scholarly journals Elements of a stochastic 3D prediction engine in larval zebrafish prey capture

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Andrew D Bolton ◽  
Martin Haesemeyer ◽  
Josua Jordi ◽  
Ulrich Schaechtle ◽  
Feras A Saad ◽  
...  
Keyword(s):  
Adaptive Behavior ◽  
Predictive Models ◽  
Prey Capture ◽  
Surprising Result ◽  
Larval Zebrafish ◽  
Simple Set ◽  
Sensorimotor Transformations ◽  
The World ◽  
Velocity Perception ◽  
Stochastic Rules

The computational principles underlying predictive capabilities in animals are poorly understood. Here, we wondered whether predictive models mediating prey capture could be reduced to a simple set of sensorimotor rules performed by a primitive organism. For this task, we chose the larval zebrafish, a tractable vertebrate that pursues and captures swimming microbes. Using a novel naturalistic 3D setup, we show that the zebrafish combines position and velocity perception to construct a future positional estimate of its prey, indicating an ability to project trajectories forward in time. Importantly, the stochasticity in the fish’s sensorimotor transformations provides a considerable advantage over equivalent noise-free strategies. This surprising result coalesces with recent findings that illustrate the benefits of biological stochasticity to adaptive behavior. In sum, our study reveals that zebrafish are equipped with a recursive prey capture algorithm, built up from simple stochastic rules, that embodies an implicit predictive model of the world.

scholarly journals Climate drivers of plague epidemiology in British India, 1898–1949

2020 ◽  
Vol 287 (1928) ◽  
pp. 20200538
Author(s):  
Warren S. D. Tennant ◽  
Mike J. Tildesley ◽  
Simon E. F. Spencer ◽  
Matt J. Keeling
Keyword(s):  
Predictive Models ◽  
Control Measures ◽  
Climate Data ◽  
British India ◽  
Middle Income ◽  
Middle Income Countries ◽  
Complex Interactions ◽  
The World ◽  
Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

Prey Capture by Larval Zebrafish: Evidence for Fine Axial Motor Control

2002 ◽  
Vol 60 (4) ◽  
pp. 207-229 ◽  
Author(s):  
Melissa A. Borla ◽  
Betsy Palecek ◽  
Seth Budick ◽  
Donald M. O’Malley
Keyword(s):  
Motor Control ◽  
Prey Capture ◽  
Larval Zebrafish

A Novel Facility to Investigate Dust Mobilization in Confined Environments with Applications to the Security of the Pharmaceutical Industry

2016 ◽  
Vol 879 ◽  
pp. 1213-1219 ◽  
Author(s):  
A. Malizia ◽  
M. Gelfusa ◽  
A. Murari ◽  
Maria Richetta ◽  
J.F. Ciparisse ◽  
...  
Keyword(s):  
Predictive Models ◽  
Critical Conditions ◽  
Initial Field ◽  
Dust Mobilization ◽  
The World ◽  
Productive Process ◽  
Pharmaceutical Industries ◽  
University Of Rome ◽  
New Applications ◽  
The University

Many pharmaceutical industries all around the world are facing the problem of dust mobilization during the productive process of medicines. This mobilization can be dangerous for the safety of the operators working in the factory and for the safety of the factory itself. It is therefore necessary to develop predictive models to simulate and forecast dust mobilization. The Quantum Electronics and Plasma Physics (QEP) Research Group of the University of Rome Tor Vergata has developed a facility to experimentally replicate dust mobilization in different critical conditions in an enclosed environment. The measurements performed with diagnostics available in the facility, provide the boundary conditions to run numerical simulations and to validate mobilization models . Even if the initial field of application of this novel facility is dust mobilization is nuclear fusion, the methodology developed can be used for the medicine industry, for the agribusiness and others. The authors will present the experimental and numerical results discussing new applications.

scholarly journals Modeling Predictive Age-Dependent and Age-Independent Symptoms and Comorbidities of Patients Seeking Treatment for COVID-19: Model Development and Validation Study (Preprint)

2020 ◽  
Author(s):  
Yingxiang Huang ◽  
Dina Radenkovic ◽  
Kevin Perez ◽  
Kari Nadeau ◽  
Eric Verdin ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Workers ◽  
Predictive Models ◽  
Model Development ◽  
Hospital Setting ◽  
World Health ◽  
Medical Attention ◽  
Age Related ◽  
Care Workers ◽  
The World

BACKGROUND The COVID-19 pandemic continues to ravage and burden hospitals around the world. The epidemic started in Wuhan, China, and was subsequently recognized by the World Health Organization as an international public health emergency and declared a pandemic in March 2020. Since then, the disruptions caused by the COVID-19 pandemic have had an unparalleled effect on all aspects of life. OBJECTIVE With increasing total hospitalization and intensive care unit admissions, a better understanding of features related to patients with COVID-19 could help health care workers stratify patients based on the risk of developing a more severe case of COVID-19. Using predictive models, we strive to select the features that are most associated with more severe cases of COVID-19. METHODS Over 3 million participants reported their potential symptoms of COVID-19, along with their comorbidities and demographic information, on a smartphone-based app. Using data from the >10,000 individuals who indicated that they had tested positive for COVID-19 in the United Kingdom, we leveraged the Elastic Net regularized binary classifier to derive the predictors that are most correlated with users having a severe enough case of COVID-19 to seek treatment in a hospital setting. We then analyzed such features in relation to age and other demographics and their longitudinal trend. RESULTS The most predictive features found include fever, use of immunosuppressant medication, use of a mobility aid, shortness of breath, and severe fatigue. Such features are age-related, and some are disproportionally high in minority populations. CONCLUSIONS Predictors selected from the predictive models can be used to stratify patients into groups based on how much medical attention they are expected to require. This could help health care workers devote valuable resources to prevent the escalation of the disease in vulnerable populations.

scholarly journals Parallel channels for motion feature extraction in the pretectum and tectum of larval zebrafish

2019 ◽  
Author(s):  
Kun Wang ◽  
Julian Hinz ◽  
Yue Zhang ◽  
Tod R. Thiele ◽  
Aristides B Arrenberg
Keyword(s):  
Feature Extraction ◽  
Visual Field ◽  
Prey Capture ◽  
Receptive Fields ◽  
Lower Visual Field ◽  
Motion Feature ◽  
Hunting Behaviour ◽  
Sensorimotor Transformations ◽  
Visual Areas ◽  
Tectal Neurons

AbstractNon-cortical visual areas in vertebrate brains extract different stimulus features, such as motion, object size and location, to support behavioural tasks. The optic tectum and pretectum, two primary visual areas, are thought to fulfil complementary biological functions in zebrafish to support prey capture and optomotor stabilisation behaviour. However, the adaptations of these brain areas to behaviourally relevant stimulus statistics are unknown. Here, we used calcium imaging to characterize the receptive fields of 1,926 motion-sensitive neurons in diencephalon and midbrain. We show that many caudal pretectal neurons have large receptive fields (RFs), whereas RFs of tectal neurons are smaller and mostly size-selective. RF centres of large-size RF neurons in the pretectum are predominantly located in the lower visual field, while tectal neurons sample the upper-nasal visual field more densely. This tectal visual field sampling matches the expected prey item locations, suggesting that the tectal magnification of the upper-nasal visual field might be an adaptation to hunting behaviour. Finally, we probed optomotor responsiveness and found that even relatively small stimuli drive optomotor swimming, if presented in the lower-temporal visual field, suggesting that the pretectum preferably samples information from this region on the ground to inform optomotor behaviour. Our characterization of the parallel processing channels for non-cortical motion feature extraction provides a basis for further investigation into the sensorimotor transformations of the zebrafish brain and its adaptations to habitat and lifestyle.

scholarly journals Recovery Ratios Reliably Anticipate COVID-19 Pandemic Progression

2020 ◽  
Author(s):  
Dan V. Nicolau ◽  
Alexander Hasson ◽  
Mona Bafadhel
Keyword(s):  
Public Health ◽  
Predictive Models ◽  
Recovery Phase ◽  
Healthcare Systems ◽  
Epidemiological Models ◽  
Health Measures ◽  
The World ◽  
Insight Into ◽  
Pandemic Wave ◽  
Gaining Insight

AbstractThe COVID-19 pandemic is placing unprecedented demands on healthcare systems worldwide and exacting a massive humanitarian toll. This makes the development of accurate predictive models imperative, not just for understanding the course of the pandemic but more importantly for gaining insight into the efficacy of public health measures and planning accordingly. Epidemiological models are forced to make assumptions about many unknowns and therefore can be unreliable. Here, taking an empirical approach, we report a 20-30 day lag between the peak of confirmed to recovered cases and the peak of daily deaths in each country, independent of the epoch of that country in its pandemic cycle. This analysis is expected to be largely independent of the proportion of the population being tested and therefore should aid in planning around the timing and easing of public health measures. Our data also suggests broad predictions for the number of fatalities, generally somewhat lower than most other models. Finally, our model suggests that the world as a whole is shortly to enter a recovery phase, at least as far as the first pandemic wave is concerned.

scholarly journals Experience, circuit dynamics, and forebrain recruitment in larval zebrafish prey capture

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Claire S Oldfield ◽  
Irene Grossrubatscher ◽  
Mario Chávez ◽  
Adam Hoagland ◽  
Alex R Huth ◽  
...  
Keyword(s):  
Prey Capture ◽  
Visual Area ◽  
Live Prey ◽  
Larval Zebrafish ◽  
Retinotopic Maps ◽  
Trigger Activity ◽  
Brain Circuitry ◽  
Evoked Activity ◽  
And Behavior ◽  
Motor Areas

Experience influences behavior, but little is known about how experience is encoded in the brain, and how changes in neural activity are implemented at a network level to improve performance. Here we investigate how differences in experience impact brain circuitry and behavior in larval zebrafish prey capture. We find that experience of live prey compared to inert food increases capture success by boosting capture initiation. In response to live prey, animals with and without prior experience of live prey show activity in visual areas (pretectum and optic tectum) and motor areas (cerebellum and hindbrain), with similar visual area retinotopic maps of prey position. However, prey-experienced animals more readily initiate capture in response to visual area activity and have greater visually-evoked activity in two forebrain areas: the telencephalon and habenula. Consequently, disruption of habenular neurons reduces capture performance in prey-experienced fish. Together, our results suggest that experience of prey strengthens prey-associated visual drive to the forebrain, and that this lowers the threshold for prey-associated visual activity to trigger activity in motor areas, thereby improving capture performance.

scholarly journals Integrating Point Process Models, Evolutionary Ecology and Traditional Knowledge Improves Landscape Archaeology—A Case from Southwest Madagascar

Geosciences ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 287
Author(s):  
Dylan S. Davis ◽  
Robert J. DiNapoli ◽  
Kristina Douglass
Keyword(s):  
Remote Sensing ◽  
Point Process ◽  
Predictive Models ◽  
Landscape Archaeology ◽  
Process Models ◽  
Point Pattern ◽  
Ecological Knowledge ◽  
Archaeological Record ◽  
The World ◽  
Point Process Models

Landscape archaeology has a long history of using predictive models to improve our knowledge of extant archaeological features around the world. Important advancements in spatial statistics, however, have been slow to enter archaeological predictive modeling. Point process models (PPMs), in particular, offer a powerful solution to explicitly model both first- and second-order properties of a point pattern. Here, we use PPMs to refine a recently developed remote sensing-based predictive algorithm applied to the archaeological record of Madagascar’s southwestern coast. This initial remote sensing model resulted in an 80% true positive rate, rapidly expanding our understanding of the archaeological record of this region. Despite the model’s success rate, it yielded a substantial number (~20%) of false positive results. In this paper, we develop a series of PPMs to improve the accuracy of this model in predicting the location of archaeological deposits in southwest Madagascar. We illustrate how PPMs, traditional ecological knowledge, remote sensing, and fieldwork can be used iteratively to improve the accuracy of predictive models and enhance interpretations of the archaeological record. We use an explicit behavioral ecology theoretical framework to formulate and test hypotheses utilizing spatial modeling methods. Our modeling process can be replicated by archaeologists around the world to assist in fieldwork logistics and planning.

Computational Statistics-Based Prediction Algorithms Using Machine Learning

2021 ◽  
pp. 64-80
Author(s):  
Venkat Narayana Rao T. ◽  
Manogna Thumukunta ◽  
Muralidhar Kurni ◽  
Saritha K.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Models ◽  
Industrial Revolution ◽  
Computational Statistics ◽  
Market Risks ◽  
The World ◽  
Lasting Impression ◽  
Stock Values

Artificial intelligence and automation are believed by many to be the new age of industrial revolution. Machine learning is an artificial intelligence section that recognizes patterns from vast amounts of data and projects useful information. Prediction, as an application of machine learning, has been sought after by all kinds of industries. Predictive models with higher efficiencies have proven effective in reducing market risks, predicting natural disasters, indicating health risks, and predicting stock values. The quality of decision making through these algorithms has left a lasting impression on several businesses and is bound to alter how the world looks at analytics. This chapter includes an introduction to machine learning and prediction using machine learning. It also sheds light on its approach and its applications.

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