Farm-level risk factors and treatment protocols for lameness in New Zealand dairy cattle.

AIMS: To identify farm-level risk factors for dairy cow lameness, and to describe lameness treatment protocols used on New Zealand dairy farms. METHODS: One hundred and nineteen farms from eight veterinary clinics within the major dairying regions of New Zealand were randomly enrolled into a cross-sectional lameness prevalence study. Each farmer completed a questionnaire on lameness risk factors and lameness treatment and management. Trained observers lameness scored cattle on two occasions, between October-December (spring, coinciding with peak lactation for most farms) and between January-March (summer, late lactation for most farms). A four-point (0-3) scoring system was used to assess lameness, with animals with a lameness score (LS) ≥2 defined as lame. At each visit, all lactating animals were scored including animals that had previously been identified lame by the farmer. Associations between the farmer-reported risk factors and lameness were determined using mixed logistic regression models in a Bayesian framework, with farm and score event as random effects. RESULTS: A lameness prevalence of 3.5% (2,113/59,631) was reported at the first LS event, and 3.3% (1,861/55,929) at the second LS event. There was a median prevalence of 2.8% (min 0, max 17.0%) from the 119 farms. Most farmers (90/117; 77%) relied on informal identification by farm staff to identify lame animals. On 65% (75/116) of farms, there was no external provider of lame cow treatments, with the farmer carrying out all lame cow treatments. Most farmers had no formal training (69/112; 62%). Animals from farms that used concrete stand-off pads during periods of inclement weather had 1.45 times the odds of lameness compared to animals on farms that did not use concrete stand-off pads (95% equal-tailed credible interval 1.07-1.88). Animals from farms that reported peak lameness incidence from January to June or all year-round, had 0.64 times odds of lameness compared to animals from farms that reported peak lameness incidence from July to December (95% equal-tailed credible interval 0.47-0.88). CONCLUSIONS: Lameness prevalence was low amongst the enrolled farms. Use of concrete stand-off pads and timing of peak lameness incidence were associated with odds of lameness. CLINICAL RELEVANCE: Veterinarians should be encouraging farmers to have formal lameness identification protocols and lameness management plans in place. There is ample opportunity to provide training to farmers for lame cow treatment. Management of cows on stand-off pads should consider the likely impact on lameness.

Prevalence of lameness on pasture-based New Zealand dairy farms: An observational study.

To understand the current impact of lameness on a system, it is important to define lameness prevalence across a range of dairy farms in that system. Prevalence estimates from dairy systems where cows are permanently managed at pasture are uncommon, although the limited data suggest that they have a lower lameness prevalence than housed cattle. One hundred and 20 farms from eight of the major dairying regions of New Zealand were randomly enrolled into a cross-sectional lameness prevalence study. On each of the farms, trained observers lameness scored cattle on two occasions, between October-December (spring, coinciding with peak lactation for most farms) and between January-March (summer, late lactation for most farms). At each visit, all lactating animals were scored using a four-point 0-3 scoring system, and included animals that had previously been identified as lame by the farmer. Animals with a lameness score (LS) ≥2 were defined as lame. Mixed logistic regression models assessed the interaction between region and season and island and season, respectively, and differences between the lameness prevalence within farm across the two seasons reported descriptively. A total of 116,317 locomotion scores over two events were conducted across the 120 farms. At the spring scoring event, 2128/60,007 (3.5 %) cows had a LS ≥2 and 1868/56,310 (3.3 %) cows had a LS ≥ 2 at the summer scoring event. At the farm level, across both scoring events, median lameness prevalence was 2.8 (interquartile range 1.5 - 4.5) %, with a range of 0.0-17.0 %. The median farm-level prevalence of LS = 3 was 0.5 % with a range of 0-4.6 %. The effect of timing of scoring was modified by region (p < 0.001), and island (p = 0.006) and at the individual farm level, differences between spring and summer farm level lameness prevalence were generally small (interquartile range: -1.8 to 1.0 %) but potentially large on individual farms (range from -12.3 % to 7.6 %). The median farm-level lameness prevalence estimate of 2.8 % across a random representative sample of New Zealand dairy farms give confidence that the overall prevalence of cattle lameness on New Zealand dairy farms is low. This adds to the growing evidence that pasture is a good management system with respect to hoof health. The evidence of strong seasonality of lameness was lacking. Instead of using lameness scoring to identify farms with large lameness problems, lameness scoring should be encouraged to farmers as a tool to improve the identification of lame animals.

Investigating the effect of prophylactic claw trimming on the interval between calving and first observed elevated locomotion score in pasture-based dairy cows.

AIMS: To evaluate, in a pasture-based dairy herd, the response to a three-time point hoof trimming regime on lameness incidence and time from calving to observation of an elevated locomotion score (LS). METHODS: This study was conducted on a 940-cow spring-calving herd in New Zealand's North Island between May 2018 and May 2019. Cows (n = 250) were randomly allocated to the hoof trimming group, with the remainder assigned to the non-trim cohort. One trained professional hoof trimmer used the five-step Dutch method to trim the hind feet of the trimming group. Throughout the subsequent production season, the whole herd was locomotion-scored fortnightly using the 4-point (0-3) Dairy NZ lameness score. Kaplan-Meier survival curves were used to assess the univariable effect of trimming on the interval between calving and first LS of ≥ 2 and first LS ≥ 1. A multivariable Cox proportional hazards regression was used to further evaluate the effect of trimming on time to elevated LS. RESULTS: Mean lameness (LS ≥ 2) prevalence was 2.6%, with 30% of cows having ≥ 4 observations during the study period when at least one LS was ≥ 2. For LS ≥ 1, mean prevalence was 40%, with 98.6% of cows having ≥ 4 observations during the study period when at least one LS was ≥ 1 during lactation. Hoof trimming had no apparent effect on the incidence of clinical lameness (LS ≥ 2) (trimmed vs. non-trimmed: 33.2% vs. 28.8%, respectively), but for LS ≥ 1, there was a small decrease in the incidence of LS ≥ 1 (trimmed vs. non-trimmed: 96.9% vs. 99.3%, respectively). The hazard of a cow having a first observed LS ≥ 2 in the control group was 0.87 (95% CI = 0.66-1.14) times that of the trimmed group; however, the hazard of a cow having a first LS ≥ 1 was 1.60 (95% CI = 1.37-1.88) times higher in the control than in the trimmed group. CONCLUSION AND CLINICAL RELEVANCE: On this farm, prophylactic hoof trimming had no clinically relevant impact on the incidence of clinical lameness and was not associated with clinically beneficial reductions in time to first observed LS ≥ 2. This may be because claw horn imbalance was not pronounced on this farm, with 53% of cows needing no trim on either hind limb on the first trimming occasion. Further research on the response to prophylactic trimming in pasture-based dairy cattle is required.

Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings.

Instantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.

Persistence of orthopaedic hoof blocks for the treatment of lame cattle kept permanently at pasture.

AIMS: To compare the retention by New Zealand dairy cows kept at pasture in a lame cow group, of three hoof block products commonly used in the remediation of lameness. METHODS: Sixty-seven farmer-presented Friesian and Friesian x Jersey dairy cows from a single herd in the Manawatu region (New Zealand) suffering from unilateral hind limb lameness attributable to a claw horn lesion (CHL) were randomly allocated to one of three treatments: foam block (FB), plastic shoe (PS) and a standard wooden block (WB). Blocks were applied to the contralateral healthy claw and checked daily by the farm staff (present/not present) and date of loss was recorded. Blocks were reassessed on Day 14 and Day 28 and then removed unless further elevation was indicated. Daily walking distances were calculated using a farm map and measurement software. Statistical analyses included a linear marginal model for distance walked until block loss and a Cox regression model for the relative hazard of a block being lost. RESULTS: Random allocation meant that differences between products in proportion used on left or right hind foot or lateral or medial claw were small. Mean distance walked/cow/day on farm tracks whilst the block was present was 0.32 (min 0.12, max 0.45) km/day; no biologically important difference between products in the mean distance walked was identified. Compared to PS, cows in the WB group were five times more likely to lose the block (HR = 4.8 (95% CI = 1.8-12.4)), while cows in the FB group were 9.5 times more likely to lose the block (HR = 9.5 (95% CI = 3.6-24.4)). CONCLUSIONS: In this study, PS were retained for much longer than either FB or WB. As cows were managed in a lame cow group for the study duration, walking distances were low and did not impact on the risk of block loss. More data are needed to define ideal block retention time. CLINICAL RELEVANCE: In cows with CHL the choice of block could be based on the type of lesion present and the expected re-epithelisation times.

[Multiple hyperplastic colon polyps in a cow]. / Multiple hyperplastische Kolonpolypen bei einem Rind.

INTRODUCTION: Reports on bovine colon polyps are rare. The present report demonstrates macro- and microscopically hyperplastic colon polyps of a seven-year-old German Simmental cow. Differential diagnoses (adenoma and adenocarcinoma) and aetiology are discussed. Even in cattle, intestinal polyps should be considered as a cause of intussusception.

INTRODUCTION: Les rapports concernant des polypes du colon chez les bovins sont rares. Le présent rapport fait état de polypes du côlon macro- et microscopiquement hyperplasiques chez une vache Simmental allemande de sept ans. Les diagnostics différentiels (adénome et adénocarcinome) et l'étiologie sont discutés. Même chez les bovins, les polypes intestinaux doivent être considérés comme une cause d'invagination.

Graduate Student Literature Review: A systematic review on the associations between nonsteroidal anti-inflammatory drug use at the time of diagnosis and treatment of claw horn lameness in dairy cattle and lameness scores, algometer readings, and lying times.

The objectives of this systematic review were to investigate the association between nonsteroidal anti-inflammatory drug (NSAID) use during the treatment of claw horn lameness in dairy cattle and locomotion score (LS), nociceptive threshold, and lying times. A total of 229 studies were initially identified and had their title and abstract screened. From this, we screened the full text of 23 articles, identifying 6 articles for inclusion in the systematic review. Of these 6, 5 reported LS, 2 reported nociceptor thresholds, and 1 reported lying times. The quality of evidence was assessed using a Cochrane risk-of-bias tool and CONSORT items reported for each included study. Due to heterogeneity between the studies, data were reported following Cochrane's Synthesis without meta-analysis guidelines. Identified heterogeneity between the studies included differences in LS systems and statistical analyses, length of time from enrollment to outcome reported, the NSAID used, concomitant treatments administered, and severity and chronicity of lameness. Recommendations are made with respect to consistency of LS reporting and analysis, along with improvements that may be noted with compulsory reporting guidelines. There were at least some concerns over the risk of bias in 4 of the studies, with risks of bias present in missing outcome data between the study groups. Within the 5 studies included with LS outcomes, there were 22 different pairwise comparisons with either NSAID or NSAID + block as the intervention, with measures of association with presence or absence of lameness as the outcome available for 20 of these comparisons. Animals in the NSAID intervention groups had a lower point estimate lameness risk than animals in the comparison groups in 3 of 8 and 9 of 14 analyses for LS outcomes <10 and ≥10 d post-treatment, respectively. However, there was no difference identified between animals in the NSAID intervention groups compared with the animals in the control group in any of these pairwise comparisons with lameness as the outcome. Twelve pairwise comparisons were reported in the 2 studies with nociceptor threshold as an outcome. Animals in the NSAID intervention groups had a greater nociceptor threshold point estimate compared with animals in the comparison groups in 6 of 6 and 1 of 6 analyses for outcomes <10 and ≥10 d post-treatment, respectively. However, no differences were identified between animals in the NSAID intervention groups and those in the comparison groups. All 4 pairwise comparisons reported in the study with lying times as an outcome found no differences between animals in the NSAID groups and those in the comparison groups. Despite the widespread use of NSAID in the treatment of claw horn lameness, there is a lack of studies of NSAID association with LS, nociceptive thresholds, or lying times. The limited evidence is consistent with no association with NSAID use and those parameters, but comparability across studies was limited by heterogeneity.

Improving motor imagery classification during induced motor perturbations.

Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets.

Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.

Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation.

An important goal in Brain-Computer Interfacing (BCI) is to find and enhance procedural strategies for users for whom BCI control is not sufficiently accurate. To address this challenge, we conducted offline analyses and online experiments to test whether the classification of different types of motor imagery could be improved when the training of the classifier was performed on the data obtained with the assistive muscular stimulation below the motor threshold. 10 healthy participants underwent three different types of experimental conditions: a) Motor imagery (MI) of hands and feet b) sensory threshold neuromuscular electrical stimulation (STM) of hands and feet while resting and c) sensory threshold neuromuscular electrical stimulation during performance of motor imagery (BOTH). Also, another group of 10 participants underwent conditions a) and c). Then, online experiments with 15 users were performed. These subjects received neurofeedback during MI using classifiers calibrated either on MI or BOTH data recorded in the same experiment. Offline analyses showed that decoding MI alone using a classifier based on BOTH resulted in a better BCI accuracy compared to using a classifier based on MI alone. Online experiments confirmed accuracy improvement of MI alone being decoded with the classifier trained on BOTH data. In addition, we observed that the performance in MI condition could be predicted on the basis of a more pronounced connectivity within sensorimotor areas in the frequency bands providing the best performance in BOTH. These finding might offer a new avenue for training SMR-based BCI systems particularly for users having difficulties to achieve efficient BCI control. It might also be an alternative strategy for users who cannot perform real movements but still have remaining afferent pathways (e.g., ALS and stroke patients).

Estimating the herd and cow level prevalence of bovine digital dermatitis on New Zealand dairy farms: A Bayesian superpopulation approach.

A cross-sectional study of 127 dairy herds distributed across four regions of New Zealand (NZ) was conducted to estimate the regional herd-level prevalence of bovine digital dermatitis (BDD) and the prevalence of cows with BDD lesions within affected herds. Each herd was visited once during the 2016-2017 lactating season and the rear feet of all cows in the milking herd were examined to detect the presence of BDD lesions. Of the 127 herds examined, 63 had at least one cow with a detected BDD lesion. Of the 59 849 cows observed, 646 cows were observed with BDD lesions. All of the herds in which BBD was detected were located in three of the four regions (Waikato, Manawatu and South Canterbury). No convincing lesions were observed on the West Coast. The probability of BDD freedom on the West Coast was predicted to be 99.97% using a Bayesian latent class model. For the three regions where BDD lesions were observed, the true herd level and cow level prevalences were estimated using a Bayesian superpopulation approach which accounted for the imperfect diagnostic method. Based on priors obtained from previous research in another region of NZ (Taranaki), the true herd level prevalences in Waikato, Manawatu and South Canterbury were estimated to be 59.2% (95% probability interval [PI]: 44.3%-73.9%), 43.3% (95%PI: 29%-59%) and 65.9% (95%PI: 49.5%-79.9%), respectively, while the true median within-herd prevalences were estimated as 3.2% (95%PI: 2%-5%), 1.7% (95%PI: 0.9%-3.1%) and 3.7% (95%PI: 2.4%-5.5%), respectively. All of these estimates except for the true herd level prevalence in Manawatu were fairly robust to changes in the priors. For Manawatu region, changing from the prior obtained in Taranaki (the best estimate of the herd level prevalence = 60%, 95% sure > 40%) to one where the mode was 50% (95% sure < 80%) reduced the posterior from 43.3% to 35.2% (95%PI: 20.1%-53.5%). The marked variation in BDD prevalence between regions and between farms highlights the need for further exploration into risk factors for disease.

SchNetPack: A Deep Learning Toolbox For Atomistic Systems.

SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks.

SchNet - A deep learning architecture for molecules and materials.

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.

The extent of tumor-infiltrating lymphocytes (TILs), along with immunomodulatory ligands, tumor-mutational burden and other biomarkers, has been demonstrated to be a marker of response to immune-checkpoint therapy in several cancers. Pathologists have therefore started to devise standardized visual approaches to quantify TILs for therapy prediction. However, despite successful standardization efforts visual TIL estimation is slow, with limited precision and lacks the ability to evaluate more complex properties such as TIL distribution patterns. Therefore, computational image analysis approaches are needed to provide standardized and efficient TIL quantification. Here, we discuss different automated TIL scoring approaches ranging from classical image segmentation, where cell boundaries are identified and the resulting objects classified according to shape properties, to machine learning-based approaches that directly classify cells without segmentation but rely on large amounts of training data. In contrast to conventional machine learning (ML) approaches that are often criticized for their "black-box" characteristics, we also discuss explainable machine learning. Such approaches render ML results interpretable and explain the computational decision-making process through high-resolution heatmaps that highlight TILs and cancer cells and therefore allow for quantification and plausibility checks in biomedical research and diagnostics.

Why build an integrated EEG-NIRS? About the advantages of hybrid bio-acquisition hardware.

OBJECTIVE: In medical applications, neuroscience and brain-computer interface research, bimodal acquisition of brain activity using Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS) is at the moment achieved by combining separate commercial devices. We have investigated quantitatively whether dedicated hybrid systems exhibit more advantageous properties. METHODS: We studied intermodality electrical crosstalk and timing jitter in two separate and one hybrid EEG-NIRS acquisition device. RESULTS: Analysis revealed significantly higher impact of electrical NIRS current crosstalk into the EEG inputs and timing jitters between EEG-NIRS markers in separate devices compared to the hybrid system. CONCLUSION: The results support hybrid acquisition systems to be advantageous in setups that require high performance in timing and signal quality.

Improving zero-training brain-computer interfaces by mixing model estimators.

OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses. Our aim is to improve overall accuracy of ERP-based BCIs without calibration. APPROACH: We consider two approaches for unsupervised classification of ERP signals. Learning from label proportions (LLP) was recently shown to be guaranteed to converge to a supervised decoder when enough data is available. In contrast, the formerly proposed expectation maximization (EM) based decoding for ERP-BCI does not have this guarantee. However, while this decoder has high variance due to random initialization of its parameters, it obtains a higher accuracy faster than LLP when the initialization is good. We introduce a method to optimally combine these two unsupervised decoding methods, letting one method's strengths compensate for the weaknesses of the other and vice versa. The new method is compared to the aforementioned methods in a resimulation of an experiment with a visual speller. MAIN RESULTS: Analysis of the experimental results shows that the new method exceeds the performance of the previous unsupervised classification approaches in terms of ERP classification accuracy and symbol selection accuracy during the spelling experiment. Furthermore, the method shows less dependency on random initialization of model parameters and is consequently more reliable. SIGNIFICANCE: Improving the accuracy and subsequent reliability of calibrationless BCIs makes these systems more appealing for frequent use.

Suspected levamisole intoxication in calves.

CASE HISTORY: A group of 32 Friesian and four Hereford calves, 3-4 months old with body weights between 100-120 kg, were purchased from a weaner sale. On arrival at the property the Hereford calves were treated with a combination anthelmintic containing 2 g/L abamectin and 80 g/L levamisole hydrochloride. Shortly afterwards they developed tremors and frothing from the mouth, and two died overnight. The Friesian calves were treated with the same anthelmintic on the following day, when some showed hypersalivation and frothing from the mouth. CLINICAL FINDINGS: Examination of the three most severely affected Friesian calves revealed severe nicotinic-type symptoms including hypersalivation, frothing from the mouth, muscle tremors, recumbency, rapid respiration, hyperaesthesia, and central nervous system depression. Other calves showed mild to moderate signs of intoxication including restlessness, tail switching, salivation, tremors, frequent defaecation, mild colic and jaw chomping. Two calves died shortly afterwards. An adverse drug event investigation revealed that the formulation and quality of the anthelmintic was within the correct specification, and that the drench gun was functioning correctly. DIAGNOSIS: Suspected levamisole intoxication due to a combination of possible overdosing, dehydration, and stress caused by transportation and prolonged yarding. CLINICAL RELEVANCE: Susceptibility to levamisole toxicity in New Zealand calves can be increased if factors like dehydration or stress are present. Levamisole has a narrow margin of safety, and overdosing in calves can easily occur if the dose rate is not based on their actual weight or health status.

Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control.

In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.

Neuromuscular electrical stimulation induced brain patterns to decode motor imagery.

OBJECTIVE: Regardless of the paradigm used to implement a brain-computer interface (BCI), all systems suffer from BCI-inefficiency. In the case of patients the inefficiency can be high. Some solutions have been proposed to overcome this problem, however they have not been completely successful yet. METHODS: EEG from 10 healthy users was recorded during neuromuscular electrical stimulation (NMES) of hands and feet and during motor imagery (MI) of the same limbs. Features and classifiers were computed using part of these data to decode MI. RESULTS: Offline analyses showed that it was possible to decode MI using a classifier based on afferent patterns induced by NMES and even infer a better model than with MI data. CONCLUSION: Afferent NMES motor patterns can support the calibration of BCI systems and be used to decode MI. SIGNIFICANCE: This finding might be a new way to train sensorimotor rhythm (SMR) based BCI systems for healthy users having difficulties to attain BCI control. It might also be an alternative to train MI-based BCIs for users who cannot perform real movements but have remaining afferents (ALS, stroke patients).