Effect of frequency compression on fricative perception between normal-hearing English and Mandarin listeners.

High-frequency speech information is susceptible to inaccurate perception in even mild to moderate forms of hearing loss. Some hearing aids employ frequency-lowering methods such as nonlinear frequency compression (NFC) to help hearing-impaired individuals access high-frequency speech information in more accessible lower-frequency regions. As such techniques cause significant spectral distortion, tests such as the S-Sh Confusion Test help optimize NFC settings to provide high-frequency audibility with the least distortion. Such tests have been traditionally based on speech contrasts pertinent to English. Here, the effects of NFC processing on fricative perception between English and Mandarin listeners are assessed. Small but significant differences in fricative discrimination were observed between the groups. The study demonstrates possible need for language-specific clinical fitting procedures for NFC.

Prediction of consumers refill frequency of LPG: A study using explainable machine learning.

Launched in 2016, the PMUY Programme of the Government of India aimed to provide 8 crore LPG connections to women in rural households over four years. After acquiring a new connection, some households appeared uninterested in ordering subsequent subsidized LPG refills, impacting programme's sustainability, and targeting strategy. We propose a prediction model using "Explainable Machine Learning" to anticipate the beneficiaries' refill frequency with a view to improving LPG-refills and social targeting. In this paper, we suggest an enhanced stacked SVM (ISS) model for classification, which is contrasted with state-of-art ML models: Random Forest (RF), SVM-RBF, Naive Bayes (NB), and Decision Tree (C5.0). Some of the performance matrices that are used to evaluate the models include accuracy, sensitivity, specificity, Cohen's Kappa statistics, Receiver Operating Characteristic curve (ROC), and area under the curve (AUC). The proposed approach, which was validated with 10-fold cross validation, produced the best overall accuracies for data splits of 50-50, 66-34, and 80-20. The "Explainable AI (XAI)" model has also been used to describe how models and features interact, and to discuss the importance of features and their contributions to prediction. The recommended XAI will aid in efficient "beneficiary targeting" and "policy interventions".

Retrospective assessment of accuracy of nine intraocular lens power calculation formulae in eyes with axial myopia.

PURPOSE: To compare the accuracy of nine conventional and newer-generation formulae in calculating intraocular lens power in eyes with axial myopia. SETTING: Tertiary eye care center, Bengaluru, India. DESIGN: Retrospective cross-sectional, comparative study conducted in India. METHODS: Patients undergoing uneventful phacoemulsification in eyes with axial length >26 mm were included. Preoperative biometry was done using Lenstar LS 900 (Haag-Streit AG, Switzerland). Single eye of patients undergoing bilateral implantation was randomly selected. Optimized lens constants were used to calculate the predicted postoperative refraction of each formula, which was then compared with the actual refractive outcomes to give the prediction errors, following which subgroup analysis was performed. The Kane formula, Barrett universal II, Emmetropia Verifying Optical (EVO) 2.0, Hill Radial Basis Function (Hill RBF) 3.0, Olsen formula, along with Wang Koch-adjusted four formulae, that is, Sanders Retzlaff Kraff/Theoretical (SRK/T), Holladay 1, Haigis, and Hoffer Q formula, were compared for intraocular lens power calculations. RESULTS: One hundred and sixty-five eyes that fulfilled all the inclusion criteria were studied. Hill RBF 3.0 had the lowest mean and median absolute prediction errors (0.355 and 0.275, respectively) compared to all formulas. In subgroup analysis (26-28, >28-30, and >30 mm), significant difference was seen only in extremely long eyes (>30 mm). The Hill RBF 3.0 formula generated the maximum percentage of eyes with refractive errors within ±0.25, ±0.5, ±0.75, and ±1 D (46%, 76.2%, 89.9%, and 95.8%, respectively). CONCLUSION: This is the first study evaluating all the formulas exclusively in the myopic eyes. Hill RBF 3 was found to be superior in accuracy to all other formulas.

Non-catalytic role of phosphoinositide 3-kinase in mesenchymal cell migration through non-canonical induction of p85ß/AP2-mediated endocytosis.

Class IA phosphoinositide 3-kinase (PI3K) galvanizes fundamental cellular processes such as migration, proliferation, and differentiation. To enable these multifaceted roles, the catalytic subunit p110 utilizes the multi-domain, regulatory subunit p85 through its inter SH2 domain (iSH2). In cell migration, its product PI(3,4,5)P3 generates locomotive activity. While non-catalytic roles are also implicated, underlying mechanisms and their relationship to PI(3,4,5)P3 signaling remain elusive. Here, we report that a disordered region of iSH2 contains AP2 binding motifs which can trigger clathrin and dynamin-mediated endocytosis independent of PI3K catalytic activity. The AP2 binding motif mutants of p85 aberrantly accumulate at focal adhesions and increase both velocity and persistency in fibroblast migration. We thus propose the dual functionality of PI3K in the control of cell motility, catalytic and non-catalytic, arising distinctly from juxtaposed regions within iSH2.

Generative models improve fairness of medical classifiers under distribution shifts.

Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and 'labeling' by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.

Insights into atypical segmental layer thicknesses and phase retardation in thick corneas using ultrahigh-resolution polarization-sensitive optical coherence tomography.

BACKGROUND: Accurately assessing corneal structural status is challenging when thickness deviates from the average. Polarization-sensitive optical coherence tomography (PS-OCT) measures tissue-specific polarization changes, providing additional contrast for accurate segmentations and aids in phase retardation (PR) measurements. Previous studies have shown PR's effectiveness in identifying sub-clinical keratoconus (KC) in asymmetric cases. Thus, this study aims to assess PR distribution in thick corneas with and without KC. METHODS: In this retrospective and cross-sectional study, 45 thick corneas from 30 Asian-Indian subjects, categorized into healthy (n = 26) and KC (n = 19) groups were analyzed. All eyes underwent standard clinical evaluations, tomographic assessments, and corneal biomechanics measurements. PR and individual layer thicknesses were measured using custom-designed ultrahigh-resolution PS-OCT. PR en-face maps were generated. Individual layer thicknesses and PR analysis was conducted across multiple zones, extending up to 8-10 mm in diameter. All eyes in the study had not undergone interventions, received topical medications, or had previous corneal disease history. RESULTS: Significant differences were found in spherical and cylindrical powers, keratometry, pachymetry, and biomechanical indices (all P < 0.01). Thickness profiles from PS-OCT showed significant differences in the 4-8 mm zones only. Bowman's layer thickness significantly differed only in the central 2 mm zone (P = 0.02). The median PR values showed marginal differences in the central 2 mm zone (P = 0.0565). Additionally, there were significant differences observed in the 2-4 mm and 4-6 mm zones (P = 0.0274 and P = 0.0456, respectively). KC eyes exhibited an atypical PR distribution and corneal thinning, while normal eyes maintained a uniform Bowman's layer thickness and PR maps with larger areas of higher PR. CONCLUSION: The study revealed distinctive PR distribution in thick corneas among healthy and KC groups. Using an ultrahigh-resolution PS-OCT the significance of Bowman's layer thickness in these groups was also emphasized. The study offered potential improvements in clinical diagnostics by enhancing our understanding of corneal structure and its altered function.

Biomechanical corneal changes induced by different flap thickness created by femtosecond laser

OBJECTIVE: To evaluate the impact of the creation of corneal flaps at different thicknesses on the biomechanical properties of swine corneas. METHOD: Twelve swine eyes were obtained to form two groups: 100 μm flap thickness and 300 μm flap thickness. Each eye was submitted to the following examinations: raster topography to investigate corneal curvature alterations, ocular response analyzer to investigate corneal hysteresis change, optical coherence tomography to measure central corneal and flap thickness and sonic wave propagation velocity as a measure of stiffness, before and immediately after flap creation. After flap amputation, surface wave velocity measurements were repeated. RESULTS: Measured flap thicknesses were statistically different for thin and thick flap groups, with an average of 108.5 + 6.9 and 307.8 + 11.5 μm respectively. Hysteresis and corneal resistance factor did not change significantly after flap creation in the thin flap group. With thicker flaps, both parameters decreased significantly from 8.0 +1.0 to 5.1 +1.5 mmHg and from 8.2 + 1.6 to 4.1 +2.5 mmHg respectively. Simulated keratometry values increased in the thick flap group (from 39.5 + 1 D to 45.9+1.2 D) after flap creation but not in the thin flap group (from 40.6 + 0.6 D to 41.4+ 1.0 D). Regarding surface wave velocity analysis, the surgical procedures induced statistically lower results in some positions. CONCLUSION: In the experimental conditions established by this model, thicker flaps presented a greater biomechanical impact on the cornea.