Naturally Acquired Bovine Besnoitiosis

The pathogenesis of bovine besnoitiosis, a disease of increasing concern within Europe, is still incompletely understood. In this study, disease progression after natural infection with the causative apicomplexan Besnoitia besnoiti was monitored in histological skin sections of 5 individual female cattle over time. High-frequency skin sampling of 2 cattle with mild and 2 with severe acute, subacute, and chronic besnoitiosis, as well as from 1 animal during subclinical disease, enabled documentation from the beginning of the disease. Skin sections were stained with hematoxylin and eosin and Giemsa, periodic acid–Schiff reaction, and anti-Besnoitia immunohistochemistry. In all 4 clinically affected animals, tachyzoite-like endozoites could be detected for the first time by immunohistochemistry, and tissue cyst evolution was monitored. Besnoitiosis-associated lesions were not detected in the animal showing the subclinical course. Because of the inconsistency of the nomenclature of Besnoitia tissue cyst layers in the literature, a new nomenclature for B. besnoiti cyst wall layers is proposed: tissue cysts consist of a hypertrophied host cell with enlarged nuclei, an intracytoplasmic parasitophorous vacuole with bradyzoites, a sometimes vacuolated inner cyst wall, and an outer cyst wall in more developed cysts. Inner and outer cyst walls can be readily distinguished by using special stains. In 1 animal, extracystic B. besnoiti zoites were immunohistochemically detected during the chronic stage. At necropsy, the 2 severely affected cows displayed large numbers of B. besnoiti cysts in a variety of tissues, including the corium of the claws, contributing mainly to the development of chronic laminitis in these 2 cases

The formation, properties and impact of secondary organic aerosol: current and emerging issues

Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and transformation of SOA is therefore required to evaluate its impact on atmospheric processes, climate and human health. The chemical and physical processes associated with SOA formation are complex and varied, and, despite considerable progress in recent years, a quantitative and predictive understanding of SOA formation does not exist and therefore represents a major research challenge in atmospheric science. This review begins with an update on the current state of knowledge on the global SOA budget and is followed by an overview of the atmospheric degradation mechanisms for SOA precursors, gas-particle partitioning theory and the analytical techniques used to determine the chemical composition of SOA. A survey of recent laboratory, field and modeling studies is also presented. The following topical and emerging issues are highlighted and discussed in detail: molecular characterization of biogenic SOA constituents, condensed phase reactions and oligomerization, the interaction of atmospheric organic components with sulfuric acid, the chemical and photochemical processing of organics in the atmospheric aqueous phase, aerosol formation from real plant emissions, interaction of atmospheric organic components with water, thermodynamics and mixtures in atmospheric models. Finally, the major challenges ahead in laboratory, field and modeling studies of SOA are discussed and recommendations for future research directions are proposed

Enhancement of the aerosol direct radiative effect by semi-volatile aerosol components: airborne measurements in North-Western Europe

A case study of atmospheric aerosol measurements exploring the impact of the vertical distribution of aerosol chemical composition upon the radiative budget in North-Western Europe is presented. Sub-micron aerosol chemical composition was measured by an Aerodyne Aerosol Mass Spectrometer (AMS) on both an airborne platform and a ground-based site at Cabauw in the Netherlands. The examined period in May 2008 was characterised by enhanced pollution loadings in North-Western Europe and was dominated by ammonium nitrate and Organic Matter (OM). Both ammonium nitrate and OM were observed to increase with altitude in the atmospheric boundary layer. This is primarily attributed to partitioning of semi-volatile gas phase species to the particle phase at reduced temperature and enhanced relative humidity. Increased ammonium nitrate concentrations in particular were found to strongly increase the ambient scattering potential of the aerosol burden, which was a consequence of the large amount of associated water as well as the enhanced mass. During particularly polluted conditions, increases in aerosol optical depth of 50–100% were estimated to occur due to the observed increase in secondary aerosol mass and associated water uptake. Furthermore, the single scattering albedo was also shown to increase with height in the boundary layer. These enhancements combined to increase the negative direct aerosol radiative forcing by close to a factor of two at the median percentile level. Such increases have major ramifications for regional climate predictions as semi-volatile components are often not included in aerosol models. The results presented here provide an ideal opportunity to test regional and global representations of both the aerosol vertical distribution and subsequent impacts in North-Western Europe. North-Western Europe can be viewed as an analogue for the possible future air quality over other polluted regions of the Northern Hemisphere, where substantial reductions in sulphur dioxide emissions have yet to occur. Anticipated reductions in sulphur dioxide in polluted regions will result in an increase in the availability of ammonia to form ammonium nitrate as opposed to ammonium sulphate. This will be most important where intensive agricultural practises occur. Our observations over North-Western Europe, a region where sulphur dioxide emissions have already been reduced, indicate that failure to include the semi-volatile behaviour of ammonium nitrate will result in significant errors in predicted aerosol direct radiative forcing. Such errors will be particularly significant on regional scales

cAMP-dependent regulation of HCN4 controls the tonic entrainment process in sinoatrial node pacemaker cells

It is highly debated how cyclic adenosine monophosphate-dependent regulation (CDR) of the major pacemaker channel HCN4 in the sinoatrial node (SAN) is involved in heart rate regulation by the autonomic nervous system. We addressed this question using a knockin mouse line expressing cyclic adenosine monophosphate-insensitive HCN4 channels. This mouse line displayed a complex cardiac phenotype characterized by sinus dysrhythmia, severe sinus bradycardia, sinus pauses and chronotropic incompetence. Furthermore, the absence of CDR leads to inappropriately enhanced heart rate responses of the SAN to vagal nerve activity in vivo. The mechanism underlying these symptoms can be explained by the presence of nonfiring pacemaker cells. We provide evidence that a tonic and mutual interaction process (tonic entrainment) between firing and nonfiring cells slows down the overall rhythm of the SAN. Most importantly, we show that the proportion of firing cells can be increased by CDR of HCN4 to efficiently oppose enhanced responses to vagal activity. In conclusion, we provide evidence for a novel role of CDR of HCN4 for the central pacemaker process in the sinoatrial node. The involvement of cAMP-dependent regulation of HCN4 in the chronotropic heart rate response is a matter of debate. Here the authors use a knockin mouse model expressing cAMP-insensitive HCN4 channels to discover an inhibitory nonfiring cell pool in the sinoatrial node and a tonic and mutual interaction between firing and nonfiring pacemaker cells that is controlled by cAMP-dependent regulation of HCN4, with implications in chronotropic heart rate responses

cAMP-dependent regulation of HCN4 controls the tonic entrainment process in sinoatrial node pacemaker cells

It is highly debated how cyclic adenosine monophosphate-dependent regulation (CDR) of the major pacemaker channel HCN4 in the sinoatrial node (SAN) is involved in heart rate regulation by the autonomic nervous system. We addressed this question using a knockin mouse line expressing cyclic adenosine monophosphate-insensitive HCN4 channels. This mouse line displayed a complex cardiac phenotype characterized by sinus dysrhythmia, severe sinus bradycardia, sinus pauses and chronotropic incompetence. Furthermore, the absence of CDR leads to inappropriately enhanced heart rate responses of the SAN to vagal nerve activity in vivo. The mechanism underlying these symptoms can be explained by the presence of nonfiring pacemaker cells. We provide evidence that a tonic and mutual interaction process (tonic entrainment) between firing and nonfiring cells slows down the overall rhythm of the SAN. Most importantly, we show that the proportion of firing cells can be increased by CDR of HCN4 to efficiently oppose enhanced responses to vagal activity. In conclusion, we provide evidence for a novel role of CDR of HCN4 for the central pacemaker process in the sinoatrial node

AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping

Background: Computer-based phenotyping of plants has risen in importance in recent years. Whilst much software has been written to aid phenotyping using image analysis, to date the vast majority has been only semi-automatic. However, such interaction is not desirable in high throughput approaches. Here, we present a system designed to analyse plant images in a completely automated manner, allowing genuine high throughput measurement of root traits. To do this we introduce a new set of proxy traits. Results: We test the system on a new, automated image capture system, the Microphenotron, which is able to image many 1000s of roots/h. A simple experiment is presented, treating the plants with differing chemical conditions to produce different phenotypes. The automated imaging setup and the new software tool was used to measure proxy traits in each well. A correlation matrix was calculated across automated and manual measures, as a validation. Some particular proxy measures are very highly correlated with the manual measures (e.g. proxy length to manual length, r2 > 0.9). This suggests that while the automated measures are not directly equivalent to classic manual measures, they can be used to indicate phenotypic differences (hence the term, proxy). In addition, the raw discriminative power of the new proxy traits was examined. Principal component analysis was calculated across all proxy measures over two phenotypically-different groups of plants. Many of the proxy traits can be used to separate the data in the two conditions. Conclusion: The new proxy traits proposed tend to correlate well with equivalent manual measures, where these exist. Additionally, the new measures display strong discriminative power. It is suggested that for particular phenotypic differences, different traits will be relevant, and not all will have meaningful manual equivalent measures. However, approaches such as PCA can be used to interrogate the resulting data to identify differences between datasets. Select images can then be carefully manually inspected if the nature of the precise differences is required. We suggest such flexible measurement approaches are necessary for fully automated, high throughput systems such as the Microphenotron

Leaf segmentation in plant phenotyping: a collation study

Image-based plant phenotyping is a growing application area of computer vision in agriculture. A key task is the segmentation of all individual leaves in images. Here we focus on the most common rosette model plants, Arabidopsis and young tobacco. Although leaves do share appearance and shape characteristics, the presence of occlusions and variability in leaf shape and pose, as well as imaging conditions, render this problem challenging. The aim of this paper is to compare several leaf segmentation solutions on a unique and first-of-its-kind dataset containing images from typical phenotyping experiments. In particular, we report and discuss methods and findings of a collection of submissions for the first Leaf Segmentation Challenge of the Computer Vision Problems in Plant Phenotyping workshop in 2014. Four methods are presented: three segment leaves by processing the distance transform in an unsupervised fashion, and the other via optimal template selection and Chamfer matching. Overall, we find that although separating plant from background can be accomplished with satisfactory accuracy (>>90 % Dice score), individual leaf segmentation and counting remain challenging when leaves overlap. Additionally, accuracy is lower for younger leaves. We find also that variability in datasets does affect outcomes. Our findings motivate further investigations and development of specialized algorithms for this particular application, and that challenges of this form are ideally suited for advancing the state of the art. Data are publicly available (online at http://​www.​plant-phenotyping.​org/​datasets) to support future challenges beyond segmentation within this application domain

Gas-to-particle partitioning of major biogenic oxidation products: a study on freshly formed and aged biogenic SOA

Secondary organic aerosols (SOAs) play a key role in climate change and air quality. Determining the fundamental parameters that distribute organic compounds between the phases is essential, as atmospheric lifetime and impacts change drastically between the gas and particle phase. In this work, gas-to-particle partitioning of major biogenic oxidation products was investigated using three different aerosol chemical characterization techniques. The aerosol collection module, the collection thermal desorption unit, and the chemical analysis of aerosols online are different aerosol sampling inlets connected to a proton-transfer reaction time-of-flight mass spectrometer (ACM-PTR-ToF-MS, TD-PTR-ToF-MS, and CHARON-PTR-ToF-MS, respectively, referred to hereafter as ACM, TD, and CHARON). These techniques were deployed at the atmosphere simulation chamber SAPHIR to perform experiments on the SOA formation and aging from different monoterpenes (β-pinene, limonene) and real plant emissions (Pinus sylvestris L.). The saturation mass concentration C* and thus the volatility of the individual ions was determined based on the simultaneous measurement of their signal in the gas and particle phase.A method to identify and exclude ions affected by thermal dissociation during desorption and ionic dissociation in the ionization chamber of the proton-transfer reaction mass spectrometer (PTR-MS) was developed and tested for each technique. Narrow volatility distributions with organic compounds in the semi-volatile (SVOCs – semi-volatile organic compounds) to intermediate-volatility (IVOCs – intermediate-volatility organic compounds) regime were found for all systems studied. Despite significant differences in the aerosol collection and desorption methods of the proton-transfer-reaction (PTR)-based techniques, a comparison of the C* values obtained with different techniques was found to be in good agreement (within 1 order of magnitude) with deviations explained by the different operating conditions of the PTR-MS.The C* of the identified organic compounds were mapped onto the two-dimensional volatility basis set (2D-VBS), and results showed a decrease in C* with increasing oxidation state. For all experiments conducted in this study, identified partitioning organic compounds accounted for 20–30&thinsp;% of the total organic mass measured from an aerosol mass spectrometer (AMS). Further comparison between observations and theoretical calculations was performed for species found in our experiments that were also identified in previous publications. Theoretical calculations based on the molecular structure of the compounds showed, within the uncertainties ranges, good agreement with the experimental C* for most SVOCs, while IVOCs deviated by up to a factor of 300. These latter differences are discussed in relation to two main processes affecting these systems: (i) possible interferences by thermal and ionic fragmentation of higher molecular-weight compounds, produced by accretion and oligomerization reactions, that fragment in the m∕z range detected by the PTR-MS and (ii) kinetic influences in the distribution between the gas and particle phase with gas-phase condensation, diffusion in the particle phase, and irreversible uptake.</p

Citizen crowds and experts: observer variability in image-based plant phenotyping

Background:Image-based plant phenotyping has become a powerful tool in unravelling genotype–environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a ‘gold-standard’ and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.Results:We compare several experienced and non-experienced observers in annotating leaf counts in images of Arabidopsis Thaliana to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability.Conclusions:While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem