Real-Time On-Site Diagnosis of Quarantine Pathogens in Plant Tissues by Nanopore-Based Sequencing

Rapid and sensitive assays for the identification of plant pathogens are necessary for the effective management of crop diseases. The main limitation of current diagnostic testing is the inability to combine broad and sensitive pathogen detection with the identification of key strains, pathovars, and subspecies. Such discrimination is necessary for quarantine pathogens, whose management is strictly dependent on genotype identification. To address these needs, we have established and evaluated a novel all-in-one diagnostic assay based on nanopore sequencing for the detection and simultaneous characterization of quarantine pathogens, using Xylella fastidiosa as a case study. The assay proved to be at least as sensitive as standard diagnostic tests and the quantitative results agreed closely with qPCR-based analysis. The same sequencing results also allowed discrimination between subspecies when present either individually or in combination. Pathogen detection and typing were achieved within 13 min of sequencing owing to the use of an internal control that allowed to stop sequencing when sufficient data had accumulated. These advantages, combined with the use of portable equipment, will facilitate the development of next-generation diagnostic assays for the efficient monitoring of other plant pathogens

Nanohaloarchaea as beneficiaries of xylan degradation by haloarchaea

Climate change, desertification, salinisation of soils and the changing hydrology of the Earth are creating or modifying microbial habitats at all scales including the oceans, saline groundwaters and brine lakes. In environments that are saline or hypersaline, the biodegradation of recalcitrant plant and animal polysaccharides can be inhibited by salt-induced microbial stress and/or by limitation of the metabolic capabilities of halophilic microbes. We recently demonstrated that the chitinolytic haloarchaeon Halomicrobium can serve as the host for an ectosymbiont, nanohaloarchaeon ‘Candidatus Nanohalobium constans’. Here, we consider whether nanohaloarchaea can benefit from the haloarchaea-mediated degradation of xylan, a major hemicellulose component of wood. Using samples of natural evaporitic brines and anthropogenic solar salterns, we describe genome-inferred trophic relations in two extremely halophilic xylan-degrading three-member consortia. We succeeded in genome assembly and closure for all members of both xylan-degrading cultures and elucidated the respective food chains within these consortia. We provide evidence that ectosymbiontic nanohaloarchaea is an active ecophysiological component of extremely halophilic xylan-degrading communities (although by proxy) in hypersaline environments. In each consortium, nanohaloarchaea occur as ectosymbionts of Haloferax, which in turn act as scavenger of oligosaccharides produced by xylan-hydrolysing Halorhabdus. We further obtained and characterised the nanohaloarchaea–host associations using microscopy, multi-omics and cultivation approaches. The current study also doubled culturable nanohaloarchaeal symbionts and demonstrated that these enigmatic nano-sized archaea can be readily isolated in binary co-cultures using an appropriate enrichment strategy. We discuss the implications of xylan degradation by halophiles in biotechnology and for the United Nation's Sustainable Development Goals

The image biomarker standardization initiative: Standardized convolutional filters for reproducible radiomics and enhanced clinical insights

Standardizing convolutional filters that enhance specific structures and patterns in medical imaging enables reproducible radiomics analyses, improving consistency and reliability for enhanced clinical insights. Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking

A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring

In type 1 diabetes (T1D) therapy, the calculation of the meal insulin bolus is performed according to a standard formula (SF) exploiting carbohydrate intake, carbohydrate-to-insulin ratio, correction factor, insulin on board, and target glucose. Recently, some approaches were proposed to account for preprandial glucose rate of change (ROC) in the SF, including those by Scheiner and by Pettus and Edelman. Here, the aim is to develop a new approach, based on neural networks (NN), to optimize and personalize the bolus calculation using continuous glucose monitoring information and some easily accessible patient parameters

The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation

In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image preprocessing, standardise the formulation and nomenclature of 169 radiomic features and share two benchmark digital phantoms for software calibration. However, to better assess the concordance of radiomic tools, more heterogeneous phantoms are needed. We created two digital phantoms, called ImSURE phantoms, having isotropic and anisotropic voxel size, respectively, and 90 regions of interest (ROIs) each. To use these phantoms, we designed a systematic feature extraction workflow including 919 different feature values (obtained from the 169 IBSI-standardised features considering all possible combinations of feature aggregation and intensity discretisation methods). The ImSURE phantoms will allow to assess the concordance of radiomic software depending on interpolation, discretisation and aggregation methods, as well as on ROI volume and shape. Eventually, we provide the feature values extracted from these phantoms using five open-source IBSI-compliant software

In silico framework for testing and comparing bolus calculator optimization techniques based on CGM sensors

Continuous Glucose Monitoring (CGM) devices have been recently approved by FDA to be used in the type 1 diabetes (T1D) treatment non-adjunctively, i.e. without confirmatory fingerstick (SMBG) measurements (Edelman, JDST, 2017). Consequently, how to tune insulin bolus relying also on glucose rate of change (ROC), has become of particular interest. Our aim is to evaluate extensively in silico empirical literature methods proposed for such a scope