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

Fumonisins affect the intestinal microbial homeostasis in broiler chickens, predisposing to necrotic enteritis

Fumonisins (FBs) are mycotoxins produced by Fusarium fungi. This study aimed to investigate the effect of these feed contaminants on the intestinal morphology and microbiota composition, and to evaluate whether FBs predispose broilers to necrotic enteritis. One-day-old broiler chicks were divided into a group fed a control diet, and a group fed a FBs contaminated diet (18.6 mg FB1+ FB2/kg feed). A significant increase in the plasma sphinganine/sphingosine ratio in the FBs-treated group (0.21 +/- 0.016) compared to the control (0.14 +/- 0.014) indicated disturbance of the sphingolipid biosynthesis. Furthermore, villus height and crypt depth of the ileum was significantly reduced by FBs. Denaturing gradient gel electrophoresis showed a shift in the microbiota composition in the ileum in the FBs group compared to the control. A reduced presence of low-GC containing operational taxonomic units in ileal digesta of birds exposed to FBs was demonstrated, and identified as a reduced abundance of Candidatus Savagella and Lactobaccilus spp. Quantification of total Clostridium perfringens in these ileal samples, previous to experimental infection, using cpa gene (alpha toxin) quantification by qPCR showed an increase in C. perfringens in chickens fed a FBs contaminated diet compared to control (7.5 +/- 0.30 versus 6.3 +/- 0.24 log10 copies/g intestinal content). After C. perfringens challenge, a higher percentage of birds developed subclinical necrotic enteritis in the group fed a FBs contaminated diet as compared to the control (44.9 +/- 2.22% versus 29.8 +/- 5.46%)

Functional illness in primary care: dysfunction versus disease

<p>Abstract</p> <p>Background</p> <p>The Biopsychosocial Model aims to integrate the biological, psychological and social components of illness, but integration is difficult in practice, particularly when patients consult with medically unexplained physical symptoms or functional illness.</p> <p>Discussion</p> <p>This Biopsychosocial Model was developed from General Systems Theory, which describes nature as a dynamic order of interacting parts and processes, from molecular to societal. Despite such conceptual progress, the biological, psychological, social and spiritual components of illness are seldom managed as an integrated whole in conventional medical practice. This is because the biomedical model can be easier to use, clinicians often have difficulty relinquishing a disease-centred approach to diagnosis, and either dismiss illness when pathology has been excluded, or explain all undifferentiated illness in terms of psychosocial factors. By contrast, traditional and complementary treatment systems describe reversible functional disturbances, and appear better at integrating the different components of illness. Conventional medicine retains the advantage of scientific method and an expanding evidence base, but needs to more effectively integrate psychosocial factors into assessment and management, notably of 'functional' illness. As an aid to integration, pathology characterised by structural change in tissues and organs is contrasted with dysfunction arising from disordered physiology or psychology that may occur independent of pathological change.</p> <p>Summary</p> <p>We propose a classification of illness that includes orthogonal dimensions of pathology and dysfunction to support a broadly based clinical approach to patients; adoption of which may lead to fewer inappropriate investigations and secondary care referrals and greater use of cognitive behavioural techniques, particularly when managing functional illness.</p

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset