Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

gcplyr: an R package for microbial growth curve data analysis.

BACKGROUND: Characterization of microbial growth is of both fundamental and applied interest. Modern platforms can automate collection of high-throughput microbial growth curves, necessitating the development of computational tools to handle and analyze these data to produce insights. RESULTS: To address this need, here I present a newly-developed R package: gcplyr. gcplyr can flexibly import growth curve data in common tabular formats, and reshapes it under a tidy framework that is flexible and extendable, enabling users to design custom analyses or plot data with popular visualization packages. gcplyr can also incorporate metadata and generate or import experimental designs to merge with data. Finally, gcplyr carries out model-free (non-parametric) analyses. These analyses do not require mathematical assumptions about microbial growth dynamics, and gcplyr is able to extract a broad range of important traits, including growth rate, doubling time, lag time, maximum density and carrying capacity, diauxie, area under the curve, extinction time, and more. CONCLUSIONS: gcplyr makes scripted analyses of growth curve data in R straightforward, streamlines common data wrangling and analysis steps, and easily integrates with common visualization and statistical analyses.

The Fertility Fix: the Boom in Facial-matching Algorithms for Donor Selection in Assisted Reproduction in Spain.

This article reads the uptake of facial-matching algorithms by fertility clinics in Spain through the lens of 'the fertility fix': a software fix to the social reconfiguration of kinship and a fixed capital investment made by competing fertility companies and firms. 'The fertility fix' is proposed as a critical, ethical lens through which to situate algorithmic facial-matching in assisted reproduction in the context of the racial politics of the face and phenotype and the spatial politics of market expansion. While an 'infertility crisis' is often mentioned when explaining the growth of the assisted reproductive technologies (ARTs) industry, the use of donated reproductive cells is tied up in societal, ecological and economic shifts. Combining Software Studies analysis with Marxist Feminist and trans*feminist perspectives on shifting re/production dynamics, the article details the role of computational technologies in promoting certain ideas and beliefs about family and fixing certain territories of capital flow.

Enhancing bug allocation in software development: a multi-criteria approach using fuzzy logic and evolutionary algorithms.

A bug tracking system (BTS) is a comprehensive data source for data-driven decision-making. Its various bug attributes can identify a BTS with ease. It results in unlabeled, fuzzy, and noisy bug reporting because some of these parameters, including severity and priority, are subjective and are instead chosen by the user's or developer's intuition rather than by adhering to a formal framework. This article proposes a hybrid, multi-criteria fuzzy-based, and multi-objective evolutionary algorithm to automate the bug management approach. The proposed approach, in a novel way, addresses the trade-offs of supporting multi-criteria decision-making to (a) gather decisive and explicit knowledge about bug reports, the developer's current workload and bug priority, (b) build metrics for computing the developer's capability score using expertise, performance, and availability (c) build metrics for relative bug importance score. Results of the experiment on five open-source projects (Mozilla, Eclipse, Net Beans, Jira, and Free desktop) demonstrate that with the proposed approach, roughly 20% of improvement can be achieved over existing approaches with the harmonic mean of precision, recall, f-measure, and accuracy of 92.05%, 89.04%, 90.05%, and 91.25%, respectively. The maximization of the throughput of the bug can be achieved effectively with the lowest cost when the number of developers or the number of bugs changes. The proposed solution addresses the following three goals: (i) improve triage accuracy for bug reports, (ii) differentiate between active and inactive developers, and (iii) identify the availability of developers according to their current workload.

3D printing traceability in healthcare using 3Diamond software.

Background: 3D printing is one of the fastest-growing technologies in medicine, but it is essential to have a system for 3D printing documentation that is accessible for not only clinical engineers and surgeons, but also quality managers and data-privacy officers in hospitals. Dedicated software such as product lifecycle management (PLM) software could enable comprehensive management and traceability of all data relevant to 3D printing tasks in a hospital and would highly beneficial. Therefore, customizable software called 3Diamond was developed for 3D printing in medicine. Methods: The software development process involved several stages, including setting specifications based on end-user requirements, design, implementation, and testing. In order to ensure the software's long-term success and smooth operation, critical phases were also considered, such as deployment and maintenance. Results: The developed software provides immediate and complete traceability of all preparations and controls, as well as management of reports, orders, stock, and post-operative follow-up of tasks related to 3D printing in a hospital. Based on user requirements, software testing is provided automatically with each release. The software was implemented in a natural clinical environment with a developed 3D printing center. Conclusion: Although 3D printing has potential for innovation in the medical profession, it is nevertheless subject to regulations. Even though there are exemptions for patient-specific products, the effects of their local legal implementations related to 3D printing cannot be fully overseen. To this end, 3Diamond provides a robust system for 3D printing documentation that is accessible to different personnel in hospitals.

scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories.

MOTIVATION: Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. RESULTS: We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. AVAILABILITY AND IMPLEMENTATION: scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.

Quantifying Sexual Dimorphism by Analyzing Ramus Flexure and Bigonial Width in Orthopantomography.

Introduction The mandible constitutes one of the skull's largest and strongest bones. Growth spurts can influence it, and it has a variety of dimorphic traits that can be used to identify sex. In addition to observing, comparing, and evaluating the potential for mandibular ramus flexure and bigonial breadth to discriminate between sexes using digital orthopantomograms (OPG), a retrospective study was conducted to examine the validity of this method for sex estimation in the Indian population. Aim and objective This study aims to quantify sexual dimorphism by analyzing two mandibular parameters, the ramus flexure and the bigonial width, using orthopantomography (OPG). The objective is to determine the accuracy of sex determination using the ramus flexure and bigonial width. Materials and methods A total of 500 OPG images (250 males and 250 females) were analyzed using the Planmeca software (Helsinki, Finland). The ramus flexure was measured as the angle formed between the tangent to the inferior border of the mandible and the tangent to the posterior border of the ramus. The bigonial width was measured as the distance between the left and right gonion points. A statistical analysis was performed to assess sexual dimorphism and determine the accuracy of sex determination using these parameters. The study employed descriptive statistics, such as means and standard deviations, and an independent t-test to determine the significance of the characteristics in relation to males and females. Results The mean bigonial width for females was 193.3068 mm (SD = 13.51669 mm) and for males was 217.6308 mm (SD = 10.87453 mm), with a statistically significant difference (p = 0.000). The 95% confidence interval for the difference in the bigonial width between males and females was between -49.97173 mm and -43.93787 mm. For the ramus flexure, the mean was 0.0000 for both males and females (SD = 0.00000), with a significant difference between males and females (p = 0.003). The 95% confidence interval for the difference in the ramus flexure between males and females was between -0.59543 and -0.12457. Conclusion The results indicated significant sexual dimorphism in both the ramus flexure and bigonial width. This study demonstrated that the ramus flexure and bigonial width, measured using orthopantomography (OPG), exhibited significant sexual dimorphism. The analysis of these mandibular parameters provided valuable information for sex determination in forensic and anthropological contexts.

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 maximises 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 to 192 SNPs were required to have a high parentage assignment rate in a real rainbow trout dataset of 1232 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.

Automated workflows using Quantitative Colour Pattern Analysis (QCPA): a guide to batch processing and downstream data analysis.

Animal and plant colouration presents a striking dimension of phenotypic variation, the study of which has driven general advances in ecology, evolution, and animal behaviour. Quantitative Colour Pattern Analysis (QCPA) is a dynamic framework for analysing colour patterns through the eyes of non-human observers. However, its extensive array of user-defined image processing and analysis tools means image analysis is often time-consuming. This hinders the full use of analytical power provided by QCPA and its application to large datasets. Here, we offer a robust and comprehensive batch script, allowing users to automate many QCPA workflows. We also provide a complimentary set of useful R scripts for downstream data extraction and analysis. The presented batch processing extension will empower users to further utilise the analytical power of QCPA and facilitate the development of customised semi-automated workflows. Such quantitatively scaled workflows are crucial for exploring colour pattern spaces and developing ever-richer frameworks for analysing organismal colouration accounting for visual perception in animals other than humans. These advances will, in turn, facilitate testing hypotheses on the function and evolution of vision and signals at quantitative and qualitative scales, which are otherwise computationally unfeasible. Supplementary Information: The online version contains supplementary material available at 10.1007/s10682-024-10291-7.

phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria.

Objectives: The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets. Materials and Methods: We developed the phoenix R package and Python module to provide researchers with intuitive and simple functions to apply the Phoenix criteria to EHR data. Results: The phoenix R package and Python module enable researchers to apply the Phoenix criteria to EHR datasets and derive the relevant indicators, total scores, and sub-scores. Discussion: The transition to the Phoenix criteria marks a major change in the conceptual definition of pediatric sepsis. Applicable across differentially resourced settings, the Phoenix criteria should help improve clinical care and research. Conclusion: The phoenix R package and Python model are freely available on CRAN, PyPi, and GitHub. These tools enable the consistent and accurate application of the Phoenix criteria to EHR datasets.

TO-LAB model: Real time Touchless Lung Abnormality detection model using USRP based machine learning algorithm.

BACKGROUND: Due to the increasing prevalence of respiratory diseases and the importance of early diagnosis. The need for non-invasive and touchless medical diagnostic solutions has become increasingly crucial in modern healthcare to detect lung abnormalities. OBJECTIVE: Existing methods for lung abnormality detection often rely on invasive and time-consuming procedures limiting their effectiveness in real-time diagnosis. This work introduces a novel Touchless Lung Abnormality (TO-LAB) detection model utilizing universal software radio peripherals (USRP) and machine learning algorithms. METHODS: The TO-LAB model integrates a blood pressure meter and an RGB-D depth-sensing camera to gather individual data without physical contact. Heart rate (HR) is analyzed through image conversion to IPPG signals, while blood pressure (BP) is obtained via analog conversion from the blood pressure meter. This touchless imaging setup facilitates the extraction of essential signal features crucial for respiratory pattern analysis. Advanced computer vision algorithms like Mel-frequency cepstral coefficients (MFCC) and Principal Component Analysis (PCA) process the acquired data to focus on breathing abnormalities. These features are then combined and inputted into a machine learning-based Multi-class SVM for breathing activity analysis. The Multi-class SVM categorizes breathing abnormalities as normal, shallow, or elevated based on the fused features. The efficiency of this TO-LAB model is evaluated with the simulated and real-time data. RESULTS: According to the findings, the proposed TO-LAB model attains the maximum accuracy of 96.15% for real time data; however, the accuracy increases to 99.54% for simulated data for the efficient classification of breathing abnormalities. CONCLUSION: From this analysis, our model attains better results in simulated data but it declines the accuracy while processing with real-time data. Moreover, this work has a significant medical impact since it presents a solution to the problem of gathering enough data during the epidemic to create a realistic model with a large dataset.

BASSA: New software tool reveals hidden details in visualisation of low-frequency animal sounds.

The study of animal sounds in biology and ecology relies heavily upon time-frequency (TF) visualisation, most commonly using the short-time Fourier transform (STFT) spectrogram. This method, however, has inherent bias towards either temporal or spectral details that can lead to misinterpretation of complex animal sounds. An ideal TF visualisation should accurately convey the structure of the sound in terms of both frequency and time, however, the STFT often cannot meet this requirement. We evaluate the accuracy of four TF visualisation methods (superlet transform [SLT], continuous wavelet transform [CWT] and two STFTs) using a synthetic test signal. We then apply these methods to visualise sounds of the Chagos blue whale, Asian elephant, southern cassowary, eastern whipbird, mulloway fish and the American crocodile. We show that the SLT visualises the test signal with 18.48%-28.08% less error than the other methods. A comparison between our visualisations of animal sounds and their literature descriptions indicates that the STFT's bias may have caused misinterpretations in describing pygmy blue whale songs and elephant rumbles. We suggest that use of the SLT to visualise low-frequency animal sounds may prevent such misinterpretations. Finally, we employ the SLT to develop 'BASSA', an open-source, GUI software application that offers a no-code, user-friendly tool for analysing short-duration recordings of low-frequency animal sounds for the Windows platform. The SLT visualises low-frequency animal sounds with improved accuracy, in a user-friendly format, minimising the risk of misinterpretation while requiring less technical expertise than the STFT. Using this method could propel advances in acoustics-driven studies of animal communication, vocal production methods, phonation and species identification.

Holographic mixed reality for planning transcatheter aortic valve replacement.

BACKGROUND: Using three-dimensional (3D) modalities for optimal pre-procedure planning in transcatheter aortic valve replacement (TAVR) is critical for procedural success. However, current methods rely on visualizing images on a two-dimensional screen, using shading and colors to create the illusion of 3D, potentially impeding the accurate comprehension of the actual anatomy structures. In contrast, a new Mixed Reality (MxR) based software enables accurate 3D visualization, imaging manipulation, and quantification of measurements. AIMS: The study aims to evaluate the feasibility, reproducibility, and accuracy of dimensions of the aortic valve complex as measured with a new holographic MxR software (ARTICOR®, Artiness srl, Milano, Italy) compared to a widely used software for pre-operative sizing and planning (3mensio Medical Imaging BV, Bilthoven, The Netherlands) . METHODS: This retrospective, observational, double-center study enrolled 100 patients with severe aortic stenosis who underwent cardiac computed tomography (CCT) before TAVR. The CCT datasets of volumetric aortic valve images were analyzed using 3Mensio and newly introduced MxR-based software. RESULTS: 98% of the CCT datasets were successfully converted into holographic models. A higher level of agreement between the two software systems was observed for linear metrics (short, long, and average diameter). In comparison, agreement was lower for area, perimeter, and annulus-to-coronary ostia distance measurements. Notably, the annulus area, annular perimeter, LVOT area, and LVOT perimeter were significantly and consistently smaller with the MxR-based software compared to the 3Mensio. Excellent interobserver reliability was demonstrated for most measurements, especially for direct linear measurements. CONCLUSIONS: Linear Measurements of the aortic valve complex using MxR-based software are reproducible compared to the standard CCT dataset analyzed with 3Mensio. MxR-based software could represent an accurate tool for the pre-procedural planning of TAVR.

Implementation of a software-based decision support tool for guideline-appropriate preoperative evaluation: a prospective agreement study.

BACKGROUND: Guideline adherence in the medical field leaves room for improvement. Digitalised decision support helps improve compliance. However, the complex nature of the guidelines makes implementation in clinical practice difficult. METHODS: This single-centre prospective study included 204 adult ASA physical status 3-4 patients undergoing elective noncardiac surgery at a German university hospital. Agreement of clearance for surgery between a guideline expert and a digital guideline support tool was investigated. The decision made by the on-duty anaesthetists (standard approach) was assessed for agreement with the expert in a cross-over design. The main outcome was the level of agreement between digital guideline support and the expert. RESULTS: The digital guideline support approach cleared 18.1% of the patients for surgery, the standard approach cleared 74.0%, and the expert approach cleared 47.5%. Agreement of the expert decision with digital guideline support (66.7%) and the standard approach (67.6%) was fair (Cohen's kappa 0.37 [interquartile range 0.26-0.48] vs 0.31 [0.21-0.42], P=0.6). Taking the expert decision as a benchmark, correct clearance using digital guideline support was 50.5%, and correct clearance using the standard approach was 44.6%. Digital guideline support incorrectly asked for additional examinations in 31.4% of the patients, whereas the standard approach did not consider conditions that would have justified additional examinations before surgery in 29.4%. CONCLUSIONS: Strict guideline adherence for clearance for surgery through digitalised decision support inadequately considered patients, clinical context. Vague formulations, weak recommendations, and low-quality evidence complicate guideline translation into explicit rules. CLINICAL TRIAL REGISTRATION: NCT04058769.

Wasm-iCARE: a portable and privacy-preserving web module to build, validate, and apply absolute risk models.

Objectives: Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform. Materials and Methods: We refactored R-iCARE into a Python package (Py-iCARE) and then compiled it to WebAssembly (Wasm-iCARE)-a portable web module, which operates within the privacy of the user's device. Results: We showcase the portability and privacy of Wasm-iCARE through 2 applications: for researchers to statistically validate risk models and to deliver them to end-users. Both applications run entirely on the client side, requiring no downloads or installations, and keep user data on-device during risk calculation. Conclusions: Wasm-iCARE fosters accessible and privacy-preserving risk tools, accelerating their validation and delivery.

Comparison of two automated CT perfusion software packages in patients with ischemic stroke presenting within 24 h of onset.

Background: We compared the ischemic core and hypoperfused tissue volumes estimated by RAPID and JLK-CTP, a newly developed automated computed tomography perfusion (CTP) analysis package. We also assessed agreement between ischemic core volumes by two software packages against early follow-up infarct volumes on diffusion-weighted images (DWI). Methods: This retrospective study analyzed 327 patients admitted to a single stroke center in Korea from January 2021 to May 2023, who underwent CTP scans within 24 h of onset. The concordance correlation coefficient (ρ) and Bland-Altman plots were utilized to compare the volumes of ischemic core and hypoperfused tissue volumes between the software packages. Agreement with early (within 3 h from CTP) follow-up infarct volumes on diffusion-weighted imaging (n = 217) was also evaluated. Results: The mean age was 70.7 ± 13.0 and 137 (41.9%) were female. Ischemic core volumes by JLK-CTP and RAPID at the threshold of relative cerebral blood flow (rCBF) < 30% showed excellent agreement (ρ = 0.958 [95% CI, 0.949 to 0.966]). Excellent agreement was also observed for time to a maximum of the residue function (T max) > 6 s between JLK-CTP and RAPID (ρ = 0.835 [95% CI, 0.806 to 0.863]). Although early follow-up infarct volume showed substantial agreement in both packages (JLK-CTP, ρ = 0.751 and RAPID, ρ = 0.632), ischemic core volumes at the threshold of rCBF <30% tended to overestimate ischemic core volumes. Conclusion: JLK-CTP and RAPID demonstrated remarkable concordance in estimating the volumes of the ischemic core and hypoperfused area based on CTP within 24 h from onset.

Inter-software reliability and agreement for follicular and luteal morphometric and echotextural ultrasonographic parameters in beef cattle.

This study aimed to compare the inter-software and inter-observer reliability and agreement for the assessment of follicular and luteal morphometry and echotexture parameters in beef crossbreed females (3/8 Bos taurus indicus and 5/8 Bos taurus taurus). B-mode and color Doppler ultrasonographic ovarian images were obtained at specific time points of estradiol-progesterone-based protocols for timed artificial insemination (TAI). Sonograms were analyzed by two observers using a licensed (IASP1) and an open access (IASP2) software package. A total of 292 snap-shot sonograms were analyzed for morphometric parameters and 504 for echotexture parameters. inter-software reliability was judged moderate to excellent (ICC or CCC=0.73-0.98), whereas inter-observer reliability for morphometric parameters was deemed good to excellent (ICC or CCC=0.82-0.98). A small percentage (up to 10.95 %) of measured parameters fell outside the limits of inter-software and inter-observer agreement. For echotexture parameters, inter-software reliability varied widely (ICC or CCC=0.16-0.95) based on the size of regions of interest (ROI), while inter-observer reliability ranged from moderate to excellent (ICC or CCC= 0.71-0.97). The highest inter-software reliability for pixel value and heterogeneity value was observed for the corpus luteum (ICCs=0.81-0.95; P>0.05), followed by the peripheral follicular antrum (ICCs=0.75-0.78; P<0.05). However, lower reliability was determined for the follicular wall (ICCs=0.08-0.33; P<0.0001) and perifollicular stroma (ICCs=0.16-0.46; P<0.05). In conclusion, both software packages showed high reproducibility for morphometric measurements, while echotexture measurements were more challenging to replicate based on ROI sizes. Caution is advised when selecting ROI sizes for echotexture measurements in bovine ovaries.

IDeRare: a lightweight and extensible open-source phenotype and exome analysis pipeline for germline rare disease diagnosis.

Objectives: Diagnosing rare diseases is an arduous and challenging process in clinical settings, resulting in the late discovery of novel variants and referral loops. To help clinicians, we built IDeRare pipelines to accelerate phenotype-genotype analysis for patients with suspected rare diseases. Materials and Methods: IDeRare pipeline is separated into phenotype and genotype parts. The phenotype utilizes our handmade Python library, while the genotype part utilizes command line (bash) and Python script to combine bioinformatics executable and Docker image. Results: We described various implementations of IDeRare phenotype and genotype parts with real-world clinical and exome data using IDeRare, accelerating the terminology conversion process and giving insight on the diagnostic pathway based on disease linkage analysis until exome analysis and HTML-based reporting for clinicians. Conclusion: IDeRare is freely available under the BSD-3 license, obtainable via GitHub. The portability of IDeRare pipeline could be easily implemented for semi-technical users and extensible for advanced users.

Predicting the odds of chronic wasting disease with Habitat Risk software.

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in captive cervids in Colorado, United States (US) in 1967, but has since spread into free-ranging white-tailed deer (Odocoileus virginianus) across the US and Canada as well as to Scandinavia and South Korea. In some areas, the disease is considered endemic in wild deer populations, and governmental wildlife agencies have employed epidemiological models to understand long-term environmental risk. However, continued rapid spread of CWD into new regions of the continent has underscored the need for extension of these models into broader tools applicable for wide use by wildlife agencies. Additionally, efforts to semi-automate models will facilitate access of technical scientific methods to broader users. We introduce software (Habitat Risk) designed to link a previously published epidemiological model with spatially referenced environmental and disease testing data to enable agency personnel to make up-to-date, localized, data-driven predictions regarding the odds of CWD detection in surrounding areas after an outbreak is discovered. Habitat Risk requires pre-processing publicly available environmental datasets and standardization of disease testing (surveillance) data, after which an autonomous computational workflow terminates in a user interface that displays an interactive map of disease risk. We demonstrated the use of the Habitat Risk software with surveillance data of white-tailed deer from Tennessee, USA.

Quantitative proteome-wide O-glycoproteomics analysis with FragPipe.

Identification of O-glycopeptides from tandem mass spectrometry data is complicated by the near complete dissociation of O-glycans from the peptide during collisional activation and by the combinatorial explosion of possible glycoforms when glycans are retained intact in electron-based activation. The recent O-Pair search method provides an elegant solution to these problems, using a collisional activation scan to identify the peptide sequence and total glycan mass, and a follow-up electron-based activation scan to localize the glycosite(s) using a graph-based algorithm in a reduced search space. Our previous O-glycoproteomics methods with MSFragger-Glyco allowed for extremely fast and sensitive identification of O-glycopeptides from collisional activation data but had limited support for site localization of glycans and quantification of glycopeptides. Here, we report an improved pipeline for O-glycoproteomics analysis that provides proteome-wide, site-specific, quantitative results by incorporating the O-Pair method as a module within FragPipe. In addition to improved search speed and sensitivity, we add flexible options for oxonium ion-based filtering of glycans and support for a variety of MS acquisition methods and provide a comparison between all software tools currently capable of O-glycosite localization in proteome-wide searches.