Expression network analysis of bovine skin infested with Rhipicephalus australis identifies pro-inflammatory genes contributing to tick susceptibility.

The skin is the primary feeding site of ticks that infest livestock animals such as cattle. The highly specialised functions of skin at the molecular level may be a factor contributing to variation in susceptibility to tick infestation; but these remain to be well defined. The aim of this study was to investigate the bovine skin transcriptomic profiles of tick-naïve and tick-infested cattle and to uncover the gene expression networks that influence contrasting phenotypes of host resistance to ticks. RNA-Seq data was obtained from skin of Brangus cattle with high (n = 5) and low (n = 6) host resistance at 0 and 12 weeks following artificial tick challenge with Rhipicephalus australis larvae. No differentially expressed genes were detected pre-infestation between high and low resistance groups, but at 12-weeks there were 229 differentially expressed genes (DEGs; FDR < 0.05), of which 212 were the target of at least 1866 transcription factors (TFs) expressed in skin. Regulatory impact factor (RIF) analysis identified 158 significant TFs (P < 0.05) of which GRHL3, and DTX1 were also DEGs in the experiment. Gene term enrichment showed the significant TFs and DEGs were enriched in processes related to immune response and biological pathways related to host response to infectious diseases. Interferon Type 1-stimulated genes, including MX2, ISG15, MX1, OAS2 were upregulated in low host resistance steers after repeated tick challenge, suggesting dysregulated wound healing and chronic inflammatory skin processes contributing to host susceptibility to ticks. The present study provides an assessment of the bovine skin transcriptome before and after repeated tick challenge and shows that the up-regulation of pro-inflammatory genes is a prominent feature in the skin of tick-susceptible animals. In addition, the identification of transcription factors with high regulatory impact provides insights into the potentially meaningful gene-gene interactions involved in the variation of phenotypes of bovine host resistance to ticks.

Use of continuous genotypes for genomic prediction in sugarcane.

Genomic selection in sugarcane faces challenges due to limited genomic tools and high genomic complexity, particularly because of its high and variable ploidy. The classification of genotypes for single nucleotide polymorphisms (SNPs) becomes difficult due to the wide range of possible allele dosages. Previous genomic studies in sugarcane used pseudo-diploid genotyping, grouping all heterozygotes into a single class. In this study, we investigate the use of continuous genotypes as a proxy for allele-dosage in genomic prediction models. The hypothesis is that continuous genotypes could better reflect allele dosage at SNPs linked to mutations affecting target traits, resulting in phenotypic variation. The dataset included genotypes of 1318 clones at 58K SNP markers, with about 26K markers filtered using standard quality controls. Predictions for tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and fiber content (Fiber) were made using parametric, non-parametric, and Bayesian methods. Continuous genotypes increased accuracy by 5%-7% for CCS and Fiber. The pseudo-diploid parametrization performed better for TCH. Reproducing kernel Hilbert spaces model with Gaussian kernel and AK4 (arc-cosine kernel with hidden layer 4) kernel outperformed other methods for TCH and CCS, suggesting that non-additive effects might influence these traits. The prevalence of low-dosage markers in the study may have limited the benefits of approximating allele-dosage information with continuous genotypes in genomic prediction models. Continuous genotypes simplify genomic prediction in polyploid crops, allowing additional markers to be used without adhering to pseudo-diploid inheritance. The approach can particularly benefit high ploidy species or emerging crops with unknown ploidy.

Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies.

Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.

Multi-breed genomic evaluation for tropical beef cattle when no pedigree information is available.

BACKGROUND: It has been challenging to implement genomic selection in multi-breed tropical beef cattle populations. If commercial (often crossbred) animals could be used in the reference population for these genomic evaluations, this could allow for very large reference populations. In tropical beef systems, such animals often have no pedigree information. Here we investigate potential models for such data, using marker heterozygosity (to model heterosis) and breed composition derived from genetic markers, as covariates in the model. Models treated breed effects as either fixed or random, and included genomic best linear unbiased prediction (GBLUP) and BayesR. A tropically-adapted beef cattle dataset of 29,391 purebred, crossbred and composite commercial animals was used to evaluate the models. RESULTS: Treating breed effects as random, in an approach analogous to genetic groups allowed partitioning of the genetic variance into within-breed and across breed-components (even with a large number of breeds), and estimation of within-breed and across-breed genomic estimated breeding values (GEBV). We demonstrate that moderately-accurate (0.30-0.43) GEBV can be calculated using these models. Treating breed effects as random gave more accurate GEBV than treating breed as fixed. A simple GBLUP model where no breed effects were fitted gave the same accuracy (and correlations of GEBV very close to 1) as a model where GEBV for within-breed and the GEBV for (random) across-breed effects were included. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy, with 3% accuracy improvement averaged across traits, especially when the validation population was less related to the reference population. Estimates of heterosis from our models were in line with previous estimates from beef cattle. A method for estimating the number of effective breed comparisons for each breed combination accumulated across contemporary groups is presented. CONCLUSIONS: When no pedigree is available, breed composition and heterosis for inclusion in multi-breed genomic evaluation can be estimated from genotypes. When GEBV were predicted for herds with no data in the reference population, BayesR resulted in the highest accuracy.

Genomic prediction with machine learning in sugarcane, a complex highly polyploid clonally propagated crop with substantial non-additive variation for key traits.

Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such, genomic prediction in sugarcane presents an interesting case for machine learning (ML) methods, which are purportedly able to deal with high levels of complexity in prediction. Here, we investigated deep learning (DL) neural networks, including multilayer networks (MLP) and convolution neural networks (CNN), and an ensemble machine learning approach, random forest (RF), for genomic prediction in sugarcane. The data set used was 2912 sugarcane clones, scored for 26,086 genome wide single nucleotide polymorphism markers, with final assessment trial data for total cane harvested (TCH), commercial cane sugar (CCS), and fiber content (Fiber). The clones in the latest trial (2017) were used as a validation set. We compared prediction accuracy of these methods to genomic best linear unbiased prediction (GBLUP) extended to include dominance and epistatic effects. The prediction accuracies from GBLUP models were up to 0.37 for TCH, 0.43 for CCS, and 0.48 for Fiber, while the optimized ML models had prediction accuracies of 0.35 for TCH, 0.38 for CCS, and 0.48 for Fiber. Both RF and DL neural network models have comparable predictive ability with the additive GBLUP model but are less accurate than the extended GBLUP model.

In it for the long run: perspectives on exploiting long-read sequencing in livestock for population scale studies of structural variants.

Studies have demonstrated that structural variants (SV) play a substantial role in the evolution of species and have an impact on Mendelian traits in the genome. However, unlike small variants (< 50 bp), it has been challenging to accurately identify and genotype SV at the population scale using short-read sequencing. Long-read sequencing technologies are becoming competitively priced and can address several of the disadvantages of short-read sequencing for the discovery and genotyping of SV. In livestock species, analysis of SV at the population scale still faces challenges due to the lack of resources, high costs, technological barriers, and computational limitations. In this review, we summarize recent progress in the characterization of SV in the major livestock species, the obstacles that still need to be overcome, as well as the future directions in this growing field. It seems timely that research communities pool resources to build global population-scale long-read sequencing consortiums for the major livestock species for which the application of genomic tools has become cost-effective.

Adaptive sampling during sequencing reveals the origins of the bovine reproductive tract microbiome across reproductive stages and sexes.

Cattle enterprises are one of the major livestock production systems globally and are forecasted to have stable growth in the next decade. To facilitate sustainable live weight production, optimal reproductive performance is essential. Microbial colonisation in the reproductive tract has been demonstrated as one of the factors contributing to bovine reproductive performance. Studies also implied that reproductive metagenomes are different at each stage of the estrous cycle. This study applied Oxford Nanopore Technologies' adaptive long-read sequencing to profile the bovine reproductive microbiome collected from tropical cattle in northern Queensland, Australia. The microbiome samples were collected from cattle of different sexes, reproductive status and locations to provide a comprehensive view of the bovine reproductive microbiome in northern Australian cattle. Ascomycota, Firmicutes and Proteobacteria were abundant phyla identified in the bovine reproductive metagenomes of Australian cattle regardless of sexes, reproductive status and location. The species level taxonomical investigation suggested that gastrointestinal metagenome and the surrounding environment were potentially the origins of the bovine reproductive metagenome. Functional profiles further affirmed this implication, revealing that the reproductive metagenomes of the prepubertal and postpartum animals were dominated by microorganisms that catabolise dietary polysaccharides as an energy substrate while that of the pregnant animals had the function of harvesting energy from aromatic compounds. Bovine reproductive metagenome investigations can be employed to trace the origins of abnormal metagenomes, which is beneficial for disease prevention and control. Additionally, our results demonstrated different reproductive metagenome diversities between cattle from two different locations. The variation in diversity within one location can serve as the indicator of abnormal reproductive metagenome, but between locations inferences cannot be made. We suggest establishing localised metagenomic indices that can be used to infer abnormal reproductive metagenomes which contribute to abortion or sub-fertility.

Metagenomic Predictions: A Review 10 years on.

Metagenomic predictions use variation in the metagenome (microbiome profile) to predict the unknown phenotype of the associated host. Metagenomic predictions were first developed 10 years ago, where they were used to predict which cattle would produce high or low levels of enteric methane. Since then, the approach has been applied to several traits and species including residual feed intake in cattle, and carcass traits, body mass index and disease state in pigs. Additionally, the method has been extended to include predictions based on other multi-dimensional data such as the metabolome, as well to combine genomic and metagenomic information. While there is still substantial optimisation required, the use of metagenomic predictions is expanding as DNA sequencing costs continue to fall and shows great promise particularly for traits heavily influenced by the microbiome such as feed efficiency and methane emissions.

Transcriptional changes in the peripheral blood leukocytes from Brangus cattle before and after tick challenge with Rhipicephalus australis.

BACKGROUND: Disease emergence and production loss caused by cattle tick infestations have focused attention on genetic selection strategies to breed beef cattle with increased tick resistance. However, the mechanisms behind host responses to tick infestation have not been fully characterised. Hence, this study examined gene expression profiles of peripheral blood leukocytes from tick-naive Brangus steers (Bos taurus x Bos indicus) at 0, 3, and 12 weeks following artificial tick challenge experiments with Rhipicephalus australis larvae. The aim of the study was to investigate the effect of tick infestation on host leukocyte response to explore genes associated with the expression of high and low host resistance to ticks. RESULTS: Animals with high (HR, n = 5) and low (LR, n = 5) host resistance were identified after repeated tick challenge. A total of 3644 unique differentially expressed genes (FDR < 0.05) were identified in the comparison of tick-exposed (both HR and LR) and tick-naive steers for the 3-week and 12-week infestation period. Enrichment analyses showed genes were involved in leukocyte chemotaxis, coagulation, and inflammatory response. The IL-17 signalling, and cytokine-cytokine interactions pathways appeared to be relevant in protection and immunopathology to tick challenge. Comparison of HR and LR phenotypes at timepoints of weeks 0, 3, and 12 showed there were 69, 8, and 4 differentially expressed genes, respectively. Most of these genes were related to immune, tissue remodelling, and angiogenesis functions, suggesting this is relevant in the development of resistance or susceptibility to tick challenge. CONCLUSIONS: This study showed the effect of tick infestation on Brangus cattle with variable phenotypes of host resistance to R. australis ticks. Steers responded to infestation by expressing leukocyte genes related to chemotaxis, cytokine secretion, and inflammatory response. The altered expression of genes from the bovine MHC complex in highly resistant animals at pre- and post- infestation stages also supports the relevance of this genomic region for disease resilience. Overall, this study offers a resource of leukocyte gene expression data on matched tick-naive and tick-infested steers relevant for the improvement of tick resistance in composite cattle.

Environmental variation effects fertility in tropical beef cattle.

The northern Australia beef cattle industry operates in harsh environmental conditions which consistently suppress female fertility. To better understand the environmental effect on cattle raised extensively in northern Australia, new environmental descriptors were defined for 54 commercial herds located across the region. Three fertility traits, based on the presence of a corpus luteum at 600 d of age, indicating puberty, (CL Presence, n = 25,176), heifer pregnancy (n = 20,989) and first lactation pregnancy (n = 10,072) were recorded. Temperature, humidity, and rainfall were obtained from publicly available data based on herd location. Being pubertal at 600 d (i.e. CL Presence) increased the likelihood of success at heifer pregnancy and first lactation pregnancy (P < 0.05), underscoring the importance of early puberty in reproductive success. A temperature humidity index (THI) of 65-70 had a significant (P < 0.05) negative effect on first lactation pregnancy rate, heifer pregnancy and puberty at 600 d of age. Area under the curve of daily THI was significant (P < 0.05) and reduced the likelihood of pregnancy at first lactation and puberty at 600 days. Deviation from long-term average rainfall was not significant (P < 0.05) for any trait. Average daily weight gain had a significant and positive relationship (P < 0.05) for heifer and first lactation pregnancy. The results indicate that chronic or cumulative heat load is more determinantal to reproductive performance than acute heat stress. The reason for the lack of a clear relationship between acute heat stress and reproductive performance is unclear but may be partially explained by peak THI and peak nutrition coinciding at the same time. Sufficient evidence was found to justify the use of average daily weight gain and chronic heat load as descriptors to define an environmental gradient.

Extensive Variation in Gene Expression is Revealed in 13 Fertility-Related Genes Using RNA-Seq, ISO-Seq, and CAGE-Seq From Brahman Cattle.

Fertility is a key driver of economic profitability in cattle production. A number of studies have identified genes associated with fertility using genome wide association studies and differential gene expression analysis; however, the genes themselves are poorly characterized in cattle. Here, we selected 13 genes from the literature which have previously been shown to have strong evidence for an association with fertility in Brahman cattle (Bos taurus indicus) or closely related breeds. We examine the expression variation of the 13 genes that are associated with cattle fertility using RNA-seq, CAGE-seq, and ISO-seq data from 11 different tissue samples from an adult Brahman cow and a Brahman fetus. Tissues examined include blood, liver, lung, kidney, muscle, spleen, ovary, and uterus from the cow and liver and lung from the fetus. The analysis revealed several novel isoforms, including seven from SERPINA7. The use of three expression characterization methodologies (5' cap selected ISO-seq, CAGE-seq, and RNA-seq) allowed the identification of isoforms that varied in their length of 5' and 3' untranslated regions, variation otherwise undetectable (collapsed as degraded RNA) in generic isoform identification pipelines. The combinations of different sequencing technologies allowed us to overcome the limitations of relatively low sequence depth in the ISO-seq data. The lower sequence depth of the ISO-seq data was also reflected in the lack of observed expression of some genes that were observed in the CAGE-seq and RNA-seq data from the same tissue. We identified allele specific expression that was tissue-specific in AR, IGF1, SOX9, STAT3, and TAF9B. Finally, we characterized an exon of TAF9B as partially nested within the neighboring gene phosphoglycerate kinase 1. As this study only examined two animals, even more transcriptional variation may be present in a genetically diverse population. This analysis reveals the large amount of transcriptional variation within mammalian fertility genes and illuminates the fact that the transcriptional landscape cannot be fully characterized using a single technology alone.

Evaluation of Host Depletion and Extraction Methods for Shotgun Metagenomic Analysis of Bovine Vaginal Samples.

The reproductive tract metagenome plays a significant role in the various reproductive system functions, including reproductive cycles, health, and fertility. One of the major challenges in bovine vaginal metagenome studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of untargeted shotgun metagenomic profiling. This is the first study comparing the effectiveness of different host depletion and DNA extraction methods for bovine vaginal metagenomic samples. The host depletion methods evaluated were slow centrifugation (Soft-spin), NEBNext Microbiome DNA Enrichment kit (NEBNext), and propidium monoazide (PMA) treatment, while the extraction methods were DNeasy Blood and Tissue extraction (DNeasy) and QIAamp DNA Microbiome extraction (QIAamp). Soft-spin and QIAamp were the most effective host depletion method and extraction methods, respectively, in reducing the number of cattle genomic content in bovine vaginal samples. The reduced host-to-microbe ratio in the extracted DNA increased the sequencing depth for microbial reads in untargeted shotgun sequencing. Bovine vaginal samples extracted with QIAamp presented taxonomical profiles which closely resembled the mock microbial composition, especially for the recovery of Gram-positive bacteria. Additionally, samples extracted with QIAamp presented extensive functional profiles with deep coverage. Overall, a combination of Soft-spin and QIAamp provided the most robust representation of the vaginal microbial community in cattle while minimizing host DNA contamination. IMPORTANCE In addition to the host tissue collected during the sampling process, bovine vaginal samples are saturated with large amounts of extracellular DNA and secreted proteins that are essential for physiological purposes, including the reproductive cycle and immune defense. Due to the high host-to-microbe genome ratio, which hampers the sequencing efficacy for metagenome samples and the recovery of the actual metagenomic profiles, bovine vaginal samples cannot benefit from the full potential of shotgun sequencing. This is the first investigation on the most effective host depletion and extraction methods for bovine vaginal metagenomic samples. This study demonstrated an effective combination of host depletion and extraction methods, which harvested higher percentages of 16S rRNA genes and microbial reads, which subsequently led to a taxonomical profile that resembled the actual community and a functional profile with deeper coverage. A representative metagenomic profile is essential for investigating the role of the bovine vaginal metagenome for both reproductive function and susceptibility to infections.

Technical note: overcoming host contamination in bovine vaginal metagenomic samples with nanopore adaptive sequencing.

Animal metagenomic studies, in which host-associated microbiomes are profiled, are an increasingly important contribution to our understanding of the physiological functions, health and susceptibility to diseases of livestock. One of the major challenges in these studies is host DNA contamination, which limits the sequencing capacity for metagenomic content and reduces the accuracy of metagenomic profiling. This is the first study comparing the effectiveness of different sequencing methods for profiling bovine vaginal metagenomic samples. We compared the new method of Oxford Nanopore Technologies (ONT) adaptive sequencing, which can be used to target or eliminate defined genetic sequences, to standard ONT sequencing, Illumina 16S rDNA amplicon sequencing, and Illumina shotgun sequencing. The efficiency of each method in recovering the metagenomic data and recalling the metagenomic profiles was assessed. ONT adaptive sequencing yielded a higher amount of metagenomic data than the other methods per 1 Gb of sequence data. The increased sequencing efficiency of ONT adaptive sequencing consequently reduced the amount of raw data needed to provide sufficient coverage for the metagenomic samples with high host-to-microbe DNA ratio. Additionally, the long reads generated by ONT adaptive sequencing retained the continuity of read information, which benefited the in-depth annotations for both taxonomical and functional profiles of the metagenome. The different methods resulted in the identification of different taxa. Genera Clostridium, which was identified at low abundances and categorized under Order "Unclassified Clostridiales" when using the 16S rDNA amplicon sequencing method, was identified to be the dominant genera in the sample when sequenced with the three other methods. Additionally, higher numbers of annotated genes were identified with ONT adaptive sequencing, which also produced high coverage on most of the commonly annotated genes. This study illustrates the advantages of ONT adaptive sequencing in improving the amount of metagenomic data derived from microbiome samples with high host-to-microbe DNA ratio and the advantage of long reads in preserving intact information for accurate annotations.

Genomic prediction using low-coverage portable Nanopore sequencing.

Most traits in livestock, crops and humans are polygenic, that is, a large number of loci contribute to genetic variation. Effects at these loci lie along a continuum ranging from common low-effect to rare high-effect variants that cumulatively contribute to the overall phenotype. Statistical methods to calculate the effect of these loci have been developed and can be used to predict phenotypes in new individuals. In agriculture, these methods are used to select superior individuals using genomic breeding values; in humans these methods are used to quantitatively measure an individual's disease risk, termed polygenic risk scores. Both fields typically use SNP array genotypes for the analysis. Recently, genotyping-by-sequencing has become popular, due to lower cost and greater genome coverage (including structural variants). Oxford Nanopore Technologies' (ONT) portable sequencers have the potential to combine the benefits genotyping-by-sequencing with portability and decreased turn-around time. This introduces the potential for in-house clinical genetic disease risk screening in humans or calculating genomic breeding values on-farm in agriculture. Here we demonstrate the potential of the later by calculating genomic breeding values for four traits in cattle using low-coverage ONT sequence data and comparing these breeding values to breeding values calculated from SNP arrays. At sequencing coverages between 2X and 4X the correlation between ONT breeding values and SNP array-based breeding values was > 0.92 when imputation was used and > 0.88 when no imputation was used. With an average sequencing coverage of 0.5x the correlation between the two methods was between 0.85 and 0.92 using imputation, depending on the trait. This suggests that ONT sequencing has potential for in clinic or on-farm genomic prediction, however, further work to validate these findings in a larger population still remains.

An Epigenetic Aging Clock for Cattle Using Portable Sequencing Technology.

Extensively grazed cattle are often mustered only once a year. Therefore, birthdates are typically unknown or inaccurate. Birthdates would be useful for deriving important traits (growth rate; calving interval), breed registrations, and making management decisions. Epigenetic clocks use methylation of DNA to predict an individual's age. An epigenetic clock for cattle could provide a solution to the challenges of industry birthdate recording. Here we derived the first epigenetic clock for tropically adapted cattle using portable sequencing devices from tail hair, a tissue which is widely used in industry for genotyping. Cattle (n = 66) with ages ranging from 0.35 to 15.7 years were sequenced using Oxford Nanopore Technologies MinION and methylation was called at CpG sites across the genome. Sites were then filtered and used to calculate a covariance relationship matrix based on methylation state. Best linear unbiased prediction was used with 10-fold cross validation to predict age. A second methylation relationship matrix was also calculated that contained sites associated with genes used in the dog and human epigenetic clocks. The correlation between predicted age and actual age was 0.71 for all sites and 0.60 for dog and human gene epigenetic clock sites. The mean absolute deviation was 1.4 years for animals aged less than 3 years of age, and 1.5 years for animals aged 3-10 years. This is the first reported epigenetic clock using industry relevant samples in cattle.

Regional Anesthesia Use in Pediatric Burn Surgery: A Descriptive Retrospective Series.

Pediatric burns are a common and often devastating injury. Treatment, reconstruction, and rehabilitation are painful experiences. For some, the experience triggers post-traumatic stress disorder and/or a chronic pain syndrome. Given the role pain plays as a major secondary disease, it must be addressed to achieve optimal healing. Regional anesthesia has been used extensively to manage postoperative pain and reduce the need for opioids following other surgical procedures in children. However, regional anesthesia is not widely used in pediatric burn care. We present a descriptive, retrospective case series of 15 pediatric burn patients who received regional anesthesia as part of their intra-operative pain management. In our patient population, we saw low levels of anesthetic as well as opioid usage with well-controlled pain. In this cohort, 93% of patients scored a 0/10 on the Face, Legs, Activity, Cry and Consolability (FLACC) scale for pain by post-anesthesia care unit (PACU) discharge, with an average PACU stay of 70 minutes. Thirty-three percent of patients received no opioids, with the average opioid dose being 0.06mg/kg morphine equivalents. This case series serves to make clinicians aware of the feasibility of use and benefits of regional anesthesia in pediatric burn patients requiring operative repair.

A linkage disequilibrium-based approach to position unmapped SNPs in crop species.

BACKGROUND: High-density SNP arrays are now available for a wide range of crop species. Despite the development of many tools for generating genetic maps, the genome position of many SNPs from these arrays is unknown. Here we propose a linkage disequilibrium (LD)-based algorithm to allocate unassigned SNPs to chromosome regions from sparse genetic maps. This algorithm was tested on sugarcane, wheat, and barley data sets. We calculated the algorithm's efficiency by masking SNPs with known locations, then assigning their position to the map with the algorithm, and finally comparing the assigned and true positions. RESULTS: In the 20-fold cross-validation, the mean proportion of masked mapped SNPs that were placed by the algorithm to a chromosome was 89.53, 94.25, and 97.23% for sugarcane, wheat, and barley, respectively. Of the markers that were placed in the genome, 98.73, 96.45 and 98.53% of the SNPs were positioned on the correct chromosome. The mean correlations between known and new estimated SNP positions were 0.97, 0.98, and 0.97 for sugarcane, wheat, and barley. The LD-based algorithm was used to assign 5920 out of 21,251 unpositioned markers to the current Q208 sugarcane genetic map, representing the highest density genetic map for this species to date. CONCLUSIONS: Our LD-based approach can be used to accurately assign unpositioned SNPs to existing genetic maps, improving genome-wide association studies and genomic prediction in crop species with fragmented and incomplete genome assemblies. This approach will facilitate genomic-assisted breeding for many orphan crops that lack genetic and genomic resources.

Piloting a New Curriculum: Guided At-Home Pediatric Regional Anesthesia Education Using a Portable Ultrasound.

Ultrasound-guided regional anesthesia is the standard of care for most regional blocks in pediatric anesthesiology.Training programs must educate physicians to perform regional blocks safely and efficiently. Hands-on learning with simulation and live models is the gold standard. The coronavirus disease 2019 (COVID-19) pandemic has greatly hampered our ability to safely hold in-person workshops. We describe an at-home, guided virtual workshop using portable ultrasound to safely continue experiential trainee education. The primary objective of this pilot virtual workshop was to develop an effective experiential learning program without the need for live child models. The main goal was to give trainees hands-on experience obtaining anatomical ultrasound images necessary for regional anesthesia blocks in a guided-virtual setting and to evaluate the effectiveness of skills acquisition. This workshop included two pediatric anesthesiology fellows and a pediatric anesthesiologist. Trainees were instructed on ultrasound-guided regional block acquisition. For two weeks, trainees acquired images/movies of regional block anatomy at home using their own children. Virtual video assistance was available. Trainees then used acquired images/movies to discuss needle and local anesthetic placement with a pediatric regional anesthesiologist. Trainees completed pre- and post-workshop surveys assessing attitudes, perceived educational efficacy, and procedural skill acquisition. The faculty member also assessed trainees' skills. The virtual workshop was successful. Trainees expressed successful active learning and increased comfort in performing regional blocks on live patients. They correctly identified relevant anatomy of acquired images/movies, as well as needle and local anesthetic placement at the time of debriefing. Faculty were pleased with trainees' initial performance of regional blocks. Adapting an in-person workshop to an at-home guided experience is a safe, feasible, and well-received method for anesthesiology trainees to obtain experiential learning of ultrasound-guided regional anesthesia. This facilitated at-home learning experience allows for hands-on skill practice while preventing exposure of child models to the hospital setting during a pandemic.

Prowler: a novel trimming algorithm for Oxford Nanopore sequence data.

MOTIVATION: Trimming and filtering tools are useful in DNA sequencing analysis because they increase the accuracy of sequence alignments and thus the reliability of results. Oxford nanopore technologies (ONT) trimming and filtering tools are currently rudimentary, generally only filtering reads based on whole read average quality. This results in discarding reads that contain regions of high-quality sequence. Here, we propose Prowler, a trimmer that uses a window-based approach inspired by algorithms used to trim short read data. Importantly, we retain the phase and read length information by optionally replacing trimmed sections with Ns. RESULTS: Prowler was applied to mammalian and bacterial datasets, to assess its effect on alignment and assembly, respectively. Compared to data filtered with Nanofilt, alignments of data trimmed with Prowler had lower error rates and more mapped reads. Assemblies of Prowler trimmed data had a lower error rate than those filtered with Nanofilt; however, this came at some cost to assembly contiguity. AVAILABILITY AND IMPLEMENTATION: Prowler is implemented in Python and is available at https://github.com/ProwlerForNanopore/ProwlerTrimmer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.