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The Presence of Background Noise Extends the Competitor Space in Native and Non-Native Spoken-Word Recognition: Insights from computational modeling

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COGNITIVE SCIENCE 2022年第2期46卷 e13110页

作者： Karaminis, Themis Hintz, Florian Scharenborg, Odette Edge Hill Univ Dept Psychol St Helens Rd Ormskirk L39 4QP England Max Planck Inst Psycholinguist Dept Psychol Language Nijmegen Netherlands Delft Univ Technol Multimedia Comp Grp Delft Netherlands

Oral communication often takes place in noisy environments, which challenge spoken-word recognition. Previous research has suggested that the presence of background noise extends the number of candidate words competing with the target word for recognition and that this extension affects the time course and accuracy of spoken-word recognition. In this study, we further investigated the temporal dynamics of competition processes in the presence of background noise, and how these vary in listeners with different language proficiency (i.e., native and non-native) using computational modeling. We developed ListenIN (Listen-In-Noise), a neural-network model based on an autoencoder architecture, which learns to map phonological forms onto meanings in two languages and simulates native and non-native spoken-word comprehension. We also examined the model's activation states during online spoken-word recognition. These analyses demonstrated that the presence of background noise increases the number of competitor words, which are engaged in phonological competition and that this happens in similar ways intra and interlinguistically and in native and non-native listening. Taken together, our results support accounts positing a "many-additional-competitors scenario" for the effects of noise on spoken-word recognition.

关键词： Spoken-word recognition Phonological competition Competitor space Noise Non-native listening computational modeling Neurocomputational model Deep neural networks

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Biological multiscale computational modeling: A promising tool for 3D bioprinting and tissue engineering

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Bioprinting 2022年 28卷

作者： dos Santos, Bianca Cristina Noritomi, Pedro Yoshito da Silva, Jorge Vicente Lopes Maia, Izaque Alves Manzini, Bruna Maria Laboratory of Three–Dimensional Technologies Center for Information Technology Renato Archer (CTI) Campinas 13069-901 Brazil

The progress of three-dimensional (3D) bioprinting techniques has driven several advances in tissue engineering (TE), which allow the obtention of biological constructs analogous to native tissues. These methods lead to the development of structures that can integrate with the extracellular matrix of the host tissue, promoting better assimilation of the implant in the injured spot. However, primary and pre-clinical researches in the regenerative medicine area still have limitations. The high cost of reagents, animal models, and the long period for completion are some challenges to be overcome. Consequently, multiscale biological simulations have stimulated researchers’ interest;they allow simulation conditions close to natural systems. Then, using computational tools, biological systems can be modeled at different scales of organization and size, creating multicellular models and allowing their application to complex tissues. Although software for multiscale biological simulations demands a high computational power, the advantages associated with in silico analysis are of great interest. In this way, the simulation contributes to the experimental results in laboratories because certain situations start to be foreseen during the modeling stages, later reducing the time and expense of materials. This review provides an overview of 3D bioprinting techniques, addressing their importance in TE development. Moreover, the main aspects of bioengineering are highlighted, focusing on multiscale modeling and the leading software used for biological computational modeling, which could be a powerful tool when integrated with 3D bioprinting and TE. © 2022 Elsevier B.V.

关键词： 3D bioprinting Bioengineering computational modeling Multiscale modeling Tissue engineering

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Phase-dependent Stimulation for Modulating Phase-amplitude Coupling: A computational modeling Approach 42

Phase-dependent Stimulation for Modulating Phase-amplitude C...

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42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Salimpour, Yousef Nayak, Anish Naydanova, Elizaveta Kim, Min Jae Hwang, Brian Y. Mills, Kelly A. Kudela, Pawel Anderson, William S. Johns Hopkins Sch Med Dept Neurosurg Funct Neurosurg Lab Baltimore MD 21218 USA Johns Hopkins Sch Med Dept Neurosci Baltimore MD USA Johns Hopkins Sch Med Dept Biomed Engn Baltimore MD USA Johns Hopkins Sch Med Dept Neurol Baltimore MD USA

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119908

Phase-amplitude coupling (PAC), in which the amplitude of a faster neural oscillation couples to the phase of a slower rhythm, is one of the most common representations of complex neuronal rhythmic activities. In a healthy brain, PAC accompanies cognitive function, and abnormal patterns of PAC have been linked to several neurological disorders. Among the various brain neuromodulation techniques, phase-dependent stimulation has a strong potential to modulate PAC levels. In this study, we utilize a computational model in the NEURON environment based on a detailed mathematical model of neuronal populations, consisting of networks with both excitatory and inhibitory neurons, to simulate PAC generation. The model was then used to investigate the modulatory effects of phase-dependent stimulation on the generated PAC. Simulated data from the model shows that stimulation locked to the phase of slower rhythms increased PAC level during stimulation. These results demonstrate the capacity of phase-dependent stimulation to modulate PAC, which could allow for applications in the treatment of neurological disorders associated with abnormal PAC, such as Parkinson's disease.

关键词： Picture archiving and communication systems computational modeling Mathematical model Couplings Oscillators Brain modeling Neurons

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Understanding Ventricular Tachyarrhythmias Related to Acute Myocardial Ischemia: A computational modeling Approach 8th

Understanding Ventricular Tachyarrhythmias Related to Acute ...

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8th Latin American Conference on Biomedical Engineering (CLAIB) / 42nd National Conference on Biomedical Engineering (CNIB)

作者： Mena, Andres Felix Rodriguez-Matas, Jose Gonzalez-Ascaso, Ana Ferrero, Jose M. Univ Zaragoza Zaragoza Spain Politecn Milan Milan Italy Univ Politecn Valencia Valencia Spain

ISBN: (纸本)9783030306489;9783030306472

This paper describes a computational approach designed to study ventricular tachyarrhythmias arising from acute myocardial ischemia. Since these cardiac disorders can frequently lead to sudden cardiac death, understanding their mechanisms is key to improving their diagnosis and therapy. The use of computational simulations based on multiscale mathematical modeling has proved to be a powerful tool in unraveling the causes of this phenomenon. In the first part of this work, we reformulated a model of the ischemic human ventricular cell to simulate the features of the action potentials during acute ischemia. We then incorporated the model into an electrophysiologically-detailed three-dimensional virtual human heart, which was able to reproduce the typical ventricular tachyarrhythmias in the first 15 min of ischemia with rhythms resembling ventricular fibrillation. The results suggest that the extracellular potassium concentration and the presence of a wash-out subendocardial zone are key factors in the vulnerability of the ischemic myocardium to arrhythmias.

关键词： computational modeling Myocardial ischemia Ventricular tachyarrhythmias Action potential Hyperkalemia

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Review on computational modeling for the property, process, product and performance (PPPP) characteristics of additively manufactured porous magnesium implants

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Bioprinting 2022年 28卷

作者： Imran, Ramsha Al Rashid, Ans Koç, Muammer Division of Sustainable Development College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha 34110 Qatar Faculty of Engineering University of Karabük Karabük 78050 Turkey

Recently, magnesium (Mg) and its alloys are gaining the attention of researchers as these materials can provide mechanical properties comparable to natural bone. Additive manufacturing (AM) techniques have also evolved in the biomedical sector owing to their precision in fabricating desired parts with varying shapes, intricacy, and porosity required for implant functionality. With increasing interest in AM of Mg-based biomedical implants, there is a pressing need to understand existing accomplishments, state-of-the-art materials, and fabrication technology and to identify remaining research gaps through a comprehensive literature review existing in this field and highlight hindrances and challenges associated. In this review study, a particular focus is placed on understanding computational modeling techniques employed for the design, manufacturing, and performance analysis of AM porous scaffolds. Therefore, progress in material synthesis and associated challenges are reviewed in this study, and conclusions and future research recommendations are drawn based on reviewed literature. This review study concludes that there is an unmet need to develop accurate, fast, and inexpensive computational modeling for AM of Mg-based implants to increase predictability capacity and capabilities of material synthesis, fabrication, and resulting product property and performance. © 2022 The Authors

关键词： 3D printing Additive manufacturing Biodegradable implants Biomaterials computational modeling

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Leveraging computational modeling to Understand Infectious Diseases

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CURRENT PATHOBIOLOGY REPORTS 2020年第4期8卷 149-161页

作者： Jenner, Adrianne L. Aogo, Rosemary A. Davis, Courtney L. Smith, Amber M. Craig, Morgan Univ Montreal Dept Math & Stat Pavillon Andre Aisenstadt Montreal PQ H3C 3J7 Canada St Justine Univ Hosp Res Ctr Montreal PQ Canada Univ Tennessee Ctr Hlth Sci Dept Pediat Memphis TN 38163 USA Pepperdine Univ Div Nat Sci Malibu CA USA

Purpose of Review computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation. Recent Findings Combining mechanistic models and machine learning algorithms has led to improvements in the treatment ofShigellaand tuberculosis through the development of novel compounds. modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps. Summary computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

关键词： Infectious diseases Viruses Parasites Bacteria Mathematics computational modeling

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Five Ways in Which computational modeling Can Help Advance Cognitive Science: Lessons From Artificial Grammar Learning

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TOPICS IN COGNITIVE SCIENCE 2020年第3期12卷 925-941页

作者： Zuidema, Willem French, Robert M. Alhama, Raquel G. Ellis, Kevin O'Donnell, Timothy J. Sainburg, Tim Gentner, Timothy Q. Univ Amsterdam Inst Log Language & Computat NL-1098 XG Amsterdam Netherlands Univ Burgundy LEAD CNRS Dijon France Max Planck Inst Psycholinguist Nijmegen Netherlands MIT Dept Brain & Cognit Sci Cambridge MA 02139 USA McGill Univ Dept Linguist Montreal PQ Canada Univ Calif San Diego Dept Psychol La Jolla CA 92093 USA Univ Calif San Diego Div Biol Sci La Jolla CA 92093 USA

There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques-even simple ones that are straightforward to use-can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

关键词： computational modeling Neural networks Formal grammars Bayesian modeling Artificial language learning Artificial grammar learning

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Image-Guidance and computational modeling to Develop and Characterize Microwave Thermal Therapy Platforms

Image-Guidance and Computational Modeling to Develop and Cha...

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作者： Faridi, Pegah Kansas State University

学位级别：Ph.D.

This dissertation focuses on the development of magnetic resonance imaging (MRI)-guided microwave thermal therapy systems for driving experimental studies in small animals, and to experimentally validate computational models of microwave ablation, which are widely employed for device design and characterization. MRI affords noninvasive monitoring of spatial temperature profiles, thereby providing a means to to quantitatively monitor and verify delivery of prescribed thermal doses in experimental studies and clinical use, as well as a means to validate thermal profiles predicted by computational models of thermal therapy. A contribution of this dissertation is the development and demonstration of a system for delivering mild hyperthermia to small animal targets, thereby providing a platform for driving basic research studies investigating the use of heating as part of cancer treatment strategies. An experimentally validated 3D computational model was employed to design and characterize a non-invasive directional water-cooled microwave hyperthermia applicator for MRI guided delivery of hypethermia in small animals. Following a parametric model-based design approach, a reflector aperture angle of 120°, S-shaped monopole antenna with 0.6 mm displacement, and a coolant flow rate of 150 ml/min were selected as applicator parameters that enable conformal delivery of mild hyperthermia to tumors in experimental animals. The system was integrated with real-time high-field 14.1 T MRI thermometry and feedback control to monitor and maintain target temperature elevations in the range of 4 – 5 °C (hypethermic range). 2 - 4 mm diameter targets positioned 1 – 3 mm from the applicator surface were heated to hyperthermic temperatures, with target coverage ratio ranging between 76 - 93 % and 11 – 26 % of non-targeted tissue heated. Another contribution of this dissertation is using computational models to determine how the fibroids altered ablation profile of a microwave applicator fo

关键词： computational modeling Microwave Thermal therapy Image-guidance Ablation

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Investigation of the Effects of Unusual Genome Architecture on the Evolution of the Ciliate Tetrahymena Using computational modeling

Investigation of the Effects of Unusual Genome Architecture ...

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作者： Zhang, Hao University of Houston

学位级别：Ph.D., Doctor of Philosophy

Tetraymena is a model organism in molecular biology and has a very unusual genome architecture, including nuclear dimorphism, amitotic division of the somatic nucleus during asexual reproduction, capability to control the copy number of chromosomes during amitosis and the ability to have facultative sex when a germline nucleus is present. Recent studies also revealed that Tetrahymena has very special evolutionary characteristics, such as the prevalence of successful asexuality within the genus and the extremely low mutation rate found in the germline nucleus. How Tetrahymena evolves such unusual characteristics is still unclear, and whether and how the unusual genome architecture contributes to the unusual evolutionary characteristics remain largely untested or uninvestigated. In this study, I investigated the effect of these unusual genome features on the evolution of Tetrahymena using computational modeling, particularly on their contributions to their successful asexuality and the extremely low mutation rate. I found that compared to mitosis, amitosis of somatic nucleus, together with copy number control, can both decelerate the operation of Muller’s ratchet to a sexual-comparable extent and speed up the adaptation to changing environments. I also found that although not expressed during asexual generations, the mutations arising in the germline nucleus can also respond to selection acting on the somatic nucleus following sexual reproduction, which rejects the previous explanation for the low germline nucleus mutation rate in Tetrahymena. Instead, my results showed that the reproduction strategies adopted by Tetrahymena, including amitosis and facultative sex, can naturally promote the reduction of mutation rate under selection. This study highlighted the contribution of unusual genome architecture to the achievement of unusual evolutionary characteristics found in Tetrahymena, which both lead to a better understanding of the evolution of this organism and elucid

关键词： Genome architecture Evolution Ciliates computational modeling

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computational modeling of psychological resilience trajectories during breast cancer treatment 19

Computational modeling of psychological resilience trajector...

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19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Manikis, Georgios C. Kourou, Konstantina Poikonen-Saksela, Paula Kondylakis, Haridimos Karademas, Evangelos Marias, Kostas Katehakis, Dimitrios G. Koumakis, Lefteris Kouroubali, Angelina Pat-Horenczyk, Ruth Fotiadis, Dimitrios I. Tsiknakis, Manolis Simos, Panagiotis FORTH ICS Computat Biomed Lab Iraklion Greece FORTH IMBB Inst Mol Biol & Biotechnol Ioannina Greece Helsinki Univ Hosp Comprehens Canc Ctr Helsinki Finland Univ Crete Iraklion Greece Hellen Mediterranean Univ Iraklion Greece Hebrew Univ Jerusalem Sch Social Work Jerusalem Israel

ISBN: (纸本)9781728146171

Coping with breast cancer and its consequences has now become a major socioeconomic challenge. The BOUNCE EU H2020 project aims at building a quantitative mathematical model of factors associated with optimal adjustment capacity to cancer. This paper gives an overview of the project targets and on the algorithmic methods focusing on modeling the psychological resilience trajectories during breast cancer treatment.

关键词： computational modeling Machine Learning Resilience Cancer

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