ABSTRACT
Purpose: To assess the safety and efficacy of subthreshold micropulse laser (SML) photo-stimulation in the management of persistent subfoveal fluid (PSF) after surgery for rhegmatogenous retinal detachment (RRD). Methods: In this pilot study, 11 eyes of 11 patients (8 men, 3 women) with long-lasting (12-18 months) PSF after surgery for RRD were evaluated before and after photostimulation with subthreshold micropulse yellow laser. Ophthalmic examination included best-corrected visual acuity (BCVA), Amsler grid test, ophthalmoscopy, autofluorescence (AF), and optical coherence tomography (OCT) with measurement of central point foveal thickness (CPFT). Primary outcome was subfoveal fluid resolution and secondary outcome was BCVA improvement. Results: The mean CPFT and BCVA were, respectively, 436.8 ± 28.8 µm and 0.25 ± 0.1 µm decimal equivalent (DE) before photostimulation and 278 ± 54.4 µm and 0.57 ± 0.2 µm DE after photostimulation, a statistically significant difference (P < 0.001). Nine (81.8%) eyes showed improved BCVA, disappearance of macular detachment on ophthalmoscopy, reduced retinal pigment epithelium distress on AF, and restored macular profile with no neuroretinal alterations on OCT scans. Conclusion: Although PSF after RRD surgery is often a self-limiting disease, our results suggest that SML photostimulation may be effective and safe in patients with clinically significant long-lasting PSF. Larger case-control studies are necessary to confirm these results.
ABSTRACT
In the analysis of empirical signals, detecting correlations that capture genuine interactions between the elements of a complex system is a challenging task with applications across disciplines. Here, we analyze a global dataset of surface air temperature (SAT) with daily resolution. Hilbert analysis is used to obtain phase, instantaneous frequency, and amplitude information of SAT seasonal cycles in different geographical zones. The analysis of the phase dynamics reveals large regions with coherent seasonality. The analysis of the instantaneous frequencies uncovers clean wave patterns formed by alternating regions of negative and positive correlations. In contrast, the analysis of the amplitude dynamics uncovers wave patterns with additional large-scale structures. These structures are interpreted as due to the fact that the amplitude dynamics is affected by processes that act in long and short time scales, while the dynamics of the instantaneous frequency is mainly governed by fast processes. Therefore, Hilbert analysis allows us to disentangle climatic processes and to track planetary atmospheric waves. Our results are relevant for the analysis of complex oscillatory signals because they offer a general strategy for uncovering interactions that act at different time scales.
ABSTRACT
Uncovering meaningful regularities in complex oscillatory signals is a challenging problem with applications across a wide range of disciplines. Here, we present a novel approach, based on the Hilbert transform (HT). We show that temporal periodicity can be uncovered by averaging the signal in a moving window of appropriated length, τ, before applying the HT. As a case study, we investigate global gridded surface air temperature (SAT) datasets. By analyzing the variation of the mean rotation period, T¯, of the Hilbert phase as a function of τ, we discover well-defined plateaus. In many geographical regions, the plateau corresponds to the expected 1-yr solar cycle; however, in regions where SAT dynamics is highly irregular, the plateaus reveal non-trivial periodicities, which can be interpreted in terms of climatic phenomena such as El Niño. In these regions, we also find that Fourier analysis is unable to detect the periodicity that emerges when τ increases and gradually washes out SAT variability. The values of T¯ obtained for different τs are then given to a standard machine learning algorithm. The results demonstrate that these features are informative and constitute a new approach for SAT time series classification. To support these results, we analyze the synthetic time series generated with a simple model and confirm that our method extracts information that is fully consistent with our knowledge of the model that generates the data. Remarkably, the variation of T¯ with τ in the synthetic data is similar to that observed in the real SAT data. This suggests that our model contains the basic mechanisms underlying the unveiled periodicities. Our results demonstrate that Hilbert analysis combined with temporal averaging is a powerful new tool for discovering hidden temporal regularity in complex oscillatory signals.
