Point-wise Data Research Articles

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data.

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Pointwise data depth for univariate and multivariate functional outlier detection

Pointwise data depth for univariate and multivariate functional outlier detection

Release of windowed multipole library for ENDF/B-VIII.0 in support of OpenMC criticality and depletion analysis

Major evaluations have been updated in the latest release of the Evaluated Nuclear Data library (ENDF/B-VIII.0) and is the currently recommended pointwise library for criticality and depletion analysis in the open-source Monte Carlo code — OpenMC. However, the currently released OpenMC library does not yet include an accompanying windowed multipole library (WMP) that enables on-the-fly Doppler broadening. This paper revisits the windowed multipole representation and fixes a major drawback that has prevented its use for depletion analysis. The new WMP library presented in this paper explicitly treats key absorption reactions as separate channels such that proper reaction rates are provided during depletion analysis. Based on this improvement, the new WMP library is processed for ENDF/B-VIII.0 and then verified against the pointwise data through direct comparison, criticality benchmarks and fuel assembly depletion. The numerical results show good agreement and efficiency with the pointwise library.

LoRa-based outdoor localization and tracking using unsupervised symbolization

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

A method for predicting the entropy and Gibbs free energy of ICl and BrCl gases based on an improved Hulburt-Hirschfelder potential

Abstract Combined with the Rydberg-Klein-Rees (RKR) data and spectral constants, a method based an improved Hulburt-Hirschfelder (IHH) potential model for calculating the molar entropy and Gibbs free energy of diatomic gases is presented. The full set of rovibrational energy is calculated by solving the one-dimension Schrödinger equation using the determined IHH potential pointwise data, and then the partition function, Gibbs free energy and entropy of the diatomic molecular gas can be determined using the quantum statistical ensemble theory. Comparing with other potentials and thermodynamic data, the application to the ground electronic state of ICl and BrCl gases shows that the IHH potential fits well with the experimental RKR data, and the calculated Gibbs free energy and molar entropy are in good agreement with the experimental values.

The effects of methylene blue during and after cardiac arrest in a porcine model; a randomized, blinded, placebo-controlled study

PurposeTo evaluate the effect of methylene blue administered as a bolus on return of spontaneous circulation (ROSC), lactate levels, vasopressor requirements, and markers of neurological injury in a clinically relevant pig model of cardiac arrest. Materials and methods40 anesthetized pigs were subjected to acute myocardial infarction and 7 min of untreated cardiac arrest. Animals were randomized into three groups: one group received saline only (controls), one group received 2 mg/kg methylene blue and saline (MB + saline), and one group received two doses of 2 mg/kg methylene blue (MB + MB). The first intervention was given after the 3rd rhythm analysis, while the second dose was administered one hour after achieving ROSC. Animals underwent intensive care and observation for six hours, followed by cerebral magnetic resonance imaging (MRI). The primary outcome for this study was development in lactate levels after cardiac arrest. Categorical data were compared using Fisher's exact test and pointwise data were analyzed using one-way analysis of variance (ANOVA) or equivalent non-parametric test. Continuous data collected over time were analyzed using a linear mixed effects model. A value of p < .05 was considered statistically significant. ResultsLactate levels increased in all groups after cardiac arrest and resuscitation, however lactate levels in the MB + MB group decreased significantly faster compared with the control group (p = .007) and the MB + saline group (p = .02). The proportion of animals achieving initial ROSC was similar across groups: 11/13 (85%) in the control group, 10/13 (77%) in the MB + saline group, and 12/14 (86%) in the MB + MB group (p = .81). Time to ROSC did not differ between groups (p = .67). There was no significant difference in accumulated norepinephrine dose between groups (p = .15). Cerebral glycerol levels were significantly lower in the MB + MB group after resuscitation compared with control group (p = .03). However, MRI data revealed no difference in apparent diffusion coefficient, cerebral blood flow, or dynamic contrast enhanced MR perfusion between groups. ConclusionTreatment with a bolus of methylene blue during cardiac arrest and after resuscitation did not significantly improve hemodynamic function. A bolus of methylene blue did not yield the neuroprotective effects that have previously been described in animals receiving methylene blue as an infusion.

Evaluation of the Consistency of Glaucomatous Visual Field Defects Using a Clustered SITA-Faster Protocol

Frontloading SITA-Faster (SFR) visual fields (2 tests per eye on the same visit) has been shown to provide repeatable perimetric data at minimal time cost. This study reports the outcomes of using frontloaded SFR in the evaluation of pointwise visual field (VF) defects in a cohort of patients with glaucoma when transitioned from SITA-Standard (SS). Prospective, cross-sectional study. A total of 144 eyes of 91 patients with confirmed or suspected glaucoma who had an SS test on a previous visit. Two SFR tests (T1, T2) per eye on the same visit. Global sensitivity, reliability indices, and pointwise deviation map probability scores from the pattern deviation grid of each patient were compared across the 3 sequential tests to evaluate the consistency of VF defects. The mean age was 68.6 years, and 79.2% of patients had a diagnosis of glaucoma. There was no significant difference in mean deviation (MD) across the 3 tests (-5.83 decibels [dB], -5.28 dB, and -5.71 dB in SS, SFR1, and SFR2, respectively, repeated-measures analysis of variance [ANOVA], P= 0.48). The frontloaded SFR tests provided repeatable VFs that confirmed existing pointwise data on the SS in 4661 (62.3%) locations, reversed an SS defect in 614 (8.2%) locations, and demonstrated a new repeatable defect in 406 (5.4%) locations of the pattern deviation grid. A new defect of at least 3 contiguous points was identified in 20.1% of eyes. The non-repeatable points on the 2 SFR tests displayed no significant difference in the distribution of defect/nondefect points based on test order or peripheral versus central locations. There was no significant difference in the rate of obtaining at least 1 reliable test result between SS and the frontloaded SFR T1 and T2 (P= 0.77). Test duration significantly decreased from SS to SFR1/2 (379 vs. 160 vs. 158 seconds, P < 0.0001). Frontloading SFR tests can provide repeatable data for the evaluation of the consistency of pattern deviation defects in glaucoma, with no observable decline in performance from test fatigue. This is achieved at equivalent duration and reliability as a single SS test. Frontloading SFR may be helpful in increasing testing frequency/quantity to meet recommended guidelines for progression analysis. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Abstract 106: Neurological Effects Of Methylene Blue In A Porcine Model Of Cardiac Arrest

Purpose To investigate the neuroprotective effects of methylene blue (MB) in a clinically relevant porcine model of cardiac arrest. Method: 40 anaesthetized pigs were subjected to myocardial infarction and seven minutes of untreated cardiac arrest. Animals were randomized into three groups: one group received saline only (control), one group received 2 mg/kg MB and saline (MB+saline), and one group received two doses of 2 mg/kg MB (MB+MB). The first intervention was given after the 3rd rhythm analysis, while the second dose was administered one hour after achieving return of spontaneous circulation (ROSC). The main investigator was blinded during resuscitation and the first hour after ROSC. Animals underwent intensive care and observation for 6 hours. Neurological outcomes were evaluated by cerebral microdialysis and serum-neuron specific enolase (NSE). Normality was assessed by qq-plot and histograms. Pointwise data were analyzed using Kruskal-Wallis test as data did not assume normal distribution. Data are presented as medians and 25 th and 75 th percentiles. A value of p&lt;0.05 was considered statistically significant. Results: The proportion of animals achieving ROSC was similar across groups: 11/13 (85%) in the control group, 10/13 (77%) in the MB+saline group, and 12/14 (86%) in the MB+MB group (p=0.81). Cerebral lactate and pyruvate levels increased after ROSC in all groups. Lactate/pyruvate ratios were comparable between groups two hours after ROSC (control: 25 (22;31); MB+saline: 24 (22;36); MB+MB: 23 (20;28); p=0.43). Cerebral glycerol levels initially increased in all groups with the highest levels at two hours after ROSC (control: 454 μmol/L (319;507); MB+saline 351 μmol/L (241;420); MB+MB 347 μmol/L (256;478); p=0.35). Glycerol levels decreased over time with no difference 6 hours after ROSC (control: 210 μmol/L (138;293); MB+saline (146 μmol/L (61;156); MB+MB: 105 μmol/L (63;159); p=0.07). Serum-NSE levels 6 hours after ROSC were not different between groups (control: 0.9 μg/L (0.7;1.5); MB+saline 1.5 μg/L (1.0;2.0); MB+MB: 1.0 μg/L (0.7;1.2); p=0.13). Conclusion Contrary to previous publications the current porcine trial did not observe any neuroprotective effects of MB when evaluated by cerebral microdialysis and NSE levels.

Ultrasonic evaluation of a steel-concrete structure using embedded Fiber Bragg Gratings (FBG) and piezoelectric sensors

Concrete is one of the most widely used materials for civil engineering structures. The casting process is particularly well suited to the deployment of buried sensors able to sample a ultrasonic wave field in three dimensions, a valuable piece of information to evaluate the internal properties of a structure, its health state and its evolution over time. Fiber Bragg Gratings (FBGs) sensors can be used as embedded acoustic receivers to acquire such point-wise ultrasonic data. The technical advantages of this sensing technology are numerous: the reduced size of the optical fiber offers a very small footprint on the studied structure, it can be used over a very broad frequency range with very small phase or amplitude distortions, FBGs are highly resistant to harsh environments such as high temperatures, salinity or radioactive environments, thus increasing the longevity of the embedded sensors. The interrogation of FBG requires dedicated state-of-the-art opto-electronics that must be adapted to the imaging problem (bandwidth, size of the heterogeneities/aggregates, nature of the source). In this study, we present a Steel-Concrete wall model equipped with several embedded optical fibers as well as several embedded and surface piezoelectric (PZT) sensors. We compare the performances of the different embedded sensors for several sources of ultrasonic waves applied on the surface of the wall for frequencies ranging from a few tens to a few hundreds of kHz. The analysis of the signals reveal that a significant part of the energy is transmitted to the concrete through the surface liner. Using a collection of random hammer impacts applied on the surface, we evaluate our ability to reconstruct empirical Green’s functions between pairs of sensors buried into the structure using passive imaging methods. These measurements will provide useful information on the internal structure of a steel-concrete model and its temporal evolution.

Analysis of Spatiotemporal Data Imputation Methods for Traffic Flow Data in Urban Networks

The increase in traffic in cities world-wide has led to a need for better traffic management systems in urban networks. Despite the advances in technology for traffic data collection, the collected data are still suffering from significant issues, such as missing data, hence the need for data imputation methods. This paper explores the spatiotemporal probabilistic principal component analysis (PPCA) based data imputation method that utilizes traffic flow data from vehicle detectors and focuses specifically on detectors in urban networks as opposed to a freeway setting. In the urban context, detectors are in a complex network, separated by traffic lights, measuring different flow directions on different types of roads. Different constructions of a spatial network are compared, from a single detector to a neighborhood and a city-wide network. Experiments are conducted on data from 285 detectors in the urban network of Surabaya, Indonesia, with a case study on the Diponegoro neighborhood. Methods are tested against both point-wise and interval-wise missing data in various scenarios. Results show that a spatial network adds robustness to the system and the choice of the subset has an impact on the imputation error. Compared to a single detector, spatiotemporal PPCA is better suited for interval-wise errors and more robust against outliers and extreme missing data. Even in the case where an entire day of data is missing, the method is still able to impute data accurately relying on other vehicle detectors in the network.

Surrogate modelling of wind fields from point-wise atmospheric turbulence measurements

We present an advanced model for the generation of synthetic wind fields that can be understood as an extension of the well-known Mann model. In contrast to such Gaussian random field models which control second-order statistics (i.e., velocity correlation tensors or spectra), we demonstrate that our extended model incorporates the effects of higherorder statistics as well. In particular, the empirically observed phenomenon of small-scale intermittency, a key feature of atmospheric turbulent flows, can be reproduced with high accuracy and at considerably low computational cost. Our method is based on a recently developed multipoint statistical description of a turbulent velocity field [J. Friedrich et al., J. Phys. Complex. 2 045006 (2021)] and consists of a superposition of multivariate Gaussian statistics with fluctuating covariances. Furthermore, we explicitly show how such superstatistical Mann fields can be constraint on a certain number of point-wise measurement data. We give an outlook on the relevance of such surrogate wind fields in the context of fatigue loads on wind turbines.

DIGITAL ELEVATION MODEL ALTERNATIVES ASSESSMENT FOR DEFORMATION ANALYSIS PURPOSES USING GNSS AND INSAR

&lt;p&gt;&lt;em&gt;Digital Elevation Model (DEM) is the starting point in the analysis performed to explain the deformation pattern changes from the Earth's surface. The estimated value of deformation based on point-wise GPS and InSAR data with a better spatial resolution must be defined in a reference frame system that reflects the phenomenon of deformation of the real physical world, e.g., orthometric height for the vertical component. Therefore, this study aims to provide alternative DEM models based on a suitable combination between the Global Geopotential Model of Earth Geopotential Model 2008 (EGM2008) and global terrain models, providing position changes with respect to the orthometric height. The alternative DEM models are (i) the global elevation model of ETOPO1 (DEM1), (ii) the modified global elevation model of SRTM30_PLUS (DEM2), and (iii) the regional elevation model of DEMNAS (DEM3). These alternative models comply with each other for the land areas with mean difference values lower than 1 meter. While for the ocean areas, we found that DEM1 and DEM2 have apparent differences due to the different types of data used. However, a similar assessment could not be performed for DEM3 as it only covers the land areas. Additionally, we compared the orthometric height from these terrain models with leveling observations for the coinciding locations. DEM3 achieves the highest accuracy with the estimated standard deviation of 11.2745 meters and is followed by DEM2 and DEM1 with the respective standard deviation of 29.4498 and 37.6872 meters. We found that these models can be used as a starting position determination for horizontal and vertical deformation analysis.&lt;/em&gt;&lt;/p&gt;

Dealing with spatial heterogeneity in pointwise-to-gridded- data comparisons

Abstract. Most studies on validation of satellite trace gas retrievals or atmospheric chemical transport models assume that pointwise measurements, which roughly represent the element of space, should compare well with satellite (model) pixels (grid box). This assumption implies that the field of interest must possess a high degree of spatial homogeneity within the pixels (grid box), which may not hold true for species with short atmospheric lifetimes or in the proximity of plumes. Results of this assumption often lead to a perception of a nonphysical discrepancy between data, resulting from different spatial scales, potentially making the comparisons prone to overinterpretation. Semivariogram is a mathematical expression of spatial variability in discrete data. Modeling the semivariogram behavior permits carrying out spatial optimal linear prediction of a random process field using kriging. Kriging can extract the spatial information (variance) pertaining to a specific scale, which in turn translates pointwise data to a gridded space with quantified uncertainty such that a grid-to-grid comparison can be made. Here, using both theoretical and real-world experiments, we demonstrate that this classical geostatistical approach can be well adapted to solving problems in evaluating model-predicted or satellite-derived atmospheric trace gases. This study suggests that satellite validation procedures using the present method must take kriging variance and satellite spatial response functions into account. We present the comparison of Ozone Monitoring Instrument (OMI) tropospheric NO2 columns against 11 Pandora spectrometer instrument (PSI) systems during the DISCOVER-AQ campaign over Houston. The least-squares fit to the paired data shows a low slope (OMI=0.76×PSI+1.18×1015 molecules cm−2, r2=0.66), which is indicative of varying biases in OMI. This perceived slope, induced by the problem of spatial scale, disappears in the comparison of the convolved kriged PSI and OMI (0.96×PSI+0.66×1015 molecules cm−2, r2=0.72), illustrating that OMI possibly has a constant systematic bias over the area. To avoid gross errors in comparisons made between gridded data vs. pointwise measurements, we argue that the concept of semivariogram (or spatial autocorrelation) should be taken into consideration, particularly if the field exhibits a strong degree of spatial heterogeneity at the scale of satellite and/or model footprints.

Robust-FusionNet: Deep Multimodal Sensor Fusion for 3-D Object Detection Under Severe Weather Conditions

The LiDAR point cloud data and camera images are distorted to a different degree under various severe weather conditions. Due to this, the traditional single modal object detection methods are unable to use the complementary information between different sensors. Consequently, these algorithms are unable to address various issues caused by severe weather conditions. Recently, the multimodal data fusion methods are applied to the road object detection under severe weather conditions. However, the multimodal algorithms suffer from low data alignment accuracy and the inability to suppress the changes in exposure under severe weather conditions. In this work, we propose a new multimodal sensor fusion object detection network. The proposed network effectively overcomes the shortcomings caused by the camera and LiDAR distortions in severe weather conditions and achieves robust environment perception. We propose: 1) point-wise aligned data fusion method based on K-means++ clustering to improve the accuracy of data alignment; 2) implicit feature pyramid network (i-FPN) to fuse the image features for suppressing the distortions caused by the changes in exposure; 3) hybrid attention mechanism (HAM) to deal with the fusion features and improve the adaptability towards different working conditions. We perform experiments using the ONCE and KITTI dataset. The experimental results and analysis show that the proposed method effectively improves the performance of multimodal deep fusion network under both clear and severe weather conditions.

A layer potential approach to functional and clinical brain imaging

In this work, we consider the inverse source recovery problem from sEEG, EEG and MEG point-wise data. We regard this as an inverse source recovery problem for L2 vector-fields normally oriented and supported on the grey/white matter interface, which together with the brain, skull and scalp form a non-homogeneous layered conductor. We assume that the quasistatic approximation of Maxwell’s equation holds for the electro-magnetic fields considered. The electric data is measured point-wise inside and outside the conductor while the magnetic data is measured only point-wise outside the conductor. These ill-posed problems are solved via Tikhonov regularization on triangulations of the interfaces and a piecewise linear model for the current on the triangles. Both in the continuous and discrete formulation the electric potential is expressed as a linear combination of double layer potentials while the magnetic flux density in the continuous case is a vector-surface integral whose discrete formulation features single layer potentials. A main feature of our approach is that these contributions can be computed exactly. Due to the consideration of the regularity conditions of the electric potential in the inverse source recovery problem, the Cauchy transmission problem for the electric potential is inadvertently solved as well. In the problem, we propagate only the electric potential while the normal derivatives at the interfaces of discontinuity of the electric conductivities are computed directly from the resulting solution. This reduces the computational complexity of the problem. There is a direct connection between the magnetic flux density and the electrical potential in conductors such as the one we explore, hence a coupling of the sEEG, EEG and MEG data for solving the respective inverse source recovery problems simultaneously is direct. We treat these problems in a unified approach that uses only single and/or double layer potentials. We provide numerical examples using realistic meshes of the head with synthetic data.

Uniqueness in determining fractional orders of derivatives and initial values

For initial boundary value problems of time-fractional diffusion-wave equations with the zero Dirichlet boundary value, we consider an inverse problem of determining orders of the fractional derivatives and initial values by three kinds of measurement data on a time interval: (i) solution in a subdomain (ii) Neumann data on a subboundary (iii) spatial averaged values. We prove that the order and initial values are uniquely determined by the above data if the solutions do not identically vanish. Our main results show that the uniqueness for the fractional order holds even if the coefficients of the fractional diffusion-wave equations and source terms within some class are unknown. Moreover we consider pointwise data for the uniqueness in the inverse problem. The proof is based on the eigenfunction expansions and the asymptotic expansions of the Mittag–Leffler functions for large time.

Deployment of Time-Resolved Particle Image Velocimetry between two PWR surrogate bundles

There is a need to expand detailed experimental data on PWR thermal hydraulics to capture larger scale features than at the sub-bundle level. However, measuring fluid velocity between PWR fuel bundles is particularly challenging due to narrow gap geometries; to date, only point-wise data are available in the literature. Here, Time-Resolved Particle Image Velocimetry (TR-PIV) is performed in this intricate configuration. To prevent glare and scattering between laser beam and rods in the test section, a very careful optimization of the optical setup is necessary. The employed design rules are summarized in this manuscript. Sample velocity fields illustrate the successful design; they correlate well with bundle motion recorded independently with motion sensors. Captured flow structures (Re≈104) between bundles will set the foundation to improve the physical understanding of hydrodynamic coupling between bundles.

Complementary time-frequency domain networks for dynamic parallel MR image reconstruction.

To introduce a novel deep learning-based approach for fast and high-quality dynamic multicoil MR reconstruction by learning a complementary time-frequency domain network that exploits spatiotemporal correlations simultaneously from complementary domains. Dynamic parallel MR image reconstruction is formulated as a multivariable minimization problem, where the data are regularized in combined temporal Fourier and spatial (x-f) domain as well as in spatiotemporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatiotemporal redundancies in complementary domains. Experiments were performed on two datasets of highly undersampled multicoil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalize well to data acquired from a different scanner and data with pathologies that were not seen in the training set. The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multicoil data ( and yielding 15 s and 10 s scan times respectively) with fast reconstruction speed (2.8 seconds). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging.

Predicting eyes at risk for rapid glaucoma progression based on an initial visual field test using machine learning.

ObjectiveTo assess whether machine learning algorithms (MLA) can predict eyes that will undergo rapid glaucoma progression based on an initial visual field (VF) test.DesignRetrospective analysis of longitudinal data.Subjects175,786 VFs (22,925 initial VFs) from 14,217 patients who completed ≥5 reliable VFs at academic glaucoma centers were included.MethodsSummary measures and reliability metrics from the initial VF and age were used to train MLA designed to predict the likelihood of rapid progression. Additionally, the neural network model was trained with point-wise threshold data in addition to summary measures, reliability metrics and age. 80% of eyes were used for a training set and 20% were used as a test set. MLA test set performance was assessed using the area under the receiver operating curve (AUC). Performance of models trained on initial VF data alone was compared to performance of models trained on data from the first two VFs.Main outcome measuresAccuracy in predicting future rapid progression defined as MD worsening more than 1 dB/year.Results1,968 eyes (8.6%) underwent rapid progression. The support vector machine model (AUC 0.72 [95% CI 0.70–0.75]) most accurately predicted rapid progression when trained on initial VF data. Artificial neural network, random forest, logistic regression and naïve Bayes classifiers produced AUC of 0.72, 0.70, 0.69, 0.68 respectively. Models trained on data from the first two VFs performed no better than top models trained on the initial VF alone. Based on the odds ratio (OR) from logistic regression and variable importance plots from the random forest model, older age (OR: 1.41 per 10 year increment [95% CI: 1.34 to 1.08]) and higher pattern standard deviation (OR: 1.31 per 5-dB increment [95% CI: 1.18 to 1.46]) were the variables in the initial VF most strongly associated with rapid progression.ConclusionsMLA can be used to predict eyes at risk for rapid progression with modest accuracy based on an initial VF test. Incorporating additional clinical data to the current model may offer opportunities to predict patients most likely to rapidly progress with even greater accuracy.

Theoretical study of the Coriolis effect in LiNa, LiK, and LiRb molecules.

The non-adiabatic electronic matrix elements, LΠΣ(R), that arise from the spin-conserving electron-rotational interactions between all mΣ+ and mΠ states, where multiplicity m = 1, 3, converging to the lowest three dissociation limits of Li-containing alkali diatomics, LiM (M = Na, K, Rb), were calculated ab initio up to large internuclear distances, R. The required electronic wavefunctions were obtained within the framework of the multi-reference configuration interaction treatment of the two-valence-electron problem constructed using small-core scalar-relativistic effective core potentials and l-independent core-polarization potentials. A least squares analysis of the ab initio functions at large internuclear distances in conjunction with long-range perturbation theory (LRPT) revealed three different asymptotic behaviors of the LΠΣ(R → +∞)-functions: const. + β[n]/Rn, characterized by n = -1, 3 and 6. The asymptotic coefficients β[n], extracted from the point-wise ab initio data, were found to be in agreement with their LRPT counterparts, which were evaluated analytically using the relevant atomic parameters. The mass dependence of the LΠΣ matrix elements was investigated analytically and numerically. To confirm the reliability of the LΠΣ(R)-functions and interatomic potentials at small and intermediate distances, the empirical q-factors available for the D1Π-states of all LiM molecules studied were compared with their theoretical counterparts derived from the present ab initio data.