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Quantitative genetics seeks to understand the role of allelic variation in trait differences. Introgression lines (ILs) contain a single genetic locus introgressed into another genetic background, and are one of the most powerful quantitative trait locus (QTL) mapping designs. However, albeit useful for QTL discovery, this homogenous background confounds genetic interactions. Here, we created a novel IL population, complementary to a previously created population, which enables identification of genetic interactions in ILs. The novel ILCB4856panel was made by crossing divergent strains of the model nematodeCaenorhabditis elegans(N2 and CB4856). The ILCB4856panel comprises a population of 145 strains with sequencing confirmed N2 introgressions in a CB4856 background from which a core set of 87 strains covering the entire genome was selected. We present three experiments demonstrating the power of the complementary IL panels. First, we performed QTL mapping identifying new regions associated with lifespan. Second, the existence of opposite-effect loci regulating heat-stress survival is demonstrated. Third, by combining ILN2and ILCB4856strains, an interacting expression QTL was uncovered. In conclusion, the complementary IL panels are a unique and ready-to-use resource to identify, resolve, and refine complex trait architectures inC. elegans.

The mycobiome (fungal microbiome) influences plants—from seed germination to full maturation. While many studies on fungal‐plant interaction studies have focused on known mutualistic and pathogenic fungi, the functional role of ubiquitous endophytic fungi remains little explored. We examined how root‐inhabiting fungi (endophytes) influence range‐expanding plant species. We isolated endophytes from three European intra‐continental range‐expanders and three congenerics that are native both in the range expander's original (southern Europe) and new (northern Europe) range. To standardize our collection, endophytes were obtained from all six plant species growing under controlled conditions in northern (new range of the range expander) and southern (native range of the range expander) soils. We cultivated, molecularly identified and tested the effects of all isolates on seed germination, and growth of seedlings and older plants. Most of the 34 isolates could not be functionally characterized based on their taxonomic identity and literature information on functions. Endophytes affected plant growth in a plant species–endophyte‐specific manner, but overall differed between range‐expanders and natives. While endophytes reduced germination and growth of range‐expanders compared to natives, they reduced seedling growth of natives more than of range‐expanders. Synthesis. We conclude that endophytic fungi have a direct effect on plant growth in a plant growth stage‐dependent manner. While these effects differed between range expanders and natives, the effect strength and significance varied among the plant genera included in the present study. Nevertheless, endophytes likely influence the establishment of newly arriving plants and influence vegetation dynamics.

Objectives To analyze the performance of radiological assessment categories and quantitative computational analysis of apparent diffusion coefficient (ADC) maps using variant machine learning algorithms to differentiate clinically significant versus insignificant prostate cancer (PCa). Methods Retrospectively, 73 patients were included in the study. The patients (mean age, 66.3 ± 7.6 years) were examined with multiparametric MRI (mpMRI) prior to radical prostatectomy (n = 33) or targeted biopsy (n = 40). The index lesion was annotated in MRI ADC and the equivalent histologic slides according to the highest Gleason Grade Group (GrG). Volumes of interest (VOIs) were determined for each lesion and normal-appearing peripheral zone. VOIs were processed by radiomic analysis. For the classification of lesions according to their clinical significance (GrG ≥ 3), principal component (PC) analysis, univariate analysis (UA) with consecutive support vector machines, neural networks, and random forest analysis were performed. Results PC analysis discriminated between benign and malignant prostate tissue. PC evaluation yielded no stratification of PCa lesions according to their clinical significance, but UA revealed differences in clinical assessment categories and radiomic features. We trained three classification models with fifteen feature subsets. We identified a subset of shape features which improved the diagnostic accuracy of the clinical assessment categories (maximum increase in diagnostic accuracy ΔAUC = + 0.05, p Conclusions The impact of radiomic features to differentiate PCa lesions according to their clinical significance remains controversial. It depends on feature selection and the employed machine learning algorithms. It can result in improvement or reduction of diagnostic performance. Key Points • Quantitative imaging features differ between normal and malignant tissue of the peripheral zone in prostate cancer. • Radiomic feature analysis of clinical routine multiparametric MRI has the potential to improve the stratification of clinically significant versus insignificant prostate cancer lesions in the peripheral zone. • Certain combinations of standard multiparametric MRI reporting and assessment categories with feature subsets and machine learning algorithms reduced the diagnostic performance over standard clinical assessment categories alone.

OBJECTIVE. The objective of our study was to evaluate the clinical performance of a new high-frequency (HF) microwave ablation (MWA) technology with spatial energy control for treatment of lung malignancies in comparison with a conventional low-frequency (LF) MWA technology. MATERIALS AND METHODS. In this retrospective study, 59 consecutive patients (mean age, 58.9 ± 12.6 [SD] years) were treated in 71 sessions using HF spatial-energy-control MWA. Parameters collected were technical success and efficacy, tumor diameter, tumor and ablation volumes, ablation time, output energy, complication rate, 90-day mortality, local tumor progression (LTP), ablative margin size, and ablation zone sphericity. Results were compared with the same parameters retrospectively collected from the last 71 conventional LF-MWA sessions. This group consisted of 56 patients (mean age, 60.3 ± 10.8 years). Statistical comparisons were performed using the Wilcoxon-Mann-Whitney test. RESULTS. Technical success was 98.6% for both technologies; technical efficacy was 97.2% for HF spatial-energy-control MWA and 95.8% for LF-MWA. The 90-day mortality rate was 5.1% (3/59) in the HF spatial-energy-control MWA group and 5.4% (3/56) in the LF-MWA group; for both groups, there were zero intraprocedural deaths. The median ablation time was 8.0 minutes for HF spatial-energy-control MWA and 10.0 minutes for LF-MWA (p < 0.0001). Complications were recorded in 21.1% (15/71) of HF spatial-energy-control MWA sessions and in 31.0% (22/71) of LF-MWA sessions (p = 0.182); of these complications, 4.2% (3/71) were major complications in the HF spatial-energy-control MWA group, and 9.9% (7/71) were major complications in the LF-MWA group. The median deviation from ideal sphericity (1.0) was 0.195 in the HF spatial-energy-control MWA group versus 0.376 in the LF-MWA group (p < 0.0001). Absolute minimal ablative margins per ablation were 7.5 ± 3.6 mm (mean ± SD) in the HF spatial-energy-control MWA group versus 4.2 ± 3.0 mm in the LF-MWA group (p < 0.0001). In the HF spatial-energy-control MWA group, LTP at 12 months was 6.5% (4/62). LTP at 12 months in the LF-MWA group was 12.5% (7/56). Differences in LTP rate (p = 0.137) and time point (p = 0.833) were not significant. CONCLUSION. HF spatial-energy-control MWA technology and conventional LFMWA technology are safe and effective for the treatment of lung malignancies independent of the MWA system used. However, HF spatial-energy-control MWA as an HF and high-energy MWA technique achieves ablation zones that are closer to an ideal sphere and achieves larger ablative margins than LF-MWA (p < 0.0001).

Air is a major conduit for the dispersal of organisms at the local and the global scale. Most research has focused on the dispersal of plants, vertebrates and human disease agents. However, the air represents a key dispersal medium also for bacteria, fungi and protists. Many of those represent potential pathogens of animals and plants and have until now gone largely unrecorded. Here we studied the turnover in composition of the entire aerobiome, the collective diversity of airborne microorganisms. For that we performed daily analyses of all prokaryotes and eukaryotes (including plants) using multi-marker high-throughput sequencing for a total of three weeks. We linked the resulting communities to local weather conditions, to assess determinants of aerobiome composition and distribution. We observed hundreds of microbial taxa, mostly belonging to spore-forming organisms including fungi, but also protists. Additionally, we detected many potential human- and plant-pathogens. Community composition fluctuated on a daily basis and was linked to concurrent weather conditions, particularly air pressure and temperature. Using network analyses, we identified taxonomically diverse groups of organisms with correlated temporal dynamics. In part, this was due to co-variation with environmental conditions, while we could also detect specific host-parasite interactions. This study provides the first full inventory of the aerobiome and identifies putative drivers of its dynamics in terms of taxon composition. This knowledge can help develop early warning systems against pathogens and improve our understanding of microbial dispersal., Environment International, 154, ISSN:0160-4120, ISSN:1873-6750

Background The nematode Caenorhabditis elegans has been extensively used to explore the relationships between complex traits, genotypes, and environments. Complex traits can vary across different genotypes of a species, and the genetic regulators of trait variation can be mapped on the genome using quantitative trait locus (QTL) analysis of recombinant inbred lines (RILs) derived from genetically and phenotypically divergent parents. Most RILs have been derived from crossing two parents from globally distant locations. However, the genetic diversity between local C. elegans populations can be as diverse as between global populations and could thus provide means of identifying genetic variation associated with complex traits relevant on a broader scale. Results To investigate the effect of local genetic variation on heritable traits, we developed a new RIL population derived from 4 parental wild isolates collected from 2 closely located sites in France: Orsay and Santeuil. We crossed these 4 genetically diverse parental isolates to generate a population of 200 multi-parental RILs and used RNA-seq to obtain sequence polymorphisms identifying almost 9000 SNPs variable between the 4 genotypes with an average spacing of 11 kb, doubling the mapping resolution relative to currently available RIL panels for many loci. The SNPs were used to construct a genetic map to facilitate QTL analysis. We measured life history traits such as lifespan, stress resistance, developmental speed, and population growth in different environments, and found substantial variation for most traits. We detected multiple QTLs for most traits, including novel QTLs not found in previous QTL analysis, including those for lifespan and pathogen responses. This shows that recombining genetic variation across C. elegans populations that are in geographical close proximity provides ample variation for QTL mapping. Conclusion Taken together, we show that using more parents than the classical two parental genotypes to construct a RIL population facilitates the detection of QTLs and that the use of wild isolates facilitates the detection of QTLs. The use of multi-parent RIL populations can further enhance our understanding of local adaptation and life history trade-offs. Electronic supplementary material The online version of this article (10.1186/s12915-019-0642-8) contains supplementary material, which is available to authorized users.

OBJECTIVES: To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data. METHODS: We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into sub cohorts with or without necessity of intensive care unit (ICU) treatment. Sub cohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test. RESULTS: We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all p < 0.001 if not otherwise stated) with occurrence of ICU stay (R = 0.74), length of ICU stay (R = 0.81), lethal outcome (R = 0.56) and length of hospital stay (R = 0.33, p < 0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R = 0.60), lactate dehydrogenase (LDH) (R = 0.60), troponin (TNTHS) (R = 0.55) and c-reactive protein (CRP) (R = 0.51). Differences (p < 0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher opacity score (12.50 vs. 4.96) and severity of laboratory data changes such as c-reactive protein (11.64 vs. 5.07 mg/dl, p < 0.01). CONCLUSIONS: Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.

Host-pathogen interactions play a major role in evolutionary selection and shape natural genetic variation. The genetically distinct Caenorhabditis elegans strains, Bristol N2 and Hawaiian CB4856, are differentially susceptible to the Orsay virus (OrV). Here, we report the dissection of the genetic architecture of susceptibility to OrV infection. We compare OrV infection in the relatively resistant wild-type CB4856 strain to the more susceptible canonical N2 strain. To gain insight into the genetic architecture of viral susceptibility, 52 fully sequenced recombinant inbred lines (CB4856 × N2 RILs) were exposed to OrV. This led to the identification of two loci on chromosome IV associated with OrV resistance. To verify the two loci and gain additional insight into the genetic architecture controlling virus infection, introgression lines (ILs) that together cover chromosome IV, were exposed to OrV. Of the 27 ILs used, 17 had an CB4856 introgression in an N2 background, and 10 had an N2 introgression in a CB4856 background. Infection of the ILs confirmed and fine-mapped the locus underlying variation in OrV susceptibility, and we found that a single nucleotide polymorphism in cul-6 may contribute to the difference in OrV susceptibility between N2 and CB4856. An allele swap experiment showed the strain CB4856 became as susceptible as the N2 strain by having an N2 cul-6 allele, although having the CB4856 cul-6 allele did not increase resistance in N2. In addition, we found that multiple strains with nonoverlapping introgressions showed a distinct infection phenotype from the parental strain, indicating that there are punctuated locations on chromosome IV determining OrV susceptibility. Thus, our findings reveal the genetic complexity of OrV susceptibility in C. elegans and suggest that viral susceptibility is governed by multiple genes. IMPORTANCE Genetic variation determines the viral susceptibility of hosts. Yet, pinpointing which genetic variants determine viral susceptibility remains challenging. Here, we have exploited the genetic tractability of the model organism Caenorhabditis elegans to dissect the genetic architecture of Orsay virus infection. Our results provide novel insight into natural determinants of Orsay virus infection.