Manual and automatic assignment of two different Aß40 amyloid fibril polymorphs using MAS solid-state NMR spectroscopy.

Amyloid fibrils from Alzheimer's amyloid-beta peptides (Aß) are found to be polymorphic. So far, 14 Aß40 fibril structures have been determined. The mechanism of why one particular protein sequence adopts so many different three-dimensional structures is yet not understood. In this work, we describe the assignment of the NMR chemical shifts of two Alzheimer's disease fibril polymorphs, P1 and P2, which are formed by the amyloid-beta peptide Aß40. The assignment is based on 13C-detected 3D NCACX and NCOCX experiments MAS solid-state NMR experiments. The fibril samples are prepared using an extensive seeding protocol in the absence and presence of the small heat shock protein αB-crystallin. In addition to manual assignments, we obtain chemical shift assignments using the automation software ARTINA. We present an analysis of the secondary chemical shifts and a discussion on the differences between the manual and automated assignment strategies.

A generalized outcome-adaptive sequential multiple assignment randomized trial design.

A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.

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

Objective: 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. Materials and Methods: 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. Results: 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. Conclusion: 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.

Solid-state NMR assignment of α-synuclein polymorph prepared from helical intermediate.

Synucleinopathies are neurodegenerative diseases characterized by the accumulation of α-synuclein protein aggregates in the neurons and glial cells. Both ex vivo and in vitro α-synuclein fibrils tend to show polymorphism. Polymorphism results in structure variations among fibrils originating from a single polypeptide/protein. The polymorphs usually have different biophysical, biochemical and pathogenic properties. The various pathologies of a single disease might be associated with distinct polymorphs. Similarly, in the case of different synucleinopathies, each condition might be associated with a different polymorph. Fibril formation is a nucleation-dependent process involving the formation of transient and heterogeneous intermediates from monomers. Polymorphs are believed to arise from heterogeneous oligomer populations because of distinct selection mechanisms in different conditions. To test this hypothesis, we isolated and incubated different intermediates during in vitro fibrillization of α-synuclein to form different polymorphs. Here, we report 13C and 15N chemical shifts and the secondary structure of fibrils prepared from the helical intermediate using solid-state nuclear magnetic spectroscopy.

"We do others' Jobs": a qualitative study of non-nursing task challenges and proposed solutions.

INTRODUCTION: Non-nursing tasks (NNTs) have become a prevalent issue among healthcare professionals, affecting nurses globally. This study delves into the experiences of Jordanian nurses regarding NNTs, aiming to uncover challenges and propose solutions within the Jordanian healthcare context. OBJECTIVE: Explore the impact of NNTs on Jordanian nurses' roles, workload, and satisfaction. Additionally, the study aims to identify various types of NNTs performed by nurses, understand their impact, and propose solutions to mitigate challenges associated with these tasks. METHODS: A qualitative-exploratory research design was employed for this study. Semi-structured interviews were conducted with Jordanian nurses using a purposeful sampling approach to ensure a diverse representation of experiences and perspectives. Thematic analysis was used to identify recurring themes and patterns related to NNTs, their challenges, and potential solutions. Ethical guidelines were strictly followed to maintain participant confidentiality and ensure the integrity of the data collected. RESULTS: Analysis of the interviews revealed four major themes: challenges of NNTs, types of NNTs, impact of NNTs, and proposed solutions. Nurses faced significant difficulties due to task ambiguity, role confusion, and increased workload from NNTs, which included administrative duties, clerical work, and tasks typically performed by other healthcare professionals. These NNTs negatively impacted nurses' effectiveness, productivity, and job satisfaction by diverting time and energy from primary nursing responsibilities, causing professional strain. To address these issues, participants suggested clearer job descriptions, stricter task assignment protocols, and systemic changes to tackle the root causes of NNTs. CONCLUSION: This study sheds light on the pervasive challenges posed by NNTs among Jordanian nurses and emphasizes the importance of addressing these issues to enhance nursing care quality and nurse well-being. By proposing actionable solutions tailored to the Jordanian context, this research contributes to the global discourse on NNTs and underscores the need for organizational support and advocacy to optimize nurses' roles and improve patient care outcomes.

Improving Diabetes Equity and Advancing Care (IDEA) to optimize team-based care at a safety-net health system for Black and Latine patients living with diabetes: study protocol for a sequential, multiple assignment, randomized trial.

BACKGROUND: Diabetes is the eighth leading cause of death in the USA. Inequities driven by structural racism and systemic oppression have led to racial/ethnic disparities in diabetes prevalence, diagnosis, and treatment. Diabetes-self management training (DSMT), remote glucose monitoring (RGM), and tailored support from a community health worker (CHW) have the potential to improve outcomes. This study will examine the implementation of these interventions in a safety-net healthcare setting. METHODS: Using implementation science and racial equity principles, this study aims to (1) evaluate the appropriateness; (2) measure fidelity; and (3) compare the effectiveness of varying the combination and sequence of three interventions. An exploratory aim will measure sustainability of intervention adherence and uptake. This mixed-methods trial employs a sequential, multiple assignment randomized trial (SMART) design, patient focus group discussions, and staff interviews. Eligible Black/Latine patients will be recruited using patient lists extracted from the electronic medical record system. After a detailed screening process, eligible patients will be invited to attend an in-person enrollment appointment. Informed consent will be obtained and patients will be randomized to either DSMT or RGM. At 6 months, patients will complete two assessments (diabetes empowerment and diabetes-related distress), and HbA1c values will be reviewed. "Responders" will be considered those who have an HbA1c that has improved by at least one percentage point. "Responders" remain in their first assigned study arm. "Nonresponders" will be randomized to either switch study arms or be paired with a CHW. At 6 months participants will complete two assessments again, and their HbA1c will be reviewed. Twelve patient focus groups, two for each intervention paths, will be conducted along with staff interviews. DISCUSSION: This study is the first, to our knowledge, that seeks to fill critical gaps in our knowledge of optimal sequence and combinations of interventions to support diabetes management among Black and Latine patients receiving care at a safety-net hospital. By achieving the study aims, we will build the evidence for optimizing equitable diabetes management and ultimately reducing racial and ethnic healthcare disparities for patients living in disinvested urban settings. TRIAL REGISTRATION: ClinicalTrials.gov: NCT06040463. Registered on September 7, 2023.

Comparison of Barrier Surveillance Algorithms for Directional Sensors and UAVs.

Border surveillance and the monitoring of critical infrastructure are essential components of regional and industrial security. In this paper, our purpose is to study the intricate nature of surveillance methods used by hybrid monitoring systems utilizing Pan-Tilt-Zoom (PTZ) cameras, modeled as directional sensors, and UAVs. We aim to accomplish three occasionally conflicting goals. Firstly, at any given moment we want to detect as many intruders as possible with special attention to newly arriving trespassers. Secondly, we consider it equally important to observe the temporal movement and behavior of each intruder group as accurately as possible. Furthermore, in addition to these objectives, we also seek to minimize the cost of sensor usage associated with surveillance. During the research, we developed and analyzed several interrelated, increasingly complex algorithms. By leveraging RL methods we also gave the system the chance to find the optimal solution on its own. As a result we have gained valuable insights into how various components of these algorithms are interconnected and coordinate. Building upon these observations, we managed to develop an efficient algorithm that takes into account all three criteria mentioned above.

An Applied Statistics dataset for human vs AI-generated answer classification.

Due to the increasing popularity of Large Language Models (LLMs) like ChatGPT, students from various fields now commonly rely on AI-powered text generation tools to complete their assignments. This poses a challenge for course instructors who struggle to identify the authenticity of submitted work. Several AI detection tools for differentiating human-generated text from AI-generated text exist for domains like medical and coding, and available generic tools do not perform well on domain-specific tasks. Those AI detection tools depend on LLM, and to train the LLM, an instruction dataset is needed that helps the LLM to learn the differences between patterns of human-generated text and AI-generated text. To help with the creation of a tool for Applied Statistics, we have created a dataset containing 4231 question-and-answer combinations. To create the dataset, first, we collected 116 questions covering a wide range of topics from Applied Statistics selected by domain experts. Second, we created a framework to randomly distribute and collect answers to the questions from students. Third, we collected answers to fifty assigned questions from each of the 100 students participating in the work. Fourth, we generated an equal number of AI-generated answers using ChatGPT. The prepared dataset will be useful for creating AI-detector tools for the Applied Statistics domain as well as benchmarking AI-detector tools, and the proposed data preparation framework will be useful for collecting data for other domains.

Artificial intelligence, ChatGPT, and dental education: Implications for reflective assignments and qualitative research.

INTRODUCTION: Reflections enable students to gain additional value from a given experience. The use of Chat Generative Pre-training Transformer (ChatGPT, OpenAI Incorporated) has gained momentum, but its impact on dental education is understudied. OBJECTIVES: To assess whether or not university instructors can differentiate reflections generated by ChatGPT from those generated by students, and to assess whether or not the content of a thematic analysis generated by ChatGPT differs from that generated by qualitative researchers on the same reflections. METHODS: Hardcopies of 20 reflections (10 generated by undergraduate dental students and 10 generated by ChatGPT) were distributed to three instructors who had at least 5 years of teaching experience. Instructors were asked to assign either 'ChatGPT' or 'student' to each reflection. Ten of these reflections (five generated by undergraduate dental students and five generated by ChatGPT) were randomly selected and distributed to two qualitative researchers who were asked to perform a brief thematic analysis with codes and themes. The same ten reflections were also thematically analyzed by ChatGPT. RESULTS: The three instructors correctly determined whether the reflections were student or ChatGPT generated 85% of the time. Most disagreements (40%) happened with the reflections generated by ChatGPT, as the instructors thought to be generated by students. The thematic analyses did not differ substantially when comparing the codes and themes produced by the two researchers with those generated by ChatGPT. CONCLUSIONS: Instructors could differentiate between reflections generated by ChatGPT or by students most of the time. The overall content of a thematic analysis generated by the artificial intelligence program ChatGPT did not differ from that generated by qualitative researchers. Overall, the promising applications of ChatGPT will likely generate a paradigm shift in (dental) health education, research, and practice.

1H, 13C and 15N assignment of self-complemented MrkA protein antigen from Klebsiella pneumoniae.

Klebsiella pneumoniae (Kp) poses an escalating threat to public health, particularly given its association with nosocomial infections and its emergence as a leading cause of neonatal sepsis, particularly in low- and middle-income countries (LMICs). Host cell adherence and biofilm formation of Kp is mediated by type 1 and type 3 fimbriae whose major fimbrial subunits are encoded by the fimA and mrkA genes, respectively. In this study, we focus on MrkA subunit, which is a 20 KDa protein whose 3D molecular structure remains elusive. We applied solution NMR to characterize a recombinant version of MrkA in which the donor strand segment situated at the protein's N-terminus is relocated to the C-terminus, preceded by a hexaglycine linker. This construct yields a self-complemented variant of MrkA. Remarkably, the self-complemented MrkA monomer loses its capacity to interact with other monomers and to extend into fimbriae structures. Here, we report the nearly complete assignment of the 13C,15N labelled self-complemented MrkA monomer. Furthermore, an examination of its internal mobility unveiled that relaxation parameters are predominantly uniform across the polypeptide sequence, except for the glycine-rich region within loop 176-181. These data pave the way to a comprehensive structural elucidation of the MrkA monomer and to structurally map the molecular interaction regions between MrkA and antigen-induced antibodies.

Researchers' Theater: Enhancing Student Engagement in an Introductory Research Course.

Research coursework can be challenging for occupational therapy students, thus potentially compromising their engagement in learning. A student engagement framework was used to design and implement an innovative assignment called Researchers' Theater with a cohort of 38 first-semester occupational therapy students. At the beginning of each class, a small group of students led a creative activity to review topics from the preceding week. Student feedback survey results and instructors' observations suggest this framework contributed to students' affective, behavioral, and cognitive engagement. Findings also highlight the potential value of student-led, game-based learning for reinforcing course content.

Increasing goals of care conversations in primary care: Study protocol for a cluster randomized, pragmatic, sequential multiple assignment randomized trial.

BACKGROUND: Goals of care conversations explore seriously ill patients' values to guide medical decision making and often inform decisions about life sustaining treatments. Ideally, conversations occur before a health crisis between patients and clinicians in the outpatient setting. In the United States Veterans Affairs (VA) healthcare system, most conversations still occur in the inpatient setting. Strategies are needed to improve implementation of outpatient, primary care goals of care conversations. METHODS: We plan a cluster randomized (clinician-level) sequential, multiple assignment randomized trial to evaluate the effectiveness of patient implementation strategies on the outcome of goals of care conversation documentation when delivered in combination with clinician implementation strategies. Across three VA healthcare system sites, we will enroll primary care clinicians with low rates of goals of care conversations and their patients with serious medical illness in the top 10th percentile of risk of hospitalization or death. We will compare the effectiveness of sequences of implementation strategies and explore how patient and site factors modify implementation strategy effects. Finally, we will conduct a mixed-methods evaluation to understand implementation strategy success or failure. The design includes two key innovations: (1) strategies that target both clinicians and patients and (2) sequential strategies with increased intensity for non-responders. CONCLUSION: This study aims to determine the effect of different sequences and combinations of implementation strategies on primary care documentation of goals of care conversations. Study partners, including the VA National Center for Ethics in Health Care and Office of Primary Care, can consider policies based on study findings.

Pushed waves, trailing edges, and extreme events: Eco-evolutionary dynamics of a geographic range shift in the owl limpet, Lottia gigantea.

As climatic variation re-shapes global biodiversity, understanding eco-evolutionary feedbacks during species range shifts is of increasing importance. Theory on range expansions distinguishes between two different forms: "pulled" and "pushed" waves. Pulled waves occur when the source of the expansion comes from low-density peripheral populations, while pushed waves occur when recruitment to the expanding edge is supplied by high-density populations closer to the species' core. How extreme events shape pushed/pulled wave expansion events, as well as trailing-edge declines/contractions, remains largely unexplored. We examined eco-evolutionary responses of a marine invertebrate (the owl limpet, Lottia gigantea) that increased in abundance during the 2014-2016 marine heatwaves near the poleward edge of its geographic range in the northeastern Pacific. We used whole-genome sequencing from 19 populations across >11 degrees of latitude to characterize genomic variation, gene flow, and demographic histories across the species' range. We estimated present-day dispersal potential and past climatic stability to identify how contemporary and historical seascape features shape genomic characteristics. Consistent with expectations of a pushed wave, we found little genomic differentiation between core and leading-edge populations, and higher genomic diversity at range edges. A large and well-mixed population in the northern edge of the species' range is likely a result of ocean current anomalies increasing larval settlement and high-dispersal potential across biogeographic boundaries. Trailing-edge populations have higher differentiation from core populations, possibly driven by local selection and limited gene flow, as well as high genomic diversity likely as a result of climatic stability during the Last Glacial Maximum. Our findings suggest that extreme events can drive poleward range expansions that carry the adaptive potential of core populations, while also cautioning that trailing-edge extirpations may threaten unique evolutionary variation. This work highlights the importance of understanding how both trailing and leading edges respond to global change and extreme events.

Adaptive Segmented Aggregation and Rate Assignment Techniques for Flexible-Length Polar Codes.

Polar codes have garnered a lot of attention from the scientific community, owing to their low-complexity implementation and provable capacity achieving capability. They have been standardized to be used for encoding information on the control channels in 5G wireless networks due to their robustness for short codeword lengths. The conventional approach to generate polar codes is to recursively use 2×2 kernels and polarize channel capacities. This approach however, has a limitation of only having the ability to generate codewords of length Norig=2n form. In order to mitigate this limitation, multiple techniques have been developed, e.g., polarization kernels of larger sizes, multi-kernel polar codes, and downsizing techniques like puncturing or shortening. However, the availability of so many design options and parameters, in turn makes the choice of design parameters quite challenging. In this paper, the authors propose a novel polar code construction technique called Adaptive Segmented Aggregation which generates polar codewords of any arbitrary codeword length. This approach involves dividing the entire codeword into smaller segments that can be independently encoded and decoded, thereby aggregated for channel processing. Additionally a rate assignment methodology has been derived for the proposed technique, that is tuned to the design requirement.

Spatial network analysis of green electricity efficiency dynamics in the Yellow River Basin cities.

Improving the green electricity efficiency (GEE), is an important issue for China's high-quality economic development. This study presents a spatial correlation network of urban GEE in the Yellow River Basin from 2012 to 2021, constructed using an improved gravity model. Social network analysis and the quadratic assignment procedure method are employed to analyze the spatial correlation characteristics and influencing factors. The findings indicate that urban GEE in the Yellow River Basin exhibits complex and stable network characteristics. The spatial network analysis reveals that Jiayuguan City, Dongying City, Dingxi City, Zibo City, and Shizuishan City occupy central positions within the network. The results indicate that spatial adjacency, GDP per capita, industrial structure, and the level of science and technology expenditure are positively related to urban GEE, while environmental regulation and average temperature are negatively related. The findings of the study have led to policy recommendations aimed at enhancing urban GEE in the Yellow River Basin.

Time-scale invariant contingency yields one-shot reinforcement learning despite extremely long delays to reinforcement.

Reinforcement learning inspires much theorizing in neuroscience, cognitive science, machine learning, and AI. A central question concerns the conditions that produce the perception of a contingency between an action and reinforcement-the assignment-of-credit problem. Contemporary models of associative and reinforcement learning do not leverage the temporal metrics (measured intervals). Our information-theoretic approach formalizes contingency by time-scale invariant temporal mutual information. It predicts that learning may proceed rapidly even with extremely long action-reinforcer delays. We show that rats can learn an action after a single reinforcement, even with a 16-min delay between the action and reinforcement (15-fold longer than any delay previously shown to support such learning). By leveraging metric temporal information, our solution obviates the need for windows of associability, exponentially decaying eligibility traces, microstimuli, or distributions over Bayesian belief states. Its three equations have no free parameters; they predict one-shot learning without iterative simulation.

APIS: an updated parentage assignment software managing triploids induced from diploid parents.

In aquaculture, sterile triploids are commonly used for production as sterility gives them potential gains in growth, yields, and quality. However, they cannot be reproduced, and DNA parentage assignment to their diploid or tetraploid parents is required to estimate breeding values for triploid phenotypes. No publicly available software has the ability to assign triploids to their parents. Here, we updated the R package APIS to support triploids induced from diploid parents. First, we created new exclusion and likelihood tables that account for the double allelic contribution of the dam and the recombination that can occur during female meiosis. As the effective recombination rate of each marker with the centromere is usually unknown, we set it at 0.5 and found that this value maximizes the assignment rate even for markers with high or low recombination rates. The number of markers needed for a high true assignment rate did not strongly depend on the proportion of missing parental genotypes. The assignment power was however affected by the quality of the markers (minor allele frequency, call rate). Altogether, 96-192 SNPs were required to have a high parentage assignment rate in a real rainbow trout dataset of 1,232 triploid progenies from 288 parents. The likelihood approach was more efficient than exclusion when the power of the marker set was limiting. When more markers were used, exclusion was more advantageous, with sensitivity reaching unity, very low false discovery rate (<0.01), and excellent specificity (0.96-0.99). Thus, APIS provides an efficient solution to assign triploids to their diploid parents.

A sequential multiple assignment randomized trial of symptom management for cancer survivors during treatment and their informal caregivers.

CONTEXT: Many cancer survivors and their informal caregivers experience multiple symptoms during the survivor's treatment. OBJECTIVE: Test relative effectiveness and optimal sequencing of two evidence-based interventions for symptom management. METHODS: In this sequential multiple assignment randomized trial (SMART), survivors of solid tumors with elevated depression or anxiety and their caregivers as dyads were initially randomized after baseline assessment in a 3:1 ratio to the Symptom Management and Survivorship Handbook (SMSH, N = 277 dyads) intervention or SMSH plus 8 weeks of telephone interpersonal counseling (TIPC, N = 97 dyads). After 4 weeks, survivors who were not responding (no improvement or worsening score on depression and/or anxiety item) to SMSH only and their caregivers were re-randomized to continue with SMSH alone (N = 44 dyads) to give it more time or to SMSH + TIPC (N = 44 dyads). Mixed effects and generalized linear models compared severity of depression, anxiety, and a summed index of 16 other symptoms over weeks 1-13 and week 17 between randomized groups and among three dynamic treatment regimes (DTRs). Dyads received SMSH only for 12 weeks (DTR1); SMSH for 12 weeks with 8 weeks of TIPC added from week 1 (DTR2); and SMSH for 4 weeks followed by the combined SMSH + TIPC for 8 weeks if no response at 4 weeks (DTR3). RESULTS: Survivors randomized initially to SMSH alone had significantly lower anxiety over weeks 1-13 compared to those randomized to the combined SMSH + TIPC. In comparing DTRs, survivor's anxiety was significantly lower at week 13 for DTR1 compared to DTR2 with no other main effects for survivors or caregivers. Exploratory moderation analyses indicated a potential benefit of adding TIPC for caregivers of non-responders with elevated baseline symptoms. CONCLUSION: SMSH + TIPC did not result in better symptom outcomes at week 17 than SMSH alone. Lower intensity SMSH may improve depression and anxiety symptoms for most survivors and their caregivers. TRIAL REGISTRATION: Clinicaltrails.gov ID number, NCT03743415; approved and posted on 11/16/2018.

Recommendations for 46,XY Disorders/Differences of Sex Development Across Two Decades: Insights from North American Pediatric Endocrinologists and Urologists.

Clinical decision-making for individuals with 46,XY disorders/differences of sex development (DSD) remains unsettled and controversial. The North American DSD Clinician Survey examines the recommendations of a large group of clinical specialists over the last two decades. Active members of the (Lawson Wilkins) Pediatric Endocrine Society and the Societies for Pediatric Urology were invited to respond to a web-based survey at three different timepoints: 2003-2004 (T1), 2010-2011 (T2), and 2019-2020 (T3). Data from 429 participants in T1, 435 in T2, and 264 in T3 were included in this study. The participants were presented with three XY newborn clinical case scenarios-micropenis, partial androgen insensitivity syndrome, and iatrogenic penile ablation-and asked for clinical management recommendations. The main outcomes assessed included the recommended gender of rearing, surgical decision-maker (parent or patient), timing of genital surgery, and age at which to disclose medical details and surgical history to the patient. For all scenarios, the overwhelming majority recommended rearing as male, including a significant increase across timepoints in those recommending a male gender of rearing for the infant with penile ablation. The proportions recommending female gender of rearing declined significantly across timepoints. In general, most recommended parents (in consultation with the physician) serve as surgical decision-makers, but these proportions declined significantly across timepoints. Recommendations on the timing of surgery varied based on the patient's gender and type of surgery. There has been a shift in recommendations away from the "optimal gender policy" regarding gender of rearing and surgical interventions for patients with XY DSD.

UnCoVar: a reproducible and scalable workflow for transparent and robust virus variant calling and lineage assignment using SARS-CoV-2 as an example.

BACKGROUND: At a global scale, the SARS-CoV-2 virus did not remain in its initial genotype for a long period of time, with the first global reports of variants of concern (VOCs) in late 2020. Subsequently, genome sequencing has become an indispensable tool for characterizing the ongoing pandemic, particularly for typing SARS-CoV-2 samples obtained from patients or environmental surveillance. For such SARS-CoV-2 typing, various in vitro and in silico workflows exist, yet to date, no systematic cross-platform validation has been reported. RESULTS: In this work, we present the first comprehensive cross-platform evaluation and validation of in silico SARS-CoV-2 typing workflows. The evaluation relies on a dataset of 54 patient-derived samples sequenced with several different in vitro approaches on all relevant state-of-the-art sequencing platforms. Moreover, we present UnCoVar, a robust, production-grade reproducible SARS-CoV-2 typing workflow that outperforms all other tested approaches in terms of precision and recall. CONCLUSIONS: In many ways, the SARS-CoV-2 pandemic has accelerated the development of techniques and analytical approaches. We believe that this can serve as a blueprint for dealing with future pandemics. Accordingly, UnCoVar is easily generalizable towards other viral pathogens and future pandemics. The fully automated workflow assembles virus genomes from patient samples, identifies existing lineages, and provides high-resolution insights into individual mutations. UnCoVar includes extensive quality control and automatically generates interactive visual reports. UnCoVar is implemented as a Snakemake workflow. The open-source code is available under a BSD 2-clause license at github.com/IKIM-Essen/uncovar.