The epidemiology of Stenotrophomonas maltophilia and Achromobacter xylosoxidans infections.

Stenotrophomonas maltophilia and Achromobacter xylosoxidans are emerging nosocomial, non-glucose fermenting, Gram-negative pathogens. In this nested case-control trial, independent predictors for S. maltophilia infections were hemodialysis and recent antibiotic usage (overall), while recent usage of fluoroquinolones, was independently associated with A. xylosoxidans infections. Infections were independently associated with multiple worse outcomes.

A framework for evaluating the performance of SMLM cluster analysis algorithms.

Single-molecule localization microscopy (SMLM) generates data in the form of coordinates of localized fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite a range of cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics to score the success of clustering algorithms in simulated conditions mimicking experimental data. We demonstrate the framework using seven diverse analysis algorithms: DBSCAN, ToMATo, KDE, FOCAL, CAML, ClusterViSu and SR-Tesseler. Given that the best performer depended on the underlying distribution of localizations, we demonstrate an analysis pipeline based on statistical similarity measures that enables the selection of the most appropriate algorithm, and the optimized analysis parameters for real SMLM data. We propose that these standard simulated conditions, metrics and analysis pipeline become the basis for future analysis algorithm development and evaluation.

A novel and robust method for counting components within bio-molecular complexes using fluorescence microscopy and statistical modelling.

Cellular biology occurs through myriad interactions between diverse molecular components, many of which assemble in to specific complexes. Various techniques can provide a qualitative survey of which components are found in a given complex. However, quantitative analysis of the absolute number of molecules within a complex (known as stoichiometry) remains challenging. Here we provide a novel method that combines fluorescence microscopy and statistical modelling to derive accurate molecular counts. We have devised a system in which batches of a given biomolecule are differentially labelled with spectrally distinct fluorescent dyes (label A or B), and mixed such that B-labelled molecules are vastly outnumbered by those with label A. Complexes, containing this component, are then simply scored as either being positive or negative for label B. The frequency of positive complexes is directly related to the stoichiometry of interaction and molecular counts can be inferred by statistical modelling. We demonstrate this method using complexes of Adenovirus particles and monoclonal antibodies, achieving counts that are in excellent agreement with previous estimates. Beyond virology, this approach is readily transferable to other experimental systems and, therefore, provides a powerful tool for quantitative molecular biology.

Improving axial resolution in Structured Illumination Microscopy using deep learning.

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM).

MOTIVATION: Many recent advancements in single-molecule localization microscopy exploit the stochastic photoswitching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation. RESULTS: Modelling the photoswitching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localizations from a single photoswitching fluorophore. This is then extended to provide the probability distribution for the number of localizations in a direct stochastic optical reconstruction microscopy experiment involving an arbitrary number of molecules. We demonstrate that when training data are available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localizations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein linker for activation of T cells on the cell surface of the T-cell immunological synapse. AVAILABILITY AND IMPLEMENTATION: Software and data available at https://github.com/lp1611/mol_count_dstorm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Testing for complete spatial randomness on three dimensional bounded convex shapes.

There is currently a gap in theory for point patterns that lie on the surface of objects, with researchers focusing on patterns that lie in a Euclidean space, typically planar and spatial data. Methodology for planar and spatial data thus relies on Euclidean geometry and is therefore inappropriate for analysis of point patterns observed in non-Euclidean spaces. Recently, there has been extensions to the analysis of point patterns on a sphere, however, many other shapes are left unexplored. This is in part due to the challenge of defining the notion of stationarity for a point process existing on such a space due to the lack of rotational and translational isometries. Here, we construct functional summary statistics for Poisson processes defined on convex shapes in three dimensions. Using the Mapping Theorem, a Poisson process can be transformed from any convex shape to a Poisson process on the unit sphere which has rotational symmetries that allow for functional summary statistics to be constructed. We present the first and second order properties of such summary statistics and demonstrate how they can be used to construct a test statistics to determine whether an observed pattern exhibits complete spatial randomness or spatial preference on the original convex space. We compare this test statistic with one constructed from an analogue L -function for inhomogeneous point processes on the sphere. A study of the Type I and II errors of our test statistics are explored through simulations on ellipsoids of varying dimensions.

Implementing Continuous Quality Improvement in an Integrated Community Urology Practice: Lessons Learned.

OBJECTIVE: To determine the effectiveness of 2 different continuous quality improvement interventions in an integrated community urology practice. We specifically assessed the impact of audited physician feedback on improving physicians' adoption of active surveillance for low-risk prostate cancer (CaP) and adherence to a prostate biopsy time-out intervention. MATERIALS AND METHODS: The electronic medical records of Genesis Healthcare Partners were analyzed between August 24, 2011 and September 30, 2020 to evaluate the performance of 2 quality interventions: audited physician feedback to improve active surveillance adoption in low-risk CaP patients, and audited physician feedback to promote adherence to an electronic medical records embedded prostate biopsy time-out template. Physician and Genesis Healthcare Partners group adherence to each quality initiative was compared before and after each intervention type using ANOVA testing. RESULTS: For active surveillance, we consistently saw an increase in active surveillance adoption for low risk CaP patients in association with continuous audited feedback (P < .001). Adherence to the prostate biopsy time-out template improved when audited feedback was provided (P < .001). CONCLUSION: The implementation of clinical guidelines into routine clinical practice remains challenging and poses an obstacle to the improvement of United States healthcare quality. Continuous quality improvement should be a dynamic process, and in our experience, audited feedback coupled with education is most effective.

Leveraging the Electronic Medical Record Improves Prostate Cancer Clinical Staging in a Community Urology Practice.

INTRODUCTION: Clinical tumor staging is an important component of risk stratification, which is central in assessment and treatment of patients with prostate cancer. We evaluated the potential of an examination based tumor staging template embedded within the electronic medical record to improve consistency and clarity of clinical tumor staging. METHODS: We conducted a retrospective analysis before template implementation (January to March 2017), followed by prospective analysis after implementation (April to August 2017). Physicians were educated on use and importance of the staging template prior to implementation. Assessment of digital rectal examination clarity included confident-explicit (cT stage documented), confident-implicit (stage inexplicit, cT stage interpreted) and unconfident (inability to discern cT stage). Clarity and consistency of tumor staging before and after template implementation were compared using a chi-square test. RESULTS: A total of 573 biopsies were analyzed: 234 before template use (40%) and 339 after (60%). In men at risk for prostate cancer explicit staging increased from 16% to 60% (p <0.001) following implementation of the staging template. Overall staging (explicit plus implicit) increased (from 74% to 92%, p <0.001), while unconfident staging decreased (from 26% to 8%, p <0.001) after implementation. In men with positive biopsies (309) explicit staging increased (from 29% to 64%, p <0.001) and overall staging increased (from 76% to 92%, p <0.001), while unconfident staging decreased (from 24% to 8%, p <0.001). CONCLUSIONS: Accurate prostate cancer clinical staging is important in risk stratification and treatment. We demonstrate the ability of a standardized, electronic medical record embedded, American Joint Committee on Cancer based template to improve the consistency and clarity of clinical staging in prostate cancer.

Microscope calibration protocol for single-molecule microscopy.

Single-molecule microscopy allows for the investigation of the dynamics of individual molecules and the visualization of subcellular structures at high spatial resolution. For single-molecule imaging experiments, and particularly those that entail the acquisition of multicolor data, calibration of the microscope and its optical components therefore needs to be carried out at a high level of accuracy. We propose here a method for calibrating a microscope at the nanometer scale, in the sense of determining optical aberrations as revealed by point source localization errors on the order of nanometers. The method is based on the imaging of a standard sample to detect and evaluate the amount of geometric aberration introduced in the optical light path. To provide support for multicolor imaging, it also includes procedures for evaluating the geometric aberration caused by a dichroic filter and the axial chromatic aberration introduced by an objective lens.

Three-year Active Surveillance Outcomes in a Contemporary Community Urology Cohort in the United States.

OBJECTIVES: To determine the 3-year outcomes of men with prostate cancer managed with active surveillance (AS) in a cohort of geographically diverse community-based urology practices. AS is the management of choice for a majority of men with lower risk prostate cancer.1,2,3 Little is known about the contemporary "real-world" follow-up and adherence rates in the most common setting of urologic care, community (private) practice.4 METHODS: We retrospectively evaluated outcomes for men diagnosed between January 1, 2013 and May 31, 2014 with National Comprehensive Cancer Network (NCCN) very low, low and intermediate risk prostate cancer who selected AS in 9 large community urology practices. We used univariate and multivariate analyses to describe associations between race, age, insurance status, family history, comorbidity, clinical stage, Gleason score, NCCN risk-group, and PSA density with discontinuation of AS. RESULTS: Five hundred and forty-eight men on AS were followed for a median of 3.35 years. 89% (492) continued to follow-up with diagnosing practice. 32% (171) discontinued AS. On multivariate analysis, increasing NCCN risk classification (Hazard ratio [HR] 1.65, P = 0.02 and HR 2.09, P < 0.01 for low and intermediate risk vs very low risk) was significantly associated with discontinuation. Among those who discontinued AS, surgery and radiation were utilized equally (47% and 53%, respectively, P = 0.48). CONCLUSION: In this community-based cohort of men on AS, a minority was lost to follow-up and adherence to AS was similar to other reports. Disease characteristics more than sociodemographic characteristics correlated with adherence to AS, while surgery and radiotherapy were utilized equally among those discontinuing AS, both suggesting guideline concordant practice of medicine.

Resolution limit of image analysis algorithms.

The resolution of an imaging system is a key property that, despite many advances in optical imaging methods, remains difficult to define and apply. Rayleigh's and Abbe's resolution criteria were developed for observations with the human eye. However, modern imaging data is typically acquired on highly sensitive cameras and often requires complex image processing algorithms to analyze. Currently, no approaches are available for evaluating the resolving capability of such image processing algorithms that are now central to the analysis of imaging data, particularly location-based imaging data. Using methods of spatial statistics, we develop a novel algorithmic resolution limit to evaluate the resolving capabilities of location-based image processing algorithms. We show how insufficient algorithmic resolution can impact the outcome of location-based image analysis and present an approach to account for algorithmic resolution in the analysis of spatial location patterns.

A HIDDEN MARKOV MODEL APPROACH TO CHARACTERIZING THE PHOTO-SWITCHING BEHAVIOR OF FLUOROPHORES.

Fluorescing molecules (fluorophores) that stochastically switch between photon-emitting and dark states underpin some of the most celebrated advancements in super-resolution microscopy. While this stochastic behavior has been heavily exploited, full characterization of the underlying models can potentially drive forward further imaging methodologies. Under the assumption that fluorophores move between fluorescing and dark states as continuous time Markov processes, the goal is to use a sequence of images to select a model and estimate the transition rates. We use a hidden Markov model to relate the observed discrete time signal to the hidden continuous time process. With imaging involving several repeat exposures of the fluorophore, we show the observed signal depends on both the current and past states of the hidden process, producing emission probabilities that depend on the transition rate parameters to be estimated. To tackle this unusual coupling of the transition and emission probabilities, we conceive transmission (transition-emission) matrices that capture all dependencies of the model. We provide a scheme of computing these matrices and adapt the forward-backward algorithm to compute a likelihood which is readily optimized to provide rate estimates. When confronted with several model proposals, combining this procedure with the Bayesian Information Criterion provides accurate model selection.

Fusing multimodal microscopy data for improved cell boundary estimation and fluorophore localization of Pseudomonas aeruginosa.

With advances in experimental technologies, the use of biological imaging has grown rapidly and there is need for procedures to combine data arising from different modalities. We propose a procedure to combine yellow fluorescence protein excitation and differential interference contrast microscopy time lapse videos to better estimate the cellular boundary of Pseudomonas aeruginosa (P. aeruginosa) and localization of it's type VI secretion system (T6SS). By approximating the shape by an ellipse, we construct a penalized objective function which accounts for both sources; the minimum of which provides an elliptical approximation to their cellular boundaries. Our approach suggests improved localization of the T6SS on the estimated cell boundary of P. aeruginosa constructed using both sources of data compared to using each in isolation.

Change in prostate cancer presentation coinciding with USPSTF screening recommendations at a community-based urology practice.

OBJECTIVE: The benefits of prostate-specific antigen (PSA)-based prostate cancer screening are controversial. We sought to determine the change in prostate cancer presentation coinciding with the release of the United States Preventative Services Task Force recommendations against screening in a high-volume community-based urology practice. METHODS: Characteristics of men presenting for an elevated PSA at a community urology practice from August 2011 to August 2015 were queried from a prospectively collected database. A retrospective analysis of presenting PSA, Gleason grade at biopsy, and prostatectomy as well as clinical and pathologic stage was performed. Kruskal-Wallis rank sum and chi-square tests were used for analysis. RESULTS: Referrals for elevated PSA decreased from 933 in year 1 to 816 by year 4 (12.5% decrease) with a concomitant reduction in biopsies performed in newly referred men from 461 to 356 (22.8% decrease, P = 0.02). The proportion of men presenting with PSAs>10 increased from 28.1% to 36.8% (P = 0.009). First-time biopsy-positivity rate increased from 48.4% to 62.4% with a rise in the proportion having Gleason≥7 from 51.6% to 69.7% (P = 0.0001). Of the 578 men who underwent radical prostatectomy, there was a 19.4% increase in Gleason≥7 tumors (P = 0.01). CONCLUSIONS: Our findings demonstrate a decrease in elevated PSA referrals, increase in PSA at the time of referral, decrease in detection of low-risk disease, and increase in detection of intermediate-/high-risk disease in a high-volume, multisite, community-based urology practice, coinciding with the United States Preventative Services Task Force recommendations against PSA screening.

3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse.

Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell's nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D. This lack of quantification tools can be explained by the relative novelty of imaging techniques such as interferometric photo-activated localisation microscopy (iPALM). Also, existing methods that could be extended to 3D SMLM are usually subject to user defined analysis parameters, which remains a major drawback. Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable parameters, relying on a model-based Bayesian approach which takes full account of the individual localisation precisions in all three dimensions. The accuracy and reliability of the method is validated using simulated data sets. This tool is then deployed on novel experimental data as a proof of concept, illustrating the recruitment of LAT to the T-cell immunological synapse in data acquired by iPALM providing ~10 nm isotropic resolution.

Aminoacyl tRNA Synthetase--Interacting Multifunctional Protein 1 Activates NK Cells via Macrophages In Vitro and In Vivo.

Aminoacyl tRNA synthetase-interacting multifunctional protein 1 (AIMP1) has been reported to have antitumor effects in various tumor models. However, mechanisms by which AIMP1 ameliorates tumorigenesis are not well understood. As NK cells are a major cell type involved in antitumor activities and AIMP1 is known to activate professional APCs, we determined whether AIMP1 induced NK cell activation directly or via these APCs. AIMP1 induced the expression of surface activation markers on murine NK cells in total splenocytes, although AIMP1 did not directly induce these activation markers of NK cells. The inductive effect of AIMP1 on NK cell activation disappeared in macrophage-depleted splenocytes, indicating that macrophages were required for the AIMP1-induced activation of NK cells. Furthermore, coculture experiments showed that AIMP1 activated NK cells in the presence of isolated macrophages, but failed to activate NK cells when cultured alone or with dendritic cells or B cells. Although AIMP1 significantly promoted TNF-α production by macrophages, the secreted TNF-α partially affected the NK cell activation. Transwell cocultivation analysis revealed that direct contact between macrophages and NK cells was required for the AIMP1-induced NK cell activation. In addition, AIMP1 significantly enhanced cytotoxicity of NK cells against Yac-1 cells. Furthermore, the in vivo administration of AIMP1 also induced NK cell activation systemically with a macrophage-dependent manner. Importantly, AIMP1 dramatically reduced the lung metastasis of melanoma cells, which was mediated by NK cells. Taken together, our results show that AIMP1 induces antitumor responses by NK cell activation mainly via macrophages.

Spectral-overlap approach to multiframe superresolution image reconstruction.

Various techniques and algorithms have been developed to improve the resolution of sensor-aliased imagery captured with multiple subpixel-displaced frames on an undersampled pixelated image plane. These dealiasing algorithms are typically known as multiframe superresolution (SR), or geometric SR to emphasize the role of the focal-plane array. Multiple low-resolution (LR) aliased frames of the same scene are captured and allocated to a common high-resolution (HR) reconstruction grid, leading to the possibility of an alias-free reconstruction, as long as the HR sampling rate is above the Nyquist rate. Allocating LR-frame irradiances to HR frames requires the use of appropriate weights. Here we present a novel approach in the spectral domain to calculating exactly weights based on spatial overlap areas, which we call the spectral-overlap (SO) method. We emphasize that the SO method is not a spectral approach but rather an approach to calculating spatial weights that uses spectral decompositions to exploit the array properties of the HR and LR pixels. The method is capable of dealing with arbitrary aliasing factors and interframe motions consisting of in-plane translations and rotations. We calculate example reconstructed HR images (the inverse problem) from synthetic aliased images for integer and for fractional aliasing factors. We show the utility of the SO-generated overlap-area weights in both noniterative and iterative reconstructions with known or unknown aliasing factor. We show how the overlap weights can be used to generate the Green's function (pixel response function) for noniterative dealiasing. In addition, we show how the overlap-area weights can be used to generate synthetic aliased images (the forward problem). We compare the SO approach to the spatial-domain geometric approach of O'Rourke and find virtually identical high accuracy but with significant enhancements in speed for SO. We also compare the SO weights to interpolated weights and find that the overlap-area weights lead to significantly smaller errors in iterative reconstructions. We consider how the SO method might be extended to account for the influence of the optical transfer function, more complex or space-variant motions, the registration process, and noise.

Prostate Biopsy Complications: A Dual Analysis.

OBJECTIVE: To measure prostate needle biopsy (PNB)-associated complications and place of treatment: inpatient hospitalization and outpatient treatment. An electronic medical record (EMR) data query is compared to a patient questionnaire survey. MATERIALS AND METHODS: A total of 2410 patients underwent 2588 biopsies and were evaluated for PNB-associated complications. Two approaches were used: (1) EMR analysis based on International Classification of Diseases, Ninth Revision, and Current Procedural Terminology coding and chart review; and (2) patient-reported questionnaire and chart review validation. Serious complications were defined as any complication leading to a related hospitalization, visit to the emergency department (ED), urgent care (UC), or doctor's office within 30 days of the biopsy. RESULTS: The EMR study revealed 69 (2.67%) serious complications leading to either hospitalization or treatment at an ED, UC, or doctor's office. Thirty serious complications led to hospitalization (1.16%), 14 patients (0.54%) were treated at the ED, 1 was managed at a UC (0.04%), and 24 (0.93%) were treated at the doctor's office. Of the 847 (35.1%) questionnaires considered appropriate for analysis, 36 (4.25%) reported treatment in either the hospital, ED, UC, or doctor's office. Nine patients (1.06%) reported being hospitalized within 30 days of the procedure, whereas 27 patients (3.19%) were treated in an outpatient setting, 8 (0.94%) at the ED, 3 (0.35%) at a UC, and 16 (1.89%) at the doctor's office. CONCLUSION: Our dual analysis study indicates a slightly greater than 1% incidence of hospitalization due to serious complications following PNB. Serious complications appear to be more frequently managed outside the hospital setting (ED, UC, and doctor's office).