Closing the Gap: A Critical Examination of Adherence, Inconsistency, and Improvements in Colonoscopy Reporting Practices.

INTRODUCTION: Comprehensive and standardized colonoscopy reports are crucial in colorectal cancer prevention, monitoring, and research. This study investigates adherence to national and international guidelines by analyzing reporting practices among 21 endoscopists in 7 German centers, with a focus on polyp reporting. METHODS: We identified and assessed German, European, American and World Health Organization provided statements to identify key elements in colonoscopy reporting. Board-certified gastroenterologists rated the relevance of each element and estimated their reporting frequency. Adherence to the identified report elements was evaluated for 874 polyps from 351 colonoscopy reports ranging from March 2021 to March 2022. RESULTS: We identified numerous recommendations for colonoscopy reporting. We categorized the reasoning behind those recommendations into clinical relevance, justification, and quality control and research. Although all elements were considered relevant by the surveyed gastroenterologists, discrepancies were observed in the evaluated reports. Particularly diminutive polyps or attributes which are rarely abnormal (e.g., surface integrity) respectively rarely performed (e.g., injection) were sparsely documented. Furthermore, the white light morphology of polyps was inconsistently documented using either the Paris classification or free text. In summary, the analysis of 874 reported polyps revealed heterogeneous adherence to the recommendations, with reporting frequencies ranging from 3% to 89%. CONCLUSION: The inhomogeneous report practices may result from implicit reporting practices and recommendations with varying clinical relevance. Future recommendations should clearly differentiate between clinical relevance and research and quality control or explanatory purposes. Additionally, the role of computer assisted documentation should be further evaluated to increase report frequencies of non-pathological findings and diminutive polyps.

Introgressions lead to reference bias in wheat RNA-seq analysis.

BACKGROUND: RNA-seq is a fundamental technique in genomics, yet reference bias, where transcripts derived from non-reference alleles are quantified less accurately, can undermine the accuracy of RNA-seq quantification and thus the conclusions made downstream. Reference bias in RNA-seq analysis has yet to be explored in complex polyploid genomes despite evidence that they are often a complex mosaic of wild relative introgressions, which introduce blocks of highly divergent genes. RESULTS: Here we use hexaploid wheat as a model complex polyploid, using both simulated and experimental data to show that RNA-seq alignment in wheat suffers from widespread reference bias which is largely driven by divergent introgressed genes. This leads to underestimation of gene expression and incorrect assessment of homoeologue expression balance. By incorporating gene models from ten wheat genome assemblies into a pantranscriptome reference, we present a novel method to reduce reference bias, which can be readily scaled to capture more variation as new genome and transcriptome data becomes available. CONCLUSIONS: This study shows that the presence of introgressions can lead to reference bias in wheat RNA-seq analysis. Caution should be exercised by researchers using non-sample reference genomes for RNA-seq alignment and novel methods, such as the one presented here, should be considered.

Meiotic recombination dynamics in plants with repeat-based holocentromeres shed light on the primary drivers of crossover patterning.

Centromeres strongly affect (epi)genomic architecture and meiotic recombination dynamics, influencing the overall distribution and frequency of crossovers. Here we show how recombination is regulated and distributed in the holocentric plant Rhynchospora breviuscula, a species with diffused centromeres. Combining immunocytochemistry, chromatin analysis and high-throughput single-pollen sequencing, we discovered that crossover frequency is distally biased, in sharp contrast to the diffused distribution of hundreds of centromeric units and (epi)genomic features. Remarkably, we found that crossovers were abolished inside centromeric units but not in their proximity, indicating the absence of a canonical centromere effect. We further propose that telomere-led synapsis of homologues is the feature that best explains the observed recombination landscape. Our results hint at the primary influence of mechanistic features of meiotic pairing and synapsis rather than (epi)genomic features and centromere organization in determining the distally biased crossover distribution in R. breviuscula, whereas centromeres and (epi)genetic properties only affect crossover positioning locally.

Integrative multi-omics analyses of date palm (Phoenix dactylifera) roots and leaves reveal how the halophyte land plant copes with sea water.

Date palm (Phoenix dactylifera L.) is able to grow and complete its life cycle while being rooted in highly saline soils. Which of the many well-known salt-tolerance strategies are combined to fine-tune this remarkable resilience is unknown. The precise location, whether in the shoot or the root, where these strategies are employed remains uncertain, leaving us unaware of how the various known salt-tolerance mechanisms are integrated to fine-tune this remarkable resilience. To address this shortcoming, we exposed date palm to a salt stress dose equivalent to seawater for up to 4 weeks and applied integrative multi-omics analyses followed by targeted metabolomics, hormone, and ion analyses. Integration of proteomic into transcriptomic data allowed a view beyond simple correlation, revealing a remarkably high degree of convergence between gene expression and protein abundance. This sheds a clear light on the acclimatization mechanisms employed, which depend on reprogramming of protein biosynthesis. For growth in highly saline habitats, date palm effectively combines various salt-tolerance mechanisms found in both halophytes and glycophytes: "avoidance" by efficient sodium and chloride exclusion at the roots, and "acclimation" by osmotic adjustment, reactive oxygen species scavenging in leaves, and remodeling of the ribosome-associated proteome in salt-exposed root cells. Combined efficiently as in P. dactylifera L., these sets of mechanisms seem to explain the palm's excellent salt stress tolerance.

Reducing scope 3 carbon emissions in gastrointestinal endoscopy: results of the prospective study of the 'Green Endoscopy Project Würzburg'.

OBJECTIVE: Carbon emissions generated by gastrointestinal endoscopy have been recognised as a critical issue. Scope 3 emissions are mainly caused by the manufacturing, packaging and transportation of purchased goods. However, to our knowledge, there are no prospective data on the efficacy of measurements aimed to reduce scope 3 emissions. DESIGN: The study was performed in a medium-sized academic endoscopy unit. Manufacturers of endoscopic consumables were requested to answer a questionnaire on fabrication, origin, packaging and transport. Based on these data, alternative products were purchased whenever possible. In addition, staff was instructed on how to avoid waste. Thereafter, the carbon footprint of each item purchased was calculated from February to May 2023 (intervention period), and scope 3 emissions were compared with the same period of the previous year (control period). RESULTS: 26 of 40 companies answered the questionnaire. 229 of 322 products were classified as unfavourable. A switch to alternative items was possible for 47/229 items (20.5%). 1666 endoscopies were performed during the intervention period compared with 1751 examinations during the control period (-4.1%). The number of instruments used decreased by 10.0% (3111 vs 3457). Using fewer and alternative products resulted in 11.5% less carbon emissions (7.09 vs 8.01 tons of carbon equivalent=tCO2 e). Separation of waste led to a reduction of 20.1% (26.55 vs 33.24 tCO2e). In total, carbon emissions could be reduced by 18.4%. CONCLUSION: Use of fewer instruments per procedure, recycling packaging material and switching to alternative products can reduce carbon emissions without impairing the endoscopic workflow.

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD: A multiclass deep learning algorithm distinguishing different endoscopic image content was trained with 10 557 images (1300 examinations, nine centers, four processors). Consecutively, the algorithm was used to calculate withdrawal time (AI prediction) and extract relevant images. Validation was performed on 100 colonoscopy videos (five centers). The reported and AI-predicted withdrawal times were compared with video-based measurement; photodocumentation was compared for documented polypectomies. RESULTS: Video-based measurement in 100 colonoscopies revealed a median absolute difference of 2.0 minutes between the measured and reported withdrawal times, compared with 0.4 minutes for AI predictions. The original photodocumentation represented the cecum in 88 examinations compared with 98/100 examinations for the AI-generated documentation. For 39/104 polypectomies, the examiners' photographs included the instrument, compared with 68 for the AI images. Lastly, we demonstrated real-time capability (10 colonoscopies). CONCLUSION : Our AI system calculates withdrawal time, provides an image report, and is real-time ready. After further validation, the system may improve standardized reporting, while decreasing the workload created by routine documentation.

Genome analyses reveal population structure and a purple stigma color gene candidate in finger millet.

Finger millet is a key food security crop widely grown in eastern Africa, India and Nepal. Long considered a 'poor man's crop', finger millet has regained attention over the past decade for its climate resilience and the nutritional qualities of its grain. To bring finger millet breeding into the 21st century, here we present the assembly and annotation of a chromosome-scale reference genome. We show that this ~1.3 million years old allotetraploid has a high level of homoeologous gene retention and lacks subgenome dominance. Population structure is mainly driven by the differential presence of large wild segments in the pericentromeric regions of several chromosomes. Trait mapping, followed by variant analysis of gene candidates, reveals that loss of purple coloration of anthers and stigma is associated with loss-of-function mutations in the finger millet orthologs of the maize R1/B1 and Arabidopsis GL3/EGL3 anthocyanin regulatory genes. Proanthocyanidin production in seed is not affected by these gene knockouts.

Artificial intelligence-based polyp size measurement in gastrointestinal endoscopy using the auxiliary waterjet as a reference.

BACKGROUND: Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary waterjet as a measurement reference. METHODS: Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a computed tomography colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the waterjet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during routine clinical practice were compared with visual estimates. RESULTS: In the silicone model, visual estimation had the largest median percentage error of 25.1 % (95 %CI 19.1 %-30.4 %), followed by biopsy forceps-based estimation: median 20.0 % (95 %CI 14.4 %-25.6 %). Poseidon gave a significantly lower median percentage error of 7.4 % (95 %CI 5.0 %-9.4 %) compared with other methods. During routine colonoscopies, Poseidon presented a significantly lower median percentage error (7.7 %, 95 %CI 6.1 %-9.3 %) than visual estimation (22.1 %, 95 %CI 15.1 %-26.9 %). CONCLUSION: In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.

Repeat-based holocentromeres influence genome architecture and karyotype evolution.

The centromere represents a single region in most eukaryotic chromosomes. However, several plant and animal lineages assemble holocentromeres along the entire chromosome length. Here, we compare genome organization and evolution as a function of centromere type by assembling chromosome-scale holocentric genomes with repeat-based holocentromeres from three beak-sedge (Rhynchospora pubera, R. breviuscula, and R. tenuis) and their closest monocentric relative, Juncus effusus. We demonstrate that transition to holocentricity affected 3D genome architecture by redefining genomic compartments, while distributing centromere function to thousands of repeat-based centromere units genome-wide. We uncover a complex genome organization in R. pubera that hides its unexpected octoploidy and describe a marked reduction in chromosome number for R. tenuis, which has only two chromosomes. We show that chromosome fusions, facilitated by repeat-based holocentromeres, promoted karyotype evolution and diploidization. Our study thus sheds light on several important aspects of genome architecture and evolution influenced by centromere organization.

Pilot study of a new freely available computer-aided polyp detection system in clinical practice.

PURPOSE: Computer-aided polyp detection (CADe) systems for colonoscopy are already presented to increase adenoma detection rate (ADR) in randomized clinical trials. Those commercially available closed systems often do not allow for data collection and algorithm optimization, for example regarding the usage of different endoscopy processors. Here, we present the first clinical experiences of a, for research purposes publicly available, CADe system. METHODS: We developed an end-to-end data acquisition and polyp detection system named EndoMind. Examiners of four centers utilizing four different endoscopy processors used EndoMind during their clinical routine. Detected polyps, ADR, time to first detection of a polyp (TFD), and system usability were evaluated (NCT05006092). RESULTS: During 41 colonoscopies, EndoMind detected 29 of 29 adenomas in 66 of 66 polyps resulting in an ADR of 41.5%. Median TFD was 130 ms (95%-CI, 80-200 ms) while maintaining a median false positive rate of 2.2% (95%-CI, 1.7-2.8%). The four participating centers rated the system using the System Usability Scale with a median of 96.3 (95%-CI, 70-100). CONCLUSION: EndoMind's ability to acquire data, detect polyps in real-time, and high usability score indicate substantial practical value for research and clinical practice. Still, clinical benefit, measured by ADR, has to be determined in a prospective randomized controlled trial.

Development and evaluation of a deep learning model to improve the usability of polyp detection systems during interventions.

BACKGROUND: The efficiency of artificial intelligence as computer-aided detection (CADe) systems for colorectal polyps has been demonstrated in several randomized trials. However, CADe systems generate many distracting detections, especially during interventions such as polypectomies. Those distracting CADe detections are often induced by the introduction of snares or biopsy forceps as the systems have not been trained for such situations. In addition, there are a significant number of non-false but not relevant detections, since the polyp has already been previously detected. All these detections have the potential to disturb the examiner's work. OBJECTIVES: Development and evaluation of a convolutional neuronal network that recognizes instruments in the endoscopic image, suppresses distracting CADe detections, and reliably detects endoscopic interventions. METHODS: A total of 580 different examination videos from 9 different centers using 4 different processor types were screened for instruments and represented the training dataset (519,856 images in total, 144,217 contained a visible instrument). The test dataset included 10 full-colonoscopy videos that were analyzed for the recognition of visible instruments and detections by a commercially available CADe system (GI Genius, Medtronic). RESULTS: The test dataset contained 153,623 images, 8.84% of those presented visible instruments (12 interventions, 19 instruments used). The convolutional neuronal network reached an overall accuracy in the detection of visible instruments of 98.59%. Sensitivity and specificity were 98.55% and 98.92%, respectively. A mean of 462.8 frames containing distracting CADe detections per colonoscopy were avoided using the convolutional neuronal network. This accounted for 95.6% of all distracting CADe detections. CONCLUSIONS: Detection of endoscopic instruments in colonoscopy using artificial intelligence technology is reliable and achieves high sensitivity and specificity. Accordingly, the new convolutional neuronal network could be used to reduce distracting CADe detections during endoscopic procedures. Thus, our study demonstrates the great potential of artificial intelligence technology beyond mucosal assessment.

Genome analysis in Avena sativa reveals hidden breeding barriers and opportunities for oat improvement.

Oat (Avena sativa L.) is an important and nutritious cereal crop, and there is a growing need to identify genes that contribute to improved oat varieties. Here we utilize a newly sequenced and annotated oat reference genome to locate and characterize quantitative trait loci (QTLs) affecting agronomic and grain-quality traits in five oat populations. We find strong and significant associations between the positions of candidate genes and QTL that affect heading date, as well as those that influence the concentrations of oil and ß-glucan in the grain. We examine genome-wide recombination profiles to confirm the presence of a large, unbalanced translocation from chromosome 1 C to 1 A, and a possible inversion on chromosome 7D. Such chromosome rearrangements appear to be common in oat, where they cause pseudo-linkage and recombination suppression, affecting the segregation, localization, and deployment of QTLs in breeding programs.

The mosaic oat genome gives insights into a uniquely healthy cereal crop.

Cultivated oat (Avena sativa L.) is an allohexaploid (AACCDD, 2n = 6x = 42) thought to have been domesticated more than 3,000 years ago while growing as a weed in wheat, emmer and barley fields in Anatolia1,2. Oat has a low carbon footprint, substantial health benefits and the potential to replace animal-based food products. However, the lack of a fully annotated reference genome has hampered efforts to deconvolute its complex evolutionary history and functional gene dynamics. Here we present a high-quality reference genome of A. sativa and close relatives of its diploid (Avena longiglumis, AA, 2n = 14) and tetraploid (Avena insularis, CCDD, 2n = 4x = 28) progenitors. We reveal the mosaic structure of the oat genome, trace large-scale genomic reorganizations in the polyploidization history of oat and illustrate a breeding barrier associated with the genome architecture of oat. We showcase detailed analyses of gene families implicated in human health and nutrition, which adds to the evidence supporting oat safety in gluten-free diets, and we perform mapping-by-sequencing of an agronomic trait related to water-use efficiency. This resource for the Avena genus will help to leverage knowledge from other cereal genomes, improve understanding of basic oat biology and accelerate genomics-assisted breeding and reanalysis of quantitative trait studies.

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy.

Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.

The Barley and Wheat Pan-Genomes.

To unlock the genetic potential in crops, multi-genome comparisons are an essential tool. Decreasing costs and improved sequencing technologies have democratized plant genome sequencing and led to a vast increase in the amount of available reference sequences on the one hand and enabled the assembly of even the largest and most complex and repetitive crops genomes such as wheat and barley. These developments have led to the era of pan-genomics in recent years. Pan-genome projects enable the definition of the core and dispensable genome for various crop species as well as the analysis of structural and functional variation and hence offer unprecedented opportunities for exploring and utilizing the genetic basis of natural variation in crops. Comparing, analyzing, and visualizing these multiple reference genomes and their diversity requires powerful and specialized computational strategies and tools.

Chromosome-scale assembly of barley cv. 'Haruna Nijo' as a resource for barley genetics.

Cultivated barley (Hordeum vulgare ssp. vulgare) is used for food, animal feed, and alcoholic beverages and is widely grown in temperate regions. Both barley and its wild progenitor (H. vulgare ssp. spontaneum) have large 5.1-Gb genomes. High-quality chromosome-scale assemblies for several representative barley genotypes, both wild and domesticated, have been constructed recently to populate the nascent barley pan-genome infrastructure. Here, we release a chromosome-scale assembly of the Japanese elite malting barley cultivar 'Haruna Nijo' using a similar methodology as in the barley pan-genome project. The 4.28-Gb assembly had a scaffold N50 size of 18.9 Mb. The assembly showed high collinearity with the barley reference genome 'Morex' cultivar, with some inversions. The pseudomolecule assembly was characterized using transcript evidence of gene projection derived from the reference genome and de novo gene annotation achieved using published full-length cDNA sequences and RNA-Seq data for 'Haruna Nijo'. We found good concordance between our whole-genome assembly and the publicly available BAC clone sequence of 'Haruna Nijo'. Interesting phenotypes have since been identified in Haruna Nijo; its genome sequence assembly will facilitate the identification of the underlying genes.

Genome sequences of three Aegilops species of the section Sitopsis reveal phylogenetic relationships and provide resources for wheat improvement.

Aegilops is a close relative of wheat (Triticum spp.), and Aegilops species in the section Sitopsis represent a rich reservoir of genetic diversity for the improvement of wheat. To understand their diversity and advance their utilization, we produced whole-genome assemblies of Aegilops longissima and Aegilops speltoides. Whole-genome comparative analysis, along with the recently sequenced Aegilops sharonensis genome, showed that the Ae. longissima and Ae. sharonensis genomes are highly similar and are most closely related to the wheat D subgenome. By contrast, the Ae. speltoides genome is more closely related to the B subgenome. Haplotype block analysis supported the idea that Ae. speltoides genome is closest to the wheat B subgenome, and highlighted variable and similar genomic regions between the three Aegilops species and wheat. Genome-wide analysis of nucleotide-binding leucine-rich repeat (NLR) genes revealed species-specific and lineage-specific NLR genes and variants, demonstrating the potential of Aegilops genomes for wheat improvement.

Advances in the agent-based modeling of economic and social behavior.

In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.