum nanostructures mediated by optical near-field interactions generate the amoebalike spatiotemporal dynamics and can be used to solve the satisfiability problem (SAT), which is the problem of judging whether a given logical proposition (a Boolean formula) is self-consistent. SAT is related to diverse application problems in artificial intelligence, information security, and bioinformatics and is a crucially important nondeterministic polynomial time (NP)-complete problem, which is believed to become intractable for conventional digital computers when the problem size increases. We show that our amoeba-inspired computing paradigm dramatically outperforms a conventional stochastic search method. These results indicate the potential for developing highly versatile nanoarchitectonic computers that realize powerful solution searching with low energy consumption.

Research Update: Programmable tandem repeat proteins inspired by squid ring teeth

NASA Astrophysics Data System (ADS)

Pena-Francesch, Abdon; Domeradzka, Natalia E.; Jung, Huihun; Barbu, Benjamin; Vural, Mert; Kikuchi, Yusuke; Allen, Benjamin D.; Demirel, Melik C.

2018-01-01

Cephalopods have evolved many interesting features that can serve as inspiration. Repetitive squid ring teeth (SRT) proteins from cephalopods exhibit properties such as strength, self-healing, and biocompatibility. These proteins have been engineered to design novel adhesives, self-healing textiles, and the assembly of 2d-layered materials. Compared to conventional polymers, repetitive proteins are easy to modify and can assemble in various morphologies and molecular architectures. This research update discusses the molecular biology and materials science of polypeptides inspired by SRT proteins, their properties, and perspectives for future applications.

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

PubMed

Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

Orientation selectivity in a multi-gated organic electrochemical transistor

NASA Astrophysics Data System (ADS)

Gkoupidenis, Paschalis; Koutsouras, Dimitrios A.; Lonjaret, Thomas; Fairfield, Jessamyn A.; Malliaras, George G.

2016-06-01

Neuromorphic devices offer promising computational paradigms that transcend the limitations of conventional technologies. A prominent example, inspired by the workings of the brain, is spatiotemporal information processing. Here we demonstrate orientation selectivity, a spatiotemporal processing function of the visual cortex, using a poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) organic electrochemical transistor with multiple gates. Spatially distributed inputs on a gate electrode array are found to correlate with the output of the transistor, leading to the ability to discriminate between different stimuli orientations. The demonstration of spatiotemporal processing in an organic electronic device paves the way for neuromorphic devices with new form factors and a facile interface with biology.

Active Hearing Mechanisms Inspire Adaptive Amplification in an Acoustic Sensor System.

PubMed

Guerreiro, Jose; Reid, Andrew; Jackson, Joseph C; Windmill, James F C

2018-06-01

Over many millions of years of evolution, nature has developed some of the most adaptable sensors and sensory systems possible, capable of sensing, conditioning and processing signals in a very power- and size-effective manner. By looking into biological sensors and systems as a source of inspiration, this paper presents the study of a bioinspired concept of signal processing at the sensor level. By exploiting a feedback control mechanism between a front-end acoustic receiver and back-end neuronal based computation, a nonlinear amplification with hysteretic behavior is created. Moreover, the transient response of the front-end acoustic receiver can also be controlled and enhanced. A theoretical model is proposed and the concept is prototyped experimentally through an embedded system setup that can provide dynamic adaptations of a sensory system comprising a MEMS microphone placed in a closed-loop feedback system. It faithfully mimics the mosquito's active hearing response as a function of the input sound intensity. This is an adaptive acoustic sensor system concept that can be exploited by sensor and system designers within acoustics and ultrasonic engineering fields.

Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller

PubMed Central

Cyr, André; Boukadoum, Mounir; Thériault, Frédéric

2014-01-01

In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors. PMID:25120464

Firefly Luciferin-Inspired Biocompatible Chemistry for Protein Labeling and In Vivo Imaging.

PubMed

Wang, Yuqi; An, Ruibing; Luo, Zhiliang; Ye, Deju

2018-04-17

Biocompatible reactions have emerged as versatile tools to build various molecular imaging probes that hold great promise for the detection of biological processes in vitro and/or in vivo. In this Minireview, we describe the recent advances in the development of a firefly luciferin-inspired biocompatible reaction between cyanobenzothiazole (CBT) and cysteine (Cys), and highlight its versatility to label proteins and build multimodality molecular imaging probes. The review starts from the general introduction of biocompatible reactions, which is followed by briefly describing the development of the firefly luciferin-inspired biocompatible chemistry. We then discuss its applications for the specific protein labeling and for the development of multimodality imaging probes (fluorescence, bioluminescence, MRI, PET, photoacoustic, etc.) that enable high sensitivity and spatial resolution imaging of redox environment, furin and caspase-3/7 activity in living cells and mice. Finally, we offer the conclusions and our perspective on the various and potential applications of this reaction. We hope that this review will contribute to the research of biocompatible reactions for their versatile applications in protein labeling and molecular imaging. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Innovation Inspired by Nature: Capabilities, Potentials and Challenges

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph

2012-01-01

Through evolution, nature came up with many effective solutions to its challenges and continually improving them. By mimicking, coping and being inspired, humans have been using Nature's solutions to address their own challenges. In recent years, the implementation of nature's capabilities has intensified with our growing understanding of the various biological and nastic mechanisms and processes. Successes include even the making of humanlike robots that perform such lifelike tasks as walking, talking, making eye-contact, interpreting speech and facial expressions, as well as many other humanlike functions. Generally, once humans are able to implement a function then, thru rapid advances in technology, capabilities are developed that can significantly exceed the original source of inspiration in Nature. Examples include flight where there is no species that can fly as high, carry so much mass, has so large dimensions and fly so fast, and operate at as such extreme conditions as our aircraft and other aerospace systems. However, using the capabilities of today's technology, there are many challenges that are not feasible to address in mimicking characteristics of species and plants. In this manuscript, state-of-the-art of biomimetic capabilities, potentials and challenges are reviewed.

Operant conditioning: a minimal components requirement in artificial spiking neurons designed for bio-inspired robot's controller.

PubMed

Cyr, André; Boukadoum, Mounir; Thériault, Frédéric

2014-01-01

In this paper, we investigate the operant conditioning (OC) learning process within a bio-inspired paradigm, using artificial spiking neural networks (ASNN) to act as robot brain controllers. In biological agents, OC results in behavioral changes learned from the consequences of previous actions, based on progressive prediction adjustment from rewarding or punishing signals. In a neurorobotics context, virtual and physical autonomous robots may benefit from a similar learning skill when facing unknown and unsupervised environments. In this work, we demonstrate that a simple invariant micro-circuit can sustain OC in multiple learning scenarios. The motivation for this new OC implementation model stems from the relatively complex alternatives that have been described in the computational literature and recent advances in neurobiology. Our elementary kernel includes only a few crucial neurons, synaptic links and originally from the integration of habituation and spike-timing dependent plasticity as learning rules. Using several tasks of incremental complexity, our results show that a minimal neural component set is sufficient to realize many OC procedures. Hence, with the proposed OC module, designing learning tasks with an ASNN and a bio-inspired robot context leads to simpler neural architectures for achieving complex behaviors.

The inorganic side of chemical biology.

PubMed

Lippard, Stephen J

2006-10-01

Bioinorganic chemistry remains a vibrant discipline at the interface of chemistry and the biological sciences. Metal ions function in numerous metalloenzymes, are incorporated into pharmaceuticals and imaging agents, and inspire the synthesis of catalysts used to achieve many chemical transformations.

State-of-the-art and outlook for biomimetic materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Richman, R.H.; Bond, G.M.; McNaughton, W.P.

1994-11-01

A remarkable diversity of structures and molecular functions has evolved in plants and animals. Many of these natural substances have properties or capabilities that belie their origins in humble, everyday, starting materials. Consequently, there is a growing awareness among scientists and engineers that biological systems can be a valuable source of inspiration for man-made materials. Emphasis in this assessment is on biomimetics; that is, the achievement of unusual materials properties or processes by mimicking novel aspects of biological systems. Five broad areas are selected for detailed investigation: Mimicking of Natural Material Designs; Biomimetic Materials Processing; Artificial Photosynthesis; Biomimetic Molecular Electronics;more » and Biomimetic Catalysis. Each of these topics is examined in terms of current activities and approaches, key aspects, unresolved issues, and implications for the power industry. Finally, the researchers, their organizations, the main thrusts of investigation, achievements, and funding agencies are summarized in tabular form.« less

Capturing Biological Activity in Natural Product Fragments by Chemical Synthesis

PubMed Central

Crane, Erika A.

2016-01-01

Abstract Natural products have had an immense influence on science and have directly led to the introduction of many drugs. Organic chemistry, and its unique ability to tailor natural products through synthesis, provides an extraordinary approach to unlock the full potential of natural products. In this Review, an approach based on natural product derived fragments is presented that can successfully address some of the current challenges in drug discovery. These fragments often display significantly reduced molecular weights, reduced structural complexity, a reduced number of synthetic steps, while retaining or even improving key biological parameters such as potency or selectivity. Examples from various stages of the drug development process up to the clinic are presented. In addition, this process can be leveraged by recent developments such as genome mining, antibody–drug conjugates, and computational approaches. All these concepts have the potential to identify the next generation of drug candidates inspired by natural products. PMID:26833854

Rapid self-assembly of complex biomolecular architectures during mussel byssus biofabrication

PubMed Central

Priemel, Tobias; Degtyar, Elena; Dean, Mason N.; Harrington, Matthew J.

2017-01-01

Protein-based biogenic materials provide important inspiration for the development of high-performance polymers. The fibrous mussel byssus, for instance, exhibits exceptional wet adhesion, abrasion resistance, toughness and self-healing capacity–properties that arise from an intricate hierarchical organization formed in minutes from a fluid secretion of over 10 different protein precursors. However, a poor understanding of this dynamic biofabrication process has hindered effective translation of byssus design principles into synthetic materials. Here, we explore mussel byssus assembly in Mytilus edulis using a synergistic combination of histological staining and confocal Raman microspectroscopy, enabling in situ tracking of specific proteins during induced thread formation from soluble precursors to solid fibres. Our findings reveal critical insights into this complex biological manufacturing process, showing that protein precursors spontaneously self-assemble into complex architectures, while maturation proceeds in subsequent regulated steps. Beyond their biological importance, these findings may guide development of advanced materials with biomedical and industrial relevance. PMID:28262668

Conceptual Elements: A Detailed Framework to Support and Assess Student Learning of Biology Core Concepts

ERIC Educational Resources Information Center

Cary, Tawnya; Branchaw, Janet

2017-01-01

The "Vision and Change in Undergraduate Biology Education: Call to Action" report has inspired and supported a nationwide movement to restructure undergraduate biology curricula to address overarching disciplinary concepts and competencies. The report outlines the concepts and competencies generally but does not provide a detailed…

Developmentally-inspired shrink-wrap polymers for mechanical induction of tissue differentiation.

PubMed

Hashmi, Basma; Zarzar, Lauren D; Mammoto, Tadanori; Mammoto, Akiko; Jiang, Amanda; Aizenberg, Joanna; Ingber, Donald E

2014-05-28

A biologically inspired thermoresponsive polymer has been developed that mechanically induces tooth differentiation in vitro and in vivo by promoting mesenchymal cell compaction as seen in each pore of the scaffold. This normally occurs during the physiological mesenchymal condensation response that triggers tooth formation in the embryo. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bio-inspired passive actuator simulating an abalone shell mechanism for structural control

NASA Astrophysics Data System (ADS)

Yang, Henry T. Y.; Lin, Chun-Hung; Bridges, Daniel; Randall, Connor J.; Hansma, Paul K.

2010-10-01

An energy dispersion mechanism called 'sacrificial bonds and hidden length', which is found in some biological systems, such as abalone shells and bones, is the inspiration for new strategies for structural control. Sacrificial bonds and hidden length can substantially increase the stiffness and enhance energy dissipation in the constituent molecules of abalone shells and bone. Having been inspired by the usefulness and effectiveness of such a mechanism, which has evolved over millions of years and countless cycles of evolutions, the authors employ the conceptual underpinnings of this mechanism to develop a bio-inspired passive actuator. This paper presents a fundamental method for optimally designing such bio-inspired passive actuators for structural control. To optimize the bio-inspired passive actuator, a simple method utilizing the force-displacement-velocity (FDV) plots based on LQR control is proposed. A linear regression approach is adopted in this research to find the initial values of the desired parameters for the bio-inspired passive actuator. The illustrative examples, conducted by numerical simulation with experimental validation, suggest that the bio-inspired passive actuator based on sacrificial bonds and hidden length may be comparable in performance to state-of-the-art semi-active actuators.

From neural-based object recognition toward microelectronic eyes

NASA Technical Reports Server (NTRS)

Sheu, Bing J.; Bang, Sa Hyun

1994-01-01

Engineering neural network systems are best known for their abilities to adapt to the changing characteristics of the surrounding environment by adjusting system parameter values during the learning process. Rapid advances in analog current-mode design techniques have made possible the implementation of major neural network functions in custom VLSI chips. An electrically programmable analog synapse cell with large dynamic range can be realized in a compact silicon area. New designs of the synapse cells, neurons, and analog processor are presented. A synapse cell based on Gilbert multiplier structure can perform the linear multiplication for back-propagation networks. A double differential-pair synapse cell can perform the Gaussian function for radial-basis network. The synapse cells can be biased in the strong inversion region for high-speed operation or biased in the subthreshold region for low-power operation. The voltage gain of the sigmoid-function neurons is externally adjustable which greatly facilitates the search of optimal solutions in certain networks. Various building blocks can be intelligently connected to form useful industrial applications. Efficient data communication is a key system-level design issue for large-scale networks. We also present analog neural processors based on perceptron architecture and Hopfield network for communication applications. Biologically inspired neural networks have played an important role towards the creation of powerful intelligent machines. Accuracy, limitations, and prospects of analog current-mode design of the biologically inspired vision processing chips and cellular neural network chips are key design issues.

A direct biocombinatorial strategy toward next generation, mussel-glue inspired saltwater adhesives.

PubMed

Wilke, Patrick; Helfricht, Nicolas; Mark, Andreas; Papastavrou, Georg; Faivre, Damien; Börner, Hans G

2014-09-10

Biological materials exhibit remarkable, purpose-adapted properties that provide a source of inspiration for designing new materials to meet the requirements of future applications. For instance, marine mussels are able to attach to a broad spectrum of hard surfaces under hostile conditions. Controlling wet-adhesion of synthetic macromolecules by analogue processes promises to strongly impact materials sciences by offering advanced coatings, adhesives, and glues. The de novo design of macromolecules to mimic complex aspects of mussel adhesion still constitutes a challenge. Phage display allows material scientists to design specifically interacting molecules with tailored affinity to material surfaces. Here, we report on the integration of enzymatic processing steps into phage display biopanning to expand the biocombinatorial procedure and enable the direct selection of enzymatically activable peptide adhesion domains. Adsorption isotherms and single molecule force spectroscopy show that those de novo peptides mimic complex aspects of bioadhesion, such as enzymatic activation (by tyrosinase), the switchability from weak to strong binders, and adsorption under hostile saltwater conditions. Furthermore, peptide-poly(ethylene oxide) conjugates are synthesized to generate protective coatings, which possess anti-fouling properties and suppress irreversible interactions with blood-plasma protein cocktails. The extended phage display procedure provides a generic way to non-natural peptide adhesion domains, which not only mimic nature but also improve biological sequence sections extractable from mussel-glue proteins. The de novo peptides manage to combine several tasks in a minimal 12-mer sequence and thus pave the way to overcome major challenges of technical wet glues.

Swarm intelligence metaheuristics for enhanced data analysis and optimization.

PubMed

Hanrahan, Grady

2011-09-21

The swarm intelligence (SI) computing paradigm has proven itself as a comprehensive means of solving complicated analytical chemistry problems by emulating biologically-inspired processes. As global optimum search metaheuristics, associated algorithms have been widely used in training neural networks, function optimization, prediction and classification, and in a variety of process-based analytical applications. The goal of this review is to provide readers with critical insight into the utility of swarm intelligence tools as methods for solving complex chemical problems. Consideration will be given to algorithm development, ease of implementation and model performance, detailing subsequent influences on a number of application areas in the analytical, bioanalytical and detection sciences.

Biologically Inspired SNN for Robot Control.

PubMed

Nichols, Eric; McDaid, Liam J; Siddique, Nazmul

2013-02-01

This paper proposes a spiking-neural-network-based robot controller inspired by the control structures of biological systems. Information is routed through the network using facilitating dynamic synapses with short-term plasticity. Learning occurs through long-term synaptic plasticity which is implemented using the temporal difference learning rule to enable the robot to learn to associate the correct movement with the appropriate input conditions. The network self-organizes to provide memories of environments that the robot encounters. A Pioneer robot simulator with laser and sonar proximity sensors is used to verify the performance of the network with a wall-following task, and the results are presented.

Biologically inspired robots elicit a robust fear response in zebrafish

NASA Astrophysics Data System (ADS)

Ladu, Fabrizio; Bartolini, Tiziana; Panitz, Sarah G.; Butail, Sachit; Macrı, Simone; Porfiri, Maurizio

2015-03-01

We investigate the behavioral response of zebrafish to three fear-evoking stimuli. In a binary choice test, zebrafish are exposed to a live allopatric predator, a biologically-inspired robot, and a computer-animated image of the live predator. A target tracking algorithm is developed to score zebrafish behavior. Unlike computer-animated images, the robotic and live predator elicit a robust avoidance response. Importantly, the robotic stimulus elicits more consistent inter-individual responses than the live predator. Results from this effort are expected to aid in hypothesis-driven studies on zebrafish fear response, by offering a valuable approach to maximize data-throughput and minimize animal subjects.

Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing.

PubMed

Kuzum, Duygu; Jeyasingh, Rakesh G D; Lee, Byoungil; Wong, H-S Philip

2012-05-09

Brain-inspired computing is an emerging field, which aims to extend the capabilities of information technology beyond digital logic. A compact nanoscale device, emulating biological synapses, is needed as the building block for brain-like computational systems. Here, we report a new nanoscale electronic synapse based on technologically mature phase change materials employed in optical data storage and nonvolatile memory applications. We utilize continuous resistance transitions in phase change materials to mimic the analog nature of biological synapses, enabling the implementation of a synaptic learning rule. We demonstrate different forms of spike-timing-dependent plasticity using the same nanoscale synapse with picojoule level energy consumption.

Signal transduction and amplification through enzyme-triggered ligand release and accelerated catalysis.

PubMed

Goggins, Sean; Marsh, Barrie J; Lubben, Anneke T; Frost, Christopher G

2015-08-01

Signal transduction and signal amplification are both important mechanisms used within biological signalling pathways. Inspired by this process, we have developed a signal amplification methodology that utilises the selectivity and high activity of enzymes in combination with the robustness and generality of an organometallic catalyst, achieving a hybrid biological and synthetic catalyst cascade. A proligand enzyme substrate was designed to selectively self-immolate in the presence of the enzyme to release a ligand that can bind to a metal pre-catalyst and accelerate the rate of a transfer hydrogenation reaction. Enzyme-triggered catalytic signal amplification was then applied to a range of catalyst substrates demonstrating that signal amplification and signal transduction can both be achieved through this methodology.

Tandem Repeat Proteins Inspired By Squid Ring Teeth

NASA Astrophysics Data System (ADS)

Pena-Francesch, Abdon

Proteins are large biomolecules consisting of long chains of amino acids that hierarchically assemble into complex structures, and provide a variety of building blocks for biological materials. The repetition of structural building blocks is a natural evolutionary strategy for increasing the complexity and stability of protein structures. However, the relationship between amino acid sequence, structure, and material properties of protein systems remains unclear due to the lack of control over the protein sequence and the intricacies of the assembly process. In order to investigate the repetition of protein building blocks, a recently discovered protein from squids is examined as an ideal protein system. Squid ring teeth are predatory appendages located inside the suction cups that provide a strong grasp of prey, and are solely composed of a group of proteins with tandem repetition of building blocks. The objective of this thesis is the understanding of sequence, structure and property relationship in repetitive protein materials inspired in squid ring teeth for the first time. Specifically, this work focuses on squid-inspired structural proteins with tandem repeat units in their sequence (i.e., repetition of alternating building blocks) that are physically cross-linked via beta-sheet structures. The research work presented here tests the hypothesis that, in these systems, increasing the number of building blocks in the polypeptide chain decreases the protein network defects and improves the material properties. Hence, the sequence, nanostructure, and properties (thermal, mechanical, and conducting) of tandem repeat squid-inspired protein materials are examined. Spectroscopic structural analysis, advanced materials characterization, and entropic elasticity theory are combined to elucidate the structure and material properties of these repetitive proteins. This approach is applied not only to native squid proteins but also to squid-inspired synthetic polypeptides that allow for a fine control of the sequence and network morphology. The results provided in this work establish a clear dependence between the repetitive building blocks, the network morphology, and the properties of squid-inspired repetitive protein materials. Increasing the number of tandem repeat units in SRT-inspired proteins led to more effective protein networks with superior properties. Through increasing tandem repetition and optimization of network morphology, highly efficient protein materials capable of withstanding deformations up to 400% of their original length, with MPa-GPa modulus, high energy absorption (50 MJ m-3), peak proton conductivity of 3.7 mS cm-1 (at pH 7, highest reported to date for biological materials), and peak thermal conductivity of 1.4 W m-1 K -1 (which exceeds that of most polymer materials) were developed. These findings introduce new design rules in the engineering of proteins based on tandem repetition and morphology control, and provide a novel framework for tailoring and optimizing the properties of protein-based materials.

Bio-Inspired Self-Cleaning Surfaces

NASA Astrophysics Data System (ADS)

Liu, Kesong; Jiang, Lei

2012-08-01

Self-cleaning surfaces have drawn a lot of interest for both fundamental research and practical applications. This review focuses on the recent progress in mechanism, preparation, and application of self-cleaning surfaces. To date, self-cleaning has been demonstrated by the following four conceptual approaches: (a) TiO2-based superhydrophilic self-cleaning, (b) lotus effect self-cleaning (superhydrophobicity with a small sliding angle), (c) gecko setae-inspired self-cleaning, and (d) underwater organisms-inspired antifouling self-cleaning. Although a number of self-cleaning products have been commercialized, the remaining challenges and future outlook of self-cleaning surfaces are also briefly addressed. Through evolution, nature, which has long been a source of inspiration for scientists and engineers, has arrived at what is optimal. We hope this review will stimulate interdisciplinary collaboration among material science, chemistry, biology, physics, nanoscience, engineering, etc., which is essential for the rational design and reproducible construction of bio-inspired multifunctional self-cleaning surfaces in practical applications.

Philosophical Basis and Some Historical Aspects of Systems Biology: From Hegel to Noble - Applications for Bioenergetic Research

PubMed Central

Saks, Valdur; Monge, Claire; Guzun, Rita

2009-01-01

We live in times of paradigmatic changes for the biological sciences. Reductionism, that for the last six decades has been the philosophical basis of biochemistry and molecular biology, is being displaced by Systems Biology, which favors the study of integrated systems. Historically, Systems Biology - defined as the higher level analysis of complex biological systems - was pioneered by Claude Bernard in physiology, Norbert Wiener with the development of cybernetics, and Erwin Schrödinger in his thermodynamic approach to the living. Systems Biology applies methods inspired by cybernetics, network analysis, and non-equilibrium dynamics of open systems. These developments follow very precisely the dialectical principles of development from thesis to antithesis to synthesis discovered by Hegel. Systems Biology opens new perspectives for studies of the integrated processes of energy metabolism in different cells. These integrated systems acquire new, system-level properties due to interaction of cellular components, such as metabolic compartmentation, channeling and functional coupling mechanisms, which are central for regulation of the energy fluxes. State of the art of these studies in the new area of Molecular System Bioenergetics is analyzed. PMID:19399243

Utilization and viability of biologically-inspired algorithms in a dynamic multiagent camera surveillance system

NASA Astrophysics Data System (ADS)

Mundhenk, Terrell N.; Dhavale, Nitin; Marmol, Salvador; Calleja, Elizabeth; Navalpakkam, Vidhya; Bellman, Kirstie; Landauer, Chris; Arbib, Michael A.; Itti, Laurent

2003-10-01

In view of the growing complexity of computational tasks and their design, we propose that certain interactive systems may be better designed by utilizing computational strategies based on the study of the human brain. Compared with current engineering paradigms, brain theory offers the promise of improved self-organization and adaptation to the current environment, freeing the programmer from having to address those issues in a procedural manner when designing and implementing large-scale complex systems. To advance this hypothesis, we discus a multi-agent surveillance system where 12 agent CPUs each with its own camera, compete and cooperate to monitor a large room. To cope with the overload of image data streaming from 12 cameras, we take inspiration from the primate"s visual system, which allows the animal to operate a real-time selection of the few most conspicuous locations in visual input. This is accomplished by having each camera agent utilize the bottom-up, saliency-based visual attention algorithm of Itti and Koch (Vision Research 2000;40(10-12):1489-1506) to scan the scene for objects of interest. Real time operation is achieved using a distributed version that runs on a 16-CPU Beowulf cluster composed of the agent computers. The algorithm guides cameras to track and monitor salient objects based on maps of color, orientation, intensity, and motion. To spread camera view points or create cooperation in monitoring highly salient targets, camera agents bias each other by increasing or decreasing the weight of different feature vectors in other cameras, using mechanisms similar to excitation and suppression that have been documented in electrophysiology, psychophysics and imaging studies of low-level visual processing. In addition, if cameras need to compete for computing resources, allocation of computational time is weighed based upon the history of each camera. A camera agent that has a history of seeing more salient targets is more likely to obtain computational resources. The system demonstrates the viability of biologically inspired systems in a real time tracking. In future work we plan on implementing additional biological mechanisms for cooperative management of both the sensor and processing resources in this system that include top down biasing for target specificity as well as novelty and the activity of the tracked object in relation to sensitive features of the environment.

Moving Bodies beyond the Social/Biological Divide: Toward Theoretical and Transdisciplinary Adventures

ERIC Educational Resources Information Center

Thorpe, Holly

2014-01-01

In this paper I call for "new forms of thinking and new ways of theorizing" the complex relations between the biological and social in sport and physical culture. I illustrate the inseparability of our biological and social bodies in sport and physical culture via the case of exercise and female reproductive hormones. Inspired by…

A new parallel DNA algorithm to solve the task scheduling problem based on inspired computational model.

PubMed

Wang, Zhaocai; Ji, Zuwen; Wang, Xiaoming; Wu, Tunhua; Huang, Wei

2017-12-01

As a promising approach to solve the computationally intractable problem, the method based on DNA computing is an emerging research area including mathematics, computer science and molecular biology. The task scheduling problem, as a well-known NP-complete problem, arranges n jobs to m individuals and finds the minimum execution time of last finished individual. In this paper, we use a biologically inspired computational model and describe a new parallel algorithm to solve the task scheduling problem by basic DNA molecular operations. In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n 2 ) time complexity. Copyright © 2017. Published by Elsevier B.V.

Biologically-Inspired Deceptive Behavior for a Robot

DTIC Science & Technology

2012-01-01

by sending false signals either intentionally or unintentionally, are essential for animals’ survival. For example, camouflage and mimicry are well...detection by both predators and their prey. While camouflage or mimicry are examples of unknowingly deceiving, a deceptive behavior can include...face this situation, where it is important to discourage an adversary from discovering a protected site, so the application of these bio -inspired

Social insects inspire human design

PubMed Central

Holbrook, C. Tate; Clark, Rebecca M.; Moore, Dani; Overson, Rick P.; Penick, Clint A.; Smith, Adrian A.

2010-01-01

The international conference ‘Social Biomimicry: Insect Societies and Human Design’, hosted by Arizona State University, USA, 18–20 February 2010, explored how the collective behaviour and nest architecture of social insects can inspire innovative and effective solutions to human design challenges. It brought together biologists, designers, engineers, computer scientists, architects and businesspeople, with the dual aims of enriching biology and advancing biomimetic design. PMID:20392721

A Bioinspired Alginate-Gum Arabic Hydrogel with Micro-/Nanoscale Structures for Controlled Drug Release in Chronic Wound Healing.

PubMed

Li, Mi; Li, Haichang; Li, Xiangguang; Zhu, Hua; Xu, Zihui; Liu, Lianqing; Ma, Jianjie; Zhang, Mingjun

2017-07-12

Biopolymeric hydrogels have drawn increasing research interest in biomaterials due to their tunable physical and chemical properties for both creating bioactive cellular microenvironment and serving as sustainable therapeutic reagents. Inspired by a naturally occurring hydrogel secreted from the carnivorous Sundew plant for trapping insects, here we have developed a bioinspired hydrogel to deliver mitsugumin 53 (MG53), an important protein in cell membrane repair, for chronic wound healing. Both chemical compositions and micro-/nanomorphological properties inherent from the natural Sundew hydrogel were mimicked using sodium alginate and gum arabic with calcium ion-mediated cross-linking. On the basis of atomic force microscopy (AFM) force measurements, an optimal sticky hydrogel scaffold was obtained through orthogonal experimental design. Imaging and mechanical analysis showed the distinct correlation between structural morphology, adhesion characteristics, and mechanical properties of the Sundew-inspired hydrogel. Combined characterization and biochemistry techniques were utilized to uncover the underlying molecular composition involved in the interactions between hydrogel and protein. In vitro drug release experiments confirmed that the Sundew-inspired hydrogel had a biphasic-kinetics release, which can facilitate both fast delivery of MG53 for improving the reepithelization process of the wounds and sustained release of the protein for treating chronic wounds. In vivo experiments showed that the Sundew-inspired hydrogel encapsulating with rhMG53 could facilitate dermal wound healing in mouse model. Together, these studies confirmed that the Sundew-inspired hydrogel has both tunable micro-/nanostructures and physicochemical properties, which enable it as a delivery vehicle for chronic wounding healing. The research may provide a new way to develop biocompatible and tunable biomaterials for sustainable drug release to meet the needs of biological activities.

Biologically Inspired Model for Inference of 3D Shape from Texture

PubMed Central

Gomez, Olman; Neumann, Heiko

2016-01-01

A biologically inspired model architecture for inferring 3D shape from texture is proposed. The model is hierarchically organized into modules roughly corresponding to visual cortical areas in the ventral stream. Initial orientation selective filtering decomposes the input into low-level orientation and spatial frequency representations. Grouping of spatially anisotropic orientation responses builds sketch-like representations of surface shape. Gradients in orientation fields and subsequent integration infers local surface geometry and globally consistent 3D depth. From the distributions in orientation responses summed in frequency, an estimate of the tilt and slant of the local surface can be obtained. The model suggests how 3D shape can be inferred from texture patterns and their image appearance in a hierarchically organized processing cascade along the cortical ventral stream. The proposed model integrates oriented texture gradient information that is encoded in distributed maps of orientation-frequency representations. The texture energy gradient information is defined by changes in the grouped summed normalized orientation-frequency response activity extracted from the textured object image. This activity is integrated by directed fields to generate a 3D shape representation of a complex object with depth ordering proportional to the fields output, with higher activity denoting larger distance in relative depth away from the viewer. PMID:27649387

Soft network composite materials with deterministic and bio-inspired designs

PubMed Central

Jang, Kyung-In; Chung, Ha Uk; Xu, Sheng; Lee, Chi Hwan; Luan, Haiwen; Jeong, Jaewoong; Cheng, Huanyu; Kim, Gwang-Tae; Han, Sang Youn; Lee, Jung Woo; Kim, Jeonghyun; Cho, Moongee; Miao, Fuxing; Yang, Yiyuan; Jung, Han Na; Flavin, Matthew; Liu, Howard; Kong, Gil Woo; Yu, Ki Jun; Rhee, Sang Il; Chung, Jeahoon; Kim, Byunggik; Kwak, Jean Won; Yun, Myoung Hee; Kim, Jin Young; Song, Young Min; Paik, Ungyu; Zhang, Yihui; Huang, Yonggang; Rogers, John A.

2015-01-01

Hard and soft structural composites found in biology provide inspiration for the design of advanced synthetic materials. Many examples of bio-inspired hard materials can be found in the literature; far less attention has been devoted to soft systems. Here we introduce deterministic routes to low-modulus thin film materials with stress/strain responses that can be tailored precisely to match the non-linear properties of biological tissues, with application opportunities that range from soft biomedical devices to constructs for tissue engineering. The approach combines a low-modulus matrix with an open, stretchable network as a structural reinforcement that can yield classes of composites with a wide range of desired mechanical responses, including anisotropic, spatially heterogeneous, hierarchical and self-similar designs. Demonstrative application examples in thin, skin-mounted electrophysiological sensors with mechanics precisely matched to the human epidermis and in soft, hydrogel-based vehicles for triggered drug release suggest their broad potential uses in biomedical devices. PMID:25782446

Actions, Observations, and Decision-Making: Biologically Inspired Strategies for Autonomous Aerial Vehicles

NASA Technical Reports Server (NTRS)

Pisanich, Greg; Ippolito, Corey; Plice, Laura; Young, Larry A.; Lau, Benton

2003-01-01

This paper details the development and demonstration of an autonomous aerial vehicle embodying search and find mission planning and execution srrategies inspired by foraging behaviors found in biology. It begins by describing key characteristics required by an aeria! explorer to support science and planetary exploration goals, and illustrates these through a hypothetical mission profile. It next outlines a conceptual bio- inspired search and find autonomy architecture that implements observations, decisions, and actions through an "ecology" of producer, consumer, and decomposer agents. Moving from concepts to development activities, it then presents the results of mission representative UAV aerial surveys at a Mars analog site. It next describes hardware and software enhancements made to a commercial small fixed-wing UAV system, which inc!nde a ncw dpvelopnent architecture that also provides hardware in the loop simulation capability. After presenting the results of simulated and actual flights of bioinspired flight algorithms, it concludes with a discussion of future development to include an expansion of system capabilities and field science support.

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

PubMed

Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

2018-04-01

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

Enhanced HMAX model with feedforward feature learning for multiclass categorization.

PubMed

Li, Yinlin; Wu, Wei; Zhang, Bo; Li, Fengfu

2015-01-01

In recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the S1 layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.

Novel locomotion via biological inspiration

NASA Astrophysics Data System (ADS)

Quinn, Roger D.; Boxerbaum, Alexander; Palmer, Luther; Chiel, Hillel; Diller, Eric; Hunt, Alexander; Bachmann, Richard

2011-05-01

Animal behavioral, physiological and neurobiological studies are providing a wealth of inspirational data for robot design and control. Several very different biologically inspired mobile robots will be reviewed. A robot called DIGbot is being developed that moves independent of the direction of gravity using Distributed Inward Gripping (DIG) as a rapid and robust attachment mechanism observed in climbing animals. DIGbot is an 18 degree of freedom hexapod with onboard power and control systems. Passive compliance in its feet, which is inspired by the flexible tarsus of the cockroach, increases the robustness of the adhesion strategy and enables DIGbot to execute large steps and stationary turns while walking on mesh screens. A Whegs™ robot, inspired by insect locomotion principles, is being developed that can be rapidly reconfigured between tracks and wheel-legs and carry GeoSystems Zipper Mast. The mechanisms that cause it to passively change its gait on irregular terrain have been integrated into its hubs for a compact and modular design. The robot is designed to move smoothly on moderately rugged terrain using its tracks and run on irregular terrain and stairs using its wheel-legs. We are also developing soft bodied robots that use peristalsis, the same method of locomotion earthworms use. We present a technique of using a braided mesh exterior to produce fluid waves of motion along the body of the robot that increase the robot's speed relative to previous designs. The concept is highly scalable, for endoscopes to water, oil or gas line inspection.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

PubMed Central

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier

2017-01-01

Background The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. PMID:28623182

Feature extraction inspired by V1 in visual cortex

NASA Astrophysics Data System (ADS)

Lv, Chao; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Xin, Peng; Zhu, Mingning; Ma, Hongqiang

2018-04-01

Target feature extraction plays an important role in pattern recognition. It is the most complicated activity in the brain mechanism of biological vision. Inspired by high properties of primary visual cortex (V1) in extracting dynamic and static features, a visual perception model was raised. Firstly, 28 spatial-temporal filters with different orientations, half-squaring operation and divisive normalization were adopted to obtain the responses of V1 simple cells; then, an adjustable parameter was added to the output weight so that the response of complex cells was got. Experimental results indicate that the proposed V1 model can perceive motion information well. Besides, it has a good edge detection capability. The model inspired by V1 has good performance in feature extraction and effectively combines brain-inspired intelligence with computer vision.

Bio-inspired engineering of cell- and virus-like nanoparticles for drug delivery.

PubMed

Parodi, Alessandro; Molinaro, Roberto; Sushnitha, Manuela; Evangelopoulos, Michael; Martinez, Jonathan O; Arrighetti, Noemi; Corbo, Claudia; Tasciotti, Ennio

2017-12-01

The engineering of future generations of nanodelivery systems aims at the creation of multifunctional vectors endowed with improved circulation, enhanced targeting and responsiveness to the biological environment. Moving past purely bio-inert systems, researchers have begun to create nanoparticles capable of proactively interacting with the biology of the body. Nature offers a wide-range of sources of inspiration for the synthesis of more effective drug delivery platforms. Because the nano-bio-interface is the key driver of nanoparticle behavior and function, the modification of nanoparticles' surfaces allows the transfer of biological properties to synthetic carriers by imparting them with a biological identity. Modulation of these surface characteristics governs nanoparticle interactions with the biological barriers they encounter. Building off these observations, we provide here an overview of virus- and cell-derived biomimetic delivery systems that combine the intrinsic hallmarks of biological membranes with the delivery capabilities of synthetic carriers. We describe the features and properties of biomimetic delivery systems, recapitulating the distinctive traits and functions of viruses, exosomes, platelets, red and white blood cells. By mimicking these biological entities, we will learn how to more efficiently interact with the human body and refine our ability to negotiate with the biological barriers that impair the therapeutic efficacy of nanoparticles. Copyright © 2017. Published by Elsevier Ltd.

Biologically inspired emotion recognition from speech

NASA Astrophysics Data System (ADS)

Caponetti, Laura; Buscicchio, Cosimo Alessandro; Castellano, Giovanna

2011-12-01

Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM) recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC) and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.

Constructing Smart Protocells with Built-In DNA Computational Core to Eliminate Exogenous Challenge.

PubMed

Lyu, Yifan; Wu, Cuichen; Heinke, Charles; Han, Da; Cai, Ren; Teng, I-Ting; Liu, Yuan; Liu, Hui; Zhang, Xiaobing; Liu, Qiaoling; Tan, Weihong

2018-06-06

A DNA reaction network is like a biological algorithm that can respond to "molecular input signals", such as biological molecules, while the artificial cell is like a microrobot whose function is powered by the encapsulated DNA reaction network. In this work, we describe the feasibility of using a DNA reaction network as the computational core of a protocell, which will perform an artificial immune response in a concise way to eliminate a mimicked pathogenic challenge. Such a DNA reaction network (RN)-powered protocell can realize the connection of logical computation and biological recognition due to the natural programmability and biological properties of DNA. Thus, the biological input molecules can be easily involved in the molecular computation and the computation process can be spatially isolated and protected by artificial bilayer membrane. We believe the strategy proposed in the current paper, i.e., using DNA RN to power artificial cells, will lay the groundwork for understanding the basic design principles of DNA algorithm-based nanodevices which will, in turn, inspire the construction of artificial cells, or protocells, that will find a place in future biomedical research.

Biologically Inspired Strategies, Algorithms and Hardware for Visual Guidance of Autonomous Helicopters

DTIC Science & Technology

2011-05-02

Dacke, J. Reinhard and M.V. Srinivasan (2010) The moment before touchdown: Landing manoeuvres of the honeybee Apis mellifera . Journal of Experimental...moment before touchdown: Landing manoeuvres of the honeybee Apis mellifera . Journal of Experimental Biology 213, 262-270. M.V. Srinivasan (2010

Bio-Inspired Metal-Coordination Dynamics: A Unique Tool for Engineering Soft Matter Mechanics

NASA Astrophysics Data System (ADS)

Holten-Andersen, Niels

Growing evidence supports a critical role of metal-coordination in soft biological material properties such as self-healing, underwater adhesion and autonomous wound plugging. Using bio-inspired metal-binding polymers, initial efforts to mimic these properties with metal-coordination crosslinked polymer materials have shown promise. In addition, with polymer network mechanics strongly coupled to coordinate crosslink dynamics material properties can be easily tuned from visco-elastic fluids to solids. Given their exploitation in desirable material applications in Nature, bio-inspired metal-coordinate complex crosslinking provides an opportunity to further advance synthetic polymer materials design. Early lessons from this pursuit are presented.

Kirigami artificial muscles with complex biologically inspired morphologies

NASA Astrophysics Data System (ADS)

Sareh, Sina; Rossiter, Jonathan

2013-01-01

In this paper we present bio-inspired smart structures which exploit the actuation of flexible ionic polymer composites and the kirigami design principle. Kirigami design is used to convert planar actuators into active 3D structures capable of large out-of-plane displacement and that replicate biological mechanisms. Here we present the burstbot, a fluid control and propulsion mechanism based on the atrioventricular cuspid valve, and the vortibot, a spiral actuator based on Vorticella campanula, a ciliate protozoa. Models derived from biological counterparts are used as a platform for design optimization and actuator performance measurement. The symmetric and asymmetric fluid interactions of the burstbot are investigated and the effectiveness in fluid transport applications is demonstrated. The vortibot actuator is geometrically optimized as a camera positioner capable of 360° scanning. Experimental results for a one-turn spiral actuator show complex actuation derived from a single degree of freedom control signal.

New Opportunities for an Ancient Material

PubMed Central

Omenetto, Fiorenzo G.; Kaplan, David L.

2011-01-01

Spiders and silkworms generate silk protein fibers that embody strength and beauty. Orb webs are fascinating feats of bioengineering in nature, displaying magnificent architectures while providing essential survival utility for spiders. The unusual combination of high strength and extensibility is a characteristic unavailable to date in synthetic materials yet is attained in nature with a relatively simple protein processed from water. This biological template suggests new directions to emulate in the pursuit of new high-performance, multifunctional materials generated with a green chemistry and processing approach. These bio-inspired and high-technology materials can lead to multifunctional material platforms that integrate with living systems for medical materials and a host of other applications. PMID:20671180

Neuron-Inspired Interpenetrative Network Composed of Cobalt-Phosphorus-Derived Nanoparticles Embedded within Porous Carbon Nanotubes for Efficient Hydrogen Production.

PubMed

Shen, Juanxia; Yang, Zhi; Ge, Mengzhan; Li, Ping; Nie, Huagui; Cai, Qiran; Gu, Cancan; Yang, Keqin; Huang, Shaoming

2016-07-13

The ongoing search for cheap and efficient hydrogen evolution reaction (HER) electrocatalysts to replace currently used catalysts based on Pt or its alloys has been considered as an prevalent strategy to produce renewable and clean hydrogen energy. Herein, inspired by the neuron structure in biological systems, we demonstrate a novel fabrication strategy via a simple two-step method for the synthesis of a neuronlike interpenetrative nanocomposite network of Co-P embedded in porous carbon nanotubes (NIN-Co-P/PCNTs). It is found that the interpenetrative network provides a natural transport path to accelerate the hydrogen production process. The embedded-type structure improves the utilization ratio of Co-P and the hollow, tubelike, and porous structure of PCNTs further promote charge and reactant transport. These factors allow the as-prepared NIN-Co-P/PCNTs to achieve a onset potential low to 43 mV, a Tafel slope as small as 40 mV/decade, an excellent stability, and a high turnover frequency value of 3.2 s(-1) at η = 0.2 V in acidic conditions. These encouraging properties derived from the neuronlike interpenetrative network structure might offer new inspiration for the preparation of more nanocomposites for applications in other catalytic and optoelectronic field.

Nanofibril scaffold assisted MEMS artificial hydrogel neuromasts for enhanced sensitivity flow sensing

PubMed Central

Kottapalli, Ajay Giri Prakash; Bora, Meghali; Asadnia, Mohsen; Miao, Jianmin; Venkatraman, Subbu S.; Triantafyllou, Michael

2016-01-01

We present the development and testing of superficial neuromast-inspired flow sensors that also attain high sensitivity and resolution through a biomimetic hyaulronic acid-based hydrogel cupula dressing. The inspiration comes from the spatially distributed neuromasts of the blind cavefish that live in completely dark undersea caves; the sensors enable the fish to form three-dimensional flow and object maps, enabling them to maneuver efficiently in cluttered environments. A canopy shaped electrospun nanofibril scaffold, inspired by the cupular fibrils, assists the drop-casting process allowing the formation of a prolate spheroid-shaped artificial cupula. Rheological and nanoindentation characterizations showed that the Young’s modulus of the artificial cupula closely matches the biological cupula (10–100 Pa). A comparative experimental study conducted to evaluate the sensitivities of the naked hair cell sensor and the cupula-dressed sensor in sensing steady-state flows demonstrated a sensitivity enhancement by 3.5–5 times due to the presence of hydrogel cupula. The novel strategies of sensor development presented in this report are applicable to the design and fabrication of other biomimetic sensors as well. The developed sensors can be used in the navigation and maneuvering of underwater robots, but can also find applications in biomedical and microfluidic devices. PMID:26763299

Neuro-Inspired Spike-Based Motion: From Dynamic Vision Sensor to Robot Motor Open-Loop Control through Spike-VITE

PubMed Central

Perez-Peña, Fernando; Morgado-Estevez, Arturo; Linares-Barranco, Alejandro; Jimenez-Fernandez, Angel; Gomez-Rodriguez, Francisco; Jimenez-Moreno, Gabriel; Lopez-Coronado, Juan

2013-01-01

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation. PMID:24264330

Neuro-inspired spike-based motion: from dynamic vision sensor to robot motor open-loop control through spike-VITE.

PubMed

Perez-Peña, Fernando; Morgado-Estevez, Arturo; Linares-Barranco, Alejandro; Jimenez-Fernandez, Angel; Gomez-Rodriguez, Francisco; Jimenez-Moreno, Gabriel; Lopez-Coronado, Juan

2013-11-20

In this paper we present a complete spike-based architecture: from a Dynamic Vision Sensor (retina) to a stereo head robotic platform. The aim of this research is to reproduce intended movements performed by humans taking into account as many features as possible from the biological point of view. This paper fills the gap between current spike silicon sensors and robotic actuators by applying a spike processing strategy to the data flows in real time. The architecture is divided into layers: the retina, visual information processing, the trajectory generator layer which uses a neuroinspired algorithm (SVITE) that can be replicated into as many times as DoF the robot has; and finally the actuation layer to supply the spikes to the robot (using PFM). All the layers do their tasks in a spike-processing mode, and they communicate each other through the neuro-inspired AER protocol. The open-loop controller is implemented on FPGA using AER interfaces developed by RTC Lab. Experimental results reveal the viability of this spike-based controller. Two main advantages are: low hardware resources (2% of a Xilinx Spartan 6) and power requirements (3.4 W) to control a robot with a high number of DoF (up to 100 for a Xilinx Spartan 6). It also evidences the suitable use of AER as a communication protocol between processing and actuation.

Biomorphodynamics: Physical-biological feedbacks that shape landscapes

USGS Publications Warehouse

Murray, A.B.; Knaapen, M.A.F.; Tal, M.; Kirwan, M.L.

2008-01-01

Plants and animals affect morphological evolution in many environments. The term "ecogeomorphology" describes studies that address such effects. In this opinion article we use the term "biomorphodynamics" to characterize a subset of ecogeomorphologic studies: those that investigate not only the effects of organisms on physical processes and morphology but also how the biological processes depend on morphology and physical forcing. The two-way coupling precipitates feedbacks, leading to interesting modes of behavior, much like the coupling between flow/sediment transport and morphology leads to rich morphodynamic behaviors. Select examples illustrate how even the basic aspects of some systems cannot be understood without considering biomorphodynamic coupling. Prominent examples include the dynamic interactions between vegetation and flow/sediment transport that can determine river channel patterns and the multifaceted biomorphodynamic feedbacks shaping tidal marshes and channel networks. These examples suggest that the effects of morphology and physical processes on biology tend to operate over the timescale of the evolution of the morphological pattern. Thus, in field studies, which represent a snapshot in the pattern evolution, these effects are often not as obvious as the effects of biology on physical processes. However, numerical modeling indicates that the influences on biology from physical processes can play a key role in shaping landscapes and that even local and temporary vegetation disturbances can steer large-scale, long-term landscape evolution. The prevalence of biomorphodynamic research is burgeoning in recent years, driven by societal need and a confluence of complex systems-inspired modeling approaches in ecology and geomorphology. To make fundamental progress in understanding the dynamics of many landscapes, our community needs to increasingly learn to look for two-way, biomorphodynamic feedbacks and to collect new types of data to support the modeling of such emergent interactions. Copyright 2008 by the American Geophysical Union.

INcreasing Security and Protection through Infrastructure REsilience: The INSPIRE Project

NASA Astrophysics Data System (ADS)

D'Antonio, Salvatore; Romano, Luigi; Khelil, Abdelmajid; Suri, Neeraj

The INSPIRE project aims at enhancing the European potential in the field of security by ensuring the protection of critical information infrastructures through (a) the identification of their vulnerabilities and (b) the development of innovative techniques for securing networked process control systems. To increase the resilience of such systems INSPIRE will develop traffic engineering algorithms, diagnostic processes and self-reconfigurable architectures along with recovery techniques. Hence, the core idea of the INSPIRE project is to protect critical information infrastructures by appropriately configuring, managing, and securing the communication network which interconnects the distributed control systems. A working prototype will be implemented as a final demonstrator of selected scenarios. Controls/Communication Experts will support project partners in the validation and demonstration activities. INSPIRE will also contribute to standardization process in order to foster multi-operator interoperability and coordinated strategies for securing lifeline systems.

A computational model of conditioning inspired by Drosophila olfactory system.

PubMed

Faghihi, Faramarz; Moustafa, Ahmed A; Heinrich, Ralf; Wörgötter, Florentin

2017-03-01

Recent studies have demonstrated that Drosophila melanogaster (briefly Drosophila) can successfully perform higher cognitive processes including second order olfactory conditioning. Understanding the neural mechanism of this behavior can help neuroscientists to unravel the principles of information processing in complex neural systems (e.g. the human brain) and to create efficient and robust robotic systems. In this work, we have developed a biologically-inspired spiking neural network which is able to execute both first and second order conditioning. Experimental studies demonstrated that volume signaling (e.g. by the gaseous transmitter nitric oxide) contributes to memory formation in vertebrates and invertebrates including insects. Based on the existing knowledge of odor encoding in Drosophila, the role of retrograde signaling in memory function, and the integration of synaptic and non-synaptic neural signaling, a neural system is implemented as Simulated fly. Simulated fly navigates in a two-dimensional environment in which it receives odors and electric shocks as sensory stimuli. The model suggests some experimental research on retrograde signaling to investigate neural mechanisms of conditioning in insects and other animals. Moreover, it illustrates a simple strategy to implement higher cognitive capabilities in machines including robots. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Inspiration of Hope in Substance Abuse Counseling

ERIC Educational Resources Information Center

Koehn, Corinne; Cutcliffe, John R.

2012-01-01

This study used a grounded theory method to explore how counselors inspire hope in clients struggling with substance abuse. Findings from 10 participants revealed that hope inspiration occurred in 3 phases and consisted of several categories of hope-inspiring processes. Implications for counseling practice, counselor education, and research are…

Diversity of Students' Beliefs about Biological Evolution

ERIC Educational Resources Information Center

Clores, Michael A.; Limjap, Auxencia A.

2006-01-01

The purpose of this study was to determine the beliefs about biological evolution held by college freshman students in one Catholic university in the Philippines. After 4 weeks of constructivist-inspired instruction, interviews and journal entries revealed that the students have diverse beliefs about the theory of evolution. They posited…

Inspired Biological Engineering: Detection and Production of Polarized Light by Animals

DTIC Science & Technology

2009-05-26

polarizers in some mantis shrimps, where the animals’ cuticles combine dichroic polarizers based on the cuticular carotenoid, astaxanthin , with a... astaxanthin in animals - the first demonstration of a pigment-based polarizer intended for signaling in any biological system. We are preparing a paper for

An Approach for Calculating Land Valuation by Using Inspire Data Models

NASA Astrophysics Data System (ADS)

Aydinoglu, A. C.; Bovkir, R.

2017-11-01

Land valuation is a highly important concept for societies and governments have always emphasis on the process especially for taxation, expropriation, market capitalization and economic activity purposes. To success an interoperable and standardised land valuation, INSPIRE data models can be very practical and effective. If data used in land valuation process produced in compliance with INSPIRE specifications, a reliable and effective land valuation process can be performed. In this study, possibility of the performing land valuation process with using the INSPIRE data models was analysed and with the help of Geographic Information Systems (GIS) a case study in Pendik was implemented. For this purpose, firstly data analysis and gathering was performed. After, different data structures were transformed according to the INSPIRE data model requirements. For each data set necessary ETL (Extract-Transform-Load) tools were produced and all data transformed according to the target data requirements. With the availability and practicability of spatial analysis tools of GIS software, land valuation calculations were performed for study area.

Photosystem Inspired Peptide Hybrid Catalysts

DTIC Science & Technology

2017-06-07

greenhouse gas , CO2 also can be an abundant C1 feedstock. Inspired by biological enzyme system, especially dehydrogenase catalyzing CO2 reduction...utilizing a potentiostat and converted to the RHE reference scale. Typically, before the electrolysis, the catholyte was saturated with CO2 gas for at...least 15 min to satisfy the saturation of pH. When the passed charge runs to 5 C, the electrolysis was stopped. Then, gas products were sampled using a

Diverted organic synthesis (DOS): accessing a new, natural product inspired, neurotrophically active scaffold through an intramolecular Pauson-Khand reaction.

PubMed

Mehta, Goverdhan; Samineni, Ramesh; Srihari, Pabbaraja; Reddy, R Gajendra; Chakravarty, Sumana

2012-09-14

Drawing inspiration from the impressive neurotrophic activity exhibited by the natural product paecilomycine A, we have designed a new natural product-like scaffold employing an intramolecular Pauson-Khand reaction. Several compounds based on the new designer scaffold exhibited promising neurotrophic activity and are worthy of further biological evaluation. Our findings also highlight the importance of a DOS strategy in creating useful therapeutical leads.

The two-process theory of face processing: modifications based on two decades of data from infants and adults.

PubMed

Johnson, Mark H; Senju, Atsushi; Tomalski, Przemyslaw

2015-03-01

Johnson and Morton (1991. Biology and Cognitive Development: The Case of Face Recognition. Blackwell, Oxford) used Gabriel Horn's work on the filial imprinting model to inspire a two-process theory of the development of face processing in humans. In this paper we review evidence accrued over the past two decades from infants and adults, and from other primates, that informs this two-process model. While work with newborns and infants has been broadly consistent with predictions from the model, further refinements and questions have been raised. With regard to adults, we discuss more recent evidence on the extension of the model to eye contact detection, and to subcortical face processing, reviewing functional imaging and patient studies. We conclude with discussion of outstanding caveats and future directions of research in this field. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling biology with HDL languages: a first step toward a genetic design automation tool inspired from microelectronics.

PubMed

Gendrault, Yves; Madec, Morgan; Lallement, Christophe; Haiech, Jacques

2014-04-01

Nowadays, synthetic biology is a hot research topic. Each day, progresses are made to improve the complexity of artificial biological functions in order to tend to complex biodevices and biosystems. Up to now, these systems are handmade by bioengineers, which require strong technical skills and leads to nonreusable development. Besides, scientific fields that share the same design approach, such as microelectronics, have already overcome several issues and designers succeed in building extremely complex systems with many evolved functions. On the other hand, in systems engineering and more specifically in microelectronics, the development of the domain has been promoted by both the improvement of technological processes and electronic design automation tools. The work presented in this paper paves the way for the adaptation of microelectronics design tools to synthetic biology. Considering the similarities and differences between the synthetic biology and microelectronics, the milestones of this adaptation are described. The first one concerns the modeling of biological mechanisms. To do so, a new formalism is proposed, based on an extension of the generalized Kirchhoff laws to biology. This way, a description of all biological mechanisms can be made with languages widely used in microelectronics. Our approach is therefore successfully validated on specific examples drawn from the literature.

Sundew-Inspired Adhesive Hydrogels Combined with Adipose-Derived Stem Cells for Wound Healing

PubMed Central

Sun, Leming; Huang, Yujian; Bian, Zehua; Petrosino, Jennifer; Fan, Zhen; Wang, Yongzhong; Park, Ki Ho; Yue, Tao; Schmidt, Michael; Galster, Scott; Ma, Jianjie; Zhu, Hua; Zhang, Mingjun

2016-01-01

The potential to harness the unique physical, chemical, and biological properties of the sundew (Drosera) plant’s adhesive hydrogels has long intrigued researchers searching for novel wound-healing applications. However, the ability to collect sufficient quantities of the sundew plant’s adhesive hydrogels is problematic and has eclipsed their therapeutic promise. Inspired by these natural hydrogels, we asked if sundew-inspired adhesive hydrogels could overcome the drawbacks associated with natural sundew hydrogels and be used in combination with stem-cell-based therapy to enhance wound-healing therapeutics. Using a bioinspired approach, we synthesized adhesive hydrogels comprised of sodium alginate, gum arabic, and calcium ions to mimic the properties of the natural sundew-derived adhesive hydrogels. We then characterized and showed that these sundew-inspired hydrogels promote wound healing through their superior adhesive strength, nanostructure, and resistance to shearing when compared to other hydrogels in vitro. In vivo, sundew-inspired hydrogels promoted a “suturing” effect to wound sites, which was demonstrated by enhanced wound closure following topical application of the hydrogels. In combination with mouse adipose-derived stem cells (ADSCs) and compared to other therapeutic biomaterials, the sundew-inspired hydrogels demonstrated superior wound-healing capabilities. Collectively, our studies show that sundew-inspired hydrogels contain ideal properties that promote wound healing and suggest that sundew-inspired-ADSCs combination therapy is an efficacious approach for treating wounds without eliciting noticeable toxicity or inflammation. PMID:26731614

Sundew-Inspired Adhesive Hydrogels Combined with Adipose-Derived Stem Cells for Wound Healing.

PubMed

Sun, Leming; Huang, Yujian; Bian, Zehua; Petrosino, Jennifer; Fan, Zhen; Wang, Yongzhong; Park, Ki Ho; Yue, Tao; Schmidt, Michael; Galster, Scott; Ma, Jianjie; Zhu, Hua; Zhang, Mingjun

2016-01-27

The potential to harness the unique physical, chemical, and biological properties of the sundew (Drosera) plant's adhesive hydrogels has long intrigued researchers searching for novel wound-healing applications. However, the ability to collect sufficient quantities of the sundew plant's adhesive hydrogels is problematic and has eclipsed their therapeutic promise. Inspired by these natural hydrogels, we asked if sundew-inspired adhesive hydrogels could overcome the drawbacks associated with natural sundew hydrogels and be used in combination with stem-cell-based therapy to enhance wound-healing therapeutics. Using a bioinspired approach, we synthesized adhesive hydrogels comprised of sodium alginate, gum arabic, and calcium ions to mimic the properties of the natural sundew-derived adhesive hydrogels. We then characterized and showed that these sundew-inspired hydrogels promote wound healing through their superior adhesive strength, nanostructure, and resistance to shearing when compared to other hydrogels in vitro. In vivo, sundew-inspired hydrogels promoted a "suturing" effect to wound sites, which was demonstrated by enhanced wound closure following topical application of the hydrogels. In combination with mouse adipose-derived stem cells (ADSCs) and compared to other therapeutic biomaterials, the sundew-inspired hydrogels demonstrated superior wound-healing capabilities. Collectively, our studies show that sundew-inspired hydrogels contain ideal properties that promote wound healing and suggest that sundew-inspired-ADSCs combination therapy is an efficacious approach for treating wounds without eliciting noticeable toxicity or inflammation.

Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?

PubMed Central

Tsotsos, John K.

2017-01-01

Much has been written about how the biological brain might represent and process visual information, and how this might inspire and inform machine vision systems. Indeed, tremendous progress has been made, and especially during the last decade in the latter area. However, a key question seems too often, if not mostly, be ignored. This question is simply: do proposed solutions scale with the reality of the brain's resources? This scaling question applies equally to brain and to machine solutions. A number of papers have examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. This set of components was termed complexity level analysis in Tsotsos (1987) and was proposed as an important complement to Marr's three levels of analysis. This paper revisits that work with the advantage that decades of hindsight can provide. PMID:28848458

Complexity Level Analysis Revisited: What Can 30 Years of Hindsight Tell Us about How the Brain Might Represent Visual Information?

PubMed

Tsotsos, John K

2017-01-01

Much has been written about how the biological brain might represent and process visual information, and how this might inspire and inform machine vision systems. Indeed, tremendous progress has been made, and especially during the last decade in the latter area. However, a key question seems too often, if not mostly, be ignored. This question is simply: do proposed solutions scale with the reality of the brain's resources? This scaling question applies equally to brain and to machine solutions. A number of papers have examined the inherent computational difficulty of visual information processing using theoretical and empirical methods. The main goal of this activity had three components: to understand the deep nature of the computational problem of visual information processing; to discover how well the computational difficulty of vision matches to the fixed resources of biological seeing systems; and, to abstract from the matching exercise the key principles that lead to the observed characteristics of biological visual performance. This set of components was termed complexity level analysis in Tsotsos (1987) and was proposed as an important complement to Marr's three levels of analysis. This paper revisits that work with the advantage that decades of hindsight can provide.

A cascade model of information processing and encoding for retinal prosthesis.

PubMed

Pei, Zhi-Jun; Gao, Guan-Xin; Hao, Bo; Qiao, Qing-Li; Ai, Hui-Jian

2016-04-01

Retinal prosthesis offers a potential treatment for individuals suffering from photoreceptor degeneration diseases. Establishing biological retinal models and simulating how the biological retina convert incoming light signal into spike trains that can be properly decoded by the brain is a key issue. Some retinal models have been presented, ranking from structural models inspired by the layered architecture to functional models originated from a set of specific physiological phenomena. However, Most of these focus on stimulus image compression, edge detection and reconstruction, but do not generate spike trains corresponding to visual image. In this study, based on state-of-the-art retinal physiological mechanism, including effective visual information extraction, static nonlinear rectification of biological systems and neurons Poisson coding, a cascade model of the retina including the out plexiform layer for information processing and the inner plexiform layer for information encoding was brought forward, which integrates both anatomic connections and functional computations of retina. Using MATLAB software, spike trains corresponding to stimulus image were numerically computed by four steps: linear spatiotemporal filtering, static nonlinear rectification, radial sampling and then Poisson spike generation. The simulated results suggested that such a cascade model could recreate visual information processing and encoding functionalities of the retina, which is helpful in developing artificial retina for the retinally blind.

Expanded experimental parameter space of semiflexible polymer assemblies through programmable nanomaterials

NASA Astrophysics Data System (ADS)

Smith, David; Schuldt, Carsten; Lorenz, Jessica; Tschirner, Teresa; Moebius-Winkler, Maximilian; Kaes, Josef; Glaser, Martin; Haendler, Tina; Schnauss, Joerg

2015-03-01

Biologically evolved materials are often used as inspiration in the development of new materials as well as examinations into the underlying physical principles governing their behavior. For instance, the biopolymer constituents of the highly dynamic cellular cytoskeleton such as actin have inspired a deep understanding of soft polymer-based materials. However, the molecular toolbox provided by biological systems has been evolutionarily optimized to carry out the necessary functions of cells, and the inability modify basic properties such as biopolymer stiffness hinders a meticulous examination of parameter space. Using actin as inspiration, we circumvent these limitations using model systems assembled from programmable materials such as DNA. Nanorods with comparable, but controllable dimensions and mechanical properties as actin can be constructed from small sets of specially designed DNA strands. In entangled gels, these allow us to systematically determine the dependence of network mechanical properties on parameters such as persistence length and crosslink strength. At higher concentrations in the presence of local attractive forces, we see a transition to highly-ordered bundled and ``aster'' phases similar to those previously characterized in systems of actin or microtubules.

Bacteriophage lambda: The path from biology to theranostic agent.

PubMed

Catalano, Carlos E

2018-03-13

Viral particles provide an attractive platform for the engineering of semisynthetic therapeutic nanoparticles. They can be modified both genetically and chemically in a defined manner to alter their surface characteristics, for targeting specific cell types, to improve their pharmacokinetic features and to attenuate (or enhance) their antigenicity. These advantages derive from a detailed understanding of virus biology, gleaned from decades of fundamental genetic, biochemical, and structural studies that have provided mechanistic insight into virus assembly pathways. In particular, bacteriophages offer significant advantages as nanoparticle platforms and several have been adapted toward the design and engineering of "designer" nanoparticles for therapeutic and diagnostic (theranostic) applications. The present review focuses on one such virus, bacteriophage lambda; I discuss the biology of lambda, the tools developed to faithfully recapitulate the lambda assembly reactions in vitro and the observations that have led to cooptation of the lambda system for nanoparticle design. This discussion illustrates how a fundamental understanding of virus assembly has allowed the rational design and construction of semisynthetic nanoparticles as potential theranostic agents and illustrates the concept of benchtop to bedside translational research. This article is categorized under: Biology-Inspired Nanomaterials> Protein and Virus-Based Structures Biology-Inspired Nanomaterials> Nucleic Acid-Based Structures. © 2018 Wiley Periodicals, Inc.

Silk materials--a road to sustainable high technology.

PubMed

Tao, Hu; Kaplan, David L; Omenetto, Fiorenzo G

2012-06-05

This review addresses the use of silk protein as a sustainable material in optics and photonics, electronics and optoelectronic applications. These options represent additional developments for this technology platform that compound the broad utility and impact of this material for medical needs that have been recently described in the literature. The favorable properties of the material certainly make a favorable case for the use of silk, yet serve as a broad inspiration to further develop biological foundries for both the synthesis and processing of Nature's materials for technological applications. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Implementation of pulse-coupled neural networks in a CNAPS environment.

PubMed

Kinser, J M; Lindblad, T

1999-01-01

Pulse coupled neural networks (PCNN's) are biologically inspired algorithms very well suited for image/signal preprocessing. While several analog implementations are proposed we suggest a digital implementation in an existing environment, the connected network of adapted processors system (CNAPS). The reason for this is two fold. First, CNAPS is a commercially available chip which has been used for several neural-network implementations. Second, the PCNN is, in almost all applications, a very efficient component of a system requiring subsequent and additional processing. This may include gating, Fourier transforms, neural classifiers, data mining, etc, with or without feedback to the PCNN.

Biologically inspired technologies using artificial muscles

NASA Technical Reports Server (NTRS)

Bar-Cohen, Yoseph

2005-01-01

One of the newest fields of biomimetics is the electroactive polymers (EAP) that are also known as artificial muscles. To take advantage of these materials, efforts are made worldwide to establish a strong infrastructure addressing the need for comprehensive analytical modeling of their response mechanism and develop effective processing and characterization techniques. The field is still in its emerging state and robust materials are still not readily available however in recent years significant progress has been made and commercial products have already started to appear. This paper covers the current state of- the-art and challenges to making artificial muscles and their potential biomimetic applications.

Development of the Biological Experimental Design Concept Inventory (BEDCI)

ERIC Educational Resources Information Center

Deane, Thomas; Nomme, Kathy; Jeffery, Erica; Pollock, Carol; Birol, Gulnur

2014-01-01

Interest in student conception of experimentation inspired the development of a fully validated 14-question inventory on experimental design in biology (BEDCI) by following established best practices in concept inventory (CI) design. This CI can be used to diagnose specific examples of non-expert-like thinking in students and to evaluate the…

Biologically inspired robots as artificial inspectors

NASA Astrophysics Data System (ADS)

Bar-Cohen, Yoseph

2002-06-01

Imagine an inspector conducting an NDE on an aircraft where you notice something is different about him - he is not real but rather he is a robot. Your first reaction would probably be to say 'it's unbelievable but he looks real' just as you would react to an artificial flower that is a good imitation. This science fiction scenario could become a reality at the trend in the development of biologically inspired technologies, and terms like artificial intelligence, artificial muscles, artificial vision and numerous others are increasingly becoming common engineering tools. For many years, the trend has been to automate processes in order to increase the efficiency of performing redundant tasks where various systems have been developed to deal with specific production line requirements. Realizing that some parts are too complex or delicate to handle in small quantities with a simple automatic system, robotic mechanisms were developed. Aircraft inspection has benefitted from this evolving technology where manipulators and crawlers are developed for rapid and reliable inspection. Advancement in robotics towards making them autonomous and possibly look like human, can potentially address the need to inspect structures that are beyond the capability of today's technology with configuration that are not predetermined. The operation of these robots may take place at harsh or hazardous environments that are too dangerous for human presence. Making such robots is becoming increasingly feasible and in this paper the state of the art will be reviewed.

Acute ethanol administration affects zebrafish preference for a biologically inspired robot.

PubMed

Spinello, Chiara; Macrì, Simone; Porfiri, Maurizio

2013-08-01

Preclinical animal models constitute a cornerstone against which the reward processes involved in drug addiction are often studied and dissected. While rodents have traditionally represented the species of choice, a growing body of literature indicates that zebrafish are emerging as a valuable model organism. Specifically, several studies demonstrate that the effects of ethanol at the level of emotional- and cognitive-related domains can be reliably investigated using zebrafish. The rapidly evolving nature of these efforts allows substantial room for the development of novel experimental paradigms suited to this freshwater species. The field of ethorobotics may prove particularly beneficial, due to its ability to convey fully controllable and easily reproducible experimental tools. In this study, we addressed the possibility of using a biologically inspired robot to investigate the emotionally related properties of ethanol in a preference task in zebrafish. To this aim, we evaluated wild-type zebrafish preference toward a robotic stimulus and addressed whether ethanol administration (0.25% and 1.00% ethanol/water concentration) may alter such preferences. In accordance with our previous studies, we observed that zebrafish exhibit a natural attraction toward the robot. Additionally, in agreement with our predictions, we showed that ethanol administration abolishes such preferences. This work is the first to demonstrate that robotic stimuli can be used in zebrafish to investigate the reward-related properties of alcohol. Copyright © 2013 Elsevier Inc. All rights reserved.

Biohybrid Control of General Linear Systems Using the Adaptive Filter Model of Cerebellum.

PubMed

Wilson, Emma D; Assaf, Tareq; Pearson, Martin J; Rossiter, Jonathan M; Dean, Paul; Anderson, Sean R; Porrill, John

2015-01-01

The adaptive filter model of the cerebellar microcircuit has been successfully applied to biological motor control problems, such as the vestibulo-ocular reflex (VOR), and to sensory processing problems, such as the adaptive cancelation of reafferent noise. It has also been successfully applied to problems in robotics, such as adaptive camera stabilization and sensor noise cancelation. In previous applications to inverse control problems, the algorithm was applied to the velocity control of a plant dominated by viscous and elastic elements. Naive application of the adaptive filter model to the displacement (as opposed to velocity) control of this plant results in unstable learning and control. To be more generally useful in engineering problems, it is essential to remove this restriction to enable the stable control of plants of any order. We address this problem here by developing a biohybrid model reference adaptive control (MRAC) scheme, which stabilizes the control algorithm for strictly proper plants. We evaluate the performance of this novel cerebellar-inspired algorithm with MRAC scheme in the experimental control of a dielectric electroactive polymer, a class of artificial muscle. The results show that the augmented cerebellar algorithm is able to accurately control the displacement response of the artificial muscle. The proposed solution not only greatly extends the practical applicability of the cerebellar-inspired algorithm, but may also shed light on cerebellar involvement in a wider range of biological control tasks.

Prospects of nanoparticle-DNA binding and its implications in medical biotechnology.

PubMed

An, Hongjie; Jin, Bo

2012-01-01

Bio-nanotechnology is a new interdisciplinary R&D area that integrates engineering and physical science with biology through the development of multifunctional devices and systems, focusing biology inspired processes or their applications, in particular in medical biotechnology. DNA based nanotechnology, in many ways, has been one of the most intensively studied fields in recent years that involves the use and the creation of bio-inspired materials and their technologies for highly selective biosensing, nanoarchitecture engineering and nanoelectronics. Increasing researches have been offered to a fundamental understanding how the interactions between the nanoparticles and DNA molecules could alter DNA molecular structure and its biochemical activities. This minor review describes the mechanisms of the nanoparticle-DNA binding and molecular interactions. We present recent discoveries and research progresses how the nanoparticle-DNA binding could vary DNA molecular structure, DNA detection, and gene therapy. We report a few case studies associated with the application of the nanoparticle-DNA binding devices in medical detection and biotechnology. The potential impacts of the nanoparticles via DNA binding on toxicity of the microorganisms are briefly discussed. The nanoparticle-DNA interactions and their impact on molecular and microbial functionalities have only drown attention in recent a few years. The information presented in this review can provide useful references for further studies on biomedical science and technology. Copyright © 2012 Elsevier Inc. All rights reserved.

The interplay of biology and technology

PubMed Central

Fields, Stanley

2001-01-01

Technologies for biological research arise in multiple ways—through serendipity, through inspired insights, and through incremental advances—and they are tightly coupled to progress in engineering. Underlying the complex dynamics of technology and biology are the different motivations of those who work in the two realms. Consideration of how methodologies emerge has implications for the planning of interdisciplinary centers and the training of the next generation of scientists. PMID:11517346

The TRP channel superfamily: insights into how structure, protein-lipid interactions and localization influence function.

PubMed

Reaves, B J; Wolstenholme, A J

2007-02-01

TRP (transient receptor potential) cationic channels are key molecules that are involved in a variety of diverse biological processes ranging from fertility to osmosensation and nociception. Increasing our knowledge of these channels will help us to understand a range of physiological and pathogenic processes, as well as highlighting potential therapeutic drug targets. The founding members of the TRP family, Drosophila TRP and TRPL (TRP-like) proteins, were identified within the last two decades and there has been a subsequent explosion in the number and type of TRP channel described. Although information is accumulating as to the function of some of the TRP channels, the activation and inactivation mechanisms, structure, and interacting proteins of many, if not most, are awaiting elucidation. The Cell and Molecular Biology of TRP Channels Meeting held at the University of Bath included speakers working on a number of the different subfamilies of TRP channels and provided a basis for highlighting both similarities and differences between these groups. As the TRP channels mediate diverse functions, this meeting also brought together an audience with wide-ranging research interests, including biochemistry, cell biology, physiology and neuroscience, and inspired lively discussion on the issues reviewed herein.

Computer Simulation of Developmental Processes and ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Citrate chemistry and biology for biomaterials design.

PubMed

Ma, Chuying; Gerhard, Ethan; Lu, Di; Yang, Jian

2018-05-04

Leveraging the multifunctional nature of citrate in chemistry and inspired by its important role in biological tissues, a class of highly versatile and functional citrate-based materials (CBBs) has been developed via facile and cost-effective polycondensation. CBBs exhibiting tunable mechanical properties and degradation rates, together with excellent biocompatibility and processability, have been successfully applied in vitro and in vivo for applications ranging from soft to hard tissue regeneration, as well as for nanomedicine designs. We summarize in the review, chemistry considerations for CBBs design to tune polymer properties and to introduce functionality with a focus on the most recent advances, biological functions of citrate in native tissues with the new notion of degradation products as cell modulator highlighted, and the applications of CBBs in wound healing, nanomedicine, orthopedic, cardiovascular, nerve and bladder tissue engineering. Given the expansive evidence for citrate's potential in biology and biomaterial science outlined in this review, it is expected that citrate based materials will continue to play an important role in regenerative engineering. Copyright © 2018 Elsevier Ltd. All rights reserved.

Vertically integrated photonic multichip module architecture for vision applications

NASA Astrophysics Data System (ADS)

Tanguay, Armand R., Jr.; Jenkins, B. Keith; von der Malsburg, Christoph; Mel, Bartlett; Holt, Gary; O'Brien, John D.; Biederman, Irving; Madhukar, Anupam; Nasiatka, Patrick; Huang, Yunsong

2000-05-01

The development of a truly smart camera, with inherent capability for low latency semi-autonomous object recognition, tracking, and optimal image capture, has remained an elusive goal notwithstanding tremendous advances in the processing power afforded by VLSI technologies. These features are essential for a number of emerging multimedia- based applications, including enhanced augmented reality systems. Recent advances in understanding of the mechanisms of biological vision systems, together with similar advances in hybrid electronic/photonic packaging technology, offer the possibility of artificial biologically-inspired vision systems with significantly different, yet complementary, strengths and weaknesses. We describe herein several system implementation architectures based on spatial and temporal integration techniques within a multilayered structure, as well as the corresponding hardware implementation of these architectures based on the hybrid vertical integration of multiple silicon VLSI vision chips by means of dense 3D photonic interconnections.

Bioinspired Design: Magnetic Freeze Casting

NASA Astrophysics Data System (ADS)

Porter, Michael Martin

Nature is the ultimate experimental scientist, having billions of years of evolution to design, test, and adapt a variety of multifunctional systems for a plethora of diverse applications. Next-generation materials that draw inspiration from the structure-property-function relationships of natural biological materials have led to many high-performance structural materials with hybrid, hierarchical architectures that fit form to function. In this dissertation, a novel materials processing method, magnetic freeze casting, is introduced to develop porous scaffolds and hybrid composites with micro-architectures that emulate bone, abalone nacre, and other hard biological materials. This method uses ice as a template to form ceramic-based materials with continuously, interconnected microstructures and magnetic fields to control the alignment of these structures in multiple directions. The resulting materials have anisotropic properties with enhanced mechanical performance that have potential applications as bone implants or lightweight structural composites, among others.

Peptide-chaperone-directed transdermal protein delivery requires energy.

PubMed

Ruan, Renquan; Jin, Peipei; Zhang, Li; Wang, Changli; Chen, Chuanjun; Ding, Weiping; Wen, Longping

2014-11-03

The biologically inspired transdermal enhanced peptide TD1 has been discovered to specifically facilitate transdermal delivery of biological macromolecules. However, the biological behavior of TD1 has not been fully defined. In this study, we find that energy is required for the TD1-mediated transdermal protein delivery through rat and human skins. Our results show that the permeation activity of TD1-hEGF, a fusion protein composed of human epidermal growth factor (hEGF) and the TD1 sequence connected with a glycine-serine linker (GGGGS), can be inhibited by the energy inhibitor, rotenone or oligomycin. In addition, adenosine triphosphate (ATP), the essential energetic molecule in organic systems, can effectively facilitate the TD1 directed permeation of the protein-based drug into the skin in a dose-dependent fashion. Our results here demonstrate a novel energy-dependent permeation process during the TD1-mediated transdermal protein delivery that could be valuable for the future development of promising new transdermal drugs.

Thermal Quantum Correlations in Photosynthetic Light-Harvesting Complexes

NASA Astrophysics Data System (ADS)

Mahdian, M.; Kouhestani, H.

2015-08-01

Photosynthesis is one of the ancient biological processes, playing crucial role converting solar energy to cellular usable currency. Environmental factors and external perturbations has forced nature to choose systems with the highest efficiency and performance. Recent theoretical and experimental studies have proved the presence of quantum properties in biological systems. Energy transfer systems like Fenna-Matthews-Olson (FMO) complex shows quantum entanglement between sites of Bacteriophylla molecules in protein environment and presence of decoherence. Complex biological systems implement more truthful mechanisms beside chemical-quantum correlations to assure system's efficiency. In this study we investigate thermal quantum correlations in FMO protein of the photosynthetic apparatus of green sulfur bacteria by quantum discord measure. The results confirmed existence of remarkable quantum correlations of of BChla pigments in room temperature. This results approve involvement of quantum correlation mechanisms for information storage and retention in living organisms that could be useful for further evolutionary studies. Inspired idea of this study is potentially interesting to practice by the same procedure in genetic data transfer mechanisms.

Design and Printing Strategies in 3D Bioprinting of Cell-Hydrogels: A Review.

PubMed

Lee, Jia Min; Yeong, Wai Yee

2016-11-01

Bioprinting is an emerging technology that allows the assembling of both living and non-living biological materials into an ideal complex layout for further tissue maturation. Bioprinting aims to produce engineered tissue or organ in a mechanized, organized, and optimized manner. Various biomaterials and techniques have been utilized to bioprint biological constructs in different shapes, sizes and resolutions. There is a need to systematically discuss and analyze the reported strategies employed to fabricate these constructs. We identified and discussed important design factors in bioprinting, namely shape and resolution, material heterogeneity, and cellular-material remodeling dynamism. Each design factors are represented by the corresponding process capabilities and printing parameters. The process-design map will inspire future biomaterials research in these aspects. Design considerations such as data processing, bio-ink formulation and process selection are discussed. Various printing and crosslinking strategies, with relevant applications, are also systematically reviewed. We categorized them into 5 general bioprinting strategies, including direct bioprinting, in-process crosslinking, post-process crosslinking, indirect bioprinting and hybrid bioprinting. The opportunities and outlook in 3D bioprinting are highlighted. This review article will serve as a framework to advance computer-aided design in bioprinting technologies. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pulse-coupled neural network implementation in FPGA

NASA Astrophysics Data System (ADS)

Waldemark, Joakim T. A.; Lindblad, Thomas; Lindsey, Clark S.; Waldemark, Karina E.; Oberg, Johnny; Millberg, Mikael

1998-03-01

Pulse Coupled Neural Networks (PCNN) are biologically inspired neural networks, mainly based on studies of the visual cortex of small mammals. The PCNN is very well suited as a pre- processor for image processing, particularly in connection with object isolation, edge detection and segmentation. Several implementations of PCNN on von Neumann computers, as well as on special parallel processing hardware devices (e.g. SIMD), exist. However, these implementations are not as flexible as required for many applications. Here we present an implementation in Field Programmable Gate Arrays (FPGA) together with a performance analysis. The FPGA hardware implementation may be considered a platform for further, extended implementations and easily expanded into various applications. The latter may include advanced on-line image analysis with close to real-time performance.

Nanomaterial interactions with biomembranes: Bridging the gap between soft matter models and biological context.

PubMed

Werner, Marco; Auth, Thorsten; Beales, Paul A; Fleury, Jean Baptiste; Höök, Fredrik; Kress, Holger; Van Lehn, Reid C; Müller, Marcus; Petrov, Eugene P; Sarkisov, Lev; Sommer, Jens-Uwe; Baulin, Vladimir A

2018-04-03

Synthetic polymers, nanoparticles, and carbon-based materials have great potential in applications including drug delivery, gene transfection, in vitro and in vivo imaging, and the alteration of biological function. Nature and humans use different design strategies to create nanomaterials: biological objects have emerged from billions of years of evolution and from adaptation to their environment resulting in high levels of structural complexity; in contrast, synthetic nanomaterials result from minimalistic but controlled design options limited by the authors' current understanding of the biological world. This conceptual mismatch makes it challenging to create synthetic nanomaterials that possess desired functions in biological media. In many biologically relevant applications, nanomaterials must enter the cell interior to perform their functions. An essential transport barrier is the cell-protecting plasma membrane and hence the understanding of its interaction with nanomaterials is a fundamental task in biotechnology. The authors present open questions in the field of nanomaterial interactions with biological membranes, including: how physical mechanisms and molecular forces acting at the nanoscale restrict or inspire design options; which levels of complexity to include next in computational and experimental models to describe how nanomaterials cross barriers via passive or active processes; and how the biological media and protein corona interfere with nanomaterial functionality. In this Perspective, the authors address these questions with the aim of offering guidelines for the development of next-generation nanomaterials that function in biological media.

Synthetic biology between challenges and risks: suggestions for a model of governance and a regulatory framework, based on fundamental rights.

PubMed

Colussi, Ilaria Anna

2013-01-01

This paper deals with the emerging synthetic biology, its challenges and risks, and tries to design a model for the governance and regulation of the field. The model is called of "prudent vigilance" (inspired by the report about synthetic biology, drafted by the U.S. Presidential Commission on Bioethics, 2010), and it entails (a) an ongoing and periodically revised process of assessment and management of all the risks and concerns, and (b) the adoption of policies - taken through "hard law" and "soft law" sources - that are based on the principle of proportionality (among benefits and risks), on a reasonable balancing between different interests and rights at stake, and are oriented by a constitutional frame, which is represented by the protection of fundamental human rights emerging in the field of synthetic biology (right to life, right to health, dignity, freedom of scientific research, right to environment). After the theoretical explanation of the model, its operability is "checked", by considering its application with reference to only one specific risk brought up by synthetic biology - biosecurity risk, i.e. the risk of bioterrorism.

Multisensory architectures for action-oriented perception

NASA Astrophysics Data System (ADS)

Alba, L.; Arena, P.; De Fiore, S.; Listán, J.; Patané, L.; Salem, A.; Scordino, G.; Webb, B.

2007-05-01

In order to solve the navigation problem of a mobile robot in an unstructured environment a versatile sensory system and efficient locomotion control algorithms are necessary. In this paper an innovative sensory system for action-oriented perception applied to a legged robot is presented. An important problem we address is how to utilize a large variety and number of sensors, while having systems that can operate in real time. Our solution is to use sensory systems that incorporate analog and parallel processing, inspired by biological systems, to reduce the required data exchange with the motor control layer. In particular, as concerns the visual system, we use the Eye-RIS v1.1 board made by Anafocus, which is based on a fully parallel mixed-signal array sensor-processor chip. The hearing sensor is inspired by the cricket hearing system and allows efficient localization of a specific sound source with a very simple analog circuit. Our robot utilizes additional sensors for touch, posture, load, distance, and heading, and thus requires customized and parallel processing for concurrent acquisition. Therefore a Field Programmable Gate Array (FPGA) based hardware was used to manage the multi-sensory acquisition and processing. This choice was made because FPGAs permit the implementation of customized digital logic blocks that can operate in parallel allowing the sensors to be driven simultaneously. With this approach the multi-sensory architecture proposed can achieve real time capabilities.

An Insect Eye Inspired Miniaturized Multi-Camera System for Endoscopic Imaging.

PubMed

Cogal, Omer; Leblebici, Yusuf

2017-02-01

In this work, we present a miniaturized high definition vision system inspired by insect eyes, with a distributed illumination method, which can work in dark environments for proximity imaging applications such as endoscopy. Our approach is based on modeling biological systems with off-the-shelf miniaturized cameras combined with digital circuit design for real time image processing. We built a 5 mm radius hemispherical compound eye, imaging a 180 ° ×180 ° degrees field of view while providing more than 1.1 megapixels (emulated ommatidias) as real-time video with an inter-ommatidial angle ∆ϕ = 0.5 ° at 18 mm radial distance. We made an FPGA implementation of the image processing system which is capable of generating 25 fps video with 1080 × 1080 pixel resolution at a 120 MHz processing clock frequency. When compared to similar size insect eye mimicking systems in literature, the system proposed in this paper features 1000 × resolution increase. To the best of our knowledge, this is the first time that a compound eye with built-in illumination idea is reported. We are offering our miniaturized imaging system for endoscopic applications like colonoscopy or laparoscopic surgery where there is a need for large field of view high definition imagery. For that purpose we tested our system inside a human colon model. We also present the resulting images and videos from the human colon model in this paper.

Cooperative Mission Concepts Using Biomorphic Explorers

NASA Technical Reports Server (NTRS)

Thakoor, S.; Miralles, C.; Martin, T.; Kahn, R.; Zurek, R.

2000-01-01

Inspired by the immense variety of naturally curious explorers (insects, animals, and birds), their wellintegrated biological sensor-processor suites, efficiently packaged in compact but highly dexterous forms, and their complex, intriguing, cooperative behavior, this paper focuses on "Biomorphic Explorers", their defination/classification, their designs, and presents planetary exploration scenarios based on the designs. Judicious blend of bio-inspired concepts and recent advances in micro-air vehicles, microsensors, microinstruments, MEMS, and microprocessors clearly suggests that the time of small, dedicated, low cost explorers that capture some of the key features of biological systems has arrived. Just as even small insects like ants, termites, honey bees etc working cooperatively in colonies can achieve big tasks, the biomorphic explorers hold the potential for obtaining science in-accessible by current large singular exploration platforms.

Mobile robots for localizing gas emission sources on landfill sites: is bio-inspiration the way to go?

PubMed

Hernandez Bennetts, Victor; Lilienthal, Achim J; Neumann, Patrick P; Trincavelli, Marco

2011-01-01

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully "translated" into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms.

Mobile Robots for Localizing Gas Emission Sources on Landfill Sites: Is Bio-Inspiration the Way to Go?

PubMed Central

Hernandez Bennetts, Victor; Lilienthal, Achim J.; Neumann, Patrick P.; Trincavelli, Marco

2011-01-01

Roboticists often take inspiration from animals for designing sensors, actuators, or algorithms that control the behavior of robots. Bio-inspiration is motivated with the uncanny ability of animals to solve complex tasks like recognizing and manipulating objects, walking on uneven terrains, or navigating to the source of an odor plume. In particular the task of tracking an odor plume up to its source has nearly exclusively been addressed using biologically inspired algorithms and robots have been developed, for example, to mimic the behavior of moths, dung beetles, or lobsters. In this paper we argue that biomimetic approaches to gas source localization are of limited use, primarily because animals differ fundamentally in their sensing and actuation capabilities from state-of-the-art gas-sensitive mobile robots. To support our claim, we compare actuation and chemical sensing available to mobile robots to the corresponding capabilities of moths. We further characterize airflow and chemosensor measurements obtained with three different robot platforms (two wheeled robots and one flying micro-drone) in four prototypical environments and show that the assumption of a constant and unidirectional airflow, which is the basis of many gas source localization approaches, is usually far from being valid. This analysis should help to identify how underlying principles, which govern the gas source tracking behavior of animals, can be usefully “translated” into gas source localization approaches that fully take into account the capabilities of mobile robots. We also describe the requirements for a reference application, monitoring of gas emissions at landfill sites with mobile robots, and discuss an engineered gas source localization approach based on statistics as an alternative to biologically inspired algorithms. PMID:22319493

Indole diterpenoid natural products as the inspiration for new synthetic methods and strategies.

PubMed

Corsello, Michael A; Kim, Junyong; Garg, Neil K

2017-09-01

Indole terpenoids comprise a large class of natural products with diverse structural topologies and a broad range of biological activities. Accordingly, indole terpenoids have and continue to serve as attractive targets for chemical synthesis. Many synthetic efforts over the past few years have focused on a subclass of this family, the indole diterpenoids. This minireview showcases the role indole diterpenoids have played in inspiring the recent development of clever synthetic strategies, and new chemical reactions.

Additive Layer Manufacturing of Biologically Inspired Short Fibre Reinforced Composites

DTIC Science & Technology

2014-03-01

combination.   It   is   frequently   the   determining   factor   for   the   type   of   fracture   mechanism  observed   [9...Thin Solid Films, 1998. 334(1–2): p. 60-64. 56. Cannas, A., Fracture Mechanics and Failure Analysis of Hollow Shaped Fibre GFRP Composites, in ACCIS...architectures inspired by nature for improving the mechanical and functional properties of engineered materials. The study was advanced on two fronts: (1

Bio-inspired smart single asymmetric hourglass nanochannels for continuous shape and ion transport control.

PubMed

Zhang, Huacheng; Hou, Xu; Yang, Zhe; Yan, Dadong; Li, Lin; Tian, Ye; Wang, Huanting; Jiang, Lei

2015-02-18

Inspired by biological asymmetric ion channels, new shape-tunable and pH-responsive asymmetric hourglass single nanochannel systems demonstrate unique ion-transport properties. It is found that the change in shape and pH cooperatively control the ion transport within the nanochannel ranging from asymmetric shape with asymmetric ion transport, to asymmetric shape with symmetric ion transport and symmetric shape with symmetric ion transport. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

DOE PAGES

James, Conrad D.; Aimone, James B.; Miner, Nadine E.; ...

2017-01-04

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classesmore » such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.« less

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

James, Conrad D.; Aimone, James B.; Miner, Nadine E.

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classesmore » such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.« less

Implementation of a Project-Based Molecular Biology Laboratory Emphasizing Protein Structure-Function Relationships in a Large Introductory Biology Laboratory Course

ERIC Educational Resources Information Center

Treacy, Daniel J.; Sankaran, Saumya M.; Gordon-Messer, Susannah; Saly, Danielle; Miller, Rebecca; Isaac, R. Stefan; Kosinski-Collins, Melissa S.

2011-01-01

In introductory laboratory courses, many universities are turning from traditional laboratories with predictable outcomes to inquiry-inspired, project-based laboratory curricula. In these labs, students are allowed to design at least some portion of their own experiment and interpret new, undiscovered data. We have redesigned the introductory…

Poly(lactic-co-glycolic acid) devices: Production and applications for sustained protein delivery.

PubMed

Lee, Parker W; Pokorski, Jonathan K

2018-03-13

Injectable or implantable poly(lactic-co-glycolic acid) (PLGA) devices for the sustained delivery of proteins have been widely studied and utilized to overcome the necessity of repeated administrations for therapeutic proteins due to poor pharmacokinetic profiles of macromolecular therapies. These devices can come in the form of microparticles, implants, or patches depending on the disease state and route of administration. Furthermore, the release rate can be tuned from weeks to months by controlling the polymer composition, geometry of the device, or introducing additives during device fabrication. Slow-release devices have become a very powerful tool for modern medicine. Production of these devices has initially focused on emulsion-based methods, relying on phase separation to encapsulate proteins within polymeric microparticles. Process parameters and the effect of additives have been thoroughly researched to ensure protein stability during device manufacturing and to control the release profile. Continuous fluidic production methods have also been utilized to create protein-laden PLGA devices through spray drying and electrospray production. Thermal processing of PLGA with solid proteins is an emerging production method that allows for continuous, high-throughput manufacturing of PLGA/protein devices. Overall, polymeric materials for protein delivery remain an emerging field of research for the creation of single administration treatments for a wide variety of disease. This review describes, in detail, methods to make PLGA devices, comparing traditional emulsion-based methods to emerging methods to fabricate protein-laden devices. This article is categorized under: Biology-Inspired Nanomaterials > Protein and Virus-Based Structures Implantable Materials and Surgical Technologies > Nanomaterials and Implants Biology-Inspired Nanomaterials > Peptide-Based Structures. © 2018 Wiley Periodicals, Inc.

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space

PubMed Central

Li, Kan; Príncipe, José C.

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime. PMID:29666568

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space.

PubMed

Li, Kan; Príncipe, José C

2018-01-01

This paper presents a novel real-time dynamic framework for quantifying time-series structure in spoken words using spikes. Audio signals are converted into multi-channel spike trains using a biologically-inspired leaky integrate-and-fire (LIF) spike generator. These spike trains are mapped into a function space of infinite dimension, i.e., a Reproducing Kernel Hilbert Space (RKHS) using point-process kernels, where a state-space model learns the dynamics of the multidimensional spike input using gradient descent learning. This kernelized recurrent system is very parsimonious and achieves the necessary memory depth via feedback of its internal states when trained discriminatively, utilizing the full context of the phoneme sequence. A main advantage of modeling nonlinear dynamics using state-space trajectories in the RKHS is that it imposes no restriction on the relationship between the exogenous input and its internal state. We are free to choose the input representation with an appropriate kernel, and changing the kernel does not impact the system nor the learning algorithm. Moreover, we show that this novel framework can outperform both traditional hidden Markov model (HMM) speech processing as well as neuromorphic implementations based on spiking neural network (SNN), yielding accurate and ultra-low power word spotters. As a proof of concept, we demonstrate its capabilities using the benchmark TI-46 digit corpus for isolated-word automatic speech recognition (ASR) or keyword spotting. Compared to HMM using Mel-frequency cepstral coefficient (MFCC) front-end without time-derivatives, our MFCC-KAARMA offered improved performance. For spike-train front-end, spike-KAARMA also outperformed state-of-the-art SNN solutions. Furthermore, compared to MFCCs, spike trains provided enhanced noise robustness in certain low signal-to-noise ratio (SNR) regime.

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

NASA Astrophysics Data System (ADS)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Modeling and Advanced Control for Sustainable Process ...

EPA Pesticide Factsheets

This book chapter introduces a novel process systems engineering framework that integrates process control with sustainability assessment tools for the simultaneous evaluation and optimization of process operations. The implemented control strategy consists of a biologically-inspired, multi-agent-based method. The sustainability and performance assessment of process operating points is carried out using the U.S. E.P.A.’s GREENSCOPE assessment tool that provides scores for the selected economic, material management, environmental and energy indicators. The indicator results supply information on whether the implementation of the controller is moving the process towards a more sustainable operation. The effectiveness of the proposed framework is illustrated through a case study of a continuous bioethanol fermentation process whose dynamics are characterized by steady-state multiplicity and oscillatory behavior. This book chapter contribution demonstrates the application of novel process control strategies for sustainability by increasing material management, energy efficiency, and pollution prevention, as needed for SHC Sustainable Uses of Wastes and Materials Management.

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

Characterization of selected elementary motion detector cells to image primitives.

PubMed

Benson, Leslie A; Barrett, Steven F; Wright, Cameron H G

2008-01-01

Developing a visual sensing system, complete with motion processing hardware and software would have many applications to current technology. It could be mounted on many autonomous vehicles to provide information about the navigational environment, as well as obstacle avoidance features. Incorporating the motion processing capabilities into the sensor requires a new approach to the algorithm implementation. This research, and that of many others, have turned to nature for inspiration. Elementary motion detector (EMD) cells are involved in a biological preprocessing network to provide information to the motion processing lobes of the house degrees y Musca domestica. This paper describes the response of the photoreceptor inputs to the EMDs. The inputs to the EMD components are tested as they are stimulated with varying image primitives. This is the first of many steps in characterizing the EMD response to image primitives.

Human development VIII: a theory of "deep" quantum chemistry and cell consciousness: quantum chemistry controls genes and biochemistry to give cells and higher organisms consciousness and complex behavior.

PubMed

Ventegodt, Søren; Hermansen, Tyge Dahl; Flensborg-Madsen, Trine; Nielsen, Maj Lyck; Merrick, Joav

2006-11-14

Deep quantum chemistry is a theory of deeply structured quantum fields carrying the biological information of the cell, making it able to remember, intend, represent the inner and outer world for comparison, understand what it "sees", and make choices on its structure, form, behavior and division. We suggest that deep quantum chemistry gives the cell consciousness and all the qualities and abilities related to consciousness. We use geometric symbolism, which is a pre-mathematical and philosophical approach to problems that cannot yet be handled mathematically. Using Occam's razor we have started with the simplest model that works; we presume this to be a many-dimensional, spiral fractal. We suggest that all the electrons of the large biological molecules' orbitals make one huge "cell-orbital", which is structured according to the spiral fractal nature of quantum fields. Consciousness of single cells, multi cellular structures as e.g. organs, multi-cellular organisms and multi-individual colonies (like ants) and human societies can thus be explained by deep quantum chemistry. When biochemical activity is strictly controlled by the quantum-mechanical super-orbital of the cell, this orbital can deliver energetic quanta as biological information, distributed through many fractal levels of the cell to guide form and behavior of an individual single or a multi-cellular organism. The top level of information is the consciousness of the cell or organism, which controls all the biochemical processes. By this speculative work inspired by Penrose and Hameroff we hope to inspire other researchers to formulate more strict and mathematically correct hypothesis on the complex and coherence nature of matter, life and consciousness.

Human Development VIII: A Theory of “Deep” Quantum Chemistry and Cell Consciousness: Quantum Chemistry Controls Genes and Biochemistry to Give Cells and Higher Organisms Consciousness and Complex Behavior

PubMed Central

Ventegodt, Søren; Hermansen, Tyge Dahl; Flensborg-Madsen, Trine; Nielsen, Maj Lyck; Merrick, Joav

2006-01-01

Deep quantum chemistry is a theory of deeply structured quantum fields carrying the biological information of the cell, making it able to remember, intend, represent the inner and outer world for comparison, understand what it “sees”, and make choices on its structure, form, behavior and division. We suggest that deep quantum chemistry gives the cell consciousness and all the qualities and abilities related to consciousness. We use geometric symbolism, which is a pre-mathematical and philosophical approach to problems that cannot yet be handled mathematically. Using Occams razor we have started with the simplest model that works; we presume this to be a many-dimensional, spiral fractal. We suggest that all the electrons of the large biological molecules orbitals make one huge “cell-orbital”, which is structured according to the spiral fractal nature of quantum fields. Consciousness of single cells, multi cellular structures as e.g. organs, multi-cellular organisms and multi-individual colonies (like ants) and human societies can thus be explained by deep quantum chemistry. When biochemical activity is strictly controlled by the quantum-mechanical super-orbital of the cell, this orbital can deliver energetic quanta as biological information, distributed through many fractal levels of the cell to guide form and behavior of an individual single or a multi-cellular organism. The top level of information is the consciousness of the cell or organism, which controls all the biochemical processes. By this speculative work inspired by Penrose and Hameroff we hope to inspire other researchers to formulate more strict and mathematically correct hypothesis on the complex and coherence nature of matter, life and consciousness. PMID:17115084

Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity

PubMed Central

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand “complex behavior” and complexity theory, and from which important biological insight can be gained. PMID:24999297

Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity.

PubMed

Liang, Jie; Qian, Hong

2010-01-01

Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand "complex behavior" and complexity theory, and from which important biological insight can be gained.

Regulation of Spatiotemporal Patterns by Biological Variability: General Principles and Applications to Dictyostelium discoideum

PubMed Central

Grace, Miriam; Hütt, Marc-Thorsten

2015-01-01

Spatiotemporal patterns often emerge from local interactions in a self-organizing fashion. In biology, the resulting patterns are also subject to the influence of the systematic differences between the system’s constituents (biological variability). This regulation of spatiotemporal patterns by biological variability is the topic of our review. We discuss several examples of correlations between cell properties and the self-organized spatiotemporal patterns, together with their relevance for biology. Our guiding, illustrative example will be spiral waves of cAMP in a colony of Dictyostelium discoideum cells. Analogous processes take place in diverse situations (such as cardiac tissue, where spiral waves occur in potentially fatal ventricular fibrillation) so a deeper understanding of this additional layer of self-organized pattern formation would be beneficial to a wide range of applications. One of the most striking differences between pattern-forming systems in physics or chemistry and those in biology is the potential importance of variability. In the former, system components are essentially identical with random fluctuations determining the details of the self-organization process and the resulting patterns. In biology, due to variability, the properties of potentially very few cells can have a driving influence on the resulting asymptotic collective state of the colony. Variability is one means of implementing a few-element control on the collective mode. Regulatory architectures, parameters of signaling cascades, and properties of structure formation processes can be "reverse-engineered" from observed spatiotemporal patterns, as different types of regulation and forms of interactions between the constituents can lead to markedly different correlations. The power of this biology-inspired view of pattern formation lies in building a bridge between two scales: the patterns as a collective state of a very large number of cells on the one hand, and the internal parameters of the single cells on the other. PMID:26562406

Using Authentic Data in High School Earth System Science Research - Inspiring Future Scientists

NASA Astrophysics Data System (ADS)

Bruck, L. F.

2006-05-01

Using authentic data in a science research class is an effective way to teach students the scientific process, problem solving, and communication skills. In Frederick County Public Schools, MD a course has been developed to hone scientific research skills, and inspire interest in careers in science and technology. The Earth System Science Research course provides eleventh and twelfth grade students an opportunity to study Earth System Science using the latest information developed through current technologies. The system approach to this course helps students understand the complexity and interrelatedness of the Earth system. Consequently students appreciate the dynamics of local and global environments as part of a complex system. This course is an elective offering designed to engage students in the study of the atmosphere, biosphere, cryosphere, geosphere, and hydrosphere. This course allows students to utilize skills and processes gained from previous science courses to study the physical, chemical, and biological aspects of the Earth system. The research component of the course makes up fifty percent of course time in which students perform independent research on the interactions within the Earth system. Students are required to produce a scientific presentation to communicate the results of their research. Posters are then presented to the scientific community. Some of these presentations have led to internships and other scientific opportunities.

Biologically inspired circuitry that mimics mammalian hearing

NASA Astrophysics Data System (ADS)

Hubbard, Allyn; Cohen, Howard; Karl, Christian; Freedman, David; Mountain, David; Ziph-Schatzberg, Leah; Nourzad Karl, Marianne; Kelsall, Sarah; Gore, Tyler; Pu, Yirong; Yang, Zibing; Xing, Xinyu; Deligeorges, Socrates

2009-05-01

We are developing low-power microcircuitry that implements classification and direction finding systems of very small size and small acoustic aperture. Our approach was inspired by the fact that small mammals are able to localize sounds despite their ears may be separated by as little as a centimeter. Gerbils, in particular are good low-frequency localizers, which is a particularly difficult task, since a wavelength at 500 Hz is on the order of two feet. Given such signals, crosscorrelation- based methods to determine direction fail badly in the presence of a small amount of noise, e.g. wind noise and noise clutter common to almost any realistic environment. Circuits are being developed using both analog and digital techniques, each of which process signals in fundamentally the same way the peripheral auditory system of mammals processes sound. A filter bank represents filtering done by the cochlea. The auditory nerve is implemented using a combination of an envelope detector, an automatic gain stage, and a unique one-bit A/D, which creates what amounts to a neural impulse. These impulses are used to extract pitch characteristics, which we use to classify sounds such as vehicles, small and large weaponry from AK-47s to 155mm cannon, including mortar launches and impacts. In addition to the pitchograms, we also use neural nets for classification.

Molecular and Cellular Biology Animations: Development and Impact on Student Learning

PubMed Central

2005-01-01

Educators often struggle when teaching cellular and molecular processes because typically they have only two-dimensional tools to teach something that plays out in four dimensions. Learning research has demonstrated that visualizing processes in three dimensions aids learning, and animations are effective visualization tools for novice learners and aid with long-term memory retention. The World Wide Web Instructional Committee at North Dakota State University has used these research results as an inspiration to develop a suite of high-quality animations of molecular and cellular processes. Currently, these animations represent transcription, translation, bacterial gene expression, messenger RNA (mRNA) processing, mRNA splicing, protein transport into an organelle, the electron transport chain, and the use of a biological gradient to drive adenosine triphosphate synthesis. These animations are integrated with an educational module that consists of First Look and Advanced Look components that feature captioned stills from the animation representing the key steps in the processes at varying levels of complexity. These animation-based educational modules are available via the World Wide Web at http://vcell.ndsu.edu/animations. An in-class research experiment demonstrated that student retention of content material was significantly better when students received a lecture coupled with the animations and then used the animation as an individual study activity. PMID:15917875

Emotion attribution to a non-humanoid robot in different social situations.

PubMed

Lakatos, Gabriella; Gácsi, Márta; Konok, Veronika; Brúder, Ildikó; Bereczky, Boróka; Korondi, Péter; Miklósi, Ádám

2014-01-01

In the last few years there was an increasing interest in building companion robots that interact in a socially acceptable way with humans. In order to interact in a meaningful way a robot has to convey intentionality and emotions of some sort in order to increase believability. We suggest that human-robot interaction should be considered as a specific form of inter-specific interaction and that human-animal interaction can provide a useful biological model for designing social robots. Dogs can provide a promising biological model since during the domestication process dogs were able to adapt to the human environment and to participate in complex social interactions. In this observational study we propose to design emotionally expressive behaviour of robots using the behaviour of dogs as inspiration and to test these dog-inspired robots with humans in inter-specific context. In two experiments (wizard-of-oz scenarios) we examined humans' ability to recognize two basic and a secondary emotion expressed by a robot. In Experiment 1 we provided our companion robot with two kinds of emotional behaviour ("happiness" and "fear"), and studied whether people attribute the appropriate emotion to the robot, and interact with it accordingly. In Experiment 2 we investigated whether participants tend to attribute guilty behaviour to a robot in a relevant context by examining whether relying on the robot's greeting behaviour human participants can detect if the robot transgressed a predetermined rule. Results of Experiment 1 showed that people readily attribute emotions to a social robot and interact with it in accordance with the expressed emotional behaviour. Results of Experiment 2 showed that people are able to recognize if the robot transgressed on the basis of its greeting behaviour. In summary, our findings showed that dog-inspired behaviour is a suitable medium for making people attribute emotional states to a non-humanoid robot.

Emotion Attribution to a Non-Humanoid Robot in Different Social Situations

PubMed Central

Lakatos, Gabriella; Gácsi, Márta; Konok, Veronika; Brúder, Ildikó; Bereczky, Boróka; Korondi, Péter; Miklósi, Ádám

2014-01-01

In the last few years there was an increasing interest in building companion robots that interact in a socially acceptable way with humans. In order to interact in a meaningful way a robot has to convey intentionality and emotions of some sort in order to increase believability. We suggest that human-robot interaction should be considered as a specific form of inter-specific interaction and that human–animal interaction can provide a useful biological model for designing social robots. Dogs can provide a promising biological model since during the domestication process dogs were able to adapt to the human environment and to participate in complex social interactions. In this observational study we propose to design emotionally expressive behaviour of robots using the behaviour of dogs as inspiration and to test these dog-inspired robots with humans in inter-specific context. In two experiments (wizard-of-oz scenarios) we examined humans' ability to recognize two basic and a secondary emotion expressed by a robot. In Experiment 1 we provided our companion robot with two kinds of emotional behaviour (“happiness” and “fear”), and studied whether people attribute the appropriate emotion to the robot, and interact with it accordingly. In Experiment 2 we investigated whether participants tend to attribute guilty behaviour to a robot in a relevant context by examining whether relying on the robot's greeting behaviour human participants can detect if the robot transgressed a predetermined rule. Results of Experiment 1 showed that people readily attribute emotions to a social robot and interact with it in accordance with the expressed emotional behaviour. Results of Experiment 2 showed that people are able to recognize if the robot transgressed on the basis of its greeting behaviour. In summary, our findings showed that dog-inspired behaviour is a suitable medium for making people attribute emotional states to a non-humanoid robot. PMID:25551218

The role of inspiration in scientific scholarship and discovery: views of theistic scientists.

PubMed

O'Grady, Kari A; Richards, P Scott

2011-01-01

This qualitative research study examined the ways those who identify themselves as theistic scientists and scholars experience inspiration, as defined as divine guidance or influence, in their scientific scholarship and discovery. It also explored participants' beliefs about how scientists and scholars can seek and prepare to receive inspiration in their work. Open-ended surveys of 450 participants from the behavioral and natural sciences and from a variety of religious backgrounds were analyzed for content themes in the areas of experiences with inspiration, preparing to receive inspiration, and further thoughts on inspiration in science. The themes extracted indicated that these scientists and scholars have experienced inspiration throughout all stages of the research process. They also believe that certain practices and virtues, such as openness to inspiration and nurturing a relationship with God, can help scientists and scholars be more prepared to receive inspiration in their work. Copyright © 2011 Elsevier Inc. All rights reserved.

MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers.

PubMed

Allot, Alexis; Chennen, Kirsley; Nevers, Yannis; Poidevin, Laetitia; Kress, Arnaud; Ripp, Raymond; Thompson, Julie Dawn; Poch, Olivier; Lecompte, Odile

2017-06-16

The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user's specific interests and provides an efficient way to share information with collaborators. Furthermore, the user's behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends. ©Alexis Allot, Kirsley Chennen, Yannis Nevers, Laetitia Poidevin, Arnaud Kress, Raymond Ripp, Julie Dawn Thompson, Olivier Poch, Odile Lecompte. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.06.2017.

A biologically inspired meta-control navigation system for the Psikharpax rat robot.

PubMed

Caluwaerts, K; Staffa, M; N'Guyen, S; Grand, C; Dollé, L; Favre-Félix, A; Girard, B; Khamassi, M

2012-06-01

A biologically inspired navigation system for the mobile rat-like robot named Psikharpax is presented, allowing for self-localization and autonomous navigation in an initially unknown environment. The ability of parts of the model (e.g. the strategy selection mechanism) to reproduce rat behavioral data in various maze tasks has been validated before in simulations. But the capacity of the model to work on a real robot platform had not been tested. This paper presents our work on the implementation on the Psikharpax robot of two independent navigation strategies (a place-based planning strategy and a cue-guided taxon strategy) and a strategy selection meta-controller. We show how our robot can memorize which was the optimal strategy in each situation, by means of a reinforcement learning algorithm. Moreover, a context detector enables the controller to quickly adapt to changes in the environment-recognized as new contexts-and to restore previously acquired strategy preferences when a previously experienced context is recognized. This produces adaptivity closer to rat behavioral performance and constitutes a computational proposition of the role of the rat prefrontal cortex in strategy shifting. Moreover, such a brain-inspired meta-controller may provide an advancement for learning architectures in robotics.

A Novel Hybrid Clonal Selection Algorithm with Combinatorial Recombination and Modified Hypermutation Operators for Global Optimization

PubMed Central

Lin, Jingjing; Jing, Honglei

2016-01-01

Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662

Maneuvering control and configuration adaptation of a biologically inspired morphing aircraft

NASA Astrophysics Data System (ADS)

Abdulrahim, Mujahid

Natural flight as a source of inspiration for aircraft design was prominent with early aircraft but became marginalized as aircraft became larger and faster. With recent interest in small unmanned air vehicles, biological inspiration is a possible technology to enhance mission performance of aircraft that are dimensionally similar to gliding birds. Serial wing joints, loosely modeling the avian skeletal structure, are used in the current study to allow significant reconfiguration of the wing shape. The wings are reconfigured to optimize aerodynamic performance and maneuvering metrics related to specific mission tasks. Wing shapes for each mission are determined and related to the seagulls, falcons, albatrosses, and non-migratory African swallows on which the aircraft are based. Variable wing geometry changes the vehicle dynamics, affording versatility in flight behavior but also requiring appropriate compensation to maintain stability and controllability. Time-varying compensation is in the form of a baseline controller which adapts to both the variable vehicle dynamics and to the changing mission requirements. Wing shape is adapted in flight to minimize a cost function which represents energy, temporal, and spatial efficiency. An optimal control architecture unifies the control and adaptation tasks.

Evolution viewed from physics, physiology and medicine.

PubMed

Noble, Denis

2017-10-06

Stochasticity is harnessed by organisms to generate functionality. Randomness does not, therefore, necessarily imply lack of function or 'blind chance' at higher levels. In this respect, biology must resemble physics in generating order from disorder. This fact is contrary to Schrödinger's idea of biology generating phenotypic order from molecular- level order, which inspired the central dogma of molecular biology. The order originates at higher levels, which constrain the components at lower levels. We now know that this includes the genome, which is controlled by patterns of transcription factors and various epigenetic and reorganization mechanisms. These processes can occur in response to environmental stress, so that the genome becomes 'a highly sensitive organ of the cell' (McClintock). Organisms have evolved to be able to cope with many variations at the molecular level. Organisms also make use of physical processes in evolution and development when it is possible to arrive at functional development without the necessity to store all information in DNA sequences. This view of development and evolution differs radically from that of neo-Darwinism with its emphasis on blind chance as the origin of variation. Blind chance is necessary, but the origin of functional variation is not at the molecular level. These observations derive from and reinforce the principle of biological relativity, which holds that there is no privileged level of causation. They also have important implications for medical science.

Bio-inspired band gap engineering of zinc oxide by intracrystalline incorporation of amino acids.

PubMed

Brif, Anastasia; Ankonina, Guy; Drathen, Christina; Pokroy, Boaz

2014-01-22

Bandgap engineering of zinc oxide semiconductors can be achieved using a bio-inspired method. During a bioInspired crystallization process, incorporation of amino acids into the crystal structure of ZnO induces lattice strain that leads to linear bandgap shifts. This allows for fine tuning of the bandgap in a bio-inspired route. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fusion of nacre, mussel, and lotus leaf: bio-inspired graphene composite paper with multifunctional integration

NASA Astrophysics Data System (ADS)

Zhong, Da; Yang, Qinglin; Guo, Lin; Dou, Shixue; Liu, Kesong; Jiang, Lei

2013-06-01

Multifunctional integration is an inherent characteristic for biological materials with multiscale structures. Learning from nature is an effective approach for scientists and engineers to construct multifunctional materials. In nature, mollusks (abalone), mussels, and the lotus have evolved different and optimized solutions to survive. Here, bio-inspired multifunctional graphene composite paper was fabricated in situ through the fusion of the different biological solutions from nacre (brick-and-mortar structure), mussel adhesive protein (adhesive property and reducing character), and the lotus leaf (self-cleaning effect). Owing to the special properties (self-polymerization, reduction, and adhesion), dopamine could be simultaneously used as a reducing agent for graphene oxide and as an adhesive, similar to the mortar in nacre, to crosslink the adjacent graphene. The resultant nacre-like graphene paper exhibited stable superhydrophobicity, self-cleaning, anti-corrosion, and remarkable mechanical properties underwater.Multifunctional integration is an inherent characteristic for biological materials with multiscale structures. Learning from nature is an effective approach for scientists and engineers to construct multifunctional materials. In nature, mollusks (abalone), mussels, and the lotus have evolved different and optimized solutions to survive. Here, bio-inspired multifunctional graphene composite paper was fabricated in situ through the fusion of the different biological solutions from nacre (brick-and-mortar structure), mussel adhesive protein (adhesive property and reducing character), and the lotus leaf (self-cleaning effect). Owing to the special properties (self-polymerization, reduction, and adhesion), dopamine could be simultaneously used as a reducing agent for graphene oxide and as an adhesive, similar to the mortar in nacre, to crosslink the adjacent graphene. The resultant nacre-like graphene paper exhibited stable superhydrophobicity, self-cleaning, anti-corrosion, and remarkable mechanical properties underwater. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr33632h

Computational evolution: taking liberties.

PubMed

Correia, Luís

2010-09-01

Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

Limited Bandwidth Recognition of Collective Behaviors in Bio-Inspired Swarms

DTIC Science & Technology

2014-05-01

Nevai, K. M. Passino, and P. Srinivasan. Stability of choice in the honey bee nest-site selection processs. Journal of Theoretical Biology , 263(1):93...and N. Franks. Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology , 218(1):1–11, 2002. [4] D. Cvetkovic, P...motion from local attraction. Journal of Theoretical Biology , 283(1):145–151, 2011. [18] G. Sukthankar and K. Sycara. Robust recognition of physical team

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment.

PubMed

Yao, Yao; Storme, Veronique; Marchal, Kathleen; Van de Peer, Yves

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Friction behavior of a microstructured polymer surface inspired by snake skin.

PubMed

Baum, Martina J; Heepe, Lars; Gorb, Stanislav N

2014-01-01

The aim of this study was to understand the influence of microstructures found on ventral scales of the biological model, Lampropeltis getula californiae, the California King Snake, on the friction behavior. For this purpose, we compared snake-inspired anisotropic microstructured surfaces to other microstructured surfaces with isotropic and anisotropic geometry. To exclude that the friction measurements were influenced by physico-chemical variations, all friction measurements were performed on the same epoxy polymer. For frictional measurements a microtribometer was used. Original data were processed by fast Fourier transformation (FFT) with a zero frequency related to the average friction and other peaks resulting from periodic stick-slip behavior. The data showed that the specific ventral surface ornamentation of snakes does not only reduce the frictional coefficient and generate anisotropic frictional properties, but also reduces stick-slip vibrations during sliding, which might be an adaptation to reduce wear. Based on this extensive comparative study of different microstructured polymer samples, it was experimentally demonstrated that the friction-induced stick-slip behavior does not solely depend on the frictional coefficient of the contact pair.

Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem

PubMed Central

Qiao, Gang; Liu, Songzuo; Bilal, Muhammad

2017-01-01

A novel portable underwater acoustic modem is proposed in this paper for covert communication between divers or underwater unmanned vehicles (UUVs) and divers at a short distance. For the first time, real dolphin calls are used in the modem to realize biologically inspired Covert Underwater Acoustic Communication (CUAC). A variety of dolphin whistles and clicks stored in an SD card inside the modem helps to realize different biomimetic CUAC algorithms based on the specified covert scenario. In this paper, the information is conveyed during the time interval between dolphin clicks. TMS320C6748 and TLV320AIC3106 are the core processors used in our unique modem for fast digital processing and interconnection with other terminals or sensors. Simulation results show that the bit error rate (BER) of the CUAC algorithm is less than 10−5 when the signal to noise ratio is over ‒5 dB. The modem was tested in an underwater pool, and a data rate of 27.1 bits per second at a distance of 10 m was achieved. PMID:29068363

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

PubMed Central

Yao, Yao; Storme, Veronique; Marchal, Kathleen

2016-01-01

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population. PMID:28028477

Novel Magnetic Nanomaterials Inspired by Magnetotactic Baterial: Topical Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prozorov, Tanya; Bazylinki, Dennis A.; Mallapragada, Surya K.

2013-05-14

Magnetotactic bacteria, known to produce magnetic nanocrystals with uniform shapes and sizes at physiological conditions, serve as an inspiration and source of a number of biological macromolecules used for the biomimetic synthesis of a variety of magnetic nanomaterials. This review discusses the current state of understanding of magnetosome biomineralization in magnetotactic bacteria, as well as the ways in which iron biomineralization processes can be utilized for tailored in vivo formation of complex magnetic nanomaterials, not occurring in magnetotactic bacteria naturally. The review assesses the current efforts on in vitro synthesis of a variety of magnetic nanoparticles using bioinspired approaches bymore » utilizing mineralization proteins from magnetotactic bacteria, and surveys biomimetic strategies for the rational synthesis of various magnetic nanomaterials under ambient conditions. Finally, this review presents magnetic characterization of nanoparticles, highlighting differences in magnetic behavior between magnetic nanoparticles produced using bioinspired in vivo and in vitro strategies, compared to those produced using conventional methods. This in turn impacts their utility in a wide range of applications for magnetic nanoparticles, which are examined in detail, where bioinspired synthesis methods have potentially provided added advantages.« less

Adhesion control by inflation: implications from biology to artificial attachment device

NASA Astrophysics Data System (ADS)

Dening, Kirstin; Heepe, Lars; Afferrante, Luciano; Carbone, Giuseppe; Gorb, Stanislav N.

2014-08-01

There is an increasing demand for materials that incorporate advanced adhesion properties, such as an ability to adhere in a reversible and controllable manner. In biological systems, these features are known from adhesive pads of the tree frog, Litoria caerulea, and the bush-cricket, Tettigonia viridissima. These species have convergently developed soft, hemispherically shaped pads that might be able to control their adhesion through active changing the curvature of the pad. Inspired by these biological systems, an artificial model system is developed here. It consists of an inflatable membrane clamped to the metallic cylinder and filled with air. Pull-off force measurements of the membrane surface were conducted in contact with the membrane at five different radii of curvature r c with (1) a smooth polyvinylsiloxane membrane and (2) mushroom-shaped adhesive microstructured membrane made of the same polymer. The hypothesis that an increased internal pressure, acting on the membrane, reduces the radius of the membrane curvature, resulting in turn in a lower pull-off force, is verified. Such an active control of adhesion, inspired by biological models, will lead to the development of industrial pick-and-drop devices with controllable adhesive properties.

A bio-inspired design of live cell biosensors

NASA Astrophysics Data System (ADS)

Marcek Chorvatova, A.; Teplicky, T.; Pavlinska, Z.; Kronekova, Z.; Trelova, D.; Razga, F.; Nemethova, V.; Uhelska, L.; Lacik, I.; Chorvat, D.

2018-02-01

The last decade has witnessed a rapid growth of nanoscale-oriented biosensors that becomes one of the most promising and rapidly growing areas of modern research. Despite significant advancements in conception of such biosensors, most are based at evaluation of molecular, or protein interactions. It is however becoming increasingly evident that functionality of a living system does not reside in genome or in individual proteins, as no real biological functionality is expressed at these levels. Instead, to comprehend the true functioning of a biological system, it is essential to understand the integrative physiological behavior of the complex molecular interactions in their natural environment and precise spatio-temporal topology. In this contribution we therefore present a new concept for creation of biosensors, bio-inspired from true functioning of living cells, while monitoring their endogenous fluorescence, or autofluorescence.

Wet self-cleaning of superhydrophobic microfiber adhesives formed from high density polyethylene.

PubMed

Lee, Jongho; Fearing, Ronald S

2012-10-30

Biologically inspired adhesives developed for switchable and controllable adhesion often require repetitive uses in general, dirty, environments. Superhydrophobic microstructures on the lotus leaf lead to exceptional self-cleaning of dirt particles on nonadhesive surfaces with water droplets. This paper describes the self-cleaning properties of a hard-polymer-based adhesive formed with high-aspect-ratio microfibers from high-density polyethylene (HDPE). The microfiber adhesive shows almost complete wet self-cleaning of dirt particles with water droplets, recovering 98% of the adhesion of the pristine microfiber adhesives. The low contact angle hysteresis indicates that the surface of microfiber adhesives is superhydrophobic. Theoretical and experimental studies reveal a design parameter, length, which can control the adhesion without affecting the superhydrophobicity. The results suggest some properties of biologically inspired adhesives can be controlled independently by adjusting design parameters.

Nanofibers of Human Tropoelastin-inspired peptides: Structural characterization and biological properties.

PubMed

Secchi, Valeria; Franchi, Stefano; Fioramonti, Marco; Polzonetti, Giovanni; Iucci, Giovanna; Bochicchio, Brigida; Battocchio, Chiara

2017-08-01

Regenerative medicine is taking great advantage from the use of biomaterials in the treatments of a wide range of diseases and injuries. Among other biomaterials, self-assembling peptides are appealing systems due to their ability to spontaneously form nanostructured hydrogels that can be directly injected into lesions. Indeed, self-assembling peptide scaffolds are expected to behave as biomimetic matrices able to surround cells, to promote specific interactions, and to control and modify cell behavior by mimicking the native environment as well. We selected three pentadecapeptides inspired by Human Tropoelastin, a natural protein of the extracellular matrix, expected to show high biocompatibility. Moreover, the here proposed self-assembling peptides (SAPs) are able to spontaneously aggregate in nanofibers in biological environment, as revealed by AFM (Atomic Force Microscopy). Peptides were characterized by XPS (X-ray Photoelectron Spectroscopy) and IRRAS (Infrared Reflection Absorption Spectroscopy) both as lyophilized (not aggregated) and as aggregated (nanofibers) samples in order to investigate some potential differences in their chemical composition and intermolecular interactions, and to analyze the surface and interface of nanofibers. Finally, an accurate investigation of the biological properties of the SAPs and of their interaction with cells was performed by culturing for the first time human Mesenchymal Stem Cells (hMSCs) in presence of SAPs. The final aim of this work was to assess if Human Tropoelastin-inspired nanostructured fibers could exert a cytotoxic effect and to evaluate their biocompatibility, cellular adhesion and proliferation. Copyright © 2017 Elsevier B.V. All rights reserved.

A discrete artificial bee colony algorithm for detecting transcription factor binding sites in DNA sequences.

PubMed

Karaboga, D; Aslan, S

2016-04-27

The great majority of biological sequences share significant similarity with other sequences as a result of evolutionary processes, and identifying these sequence similarities is one of the most challenging problems in bioinformatics. In this paper, we present a discrete artificial bee colony (ABC) algorithm, which is inspired by the intelligent foraging behavior of real honey bees, for the detection of highly conserved residue patterns or motifs within sequences. Experimental studies on three different data sets showed that the proposed discrete model, by adhering to the fundamental scheme of the ABC algorithm, produced competitive or better results than other metaheuristic motif discovery techniques.

Ionic current devices-Recent progress in the merging of electronic, microfluidic, and biomimetic structures.

PubMed

Koo, Hyung-Jun; Velev, Orlin D

2013-05-09

We review the recent progress in the emerging area of devices and circuits operating on the basis of ionic currents. These devices operate at the intersection of electrochemistry, electronics, and microfluidics, and their potential applications are inspired by essential biological processes such as neural transmission. Ionic current rectification has been demonstrated in diode-like devices containing electrolyte solutions, hydrogel, or hydrated nanofilms. More complex functions have been realized in ionic current based transistors, solar cells, and switching memory devices. Microfluidic channels and networks-an intrinsic component of the ionic devices-could play the role of wires and circuits in conventional electronics.

Evolutionary crossroads in developmental biology: Cnidaria

PubMed Central

Technau, Ulrich; Steele, Robert E.

2011-01-01

There is growing interest in the use of cnidarians (corals, sea anemones, jellyfish and hydroids) to investigate the evolution of key aspects of animal development, such as the formation of the third germ layer (mesoderm), the nervous system and the generation of bilaterality. The recent sequencing of the Nematostella and Hydra genomes, and the establishment of methods for manipulating gene expression, have inspired new research efforts using cnidarians. Here, we present the main features of cnidarian models and their advantages for research, and summarize key recent findings using these models that have informed our understanding of the evolution of the developmental processes underlying metazoan body plan formation. PMID:21389047

Evolutionary crossroads in developmental biology: Cnidaria.

PubMed

Technau, Ulrich; Steele, Robert E

2011-04-01

There is growing interest in the use of cnidarians (corals, sea anemones, jellyfish and hydroids) to investigate the evolution of key aspects of animal development, such as the formation of the third germ layer (mesoderm), the nervous system and the generation of bilaterality. The recent sequencing of the Nematostella and Hydra genomes, and the establishment of methods for manipulating gene expression, have inspired new research efforts using cnidarians. Here, we present the main features of cnidarian models and their advantages for research, and summarize key recent findings using these models that have informed our understanding of the evolution of the developmental processes underlying metazoan body plan formation.

Correlated randomness: Some examples of exotic statistical physics

NASA Astrophysics Data System (ADS)

Stanley, H. Eugene

2005-05-01

One challenge of biology, medicine, and economics is that the systems treated by these sciences have no perfect metronome in time and no perfect spatial architecture -- crystalline or otherwise. Nonetheless, as if by magic, out of nothing but randomness one finds remarkably fine-tuned processes in time and remarkably fine-tuned structures in space. To understand this `miracle', one might consider placing aside the human tendency to see the universe as a machine. Instead, one might address the challenge of uncovering how, through randomness (albeit, as we shall see, strongly correlated randomness), one can arrive at many spatial and temporal patterns in biology, medicine, and economics. Inspired by principles developed by statistical physics over the past 50 years -- scale invariance and universality -- we review some recent applications of correlated randomness to fields that might startle Boltzmann if he were alive today.