Abstract Motivation The Oxford Nanopore Technologies’ sequencing platform offers a path towards bedside genomics, producing long reads that can completely cover a gene of interest, and detect any known or novel variant the gene contains. However, the analysis of these long reads to identify actionable genotypes remains challenging and typically requires customization depending on the target gene. Results Here, we describe a generic algorithm to accurately reconstruct allele sequences derived from long-reads of amplicon-based data. Rather than calling variants directly from these long-reads, our method takes a “sequence-first” approach, performing an unbiased reconstruction of the underlying amplicon sequences to generate high-confidence reconstructed allele sequences. This is done without user input of the target gene, allowing for any source amplicon to be reconstructed. These high-confidence reconstructed allele sequences are then compared to the genomic reference sequence of the gene to infer the specific diplotype present in the sample. This approach is agnostic towards the number of genes and alleles present and readily detects novel variants. We demonstrate our approach using three independent data sets for CYP2D6, a diverse and complex gene with over 175 known alleles of clinical significance. We show how our approach can accurately recover validated CYP2D6 diplotypes from 20 Coriell samples covering 14 distinct alleles, using different amplicons, flow cell versions, and depths. This includes inferring occurrences of allele duplication events from relative abundances of each allele, a critical factor for ascribing functional effects to a diplotype. Further, we demonstrate our approach’s utility for other genomic regions, including HLA. Availability Custom code is available at the following GitHub repository, along with instructions for use and test data: https://github.com/scottdbrown/allele-reconstruction-long-read-amplicon-data. A snapshot of the code at the time of publication is available on Zenodo.org; doi 10.5281/zenodo.19716004. Raw .fastq sequence data for our three sequencing runs is available at the SRA under Bioproject PRJNA1357883 (https://www.ncbi.nlm.nih.gov/bioproject/1357883).

In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasize predictive accuracy, but there is a growing focus on incorporating uncertainty measures. This paper addresses localized uncertainty quantification in random forests. While current methods often rely on quantile regression or Monte Carlo techniques, we propose a new approach using naturally occurring test sets and similarity measures (proximities) typically viewed as byproducts of random forests. Specifically, we form localized distributions of OOB errors around nearby points, defined using the proximities, to create prediction intervals for regression and trust scores for classification. By varying the number of nearby points, our intervals can be adjusted to achieve the desired coverage while retaining the flexibility that reflects the certainty of individual predictions. For classification, excluding points identified as unclassifiable by our method generally enhances the accuracy of the model and provides higher accuracy-rejection AUC scores than competing methods.

Modern marine navigation places significant physical and mental demands on officers stationed on ship bridges, primarily due to the continuous observation and evaluation of real-time navigational information displayed on scattered electronic equipment. To alleviate the high cognitive load experienced by marine officers and allow them to focus on essential tasks during complex situations, the integration of head-up displays (HUDs) in marine applications has emerged as a promising solution. HUDs offer the potential to provide crucial information, enhancing the accessibility and organization of previously disordered data. However, there is limited information on the impact of HUDs on marine officers, which has been explored in the presented work. In this work, a novel immersive navigation experiment with three conditions: traditional display (NonAR), augmented reality (AR) based information presentation, and a variant of AR with essential information only (AR-Indicator) has been conducted. The objective is to explore the effects of these three conditions on navigation performance and mental workload. Our findings indicate that the AR-based information presentation, specifically the variant that includes only essential information, is preferred by participants and showed performance improvements measured by time to complete tasks, gaze duration, and pupil dilation compared to the traditional display and full AR condition. These results have an impact on the design and development of HUDs in marine-related tasks. This pilot research sheds light on the potential benefits of HUDs in improving maritime navigation and paves the way for further advancements in this field.

Objects on Earth with 3-dimensional geometries cast shadows onto the underlying background due to the obstruction of direct solar radiation. This phenomenon is considered for a multiclass subpixel target detection scenario, where the mixed pixels consist of materials from varying percentages of an illuminated target and partially shaded background. Hyperspectral data collected from UAS-based instruments and novel subpixel targets were used for the analyses. The novel targets enabled empirical observations of fully illuminated targets on partially shaded backgrounds. To assist in developing inferences on the impacts of detection, model reflectance spectra were generated for mixed pixels with fully illuminated and fully shaded background conditions. The modeling approach was validated with the empirical observations. The results imply the shadow effect is an important system parameter for subpixel target percentages of 20% or less, for the multiclass scenario and particular targets explored.

BACKGROUND: Sequence verification is essential for plasmids used as critical reagents or therapeutic products. Typically, high-quality plasmid sequence is achieved through capillary-based Sanger sequencing, requiring customized sets of primers for each plasmid. This process can become expensive, particularly for applications where the validated sequence needs to be produced within a regulated and quality-controlled environment for downstream clinical research applications. RESULTS: Here, we describe a cost-effective and accurate plasmid sequencing and consensus generation procedure using the Oxford Nanopore Technologies' MinION device as an alternative to capillary-based plasmid sequencing options. This procedure can verify the identity of a pure population of plasmid, either confirming it matches the known and expected sequence, or identifying mutations present in the plasmid if any exist. We use a full MinION flow cell per plasmid, maximizing available data and allowing for stringent quality filters. Pseudopairing reads for consensus base calling reduces read error rates from 5.3 to 0.53%, and our pileup consensus approach provides per-base counts and confidence scores, allowing for interpretation of the certainty of the resulting consensus sequences. For pure plasmid samples, we demonstrate 100% accuracy in the resulting consensus sequence, and the sensitivity to detect small mutations such as insertions, deletions, and single nucleotide variants. In test cases where the sequenced pool of plasmids contains subclonal templates, detection sensitivity is similar to that of traditional capillary sequencing. CONCLUSIONS: Our pipeline can provide significant cost savings compared to outsourcing clinical-grade sequencing of plasmids, making generation of high-quality plasmid sequence for clinical sequence verification more accessible. While other long-read-based methods offer higher-throughput and less cost, our pipeline produces complete and accurate sequence verification for cases where absolute sequence accuracy is required.

A new radiometry and design framework has been introduced in the latest Digital Imaging and Remote Sensing Image Generation model (DIRSIG5) that allows for faster simulations while streamlining the generation of high-fidelity radiometric data. The same framework that allows for improved computational performance has also modularized simulation components to allow for extensive interchangeability based on simulation needs. This new framework includes several plugin interfaces that facilitate native and 3rd party extensions to the model. A sensor plugin and its interaction with the internal radiometry engine is described and then demonstrated by modeling systems with rolling shutters and time-delayed integration.

Motivation: In T-cell lymphoma, malignant T cells arising from a founding clone share an identical T cell receptor (TCR) and can be identified by the over-representation of this TCR relative to TCRs from the patient's repertoire of normal T cells. Here, we demonstrate that TCR information extracted from RNA-seq data can provide a higher resolution view of peripheral T cell lymphomas (PTCLs) than that provided by conventional methods. Results: For 60 subjects with PTCL, flow cytometry/FACS was used to identify and sort aberrant T cell populations from diagnostic lymph node cell suspensions. For samples that did not appear to contain aberrant T cell populations, T helper (T H ), T follicular helper (T FH ) and cytotoxic T lymphocyte (CTL) subsets were sorted. RNA-seq was performed on sorted T cell populations, and TCR alpha and beta chain sequences were extracted and quantified directly from the RNA-seq data. 96% of the immunophenotypically aberrant samples had a dominant T cell clone readily identifiable by RNA-seq. Of the samples where no aberrant population was found by flow cytometry, 80% had a dominant clone by RNA-seq. This demonstrates the increased sensitivity and diagnostic ability of RNA-seq over flow cytometry and shows that the presence of a normal immunophenotype does not exclude clonality. Availability and Implementation: R scripts used in the processing of the data are available online at https://www.github.com/scottdbrown/RNAseq-TcellClonality. Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Few studies have evaluated how per-pixel structural configurations could impact spectral response. This has an impact on how we assess especially large area/global ecosystems. In an effort to understand this impact of sub-pixel structural variation on large-footprint imaging spectroscopy, a simulation approach was used, which provides precise knowledge of target geometry and radiometry. We demonstrated the validity of the proposed simulation in terms of one such structural metric of interest, namely leaf area index (LAI). LAI is a key vegetation structural parameter, which has implications for predicting ecosystems' foliar spatial distribution, health, photosynthesis, transpiration, and energy transfer. Simulated LAI measurements were validated with field data obtained from AccuPAR measurements (R2= 0.76) and by comparison to NDVI data obtained from simulated AVIRIS imagery (R2= 0.92−0.65, depending on sampling interval). These data were used to propose an appropriate sampling protocol for LAI data collection, thus providing for efficient data collection, while minimizing variability of individual measurements. These efforts will support preparatory science experiments towards understanding the phenomenology of NASA's next-generation imaging spectrometer, HyspIRI.

Speed-accuracy tradeoff (SAT) is an adaptive process balancing urgency and caution when making decisions. Computational cognitive theories, known as "evidence accumulation models", have explained SATs via a manipulation of the amount of evidence necessary to trigger response selection. New light has been shed on these processes by single-cell recordings from monkeys who were adjusting their SAT settings. Those data have been interpreted as inconsistent with existing evidence accumulation theories, prompting the addition of new mechanisms to the models. We show that this interpretation was wrong, by demonstrating that the neural spiking data, and the behavioural data are consistent with existing evidence accumulation theories, without positing additional mechanisms. Our approach succeeds by using the neural data to provide constraints on the cognitive model. Open questions remain about the locus of the link between certain elements of the cognitive models and the neurophysiology, and about the relationship between activity in cortical neurons identified with decision-making vs. activity in downstream areas more closely linked with motor effectors.

To advance the science of lidar sensing of complex ice and snow surfaces as well as in support of the upcoming ICESat- 2 mission, this paper establishes a framework to theoretically study a spaceborne micropulselidar returns from snow and ice surfaces. First, the anticipated lidar return characteristics for a sloped non-penetrating surface is studied when measured by a multiple-channel photon-counting detector. Second, an analytical snow reflectance model based on experimental observations is applied in synthetic scene. Based on the simulation results, the spaceborne photon-counting lidar system considered here is seen to have moderate detectability on snow surfaces. In addition, for the penetrating snow model considered here, it is shown that slightly sloped snow terrain with larger snow grain size will result in smaller elevation bias.

Lidar sensing has been found to be a useful method of monitoring the dynamics and mass balance of glaciers, ice caps, and ice sheets. However, it is also known that ice surfaces can have complex 3-dimensional structure, which can challenge their accurate retrieval with lidar sensing. In support of future lidar sensing satellite missions, such as the upcoming ICESat-2, a joint research project was recently initiated between the Rochester Institute of Technology (RIT) and the University at Buffalo to study lidar sensing of complex ice surfaces. This effort is supported by NASA's Remote Sensing Theory program and is aimed at advancing the science of lidar sensing. The general approach is to 1) define realistic complex ice surfaces, 2) render lidar image simulations, and 3) compare the resulting data to the known surfaces to gain insight into the phenomenology of lidar sensing of snow and ice. The project will build on existing scientific understanding of light scattering from snow and ice as well as lidar sensor system modeling with a systems engineering end-to-end perspective. Initial results show the simulations capturing realistic scattering of photons in snow volumes and the resulting point clouds measured by a model spaceborne lidar system.

The visually-realistic yet radiometrically-accurate simulation of long-wave infrared (LWIR) imagery is a problem that has plagued members of industry and academia alike. Simulating texture in LWIR imagery is a more complex task due to temperature variation which is a function of solar absorptivity, surface orientation, shadowing and bulk thermal properties. In order to deal with these additional sources of variability, we have improved the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model to allow the use of thermal property texture maps to control various thermal properties. This paper presents these methods and applies them in order to simulate LWIR imagery taken over natural desert scenes in Trona, CA. The resulting imagery demonstrates that these new methodologies for modeling thermodynamic phenomena and the utilization of an enhanced DIRSIG tool improve the average root mean-squared error (RMSE) of our synthetic LWIR imagery by up to 88%.

We measure the ability of human observers to predict the next datum in a sequence that is generated by a simple statistical process undergoing change at random points in time. Accurate performance in this task requires the identification of changepoints. We assess individual differences between observers both empirically, and using two kinds of models: a Bayesian approach for change detection and a family of cognitively plausible fast and frugal models. Some individuals detect too many changes and hence perform sub-optimally due to excess variability. Other individuals do not detect enough changes, and perform sub-optimally because they fail to notice short-term temporal trends. 1 I n t r o d u c t i o n Decision-making often requires a rapid response to change. For example, stock analysts need to quickly detect changes in the market in order to adjust investment strategies. Coaches need to track changes in a player’s performance in order to adjust strategy. When tracking changes, there are costs involved when either more or less changes are observed than actually occurred. For example, when using an overly conservative change detection criterion, a stock analyst might miss important short-term trends and interpret them as random fluctuations instead. On the other hand, a change may also be detected too readily. For example, in basketball, a player who makes a series of consecutive baskets is often identified as a “hot hand” player whose underlying ability is perceived to have suddenly increased [1,2]. This might lead to sub-optimal passing strategies, based on random fluctuations. We are interested in explaining individual differences in a sequential prediction task. Observers are shown stimuli generated from a simple statistical process with the task of predicting the next datum in the sequence. The latent parameters of the statistical process change discretely at random points in time. Performance in this task depends on the accurate detection of those changepoints, as well as inference about future outcomes based on the outcomes that followed the most recent inferred changepoint. There is much prior research in statistics on the problem of identifying changepoints [3,4,5]. In this paper, we adopt a Bayesian approach to the changepoint identification problem and develop a simple inference procedure to predict the next datum in a sequence. The Bayesian model serves as an ideal observer model and is useful to characterize the ways in which individuals deviate from optimality. The plan of the paper is as follows. We first introduce the sequential prediction task and discuss a Bayesian analysis of this prediction problem. We then discuss the results from a few individuals in this prediction task and show how the Bayesian approach can capture individual differences with a single “twitchiness” parameter that describes how readily changes are perceived in random sequences. We will show that some individuals are too twitchy: their performance is too variable because they base their predictions on too little of the recent data. Other individuals are not twitchy enough, and they fail to capture fast changes in the data. We also show how behavior can be explained with a set of fast and frugal models [6]. These are cognitively realistic models that operate under plausible computational constraints. 2 A p r e d i c t i o n t a s k w i t h m u l t i p l e c h a n g e p o i n t s In the prediction task, stimuli are presented sequentially and the task is to predict the next stimulus in the sequence. After t trials, the observer has been presented with stimuli y1, y2, …, yt and the task is to make a prediction about yt+1. After the prediction is made, the actual outcome yt+1 is revealed and the next trial proceeds to the prediction of yt+2. This procedure starts with y1 and is repeated for T trials. The observations yt are D-dimensional vectors with elements sampled from binomial distributions. The parameters of those distributions change discretely at random points in time such that the mean increases or decreases after a change point. This generates a sequence of observation vectors, y1, y2, …, yT, where each yt = {yt,1 … yt,D}. Each of the yt,d is sampled from a binomial distribution Bin(θt,d,K), so 0 ≤ yt,d ≤ K. The parameter vector θt ={θt,1 … θt,D} changes depending on the locations of the changepoints. At each time step, x is a binary indicator for the occurrence of a t changepoint occurring at time t+1. The parameter α determines the probability of a change occurring in the sequence. The generative model is specified by the following algorithm: For d=1..D sample θ1,d from a Uniform(0,1) distribution