Mediaboss Marketing

Specialty

Please choose I'm not a medical professional. Allergy and Immunology Anatomy Anesthesiology Cardiac/Thoracic/Vascular Surgery Cardiology Critical Care Dentistry Dermatology Diabetes and Endocrinology Emergency Medicine Epidemiology and Public Health Family Medicine Forensic Medicine Gastroenterology General Practice Genetics Geriatrics Health Policy Hematology HIV/AIDS Hospital-based Medicine I'm not a medical professional. Infectious Disease Integrative/Complementary Medicine Internal Medicine Internal Medicine-Pediatrics Medical Education and Simulation Medical Physics Medical Student Nephrology Neurological Surgery Neurology Nuclear Medicine Nutrition Obstetrics and Gynecology Occupational Health Oncology Ophthalmology Optometry Oral Medicine Orthopaedics Osteopathic Medicine Otolaryngology Pain Management Palliative Care Pathology Pediatrics Pediatric Surgery Physical Medicine and Rehabilitation Plastic Surgery Podiatry Preventive Medicine Psychiatry Psychology Pulmonology Radiation Oncology Radiology Rheumatology Substance Use and Addiction Surgery Therapeutics Trauma Urology Miscellaneous

Visit link:
Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning - Cureus

Tags:

Nothing is built to last, not even the stuff we create to last as long as possible. Everything eventually degrades, especially art, and many people make careers and hobbies out of restoring timeworn items. AI could provide a useful second pair of eyes during the process.

Was Rahman pointed out that machine learning has served a vital role in art restoration by figuring out the most likely missing pieces that need replacing. Consider the exorcism scene in "Invincible;" Machine learning cuts down on the time-consuming, mind-numbing work human restorers have to carry out. To be fair, machine learning is technically different from AI, but it is also a subset of AI, so since we can use machine learning in art restoration, it stands to reason we could use AI, too.

Rahman also stated machine learning helps guide art restorers and is generally more accurate than prior techniques. More importantly, Rahman believes AI programs assigned to art restoration could prevent botched attempts that are the product of human error or when someone's pride exceeds their talent. Rahman cited the disastrous event when a furniture restorer forever disfigured Bartolom Esteban Murillo's Immaculate Conception, but that is far from the only case where an AI could come in handy. After all, someone once tried restoring EliasGarcia Martinez' Ecce Homofresco andaccidentally birthed what is colloquially known as "Monkey Christ."

While a steady hand and preternatural skill are necessary to rekindle the glory of an old painting or sculpture, Rahman believes AI could provide a guiding hand that improves the result's quality, provided the restorer already knows what they're doing.

Read more here:
An AI Ethics Researcher's Take On The Future Of Machine Learning In The Art World - SlashGear

Tags:

The following is a summary of Improving emotion perception in cochlear implant users: insights from machine learning analysis of EEG signals, published in the April 2024 issue of Neurology by Paquette al.

Cochlear implants provide some hearing restoration, but limited emotional perception in sound hinders social interaction, making it essential to study remaining emotion perception abilities for future rehabilitation programs.

Researchers conducted a retrospective study to investigate the remaining emotion perception abilities in cochlear implant users, aiming to improve rehabilitation programs by understanding how well they can still perceive emotions in sound.

They explored the neural basis of these remaining abilities by examining if machine learning methods could detect emotion-related brain patterns in 22 cochlear implant users. Employing a random forest classifier on available EEG data, they aimed to predict auditory emotions (vocal and musical) from participants brain responses.

The results showed consistent emotion-specific biomarkers in cochlear implant users, which could potentially be utilized in developing effective rehabilitation programs integrating emotion perception training.

Investigators concluded that the study demonstrated the promise of machine learning for enhancing cochlear implant user outcomes, especially regarding emotion perception.

Source: bmcneurol.biomedcentral.com/articles/10.1186/s12883-024-03616-0

More:
Enhancing Emotion Recognition in Users with Cochlear Implant Through Machine Learning and EEG Analysis - Physician's Weekly

Tags:

COLUMBUS, Ohio, April 22, 2024 Researchers at Ohio State University are pioneering the field of imageomics. Founded on advancements in machine learning and computer vision, the researchers are using imageomics to explore fundamental questions about biological processes by combining images of living organisms with computer-enabled analysis.

The field was the subject of a presentation by Wei-Lun Chao, an investigator at Ohio State Universitys Imageomics Institute and a distinguished assistant professor, during the annual meeting of the American Association for the Advancement of Science (AAAS). The presentation focused on the fields application for micro- to macro-level problems by turning research questions into computable problems.

Nowadays we have many rapid advances in machine learning and computer vision techniques, said Chao. If we use them appropriately, they could really help scientists solve critical but laborious problems.

Traditional methods for image classification with trait detection require a huge amount of human annotation, but our method doesnt, said Chao. We were inspired to develop our algorithm through how biologists and ecologists look for traits to differentiate various species of biological organisms.

Chao said that one of the most challenging parts of fostering imageomics research is integrating different parts of scientific culture to collect enough data and form novel scientific hypotheses from them. That being said, he is enthusiastic about its potential to allow for the natural world to be seen within multiple fields.

What we really want is for AI to have strong integration with scientific knowledge, and I would say imageomics is a great starting point towards that, he said.

Chaos AAAS presentation, An Imageomics Perspective of Machine Learning and Computer Vision: Micro to Global, was part of the session Imageomics: Powering Machine Learning for Understanding Biological Traits.

Continue reading here:
Imageomics Applies AI and Vision Advancements to Biological Questions - Photonics.com

Tags:

Grimaldi, D. & Engel, M. S. Evolution of the Insects (Cambridge Univ. Press, 2005).

Deora, T., Gundiah, N. & Sane, S. P. Mechanics of the thorax in flies. J. Exp. Biol. 220, 13821395 (2017).

Article PubMed Google Scholar

Gu, J. et al. Recent advances in convolutional neural networks. Pattern Recognit. 77, 354377 (2018).

Article ADS Google Scholar

Kramer, M. A. Nonlinear principal component analysis using autoassociative neural networks. AlChE J. 37, 233243 (1991).

Article ADS CAS Google Scholar

Pringle, J. W. S. The excitation and contraction of the flight muscles of insects. J. Physiol. 108, 226232 (1949).

Article CAS PubMed PubMed Central Google Scholar

Josephson, R. K., Malamud, J. G. & Stokes, D. R. Asynchronous muscle: a primer. J. Exp. Biol. 203, 27132722 (2000).

Article CAS PubMed Google Scholar

Gau, J. et al. Bridging two insect flight modes in evolution, physiology and robophysics. Nature 622, 767774 (2023).

Article ADS CAS PubMed PubMed Central Google Scholar

Boettiger, E. G. & Furshpan, E. The mechanics of flight movements in diptera. Biol. Bull. 102, 200211 (1952).

Article Google Scholar

Pringle, J. W. S. Insect Flight (Cambridge Univ. Press, 1957).

Miyan, J. A. & Ewing, A. W. How Diptera move their wings: a re-examination of the wing base articulation and muscle systems concerned with flight. Phil. Trans. R. Soc. B 311, 271302 (1985).

ADS Google Scholar

Wisser, A. Wing beat of Calliphora erythrocephala: turning axis and gearbox of the wing base (Insecta, Diptera). Zoomorph. 107, 359369 (1988).

Article Google Scholar

Ennos, R. A. A comparative study of the flight mechanism of diptera. J. Exp. Biol. 127, 355372 (1987).

Article Google Scholar

Dickinson, M. H. & Tu, M. S. The function of dipteran flight muscle. Comp. Biochem. Physiol. A 116, 223238 (1997).

Article Google Scholar

Nalbach, G. The gear change mechanism of the blowfly (Calliphora erythrocephala) in tethered flight. J. Comp. Physiol. A 165, 321331 (1989).

Article Google Scholar

Walker, S. M., Thomas, A. L. R. & Taylor, G. K. Operation of the alula as an indicator of gear change in hoverflies. J. R. Soc. Inter. 9, 11941207 (2011).

Article Google Scholar

Walker, S. M. et al. In vivo time-resolved microtomography reveals the mechanics of the blowfly flight motor. PLoS Biol. 12, e1001823 (2014).

Article PubMed PubMed Central Google Scholar

Wisser, A. & Nachtigall, W. Functional-morphological investigations on the flight muscles and their insertion points in the blowfly Calliphora erythrocephala (Insecta, Diptera). Zoomorph. 104, 188195 (1984).

Article Google Scholar

Heide, G. Funktion der nicht-fibrillaren Flugmuskeln von Calliphora. I. Lage Insertionsstellen und Innervierungsmuster der Muskeln. Zool. Jahrb., Abt. allg. Zool. Physiol. Tiere 76, 8798 (1971).

Google Scholar

Fabian, B., Schneeberg, K. & Beutel, R. G. Comparative thoracic anatomy of the wild type and wingless (wg1cn1) mutant of Drosophila melanogaster (Diptera). Arth. Struct. Dev. 45, 611636 (2016).

Article Google Scholar

Tu, M. & Dickinson, M. Modulation of negative work output from a steering muscle of the blowfly Calliphora vicina. J. Exp. Biol. 192, 207224 (1994).

Article CAS PubMed Google Scholar

Tu, M. S. & Dickinson, M. H. The control of wing kinematics by two steering muscles of the blowfly (Calliphora vicina). J. Comp. Physiol. A 178, 813830 (1996).

Article CAS PubMed Google Scholar

Muijres, F. T., Iwasaki, N. A., Elzinga, M. J., Melis, J. M. & Dickinson, M. H. Flies compensate for unilateral wing damage through modular adjustments of wing and body kinematics. Interface Focus 7, 20160103 (2017).

Article PubMed PubMed Central Google Scholar

OSullivan, A. et al. Multifunctional wing motor control of song and flight. Curr. Biol. 28, 27052717.e4 (2018).

Article PubMed Google Scholar

Azevedo, A. et al. Tools for comprehensive reconstruction and analysis of Drosophila motor circuits. Preprint at BioRxiv https://doi.org/10.1101/2022.12.15.520299 (2022).

Donovan, E. R. et al. Muscle activation patterns and motoranatomy of Annas hummingbirds Calypte anna and zebra finches Taeniopygia guttata. Physiol. Biochem. Zool. 86, 2746 (2013).

Article PubMed Google Scholar

Bashivan, P., Kar, K. & DiCarlo, J. J. Neural population control via deep image synthesis. Science 364, eaav9436 (2019).

Article CAS PubMed Google Scholar

Lindsay, T., Sustar, A. & Dickinson, M. The function and organization of the motor system controlling flight maneuvers in flies. Curr. Biol. 27, 345358 (2017).

Article CAS PubMed Google Scholar

Reiser, M. B. & Dickinson, M. H. A modular display system for insect behavioral neuroscience. J. Neurosci. Meth. 167, 127139 (2008).

Article Google Scholar

Albawi, S., Mohammed, T. A. & Al-Zawi, S. Understanding of a convolutional neural network. In 2017 International Conference on Engineering and Technology (ICET) 16 https://doi.org/10.1109/ICEngTechnol.2017.8308186 (2017).

Kennedy, J. & Eberhart, R. Particle swarm optimization. In Proc. ICNN95International Conference on Neural Networks Vol. 4, 19421948 (1995).

Dana, H. et al. High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat. Methods 16, 649657 (2019).

Article CAS PubMed Google Scholar

Muijres, F. T., Elzinga, M. J., Melis, J. M. & Dickinson, M. H. Flies evade looming targets by executing rapid visually directed banked turns. Science 344, 172177 (2014).

Article ADS CAS PubMed Google Scholar

Gordon, S. & Dickinson, M. H. Role of calcium in the regulation of mechanical power in insect flight. Proc. Natl Acad. Sci. USA 103, 43114315 (2006).

Article ADS CAS PubMed PubMed Central Google Scholar

Nachtigall, W. & Wilson, D. M. Neuro-muscular control of dipteran flight. J. Exp. Biol. 47, 7797 (1967).

Article CAS PubMed Google Scholar

Heide, G. & Gtz, K. G. Optomotor control of course and altitude in Drosophila melanogaster is correlated with distinct activities of at least three pairs of flight steering muscles. J. Exp. Biol. 199, 17111726 (1996).

Article CAS PubMed Google Scholar

Balint, C. N. & Dickinson, M. H. The correlation between wing kinematics and steering muscle activity in the blowfly Calliphora vicina. J. Exp. Biol. 204, 42134226 (2001).

Article CAS PubMed Google Scholar

Elzinga, M. J., Dickson, W. B. & Dickinson, M. H. The influence of sensory delay on the yaw dynamics of a flapping insect. J. R. Soc. Interface 9, 16851696 (2012).

Article PubMed Google Scholar

Dickinson, M. H., Lehmann, F.-O. & Sane, S. P. Wing rotation and the aerodynamic basis of insect flight. Science 284, 19541960 (1999).

Article CAS PubMed Google Scholar

Lehmann, F. O. & Dickinson, M. H. The changes in power requirements and muscle efficiency during elevated force production in the fruit fly Drosophila melanogaster. J. Exp. Biol. 200, 11331143 (1997).

Article CAS PubMed Google Scholar

Lucia, S., Ttulea-Codrean, A., Schoppmeyer, C. & Engell, S. Rapid development of modular and sustainable nonlinear model predictive control solutions. Control Eng. Pract. 60, 5162 (2017).

Article Google Scholar

Cheng, B., Fry, S. N., Huang, Q. & Deng, X. Aerodynamic damping during rapid flight maneuvers in the fruit fly Drosophila. J. Exp. Biol. 213, 602612 (2010).

Article CAS PubMed Google Scholar

Collett, T. S. & Land, M. F. Visual control of flight behaviour in the hoverfly, Syritta pipiens L. J. Comp. Physiol. 99, 166 (1975).

Article Google Scholar

Muijres, F. T., Elzinga, M. J., Iwasaki, N. A. & Dickinson, M. H. Body saccades of Drosophila consist of stereotyped banked turns. J. Exp. Biol. 218, 864875 (2015).

Article PubMed Google Scholar

Syme, D. A. & Josephson, R. K. How to build fast muscles: synchronous and asynchronous designs. Integr. Comp. Biol. 42, 762770 (2002).

Article PubMed Google Scholar

Snodgrass, R. E. Principles of Insect Morphology (Cornell Univ. Press, 2018).

Williams, C. M. & Williams, M. V. The flight muscles of Drosophila repleta. J. Morphol. 72, 589599 (1943).

Article Google Scholar

Wootton, R. The geometry and mechanics of insect wing deformations in flight: a modelling approach. Insects 11, 446 (2020).

Article PubMed PubMed Central Google Scholar

Lerch, S. et al. Resilin matrix distribution, variability and function in Drosophila. BMC Biol. 18, 195 (2020).

Article CAS PubMed PubMed Central Google Scholar

Weis-Fogh, T. A rubber-like protein in insect cuticle. J. Exp. Biol. 37, 889907 (1960).

Article CAS Google Scholar

Weis-Fogh, T. Energetics of hovering flight in hummingbirds and in Drosophila. J. Exp. Biol. 56, 79104 (1972).

Article Google Scholar

Ellington, C. P. The aerodynamics of hovering insect flight. VI. Lift and power requirements. Phil. Trans. R. Soc. B 305, 145181 (1984).

ADS Google Scholar

Alexander, R. M. & Bennet-Clark, H. C. Storage of elastic strain energy in muscle and other tissues. Nature 265, 114117 (1977).

Article ADS CAS PubMed Google Scholar

Mronz, M. & Lehmann, F.-O. The free-flight response of Drosophila to motion of the visual environment. J. Exp. Biol. 211, 20262045 (2008).

Article PubMed Google Scholar

More here:
Machine learning reveals the control mechanics of an insect wing hinge - Nature.com

Tags: