Allele Frequencies of Genetic Variants Associated with Varroa Drone Brood Resistance (DBR) in Apis mellifera Subspecies across the European Continent.

Implementation of marker-assisted selection (MAS) in modern beekeeping would improve sustainability, especially in breeding programs aiming for resilience against the parasitic mite Varroa destructor. Selecting honey bee colonies for natural resistance traits, such as brood-intrinsic suppression of varroa mite reproduction, reduces the use of chemical acaricides while respecting local adaptation. In 2019, eight genomic variants associated with varroa non-reproduction in drone brood were discovered in a single colony from the Amsterdam Water Dune population in the Netherlands. Recently, a new study tested the applicability of these eight genetic variants for the same phenotype on a population-wide scale in Flanders, Belgium. As the properties of some variants varied between the two studies, one hypothesized that the difference in genetic ancestry of the sampled colonies may underly these contribution shifts. In order to frame this, we determined the allele frequencies of the eight genetic variants in more than 360 Apis mellifera colonies across the European continent and found that variant type allele frequencies of these variants are primarily related to the A. mellifera subspecies or phylogenetic honey bee lineage. Our results confirm that population-specific genetic markers should always be evaluated in a new population prior to using them in MAS programs.

Quantitative assessments of honeybee colony's response to an artificial vibrational pulse resulting in non-invasive measurements of colony's overall mobility and restfulness.

In this work we aim to provide a quantitative method allowing the probing of the physiological status of honeybee colonies by providing them with a gentle, short, external artificial vibrational shockwave, and recording their response. The knock is provided by an external electromagnetic shaker attached to the outer wall of a hive, driven by a computer with a 0.1 s long, monochromatic vibration at 340Hz set to an amplitude that occasionally yields a mild response from the bees, recorded by an accelerometer placed in the middle of the central frame of the colony. To avoid habituation, the stimulus is supplied at randomised times, approximately every hour. The method is pioneered with a pilot study on a single colony hosted indoors, then extended onto eight outdoors colonies. The results show that we can quantitatively sense the colony's overall mobility, independently from another physiological aspect, which is phenomenologically explored. Using this, a colony that is queenless is easily discriminated from the others.

Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.

Honey bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees (Apis mellifera) have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies' exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony's health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project's data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.

A practical method to simulate realistic reduced-exposure CT images by the addition of computationally generated noise.

Computed tomography (CT) scanning protocols should be optimized to minimize the radiation dose necessary for imaging. The addition of computationally generated noise to the CT images facilitates dose reduction. The objective of this study was to develop a noise addition method that reproduces the complexity of the noise texture present in clinical images with directionality that varies over images according to the underlying anatomy, requiring only Digital Imaging and Communications in Medicine (DICOM) images as input data and commonly available phantoms for calibration. The developed method is based on the estimation of projection data by forward projection from images, the addition of Poisson noise, and the reconstruction of new images. The method was validated by applying it to images acquired from cylindrical and thoracic phantoms using source images with exposures up to 49 mAs and target images between 39 and 5 mAs. 2D noise spectra were derived for regions of interest in the generated low-dose images and compared with those from the scanner-acquired low-dose images. The root mean square difference between the standard deviations of noise was 4%, except for very low exposures in peripheral regions of the cylindrical phantom. The noise spectra from the corresponding regions of interest exhibited remarkable agreement, indicating that the complex nature of the noise was reproduced. A practical method for adding noise to CT images was presented, and the magnitudes of noise and spectral content were validated. This method may be used to optimize CT imaging.

Attempted symmetry affects dynamic gait stability in individuals with lower-limb amputation.

BACKGROUND: Gait in people with lower limb amputation (LLA) is typically asymmetrical. Reducing this asymmetry is often attempted to minimise the impact of secondary health issues. However, temporal-spatial asymmetry in gait of people with LLA has also been shown to underpin dynamic stability. RESEARCH QUESTION: The current study aimed to identify the effects of acute attempts to achieve temporal-spatial symmetry on the dynamic stability of people with unilateral transtibial amputation (UTA). The secondary aim of this study was to identify the corresponding biomechanical adaptations during attempted symmetrical gait. METHODS: Eleven people with UTA walked along a 15 m walkway in four different conditions: normal (NORM), attempted symmetrical step length and step frequency (SYMSL+SF) attempted symmetrical step length (SYMSL) and attempted symmetrical step frequency (SYMSF). Dynamic stability was measured using the backward (BW) and medio-lateral (ML) margins of stability (MoS). RESULTS: Results indicate that attempting SYMSF had a positive effect on gait stability in BW and ML directions, while attempting SYMSL had a potentially negative effect, although these results did not appear to be significant. The absence of clustering in principal component analysis, supported the lack of significant results, indicating no features differentiating between conditions of attempted symmetry. Conversely, there was clustering by limbs which were associated with differences in knee and ankle joint angles between the prosthetic and non-prosthetic limbs, and clustering by individuals highlighting the importance of patient-specific analysis. CONCLUSION: The data suggests that attempted symmetrical gait reduces asymmetry but also affects dynamic stability.

Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

Real-world gait analysis can aid in clinical assessments and influence related interventions, free from the restrictions of a laboratory setting. Using individual accelerometers, we aimed to use a simple machine learning method to quantify the performance of the discrimination between three self-selected cyclical locomotion types using accelerometers placed at frequently referenced attachment locations. Thirty-five participants walked along a 10 m walkway at three different speeds. Triaxial accelerometers were attached to the sacrum, thighs and shanks. Slabs of magnitude, three-second-long accelerometer data were transformed into two-dimensional Fourier spectra. Principal component analysis was undertaken for data reduction and feature selection, followed by discriminant function analysis for classification. Accuracy was quantified by calculating scalar accounting for the distances between the three centroids and the scatter of each category's cloud. The algorithm could successfully discriminate between gait modalities with 91% accuracy at the sacrum, 90% at the shanks and 87% at the thighs. Modalities were discriminated with high accuracy in all three sensor locations, where the most accurate location was the sacrum. Future research will focus on optimising the data processing of information from sensor locations that are advantageous for practical reasons, e.g., shank for prosthetic and orthotic devices.

Automated, non-invasive Varroa mite detection by vibrational measurements of gait combined with machine learning.

Little is known about mite gait, but it has been suggested that there could be greater variation in locomotory styles for arachnids than insects. The Varroa destructor mite is a devastating ectoparasite of the honeybee. We aim to automatically detect Varroa-specific signals in long-term vibrational recordings of honeybee hives and additionally provide the first quantification and characterisation of Varroa gait through the analysis of its unique vibrational trace. These vibrations are used as part of a novel approach to achieve remote, non-invasive Varroa monitoring in honeybee colonies, requiring discrimination between mite and honeybee signals. We measure the vibrations occurring in samples of freshly collected capped brood-comb, and through combined critical listening and video recordings we build a training database for discrimination and classification purposes. In searching for a suitable vibrational feature, we demonstrate the outstanding value of two-dimensional-Fourier-transforms in invertebrate vibration analysis. Discrimination was less reliable when testing datasets comprising of Varroa within capped brood-cells, where Varroa induced signals are weaker than those produced on the cell surface. We here advance knowledge of Varroa vibration and locomotion, whilst expanding upon the remote detection strategies available for its control.

A Monitoring System for Carbon Dioxide in Honeybee Hives: An Indicator of Colony Health.

Non-dispersive infra-red (NDIR) detectors have become the dominant method for measuring atmospheric CO2, which is thought to be an important gas for honeybee colony health. In this work we describe a microcontroller-based system used to collect data from Senserion SCD41 NDIR sensors placed in the crown boards and queen excluders of honeybee colonies. The same sensors also provide relative humidity and temperature data. Several months of data have been recorded from four different hives. The mass change measurements, from hive scales, when foragers leave the hive were compared with the data from the gas sensors. Our data suggest that it is possible to estimate the colony size from the change in measured CO2, however no such link with the humidity is observed. Data are presented showing the CO2 decreasing over many weeks as a colony dies.

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.

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.

Design and testing of microbubble-based MRI contrast agents for gastric pressure measurement.

PURPOSE: This work demonstrates specifically tailored microbubble-based preparations and their suitability as MRI contrast agents for ingestion and measuring temporal and spatial pressure variation in the human stomach. METHODS: Enhanced alginate spheres were prepared by incorporating gas-filled microbubbles into sodium alginate solution followed by the polymerization of the mixture in an aqueous calcium lactate solution. The microbubbles were prepared with a phospholipid shell and perfluorocarbon gas filling, using a mechanical cavitational agitation regime. The NMR signal changes to externally applied pressure and coming from the enhanced alginate spheres were acquired and compared with that of alginate spheres without microbubbles. In vivo investigations were also carried out on healthy volunteers to measure the pressure variation in the stomach. RESULTS: The MR signal changes in the contrast agent exhibits a linear sensitivity of approximately 40% per bar, as opposed to no measurable signal change seen in the control gas-free spheres. This novel contrast agent also demonstrates an excellent stability in simulated gastric conditions, including at body temperature. In vivo studies showed that the signal change exhibited in the meal within the antrum region is between 5% and 10%, but appears to come from both pressure changes and partial volume artifacts. CONCLUSION: This study demonstrates that alginate spheres with microbubbles can be used as an MRI contrast agent to measure pressure changes. The peristaltic movement within the stomach is seen to substantially alter the overall signal intensity of the contrast agent meal. Future work must focus on improving the contrast agent's sensitivity to pressure changes.

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.

Correction: Long-term trends in the honeybee 'whooping signal' revealed by automated detection.

[This corrects the article DOI: 10.1371/journal.pone.0171162.].

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 non-invasive 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.

Honeybee Colony Vibrational Measurements to Highlight the Brood Cycle.

Insect pollination is of great importance to crop production worldwide and honey bees are amongst its chief facilitators. Because of the decline of managed colonies, the use of sensor technology is growing in popularity and it is of interest to develop new methods which can more accurately and less invasively assess honey bee colony status. Our approach is to use accelerometers to measure vibrations in order to provide information on colony activity and development. The accelerometers provide amplitude and frequency information which is recorded every three minutes and analysed for night time only. Vibrational data were validated by comparison to visual inspection data, particularly the brood development. We show a strong correlation between vibrational amplitude data and the brood cycle in the vicinity of the sensor. We have further explored the minimum data that is required, when frequency information is also included, to accurately predict the current point in the brood cycle. Such a technique should enable beekeepers to reduce the frequency with which visual inspections are required, reducing the stress this places on the colony and saving the beekeeper time.

Bacterially synthesized ferrite nanoparticles for magnetic hyperthermia applications.

Magnetic hyperthermia uses AC stimulation of magnetic nanoparticles to generate heat for cancer cell destruction. Whilst nanoparticles produced inside magnetotactic bacteria have shown amongst the highest reported heating to date, these particles are magnetically blocked so that strong heating occurs only for mobile particles, unless magnetic field parameters are far outside clinical limits. Here, nanoparticles extracellularly produced by the bacteria Geobacter sulfurreducens are investigated that contain Co or Zn dopants to tune the magnetic anisotropy, saturation magnetization and nanoparticle sizes, enabling heating within clinical field constraints. The heating mechanisms specific to either Co or Zn doping are determined from frequency dependent specific absorption rate (SAR) measurements and innovative AC susceptometry simulations that use a realistic model concerning clusters of polydisperse nanoparticles in suspension. Whilst both particle types undergo magnetization relaxation and show heating effects in water under low AC frequency and field, only Zn doped particles maintain relaxation combined with hysteresis losses even when immobilized. This magnetic heating process could prove important in the biological environment where nanoparticle mobility may not be possible. Obtained SARs are discussed regarding clinical conditions which, together with their enhanced MRI contrast, indicate that biogenic Zn doped particles are promising for combined diagnostics and cancer therapy.

Influence of polymerisation conditions on the properties of polymer/clay nanocomposite hydrogels.

Free-radical polymerisation of acrylamide derivatives in the presence of exfoliated clay platelets has recently emerged as a new technique for the synthesis of strong and tough nanocomposite hydrogels (NCHs) with a unique hybrid organic/inorganic network structure. The central intent of many research studies in the field of NCHs conducted so far was to change hydrogel properties with the introduction of various clays and variation of the clay content. Here, we demonstrate that the properties of NCHs significantly vary depending on initiating conditions used for hydrogel synthesis via in situ polymerisation in solutions of high monomer concentrations (above 1 mol L(-1)). A unique, complementary combination of real-time dynamic rheology and pulsed NMR/MRI has been used to study the influence of the composition of a redox initiating system on the gelation process and hydrogel properties. The molar ratio of the persulphate initiator to tertiary amine activator affects the polymerisation kinetics, morphology and mechanical properties of the hydrogels. We further show that activator-dominated systems tend to produce hydrogels with higher storage modulus and lower water intake. This trend is attributed to the increase in the cross-linking degree. From the analysis of the water state in NCH and hydrogels prepared with and without an organic cross-linker, it was concluded that clay platelets did not form covalent bonds with polymer molecules but contributed to the formation of a physical network. There is evidence of self-crosslinking of polymer chains during acrylamide polymerisation at high monomer concentration. The composition of the initiating system influences the number of formed self-crosslinks.

Detection of virgin olive oil adulteration using low field unilateral NMR.

The detection of adulteration in edible oils is a concern in the food industry, especially for the higher priced virgin olive oils. This article presents a low field unilateral nuclear magnetic resonance (NMR) method for the detection of the adulteration of virgin olive oil that can be performed through sealed bottles providing a non-destructive screening technique. Adulterations of an extra virgin olive oil with different percentages of sunflower oil and red palm oil were measured with a commercial unilateral instrument, the profile NMR-Mouse. The NMR signal was processed using a 2-dimensional Inverse Laplace transformation to analyze the transverse relaxation and self-diffusion behaviors of different oils. The obtained results demonstrated the feasibility of detecting adulterations of olive oil with percentages of at least 10% of sunflower and red palm oils.

Quantitation of MRI sensitivity to quasi-monodisperse microbubble contrast agents for spatially resolved manometry.

PURPOSE: The direct in-vivo measurement of fluid pressure cannot be achieved with MRI unless it is done with the contribution of a contrast agent. No such contrast agents are currently available commercially, whilst those demonstrated previously only produced qualitative results due to their broad size distribution. Our aim is to quantitate then model the MR sensitivity to the presence of quasi-monodisperse microbubble populations. METHODS: Lipid stabilised microbubble populations with mean radius 1.2 ± 0.8 µm have been produced by mechanical agitation. Contrast agents with increasing volume fraction of bubbles up to 4% were formed and the contribution the bubbles bring to the relaxation rate was quantitated. A periodic pressure change was also continuously applied to the same contrast agent, until MR signal changes were only due to bubble radius change and not due to a change in bubble density. RESULTS: The MR data compared favourably with the prediction of an improved numerical simulation. An excellent MR sensitivity of 23 % bar(-1) has been demonstrated. CONCLUSION: This work opens up the possibility of generating microbubble preparations tailored to specific applications with optimised MR sensitivity, in particular MRI based in-vivo manometry.

Numerical classification of curvilinear structures for the identification of pistol barrels.

This paper demonstrates a numerical pattern recognition method applied to curvilinear image structures. These structures are extracted from physical cross-sections of cast internal pistol barrel surfaces. Variations in structure arise from gun design and manufacturing method providing a basis for discrimination and identification. Binarised curvilinear land transition images are processed with fast Fourier transform on which principal component analysis is performed. One-way analysis of variance (95% confidence interval) concludes significant differentiation between 11 barrel manufacturers when calculating weighted Euclidean distance between any trio of land transitions and an average land transition for each barrel in the database. The proposed methodology is therefore a promising novel approach for the classification and identification of firearms.