1,486 research outputs found

    Computational techniques to interpret the neural code underlying complex cognitive processes

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    Advances in large-scale neural recording technology have significantly improved the capacity to further elucidate the neural code underlying complex cognitive processes. This thesis aimed to investigate two research questions in rodent models. First, what is the role of the hippocampus in memory and specifically what is the underlying neural code that contributes to spatial memory and navigational decision-making. Second, how is social cognition represented in the medial prefrontal cortex at the level of individual neurons. To start, the thesis begins by investigating memory and social cognition in the context of healthy and diseased states that use non-invasive methods (i.e. fMRI and animal behavioural studies). The main body of the thesis then shifts to developing our fundamental understanding of the neural mechanisms underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging and behaviour preprocessing computational pipelines were developed and optimised for use in social interaction and spatial navigation experimental analysis. In parallel, a review was conducted on methods for multivariate/neural population analysis. A comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise and behavioural complexity. Furthermore, the review visualises how NML can be applied to disease states in the brain and introduces the secondary analyses that can be used to enhance or characterise a neural manifold. Lastly, the preprocessing and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored how neural firing in the medial Prefrontal cortex changed as a function of the social dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble of behavioural-tuned neurons that fire preferentially during specific behaviours such as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s behaviour. Furthermore, in dominant animals, the neural population exhibited greater average firing than that of subordinate animals. Next, to investigate spatial memory, a spatial recency task was used, where rats learnt to navigate towards one of three reward locations and then recall the rewarded location of the session. During the task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active in the decision period before the animal navigates to the rewarded location. The result posits that prospective replay of neural sequences in the hippocampal CA1 region could provide a mechanism by which decision-making is supported

    Measuring the Health and Development of School-age Zimbabwean Children

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    Health, growth and development during mid-childhood (from 5 to 14 years) are poorly characterised, and this period has been termed the ‘missing middle’. This thesis describes the piloting and application of the School-Age Health, Activity, Resilience, Anthropometry and Neurocognitive (SAHARAN) toolbox to measure growth, cognitive and physical function amongst the SHINE cohort in rural Zimbabwe. The SHINE cluster-randomised trial tested the effects of a household WASH intervention and/or infant and young child feeding (IYCF) on child stunting and anaemia at age 18 months in rural Zimbabwe. SHINE showed that IYCF modestly increased linear growth and reduced stunting by age 18 months, while WASH had no effects. The SAHARAN toolbox was used to measure 1000 HIV-unexposed children (250 in each intervention arm), and 275 HIV-exposed children within the SHINE cohort to evaluate long-term outcomes. Children were re-enrolled at age seven years to evaluate growth, body composition, cognitive and physical function. Four main findings are presented from the SAHARAN toolbox measurements of this cohort. Firstly, child sex, growth and contemporary environmental conditions are associated with school-age physical and cognitive function at seven years. Secondly, early-life growth and baseline environmental conditions suggest the impact of early-life trajectories on multiple aspects of school-age growth, physical and cognitive function. Thirdly, the long-term impact of HIV-exposure in pregnancy is explored, which indicate reduced cognitive function, cardiovascular fitness and head circumference by age 7 years. Finally, associations with the SHINE trial early life interventions are explored, demonstrating that the SHINE early-life nutrition intervention has minimal impact by 7 years of age, except marginally stronger handgrip strength. The public health implications advocate that child interventions need to be earlier (including antenatal), broader (incorporating nurturing care), deeper (providing transformational WASH) and longer (supporting throughout childhood), as well as targeting particularly vulnerable groups such as children born HIV-free

    Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning

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    Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and direct surgical procedures, and to track the development of bone-related diseases. This often involves radiologists who have to annotate bones manually or in a semi-automatic way, which is a time consuming task. Their annotation workload can be reduced by automated segmentation and detection of individual bones. This automation of distinct bone segmentation not only has the potential to accelerate current workflows but also opens up new possibilities for processing and presenting medical data for planning, navigation, and education. In this thesis, we explored the use of deep learning for automating the segmentation of all individual bones within an upper-body CT scan. To do so, we had to find a network architec- ture that provides a good trade-off between the problem’s high computational demands and the results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out to eliminate the most prevalent types of error. To do so, we introduced an novel method called binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin- guishing bone from non-bone is conducted separately from identifying the individual bones. Both predictions are then merged, which leads to superior results. Another type of error is tack- led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input into the network while keeping the growth of additional pixels in check. Overall, we present a deep-learning-based method that reliably segments most of the over one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter quickly enough to be used in interactive software. Our algorithm has been included in our groups virtual reality medical image visualisation software SpectoVR with the plan to be used as one of the puzzle piece in surgical planning and navigation, as well as in the education of future doctors

    Characterising the neck motor system of the blowfly

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    Flying insects use visual, mechanosensory, and proprioceptive information to control their movements, both when on the ground and when airborne. Exploiting visual information for motor control is significantly simplified if the eyes remain aligned with the external horizon. In fast flying insects, head rotations relative to the body enable gaze stabilisation during highspeed manoeuvres or externally caused attitude changes due to turbulent air. Previous behavioural studies into gaze stabilisation suffered from the dynamic properties of the supplying sensor systems and those of the neck motor system being convolved. Specifically, stabilisation of the head in Dipteran flies responding to induced thorax roll involves feed forward information from the mechanosensory halteres, as well as feedback information from the visual systems. To fully understand the functional design of the blowfly gaze stabilisation system as a whole, the neck motor system needs to be investigated independently. Through X-ray micro-computed tomography (μCT), high resolution 3D data has become available, and using staining techniques developed in collaboration with the Natural History Museum London, detailed anatomical data can be extracted. This resulted in a full 3- dimensional anatomical representation of the 21 neck muscle pairs and neighbouring cuticula structures which comprise the blowfly neck motor system. Currently, on the work presented in my PhD thesis, μCT data are being used to infer function from structure by creating a biomechanical model of the neck motor system. This effort aims to determine the specific function of each muscle individually, and is likely to inform the design of artificial gaze stabilisation systems. Any such design would incorporate both sensory and motor systems as well as the control architecture converting sensor signals into motor commands under the given physical constraints of the system as a whole.Open Acces

    Re-evaluation of the risks to public health related to the presence of bisphenol A (BPA) in foodstuffs

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    Publisher Copyright: © 2023 European Food Safety Authority. EFSA Journal published by Wiley-VCH GmbH on behalf of European Food Safety Authority.In 2015, EFSA established a temporary tolerable daily intake (t-TDI) for BPA of 4 μg/kg body weight (bw) per day. In 2016, the European Commission mandated EFSA to re-evaluate the risks to public health from the presence of BPA in foodstuffs and to establish a tolerable daily intake (TDI). For this re-evaluation, a pre-established protocol was used that had undergone public consultation. The CEP Panel concluded that it is Unlikely to Very Unlikely that BPA presents a genotoxic hazard through a direct mechanism. Taking into consideration the evidence from animal data and support from human observational studies, the immune system was identified as most sensitive to BPA exposure. An effect on Th17 cells in mice was identified as the critical effect; these cells are pivotal in cellular immune mechanisms and involved in the development of inflammatory conditions, including autoimmunity and lung inflammation. A reference point (RP) of 8.2 ng/kg bw per day, expressed as human equivalent dose, was identified for the critical effect. Uncertainty analysis assessed a probability of 57–73% that the lowest estimated Benchmark Dose (BMD) for other health effects was below the RP based on Th17 cells. In view of this, the CEP Panel judged that an additional uncertainty factor (UF) of 2 was needed for establishing the TDI. Applying an overall UF of 50 to the RP, a TDI of 0.2 ng BPA/kg bw per day was established. Comparison of this TDI with the dietary exposure estimates from the 2015 EFSA opinion showed that both the mean and the 95th percentile dietary exposures in all age groups exceeded the TDI by two to three orders of magnitude. Even considering the uncertainty in the exposure assessment, the exceedance being so large, the CEP Panel concluded that there is a health concern from dietary BPA exposure.Peer reviewe

    Towards Robot Autonomy in Medical Procedures Via Visual Localization and Motion Planning

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    Robots performing medical procedures with autonomous capabilities have the potential to positively effect patient care and healthcare system efficiency. These benefits can be realized by autonomous robots facilitating novel procedures, increasing operative efficiency, standardizing intra- and inter-physician performance, democratizing specialized care, and focusing the physician’s time on subtasks that best leverage their expertise. However, enabling medical robots to act autonomously in a procedural environment is extremely challenging. The deforming and unstructured nature of the environment, the lack of features in the anatomy, and sensor size constraints coupled with the millimeter level accuracy required for safe medical procedures introduce a host of challenges not faced by robots operating in structured environments such as factories or warehouses. Robot motion planning and localization are two fundamental abilities for enabling robot autonomy. Motion planning methods compute a sequence of safe and feasible motions for a robot to accomplish a specified task, where safe and feasible are defined by constraints with respect to the robot and its environment. Localization methods estimate the position and orientation of a robot in its environment. Developing such methods for medical robots that overcome the unique challenges in procedural environments is critical for enabling medical robot autonomy. In this dissertation, I developed and evaluated motion planning and localization algorithms towards robot autonomy in medical procedures. A majority of my work was done in the context of an autonomous medical robot built for enhanced lung nodule biopsy. First, I developed a dataset of medical environments spanning various organs and procedures to foster future research into medical robots and automation. I used this data in my own work described throughout this dissertation. Next, I used motion planning to characterize the capabilities of the lung nodule biopsy robot compared to existing clinical tools and I highlighted trade-offs in robot design considerations. Then, I conducted a study to experimentally demonstrate the benefits of the autonomous lung robot in accessing otherwise hard-to-reach lung nodules. I showed that the robot enables access to lung regions beyond the reach of existing clinical tools with millimeter-level accuracy sufficient for accessing the smallest clinically operable nodules. Next, I developed a localization method to estimate the bronchoscope’s position and orientation in the airways with respect to a preoperatively planned needle insertion pose. The method can be used by robotic bronchoscopy systems and by traditional manually navigated bronchoscopes. The method is designed to overcome challenges with tissue motion and visual homogeneity in the airways. I demonstrated the success of this method in simulated lungs undergoing respiratory motion and showed the method’s ability to generalize across patients.Doctor of Philosoph

    Gut-brain interactions affecting metabolic health and central appetite regulation in diabetes, obesity and aging

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    The central aim of this thesis was to study the effects of gut microbiota on host energy metabolism and central regulation of appetite. We specifically studied the interaction between gut microbiota-derived short-chain fatty acids (SCFAs), postprandial glucose metabolism and central regulation of appetite. In addition, we studied probable determinants that affect this interaction, specifically: host genetics, bariatric surgery, dietary intake and hypoglycemic medication.First, we studied the involvement of microbiota-derived short-chain fatty acids in glucose tolerance. In an observational study we found an association of intestinal availability of SCFAs acetate and butyrate with postprandial insulin and glucose responses. Hereafter, we performed a clinical trial, administering acetate intravenously at a constant rate and studied the effects on glucose tolerance and central regulation of appetite. The acetate intervention did not have a significant effect on these outcome measures, suggesting the association between increased gastrointestinal SCFAs and metabolic health, as observed in the observational study, is not paralleled when inducing acute plasma elevations.Second, we looked at other determinants affecting gut-brain interactions in metabolic health and central appetite signaling. Therefore, we studied the relation between the microbiota and central appetite regulation in identical twin pairs discordant for BMI. Second, we studied the relation between microbial composition and post-surgery gastrointestinal symptoms upon bariatric surgery. Third, we report the effects of increased protein intake on host microbiota composition and central regulation of appetite. Finally, we explored the effects of combination therapy with GLP-1 agonist exenatide and SGLT2 inhibitor dapagliflozin on brain responses to food stimuli

    2023 GREAT Day Program

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    SUNY Geneseo’s Seventeenth Annual GREAT Day. Geneseo Recognizing Excellence, Achievement & Talent Day is a college-wide symposium celebrating the creative and scholarly endeavors of our students. http://www.geneseo.edu/great_dayhttps://knightscholar.geneseo.edu/program-2007/1017/thumbnail.jp

    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!)

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    (b2023 to 2014) The UNBELIEVABLE similarities between the ideas of some people (2006-2016) and my ideas (2002-2008) in physics (quantum mechanics, cosmology), cognitive neuroscience, philosophy of mind, and philosophy (this manuscript would require a REVOLUTION in international academy environment!
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