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Chlamtac, I., and A. Lerner. "Fair Algorithms for Maximal Link Activation in Multihop Radio Networks." IEEE Transactions on Communications 35, no. 7 (July 1987): 739–46. http://dx.doi.org/10.1109/tcom.1987.1096847.
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Grenson, Pierre, and Eric Garnier. "Distortion reconstruction in S-ducts from wall static pressure measurements." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 5 (May 8, 2018): 1134–55. http://dx.doi.org/10.1108/hff-06-2017-0232.
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Purpose This paper aims to report the attempts for predicting “on-the-fly” flow distortion in the engine entrance plane of a highly curved S-duct from wall static pressure measurements. Such a technology would be indispensable to trigger active flow control devices to mitigate the intense flow separations which occur in specific flight conditions. Design/methodology/approach Evaluation of different reconstruction algorithms is performed on the basis of data extracted from a Zonal Detached Eddy Simulation (ZDES) of a well-documented S-Duct (Garnier et al., AIAA J., 2015). Contrary to RANS methods, such a hybrid approach makes unsteady distortions available, which are necessary information for reconstruction algorithm assessment. Findings The best reconstruction accuracy is obtained with the artificial neural network (ANN) but the improvement compared to the classical linear stochastic estimation (LSE) is minor. The different inlet distortion coefficients are not reconstructed with the same accuracy. KA2 coefficient is finally identified as the more suited for activation of the control device. Originality/value LSE and its second-order variant (quadratic stochastic estimation [QSE]) are applied for reconstructing instantaneous stagnation pressure in the flow field. The potential improvement of an algorithm based on an ANN is also evaluated. The statistical link between the wall sensors and 40-Kulite rake sensors are carefully discussed and the accuracy of the reconstruction of the most used distortion coefficients (DC60, RDI, CDI and KA2) is quantified for each estimation technique.
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Syed, Zareen, Tim Finin, and Anupam Joshi. "Wikipedia as an Ontology for Describing Documents." Proceedings of the International AAAI Conference on Web and Social Media 2, no. 1 (September 25, 2021): 136–44. http://dx.doi.org/10.1609/icwsm.v2i1.18627.
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Identifying topics and concepts associated with a set of documents is a task common to many applications. It can help in the annotation and categorization of documents and be used to model a person's current interests for improving search results, business intelligence or selecting appropriate advertisements. One approach is to associate a document with a set of topics selected from a fixed ontology or vocabulary of terms. We have investigated using Wikipedia's articles and associated pages as a topic ontology for this purpose. The benefits are that the ontology terms are developed through a social process, maintained and kept current by the Wikipedia community, represent a consensus view, and have meaning that can be understood simply by reading the associated Wikipedia page. We use Wikipedia articles and the category and article link graphs to predict concepts common to a set of documents. We describe several algorithms to aggregate and refine results, including the use of spreading activation to select the most appropriate terms. While the Wikipedia category graph can be used to predict generalized concepts, the article links graph helps by predicting more specific concepts and concepts not in the category hierarchy. Our experiments demonstrate the feasibility of extending the category system with new concepts identified as a union of pages from the page link graph.
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Jablonska, Ewa, Patryk Gorniak, Weronika Prusisz, Przemyslaw Kiliszek, Maciej Szydlowski, Tomasz Sewastianik, Emilia Bialopiotrowicz, et al. "Downregulation of Deptor By MiR-155 Promotes Cell Survival through Activation of PI3K/AKT and NFkB Signaling in ABC-Type Diffuse Large B-Cell Lymphomas." Blood 128, no. 22 (December 2, 2016): 1761. http://dx.doi.org/10.1182/blood.v128.22.1761.1761.
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Abstract Introduction MiR-155 expression in DLBCLs is induced by NFkB and directly targets key regulators of B-cell maturation, motility and BCR signaling. In this study, we searched for new targets of miR-155 potentially involved in deregulation of BCR-PI3K/AKT and NFkB signaling in DLBCLs. We identified two new miR-155 targets: c-CBL (SYK ubiquitin E3 ligase) and DEPTOR (mTOR phosphatase). DEPTOR suppresses mTOR activity and in different tumors plays a role of either a tumor suppressor or an oncogene. Since the role of DEPTOR in DLCBLs cells has not been determined, we assessed the consequences of its inhibition in this malignancy. Methods Predicted miR-155 targets were validated with 3'UTR luciferase reporter assays. MiR-155 expression was modulated through transfection with miR-155 mimic or miR-155 inhibitor. The DEPTOR silencing in DLBCL cell was achieved with retroviral shRNA vector. DEPTOR mRNA expression and survival of DLBCL patients was determined using publicly available microarray data (Lenz et al, 2008, GEO accession GSE10846). Immunohistochemical assessment of DEPTOR expression was performed in 76 newly diagnosed DLBCL patients with available GCB/non-GCB designations based on Hans algorithm. Results Using miRNA target finding algorithms, we identified miR-155-matching sequences in 3'UTRs of two genes involved in SYK/PI3K/AKT pathway regulation: c-CBL and DEPTOR. MiR-155 suppressed luciferase activities of vectors containing 3'UTR fragments from c-CBL and DEPTOR genes with wild-type, but not mutant miR-155 seed sequence. To establish a link between miR-155 and c-CBL or DEPTOR, DLBCL cell lines were transfected with a control non-targeting miR or miR-155 mimic. Introduction of miR-155 resulted in decreased expression of c-CBL and DEPTOR, accompanied by a marked increase in phospho-AKT level. Inhibition of endogenous miR-155 in U2932 cell line by anti-miR-155 exhibited opposite effects. Using publicly available gene expression data (Lenz et al, 2008, GEO accession GSE10846), we found that miR-155 exhibited a reciprocal expression pattern with DEPTOR and c-CBL (r =-0.15; p=.002 and r=-0.189; p<.0001), indicating that miR-155 likely modulates expression of c-CBL and DEPTOR in primary tumors. Since the role of DEPTOR in DLBCL has not been previously addressed, we investigated its expression in a series of 76 newly diagnosed DLBCL patients by immunohistochemistry. Complete loss of DEPTOR expression was noted solely in non-GCB tumors (9 of 37; 24%). Additional 12 patients exhibited low expression of this protein. In contrast, none of the 39 GCB tumors stained negative for DEPTOR (Chi-square, p=.005). DEPTOR mRNA greater than mean in primary DLBCL biopsies was associated with longer overall survival (OS; P=0.016). To elucidate the function of DEPTOR in DLBCL cells, we silenced the expression of this protein with shRNA. Attenuated protein level of DEPTOR markedly enhanced AKT activity and promoted proliferation of DHL4 cells. DHL4 cells with silenced DEPTOR were also less prone to starvation- or SYK inhibition- induced apoptosis. Since AKT has been reported to stimulate activity of NFκB, we hypothesized that miR-155-induced decrease in DEPTOR expression would affect NFκB signaling. Consistent with this, inhibition of endogenous miR-155 in U2932 cells led to marked downregulation of NFĸB-controlled genes (CD40, BFL-1, RelB, IĸBα, A20, MIR155HG) and sensitized U2932 cells to ibrutinib, indicating that miR-155 amplifies NFĸB signaling in these cells. Conclusions Taken together, our data underscore the role of miR-155 in the regulation of AKT and NFkB prosurvival signaling in DLBCL. By targeting multiple negative regulators of PI3K/AKT pathway, miR-155 creates a feed - forward loop leading to increased NFkB activity. We also show that DEPTOR protein expression is decreased or lost selectively in a large fraction of ABC-DLBCLs. Since DEPTOR modulates AKT activity and its silencing promotes proliferation of DLBCL cells, these data suggest that DEPTOR functions as a tumor suppressor in ABC-DLBCLs. Disclosures Prochorec-Sobieszek: Roche: Other: travel, accommodation. Warzocha:BMS: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Juszczynski:Selvita S.A.: Consultancy, Membership on an entity's Board of Directors or advisory committees.
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CHINNASARN, KRISANA, CHIDCHANOK LURSINSAP, and VASILE PALADE. "BLIND SEPARATION OF MIXED KURTOSIS SIGNED SIGNALS USING PARTIAL OBSERVATIONS AND LOW COMPLEXITY ACTIVATION FUNCTIONS." International Journal of Computational Intelligence and Applications 04, no. 02 (June 2004): 207–23. http://dx.doi.org/10.1142/s1469026804001239.
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Although several highly accurate blind source separation algorithms have already been proposed in the literature, these algorithms must store and process the whole data set which may be tremendous in some situations. This makes the blind source separation infeasible and not realisable on VLSI level, due to a large memory requirement and costly computation. This paper concerns the algorithms for solving the problem of tremendous data sets and high computational complexity, so that the algorithms could be run on-line and implementable on VLSI level with acceptable accuracy. Our approach is to partition the observed signals into several parts and to extract the partitioned observations with a simple activation function performing only the "shift-and-add" micro-operation. No division, multiplication and exponential operations are needed. Moreover, obtaining an optimal initial de-mixing weight matrix for speeding up the separating time will be also presented. The proposed algorithm is tested on some benchmarks available online. The experimental results show that our solution provides comparable efficiency with other approaches, but lower space and time complexity.
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Pellegrini, Riccardo, Andrea Serani, Giampaolo Liuzzi, Francesco Rinaldi, Stefano Lucidi, and Matteo Diez. "Hybridization of Multi-Objective Deterministic Particle Swarm with Derivative-Free Local Searches." Mathematics 8, no. 4 (April 7, 2020): 546. http://dx.doi.org/10.3390/math8040546.
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The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts.
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Guo, Yuan Hua, and Chun Lun Huang. "Functional Link Artificial Neural Networks Filter for Gaussian Noise." Applied Mechanics and Materials 347-350 (August 2013): 2580–85. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2580.
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In this paper, FLANN(functional link ANN) filter is presented for Gaussian noise. FLANN is a singer layer with expanded input vectors and has lower computational cost than MLP(multilayer perceptron). Three types of functional expansion are discussed. BP(back propagation algorithm) for nonlinear activation function and matrix calculation for identical activation function are exploited for training FLANN. Simulation shows that convergence is not guaranteed in BP and related to the initial weight matrix and training images, and that linear FLANN trained by matrix calculation performs better than both nonlinear FLANN trained by BP and Wiener filter in detail region in environment of Gaussian noise
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Belcaid, A., and M. Douimi. "A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy." International Journal on Artificial Intelligence Tools 29, no. 06 (September 2020): 2050018. http://dx.doi.org/10.1142/s0218213020500189.
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In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection. The proposed approach considers a local blanket of a global Markov Random Field (MRF) representing the signal and its noisy observation. For each window, we define and solve the local energy minimization problem to deduce the gradient on each edge of the MRF graph. The gradient is then processed by an activation function to filter the weak features and produce the final jumps. We demonstrate the effectiveness of our method by comparing its running time and several detection metrics with state of the art algorithms.
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Bonn, B., E. Bourtsoukidis, T. S. Sun, H. Bingemer, L. Rondo, U. Javed, J. Li, et al. "The link between atmospheric radicals and newly formed particles at a spruce forest site in Germany." Atmospheric Chemistry and Physics Discussions 13, no. 10 (October 24, 2013): 27501–60. http://dx.doi.org/10.5194/acpd-13-27501-2013.
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Abstract. It has been claimed for more than a century that atmospheric new particle formation is primarily influenced by the presence of sulphuric acid. However, the activation process of sulphuric acid related clusters into detectable particles is still an unresolved topic. In this study we focus on the PARADE campaign measurements conducted during August/September 2011 at Mt. Kleiner Feldberg in central Germany. During this campaign a set of radicals, organic and inorganic compounds and oxidants and aerosol properties were measured or calculated. We compared a range of organic and inorganic nucleation theories, evaluating their ability to simulate measured particle formation rates at 3 nm in diameter (J3) for a variety of different conditions. Nucleation mechanisms involving only sulphuric acid tentatively captured the observed noon-time daily maximum in J3, but displayed an increasing difference to J3 measurements during the rest of the diurnal cycle. Including large organic radicals, i.e. organic peroxy radicals (RO2) deriving from monoterpenes and their oxidation products in the nucleation mechanism improved the correlation between observed and simulated J3. This supports a recently proposed empirical relationship for new particle formation that has been used in global models. However, the best match between theory and measurements for the site of interest was found for an activation process based on large organic peroxy radicals and stabilized Criegee intermediates (sCI). This novel laboratory derived algorithm simulated the daily pattern and intensity of J3 observed in the ambient data. In this algorithm organic derived radicals are involved in activation and growth and link the formation rate of smallest aerosol particles with OH during daytime and NO3 during nighttime. Because of the RO2s lifetime is controlled by HO2 and NO we conclude that peroxy radicals and NO seem to play an important role for ambient radical chemistry not only with respect to oxidation capacity but also for the activation process of new particle formation. This is supposed to have significant impact of atmospheric radical species on aerosol chemistry and should to be taken into account when studying the impact of new particles in climate feedback cycles.
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Bonn, B., E. Bourtsoukidis, T. S. Sun, H. Bingemer, L. Rondo, U. Javed, J. Li, et al. "The link between atmospheric radicals and newly formed particles at a spruce forest site in Germany." Atmospheric Chemistry and Physics 14, no. 19 (October 15, 2014): 10823–43. http://dx.doi.org/10.5194/acp-14-10823-2014.
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Abstract. It has been claimed for more than a century that atmospheric new particle formation is primarily influenced by the presence of sulfuric acid. However, the activation process of sulfuric acid related clusters into detectable particles is still an unresolved topic. In this study we focus on the PARADE campaign measurements conducted during August/September 2011 at Mt Kleiner Feldberg in central Germany. During this campaign a set of radicals, organic and inorganic compounds and oxidants and aerosol properties were measured or calculated. We compared a range of organic and inorganic nucleation theories, evaluating their ability to simulate measured particle formation rates at 3 nm in diameter (J3) for a variety of different conditions. Nucleation mechanisms involving only sulfuric acid tentatively captured the observed noon-time daily maximum in J3, but displayed an increasing difference to J3 measurements during the rest of the diurnal cycle. Including large organic radicals, i.e. organic peroxy radicals (RO2) deriving from monoterpenes and their oxidation products, in the nucleation mechanism improved the correlation between observed and simulated J3. This supports a recently proposed empirical relationship for new particle formation that has been used in global models. However, the best match between theory and measurements for the site of interest was found for an activation process based on large organic peroxy radicals and stabilised Criegee intermediates (sCI). This novel laboratory-derived algorithm simulated the daily pattern and intensity of J3 observed in the ambient data. In this algorithm organic derived radicals are involved in activation and growth and link the formation rate of smallest aerosol particles with OH during daytime and NO3 during night-time. Because the RO2 lifetime is controlled by HO2 and NO we conclude that peroxy radicals and NO seem to play an important role for ambient radical chemistry not only with respect to oxidation capacity but also for the activation process of new particle formation. This is supposed to have significant impact of atmospheric radical species on aerosol chemistry and should be taken into account when studying the impact of new particles in climate feedback cycles.
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Porr, Bernd, and Paul Miller. "Forward propagation closed loop learning." Adaptive Behavior 28, no. 3 (May 31, 2019): 181–94. http://dx.doi.org/10.1177/1059712319851070.
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For an autonomous agent, the inputs are the sensory data that inform the agent of the state of the world, and the outputs are their actions, which act on the world and consequently produce new sensory inputs. The agent only knows of its own actions via their effect on future inputs; therefore desired states, and error signals, are most naturally defined in terms of the inputs. Most machine learning algorithms, however, operate in terms of desired outputs. For example, backpropagation takes target output values and propagates the corresponding error backwards through the network in order to change the weights. In closed loop settings, it is far more obvious how to define desired sensory inputs than desired actions, however. To train a deep network using errors defined in the input space would call for an algorithm that can propagate those errors forwards through the network, from input layer to output layer, in much the same way that activations are propagated. In this article, we present a novel learning algorithm which performs such ‘forward-propagation’ of errors. We demonstrate its performance, first in a simple line follower and then in a 1st person shooter game.
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Taylor, David R., Kim Young, and Michael J. Korrer. "The synergy of TLR and STING in cancer immunity." Journal of Immunology 204, no. 1_Supplement (May 1, 2020): 91.30. http://dx.doi.org/10.4049/jimmunol.204.supp.91.30.
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Abstract Adjuvants are essential components of cancer vaccine formulations to promote effective immune responses. We initially screened multiple immune-activating targets, including TLR and STING adjuvants using in-vitro assays to test their ability to activate bone marrow DC (BMDC) and showed STING had the most potent activation (i.e., MHC and co-stimulatory molecules) on DCs. Since STING and TLR signaling is non-redundant, TLR and STING adjuvants were combined to enhance DC activation. We utilized the multidimensional synergy of combinations (MuSyc), a novel synergy algorithm, to assess molecular synergy in combining STING and TLR adjuvants, and we noted that the combination of R848 (TLR7-8) plus STING agonists provided a synergistic efficacious and potent response on BMDCs. From this data-set, we generated a MuSYC synergy strategy where we will utilize max signal dose for STING adjuvant and ten times less of max signal dose for R848 (TLR7-8) to determine synergy for other assays. We tested this same MuSYC strategy for activation of human monocytic cell line THP-1 and determined that the synergy strategy induced similar or better activation effects compared to the max signal dose for both adjuvants. Finally, we tested the in-vivo anti-tumor immune response for the B16 melanoma and MOC2 head/neck tumor-bearing vaccination models that resulted in an improved anti-tumor response for the combinatorial STING-TLR adjuvanted vaccines compared to the single agents.
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Hatzis, Pantelis, Laurens G. van der Flier, Marc A. van Driel, Victor Guryev, Fiona Nielsen, Sergei Denissov, Isaäc J. Nijman, et al. "Genome-Wide Pattern of TCF7L2/TCF4 Chromatin Occupancy in Colorectal Cancer Cells." Molecular and Cellular Biology 28, no. 8 (February 11, 2008): 2732–44. http://dx.doi.org/10.1128/mcb.02175-07.
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ABSTRACT Wnt signaling activates gene expression through the induced formation of complexes between DNA-binding T-cell factors (TCFs) and the transcriptional coactivator β-catenin. In colorectal cancer, activating Wnt pathway mutations transform epithelial cells through the inappropriate activation of a TCF7L2/TCF4 target gene program. Through a DNA array-based genome-wide analysis of TCF4 chromatin occupancy, we have identified 6,868 high-confidence TCF4-binding sites in the LS174T colorectal cancer cell line. Most TCF4-binding sites are located at large distances from transcription start sites, while target genes are frequently “decorated” by multiple binding sites. Motif discovery algorithms define the in vivo-occupied TCF4-binding site as evolutionarily conserved A-C/G-A/T-T-C-A-A-A-G motifs. The TCF4-binding regions significantly correlate with Wnt-responsive gene expression profiles derived from primary human adenomas and often behave as β-catenin/TCF4-dependent enhancers in transient reporter assays.
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Lytaev, Sergey. "Psychological and Neurophysiological Screening Investigation of the Collective and Personal Stress Resilience." Behavioral Sciences 13, no. 3 (March 15, 2023): 258. http://dx.doi.org/10.3390/bs13030258.
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Methodological approaches to assess the human cognitive status are constantly evolving. At the same time, the creation of new assessment methods is accompanied by traditional research. This paper discusses the direction of research on the search for markers of stress resilience. The basis for the formation of the research algorithm was the assessment of activation factors of emotional states, including preceding stress–sensory (cognitive and informational) and psycho-emotional factors. This was determined using methodological techniques, stress factors, working conditions in professional teams, etc. For an express analysis (25–40 min) of diagnosing stress resistance, a research algorithm was justified, consisting of clinical and psychological testing, as well as EEG with traditional tests and analysis of indicators and spectra. Therefore, this research was aimed at the psychological and neurophysiological substantiation of approaches to express algorithms for assessing cognitive functions and resilience to stress under time deficit. A study on 102 healthy subjects and 38 outpatients of a neuropsychiatric clinic was performed. Basic outcomes: the integrative indicator SCL-90-R—”general index of severity” has a high statistical significance (p < 0.05) in both healthy subjects and neuropsychiatric outpatients. The effectiveness of the Mini-Mult test in conditions of time deficit is determined by the results of the scales of hypochondria, depression, hysteria, paranoia, psychasthenia, schizoid and hypomania (p < 0.05). Furthermore, we used a line of logical thinking techniques. A line of four logical methods is highly informative in assessing the mental status in conditions of time deficit. EEG power indices and spectra in theta, delta and alpha frequency ranges are an effective reflection of cognitive status. In this article, a testing algorithm as a variant for assessing neurocognitive status in screening studies of large groups is discussed.
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Maton, Maxime, Philippe Bogaerts, and Alain Vande Wouwer. "Hybrid Dynamic Models of Bioprocesses Based on Elementary Flux Modes and Multilayer Perceptrons." Processes 10, no. 10 (October 14, 2022): 2084. http://dx.doi.org/10.3390/pr10102084.
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The derivation of minimal bioreaction models is of primary importance to develop monitoring and control strategies of cell/microorganism culture production. These minimal bioreaction models can be obtained based on the selection of a basis of elementary flux modes (EFMs) using an algorithm starting from a relatively large set of EFMs and progressively reducing their numbers based on geometric and least-squares residual criteria. The reaction rates associated with the selected EFMs usually have complex features resulting from the combination of different activation, inhibition and saturation effects from several culture species. Multilayer perceptrons (MLPs) are used in order to undertake the representation of these rates, resulting in a hybrid dynamic model combining the mass-balance equations provided by the EFMs to the rate equations described by the MLPs. To further reduce the number of kinetic parameters of the model, pruning algorithms for the MLPs are also considered. The whole procedure ends up with reduced-order macroscopic models that show promising prediction results, as illustrated with data of perfusion cultures of hybridoma cell line HB-58.
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Alba, Josephine, Maria Montagna, and Marco D’Abramo. "Modelling the Activation Pathways in Full-Length Src Kinase." Biophysica 1, no. 2 (June 11, 2021): 238–48. http://dx.doi.org/10.3390/biophysica1020018.
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Src kinases play fundamental roles in several crucial cell processes. Their activity is tightly regulated by conformational transitions between the active and the inactive forms, which are carried out by complex protein structural rearrangements. Here, we present an in-depth study of such structural transitions coupling extensive all-atoms molecular dynamic simulations coupled to an algorithm able to drive the system between defined conformational states. Our results, in line with the available experimental data, confirm the complexity of such a process indicating the main molecular determinants involved. Moreover, the role of an Src inhibitor—able to bind to the protein inactive state—is discussed and compared with available experimental data.
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Kumar, R. Suresh, and P. Manimegalai. "Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals." Current Signal Transduction Therapy 15, no. 1 (July 31, 2020): 77–83. http://dx.doi.org/10.2174/1574362414666190613142424.
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Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.
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Kabus, Desmond, Louise Arno, Lore Leenknegt, Alexander V. Panfilov, and Hans Dierckx. "Numerical methods for the detection of phase defect structures in excitable media." PLOS ONE 17, no. 7 (July 12, 2022): e0271351. http://dx.doi.org/10.1371/journal.pone.0271351.
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Electrical waves that rotate in the heart organize dangerous cardiac arrhythmias. Finding the region around which such rotation occurs is one of the most important practical questions for arrhythmia management. For many years, the main method for finding such regions was so-called phase mapping, in which a continuous phase was assigned to points in the heart based on their excitation status and defining the rotation region as a point of phase singularity. Recent analysis, however, showed that in many rotation regimes there exist phase discontinuities and the region of rotation must be defined not as a point of phase singularity, but as a phase defect line. In this paper, we use this novel methodology and perform a comparative study of three different phase definitions applied to in silico data and to experimental data obtained from optical voltage mapping experiments on monolayers of human atrial myocytes. We introduce new phase defect detection algorithms and compare them with those that appeared in literature already. We find that the phase definition is more important than the algorithm to identify sudden spatial phase variations. Sharp phase defect lines can be obtained from a phase derived from local activation times observed during one cycle of arrhythmia. Alternatively, similar quality can be obtained from a reparameterization of the classical phase obtained from observation of a single timeframe of transmembrane potential. We found that the phase defect line length was (35.9 ± 6.2)mm in the Fenton-Karma model and (4.01 ± 0.55)mm in cardiac human atrial myocyte monolayers. As local activation times are obtained during standard clinical cardiac mapping, the methods are also suitable to be applied to clinical datasets. All studied methods are publicly available and can be downloaded from an institutional web-server.
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Tahami, Ehsan, Amir Homayoun Jafari, and Ali Fallah. "LEARNING TO CONTROL THE THREE-LINK MUSCULOSKELETAL ARM USING ACTOR–CRITIC REINFORCEMENT LEARNING ALGORITHM DURING REACHING MOVEMENT." Biomedical Engineering: Applications, Basis and Communications 26, no. 05 (September 26, 2014): 1450064. http://dx.doi.org/10.4015/s1016237214500641.
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Learning to control the planar three-link musculoskeletal arm by using an Actor–Critic learning algorithm during reaching movements to stationary target is presented. The arm model used in this study includes three skeletal links (hand, forearm and upper arm), three joints (wrist, elbow and shoulder without redundancy) and six nonlinear monoarticular muscles with redundancy which are modeled based on Hill model. The learning system is composed of Actor and Critic parts. For each part, a single layer neural network is used. This learning system applies six activation commands to six muscles at each instant of time. It also uses a reinforcement (reward) feedback for learning process and controlling the arm movement direction. The results showed that with a learning rate α = 0.9 and after 20 episodes, Mean square error (MSE), average reward and average time of reaching the target are gradually converged to the values: 0.0056, 0.02262 and 187 s, respectively. After the 20th episode, the learning will be completed. The research suggests a new direction for designation of learning-based controllers for functional electrical stimulation (FES) applications and for arm movement of autonomous robots.
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Zhang, Anlong, Zhiyun Lin, Bo Wang, and Zhimin Han. "Nonlinear Model Predictive Control of Single-Link Flexible-Joint Robot Using Recurrent Neural Network and Differential Evolution Optimization." Electronics 10, no. 19 (October 6, 2021): 2426. http://dx.doi.org/10.3390/electronics10192426.
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A recurrent neural network (RNN) and differential evolution optimization (DEO) based nonlinear model predictive control (NMPC) technique is proposed for position control of a single-link flexible-joint (FJ) robot. First, a simple three-layer recurrent neural network with rectified linear units as an activation function (ReLU-RNN) is employed for approximating the system dynamic model. Then, using the RNN predictive model and model predictive control (MPC) scheme, an RNN and DEO based NMPC controller is designed, and the DEO algorithm is used to solve the controller. Finally, comparing numerical simulation findings demonstrates the efficiency and performance of the proposed approach. The merit of this method is that not only is the control precision satisfied, but also the overshoots and the residual vibration are well suppressed.
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Nowshiravan Rahatabad, Fereidoun. "Modeling and Control Human Arm With FuzzyGenetic Muscle Model Based on Reinforcement Learning: The Muscle Activation Method." International Clinical Neuroscience Journal 7, no. 3 (June 21, 2020): 138–46. http://dx.doi.org/10.34172/icnj.2020.15.
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Background: The central nervous system (CNS) is optimizing arm movements to reduce some kind of cost function. Simulating parts of the nervous system is one way of obtaining accurate information about the neurological and treatment of neuromuscular diseases. The primary purpose of this paper is to model and control the human arm in a reaching movement based on reinforcement learning (RL) theory. Methods: First, Zajac’s muscle model has improved by a fuzzy system. Second, the proposed muscle model applied to the 6 muscles, which are responsible for a two-link arm that moves in the horizontal plane. Third, the model parameters are approximated based on the genetic algorithm (GA). Experimental data recorded from healthy subjects for assessing the approach. At last, the RL algorithm has utilized to guide the arm for reaching tasks. Results: The results show that: (1) The proposed system is temporally similar to a real arm movement. (2) The RL algorithm can generate the motor commands obtained from electromyographies (EMGs). (3) The similarity of obtained activation function from the system has compared with the real data activation function, which may prove the possibility of RL in the CNS (basal ganglia). Finally, in order to have a graphical and effective representation of the arm model, the virtual reality environment of MATLAB has been used. Conclusion: Since the RL method is a representative of the brain’s control function, it has some features, such as better settling time, not having any peek overshoot, and robustness.
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Abbasi, Ali, Hossein Kazemi Karegar, and Tohid Soleymani Aghdam. "Inter-trip links incorporated optimal protection coordination." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 72. http://dx.doi.org/10.11591/ijece.v10i1.pp72-79.
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Due to advances in smart grid, different communication links as delay, inter-trip and activation are used between relays to enhance the protection system performance. In this paper, the effect of inter-trip links on optimal coordination of directional overcurrent relays (DOCRs) is analytically investigated and modelled. Moreover, an index is proposed to find the optimum locations for inter-trip link installation to reach the minimal fault clearance times under the selectivity constraint. Then a method is proposed to determine the candidate locations of inter-trip links and the associated reduced operating times. An Exhaustive search approach is also used to validate the efficiency of the proposed method. The method is simulated and tested on distribution network of IEEE 33 bus using the Power Factory software and MATLAB optimization toolbox. Genetic algorithm is used as an optimization tool to find optimal settings of relays. The results indicate the capability of proposed method in optimal protection coordination with optimum inter-trips.
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Al-Azzawi, Nemir Ahmed. "Microscopy images segmentation algorithm based on shearlet neural network." Bulletin of Electrical Engineering and Informatics 10, no. 2 (April 1, 2021): 724–31. http://dx.doi.org/10.11591/eei.v10i2.2743.
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Microscopic images are becoming important and need to be studied to know the details and how-to quantitatively evaluate decellularization. Most of the existing research focuses on deep learning-based techniques that lack simplification for decellularization. A new computational method for the segmentation microscopy images based on the shearlet neural network (SNN) has been introduced. The proposal is to link the concept of shearlets transform and neural networks into a single unit. The method contains a feed-forward neural network and uses a single hidden layer. The activation functions are depending on the standard shearlet transform. The proposed SNN is a powerful technology for segmenting an electron microscopic image that is trained without relying on the pre-information of the data. The shearlet neural networks capture the features of full accuracy and contextual information, respectively. The expected value for specific inputs is estimated by learning the functional configuration of a network for the sequence of observed value. Experimental results on the segmentation of two-dimensional microscopy images are promising and confirm the benefits of the proposed approach. Lastly, we investigate on a challenging datasets ISBI 2012 that our method (SNN) achieves superior outcomes when compared to classical and deep learning-based methods.
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Iqbal, Kamran, and Anindo Roy. "Stabilizing PID Controllers for a Single-Link Biomechanical Model with Position, Velocity, and Force Feedback." Journal of Biomechanical Engineering 126, no. 6 (December 1, 2004): 838–43. http://dx.doi.org/10.1115/1.1824134.
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In this paper we address the problem of PID stabilization of a single-link inverted pendulum-based biomechanical model with force feedback, two levels of position and velocity feedback, and with delays in all the feedback loops. The novelty of the proposed model lies in its physiological relevance, whereby both small and medium latency sensory feedbacks from muscle spindle (MS), and force feedback from Golgi tendon organ (GTO) are included in the formulation. The biomechanical model also includes active and passive viscoelastic feedback from Hill-type muscle model and a second-order low-pass function for muscle activation. The central nervous system (CNS) regulation of postural movement is represented by a proportional-integral-derivative (PID) controller. Pade´ approximation of delay terms is employed to arrive at an overall rational transfer function of the biomechanical model. The Hermite–Biehler theorem is then used to derive stability results, leading to the existence of stabilizing PID controllers. An algorithm for selection of stabilizing feedback gains is developed using the linear matrix inequality (LMI) approach.
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García Cabello, Julia. "Mathematical Neural Networks." Axioms 11, no. 2 (February 17, 2022): 80. http://dx.doi.org/10.3390/axioms11020080.
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ANNs succeed in several tasks for real scenarios due to their high learning abilities. This paper focuses on theoretical aspects of ANNs to enhance the capacity of implementing those modifications that make ANNs absorb the defining features of each scenario. This work may be also encompassed within the trend devoted to providing mathematical explanations of ANN performance, with special attention to activation functions. The base algorithm has been mathematically decoded to analyse the required features of activation functions regarding their impact on the training process and on the applicability of the Universal Approximation Theorem. Particularly, significant new results to identify those activation functions which undergo some usual failings (gradient preserving) are presented here. This is the first paper—to the best of the author’s knowledge—that stresses the role of injectivity for activation functions, which has received scant attention in literature but has great incidence on the ANN performance. In this line, a characterization of injective activation functions has been provided related to monotonic functions which satisfy the classical contractive condition as a particular case of Lipschitz functions. A summary table on these is also provided, targeted at documenting how to select the best activation function for each situation.
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Tair, Milan, Aleksandar Mihajlovic, Nikola Savanovic, and Marko Sarac. "A multi-layered image format for the web with an adaptive layer selection algorithm." Serbian Journal of Electrical Engineering 14, no. 2 (2017): 177–97. http://dx.doi.org/10.2298/sjee161010001t.
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In this paper we present a proposed multi-layered image format for use on the web. The format implements an algorithm for selecting adequate layer images depending on the image container's surroundings and size. The layer selection depends on the weighted average brightness of the underlying web page background within the bounds of the image. The proposed image format supports multiple image layers with adjoined thresholds and activation conditions. Depending on these conditions and the underlying background, a layer's visibility will be adequately set. The selection algorithm takes into account the background brightness, each layer's adjoined threshold values, and other newly introduced layer conditions. <br><br><font color="red"><b> This article has been corrected. Link to the correction <u><a href="http://dx.doi.org/10.2298/SJEE1803371E">10.2298/SJEE1803371E</a><u></b></font>
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Li, Wenguo, Zhizeng Luo, and Xugang Xi. "Movement Trajectory Recognition of Sign Language Based on Optimized Dynamic Time Warping." Electronics 9, no. 9 (August 29, 2020): 1400. http://dx.doi.org/10.3390/electronics9091400.
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Movement trajectory recognition is the key link of sign language (SL) translation research, which directly affects the accuracy of SL translation results. A new method is proposed for the accurate recognition of movement trajectory. First, the gesture motion information collected should be converted into a fixed coordinate system by the coordinate transformation. The SL movement trajectory is reconstructed using the adaptive Simpson algorithm to maintain the originality and integrity of the trajectory. The algorithm is then extended to multidimensional time series by using Mahalanobis distance (MD). The activation function of generalized linear regression (GLR) is modified to optimize the dynamic time warping (DTW) algorithm, which ensures that the local shape characteristics are considered for the global amplitude characteristics and avoids the problem of abnormal matching in the process of trajectory recognition. Finally, the similarity measure method is used to calculate the distance between two warped trajectories, to judge whether they are classified to the same category. Experimental results show that this method is effective for the recognition of SL movement trajectory, and the accuracy of trajectory recognition is 86.25%. The difference ratio between the inter-class features and intra-class features of the movement trajectory is 20, and the generalization ability of the algorithm can be effectively improved.
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Guliyev, Namig J., and Vugar E. Ismailov. "A Single Hidden Layer Feedforward Network with Only One Neuron in the Hidden Layer Can Approximate Any Univariate Function." Neural Computation 28, no. 7 (July 2016): 1289–304. http://dx.doi.org/10.1162/neco_a_00849.
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The possibility of approximating a continuous function on a compact subset of the real line by a feedforward single hidden layer neural network with a sigmoidal activation function has been studied in many papers. Such networks can approximate an arbitrary continuous function provided that an unlimited number of neurons in a hidden layer is permitted. In this note, we consider constructive approximation on any finite interval of [Formula: see text] by neural networks with only one neuron in the hidden layer. We construct algorithmically a smooth, sigmoidal, almost monotone activation function [Formula: see text] providing approximation to an arbitrary continuous function within any degree of accuracy. This algorithm is implemented in a computer program, which computes the value of [Formula: see text] at any reasonable point of the real axis.
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Gharsellaoui, Hamza, Mohamed Khalgui, and Samir Ben Ahmed. "New Optimal Preemptively Scheduling for Real-Time Reconfigurable Sporadic Tasks Based on Earliest Deadline First Algorithm." International Journal of Advanced Pervasive and Ubiquitous Computing 4, no. 2 (April 2012): 65–81. http://dx.doi.org/10.4018/japuc.2012040106.
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This paper examines the problem of scheduling the mixed workload of both sporadic (on-line) and periodic (off-line) tasks on uniprocessor in a hard real-time environment. The authors introduce an optimal earliest deadline scheduling algorithm to optimize response time while ensuring that all periodic tasks meet their deadlines and to accept as many sporadic tasks. A necessary and sufficient schedulability test is presented, and an efficient O(n+m) guarantee algorithm is proposed. This optimal algorithm results in dynamic scheduling solutions. They are presented by a proposed intelligent agent-based architecture where a software agent is used to evaluate the response time, to calculate the processor utilization factor and also to verify the satisfaction of real-time deadlines. The agent dynamically provides technical solutions for users where the system becomes unfeasible by sending sporadic tasks to idle times, by modifying the deadlines of tasks, the worst case execution times (WCETs), the activation time, by tolerating some non critical tasks according to the (m, n) firm and a reasonable cost, or in the worst case by removing some non hard (soft) tasks according to predefined heuristic. The authors implement the agent to support these services which are applied to extensive experiments with real-life design examples in order to demonstrate the effectiveness and the excellent performance of the new optimal algorithm in normal and overload conditions.
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K.Venkatesh, G., and P. V.Rao. "Performance analysis of a novel method for fast handovers in TDD and FDD for long term evolution." International Journal of Engineering & Technology 7, no. 1.9 (March 1, 2018): 115. http://dx.doi.org/10.14419/ijet.v7i1.9.9745.
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The LTE Long Term Evolution highly developed Technology, Handover is the essential function of the mobility of user in cellular networks in Time Division Duplex as well as Frequency Division Duplex. Handover is one of the essential that can affect the [QoS] Quality of Service with Capacity of Mobile Broadband Networks. Within mobile cellular network communication systems, a (spectrum) limited shared resource needs to be shared with all the users, so full duplex communication is achieved. This paper involves studying diverse Hand over delay parameters and also focus on reducing “Hard Handover delay” by minimizing interruption time, activation time, wireless channel accesses time as well as the wireless link transmission delay. Technique is developed in order to reduce the handover delay time in Time Division Duplex network which too reduces the wireless channel access time and the wireless link transmission delay. A novel handover algorithm is developed which would decreases the handover delay time and access time inside mobile network environment.Additional work may be conceded on to obtain enhanced performance and Quality of service in Time Division Duplex mobile network.
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Scheibye-Knudsen, Morten, Anne Tseng, Martin Borch Jensen, Karsten Scheibye-Alsing, Evandro Fei Fang, Teruaki Iyama, Sanjay Kumar Bharti, et al. "Cockayne syndrome group A and B proteins converge on transcription-linked resolution of non-B DNA." Proceedings of the National Academy of Sciences 113, no. 44 (October 18, 2016): 12502–7. http://dx.doi.org/10.1073/pnas.1610198113.
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Cockayne syndrome is a neurodegenerative accelerated aging disorder caused by mutations in the CSA or CSB genes. Although the pathogenesis of Cockayne syndrome has remained elusive, recent work implicates mitochondrial dysfunction in the disease progression. Here, we present evidence that loss of CSA or CSB in a neuroblastoma cell line converges on mitochondrial dysfunction caused by defects in ribosomal DNA transcription and activation of the DNA damage sensor poly-ADP ribose polymerase 1 (PARP1). Indeed, inhibition of ribosomal DNA transcription leads to mitochondrial dysfunction in a number of cell lines. Furthermore, machine-learning algorithms predict that diseases with defects in ribosomal DNA (rDNA) transcription have mitochondrial dysfunction, and, accordingly, this is found when factors involved in rDNA transcription are knocked down. Mechanistically, loss of CSA or CSB leads to polymerase stalling at non-B DNA in a neuroblastoma cell line, in particular at G-quadruplex structures, and recombinant CSB can melt G-quadruplex structures. Indeed, stabilization of G-quadruplex structures activates PARP1 and leads to accelerated aging in Caenorhabditis elegans. In conclusion, this work supports a role for impaired ribosomal DNA transcription in Cockayne syndrome and suggests that transcription-coupled resolution of secondary structures may be a mechanism to repress spurious activation of a DNA damage response.
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Song, Yongchao, Tao Huang, Xin Fu, Yahong Jiang, Jindong Xu, Jindong Zhao, Weiqing Yan, and Xuan Wang. "A Novel Lane Line Detection Algorithm for Driverless Geographic Information Perception Using Mixed-Attention Mechanism ResNet and Row Anchor Classification." ISPRS International Journal of Geo-Information 12, no. 3 (March 20, 2023): 132. http://dx.doi.org/10.3390/ijgi12030132.
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Lane line detection is a fundamental and critical task for geographic information perception of driverless and advanced assisted driving. However, the traditional lane line detection method relies on manual adjustment of parameters, and has poor universality, a heavy workload, and poor robustness. Most deep learning-based methods make it difficult to effectively balance accuracy and efficiency. To improve the comprehensive perception ability of lane line geographic information in a natural traffic environment, a lane line detection algorithm based on a mixed-attention mechanism residual network (ResNet) and row anchor classification is proposed. A mixed-attention mechanism is added after the backbone network convolution, normalization and activation layers, respectively, so that the model can focus more on important lane line features to improve the pertinence and efficiency of feature extraction. In addition, to achieve faster detection speed and solve the problem of no vision, the method of lane line location selection and classification based on the row direction is used to detect whether there are lane lines in each candidate point according to the row anchor, reducing the high computational complexity caused by segmentation on a pixel-by-pixel basis of traditional semantic segmentation. Based on TuSimple and CurveLane datasets, multi-scene, multi-environment, multi-linear road image datasets and video sequences are integrated and self-built, and several experiments are designed and tested to verify the effectiveness of the proposed method. The test accuracy of the mixed-attention mechanism network model reached 95.96%, and the average time efficiency is nearly 180 FPS, which can achieve a high level of accuracy and real-time detection process. Therefore, the proposed method can meet the safety perception effect of lane line geographic information in natural traffic environments, and achieve an effective balance between the accuracy and efficiency of actual road application scenarios.
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Kuźnar, Małgorzata, and Augustyn Lorenc. "A Method of Predicting Wear and Damage of Pantograph Sliding Strips Based on Artificial Neural Networks." Materials 15, no. 1 (December 23, 2021): 98. http://dx.doi.org/10.3390/ma15010098.
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The impact of the pantograph of a rail vehicle on the overhead contact line depends on many factors. Among other things, the type of pantograph, i.e., the material of the sliding strip, influences the wear and possible damage to the sliding strip. The possibility of predicting pantograph failures may make it possible to reduce the number of these kinds of failures. This article presents a method for predicting the technical state of the pantograph by using artificial neural networks. The presented method enables the prediction of the wear and damage of the pantograph, with particular emphasis on carbon sliding strips. The paper compares 12 predictive models based on regression algorithms, where different training algorithms and activation functions were used. Two different types of training data were also used. Such a distinction made it possible to determine the optimal structure of the input and output data teaching the neural network, as well as the determination of the best structure and parameters of the model enabling the prediction of the technical condition of the current collector.
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Jiang, Yuning, and Jinhua Li. "Generative Adversarial Network for Image Super-Resolution Combining Texture Loss." Applied Sciences 10, no. 5 (March 3, 2020): 1729. http://dx.doi.org/10.3390/app10051729.
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Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.
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van Alphen, Carolien, Jacqueline Cloos, Robin Beekhof, David G. J. Cucchi, Sander R. Piersma, Jaco C. Knol, Alex A. Henneman, et al. "Phosphotyrosine-based Phosphoproteomics for Target Identification and Drug Response Prediction in AML Cell Lines." Molecular & Cellular Proteomics 19, no. 5 (February 26, 2020): 884–99. http://dx.doi.org/10.1074/mcp.ra119.001504.
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Acute myeloid leukemia (AML) is a clonal disorder arising from hematopoietic myeloid progenitors. Aberrantly activated tyrosine kinases (TK) are involved in leukemogenesis and are associated with poor treatment outcome. Kinase inhibitor (KI) treatment has shown promise in improving patient outcome in AML. However, inhibitor selection for patients is suboptimal.In a preclinical effort to address KI selection, we analyzed a panel of 16 AML cell lines using phosphotyrosine (pY) enrichment-based, label-free phosphoproteomics. The Integrative Inferred Kinase Activity (INKA) algorithm was used to identify hyperphosphorylated, active kinases as candidates for KI treatment, and efficacy of selected KIs was tested.Heterogeneous signaling was observed with between 241 and 2764 phosphopeptides detected per cell line. Of 4853 identified phosphopeptides with 4229 phosphosites, 4459 phosphopeptides (4430 pY) were linked to 3605 class I sites (3525 pY). INKA analysis in single cell lines successfully pinpointed driver kinases (PDGFRA, JAK2, KIT and FLT3) corresponding with activating mutations present in these cell lines. Furthermore, potential receptor tyrosine kinase (RTK) drivers, undetected by standard molecular analyses, were identified in four cell lines (FGFR1 in KG-1 and KG-1a, PDGFRA in Kasumi-3, and FLT3 in MM6). These cell lines proved highly sensitive to specific KIs. Six AML cell lines without a clear RTK driver showed evidence of MAPK1/3 activation, indicative of the presence of activating upstream RAS mutations. Importantly, FLT3 phosphorylation was demonstrated in two clinical AML samples with a FLT3 internal tandem duplication (ITD) mutation.Our data show the potential of pY-phosphoproteomics and INKA analysis to provide insight in AML TK signaling and identify hyperactive kinases as potential targets for treatment in AML cell lines. These results warrant future investigation of clinical samples to further our understanding of TK phosphorylation in relation to clinical response in the individual patient.
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Henna, Mohamed Younes E., and Sayed A. Nagy. "Prevention of Cascaded Events of Distance Relay Zone Three Using Logic Controls." Conference Papers in Engineering 2013 (June 2, 2013): 1–7. http://dx.doi.org/10.1155/2013/198021.
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This paper presents a new method to prevent cascaded events caused by zone 3 elements of distance relays due to transmission line overload by using logic controls. A proposed adaptive distance relay algorithm provides a new concept to distinguish between actual faults and flow transfers and secures time to perform remedial controls by a defense system during cascaded events. Maloperation of distance relay is a very critical situation, leading to more operation of other distance relays and finally partial or total blackout. Logic controls are used to insure the operating decision through observing the system parameters and compare it with a setting already being input to the system. When distance relay is activated and the logic controls find that the system is healthy (unfaulted) and that the activation resulted from transmission line overload not real fault, the operation signal will be blocked to protect the system from maloperation.
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Guadalupi, Valentina, Giacomo Cartenì, Roberto Iacovelli, Camillo Porta, Giovanni Pappagallo, Riccardo Ricotta, and Giuseppe Procopio. "Second-line treatment in renal cell carcinoma: clinical experience and decision making." Therapeutic Advances in Urology 13 (January 2021): 175628722110228. http://dx.doi.org/10.1177/17562872211022870.
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Currently, conventional treatments for metastatic RCC (mRCC) include immune-based combination regimens and/or targeted therapies, the latter mainly acting on angiogenesis, a key element of the process of tumor growth and spread. Although these agents proved able to improve patients’ outcomes, drug resistance and disease progression are still experienced by a substantial number of VEGFR-TKIs-treated mRCC patients. Following the inhibition of the VEGF/VEGFRs axis, two strategies have emerged: either specifically targeting resistance pathways, at the same time continuing to inhibit angiogenesis, or using a completely different approach aimed at re-activating the immune system through the use of inhibitors of specific negative immune checkpoints. These two approaches, practically represented by the use of either cabozantinib or nivolumab, seem to remain a rational therapeutic approach also when first-line immune-based combinations are used. The objective of this study is to design a preferential therapeutic pathway for the second-line treatment of mRCC. The procedure applied in this project was a group discussion, based on the Nominal Group Technique (NGT) method in a meeting session, aimed at defining the therapeutic choice for the second-line treatment of mRCC. The NGT process defined the most relevant parameters that, according to the interviewed panelists, clinicians should consider for the selection of the second-line therapy in the context of advanced renal cell carcinoma of mRCC. The algorithm developed for the treatment selection as a result of this process should thus be considered by clinicians as reference for therapy selection. Plain language summary The result of this paper was the definition of an algorithm intended to suggest a preferential therapeutic pathway considering both the outputs of the Nominal Group Technique (NGT) process and the actual clinical practice and the experience of selected panelists. During the NGT process and the discussion phase, panelists defined the most important parameters to be included in the algorithm that are important for the treatment definition. Cabozantinib and nivolumab are identified as the most reasonable therapeutic options for patients progressing after first-line treatment and are the medication options included in the algorithm for therapy selection.
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TAHAMI, EHSAN, AMIR HOMAYOUN JAFARI, and ALI FALLAH. "APPLICATION OF AN EVOLUTIONARY ACTOR–CRITIC REINFORCEMENT LEARNING METHOD FOR THE CONTROL OF A THREE-LINK MUSCULOSKELETAL ARM DURING A REACHING MOVEMENT." Journal of Mechanics in Medicine and Biology 13, no. 02 (April 2013): 1350040. http://dx.doi.org/10.1142/s0219519413500401.
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In this paper, the control of a planar three-link musculoskeletal arm by using a revolutionary actor–critic reinforcement learning (RL) method during a reaching movement to a stationary target is presented. The arm model used in this study included three skeletal links (wrist, forearm, and upper arm), three joints (wrist, elbow, and shoulder without redundancy), and six non-linear monoarticular muscles (with redundancy), which were based on the Hill model. The learning control system was composed of actor, critic, and genetic algorithm (GA) parts. Two single-layer neural networks were used for each part of the actor and critic. This learning control system was used to apply six activation commands to six monoarticular muscles at each instant of time. It also used a reinforcement (reward) feedback for the learning process and controlling the direction of arm movement. Also, the GA was implemented to select the best learning rates for actor–critic neural networks. The results showed that mean square error (MSE) and average episode time gradually decrease and average reward gradually increases to constant values during the learning of the control policy. Furthermore, when learning was complete, optimal values of learning rates were selected.
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Popova, A. V., K. S. Shulenin, D. V. Cherkashin, S. N. Shulenin, G. G. Kutelev, and D. S. Bogdanov. "Acute myocardial infarction and pulmonary embolism in a young serviceman with a mutation of the plasminogen activator inhibitor gene type 1." Marine Medicine 9, no. 1 (April 20, 2023): 87–94. http://dx.doi.org/10.22328/2413-5747-2023-9-1-87-94.
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The clinical case of acute myocardial infarction and pulmonary embolism in a young soldier with the first occurred protracted attack of angina. The survey found the link between these conditions and gene mutation of type 1 plasminogen activator inhibitor that plays a key role in fibrinolysis by inhibiting the formation of plasmin and leading to slowdown in fibrinolysis processes and a longer-term persistence of blood clot. It is proven that homozygous 4G/4G mutation found in the patient results in the development of arterial and venous thrombosis at a young age and is associated with a tendency to relapse. It clearly shows that hereditary thrombophilia is characterized by the absence of obvious provocative factor and single universal diagnostic algorithm; the diagnosis is based on the comprehensive evaluation of laboratory data.
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Wang, Steve S., and Daniel J. Ehrlich. "Image-Based Phenotypic Screening with Human Primary T Cells Using One-Dimensional Imaging Cytometry with Self-Tuning Statistical-Gating Algorithms." SLAS DISCOVERY: Advancing the Science of Drug Discovery 22, no. 8 (April 26, 2017): 985–94. http://dx.doi.org/10.1177/2472555217705953.
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The parallel microfluidic cytometer (PMC) is an imaging flow cytometer that operates on statistical analysis of low-pixel-count, one-dimensional (1D) line scans. It is highly efficient in data collection and operates on suspension cells. In this article, we present a supervised automated pipeline for the PMC that minimizes operator intervention by incorporating multivariate logistic regression for data scoring. We test the self-tuning statistical algorithms in a human primary T-cell activation assay in flow using nuclear factor of activated T cells (NFAT) translocation as a readout and readily achieve an average Z′ of 0.55 and strictly standardized mean difference of 13 with standard phorbol myristate acetate/ionomycin induction. To implement the tests, we routinely load 4 µL samples and can readout 3000 to 9000 independent conditions from 15 mL of primary human blood (buffy coat fraction). We conclude that the new technology will support primary-cell protein-localization assays and “on-the-fly” data scoring at a sample throughput of more than 100,000 wells per day and that it is, in principle, consistent with a primary pharmaceutical screen.
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Pyatnitskiy, Mikhail A., Viktoriia A. Arzumanian, Sergey P. Radko, Konstantin G. Ptitsyn, Igor V. Vakhrushev, Ekaterina V. Poverennaya, and Elena A. Ponomarenko. "Oxford Nanopore MinION Direct RNA-Seq for Systems Biology." Biology 10, no. 11 (November 4, 2021): 1131. http://dx.doi.org/10.3390/biology10111131.
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Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed—ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.
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Ciobanu, Oana A., Sorina C. Martin, Vlad Herlea, and Simona Fica. "Insights into Epigenetic Changes Related to Genetic Variants and Cells-of-Origin of Pancreatic Neuroendocrine Tumors: An Algorithm for Practical Workup." Cancers 14, no. 18 (September 13, 2022): 4444. http://dx.doi.org/10.3390/cancers14184444.
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Current knowledge on the molecular landscape of pancreatic neuroendocrine tumors (PanNETs) has advanced significantly. Still, the cellular origin of PanNETs is uncertain and the associated mechanisms remain largely unknown. DAXX/ATRX and MEN1 are the three most frequently altered genes that drive PanNETs. They are recognized as a link between genetics and epigenetics. Moreover, the acknowledged impact on DNA methylation by somatic mutations in MEN1 is a valid hallmark of epigenetic mechanism. DAXX/ATRX and MEN1 can be studied at the immunohistochemical level as a reliable surrogate for sequencing. DAXX/ATRX mutations promote alternative lengthening of telomeres (ALT) activation, determined by specific fluorescence in situ hybridization (FISH) analysis. ALT phenotype is considered a significant predictor of worse prognosis and a marker of pancreatic origin. Additionally, ARX/PDX1 expression is linked to important epigenomic alterations and can be used as lineage associated immunohistochemical marker. Herein, ARX/PDX1 association with DAXX/ATRX/MEN1 and ALT can be studied through pathological assessment, as these biomarkers may provide important clues to the mechanism underlying disease pathogenesis. In this review, we present an overview of a new approach to tumor stratification based on genetic and epigenetic characteristics as well as cellular origin, with prognostic consequences.
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Liu, Xiaofei. "Dimensional Defect Detection Research on the Sculpture Surface Combining the Convolutional Neural Network and Gabor." Wireless Communications and Mobile Computing 2022 (September 5, 2022): 1–11. http://dx.doi.org/10.1155/2022/8259265.
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Thematic sculpture creation is an important part of sculpture creation. In particular, the current thematic sculpture creation in China has broken the shackles of serving politics and has shown a variety of aspects such as theme selection, modelling method, material language, and creative purpose. Since the new century, Chinese thematic sculptors have made in-depth analysis and research on the diversity of creative objects, the infinite possibilities of creative expression, and the diversity of creative purposes, especially through the phenomenon of different thematic sculptures to explore their existing predicaments, and give them reflection. If the time is too long, it will reduce the production efficiency of the sculpture production line and increase the operating cost of the enterprise. The Gabor wavelet transform is used to obtain the texture features of the sculpture surface, and the generated high-dimensional features are subjected to random projection dimension reduction. Finally, the binarization algorithm can be used to detect the defect location quickly and accurately. The feature map is extracted by selecting a deep network. The neural network algorithm structure for the sculpture dataset is presented, and the activation function and regularization method are improved on this basis.
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Chen, Binshen, Kai Xu, Yiming Zhang, Peng Xu, Chaoming Li, Jun Liu, and Yawen Xu. "LncRNA ERVH48-1 Contributes to the Drug Resistance of Prostate Cancer and Proliferation through Sponging of miR-4784 to the Activation of the Wnt/β-Catenin Pathway." Cancers 15, no. 6 (March 22, 2023): 1902. http://dx.doi.org/10.3390/cancers15061902.
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Long noncoding RNAs (LncRNAs) are very important in the way that docetaxel resistance (DR) happens in prostate cancer (PCa) patients. ImmuneScore and StromalScore were calculated using PCa-related expression data from TCGA and the ESTIMATE algorithm. We finally found the DEGs that were related to the immune system and the stroma of the patients by making profiles of the DEGs in ImmuneScore and StromalScore. The CancerSubtypes algorithm identified prognosis-related PCa subtypes, and the GSVA assessed their pathway activity. A UniCox regression analysis was used to identify a prognosis-related differential gene set. We then used intersection analysis to identify immunological and prognostic (IP)-related genes (IPGs). The coexpression of long noncoding RNAs (lncRNAs) and IPGs was used to identify IP-related lncRNAs (IPLs). Three methods (SVM-RFE, random forest, and LASSO) were used to find genes that overlap in the GEO database. A gene signature was then validated by building an ROC curve. CIBERSORT technology was used to look at the possibility of a link between the gene signature and immune cells. LncRNA–miRNA pairs and miRNA–mRNA pairs from the miRDB and TargetScan databases were used to construct the ERVH48-1-miR-4784-WNT2B ceRNA regulation network. The concentration of docetaxel elevated the expression of ERVH48-1. Overexpression of ERVH48-1 increased PCa-DR cell proliferation, invasion, and migration while inhibiting apoptosis. ERVH48-1 increased the tumorigenicity of PCa-DR cells in nude mice. ERVH48-1, acting as a ceRNA, targeted miR-4784 to increase WNT2B expression. ICG001 therapy increased Wnt/-catenin signaling activity in PCa-DR cells by inhibiting ERVH48-1. Finally, ERVH48-1 increased docetaxel resistance in a WNT2B-dependent manner via the miR-4784/Wnt/-catenin pathway.
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Szablicki, Mateusz, Piotr Rzepka, and Adrian Halinka. "Simulation Verification of Overcurrent Protection Operation in Power Networks Integrating Renewable Energy Sources in Energy Communities." Energies 14, no. 8 (April 14, 2021): 2193. http://dx.doi.org/10.3390/en14082193.
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This publication discusses the risks of further use of classical overcurrent protections in modern power systems. The increasing penetration of renewable energy sources has caused a lot of challenges, among other things, the development of energy communities that balance local generation and consumption. Usually the interconnection line between the energy community and power systems are only used to balance the shortage or overflow of energy. As a result, most of the time these connections can be low loaded. Such a state can cause incorrect operation of power system protection approached, because the current level values are smaller than the required activation level for the protections. This article presents the potential incorrect operation of digital power system protection with overcurrent function. The obtained simulation results clearly show that the correctness of protection operation is strongly dependent on the level load of lines and the parameters and structure of the protection decision algorithms. These problems occur during low load line periods because these were not taken into account during the classical digital protection design stage. In the future this can cause problems with the fulfillment of the basic protection requirements of stability, speed, sensitivity. This publication suggests extra problems for power system protection research.
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Lee, Sang Hyeon, Myeong-in Choi, SangHoon Lee, SoungHoan Park, and Sehyun Park. "Minimizing Energy Loss over Distance and Activating the Energy Trading System in Microgrid." MATEC Web of Conferences 260 (2019): 01003. http://dx.doi.org/10.1051/matecconf/201926001003.
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As small-scale distributed energy is gradually expanding, commercialization of peer to peer(P2P) energy trading that freely exchanges energy among individuals in various countries is being commercialized, and the Microgrids (MGs) are considered to be an optimal platform for P2P energy trading. Although conducting electricity trade among individuals without going through power companies is still in its infancy, it is expected to expand gradually as the awareness of the shared economy grows and the MG spreads. Research on more efficient trading systems is needed while trading energy in MG. Therefore we propose a more efficient energy trading system that minimizes the loss in proportion to the distance of the power line when energy trading is performed in the MG. We have constructed a virtual MG environment and experimented with energy trading scenarios. As a result, when the algorithm is applied, loss in proportion to the distance is reduced by 2.495% and energy trading becomes more active. The amount of energy and the number of trades increased by 1.5 times during the energy trading process.
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Huang, Xiang Jun, Chao Zhang, and Qing Hua Zheng. "Generating Personalized Navigation Learning Path Based on Knowledge Map." International Journal of Technology and Educational Marketing 4, no. 2 (July 2014): 1–17. http://dx.doi.org/10.4018/ijtem.2014070101.
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With a rapid development of Internet, E-Learning is becoming a new learning mode. E-Learning is not limited by time and space. It also has a large number of on-line learning resource. However, it has many disadvantages for students, such as information overload, disorientation, low learning efficiency, low user satisfaction and so on. Our aim is to improve learning efficiency and user satisfaction by overcoming information overload and disorientation of E-Learning system. This paper proposes an algorithm by combining Spreading-Activation Theory and techniques of classifying and sorting knowledge. The algorithm can generate a near optimal navigation learning path(NLP) based on a student's target knowledge unit(TKU) and knowledge map(KM) which it belongs to. NLP provides students an appropriate learning instruction to effectively eliminate disorientation during the process when they are learning interested knowledge units. The essential tasks of the algorithm is to filter redundant information and sort candidate knowledge units. So its realization process can be divided into three phrases: first, generating candidate complement map to overcome information overload. Because the candidate complement map only contains essential candidate knowledge units and learning dependencies among them to master TKU. Second, constructing learning features to discrete the candidate complement map to implement techniques of sorting knowledge conveniently. Final, sorting candidate knowledge units to get an appropriate NLP by using a Secondary Sort Strategy(SSS). The experimental results have shown that our method is sound for improving learning efficiency and users' satisfaction.
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Chu, Wing-Keung, Li-Man Hung, Chun-Wei Hou, and Jan-Kan Chen. "MicroRNA 630 Represses NANOG Expression through Transcriptional and Post-Transcriptional Regulation in Human Embryonal Carcinoma Cells." International Journal of Molecular Sciences 23, no. 1 (December 21, 2021): 46. http://dx.doi.org/10.3390/ijms23010046.
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The pluripotent transcription factor NANOG is essential for maintaining embryonic stem cells and driving tumorigenesis. We previously showed that PKC activity is involved in the regulation of NANOG expression. To explore the possible involvement of microRNAs in regulating the expression of key pluripotency factors, we performed a genome-wide analysis of microRNA expression in the embryonal carcinoma cell line NT2/D1 in the presence of the PKC activator, PMA. We found that MIR630 was significantly upregulated in PMA-treated cells. Experimentally, we showed that transfection of MIR630 mimic into embryonal carcinoma cell lines directly targeted the 3′UTR of OCT4, SOX2, and NANOG and markedly suppressed their expression. RNAhybrid and RNA22 algorithms were used to predict miRNA target sites in the NANOG 3′UTR, four possible target sites of MIR630 were identified. To examine the functional interaction between MIR630 and NANOG mRNA, the predicted MIR630 target sites in the NANOG 3′UTR were deleted and the activity of the reporters were compared. After targeted mutation of the predicted MIR630 target sites, the MIR630 mimic inhibited NANOG significantly less than the wild-type reporters. It is worth noting that mutation of a single putative binding site in the 3′UTR of NANOG did not completely abolish MIR630-mediated suppression, suggesting that MIR630 in the NANOG 3′UTR may have multiple binding sites and act together to maximally repress NANOG expression. Interestingly, MIR630 mimics significantly downregulated NANOG gene transcription. Exogenous expression of OCT4, SOX2, and NANOG lacking the 3′UTR almost completely rescued the reduced transcriptional activity of MIR630. MIR630 mediated the expression of differentiation markers in NT2/D1 cells, suggesting that MIR630 leads to the differentiation of NT2/D1 cell. Our findings show that MIR630 represses NANOG through transcriptional and post-transcriptional regulation, suggesting a direct link between core pluripotency factors and MIR630.
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German, L. A., A. Yu Popov, A. V. Samorukov, D. V. Ishkin, D. V. Yakunin, and K. S. Subkhanverdiev. "A new algorithm for automating power supply of an AC traction network with a sectioning points on switches." Vestnik of the Railway Research Institute 76, no. 5 (October 28, 2017): 266–72. http://dx.doi.org/10.21780/2223-9731-2017-76-5-266-272.
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The article considers modern sectioning points on contact network and determining the location of the damage, imthe switches, which differs by the introduction of new functions for plemented in the IntTer smart terminal. These new functions are determining a stable (or passing) short circuit in the disconnected deciding whether to perform a quick automatic restart of the circuit breakers after an emergency shutdown or finding the fault zone without testing the insulation of the contact network. The principle of operation of the functions under consideration is analyzed in the article. The limits of the optimum time of the current-free pause of the circuit breaker for automatic re-activation (AR) for each inter-substation zone are established. The paper describes the existing algorithm for automating power supply of an AC traction network (the normal reclosing algorithm) and indicates the unfavorable consequences from its application, in particular, related to the long time of the absence of voltage in the contact network. It was noted that the decision to reduce the voltage recovery time performed by introducing the function of determining the passing (or stable) short-circuit in the switched-off contact network in traction substations is due to the cost of additional high-voltage equipment and requires a rethinking. Thus, a new algorithm for automating the power supply of the traction network (the BAR algorithm) is presented, which is quite easily implemented at the modern sectioning post, which has voltage transformers for each supply line of the contact network, in contrast to the supply lines of the substation contact network and is caused by the transfer of the function of determining the passing (or steady ) short circuit to the sectioning point. In the article, the issue of performing the automatic reclosure of the power line of the traction substation is considered dependent on the successful operation of the BAR algorithm of the sectioning point. It is established that the most rational option is the implementation of a dependent automatic reclosing of a traction substation using telemechanics. The authors analyze the possibility of manufacturing compact sections of the ac contact network on the basis of single-phase re-closers OR-27.5 kV. It is noted that it is advisable to use a new automation scheme in conjunction with the sectionalization points on reclosers.
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Taylor, David, and Michael Korrer. "581 Multi-dimensional Synergy of Combinations (MuSYC) Algorithm Optimizes Combinatorial STING and TLR Adjuvant Cancer Vaccines." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A611. http://dx.doi.org/10.1136/jitc-2021-sitc2021.581.
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BackgroundOptimized cancer vaccine’s T cell priming potential can promote their translation in the current clinical climate of immune checkpoint inhibitor approval for many cancers.MethodsTo rigorously optimize adjuvant combinations that would effectuate an improved in vivo anti-tumor response, we utilized a novel algorithm, the multi-dimensional synergy of combinations (MuSYC), to maximize efficacy and minimize dosing for various classes of adjuvant combinations (Figure 1).ResultsIn-vitro, the MuSYC algorithm characterized the combination of R848 (TLR7/8 adjuvant) and STING agonist as synergistically efficacious and potent in activating murine bone marrow-derived dendritic cells (mBMDCs) and human monocytic cell line THP-1. These two selected adjuvants were then used to generate a MuSYC-derived optimized combination strategy for optimal in vivo priming. Finally, using B16 melanoma and MOC1 head and neck models, MuSYC-optimized cancer vaccines had the best anti-tumor response associated with increased tumor-infiltrating lymphocytes and changes in myeloid infiltration.Abstract 581 Figure 1ConclusionsCumulatively, we believe our MuSYC-centered approach will optimize translatable adjuvant combinations to improve cancer immunotherapy.