31 research outputs found
Odour-background segregation and source localisation using fast olfactory processing
No full textThe hungry insect relies on olfaction to find food patches. However the natural environment is full of different odours made up of a variety of odorants intermingling together. So how can the insect recognise which odorants belong to a patch of good food that is worth visiting? Segregating appetitive food odorants poses a difficult challenge for the insect, as it must separate target odorants from mixtures of odorants that come together from a variety of sources, a process called odour background segregation. During flight, the insect can use spatial and temporal information in turbulent odour plumes to determine whether odorants come from one source or multiple sources. The insect olfactory system can process odorants rapidly, matching the resolution of other senses such as vision and audition. When the insect arrives at an odour source, it must forage alongside other insects to localise the odour to its source and find its food reward. The insect could potentially gain information from other nearby conspecifics that would increase its success in foraging. Thus, during the search for food, the insect olfactory system must process information from odour plume structure and odour valence, from memories about good food sources and from social information transfer between conspecifics. This thesis concentrates on what information insects use during olfactory search to locate a food source and the neural mechanisms that convert an olfactory stimulus into a behavioural response. Two of the chapters focus on odour background segregation, primarily what temporal information insects (honey bees and fruit flies) can use to segregate an appetitive odorant from a background odour mixture. The last chapter focuses on social information transfer between insects (fruit flies) at an odorous food source, and whether such information can improve foraging. Firstly, I asked whether insects were able to distinguish a target odorant from an odour background using odorant onset asynchrony, when the insect had never experienced this target odorant alone. I addressed this question using Apis mellifera, the honey bee, by appetitively conditioning fixed honey bees to a target odorant while presenting a complex background mixture. In my first chapter, I demonstrated that honey bees could separate an unknown target odorant from a mixture using odorant onset asynchrony, however the onset asynchrony was in the range of seconds, two orders of magnitude larger than previously reported for segregation of known odorants. This implies that segregation of unknown odorants may depend on other neural mechanisms such as sensory adaptation. Secondly, I asked what the behaviourally relevant timescales of temporal stimulus cues were for odour source segregation. I addressed this by presenting Drosophila melanogaster with pulses of binary mixtures of attractive and aversive odorants in a wind tunnel, examining their responses to different onset asynchrony times and odorant combinations. In my second chapter, I demonstrated that fruit flies can distinguish between synchronous and asynchronous mixtures of odorants of opposing valence, therefore could use this information for determining the number of odour sources in their environment. Thirdly, I asked whether social interactions between insects could affect their proficiency of foraging and their memory expression. I addressed this using an automated conditioning assay for Drosophila melanogaster, where I conditioned flies in different group sizes to associate an odorant with food, and tested their short-term memory for the conditioned odorant. In my third chapter, I demonstrated that the associative memory of the conditioned odorant is extended for flies conditioned and tested in larger groups, compared to flies conditioned and tested as individuals or in pairs. This extended memory expression could be due to the increased number of social interactions between flies in the larger group, through which flies could transfer information about the location and quality of the food source. Altogether, these three chapters provide evidence that insects can use temporal information to segregate relevant odorant stimuli from background mixtures and can use social information to improve source localisation.publishe
Segregation of unknown odors from mixtures based on stimulus onset asynchrony in honey bees
No full textAnimals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.publishe
Investigating the impact of NHS based ovarian cancer screening
Get PDFthe UK ovarian cancer is the fifth most common cancer in females and after uterine cancer, the second most common gynaecological cancer. There were 6,596 new cases diagnosed in the UK in 2006. The majority of women who develop ovarian cancer have few symptoms until the cancer has spread. A systematic review of published literature was performed to include randomised control trials, case control or cohort studies. It is apparent from the literature on ovarian cancer screening that internationally extensive research is performed however, there is lack of consensus on who to offer screening to, and the most efficacious way of offering it. Annual screening was found to be inadequate for early cancer detection as several studies report advanced stage disease or found that women were developing symptoms in the interim period of screening visits.
The retrospective studies performed at Milton Keynes Hospital demonstrated that ovarian cancer affects a wide age range with many women having no family history of ovarian or breast cancer. Many cases were found to have early stage ovarian cancer however, the largest group of women were found to have extensive metastatic disease at time of diagnosis. 80% of cases reviewed experienced abdominal or pelvic pains often with distension. Five patients were found to have a CA125 value in the normal range, one of which had advanced disease, indicating the limitations of this biomarker. The impact and costs associated with screening in the NHS setting vary considerably with inclusion criteria used. The UK National Screening Committee will have to decide once the findings of UKCTOCS are published in 2010/11 as to the cost benefit of offering NHS based ovarian cancer screening. An annual cost of at least £1.3 million should be expected per NHS trust, in addition to individual trusts needs for equipment, staff and additional facilities required to offer such screening
Data_Sheet_1_Segregation of Unknown Odors From Mixtures Based on Stimulus Onset Asynchrony in Honey Bees.CSV
No full textAnimals use olfaction to search for distant objects. Unlike vision, where objects are spaced out, olfactory information mixes when it reaches olfactory organs. Therefore, efficient olfactory search requires segregating odors that are mixed with background odors. Animals can segregate known odors by detecting short differences in the arrival of mixed odorants (stimulus onset asynchrony). However, it is unclear whether animals can also use stimulus onset asynchrony to segregate odorants that they had no previous experience with and which have no innate or learned relevance (unknown odorants). Using behavioral experiments in honey bees, we here show that stimulus onset asynchrony also improves segregation of those unknown odorants. The stimulus onset asynchrony necessary to segregate unknown odorants is in the range of seconds, which is two orders of magnitude larger than the previously reported stimulus asynchrony sufficient for segregating known odorants. We propose that for unknown odorants, segregating odorant A from a mixture with B requires sensory adaptation to B.</p
Olfactory Object Recognition Based on Fine-Scale Stimulus Timing in Drosophila
No full textSummary: Odorants of behaviorally relevant objects (e.g., food sources) intermingle with those from other sources. Therefore to determine whether an odor source is good or bad—without actually visiting it—animals first need to segregate the odorants from different sources. To do so, animals could use temporal stimulus cues, because odorants from one source exhibit correlated fluctuations, whereas odorants from different sources are less correlated. However, the behaviorally relevant timescales of temporal stimulus cues for odor source segregation remain unclear. Using behavioral experiments with free-flying flies, we show that (1) odorant onset asynchrony increases flies' attraction to a mixture of two odorants with opposing innate or learned valence and (2) attraction does not increase when the attractive odorant arrives first. These data suggest that flies can use stimulus onset asynchrony for odor source segregation and imply temporally precise neural mechanisms for encoding odors and for segregating them into distinct objects. : Biological Sciences; Entomology; Behavioral Neuroscience Subject Areas: Biological Sciences, Entomology, Behavioral Neuroscienc
S13 Fig -
No full textWalking speed and stopping behavior in temporally diverse fictive odor environments: A) Average walking speed of tracked agents in the different temporal environments. Here we exclude flies with speeds less than 2mm/s since that is the threshold for being considered as stopped. Grey shading denotes SEM. Between 47 and 205 trajectories contribute to each time point. B) Stop-to-walk transition rate (blue) and walk-to-stop transition rate (orange) as a function of time (Materials and Methods for more details on estimating these rates). Between 8 and 86 trajectories contribute to the stop-to-walk rate at each time point and 37 and 208 contribute to the walk-to-stop rate, before smoothing. For A) and B) the red bars denote the fictive odor signal as in other figures. (EPS)</p
Flies use the frequency and intermittency of odor signals to navigate upwind across temporally diverse fictive odor environments.
No full textA. Population mean orientation response across 45 fictive odor environments. Each environment projected stimuli with a fixed duration and frequency. Each row represents a different tested frequency: 0.2 Hz, 0.5 Hz, 1 Hz, 1.5 Hz, 1.75 Hz, 2 Hz, 2.5 Hz, 3 Hz, 4 Hz and 5 Hz, from top to bottom. Each column represents a different duration: 0.02 s, 0.05 s, 0.1 s, 0.25 s, 0.5 s and 1 s from left to right. Red bars denote the signal simultaneously encountered by all flies within an experiment (Materials and Methods). Upwind is 180°, downwind is 0°. Grey-blue dashed line: crosswind direction (90°). Black: Population mean orientation; orientation was flipped over 180° as before. Grey shading: SEM for each time point (recording rate = 60 Hz). Between 176 and 407 trajectories were recorded per environment. Between 72 and 237 trajectories were recorded per time point across all environments. B. Instantaneous angular velocity of flies as a function of their orientation during the ON block (0–15 s). Upwind is at 180°, downwind at 0°. Orientation was split into 8 bins with width 22.5°. Vertical dashed line indicates crosswind orientation 90°. Positive (negative) angular velocities correspond to upwind (downwind) turning. Black horizontal line at 0°/s indicates no change in orientation. Color indicates environment frequency; yellow: low frequencies (0.2 Hz, 0.5 Hz), orange: medium frequencies (1 Hz, 1.5 Hz, 1.75 Hz, 2 Hz, 2.5 Hz, 3 Hz), red: high frequencies (4 Hz, 5 Hz). C. Mean angular velocity of individual flies oriented within the crosswind range (90°± 22.5°; grey shading in B) over duration of ON block (0–15 s) as a function of environment frequency (left), duration (middle) and intermittency (right). Each point represents a different environment with defined frequency and duration. Error bars: SEM for that environment. Dotted grey line represents no mean change in orientation. ρ value is the Pearson’s correlation coefficient between mean angular velocity and the temporal feature (frequency, duration, intermittency), obtained from linear least-squares regression. Correlations with environment frequency and intermittency were significantly different from 0 (frequency: ρ = 0.63, p < 0.001; intermittency: ρ = 0.55, p < 0.001). Correlation with duration was not significantly different from 0 (ρ = 0.07, p = 0.633).</p
A single model with fixed parameters captures general trends in angular speed across a spectrum of temporally diverse fictive odor environments.
No full textPopulation mean angular speed (grey) and model predictions (pink). Grey shading denotes standard error of the mean while pink shading denotes simulated standard deviation (see Materials and Methods). Trends in population angular speed, which is modelled as the result of dynamic turn rates and mean turn angular speeds (Eqs 2 and 3, respectively), are well captured by the model across all 45 experiments.</p
Population mean behavioral responses of flies navigating fictive odor stimuli are similar in laminar and complex wind.
No full textMean population orientation response measured from flies navigating one of 6 from the 45 fictive odor environments: 0.2 Hz 1 s, 0.5 Hz 0.1s, 0.5 Hz 1s, 1 Hz 0.5 s, 2 Hz 0.1 s, 2 Hz 0.25 s (from left to right), in either laminar (green) or complex (purple) wind structure. 235–485 trajectories were recorded per odor and wind environment, and 104–242 trajectories were recorded per time frame (recording rate = 60 frames per second). Red bars indicate odor presence. (EPS)</p
Temporal novelty detection and offset response together can predict turn rate, angular speed given turning, and angular speed dynamics.
No full textA. Population mean turn rate (top) and mean turn speed (bottom) from four of the 45 odor environments: 0.2 Hz 1 s, 0.5 Hz 0.25 s, 1.5 Hz 0.1s s, 3 Hz 0.25 s. Grey shading: mean ± SEM. We used a 0.25s sliding window shifted by 1 frame (0.016s) to obtain turn rate and turn speed (see Materials and Methods). Pink line: mean predicted turn rate over time (λ(t), top) and mean predicted turn speed over time (bottom). Parameters of Eqs [1–3] were estimated using Maximum Likelihood Estimation (Materials and Methods). Pink shading: standard deviation obtained from repeated simulation of model prediction (Materials and Methods and S9 Fig caption). Red bars: fictive odor pulses. We had 8–103 turns per frame across all 45 odor environments. B. Model of turn dynamics. Flies initiate discrete turn events (green) with a defined mean turn speed. Turn initiations are modelled as an inhomogeneous Poisson process with rate λ(t) calculated as a linear combination of a baseline turn rate, a temporal novelty detector (N(t), blue), and an offset detector (Off(t), yellow) (Eq 2). Mean turn speed was modelled similarly (Eq 3). Fits and their errors are shown in pink in A. C. Grey: Mean angular speed of the flies. Grey shade: mean ± SEM. Pink: model prediction for the mean, pink shade: mean ± SEM.</p
