Intelligent image-based colourimetric tests using machine learning framework for lateral flow assays

This paper aims to deliberately examine the scope of an intelligent colourimetric test that fulfils ASSURED criteria (Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free, and Deliverable) and demonstrate the claim as well. This paper presents an investigation into an intelligent image-based system to perform automatic paper-based colourimetric tests in real-time to provide a proof-of-concept for a dry-chemical based or microfluidic, stable and semi-quantitative assay using a larger dataset with diverse conditions. The universal pH indicator papers were utilised as a case study. Unlike the works done in the literature, this work performs multiclass colourimetric tests using histogram based image processing and machine learning algorithm without any user intervention. The proposed image processing framework is based on colour channel separation, global thresholding, morphological operation and object detection. We have also deployed a server based convolutional neural network framework for image classification using inductive transfer learning on a mobile platform. The results obtained by both traditional machine learning and pre-trained model-based deep learning were critically analysed with the set evaluation criteria (ASSURED criteria). The features were optimised using univariate analysis and exploratory data analysis to improve the performance. The image processing algorithm showed >98% accuracy while the classification accuracy by Least Squares Support Vector Machine (LS- SVM) was 100%. On the other hand, the deep learning technique provided >86% accuracy, which could be further improved with a large amount of data. The k-fold cross validated LS- SVM based final system, examined on different datasets, confirmed the robustness and reliability of the presented approach, which was further validated using statistical analysis. The understaffed and resource limited healthcare system can benefit from such an easy-to-use technology to support remote aid workers, assist in elderly care and promote personalised healthcare by eliminating the subjectivity of interpretation

Transcriptomic Analysis Reveals Selective Metabolic Adaptation of Streptococcus suis to Porcine Blood and Cerebrospinal Fluid

Streptococcus suis is a zoonotic pathogen that can cause severe pathologies such as septicemia and meningitis in its natural porcine host as well as in humans. Establishment of disease requires not only virulence of the infecting strain but also an appropriate metabolic activity of the pathogen in its host environment. However, it is yet largely unknown how the streptococcal metabolism adapts to the different host niches encountered during infection. Our previous isotopologue profiling studies on S. suis grown in porcine blood and cerebrospinal fluid (CSF) revealed conserved activities of central carbon metabolism in both body fluids. On the other hand, they suggested differences in the de novo amino acid biosynthesis. This prompted us to further dissect S. suis adaptation to porcine blood and CSF by RNA deep sequencing (RNA-seq). In blood, the majority of differentially expressed genes were associated with transport of alternative carbohydrate sources and the carbohydrate metabolism (pentose phosphate pathway, glycogen metabolism). In CSF, predominantly genes involved in the biosynthesis of branched-chain and aromatic amino acids were differentially expressed. Especially, isoleucine biosynthesis seems to be of major importance for S. suis in CSF because several related biosynthetic genes were more highly expressed. In conclusion, our data revealed niche-specific metabolic gene activity which emphasizes a selective adaptation of S. suis to host environments

FIGURE 3. Mean pairwise uncorrected genetic distances p in Two new species in the annelid genus Stygocapitella (Orbiniida, Parergodrilidae) with comments on their biogeography

FIGURE 3. Mean pairwise uncorrected genetic distances p for COI (A) and 18 S (B) as well as morphological disparity (C) between and within the different regions as well as the Australian populations. Plot of the genetic distances for COI (D) or 18 S (E) versus the morphological disparity between and within the different regions. Standard deviations are also indicated by error bars. Aus = Australia, Eur = Europe, GB = Gnarabup Beach, MMD = multidimensional morphological disparity, SA = South Africa, SB = Sarge Bay

Transcriptomic Analysis Reveals Selective Metabolic Adaptation of Streptococcus suis to Porcine Blood and Cerebrospinal Fluid.

Streptococcus suis is a zoonotic pathogen that can cause severe pathologies such as septicemia and meningitis in its natural porcine host as well as in humans. Establishment of disease requires not only virulence of the infecting strain but also an appropriate metabolic activity of the pathogen in its host environment. However, it is yet largely unknown how the streptococcal metabolism adapts to the different host niches encountered during infection. Our previous isotopologue profiling studies on S. suis grown in porcine blood and cerebrospinal fluid (CSF) revealed conserved activities of central carbon metabolism in both body fluids. On the other hand, they suggested differences in the de novo amino acid biosynthesis. This prompted us to further dissect S. suis adaptation to porcine blood and CSF by RNA deep sequencing (RNA-seq). In blood, the majority of differentially expressed genes were associated with transport of alternative carbohydrate sources and the carbohydrate metabolism (pentose phosphate pathway, glycogen metabolism). In CSF, predominantly genes involved in the biosynthesis of branched-chain and aromatic amino acids were differentially expressed. Especially, isoleucine biosynthesis seems to be of major importance for S. suis in CSF because several related biosynthetic genes were more highly expressed. In conclusion, our data revealed niche-specific metabolic gene activity which emphasizes a selective adaptation of S. suis to host environments