Extensive vibrational characterisation and long-term monitoring of honeybee dorso-ventral abdominal vibration signals

Abstract A very common honeybee signal is the dorso-ventral abdominal vibration (DVAV) signal, widely accepted as a modulatory signal meaning: “prepare for greater activity”. In this study, using ultra-sensitive accelerometer technology embedded in the honeycomb, we visually confirm the one-to-one relationship between a DVAV signal being produced and the resulting accelerometer waveform, allowing the measurement of DVAV signals without relying on any visual inspection. We then demonstrate a novel method for the continuous in-situ non-invasive automated monitoring of this honeybee signal, not previously known to induce any vibration into the honeycomb, and most often inaudible to human hearing. We monitored a total of three hives in the UK and France, showing that the signal is very common, highly repeatable and occurs more frequently at night, exhibiting a distinct decrease in instances and increase in amplitude towards mid-afternoon. We also show an unprecedented increase in the cumulative amplitude of DVAV signals occurring in the hours preceding and following a primary swarm. We conclude that DVAV signals may have additional functions beyond solely being a foraging activation signal, and that the amplitude of the signal might be indicative of the switching of its purpose

Long-term trends in the honeybee ‘whooping signal’ revealed by automated detection

It is known that honeybees use vibrational communication pathways to transfer information. One honeybee signal that has been previously investigated is the short vibrational pulse named the ‘stop signal’, because its inhibitory effect is generally the most accepted interpretation. The present study demonstrates long term (over 9 months) automated in-situ noninvasive monitoring of a honeybee vibrational pulse with the same characteristics of what has previously been described as a stop signal using ultra-sensitive accelerometers embedded in the honeycomb located at the heart of honeybee colonies. We show that the signal is very common and highly repeatable, occurring mainly at night with a distinct decrease in instances towards midday, and that it can be elicited en masse from bees following the gentle shaking or knocking of their hive with distinct evidence of habituation. The results of our study suggest that this vibrational pulse is generated under many different circumstances, thereby unifying previous publication’s conflicting definitions, and we demonstrate that this pulse can be generated in response to a surprise stimulus. This work suggests that, using an artificial stimulus and monitoring the changes in the features of this signal could provide a sensitive tool to assess colony status

Natalizumab therapy, 2013

Multiple sclerosis (MS) is the most common chronic disease of the central nervous system in young adults. No curative therapy is known. Currently, six drugs are available that can reduce the activity of MS. The first-line drugs can completely reduce the activity of the disease in nearly two-thirds of the patients. In the remainder, who suffer from breakthrough disease, the condition of the patient worsens, and second-line therapies must be used. The second-line drug natalizumab exhibits almost double efficacy of the first-line drugs, but also have less favourable adverse effects. As a severe side-effect for instance, natalizumab carries the risk of the development of progressive multifocal leucoencephalopathy (PML), caused by a polyoma virus, the JC virus. There are three major risk factors for PML: an anti-JCV antibody status, a long duration of natalizumab treatment and prior immunosuppressant therapy. The lowest-risk group (1:14 286) comprises of patients who are anti-JCV antibody-negative, in whom the prior immunosuppressant use and duration of natalizumab therapy do not influence the risk of PML. With no prior immunosuppressant treatment, the incidence of PML increases to 1 in 192 patients after 2 years among those who are anti-JCV antibody-positive. These data may lead the physician to decide to discontinue natalizumab treatment. The half-life of natalizumab is three months; during this time other therapies can not be administered and the patients encounter the rebound effect: as the patients receiving natalizumab therapy displayed a high disease activity before treatment, the rebound effect can lead to relapses. After the termination of natalizumab second-line disease-modifying therapy with fingolimod may be introduce; no PML cases occur in response to fingolimod treatment. In the large majority of patients taking natalizumab who do not develop PML, this drug is highly effective and can prevent the progression of MS. The benefit of therapy and the risk of PML must be considered on an individual basis, with regard to the disease activity, the progression and the MRI activity, before natalizumab therapy is implemented

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses

Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statistical methods, such as Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), have been adopted since they have the potential to overcome these data handling issues. The aim of the current study was to develop and optimise a specific machine learning algorithm for processing human locomotion data. Twenty participants ran at a self-selected speed across a 15m runway in barefoot and shod conditions. Ground reaction forces (BW) and kinematics were measured at 1000 Hz and 100 Hz, respectively from which joint angles (°), joint moments (N.m.kg-1) and joint powers (W.kg-1) for the hip, knee and ankle joints were calculated in all three anatomical planes. Using PCA and DFA, power spectra of the kinematic and kinetic variables were used as a training database for the development of a machine learning algorithm. All possible combinations of 10 out of 20 participants were explored to find the iteration of individuals that would optimise the machine learning algorithm. The results showed that the algorithm was able to successfully predict whether a participant ran shod or barefoot in 93.5% of cases. To the authors’ knowledge, this is the first study to optimise the development of a machine learning algorithm

Robust spatially resolved pressure measurements using MRI with novel buoyant advection-free preparations of stable microbubbles in polysaccharide gels

MRI of fluids containing lipid coated microbubbles has been shown to be an effective tool for measuring the local fluid pressure. However, the intrinsically buoyant nature of these microbubbles precludes lengthy measurements due to their vertical migration under gravity and pressure-induced coalescence. A novel preparation is presented which is shown to minimize both these effects for at least 25 min. By using a 2% polysaccharide gel base with a small concentration of glycerol and 1,2-distearoyl-sn-glycero-3-phosphocholine coated gas microbubbles, MR measurements are made for pressures between 0.95 and 1.44 bar. The signal drifts due to migration and amalgamation are shown to be minimized for such an experiment whilst yielding very high NMR sensitivities up to 38% signal change per bar

Honey bee vibration monitoring using the 805M1 accelerometer

In this work we demonstrate that the 805M1 single axis analogue output accelerometer can be used to monitor honey bee activity and requires only a low cost microcontroller with an audio shield to log the data. We present accelerometer output signals demonstrating the ability to capture individual honey bee ‘whooping’ signals as well as long term amplitude monitoring, indicating the brood cycle, using this affordable measurement system

Machine learning analysis for quantitative discrimination of dried blood droplets

One of the most interesting and everyday natural phenomenon is the formation of different patterns after the evaporation of liquid droplets on a solid surface. The analysis of dried patterns from blood droplets has recently gained a lot of attention, experimentally and theoretically, due to its potential application in diagnostic medicine and forensic science. This paper presents evidence that images of dried blood droplets have a signature revealing the exhaustion level of the person, and discloses an entirely novel approach to studying human dried blood droplet patterns. We took blood samples from 30 healthy young male volunteers before and after exhaustive exercise, which is well known to cause large changes to blood chemistry. We objectively and quantitatively analysed 1800 images of dried blood droplets, developing sophisticated image processing analysis routines and optimising a multivariate statistical machine learning algorithm. We looked for statistically relevant correlations between the patterns in the dried blood droplets and exercise-induced changes in blood chemistry. An analysis of the various measured physiological parameters was also investigated. We found that when our machine learning algorithm, which optimises a statistical model combining Principal Component Analysis (PCA) as an unsupervised learning method and Linear Discriminant Analysis (LDA) as a supervised learning method, is applied on the logarithmic power spectrum of the images, it can provide up to 95% prediction accuracy, in discriminating the physiological conditions, i.e., before or after physical exercise. This correlation is strongest when all ten images taken per volunteer per condition are averaged, rather than treated individually. Having demonstrated proof-of-principle, this method can be applied to identify diseases

The prediction of swarming in honeybee colonies using vibrational spectra

In this work, we disclose a non-invasive method for the monitoring and predicting of the swarming process within honeybee colonies, using vibro-acoustic information. Two machine learning algorithms are presented for the prediction of swarming, based on vibration data recorded using accelerometers placed in the heart of honeybee hives. Both algorithms successfully discriminate between colonies intending and not intending to swarm with a high degree of accuracy, over 90% for each method, with successful swarming prediction up to 30 days prior to the event. We show that instantaneous vibrational spectra predict the swarming within the swarming season only, and that this limitation can be lifted provided that the history of the evolution of the spectra is accounted for. We also disclose queen toots and quacks, showing statistics of the occurrence of queen pipes over the entire swarming season. From this we were able to determine that (1) tooting always precedes quacking, (2) under natural conditions there is a 4 to 7 day period without queen tooting following the exit of the primary swarm, and (3) human intervention, such as queen clipping and the opening of a hive, causes strong interferences with important mechanisms for the prevention of simultaneous rival queen emergence

A spatially resolved temperature measurement system for a honeybee colony brood box

Honeybee colonies depend on suitable temperatures for successful development. We demonstrate the use of a spatially resolved temperature measurement system for a honeybee colony by producing ten custom frames which results into four hundred eighty sensors across the hive. A first prototype used four layers of wax to embed the sensors, however, the honeybees rejected these and removed the wax before building new, irregular honeycomb. A second system using a single sheet of wax onto which the sensors were laid was accepted by the colony, and normal honeycomb was built. We showcase some of the data collected from this system

Possible Case of Maternal Transmission of Feline Spongiform Encephalopathy in a Captive Cheetah

Feline spongiform encephalopathy (FSE) is considered to be related to bovine spongiform encephalopathy (BSE) and has been reported in domestic cats as well as in captive wild cats including cheetahs, first in the United Kingdom (UK) and then in other European countries. In France, several cases were described in cheetahs either imported from UK or born in France. Here we report details of two other FSE cases in captive cheetah including a 2nd case of FSE in a cheetah born in France, most likely due to maternal transmission. Complete prion protein immunohistochemical study on both brains and peripheral organs showed the close likeness between the two cases. In addition, transmission studies to the TgOvPrP4 mouse line were also performed, for comparison with the transmission of cattle BSE. The TgOvPrP4 mouse brains infected with cattle BSE and cheetah FSE revealed similar vacuolar lesion profiles, PrPd brain mapping with occurrence of typical florid plaques. Collectively, these data indicate that they harbor the same strain of agent as the cattle BSE agent. This new observation may have some impact on our knowledge of vertical transmission of BSE agent-linked TSEs such as in housecat FSE, or vCJD