Studies of Local DNA Backbone Conformation and Conformational Disorder Using Site-Specific Exciton-Coupled Dimer Probe Spectroscopy.

The processes of genome expression, regulation, and repair require direct interactions between proteins and DNA at specific sites located at and near single-stranded-double-stranded DNA (ssDNA-dsDNA) junctions. Here, we review the application of recently developed spectroscopic methods and analyses that combine linear absorbance and circular dichroism spectroscopy with nonlinear 2D fluorescence spectroscopy to study the local conformations and conformational disorder of the sugar-phosphate backbones of ssDNA-dsDNA fork constructs that have been internally labeled with exciton-coupled cyanine (iCy3)2 dimer probes. With the application of these methods, the (iCy3)2 dimer can serve as a reliable probe of the mean local conformations and conformational distributions of the sugar-phosphate backbones of dsDNA at various critical positions. The results of our studies suggest a possible structural framework for understanding the roles of DNA breathing in driving the processes of protein-DNA complex assembly and function.

Comparing the Accuracy of Peripapillary OCT Scans and Visual Fields to Detect Glaucoma Worsening.

PURPOSE: To compare the accuracy of detecting moderate and rapid rates of glaucoma worsening over a 2-year period with different numbers of OCT scans and visual field (VF) tests in a large sample of glaucoma and glaucoma suspect eyes. DESIGN: Descriptive and simulation study. PARTICIPANTS: The OCT sample comprised 12 150 eyes from 7392 adults with glaucoma or glaucoma suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. The VF sample comprised 20 583 eyes from 10 958 adults from the same database. All eyes had undergone at least 5 measurements over follow-up from the Zeiss Cirrus OCT or Humphrey Field Analyzer. METHODS: Within-eye rates of change in retinal nerve fiber layer (RNFL) thickness and mean deviation (MD) were measured using linear regression. For each measured rate, simulated measurements of RNFL thickness and MD were generated using the distributions of residuals. Simulated rates of change for different numbers of OCT scans and VF tests over a 2-year period were used to estimate the accuracy of detecting moderate (75th percentile) and rapid (90th percentile) worsening for OCT and VF. Accuracy was defined as the percentage of simulated eyes in which the true rate of worsening (the rate without measurement error) was at or less than a criterion rate (e.g., 75th or 90th percentile). MAIN OUTCOME MEASURES: The accuracy of diagnosing moderate and rapid rates of glaucoma worsening for different numbers of OCT scans and VF tests over a 2-year period. RESULTS: Accuracy was less than 50% for both OCT and VF when diagnosing worsening after a 2-year period. OCT accuracy was 5 to 10 percentage points higher than VF accuracy at detecting moderate worsening and 10 to 15 percentage points higher for rapid worsening. Accuracy increased by more than 17 percentage points when using both OCT and VF to detect worsening, that is, when relying on either OCT or VF to be accurate. CONCLUSIONS: More frequent OCT scans and VF tests are needed to improve the accuracy of diagnosing glaucoma worsening. Accuracy greatly increases when relying on both OCT and VF to detect worsening. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Evidence-Based Guidelines for the Number of Peripapillary OCT Scans Needed to Detect Glaucoma Worsening.

PURPOSE: To estimate the number of OCT scans necessary to detect moderate and rapid rates of retinal nerve fiber layer (RNFL) thickness worsening at different levels of accuracy using a large sample of glaucoma and glaucoma-suspect eyes. DESIGN: Descriptive and simulation study. PARTICIPANTS: Twelve thousand one hundred fifty eyes from 7392 adult patients with glaucoma or glaucoma-suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. All eyes had at least 5 measurements of RNFL thickness on the Cirrus OCT (Carl Zeiss Meditec) with signal strength of 6 or more. METHODS: Rates of RNFL worsening for average RNFL thickness and for the 4 quadrants were measured using linear regression. Simulations were used to estimate the accuracy of detecting worsening-defined as the percentage of patients in whom the true rate of RNFL worsening was at or less than different criterion rates of worsening when the OCT-measured rate was also at or less than these criterion rates-for two different measurement strategies: evenly spaced (equal time intervals between measurements) and clustered (approximately half the measurements at each end point of the period). MAIN OUTCOME MEASURES: The 75th percentile (moderate) and 90th percentile (rapid) rates of RNFL worsening for average RNFL thickness and the accuracy of diagnosing worsening at these moderate and rapid rates. RESULTS: The 75th and 90th percentile rates of worsening for average RNFL thickness were -1.09 µm/year and -2.35 µm/year, respectively. Simulations showed that, for the average measurement frequency in our sample of approximately 3 OCT scans over a 2-year period, moderate and rapid RNFL worsening were diagnosed accurately only 47% and 40% of the time, respectively. Estimates for the number of OCT scans needed to achieve a range of accuracy levels are provided. For example, 60% accuracy requires 7 measurements to detect both moderate and rapid worsening within a 2-year period if the more efficient clustered measurement strategy is used. CONCLUSIONS: To diagnose RNFL worsening more accurately, the number of OCT scans must be increased compared with current clinical practice. A clustered measurement strategy reduces the number of scans required compared with evenly spacing measurements.

Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network.

PURPOSE: To identify visual field (VF) worsening from longitudinal OCT data using a gated transformer network (GTN) and to examine how GTN performance varies for different definitions of VF worsening and different stages of glaucoma severity at baseline. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: A total of 4211 eyes (2666 patients) followed up at the Johns Hopkins Wilmer Eye Institute with at least 5 reliable VF results and 1 reliable OCT scan within 1 year of each reliable VF test. METHODS: For each eye, we used 3 trend-based methods (mean deviation [MD] slope, VF index slope, and pointwise linear regression) and 3 event-based methods (Guided Progression Analysis, Collaborative Initial Glaucoma Treatment Study scoring system, and Advanced Glaucoma Intervention Study [AGIS] scoring system) to define VF worsening. Additionally, we developed a "majority of 6" algorithm (M6) that classifies an eye as worsening if 4 or more of the 6 aforementioned methods classified the eye as worsening. Using these 7 reference standards for VF worsening, we trained 7 GTNs that accept a series of at least 5 as input OCT scans and provide as output a probability of VF worsening. Gated transformer network performance was compared with non-deep learning models with the same serial OCT input from previous studies-linear mixed-effects models (MEMs) and naive Bayes classifiers (NBCs)-using the same training sets and reference standards as for the GTN. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC). RESULTS: The M6 labeled 63 eyes (1.50%) as worsening. The GTN achieved an AUC of 0.97 (95% confidence interval, 0.88-1.00) when trained with M6. Gated transformer networks trained and optimized with the other 6 reference standards showed an AUC ranging from 0.78 (MD slope) to 0.89 (AGIS). The 7 GTNs outperformed all 7 MEMs and all 7 NBCs accordingly. Gated transformer network performance was worse for eyes with more severe glaucoma at baseline. CONCLUSIONS: Gated transformer network models trained with OCT data may be used to identify VF worsening. After further validation, implementing such models in clinical practice may allow us to track functional worsening of glaucoma with less onerous structural testing. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Spin-Polarized Photoluminescence in Au25 (SC8 H9 )18 Monolayer-Protected Clusters.

Here, the observation of spin-polarized emission for the Au25 (SC8 H9 )18 monolayer-protected cluster (MPC) is reported. Variable-temperature variable-field magnetic circular photoluminescence (VTV H ⇀ -MCPL) measurements are combined with VT-PL spectroscopy to provide state-resolved characterization of the transient electronic structure and spin-polarized electron-hole recombination dynamics of Au25 (SC8 H9 )18 . Through analysis of VTV H ⇀ -MCPL measurements, a low energy (1.64 eV) emission peak is assigned to intraband relaxation between core-metal-localized superatom-D to -P orbitals. Two higher energy interband components (1.78 eV, 1.94 eV) are assigned to relaxation from superatom-D orbitals to states localized to the inorganic semirings. For both intraband superatom-based or interband relaxation mechanisms, the extent of spin-polarization, quantified as the degree of circular polarization (DOCP), is determined by state-specific electron-vibration coupling strengths and energy separations of bright and dark electronic fine-structure levels. At low temperatures (<60 K), metal-metal superatom-based intraband transitions dominate the global PL emission. At higher temperatures (>60 K), interband ligand-based emission is dominant. In the low-temperature PL regime, increased sample temperature results in larger global PL intensity. In the high-temperature regime, increased temperature quenches interband radiative recombination. The relative intensity for each PL mechanism is discussed in terms of state-specific electronic-vibrational coupling strengths and related to the total angular momentum, quantified by Landé g-factors.

State-Resolved Metal Nanoparticle Dynamics Viewed through the Combined Lenses of Ultrafast and Magneto-optical Spectroscopies.

Electronic carrier dynamics play pivotal roles in the functional properties of nanomaterials. For colloidal metals, the mechanisms and influences of these dynamics are structure dependent. The coherent carrier dynamics of collective plasmon modes for nanoparticles (approximately 2 nm and larger) determine optical amplification factors that are important to applied spectroscopy techniques. In the nanocluster domain (sub-2 nm), carrier coupling to vibrational modes affects photoluminescence yields. The performance of photocatalytic materials featuring both nanoparticles and nanoclusters also depends on the relaxation dynamics of nonequilibrium charge carriers. The challenges for developing comprehensive descriptions of carrier dynamics spanning both domains are multifold. Plasmon coherences are short-lived, persisting for only tens of femtoseconds. Nanoclusters exhibit discrete carrier dynamics that can persist for microseconds in some cases. On this time scale, many state-dependent processes, including vibrational relaxation, charge transfer, and spin conversion, affect carrier dynamics in ways that are nonscalable but, rather, structure specific. Hence, state-resolved spectroscopy methods are needed for understanding carrier dynamics in the nanocluster domain. Based on these considerations, a detailed understanding of structure-dependent carrier dynamics across length scales requires an appropriate combination of spectroscopic methods. Plasmon mode-specific dynamics can be obtained through ultrafast correlated light and electron microscopy (UCLEM), which pairs interferometric nonlinear optical (INLO) with electron imaging methods. INLO yields nanostructure spectral resonance responses, which capture the system's homogeneous line width and coherence dynamics. State-resolved nanocluster dynamics can be obtained by pairing ultrafast with magnetic-optical spectroscopy methods. In particular, variable-temperature variable-field (VTVH) spectroscopies allow quantification of transient, excited states, providing quantification of important parameters such as spin and orbital angular momenta as well as the energy gaps that separate electronic fine structure states. Ultrafast two-dimensional electronic spectroscopy (2DES) can be used to understand how these details influence state-to-state carrier dynamics. In combination, VTVH and 2DES methods can provide chemists with detailed information regarding the structure-dependent and state-specific flow of energy through metal nanoclusters. In this Account, we highlight recent advances toward understanding structure-dependent carrier dynamics for metals spanning the sub-nanometer to tens of nanometers length scale. We demonstrate the use of UCLEM methods for arresting interband scattering effects. For sub-nanometer thiol-protected nanoclusters, we discuss the effectiveness of VTVH for distinguishing state-specific radiative recombination originating from a gold core versus organometallic protecting layers. This state specificity is refined further using femtosecond 2DES and two-color methods to isolate so-called superatom state dynamics and vibrationally mediated spin-conversion and emission processes. Finally, we discuss prospects for merging VTVH and 2DES methods into a single platform.

Superatom spin-state dynamics of structurally precise metal monolayer-protected clusters (MPCs).

Electronic spin-state dynamics were studied for a series of Au25(SC8H9)18 q and Au24Pd(SC8H9)18 monolayer-protected clusters (MPCs) prepared in a series of oxidation states, q, including q = -1, 0, +1. These clusters were chosen for study because Au25(SC8H9)18 -1 is a closed-shell superatomic cluster, but Au25(SC8H9)18 0 is an open-shell (7-electron) system; Au25(SC8H9)18 +1 and PdAu24(SC8H9)18 0 are isoelectronic (6-electron) closed-shell systems. Carrier dynamics for electronic fine structure spin states were isolated using femtosecond time-resolved circularly polarized transient-absorption spectroscopy (fs-CPTA). Excitation energies of 1.82 eV and 1.97 eV were chosen for these measurements on Au25(SC8H9)18 0 in order to achieve resonance matching with electronic fine structure transitions within the superatomic P- and D-orbital manifolds; 1.82-eV excited an unpaired Pz electron to D states, whereas 1.97-eV was resonant with transitions between filled Px and Py subshells and higher-energy D orbitals. fs-CPTA measurements revealed multiple spin-polarized transient signals for neutral (open shell) Au25(SC8H9)18, following 1.82-eV excitation, which persisted for several picoseconds; time constants of 5.03 ± 0.38 ps and 2.36 ± 0.59 ps were measured using 2.43 and 2.14 eV probes, respectively. Polarization-dependent fs-CPTA measurements of PdAu24(SC8H9)18 clusters exhibit no spin-conversion dynamics, similar to the isoelectronic Au25(SC8H9)18 +1 counterpart. These observations of cluster-specific dynamics resulted from spin-polarized superatom P to D excitation, via an unpaired Pz electron of the open-shell seven-electron Au25(SC8H9)18 MPC. These results suggest that MPCs may serve as structurally well-defined prototypes for understanding spin and quantum state dynamics in nanoscale metal systems.

Variable-temperature variable-field magnetic circular photoluminescence (VTVH-MCPL) spectroscopy for electronic-structure determination in nanoscale chemical systems.

In this Letter, we describe variable-temperature variable-field magnetic circular photoluminescence (VTVH-MCPL) spectroscopy as a complementary technique to absorption-based magnetic circular dichroism. A paramagnetic model system, Au25(SC8H9)18, is chosen to demonstrate the information content that is obtained from VTVH-MCPL. Specifically, the methods and analyses for the determination of electronic Landé g-factors, zero-field energy splittings, and relative A-, B-, and C-term contributions to the MCPL response are detailed. The determination of these system properties from photoluminescence data suggests the feasibility of point-source-based super-resolution magneto-optical microscopy.

Chiral nanostructures studied using polarization-dependent NOLES imaging.

The Nonlinear Optical Localization using Electromagnetic Surface fields (NOLES) imaging technique was used to generate optical images in which the position of a chiral object could be determined with nanometer precision. Asymmetric gold bowtie nanostructures were used as a model system with 2D chirality. The bowties functioned as a chiral nonlinear medium that converted the fundamental of a Ti:sapphire laser to its second harmonic frequency. The bowties consisted of two lithographically prepared equilateral triangles (base = 75 nm, height = 85 nm, thickness = 25 nm) separated by a 20 nm gap. Asymmetric bowties were formed by lateral displacement of one triangle by 10 nm, yielding C2 point group symmetry. The chirality of the bowtie nanostructures was confirmed via nonzero second-harmonic generation circular dichroism (SHG-CDR) ratios, which came from single-particle SHG measurements. The SHG-CDR ratios were validated using numerical finite difference time domain simulations that quantified the relative magnitudes of gap-localized electromagnetic fields at the harmonic frequency resulting from excitation by left and right circularly (LCP and RCP) and linearly polarized fundamental waves. The relative electric dipolar and magnetic dipolar contributions to the SHG responses were determined using single-particle continuous polarization variation (CPV) SHG measurements. The spatial localization precision obtainable for individual chiral nanostructures was determined by statistical analysis of the SHG image point spread function. Our results demonstrated that both the chiral image contrast, which resulted from LCP and RCP excitation, and the corresponding localization precision was dependent upon the relative magnetic dipole/electric dipole ratio (G/F). A localization precision of 1.13 ± 0.13 nm and left-to-right image enhancements of 400% were obtained for bowties with the highest G/F ratios using 5 s frame exposure times. The polarization dependence and magnetic dipole amplification confirmed here demonstrate that the NOLES imaging technique is a powerful method for studying chiral specimens with high spatial precision.

The Impact of Social Vulnerability on Structural and Functional Glaucoma Severity, Worsening, and Variability.

PURPOSE: To determine the associations between social vulnerability index (SVI) and baseline severity, worsening, and variability of glaucoma, as assessed by visual field (VF) and OCT. DESIGN: Retrospective longitudinal cohort study. PARTICIPANTS: Adults with glaucoma or glaucoma suspect status in 1 or both eyes. Visual fields were derived from 7897 eyes from 4482 patients, while OCTs were derived from 6271 eyes from 3976 patients. All eyes had a minimum of 5 tests over follow-up using either the Humphrey Field Analyzer or the Cirrus HD-OCT. METHODS: Social vulnerability index, which measures neighborhood-level environmental factors, was linked to patients' addresses at the census tract level. Rates of change in mean deviation (MD) and retinal nerve fiber layer (RNFL) thickness were computed using linear regression. The slope of the regression line was used to assess worsening, while the standard deviation of residuals was used as a measure of variability. Multivariable linear mixed-effects models were used to investigate the impact of SVI on baseline, worsening, and variability in both MD and RNFL. We further explored the interaction effect of mean intraocular pressure (IOP) and SVI on worsening in MD and RNFL. MAIN OUTCOME MEASURES: Glaucoma severity defined based on baseline MD and RNFL thickness. Worsening defined as MD and RNFL slope. Variability defined as the standard deviation of the residuals obtained from MD and RNFL slopes. RESULTS: Increased (worse) SVI was significantly associated with worse baseline MD (ß = -1.07 dB, 95% confidence interval [CI]: [-1.54, -0.60]), thicker baseline RNFL (ß = 2.46 µm, 95% CI: [0.75, 4.17]), greater rates of RNFL loss (ß = -0.12 µm, 95% CI: [-0.23, -0.02]), and greater VF variability (ß = 0.16 dB, 95% CI: [0.07, 0.24]). Having worse SVI was associated with worse RNFL loss with increases in IOP (ßinteraction = -0.07, 95% CI: [-0.12, -0.02]). CONCLUSIONS: Increased SVI score is associated with worse functional (VF) loss at baseline, higher rates of structural (OCT) worsening over time, higher VF variability, and a greater effect of IOP on RNFL loss. Further studies are needed to enhance our understanding of these relationships and establish their cause. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Deep learning-based identification of eyes at risk for glaucoma surgery.

To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retrospective study. 4898 unique eyes from 4038 adult glaucoma or glaucoma-suspect patients who underwent surgery for uncontrolled glaucoma (trabeculectomy, tube shunt, xen, or diode surgery) between 2013 and 2021, or did not undergo glaucoma surgery but had 3 or more ophthalmology visits. We constructed a DLM to predict the occurrence of glaucoma surgery within various time horizons from a baseline visit. Model inputs included spatially oriented visual field (VF) and optical coherence tomography (OCT) data as well as clinical and demographic features. Separate DLMs with the same architecture were trained to predict the occurrence of surgery within 3 months, within 3-6 months, within 6 months-1 year, within 1-2 years, within 2-3 years, within 3-4 years, and within 4-5 years from the baseline visit. Included eyes were randomly split into 60%, 20%, and 20% for training, validation, and testing. DLM performance was measured using area under the receiver operating characteristic curve (AUC) and precision-recall curve (PRC). Shapley additive explanations (SHAP) were utilized to assess the importance of different features. Model prediction of surgery for uncontrolled glaucoma within 3 months had the best AUC of 0.92 (95% CI 0.88, 0.96). DLMs achieved clinically useful AUC values (> 0.8) for all models that predicted the occurrence of surgery within 3 years. According to SHAP analysis, all 7 models placed intraocular pressure (IOP) within the five most important features in predicting the occurrence of glaucoma surgery. Mean deviation (MD) and average retinal nerve fiber layer (RNFL) thickness were listed among the top 5 most important features by 6 of the 7 models. DLMs can successfully identify eyes requiring surgery for uncontrolled glaucoma within specific time horizons. Predictive performance decreases as the time horizon for forecasting surgery increases. Implementing prediction models in a clinical setting may help identify patients that should be referred to a glaucoma specialist for surgical evaluation.

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory.

Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.

Detecting Visual Field Worsening From Optic Nerve Head and Macular Optical Coherence Tomography Thickness Measurements.

Purpose: Compare the use of optic disc and macular optical coherence tomography measurements to predict glaucomatous visual field (VF) worsening. Methods: Machine learning and statistical models were trained on 924 eyes (924 patients) with circumpapillary retinal nerve fiber layer (cp-RNFL) or ganglion cell inner plexiform layer (GC-IPL) thickness measurements. The probability of 24-2 VF worsening was predicted using both trend-based and event-based progression definitions of VF worsening. Additionally, the cp-RNFL and GC-IPL predictions were combined to produce a combined prediction. A held-out test set of 617 eyes was used to calculate the area under the curve (AUC) to compare cp-RNFL, GC-IPL, and combined predictions. Results: The AUCs for cp-RNFL, GC-IPL, and combined predictions with the statistical and machine learning models were 0.72, 0.69, 0.73, and 0.78, 0.75, 0.81, respectively, when using trend-based analysis as ground truth. The differences in performance between the cp-RNFL, GC-IPL, and combined predictions were not statistically significant. AUCs were highest in glaucoma suspects using cp-RNFL predictions and highest in moderate/advanced glaucoma using GC-IPL predictions. The AUCs for the statistical and machine learning models were 0.63, 0.68, 0.69, and 0.72, 0.69, 0.73, respectively, when using event-based analysis. AUCs decreased with increasing disease severity for all predictions. Conclusions: cp-RNFL and GC-IPL similarly predicted VF worsening overall, but cp-RNFL performed best in early glaucoma stages and GC-IPL in later stages. Combining both did not enhance detection significantly. Translational Relevance: cp-RNFL best predicted trend-based 24-2 VF progression in early-stage disease, while GC-IPL best predicted progression in late-stage disease. Combining both features led to minimal improvement in predicting progression.

Studies of DNA breathing in exciton-coupled (iCy3)2 dimer-labeled DNA constructs by polarization-sweep single-molecule fluorescence (PS-SMF) microscopy.

Local fluctuations of the sugar-phosphate backbones and bases of DNA (a form of DNA 'breathing') play a central role in the assembly of protein-DNA complexes. We present a single-molecule fluorescence method to sensitively measure the local conformational fluctuations of exciton-coupled cyanine [(iCy3)2] dimer-labeled DNA fork constructs in which the dimer probes are placed at varying positions relative to the DNA fork junction. These systems exhibit spectroscopic signals that are sensitive to the local conformations adopted by the sugar-phosphate backbones and bases immediately surrounding the dimer probe label positions. The (iCy3)2 dimer has one symmetric (+) and one anti-symmetric (-) exciton with respective transition dipole moments oriented perpendicular to one another. We excite single molecule samples using a continuous-wave, linearly polarized laser with its polarization direction rotated at a frequency of 1 MHz. The ensuing fluorescence signal is modulated as the laser polarization alternately excites the symmetric and anti-symmetric excitons of the (iCy3)2 dimer probe. Phase-sensitive detection of the signal at the photon-counting level provides information about the distribution of local conformations and conformational dynamics. We analyze our data using a kinetic network model, which we use to parametrize the free energy surface of the system. In addition to observing DNA breathing at and near ss-dsDNA junctions, the approach can be used to study the effects of proteins that bind and function at these sites.

Opportunities for Improving Glaucoma Clinical Trials via Deep Learning-Based Identification of Patients with Low Visual Field Variability.

PURPOSE: Develop and evaluate the performance of a deep learning model (DLM) that forecasts eyes with low future visual field (VF) variability, and study the impact of using this DLM on sample size requirements for neuroprotective trials. DESIGN: Retrospective cohort and simulation study. METHODS: We included 1 eye per patient with baseline reliable VFs, OCT, clinical measures (demographics, intraocular pressure, and visual acuity), and 5 subsequent reliable VFs to forecast VF variability using DLMs and perform sample size estimates. We estimated sample size for 3 groups of eyes: all eyes (AE), low variability eyes (LVE: the subset of AE with a standard deviation of mean deviation [MD] slope residuals in the bottom 25th percentile), and DLM-predicted low variability eyes (DLPE: the subset of AE predicted to be low variability by the DLM). Deep learning models using only baseline VF/OCT/clinical data as input (DLM1), or also using a second VF (DLM2) were constructed to predict low VF variability (DLPE1 and DLPE2, respectively). Data were split 60/10/30 into train/val/test. Clinical trial simulations were performed only on the test set. We estimated the sample size necessary to detect treatment effects of 20% to 50% in MD slope with 80% power. Power was defined as the percentage of simulated clinical trials where the MD slope was significantly worse from the control. Clinical trials were simulated with visits every 3 months with a total of 10 visits. RESULTS: A total of 2817 eyes were included in the analysis. Deep learning models 1 and 2 achieved an area under the receiver operating characteristic curve of 0.73 (95% confidence interval [CI]: 0.68, 0.76) and 0.82 (95% CI: 0.78, 0.85) in forecasting low VF variability. When compared with including AE, using DLPE1 and DLPE2 reduced sample size to achieve 80% power by 30% and 38% for 30% treatment effect, and 31% and 38% for 50% treatment effect. CONCLUSIONS: Deep learning models can forecast eyes with low VF variability using data from a single baseline clinical visit. This can reduce sample size requirements, and potentially reduce the burden of future glaucoma clinical trials. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Spectroscopic approaches for studies of site-specific DNA base and backbone 'breathing' using exciton-coupled dimer-labeled DNA.

DNA regulation and repair processes require direct interactions between proteins and DNA at specific sites. Local fluctuations of the sugar-phosphate backbones and bases of DNA (a form of DNA 'breathing') play a central role in such processes. Here we review the development and application of novel spectroscopic methods and analyses - both at the ensemble and single-molecule levels - to study structural and dynamic properties of exciton-coupled cyanine and fluorescent nucleobase analogue dimer-labeled DNA constructs at key positions involved in protein-DNA complex assembly and function. The exciton-coupled dimer probes act as 'sensors' of the local conformations adopted by the sugar-phosphate backbones and bases immediately surrounding the dimer probes. These methods can be used to study the mechanisms of protein binding and function at these sites.

Studies of DNA 'Breathing' by Polarization-Sweep Single-Molecule Fluorescence Microscopy of Exciton-Coupled (iCy3)2 Dimer-Labeled DNA Fork Constructs.

Local fluctuations of the sugar-phosphate backbones and bases of DNA (often called DNA 'breathing') play a variety of critical roles in controlling the functional interactions of the DNA genome with the protein complexes that regulate it. Here, we present a single-molecule fluorescence method that we have used to measure and characterize such conformational fluctuations at and near biologically important positions in model DNA replication fork constructs labeled with exciton-coupled cyanine [(iCy3)2] dimer probes. Previous work has shown that the constructs that we tested here exhibit a broad range of spectral properties at the ensemble level, and these differences can be structurally and dynamically interpreted using our present methodology at the single-molecule level. The (iCy3)2 dimer has one symmetric (+) and one antisymmetric (-) exciton, with the respective transition dipole moments oriented perpendicular to one another. We excite single-molecule samples using a continuous-wave linearly polarized laser, with the polarization direction continuously rotated at the frequency of 1 MHz. The ensuing fluorescence signal is modulated as the laser polarization alternately excites the symmetric and antisymmetric excitons of the (iCy3)2 dimer probe. Phase-sensitive detection of the modulated signal provides information about the distribution of local conformations and the conformational interconversion dynamics of the (iCy3)2 probe. We find that at most construct positions that we examined, the (iCy3)2 dimer-labeled DNA fork constructs can adopt four topologically distinct conformational macrostates. These results suggest that in addition to observing DNA breathing at and near ss-dsDNA junctions, our new methodology should be useful to determine which of these pre-existing macrostates are recognized by, bind to, and are stabilized by various genome-regulatory proteins.

A deep learning model incorporating spatial and temporal information successfully detects visual field worsening using a consensus based approach.

Glaucoma is a leading cause of irreversible blindness, and its worsening is most often monitored with visual field (VF) testing. Deep learning models (DLM) may help identify VF worsening consistently and reproducibly. In this study, we developed and investigated the performance of a DLM on a large population of glaucoma patients. We included 5099 patients (8705 eyes) seen at one institute from June 1990 to June 2020 that had VF testing as well as clinician assessment of VF worsening. Since there is no gold standard to identify VF worsening, we used a consensus of six commonly used algorithmic methods which include global regressions as well as point-wise change in the VFs. We used the consensus decision as a reference standard to train/test the DLM and evaluate clinician performance. 80%, 10%, and 10% of patients were included in training, validation, and test sets, respectively. Of the 873 eyes in the test set, 309 [60.6%] were from females and the median age was 62.4; (IQR 54.8-68.9). The DLM achieved an AUC of 0.94 (95% CI 0.93-0.99). Even after removing the 6 most recent VFs, providing fewer data points to the model, the DLM successfully identified worsening with an AUC of 0.78 (95% CI 0.72-0.84). Clinician assessment of worsening (based on documentation from the health record at the time of the final VF in each eye) had an AUC of 0.64 (95% CI 0.63-0.66). Both the DLM and clinician performed worse when the initial disease was more severe. This data shows that a DLM trained on a consensus of methods to define worsening successfully identified VF worsening and could help guide clinicians during routine clinical care.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.

PURPOSE: To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data. DESIGN: A retrospective cohort study. SUBJECTS: In total, 4536 eyes from 2962 patients. Overall, 263 (5.80%) eyes underwent rapid VF worsening (mean deviation slope less than -1 dB/year across all VFs). METHODS: We included eyes that met the following criteria: (1) followed for glaucoma or suspect status; (2) had at least 5 longitudinal reliable VFs (VF1, VF2, VF3, VF4, and VF5); and (3) had 1 reliable baseline OCT scan (OCT1) and 1 set of baseline clinical measurements (clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict the eye's risk of rapid VF worsening across the 5 VFs. We compared the performance of models with differing inputs by computing area under the curve (AUC) in the test set. Specifically, we trained models with the following inputs: (1) model V: VF1; (2) VC: VF1+ Clinical1; (3) VO: VF1+ OCT1; (4) VOC: VF1+ Clinical1+ OCT1; (5) V2: VF1 + VF2; (6) V2OC: VF1 + VF2 + Clinical1 + OCT1; (7) V3: VF1 + VF2 + VF3; and (8) V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1. MAIN OUTCOME MEASURES: The AUC of DLMs when forecasting rapidly worsening eyes. RESULTS: Model V3OC best forecasted rapid worsening with an AUC (95% confidence interval [CI]) of 0.87 (0.77-0.97). Remaining models in descending order of performance and their respective AUC (95% CI) were as follows: (1) model V3 (0.84 [0.74-0.95]), (2) model V2OC (0.81 [0.70-0.92]), (3) model V2 (0.81 [0.70-0.82]), (4) model VOC (0.77 [0.65-0.88]), (5) model VO (0.75 [0.64-0.88]), (6) model VC (0.75 [0.63-0.87]), and (7) model V (0.74 [0.62-0.86]). CONCLUSIONS: Deep learning models can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Impact of Achieving Target Intraocular Pressure on Glaucomatous Retinal Nerve Fiber Layer Thinning in a Treated Clinical Population.

PURPOSE: To estimate the effect of being below and above the clinician-set target intraocular pressure (IOP) on rates of glaucomatous retinal nerve fiber layer (RNFL) thinning in a treated real-world clinical population. DESIGN: Retrospective cohort study. METHODS: A total of 3256 eyes (1923 patients) with ≥5 reliable optical coherence tomography scans and 1 baseline visual field test were included. Linear mixed-effects modeling estimated the effects of the primary independent variables (mean target difference [measured IOP - target IOP] and mean IOP, mm Hg) on the primary dependent variable (RNFL slope, µm/y) while accounting for additional confounding variables (age, biological sex, race, baseline RNFL, baseline pachymetry, and disease severity). A spline term accounted for differential effects when above (target difference >0 mm Hg) and below (target difference ≤0 mm Hg) target pressure. RESULTS: Eyes below and above target had significantly different mean RNFL slopes (-0.44 vs -0.71 µm/y, P < .001). Each 1 mm Hg increase above target had a 0.143 µm/y faster rate of RNFL thinning (P < .001). Separating by disease severity, suspect, mild, moderate, and advanced glaucoma had 0.135 (P = .002), 0.116 (P = .009), 0.203 (P = .02), and 0.65 (P = .22) µm/y faster rates of RNFL thinning per 1 mm Hg increase, respectively. CONCLUSIONS: Being above the clinician-set target pressure is associated with more rapid RNFL thinning in suspect, mild, and moderate glaucoma. Faster rates of thinning were also present in advanced glaucoma, but statistical significance was limited by the lower sample size of eyes above target and the optical coherence tomography floor effect.