Relevant bibliographies by topics / Dead cell recognition

Academic literature on the topic 'Dead cell recognition'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Dead cell recognition"

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Shiotani, Shigetoshi, Toshio Fukuda, Fumihito Arai, Naokazu Takeuchi, Kyosuke Sasaki, and Tatsuyuki Kinosita. "Cell Recognition by Image Processing : Recognition of Dead or Living Plant Cells by Neural Network." JSME international journal. Ser. C, Dynamics, control, robotics, design and manufacturing 37, no. 1 (1994): 202–8. http://dx.doi.org/10.1299/jsmec1993.37.202.

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Hochreiter-Hufford, A., and K. S. Ravichandran. "Clearing the Dead: Apoptotic Cell Sensing, Recognition, Engulfment, and Digestion." Cold Spring Harbor Perspectives in Biology 5, no. 1 (January 1, 2013): a008748. http://dx.doi.org/10.1101/cshperspect.a008748.

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FUKUDA, Toshio, Shigetoshi SHIOTANI, Fumihito ARAI, Naokazu TAKEUCHI, Kyosuke SASAKI, and Tatsuyuki KINOSHITA. "Cell Recognition by Image Processing. 1st Report. Recognition of Dead or Alive Plant Cells by Neural Network." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 542 (1991): 3189–96. http://dx.doi.org/10.1299/kikaic.57.3189.

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Cao, Longxing, Haishuang Chang, Xiangyi Shi, Chao Peng, and Yongning He. "Keratin mediates the recognition of apoptotic and necrotic cells through dendritic cell receptor DEC205/CD205." Proceedings of the National Academy of Sciences 113, no. 47 (November 7, 2016): 13438–43. http://dx.doi.org/10.1073/pnas.1609331113.

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Clearance of dead cells is critical for maintaining homeostasis and prevents autoimmunity and inflammation. When cells undergo apoptosis and necrosis, specific markers are exposed and recognized by the receptors on phagocytes. DEC205 (CD205) is an endocytotic receptor on dendritic cells with antigen presentation function and has been widely used in immune therapies for vaccine generation. It has been shown that human DEC205 recognizes apoptotic and necrotic cells in a pH-dependent fashion. However, the natural ligand(s) of DEC205 remains unknown. Here we find that keratins are the cellular ligands of human DEC205. DEC205 binds to keratins specifically at acidic, but not basic, pH through its N-terminal domains. Keratins form intermediate filaments and are important for maintaining the strength of cells and tissues. Our results suggest that keratins also function as cell markers of apoptotic and necrotic cells and mediate a pH-dependent pathway for the immune recognition of dead cells.
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Taschuk, Frances, and Sara Cherry. "DEAD-Box Helicases: Sensors, Regulators, and Effectors for Antiviral Defense." Viruses 12, no. 2 (February 5, 2020): 181. http://dx.doi.org/10.3390/v12020181.

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DEAD-box helicases are a large family of conserved RNA-binding proteins that belong to the broader group of cellular DExD/H helicases. Members of the DEAD-box helicase family have roles throughout cellular RNA metabolism from biogenesis to decay. Moreover, there is emerging evidence that cellular RNA helicases, including DEAD-box helicases, play roles in the recognition of foreign nucleic acids and the modulation of viral infection. As intracellular parasites, viruses must evade detection by innate immune sensing mechanisms and degradation by cellular machinery while also manipulating host cell processes to facilitate replication. The ability of DEAD-box helicases to recognize RNA in a sequence-independent manner, as well as the breadth of cellular functions carried out by members of this family, lead them to influence innate recognition and viral infections in multiple ways. Indeed, DEAD-box helicases have been shown to contribute to intracellular immune sensing, act as antiviral effectors, and even to be coopted by viruses to promote their replication. However, our understanding of the mechanisms underlying these interactions, as well as the cellular roles of DEAD-box helicases themselves, is limited in many cases. We will discuss the diverse roles that members of the DEAD-box helicase family play during viral infections.
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Turkyilmaz, Serhan, Douglas R. Rice, Rachael Palumbo, and Bradley D. Smith. "Selective recognition of anionic cell membranes using targeted liposomes coated with zinc(ii)-bis(dipicolylamine) affinity units." Org. Biomol. Chem. 12, no. 30 (2014): 5645–55. http://dx.doi.org/10.1039/c4ob00924j.

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Liposomes containing phospholipid-PEG conjugates with terminal zinc(ıı)-bis(dipicolylamine) affinity units selectively target anionic membrane surfaces including the exterior of bacterial and dead/dying mammalian cells.
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Tanner, N. Kyle. "The Newly Identified Q Motif of DEAD Box Heicases IS Involved in Adenine Recognition." Cell Cycle 2, no. 1 (January 2003): 18–19. http://dx.doi.org/10.4161/cc.2.1.296.

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Li, Qiong, Xin Sun, Junyu Dong, Shuqun Song, Tongtong Zhang, Dan Liu, Han Zhang, and Shuai Han. "Developing a microscopic image dataset in support of intelligent phytoplankton detection using deep learning." ICES Journal of Marine Science 77, no. 4 (September 20, 2019): 1427–39. http://dx.doi.org/10.1093/icesjms/fsz171.

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Abstract Phytoplankton plays an important role in marine ecological environment and aquaculture. However, the recognition and detection of phytoplankton rely on manual operations. As the foundation of achieving intelligence and releasing human labour, a phytoplankton microscopic image dataset PMID2019 for phytoplankton automated detection is presented. The PMID2019 dataset contains 10 819 phytoplankton microscopic images of 24 different categories. We leverage microscopes to collect images of phytoplankton in the laboratory environment. Each object in the images is manually labelled with a bounding box and category of ground-truth. In addition, living cells move quickly making it difficult to capture images of them. In order to generalize the dataset for in situ applications, we further utilize Cycle-GAN to achieve the domain migration between dead and living cell samples. We built a synthetic dataset to generate the corresponding living cell samples from the original dead ones. The PMID2019 dataset will not only benefit the development of phytoplankton microscopic vision technology in the future, but also can be widely used to assess the performance of the state-of-the-art object detection algorithms for phytoplankton recognition. Finally, we illustrate the performances of some state-of-the-art object detection algorithms, which may provide new ideas for monitoring marine ecosystems.
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Cocco, Regina E., and David S. Ucker. "Distinct Modes of Macrophage Recognition for Apoptotic and Necrotic Cells Are Not Specified Exclusively by Phosphatidylserine Exposure." Molecular Biology of the Cell 12, no. 4 (April 2001): 919–30. http://dx.doi.org/10.1091/mbc.12.4.919.

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The distinction between physiological (apoptotic) and pathological (necrotic) cell deaths reflects mechanistic differences in cellular disintegration and is of functional significance with respect to the outcomes that are triggered by the cell corpses. Mechanistically, apoptotic cells die via an active and ordered pathway; necrotic deaths, conversely, are chaotic and passive. Macrophages and other phagocytic cells recognize and engulf these dead cells. This clearance is believed to reveal an innate immunity, associated with inflammation in cases of pathological but not physiological cell deaths. Using objective and quantitative measures to assess these processes, we find that macrophages bind and engulf native apoptotic and necrotic cells to similar extents and with similar kinetics. However, recognition of these two classes of dying cells occurs via distinct and noncompeting mechanisms. Phosphatidylserine, which is externalized on both apoptotic and necrotic cells, is not a specific ligand for the recognition of either one. The distinct modes of recognition for these different corpses are linked to opposing responses from engulfing macrophages. Necrotic cells, when recognized, enhance proinflammatory responses of activated macrophages, although they are not sufficient to trigger macrophage activation. In marked contrast, apoptotic cells profoundly inhibit phlogistic macrophage responses; this represents a cell-associated, dominant-acting anti-inflammatory signaling activity acquired posttranslationally during the process of physiological cell death.
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Hood, M. E., and H. D. Shew. "Initial Cellular Interactions Between Thielaviopsis basicola and Tobacco Root Hairs." Phytopathology® 87, no. 3 (March 1997): 228–35. http://dx.doi.org/10.1094/phyto.1997.87.3.228.

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Cellular events that occur during the initial interactions between Thielaviopsis basicola and root hairs of tobacco (Nicotiana tabacum) were examined microscopically. Time-course documentation of the infection process indicated a dynamic interaction between T. basicola and the living host cell. Upon root hair contact and recognition, the vegetative apex of T. basicola rapidly differentiated to form infection structures, and the host cell responded cytologically. Penetration was achieved by threadlike hyphae that subsequently developed distal swellings, and intracellular hyphae of sickle-shaped morphology advanced from the distal swelling and colonized the cell. Streaming of the host cytoplasm became aggregated near the infection site prior to penetration and accumulated around the infecting hyphae as long as the host cell was viable. Substantial callose deposition, in the form of a bell-shaped collar around infection structures, resulted from the cytological activity at the infection site. Penetration of dead root hairs was common, but did not lead to the development of infection structures or to a sustained association with the host tissue; T. basicola exited dead root hairs and resumed vegetative growth. The establishment of the parasitic relationship by T. basicola was characteristic of hemibiotrophic fungi in that, initially, biotrophic infection led to tissue colonization, and host cell survival was limited under parasitism.
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Book chapters on the topic "Dead cell recognition"

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Ayliffe, Michael, Ming Luo, Justin Faris, and Evans Lagudah. "Disease Resistance." In Wheat Improvement, 341–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_19.

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AbstractWheat plants are infected by diverse pathogens of economic significance. They include biotrophic pathogens like mildews and rusts that require living plant cells to proliferate. By contrast necrotrophic pathogens that cause diseases such as tan spot, Septoria nodurum blotch and spot blotch require dead or dying cells to acquire nutrients. Pioneering studies in the flax plant-flax rust pathosystem led to the ‘gene-for-gene’ hypothesis which posits that a resistance gene product in the host plant recognizes a corresponding pathogen gene product, resulting in disease resistance. In contrast, necrotrophic wheat pathosystems have an ‘inverse gene-for-gene’ system whereby recognition of a necrotrophic fungal product by a dominant host gene product causes disease susceptibility, and the lack of recognition of this pathogen molecule leads to resistance. More than 300 resistance/susceptibility genes have been identified genetically in wheat and of those cloned the majority encode nucleotide binding, leucine rich repeat immune receptors. Other resistance gene types are also present in wheat, in particular adult plant resistance genes. Advances in mutational genomics and the wheat pan-genome are accelerating causative disease resistance/susceptibility gene discovery. This has enabled multiple disease resistance genes to be engineered as a transgenic gene stack for developing more durable disease resistance in wheat.
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Verma, Meenakshi, Veena Choudhary, and S. K. Dhawan. "Thermoplastic Polyurethane Graphene Nanocomposites for EMI Shielding." In Smart Materials Design for Electromagnetic Interference Shielding Applications, 153–212. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815036428122010007.

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Interference and chaos among the various electromagnetic signals are becoming the primary challenge of the current era that relies on wireless communication. Electromagnetic pollution is the overabundance of electromagnetic radiation emitted by electronic devices, like cell phones, cordless phones, Wi-Fi routers, or Bluetooth-enabled equipment, and our relationship with these devices has become more and more intimate. The potential effects of electromagnetic pollution, both in terms of its interaction with electronic devices as well as biological species, are serious concerns for the research community. EMI shielding reduces electromagnetic interference among the electronic components. Therefore, protection from such harmful radiations must be acquired by either blocking or shielding these unavoidable severe electromagnetic radiations. Metals have been typically used as the material of choice for shielding applications, but heavy weight, corrosion susceptibility, and cumbersome processing methods make them unsuitable for both researchers and users. Alternatively, polymer nanocomposites have gained tremendous attention as electromagnetic interference (EMI) shielding materials owing to their facile synthesis, ease of processing, and low cost. Different thermoplastic and thermoset polymer matrices have been explored for the development of lightweight composite material for EMI shielding applications. Among the thermoplastic polymers, thermoplastic polyurethanes (TPU) have attracted a great deal of recognition due to their combination of properties, such as flexibility, stretchability, transparency, good wear and weather resistance, better abrasion and chemical resistance, and better mechanical properties. Although graphene and carbon nanotubes have been explored as conducting fillers in polyurethane matrix for the development of EMI shields, no reports are available using a combination of these fillers along with magnetic nanoparticles in thermoplastic polyurethane matrix.
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Jacqulyn, Varsha, Mrs Thiruchelvi R., and Mrs Rajakumari K. "Internet of Medical Things (IoMT) – Medical Applications and it’s Cyber Security." In Applications of Artificial Intelligence and Machine Learning in Healthcare. Technoarete Publishing, 2022. http://dx.doi.org/10.36647/aaimlh/2022.01.b1.ch010.

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The Internet of Things (IoT) is a huge deal all around the world. It can be used in a variety of fields, including industrial, healthcare, agriculture, and the environment. Due to a lack of interaction among patients and doctors in the past, correct diagnosis was difficult to come by, and fatality rates were high. They were incapable of dealing with epidemics and pandemics. The Internet of Medical Things (IoMT) was recently developed to better and improve the healthcare profession. IoMT is a medical gadget that allows for increased patient convenience, price healthcare treatments, improved medical therapies, and more customized care. Wearable gadgets have been on the rise, with several benefits in terms of keeping watch of vitals and healthcare, igniting the growth of the Internet of Medical Things (IoMT). It allows electronic technologies to collect, analyze, and send information to cloud systems. This allows you to examine the patient's health status at any time and from anywhere, including body temperature, pulse rate, hearing beats utilizing ECG sensors, temperature, pressure, and observation. Digital gadgets have been designed and are widely used as a result of these systems. Doctors may keep track of a patient's health status in real time and prescribe medicine, vitamins supplements, and healthcare advice to them through. IoMT aids in the detection of body changes such as the growth of irregular masses of undifferentiated cells, the recognition of neural disorders, the checking of diabetic patients' glucose levels, the detection of psychiatric conditions using heartbeat, the real-time tracking of chronic disease symptoms, the screening of internal organ dysfunction, and the monitoring of cardiac blocks that cause heart attacks. IoMT assisted doctors in identifying and diagnosing COVID-19-affected patients and assisting them in providing appropriate therapy remotely. Clinical decisions were made with the help of IoMT as Artificial Intelligence (AI), telemedicine, and sensor technologies advanced. In the context of smart cities, IoMT introduces a new issue in the healthcare sphere. This chapter provides an outline as to how IoMT operates and how it has improved the lives of many patients through the use of modern technologies. This also includes a design and overview of IoMT, as well as numerous activities in the health-care industry, pop-up technologies, and several IoMT case studies in medical applications. It also lays out the IoMT's cybersecurity rules and indicates issues which need to be tackled at the parliamentary and community sectors.
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"really what was at issue, so much as the means by which the inevitable outcome would be accomplished, and it is precisely those means which are problematized by the riddle structure. As usual, the answer is provided retrospectively and within the dramatic frame, but in this case the solution involves the introduc­ tion of new ‘facts’ of which the reader has hitherto been quite unaware. That night, in their prison cell, Theagenes and Charikleia talk over the day’s remarkable events. Charikleia suddenly remem­ bers a dream vision of her now dead mentor Kalasiris that had visited her the previous night and delivered this prophecy: If you wear pantarbe fear-all, fear not the power of flame Miracles may come to pass; for Fate ’tis easy game. (8.11.2) The solution to the riddle is itself a riddle, which Charikleia elucidates for her sceptical beloved: thinking she was about to die, she had secreted about herself the recognition tokens left her by her mother, including a ring set with the jewel called pantarbe and engraved with mystic characters. This, she surmises, protected her from the fire (8.11.7-8). Heliodoros’ manipulation of his narrative is obvious. Any ‘honest’ writer would have narrated this self-evidently important dream in its proper chronological place. The postponement is half­ heartedly explained within the dramatic frame by the suggestion that Charikleia simply forgot about it, but this is only for form’s sake.8 Heliodoros is deliberately withholding information, to induce puzzlement and speculation, to encourage the reader to take, in Umberto Eco’s notorious phrase, ‘inferential walks’. In comparison with the other riddles we have discussed, this one may seem adversarial rather than collaborative. Rather than slowly releasing material which will guide the reader safely to the correct solution, Heliodoros’ aim appears to be to keep us in the dark until such time as it suits him to tell us something we could not have otherwise known. But, although the author is playing more roughly here, he is still observing the rules: the clues are there, though probably their significance is realized only in retrospect. As Charikleia goes to face trial, intending to denounce herself and find release from the torment of her existence, Helio­ doros duly records that she wore her recognition tokens ‘as a kind of burial shroud, fastened around her waist beneath her clothes’ (8.9.8). And this reference to the tokens takes us back, across half." In Greek Literature in the Roman Period and in Late Antiquity, 324. Routledge, 2014. http://dx.doi.org/10.4324/9780203616895-39.

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Conference papers on the topic "Dead cell recognition"

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Wang, Qingqing, and Yue Lu. "A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/411.

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Handwritten string recognition has been struggling with connected patterns fiercely. Segmentation-free and over-segmentation frameworks are commonly applied to deal with this issue. For the past years, RNN combining with CTC has occupied the domain of segmentation-free handwritten string recognition, while CNN is just employed as a single character recognizer in the over-segmentation framework. The main challenges for CNN to directly recognize handwritten strings are the appropriate processing of arbitrary input string length, which implies arbitrary input image size, and reasonable design of the output layer. In this paper, we propose a sequence labeling convolutional network for the recognition of handwritten strings, in particular, the connected patterns. We properly design the structure of the network to predict how many characters present in the input images and what exactly they are at every position. Spatial pyramid pooling (SPP) is utilized with a new implementation to handle arbitrary string length. Moreover, we propose a more flexible pooling strategy called FSPP to adapt the network to the straightforward recognition of long strings better. Experiments conducted on handwritten digital strings from two benchmark datasets and our own cell-phone number dataset demonstrate the superiority of the proposed network.
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Lee, Dennis W. K., Y. K. Yan, and W. M. Leung. "Uncertainty Estimation for Force Measurements." In NCSL International Workshop & Symposium. NCSL International, 2014. http://dx.doi.org/10.51843/wsproceedings.2014.15.

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Load cells are force measurement transducers which form integral parts of systems for measurement of weight, torque, impact, acceleration and other quantities. Particularly in the construction industry, load cells are extensively used for calibrating force machines and determining the strengths of construction materials. Load cells are calibrated against standard masses using standard force machines based on principles of deadweight or hydraulic amplification. For international recognition purpose, load cells are calibrated in accordance with international or national standards such as ISO 376, BS 1610, EN 10002-3. However, these standards do not provide guidelines for evaluation of measurement data and expression of measurement uncertainty. There is another complication. Load cells are transducers that give out deflection values in response to the applied forces. A load cell is calibrated at specific test points only and the behavior of the test unit is expressed graphically by plotting the indicated output value against the applied force (known as a response curve). Hence, measurement results for load cells are expressed in terms of calibration coefficients, which are used to reproduce the response curve. This made the evaluation and expression of measurement uncertainty a complicated process. The document JCGM 100 "Evaluation of measurement data - Guide to the expression of uncertainty in measurement (GUM)" provides a framework for uncertainty evaluation. However, the GUM does not provide specific guidelines for uncertainty estimates for load cells, in particularly, to deal with errors concerning curve fitting and interpolation. It is also known that GUM has certain limitations which render it unreliable when there is prominent nonlinearity in the model or there are dominant uncertainty contributions. In this paper, we not only demonstrate how to use the GUM framework to estimate uncertainties of a load cell but also apply the method stipulated in the "Supplement 1 to the GUM - Propagation of distributions using a Monte Carlo method (JCGM 101)" to validate the GUM uncertainty framework.
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Lu, Yong, Rajit Gadh, and Timothy J. Tautges. "Decomposition Based Feature Recognition Using Constrained and Aggregated Half-Space Partitioning." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dfm-14038.

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Abstract Decomposition based feature recognition (DBFR) has drawn attention over the years. It has two stages: decomposition and aggregation. At the decomposition stage, the CAD model is partitioned into minimal cells. At the aggregation stage, the decomposed individual cells are composed in different combinations and these combinations are matched with predefined feature patterns to retrieve features in the model. The DBFR technique shows promises to deal with interactive features. However, DBFR algorithms suffer from the combinatorial problem in both the partitioning and the composing stages. This paper proposes a novel decomposition based feature recognition technique using the constrained and aggregated half-space partitioning. The constrained and aggregated half-space is defined in the occupation of a volume in the Euclidean space, bounded by multiple surfaces. The decomposition approach based on this concept can largely avoid over-cuttings. It tends to produce partitions that can be directly matched with feature patterns. Different from other DBFR algorithms, pattern matching is also introduced in the decomposition stage. Thus it further shrinks the space of combination and feature determination. Some algorithms are also proposed to do efficient volume combinations at the aggregation stage.
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