Correct Assignment Research Articles

Abstract 4118724: Validity of a NYHA Classification Guide with Six Minute Walk Test Outcomes

Background: The New York Heart Association (NYHA) classification is a subjective tool that is commonly used in clinical practice to assess symptoms and functional capacity of patients with heart failure (HF). Correct assignment of NYHA is essential to facilitate evidence-based management. Research Question: What is the validity of the CLASS-HF Guide compared to the 6-minute walk test (6MWT)? Purpose: To examine the validity of the new investigator-developed CLASS-HF guide to assist in appropriate assignment of NYHA Class relative to the 6MWT. Methods: A multi-site, cross-sectional study in three cardiology clinical sites (two specializing in HF) recruited 103 patients in various classes and stages of HF. Providers assigned patients their NYHA Classification using the CLASS-HF guide. Patients then performed the 6MWT with test staff blinded to the assigned NYHA class. Exertion, dyspnea, and walk distance were captured post-test. The validity of the guide-assisted classification was then examined for convergent validity with 6MWT outcomes. Data analysis was performed with correlations, ANOVA, and multivariable regression. Results: Of the 103 total participants, 65.1% were male, 18.4% were non-White, with an average age of 66.0 ± 15.5 years old. A little less than one-third (30.1%) had HFpEF (LVEF ≥ 50%). Provider-assigned NYHA Class was 22.3% I, 38.8% II, 35.0% III and 3.9% IV. The average distance walked during the 6MWT by class was: 367.1 ± 85.6 m for I, 343.7 ± 104.7 m for II, 261.6 ± 73.9 m for III, and 184.6 ± 114.0 m for IV. Convergent validity of NYHA class with Borg exertion (Spearman’s r = .546, p < .001) and dyspnea ( r = .504, p < .001) was strong. A statistically significant inverse correlation was found between NYHA assigned class and meters walked during the 6MWT ( r = -.469, p < .001), with significant mean differences (ANOVA F (3,99) = 10.72, p < .001) in distance walked for: NYHA Class I vs. III ( md = 105.5 m), I vs. IV ( md = 182.6 m), II vs. III ( md = 82.1 m), II vs. IV ( md = 159.2 m). Increasing NYHA class remained significantly associated with lower 6MWT distance ( F (3,90)=5.22, p = .002) in multivariable regression (Adj. R -squared= .575) controlling for age, sex, race/ethnicity, diagnosis, site, Borg exertion and dyspnea, and 6MWT stopping/pausing. Conclusion: Validity evidence was found for NYHA class assignment after use of the CLASS-HF guide with respect to 6MWT distance and post-test perceived exertion and dyspnea.

Comprehensive two dimensional gas chromatography – Time of flight mass spectrometry (GC×GC-TOFMS) for the investigation of botanical origin of raw spirits

Comprehensive two dimensional gas chromatography – time of flight mass spectrometry (GC × GC-TOFMS) with sample introduction using headspace solid phase microextraction (HS-SPME) was used for the botanical classification of raw spirits obtained from C3 (corn and sorghum) and C4 (rye, wheat and potato) plants. 45 spirit samples representing these raw materials (10 spirits produced from rye, corn, wheat and potato, and 5 from sorghum) were analyzed. Volatile compounds profiles were compared by PCA, and after removal of outliers samples were subjected to the classification model. OPLS-DA model was built (R2Y = 0.924 Q2Y = 0.895) that enabled clear separation of all tested spirits of different botanical origin. The model was validated with training and testing sets and 100 % correct assignment was achieved. GC × GC proved to be a method that not only can be used as a tool for botanic origin of raw spirits, but also provides detailed information of volatile fermentation by-products, characteristic for particular spirit.

RoadSitu: Leveraging Road Video Frame Extraction and Three-Stage Transformers for Situation Recognition

Situation recognition is an crucial problem in scene understanding, activity understanding, and action reasoning as it provides a structured representation of the main activity depicted in the image.Semantic role labeling is crucial to situation recognition, which is challenging because a single action can have multiple meanings and purposes depending on its context. Understanding images beyond the highlighted actions requires inferences about the context of the scene, the objects, and their role in the captured event. Recently, situation recognition (SR) has been introduced, which jointly derives a collection of the action (activity), meaning-role, and noun (entities) pairs in the form of moving images. To label these frames as action frames, we must assign nouns (entities) to the role based on the content of the observed image. One of the main challenges is managing the complex dependencies between the assigned roles (nouns) and the predicted action, as the correct role assignment often depends on the accuracy of the action prediction. We introduce, RoadSitu, a road situation recognition that involves generating a structured summary of what is happening in a road scenario using an action and the semantic roles played by agents from a video frame. The action can describe a diverse set of situations, and the same agent can play various roles depending on the situation depicted in the video frame. Therefore, a situation recognition model needs to understand the context of each video frame and the visual-linguistic meaning of the semantic roles of that particular frame. One of the main challenges in this work is the complex task of annotating video frames with semantic roles and handling the structured dependencies between the assigned roles (nouns) and the predicted action (activity). Additionally, the sparsity of meaningful semantic information within road scenarios poses further difficulties. To overcome these challenges, we introduce a novel approach where action recognition and noun estimation work together interactively to form structured summaries of each situation. In experiments using a road video dataset obtained from a South Korean company, RoadSitu achieved significant improvements across various performance metrics, with a Top-1 verb accuracy of 43.46%, Top-5 verb accuracy of 72.48%, and value accuracy of 34.21%, outperforming baseline models such as GSRTR and JSL by 2.4% and 3.86% in Top-1 verb accuracy, respectively. These results demonstrate the effectiveness of our model in handling complex road scenarios.

Clustering by detecting skeletal structure and identifying density fluctuation

Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. Handling clusters with arbitrary shape and various density; 2. Finding cluster centers automatically; 3. Low parameter sensitivity and computational complexity. In this context, a novel algorithm namely clustering by detecting skeletal structure and identifying density fluctuation (CSSDF) was presented. In CSSDF, an efficient strategy based on density and local information of neighborhood is firstly proposed to detect the skeletal structure, which can collect the local information and identify the rough distribution of data. With the identified distribution information, a method takes expanded neighborhood and density fluctuation into consideration is proposed to further collect global information of data, which can assign all skeleton points and find cluster centers. To sum up, CSSDF can not only discover the underlying structure of data regardless of its’ distribution, but also ensure the correct assignment of all skeleton points and thus lead to a satisfying clustering performance. In addition, the computational complexity of the proposed approach is O(nlogn), which makes it possible to deal with some large clustering problem.

Ruminant microbiome data are skewed and unFAIR, undermining their usefulness for sustainable production improvement

The ruminant microbiome plays a key role in the health, feed utilization and environmental impact of ruminant production systems. Microbiome research provides insights to reduce the environmental footprint and improve meat and milk production from ruminants. However, the microbiome composition depends on the ruminant species, habitat and diet, highlighting the importance of having a good representation of ruminant microbiomes in their local environment to translate research findings into beneficial approaches. This information is currently lacking. In this study, we examined the metadata of farmed ruminant microbiome studies to determine global representativeness and summarized information by ruminant species, geographic location, body site, and host information. We accessed data from the International Nucleotide Sequence Database Collaboration via the National Center for Biotechnology Information database. We retrieved 47,628 sample metadata, with cattle accounting for more than two-thirds of the samples. In contrast, goats, which have a similar global population to cattle, were underrepresented with less than 4% of the total samples. Most samples originated in Western Europe, North America, Australasia and China but countries with large ruminant populations in South America, Africa, Asia, and Eastern Europe were underrepresented. Microbiomes from the gastrointestinal tract were the most frequently studied, comprising about 87% of all samples. Additionally, the number of samples from other body sites such as the respiratory tract, milk, skin, reproductive tract, and fetal tissue, has markedly increased over the past decade. More than 40% of the samples lacked basic information and many were retrieved from generic taxonomic classifications where the ruminant species was manually recovered. The lack of basic information such as age, breed or sex can limit the reusability of the data for further analysis and follow-up studies. This requires correct taxonomic assignment of the ruminant host and basic metadata information using accepted ontologies adapted to host-associated microbiomes. Repositories should require this information as a condition of acceptance. The results of this survey highlight the need to encourage studies of the ruminant microbiome from underrepresented ruminant species and countries worldwide. This shortfall in information poses a challenge for the development of microbiome-based strategies to meet sustainability requirements, particularly in areas with expanding livestock production systems.

The scent gland composition of the Mangshan pit viper, Protobothrops mangshanensis.

The Mangshan pit viper, Protobothrops mangshanensis, is a rare, highly endangered snake native to the mountainous regions of Hunan Province in China. Snakes possess abdominal scent glands, which have been chemically studied in several species. These glands can contain various lipids and peptides, but very often also complex mixtures of carboxylic acids. We report here the occurrence of novel methyl-branched unsaturated acids found in the secretions of six captive individuals living in a zoo. The structures of these compounds, 4.6-dimethylalk-5-enoates in a homologous series from C11-C16, were characterized by GC-MS and GC-IR analysis and various microderivatization reactions including hydrogenation and esterification leading to methyl and pyridylmethyl esters. In addition, dimethyloxazoline formation helped to localize the double bond. The synthesis of methyl 4,6-dimethyldodec-5-enoate allowed the correct assignment of structures and showed the (E)-configuration of the double bond for the major naturally occurring diastereomers. These acids occur in small amounts compared to the major glandular components, cholesterol, and 1-O-hexadecylglycerol, as well as other common long-chain alcohols and amides. Although a general defensive function has been proposed for snake abdominal scent glands, the specific chemistry and moderate amounts of acids reported here may suggest a function in chemical signaling for the Mangshan pit viper. In addition, proline-containing diketopiperazines were identified for the first time in snake scent glands, although an artificial formation from amino acids likely present in the secretion cannot be excluded.

Applications of Probabilistic Genotyping Method for Combining Evidence Across Microhaplotype DNA Mixture Profiles.

In cases of serious crimes that involve challenging DNA samples from the perpetrator (e.g., a minor contributor to a mixture), there is justification to combine different mixture profiles. In our previous study, we developed a massively parallel sequencing (MPS)-based assay targeting 140 microhaplotype markers. In this study, we extended the use of the microhaplotype panel to common scenarios, such as determining the presence of a common contributor or relatedness between different mixture profiles when no reference source is available. Data interpretation was performed using the R package KinMix. Our findings revealed that correct assignments of a common contributor and relatedness were made between relatively balanced mixtures. However, when profiles suffered from allele imbalance, inclusive assignments were significantly associated with the suspect's mixture proportion. Additionally, our analysis showed zero false-positive rates in the studied scenarios. These results indicate that microhaplotype data can be reliably interpreted for identifying a common donor or related donors among different mixtures. Further research based on larger sample sizes may yield more reliable results, which could assist in solving issues related to complex scenarios where multiple mixed profiles were involved.

Contrastive clustering based on generalized bias-variance decomposition

Contrastive learning demonstrates remarkable generalization performance but lacks theoretical understanding, while contrastive clustering achieves promising performance but exhibits some shortcomings. We first introduce a generalized bias-variance decomposition to study contrastive learning, then present the concept of the conformal field, which unifies instance-level contrastive loss and cluster-level de-redundancy loss (Barlow Twins). Finally, we integrate the conformal field and self-labeling to propose the outstanding contrastive clustering model D3CF. D3CF consists of two novel stages: the pre-training stage simultaneously performs instance-level contrastive learning and multi-view cluster-level redundancy reduction, bringing positive samples together and separating negative samples in the row and column space of the augmented feature matrix; to alleviate the adverse effects caused by false-negative pairs and misclustered assignments in the pre-training stage, the boosting stage enhances contrastive learning from single-positive pairs to multiple-positive pairs by leveraging cross-sample similarities, while utilizing pseudo-labels with high confidence criteria for self-labeling to correct clustering assignments. Extensive experiments on six image benchmark datasets and two text benchmarks demonstrate D3CF’s superior performance and validate the effectiveness of its components. Particularly on CIFAR-10, ImageNet-10, and STL-10, D3CF achieves average accuracies of 89.5%, 97%, and 91%, improving NMI by 5.2%, 4.8%, and 2.1%, and ARI by 7%, 7.3%, and 7.3% over the closest baseline.

B-236 New real-time PCR test for APOE genotyping in patients with Alzheimer’s disease before anti-amyloid therapy

Abstract Background In 2023, the US Food and Drug Administration approved LEQEMBI, a monoclonal antibody therapy targeting beta-amyloid plaques, for patients with early-stage Alzheimer’s disease (AD). However, anti-amyloid drug trials demonstrated an elevated risk for amyloid-related imaging abnormalities with cerebral edema (ARIA-E) in carriers of the AD risk allele apolipoprotein E epsilon 4 (APOE-ɛ4), especially in ɛ4/ɛ4 homozygous individuals [1-3]. Here, we report on the evaluation of a new real-time polymerase chain reaction (PCR) test for APOE genotyping. Methods The EURORealTime APOE (EUROIMMUN), which enables simultaneous testing for all six APOE genotypes (ɛ2/ɛ2, ɛ2/ɛ3, ɛ2/ɛ4, ɛ3/ɛ3, ɛ3/ɛ4, and ɛ4/ɛ4) by real-time PCR, was performed using genomic DNA (gDNA) isolated from EDTA blood samples. The PCR amplification results were evaluated using the EURORealTime Analysis Software (EUROIMMUN). Samples from healthy blood donors (HBD) were used to assess intra-assay, inter-assay and between-lot precision (n=6) as well as analytical sensitivity (n=21), with each APOE genotype represented by at least one sample. Assay accuracy was analyzed by comparing genotype assignments to the results of the CE-IVD-marked EUROArray APOE (EUROIMMUN) and bidirectional Sanger sequencing using 110 samples (100 AD, 10 HBD). APOE genotype frequencies were compared between patients (100 AD) and controls (250 HBD). Results Precision testing of the EURORealTime APOE resulted in 100% correct genotype calls. Assessment of the analytical sensitivity revealed valid and correct genotype assignment in all 21 samples, confirming 1 ng/µl as the recommended minimum gDNA concentration. The analytical accuracy amounted to 100% based on agreement with the two reference methods in 110/110 cases. Comparison of the APOE genotype distribution showed a higher proportion of ɛ4 allele carriers (i.e., ɛ2/ɛ4, ɛ3/ɛ4, and ɛ4/ɛ4) in AD patients than in HBD (50.0% vs. 25.2%). The prevalence of the ɛ4/ɛ4 genotype in these cohorts was 25.0% for AD patients compared with 2.8% for HBD. Conclusions The well-established high prevalence of the APOE-ɛ4/ɛ4 genotype in AD patients, together with the increased risk of ARIA-E in these homozygous carriers, substantiates the relevance of APOE genotyping prior to anti-amyloid treatment. The EURORealTime APOE test provides sensitive and reliable determination of APOE alleles, thus presenting a suitable tool to improve risk stratification for potential LEQEMBI recipients.

Two Million Contributions From Citizen Scientists Accelerate the Digitization of the Berlin Herbarium

In 2016, the Botanic Garden Berlin decided to translate into German, the citizen science platform "Les Herbonautes" (Rouhan et al. 2016, developed at the Natural History Museum in Paris) and to integrate it into the digitization workflows of Herbarium Berolinense. "Die Herbonauten" is a web-based platform on which citizen scientists can contribute to the transcription of digital herbarium specimens and their labels. Using a quiz module, users first learn about the various information elements on herbarium labels and their correct semantic assignment and recording. The actual data entry then takes place in so-called "missions," in which herbarium specimens are compiled, e.g., to support work on specific scientific questions. In addition to data entry, there is a forum in which users can discuss problems and their solutions, for example if there is disagreement about the correct transcription of a text element. The forum is moderated by scientific staff of the Herbarium Berlin, who provide support particularly when the knowledge of the collection curators is needed to solve problems. In the course of numerous missions, the knowledge about transcriptions of herbarium specimens was summarized and structured in an evolving and extensive wiki system in German. This wiki has developed into a unique and valuable knowledge base for which an English translation is planned for use by an international audience. To date, 640 registered Herbonauten have made around two million data contributions. Citizen science has thus become an indispensable component of the herbarium's information management. For the further development of the platform, we are experimenting, among other things, with expanding the scope, e.g., to include the recording of character data, and with combining citizen science approaches with AI-based transcription algorithms. In the context of the EU project, Transforming European Taxonomy through Training, Research and Innovations (TETTRIs), the Herbonauten carry out joint missions with other citizen science platforms, where selected specimens are processed by communities that are particularly qualified, for example because they have the necessary knowledge of languages or local geography.

Bridging the gap between molecular and genomic epidemiology in tuberculosis: inferring MIRU-VNTR patterns from genomic data.

The transition from molecular epidemiology in tuberculosis (TB), based on the analysis of repetitive regions (VNTR-based genotyping), to genomic epidemiology transforms in the precision with which we track transmission. However, short-read sequencing, the most common method for performing genomic analysis, is poor at analyzing repetitive regions. This means that we face a gap between the new genomic data and the large amount of information stored in historical databases, which is also an obstacle to cross-national surveillance involving settings where only molecular data are available. Long-read sequencing could help bridge this knowledge gap by allowing analysis of repetitive regions. Our study demonstrates that MIRU-VNTR patterns can be successfully inferred from long-read sequences, allowing the correct assignment of new cases as clustered/orphan by linking new data extracted from genomic analysis to historical MIRU-VNTR databases. Our data may provide a starting point for bridging the knowledge gap between the molecular and genomic eras in tuberculosis epidemiology.

Genetic homogeneity and weak signatures of local adaptation in the marine mussel Mytilus chilensis

The natural populations of the marine mussel Mytilus chilensis and the associated aquaculture industry forms a sensitive social-ecological system that relies on the released propagules for cultivation in the highly heterogeneous environment (temperature, productivity, and salinity) of northern Patagonia (42–44 °S). We assessed spatial genetic structure, signals of local adaptation, and population assignment of M. chilensis analyzing 5963 SNPs from 125 individuals across six natural populations sampled over two consecutive years along the southeast Pacific coast (39° 25′ to 43° 07′ S, ~ 430 km). Neutral and putatively adaptive loci revealed high genetic diversity and low genetic differentiation among populations. Of the whole dataset, less than 1% (50) of loci were identified as putatively adaptive through multiple approaches, with only 0.1% detected in by all of them, and only two loci of them were correlated with environmental variables. No evidence of Isolation by Environment (IBE) was found, albeit a slight differentiation in the southern sampling location (Yaldad). These results suggest that the genetic structure observed is primarily shaped by neutral processes with weak signals of local adaptation. Gene-flow appears to be the main evolutionary force influencing the species’ population genetic structure. Because of the importance for the industry, the probability of correct assignment of individuals to their population of origin using allelic frequencies was evaluated. Analyses exhibited relatively low probabilities (< 50% for four out of six sites) of accurately assigning individuals to their geographic origin, with a limited success of SNP markers the for such purposes. Likely, species' high dispersal capacity, seed translocation, and the spill-over effect of mussel aquaculture prevents population genetic differentiation through high effective gene flow, hindering local genetic adaptation.

Taxonomic reintroduction of the holarctic saprotrophic fungus Crepidotus cinnamomeus

Crepidotus is a genus of common saprotrophic fungi well known especially in the Northern Hemisphere, but distribution patterns of individual species are not sufficiently understood. We redefined a taxonomic circumscription of Crepidotus cinnamomeus based on morphological and molecular congruencies between the type material and recent collections. The species is well delimited from other similar and currently accepted species of the genus. Crepidotus cinnamomeus was found to have a broad holarctic distribution with occurrences in North America, Europe and Asia where it grows on twigs and branches of deciduous trees and shrubs in preferably cold humid habitats. Here we present the first multilocus phylogeny of the genus, including portions of the RPB2 gene. Our study highlights the importance of sufficient sampling from broader areas supported by sequence data, which is essential for estimation of species delimitation, distribution and correct name assignment for Crepidotus species.

THE GENUS ORBITOLINA D’ORBIGNY, 1850 (LARGER BENTHIC FORAMINIFERA) AND ITS CONSTITUENT SPECIES: NOTES ON IDENTITY AND STRATIGRAPHIC RANGES

Following a literature review of the species of Orbitolina and its allies, the identity criteria and stratigraphic ranges of the six potential constituent species of the commonly encountered mid-Cretaceous foraminiferal genus Orbitolina d’Orbigny, 1850 (sensu stricto) are reviewed based on published material. It is noted that correct taxonomic assignment of Orbitolina species requires observation and measurement of both the embryonic apparatus in centred axial thin-sections and of the chamber passage shape in tangential sections. As a consequence, many illustrations purportedly of Orbitolina sensu stricto in the published literature cannot confidently be identified to species level, and such specimens (including new material) should be described or re-described as Orbitolina spp. The genus Orbitolina has a range of late Albian – middle Cenomanian. Species of Orbitolina, when identifiable, have shorter ranges. Orbitolina concava (Lamarck 1816) is restricted to the early – middle Cenomanian, and its inception thus forms a potential proxy for the base of the Cenomanian in carbonate platform settings, but care needs to be taken in its identification and separation from other species of Orbitolina that have at least partial late Albian ranges.

Raman spectra of oxidized sulfur species in hydrothermal fluids

Raman spectroscopic determination of sulfur species molalities in hydrothermal fluids requires correct assignment and knowledge of the scattering efficiencies of Raman bands. Therefore, we studied the Raman spectra of NaHSO4 and H2SO4 solutions experimentally to 700 °C, and of Na2SO4, NaHSO4, H2SO4, and H2SO3 solutions by ab initio molecular dynamics simulation at 727 °C. The results indicate that the scattering efficiencies of the νs(SO3), νas(SO3), and ν(S–OH) Raman bands of HSO4−(aq) depend on the H+ activity. The asymmetric shape of the νs(SO3) Raman band of HSO4−(aq) becomes more symmetric with increasing temperature, which correlates with decreasing hydrogen bonding in the molecular environment. Proton activity and ion pairing do not have a large effect on the change in the band asymmetry with temperature, and a resonance effect on the band shape is not observed. Therefore, we attribute the asymmetric shape of the νs(SO3) Raman band of HSO4−(aq) mostly to hydrogen bonding of the proton in the H–OSO3− molecule with water in its environment. The AIMD simulations clarify assignments of Raman bands of H2SO40, specifically to νs(SO2) and νas(SO2) at ∼1140 cm−1 and ∼1370 cm−1, to νs(SO4) and νas(SO4) at ∼970 cm−1 and ∼1220 cm−1, and to νs(S–(OH)2) and νas(S–(OH)2) at ∼750 cm−1 and ∼840 cm−1. In addition, the experiments showed that diamond is not inert to H2SO4 at high temperatures as reduction of S(VI) to S(IV) produces SO20 and oxidation of diamond generates CO20.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

"Covering provider": an effort to streamline clinical communication chaos.

This report describes a root cause analysis of incorrect provider assignments and a standardized workflow developed to improve the clarity and accuracy of provider assignments. A multidisciplinary working group involving housestaff was assembled. Key drivers were identified using value stream mapping and fishbone analysis. A report was developed to allow for the analysis of correct provider assignments. A standardized workflow was created and piloted with a single service line. Pre- and post-pilot surveys were administered to nursing staff and participating housestaff on the unit. Four key drivers were identified. A standardized workflow was created with an exclusive treatment team role in Epic held by a single provider at any given time, with a corresponding patient list column displaying provider information for each patient. Pre- and post-survey responses report decreased confusion, decreased provider identification errors, and increased user satisfaction among RNs and residents with sustained uptake over time. This work demonstrates structured root cause analysis, notably engaging housestaff, to develop a standardized workflow for an understudied and growing problem. The development of tools and strategies to address the widespread burdens resulting from clinical communication failures is needed.

Methylation Analysis of Colitis-Associated Colorectal Carcinomas.

Ulcerative colitis is a well-known inflammatory bowel disease. Patients have an increased risk of developing colitis associated carcinoma (CAC). It is important for patient management to be able to distinguish between ulcerative colitis associated carcinoma and sporadic carcinoma (sCRC). However, this distinction is frequently very challenging. It is not readily possible to differentiate this histologically. However, the diagnosis is crucial for the patient's further treatment and follow-up. An attempt was therefore made to develop a diagnostic regime that would enable a reliable distinction between sCRC and CAC. We screened 96 patients analyzing more than 850,000 methylation hotspots, to detect distinct epigenetic patterns between both types of carcinomas. Patients with sporadic carcinoma and colitis-associated carcinoma as well as patients with normal colon and patients with confirmed ulcerative colitis without neoplasia were used for the analysis. By extensively filtering the results, methylation sites relevant to distinguish between CAC and sCRC were identified. After the results were filtered, three methylation sites relevant to distinguish between CAC and sCRC were identified. For this purpose, methylation limit values were defined, which favor the samples as CAC or sCRC up to a certain methylation value of the methylation sites. The combination of three methylation sites allows a correct assignment to CAC or sCRC in 94.5% of the cases. The results show that these three methylation sites are promising markers in the diagnosis of CAC vs sCRC. Nevertheless, the diagnosis should always be made in conjunction with histomorphological analyses.

A deep learning method for reflective boundary estimation

Environment estimation is a challenging task in reverberant settings such as the underwater and indoor acoustic domains. The locations of reflective boundaries, for example, can be estimated using acoustic echoes and leveraged for subsequent, more accurate localization and mapping. Current boundary estimation methods are constrained to high signal-to-noise ratios or are customized to specific environments. Existing methods also often require a correct assignment of echoes to boundaries, which is difficult if spurious echoes are detected. To evade these limitations, a convolutional neural network (NN) method is developed for robust two-dimensional boundary estimation, given known emitter and receiver locations. A Hough transform-inspired algorithm is leveraged to transform echo times of arrival into images, which are amenable to multi-resolution regression by NNs. The same architecture is trained on transform images of different resolutions to obtain diverse NNs, deployed sequentially for increasingly refined boundary estimation. A correct echo labeling solution is not required, and the method is robust to reverberation. The proposed method is tested in simulation and for real data from a water tank, where it outperforms state-of-the-art alternatives. These results are encouraging for the future development of data-driven three-dimensional environment estimation with high practical value in underwater acoustic detection and tracking.

PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.