High-resolution Typing Research Articles

Probing red supergiant atmospheres and winds with early-time, high-cadence, high-resolution type II supernova spectra

High-cadence high-resolution spectroscopic observations of infant Type II supernovae (SNe) represent an exquisite probe of the atmospheres and winds of exploding red-supergiant (RSG) stars. Using radiation hydrodynamics and radiative transfer calculations, we studied the gas and radiation properties during and after the phase of shock breakout, considering RSG star progenitors enshrouded within a circumstellar material (CSM) that varies in terms of the extent, density, and velocity profile. In all cases, the original, unadulterated CSM structure is probed only at the onset of shock breakout, seen in high-resolution spectra as narrow, often blueshifted emission components, possibly with an additional absorption trough. As the SN luminosity rises during breakout, radiative acceleration of the unshocked CSM starts, leading to a broadening of the ``narrow'' lines by several 100 (up to several 1000) depending on the CSM properties. This acceleration is at its maximum close to the shock, where the radiative flux is greater and thus typically masked by optical-depth effects. Generally, the narrow-line broadening is greater for more compact, tenuous CSM because of the proximity to the shock where the flux is born; it is smaller in the denser and more extended CSM. Narrow-line emission should show a broadening that slowly increases first (the line forms further out in the original wind), then sharply rises (the line forms in a region that is radiatively accelerated), before decreasing until late times (the line forms further away in regions more weakly accelerated). Radiative acceleration is expected to inhibit X-ray emission during the early (IIn) phase. Although high spectral resolution is critical at the earliest times to probe the original slow wind, the radiative acceleration and the associated line broadening may be captured with medium resolution. This would allow for a simultaneous view of narrow, Doppler-broadened line emission, as well as extended, electron-scattering broadened emission.

Association analysis of T and B-cell epitopes with humoral alloimmunisation in kidney transplantation: A Tunisian cohort study.

HLA matching is critical for successful kidney transplantation. This study aimed to investigate the impact of eplet mismatches and Predicted Indirectly Recognizable HLA Epitopes (PIRCHE-II) scores on the development of de novo donor-specific antibodies (dnDSA) and graft survival in a Tunisian cohort, characterized by a high prevalence of living donors and significant genetic diversity in HLA profiles. This retrospective study included 112 adult kidney transplant recipients who underwent transplantation between 2012 and 2018. Donor and recipient HLA typing was performed using One Lambda Labtype PCR-SSO and PCR-SSP kits, with high-resolution typing inferred using validated tools and reference datasets. Luminex DSA screening (One Lambda) was conducted at transplantation, during follow-up for graft dysfunction or proteinuria, and at the study's conclusion. Eplet mismatches and PIRCHE-II scores were calculated using EpVix and PIRCHE-II software. Nineteen patients (17%) developed dnDSA, predominantly targeting HLA-DQ, with a median detection timeframe of 50months (range: 11-106months) post-transplantation. Male donor sex was negatively associated with dnDSA development, while prolonged cold ischemia time was a significant risk factor. Molecular HLA-DQ mismatches and high PIRCHE-II scores were significantly associated with dnDSA. Factors independently associated with reduced death-censored graft survival included history of transfusion after transplantation, allelic DRB1 mismatch, dnDSA development, and elevated PIRCHE-II score. Our findings highlight the critical role of HLA-DQ compatibility in kidney transplantation and suggest that molecular HLA-DQ matching should be considered in kidney allocation strategies to minimise alloimmune responses and improve long-term graft outcomes.

Clinical Applicability of 2-Field High-Resolution and Extended HLA-Allele Typing in Deceased Donor Kidney Allocation.

HLA-compatibility remains an important triage test for deceased donor kidney allocation. Low-intermediate resolution donor HLA-typing is typically available at allocation, but its accuracy in assigning pre-transplant donor-specific anti-HLA antibody (DSA) and HLA mismatches compared to 2-field high-resolution typing is poorly characterised. Consecutive deceased donor/recipient pairs from a single centre between 2016 and 2020 were included. Majority of donor typing at HLA-ABDRB1 loci were performed at low-intermediate resolution, with 2-field high-resolution NGS typing across extended loci performed by NGS-technique post-transplantation. We compared the two typing methods for (1) accuracy of pre-transplant DSA assignment; (2) misassignment of HLA-antigen/allele mismatches and performance of each model for acute rejection and (3) proportion of recipients who developed de novo DSA (dnDSA) when matched at antigen but mismatched at allele level. Of 179 deceased donor/recipient pairs, 157 donors had low-intermediate resolution typing and 22 with high-resolution ONT typing. Sixty-two recipients (35%) had potential pre-transplant DSAs, with incorrect assignment of allele-specific Class I and II actual DSAs in 31% and 53% of cases, respectively. NGS typing identified 59 (33%) additional HLA-DRB1 allele mismatches. ONT typing accurately assigned pre-transplant DSAs and allele mismatches in all cases. Seven (4%) recipients with antigen/allele level discordance developed dnDSAs, majority HLA-DQ antibodies. Two-field high-resolution donor HLA typing may provide a more accurate transplant immunological risk assessment and identify those at risk of developing dnDSA to matched HLA antigen.

Ecological Systems Classification: Integrating Machine Learning, Ancillary Modeling, and Sentinel-2 Satellite Imagery

Spatial land cover depictions are essential for ecological and environmental management. The thematic resolution of land cover and vegetation maps is also a significant factor affecting the ability to effectively develop policy and land management decisions based on spatial data. Natural resource and conservation planners often seek to develop strategies at broad scales; however, high-quality spatial data depicting current vegetation and ecosystem types over large areas are often unavailable. Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. A RandomForest land cover classification of Sentinel-2 imagery was generated and further articulated into ecological types using a comprehensive set of secondary modeling approaches. A total of 123 types were mapped in Arkansas, including common cultural and ruderal land cover and vegetation such as pine plantations and developed types. Ozark–Ouachita Dry–Mesic Forest covered the most area, 17.51% of the state. Row Crops covered 17.16%. Twenty-five pine or pine plantation types covered 19.73% of the state, with Ozark–Ouachita pine woodland or mature pine plantation covering 6.15%. Field survey points were used to assess the quality of the mapped ecological systems. The approaches presented here provide a framework for finer resolution mapping of ecological systems at broad scales in other regions.

Novel Scoring System for Ranking Hematopoietic Stem Cell Transplantation.

When human leukocyte antigen (HLA)-matched donors are not available for hematopoietic stem cell transplants (HSCT), there are no well-accepted guidelines for ranking 7/8 HLA-matched unrelated donors to achieve optimal transplant outcomes. A novel scoring system for ranking HLA mismatches for these donors was investigated. High-resolution HLA types were used to determine amino acid mismatches located in the HLA antigen-recognition domain. The location and physicochemical properties of mismatched amino acids were used to assign scores for peptide binding, T-cell receptor docking, and HLA structure/function. The scores were tested using a cohort of 2319 patients with leukemia or myelodysplastic syndrome who received their first unrelated donor transplant using conventional graft-versus-host disease (GVHD) prophylaxis between 2000 and 2014. Donors were 7/8 HLA-matched with a single HLA Class I mismatch. Primary outcomes were overall survival and acute GVHD. The scores did not significantly (p<0.01) associate with transplant outcomes, although a Peptide Score = 0 (i.e., no differences in peptide binding; N=146, 6.3%) appears to have lower transplant-related mortality (TRM) compared to higher scores (p=0.019). HLA mismatches with Peptide Score=0 were predominately HLA-C*03:03/03:04 (62%), previously reported to be a permissive mismatch, and a group of 28 other HLA mismatches (38%) that showed similar associations with TRM. This study suggests that HLA mismatches that do not alter peptide binding or orientation (Peptide Score=0) could expand the number of permissive HLA mismatches. Further investigation is needed to confirm this observation and to explore alternative scoring systems for ranking HLA mismatched donors.

Optical Image Processing using Eulerian Amplification

Recent advancements in digital photography, particularly through mobile phone cameras, microscopes, and satellite imaging, have made high-resolution image capture increasingly accessible. However, these captured images often contain subtle changes in color resolution and density that are challenging to observe with the naked eye. This study explores the application of Eulerian amplification as a method to detect and enhance such small-amplitude variations, making them visible for improved analysis. Unlike traditional approaches, Eulerian methods are particularly effective for processing smooth structures and high-quality printed materials where minor amplifications in color can reveal significant grading details. Using a pixel-by-pixel Eulerian path analysis, this method allows for a detailed comparison of color intensity values on a fine 8-bit scale, which ranges from 0 to 255. Through this analysis, color distribution patterns are visualized across the spatial structure of the image, which is particularly beneficial for identifying specific variations in quality in printed materials, such as highly graded cards. To manage large image structures, spatial decomposition using a multi-level Gaussian pyramid technique is employed. By applying temporal filtering within select frequency bands (0.4–4 Hz) at a coarse pyramid level, the algorithm effectively pools spatial data, enabling the detection of color density shifts that may indicate image defects. The amplified image data is further processed using a classifier neural network, which provides high sensitivity and specificity in detecting edge defects, image centering issues, scuffs, and breakages. The system demonstrates promising results in identifying true positives while maintaining low false-negative rates, thus confirming the reliability of deep machine learning algorithms in high-sensitivity defect detection. This paper presents the significance of Eulerian amplification in optical image processing, offering a robust tool for precise, automated defect detection across a variety of high-resolution image types.

New insights into hematopoietic cell transplantation in ALL: Who should be transplanted, when, and how

Advancements in multi-agent chemotherapy through clinical trials conducted by multi-institutional collaborative groups worldwide, along with risk stratification using molecular genetic features and treatment responses, including minimal residual disease, have significantly improved outcomes for children and adolescents with acute lymphoblastic leukemia (ALL) over the past half-century. However, for a certain number of high-risk patients, including those with relapsed or refractory disease, for whom existing chemotherapy alone is insufficient for cure, allogeneic hematopoietic cell transplantation (HCT) has provided a potential opportunity for leukemia cure. For these patients, the appropriate selection of donor and stem cell source, conditioning regimen, timing of transplantation, and comprehensive supportive care, including effective graft-versus-host disease prophylaxis, are prerequisites for successful HCT. While HCT from a human leukocyte antigen (HLA)-matched sibling has traditionally been the preferred option, less than 25 % of patients currently have such a donor in developed countries. Consequently, alternative donor HCT options, such as those from matched unrelated donors identified through high-resolution HLA typing, unrelated cord blood donors, and more recently, haploidentical donors using post-transplant cyclophosphamide or TCRαβ+/CD19+ cell-depleted grafts, are providing broader access to HCT for patients lacking matched sibling donors. Nonetheless, HCT carries the risk of various acute and late toxicities. In particular, the use of myeloablative conditioning with total body irradiation, a standard in pediatric ALL, is associated with significant long-term sequelae. As our understanding of the pathophysiology of the disease improves and novel molecular targeted therapies and immunotherapies are developed, the indication for HCT in pediatric ALL is becoming more selective, leading to a gradual decrease in the number of transplants performed. However, further optimization and evolution of allogeneic HCT are needed to both maximize its anti-leukemia effects and minimize transplant-related complications, as there remain cases that undoubtedly require HCT for the cure of leukemia.

Distributions of MICA and MICB Alleles Typed by Amplicon-Based Next-Generation Sequencing in South Koreans.

Major histocompatibility complex class I chain-related genes A and B (MICA and MICB) play a role as ligands in activating the NKG2D receptor expressed in natural killer cells, γδ T-cells and αβ CD8 T-cells and have been defined in human diseases and haematopoietic stem cell transplantation (HSCT). MICA and MICB alleles were genotyped at the three-field level by amplicon-based next-generation sequencing (NGS) using a MiSeqDx system and compared with the results from previous studies in healthy South Korean donors. Exons 2-5 of MICA and exons 2-4 of MICB were amplified using a multiplex polymerase chain reaction (PCR). Sequence reads of ≥ 51 depth counts were consistently obtained for each sample exon, and target exons were determined to match reference sequences contained in the IPD-IMGT/HLA database. MICA and MICB alleles were tested using exon combinations. The program was designed to recognise specific sequences and discriminate between the MICA*008:01:01/*027 alleles. A total of 22 alleles were found in MICA and MICB. We observed 1 HLA-C ~ HLA-B ~ MICA ~ MICB ~ HLA-DRB1 haplotype with significant linkage disequilibrium between alleles at all neighbouring HLA loci. These results are consistent with previous microarray results. Genotyping of MICA and MICB was possible using 11-loci HLA genes. We updated the distribution of MICA and MICB based on three-field allele and haplotype frequencies containing linkage disequilibrium in South Koreans using amplicon-based NGS. These data suggest that high-resolution MICA and MICB typing data obtained using NGS may aid in performing HSCT and disease association studies.

247 - Performance of high-resolution type deep learning reconstruction

247 - Performance of high-resolution type deep learning reconstruction

HLA-C Peptide Repertoires as Predictors of Clinical Response during Early SARS-CoV-2 Infection.

The human leukocyte antigen (HLA) system plays a pivotal role in the immune response to viral infections, mediating the presentation of viral peptides to T cells and influencing both the strength and specificity of the host immune response. Variations in HLA genotypes across individuals lead to differences in susceptibility to viral infection and severity of illness. This study uses observations from the early phase of the COVID-19 pandemic to explore how specific HLA class I molecules affect clinical responses to SARS-CoV-2 infection. By analyzing paired high-resolution HLA types and viral genomic sequences from 60 patients, we assess the relationship between predicted HLA class I peptide binding repertoires and infection severity as measured by the sequential organ failure assessment score. This approach leverages functional convergence across HLA-C alleles to identify relationships that may otherwise be inaccessible due to allelic diversity and limitations in sample size. Surprisingly, our findings show that severely symptomatic infection in this cohort is associated with disproportionately abundant binding of SARS-CoV-2 structural and non-structural protein epitopes by patient HLA-C molecules. In addition, the extent of overlap between a given patient's predicted HLA-C and HLA-A peptide binding repertoires correlates with worse prognoses in this cohort. The findings highlight immunologic mechanisms linking HLA-C molecules with the human response to viral pathogens that warrant further investigation.

High-throughput, high-resolution HLA typing using NGS-Pronto nanopore sequencing – a multicentre performance study

High-throughput, high-resolution HLA typing using NGS-Pronto nanopore sequencing – a multicentre performance study

Electrical conductivity profiling for rapid contamination assessment in unsaturated zones: A case study of an MSW landfill

This study investigates the potential of using bulk soil electrical conductivity (ECbulk) to predict pore water conductivity (ECpw) for assessing the contamination in the unsaturated zone of an old municipal solid waste (MSW) landfill. ECbulk, ECpw, and water content were evaluated with depth at an old MSW landfill in Bhalswa, Delhi, using the Hydraulic Profiling Tool (HPT) and a dual tube soil sampling system. This data was also supplemented by a cone penetration test (CPTu) for high-resolution soil type identification. The correlation of ECbulk with ECpw was primarily influenced by volumetric water content and mineral conductivity with the latter being negligible at this site due to the high conductivity of the leachate. A reasonable linear correlation between normalized EC (ECbulk/ECpw) was observed with volumetric water content, except at low water contents. ECbulk and ECpw profiles with depth indicated attenuation of contaminants in clay layers, while sand layers exhibited constant profile with depth. ECpw was contributed by macro ions generally found in the leachate, including Na+, Mg2+, K+, Ca2+, NH4+, Cl−, SO42−, and HCO3−, as demonstrated by a strong correlation with their cumulative ionic strength. The results indicate that ECbulk profile can be used as a rapid semi-quantitative method for assessing contaminant migration in the unsaturated soil zone, supporting the remediation or control strategies at old landfills.

Human Leukocyte Antigen Mismatching and Survival in Contemporary Hematopoietic Cell Transplantation for Hematologic Malignancies.

PURPOSEHuman leukocyte antigen (HLA) mismatching can reduce survival of patients with blood cancer after hematopoietic cell transplantation (HCT). How recent advances in HCT practice, in particular graft-versus-host disease (GVHD) prophylaxis by post-transplantation cyclophosphamide (PTCy), influence HLA risk associations is unknown.PATIENTS AND METHODSThe study included 17,292 unrelated HCTs with 6-locus high-resolution HLA typing, performed mainly for acute leukemia or related myeloid neoplasms between 2016 and 2020, including 1,523 transplants with PTCy. HLA risk associations were evaluated by multivariable Cox regression models, with overall survival (OS) as primary end point.RESULTSOS was lower in HLA mismatched compared with fully matched transplants (hazard ratio [HR], 1.23 [99% CI, 1.14 to 1.33]; P < .001). This was driven by class I HLA-A, HLA-B, HLA-C (HR, 1.29 [99% CI, 1.19 to 1.41]; P < .001) but not class II HLA-DRB1 and HLA-DQB1 (HR, 1.07 [99% CI, 0.93 to 1.23]; P = .19). Class I antigen-level mismatches were associated with worse OS than allele-level mismatches (HR, 1.36 [99% CI, 1.24 to 1.49]; P < .001), as were class I graft-versus-host peptide-binding motif (PBM) mismatches compared with matches (HR, 1.42 [99% CI, 1.28 to 1.59]; P < .001). The use of PTCy improved GVHD, relapse-free survival compared with conventional prophylaxis in HLA-matched transplants (HR, 0.77 [0.66 to 0.9]; P < .001). HLA mismatching increased mortality in PTCy transplants (HR, 1.32 [1.04 to 1.68]; P = .003) similarly as in non-PTCy transplants (interaction P = .43).CONCLUSIONClass I but not class II HLA mismatches, especially at the antigen and PBM level, are associated with inferior survival in contemporary unrelated HCT. These effects are not significantly different between non-PTCy compared with PTCy transplants. Optimized HLA matching should still be considered in modern HCT.

Cross-species imputation and comparison of single-cell transcriptomic profiles.

Cross-species comparison and prediction of gene expression profiles are important to understand regulatory changes during evolution and to transfer knowledge learned from model organisms to humans. Single-cell RNA-seq (scRNA-seq) profiles enable us to capture gene expression profiles with respect to variations among individual cells; however, cross-species comparison of scRNA-seq profiles is challenging because of data sparsity, batch effects, and the lack of one-to-one cell matching across species. Moreover, single-cell profiles are challenging to obtain in certain biological contexts, limiting the scope of hypothesis generation. Here we developed Icebear, a neural network framework that decomposes single-cell measurements into factors representing cell identity, species, and batch factors. Icebear enables accurate prediction of single-cell gene expression profiles across species, thereby providing high-resolution cell type and disease profiles in under-characterized contexts. Icebear also facilitates direct cross-species comparison of single-cell expression profiles for conserved genes that are located on the X chromosome in eutherian mammals but on autosomes in chicken. This comparison, for the first time, revealed evolutionary and diverse adaptations of X-chromosome upregulation in mammals.

High resolution HLA genotyping with third generation sequencing technology-A multicentre study.

Molecular HLA typing techniques are currently undergoing a rapid evolution. While real-time PCR is established as the standard method in tissue typing laboratories regarding allocation of solid organs, next generation sequencing (NGS) for high-resolution HLA typing is becoming indispensable but is not yet suitable for deceased donors. By contrast, high-resolution typing is essential for stem cell transplantation and is increasingly required for questions relating to various disease associations. In this multicentre clinical study, the TGS technique using nanopore sequencing is investigated applying NanoTYPE™ kit and NanoTYPER™ software (Omixon Biocomputing Ltd., Budapest, Hungary) regarding the concordance of the results with NGS and its practicability in diagnostic laboratories. The results of 381 samples show a concordance of 99.58% for 11 HLA loci, HLA-A, -B, -C, -DRB1, -DRB3, -DRB4, -DRB5, -DQA1, -DQB1, -DPA1 and -DPB1. The quality control (QC) data shows a very high quality of the sequencing performed in each laboratory, 34,926 (97.15%) QC values were returned as 'passed', 862 (2.4%) as 'inspect' and 162 (0.45%) as 'failed'. We show that an 'inspect' or 'failed' QC warning does not automatically lead to incorrect HLA typing. The advantages of nanopore sequencing are speed, flexibility, reusability of the flow cells and easy implementation in the laboratory. There are challenges, such as exon coverage and the handling of large amounts of data. Finally, nanopore sequencing presents potential for applications in basic research within the field of epigenetics and genomics and holds significance for clinical concerns.

Can genomics and meteorology predict outbreaks of legionellosis in urban settings?

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaire's disease (LD). Globally, reported cases of LD have risen fourfold from 2000 to 2014. In 2016, Sydney, Australia was the epicenter of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole-genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones, and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The core genome multi-locus sequence typing scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high-resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce.IMPORTANCEWe investigated the genomic and meteorological influences of infections caused by Legionella pneumophila in Sydney, Australia. Our study contributes to a knowledge gap of factors that drive outbreaks of legionellosis compared to sporadic infections in urban settings. In such cases, clinical isolates can be rare, and thus, other data are needed to inform decision-making around control measures. The study revealed that core genome multi-locus sequence typing is a reliable and adaptable technique when investigating Lpsg1 outbreaks. In Sydney, the genomic profile of Lpsg1 was dominated by a single clone, which was linked to numerous community cases over a period of 40 years. Interestingly, the peak in legionellosis cases during Autumn was not associated with this prevalent outbreak clone. Incorporating meteorological data with Lpsg1 genomics can support risk assessment strategies for legionellosis in urban environments, and this approach may be relevant for other densely populated regions globally.

Genetic study of a rare Chinese pedigree with a recombination occurring between the HLA-A/C loci in both parents

To analyze a Chinese pedigree with a recombination occurring between the HLA-A/C loci in both parents. A patient who was planning to undergo hematopoietic stem cell transplantation due to "aplastic anemia" in February 2022 was selected as the study subject. Peripheral blood samples were collected from the patient, his parents and brother. HLA-A/C/B/DRB1/DQB1 high-resolution typing was carried out by using sequence-based typing and sequence-specific oligonucleotides. The recombination was identified by pedigree analysis. The HLA haplotype of each individual was identified by genealogical analysis. The parentage possibility was determined by short tandem repeat analysis. HLA-A/C/B/DRB1/DRB345/DQA1/DQB1/DPA1/DPB1 were determined with next-generation high-throughput sequence-based typing. The recombination sites were analyzed by family study. The high parentage possibilities of the family was confirmed by short tandem repeat analysis. Recombination was found between the HLA-A*24:02 A*33:03/C*14:03 in the paternally transmitted haplotype, whilst HLA-A*01:01 A*03:01/C*08:02 was found in the maternally transmitted haplotype, which had resulted in two novel HLA haplotypes in the proband. A rare case with simultaneous recombination of the paternal and maternal HLA-A/C loci has been discovered, which may facilitate further study of the mechanisms of the HLA recombination.

Comparison of CRISPR typing and conventional molecular methods for distinguishing Laribacter hongkongensis isolates from fish, frogs and humans

High-resolution and efficient typing for Laribacter hongkongensis (L. hongkongensis) is essential for epidemiological investigation of such emerging foodborne pathogens. Clustered regularly interspaced short palindromic repeats (CRISPR) typing is an innovative molecular method that shows great promise for L. hongkongensis typing. Here, we explored the CRISPR typing method by combining CRISPR1 and CRISPR2 loci to characterize a collection of 109 L. hongkongensis isolates from humans and animals and compared it to current molecular methods such as pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST). The results showed that all three methods have high discriminatory power (diversity index was 0.9902 for PFGE, 0.9663 for CRISPR and 0.9562 for MLST); strong congruence was observed between them (Rand index was 0.969 between CRISPR and PFGE, 0.953 between CRISPR and MLST, 0.958 between PFGE and MLST). CRISPR typing could well distinguish the isolates in the same STs or PFGE profiles, and the genetic information contained by the CRISPR array is useful for deep phylogenetic typing. We demonstrate that rapid CRISPR typing is a practical genetic fingerprinting tool with high resolution, comparable ease of use and lower cost, ability to track the source of various groups of L. hongkongensis strains and indication of genetic characteristics.

Earth Observation based multi-scale analysis of crop diversity in the European Union: First insights for agro-environmental policies

Characterizing and quantifying crop diversity (“effective number of crops”) across scales is needed to understand a wide range of issues related to resilience of farms and the agricultural sector, the provision of ecosystem services, and ultimately to provide a scientific basis for effective agro-environmental policies. We use a novel European Union (EU) wide satellite-derived product at 10 m spatial resolution to produce datasets of crop diversity across spatial (1–100 km) and administrative scales for the year 2018. We focus on the 27 EU countries and the United Kingdom. We define local crop diversity (α-diversity) at the 1 km scale corresponding to large farms or clusters of small-to-medium sized farms. Across countries, the α crop diversity ranges from 2.3 to 4.4 with the highest levels achieved by systems dominated by a high number of small farms (less than 10 ha on average). Computed at grid level aggregation, γ-diversity (the number and area of crops that are grown independently from the precise location, for landscape region, and country levels) increases rapidly from 2.85 at 1 km to 3.86 at 10 km and levels off 4.27 at 100 km. Such diversity levels are higher than that reported for the U.S.A., likely related to differences in farm structure and practices. β-diversity, the ratio of γ and α diversity, provides a measure of the diversity between agroecosystems and ranges from 1.2 to 2.3 across EU countries. Based on the magnitude and change of γ-diversity across scales, we classify countries’ diversity in four groups with possible consequences for regional to national agro-environmental policy recommendations, in particular the monitoring activities and indicator development of interventions for the implementation of the Common Agricultural Policy (CAP) in the EU. Forthcoming annual high-resolution continental Copernicus crop type maps will facilitate temporal comparisons. Various ecosystem co-variates are to be explored for deeper understanding of the link of crop diversity to agro-ecosystem services.

DODGE: automated point source bacterial outbreak detection using cumulative long term genomic surveillance.

The reliable and timely recognition of outbreaks is a key component of public health surveillance for foodborne diseases. Whole genome sequencing (WGS) offers high resolution typing of foodborne bacterial pathogens and facilitates the accurate detection of outbreaks. This detection relies on grouping WGS data into clusters at an appropriate genetic threshold. However, methods and tools for selecting and adjusting such thresholds according to the required resolution of surveillance and epidemiological context are lacking. Here we present DODGE (Dynamic Outbreak Detection for Genomic Epidemiology), an algorithm to dynamically select and compare these genetic thresholds. DODGE can analyse expanding datasets over time and clusters that are predicted to correspond to outbreaks (or "investigation clusters") can be named with established genomic nomenclature systems to facilitate integrated analysis across jurisdictions. DODGE was tested in two real-world Salmonella genomic surveillance datasets of different duration, 2 months from Australia and 9 years from the United Kingdom. In both cases only a minority of isolates were identified as investigation clusters. Two known outbreaks in the United Kingdom dataset were detected by DODGE and were recognized at an earlier timepoint than the outbreaks were reported. These findings demonstrated the potential of the DODGE approach to improve the effectiveness and timeliness of genomic surveillance for foodborne diseases and the effectiveness of the algorithm developed. DODGE is freely available at https://github.com/LanLab/dodge and can easily be installed using Conda.