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.

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 Manawatū 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.

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.

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.

Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.

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.

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.

Importance of the effective strong ion difference of an intravenous solution in the treatment of diarrheic calves with naturally acquired acidemia and strong ion (metabolic) acidosis

BACKGROUND

The effect of sodium bicarbonate on acid-base balance in metabolic acidosis is interpreted differently by Henderson-Hasselbalch and strong ion acid-base approaches. Application of the traditional bicarbonate-centric approach indicates that bicarbonate administration corrects the metabolic acidosis by buffering hydrogen ions, whereas strong ion difference theory indicates that the co-administration of the strong cation sodium with a volatile buffer (bicarbonate) corrects the strong ion acidosis by increasing the strong ion difference (SID) in plasma.

OBJECTIVE

To investigate the relative importance of the effective SID of IV solutions in correcting acidemia in calves with diarrhea.

ANIMALS

Twenty-two Holstein-Friesian calves (4-21 days old) with naturally acquired diarrhea and strong ion (metabolic) acidosis.

METHODS

Calves were randomly assigned to IV treatment with a solution of sodium bicarbonate (1.4%) or sodium gluconate (3.26%). Fluids were administered over 4 hours and the effect on acid-base balance was determined.

RESULTS

Calves suffered from acidemia owing to moderate to strong ion acidosis arising from hyponatremia and hyper-D-lactatemia. Sodium bicarbonate infusion was effective in correcting the strong ion acidosis. In contrast, sodium gluconate infusion did not change blood pH, presumably because the strong anion gluconate was minimally metabolized.

CONCLUSIONS

A solution containing a high effective SID (sodium bicarbonate) is much more effective in alkalinizing diarrheic calves with strong ion acidosis than a solution with a low effective SID (sodium gluconate). Sodium gluconate is ineffective in correcting acidemia, which can be explained using traditional acid-base theory but requires a new parameter, effective SID, to be understood using the strong ion approach.

Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges

In recent years, new research has brought the field of electroencephalogram (EEG)-based brain–computer interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely, “Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user–machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human–computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices.

An introduction to kernel-based learning algorithms

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

Improving speed and accuracy of brain-computer interfaces using readiness potential features

To enhance human interaction with machines, research interest is growing to develop a 'brain-computer interface', which allows communication of a human with a machine only by use of brain signals. So far, the applicability of such an interface is strongly limited by low bit-transfer rates, slow response times and long training sessions for the subject. The Berlin Brain-Computer Interface (BBCI) project is guided by the idea to train a computer by advanced machine learning techniques both to improve classification performance and to reduce the need of subject training. In this paper we present two directions in which brain-computer interfacing can be enhanced by exploiting the lateralized readiness potential: (1) for establishing a rapid response BCI system that can predict the laterality of upcoming finger movements before EMG onset even in time critical contexts, and (2) to improve information transfer rates in the common BCI approach relying on imagined limb movements.

Noise robust estimates of correlation dimension and K2 entropy

Using Gaussian kernels to define the correlation sum we derive simple formulas that correct the noise bias in estimates of the correlation dimension and K2 entropy of chaotic time series. The corrections are only based on the difference of correlation dimensions for adjacent embedding dimensions and hence preserve the full functional dependencies on both the scale parameter and embedding dimension. It is shown theoretically that the estimates, which are derived for additive white Gaussian noise, are also robust for moderately colored noise. Simulations underline the usefulness of the proposed correction schemes. It is demonstrated that the method gives satisfactory results also for non-Gaussian and dynamical noise.

Engineering support vector machine kernels that recognize translation initiation sites

MOTIVATION

In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS).

RESULTS

The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

Identification of nonstationary dynamics in physiological recordings

We present a novel framework for the analysis of time series from dynamical systems that alternate between different operating modes. The method simultaneously segments and identifies the dynamical modes by using predictive models. In extension to previous approaches, it allows an identification of smooth transition between successive modes. The method can be used for analysis, diagnosis, prediction, and control. In an application to EEG and respiratory data recorded from humans during afternoon naps, the obtained segmentations of the data agree with the sleep stage segmentation of a medical expert to a large extent. However, in contrast to the manual segmentation, our method does not require a priori knowledge about physiology. Moreover, it has a high temporal resolution and reveals previously unclassified details of the transitions. In particular, a parameter is found that is potentially helpful for vigilance monitoring. We expect that the method will generally be useful for the analysis of nonstationary dynamical systems, which are abundant in medicine, chemistry, biology and engineering.

Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans

We apply a recently developed multivariate statistical data analysis technique--so called blind source separation (BSS) by independent component analysis--to process magnetoencephalogram recordings of near-dc fields. The extraction of near-dc fields from MEG recordings has great relevance for medical applications since slowly varying dc-phenomena have been found, e.g., in cerebral anoxia and spreading depression in animals. Comparing several BSS approaches, it turns out that an algorithm based on temporal decorrelation successfully extracted a dc-component which was induced in the auditory cortex by presentation of music. The task is challenging because of the limited amount of available data and the corruption by outliers, which makes it an interesting real-world testbed for studying the robustness of ICA methods.

Artifact reduction in magnetoneurography based on time-delayed second-order correlations

Artifacts in magnetoneurography data due to endogenous biological noise sources, like the cardiac signal, can be four orders of magnitude higher than the signal of interest. Therefore, it is important to establish effective artifact reduction methods. We propose a blind source separation algorithm using only second-order temporal correlations for cleaning biomagnetic measurements of evoked responses in the peripheral nervous system. The algorithm showed its efficiency by eliminating disturbances originating from biological and technical noise sources and successfully extracting the signal of interest. This yields a significant improvement of the neuro-magnetic source analysis.

A numerical study on learning curves in stochastic multilayer feedforward networks

The universal asymptotic scaling laws proposed by Amari et al. are studied in large scale simulations using a CM5. Small stochastic multilayer feedforward networks trained with backpropagation are investigated. In the range of a large number of training patterns t, the asymptotic generalization error scales as 1/t as predicted. For a medium range t a faster 1/t2 scaling is observed. This effect is explained by using higher order corrections of the likelihood expansion. It is shown for small t that the scaling law changes drastically, when the network undergoes a transition from strong overfitting to effective learning.

Afriwaste The fixation and stabilisation of hazardous waste : Rendering waste into a product safe for landfills

The disposal of hazardous and toxic wastes is an area where utmost care and responsibility needs to be exercised. A certain (and mostly acceptable) level of care and responsibility has been legislated and is in place in most developed economies (UK, USA, Canada, Europe, etc.). This is, however, generally not the case in under-developed or developing economies, South Africa being no exception.This paper reflects on various disposal methods and describes a potentially economic alternative to existing methods of the disposal of toxic and hazardous wastes. These existing methods are: Disposal in Class I landfill sites and destruction via incineration.Although incineration (which entails the total destruction of toxic compounds) is the preferred method of disposal, an alternative solution is the fixation of these wastes using specially formulated cementitious agents. The Fixation Solution can be economically feasible, especially in developing economies.

The effect of LDL and modified LDL on macrophage secretion products

The role that lipid uptake might play in macrophage activation was investigated using mouse peritoneal macrophages in vitro. Incubation with acetylated LDL for 48 hours resulted in a 12 fold increase in cholesterol ester content in macrophages; incubation with oxidized LDL resulted in a 6 fold increase in cholesterol ester, while incubation with native LDL did not result in cholesterol accumulation. Incubation of macrophages with acetylated LDL or oxidized LDL produced no change in macrophage production of plasminogen activator or secretion of interleukin 1 or superoxide anion.

Endothelial regeneration in hypertensive and genetically hypercholesterolemic rats

Endothelial regeneration after a narrow, superficial aortic injury was studied in rats with chronic Goldblatt hypertension, genetic hypercholesterolemia, or a combination of hypertension and genetic hypercholesterolemia. In all groups, endothelial continuity was restored within 24 to 36 hours by a combination of endothelial migration and proliferation. A line of increased endothelial density covering the previous wound was seen through 16 weeks after injury. Intimal thickening after injury did not occur in any of the groups. These results indicate that hypertension and hypercholesterolemia neither delay endothelial regeneration nor cause intimal thickening after a small injury in the rat.

The characteristics and metabolism of a genetically hypercholesterolemic strain of rats (RICO)

A genetically hypercholesterolemic strain of rats was selectively bred, starting from an ordinary albino mutant of Rattus norvegicus. The new strain was given the designation RICO, standing for rats with increased cholesterol. In these animals, hypercholesterolemia is established, in both sexes, one day after weaning, and it increases progressively thereafter. It is due to elevated concentrations of LDL- and HDL-cholesterol. As in the ordinary rat, the HDL fraction makes up the main part of the serum cholesterol in the RICO rat. Metabolic studies revealed that in the RICO strain the overall rate of hepatic cholesterol synthesis is accelerated, as a result of higher than normal activity of 3-hydroxy-3-methylglutaryl-CoA reductase. The activity of cholesterol-7 alpha-hydroxylase is decreased in RICO rats, indicating a lower than normal rate of cholesterol catabolism. No difference was found between RICO and ordinary rats with respect to fecal excretion of bile acids and cholesterol.