Search

SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.

In medical imaging, deep learning-based solutions have achieved state-of-the-art performance. However, reliability restricts the integration of deep learning into practical medical workflows since conventional deep learning frameworks cannot quantitatively assess model uncertainty. In this work, we propose to address this shortcoming by utilizing a Bayesian deep network capable of estimating uncertainty to assess oral cancer image classification reliability. We evaluate the model using a large intraoral cheek mucosa image dataset captured using our customized device from high-risk population to show that meaningful uncertainty information can be produced. In addition, our experiments show improved accuracy by uncertainty-informed referral. The accuracy of retained data reaches roughly 90% when referring either 10% of all cases or referring cases whose uncertainty value is greater than 0.3. The performance can be further improved by referring more patients. The experiments show the model is capable of identifying difficult cases needing further inspection.

SignificanceEarly detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification.AimTo reduce the class bias caused by data imbalance.ApproachWe collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings.ResultsBy applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications.ConclusionsExperimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.

Global use of energy-inefficient mechanical vapor-compression air conditioning (AC) is increasing dramatically for home cooling. Direct evaporative coolers (EC) offer substantial energy savings, and may provide a sustainable alternative to AC for homes in hot, dry climates. One drawback of ECs is the potential for infiltration of outdoor air pollution into homes. Prior studies on this topic are limited by small sample sizes and a lack of comparison homes. In this study, we used aerosol photometers to sample indoor and outdoor fine particulate matter (PM2.5) from 16 homes with AC and 14 homes with EC in Utah County, Utah (USA) between July 2022 and August 2023. We observed a significantly larger infiltration factor (Fin) of outdoor PM2.5 in EC vs. AC homes (0.39 vs. 0.12, p = 0.026) during summer. Fin significantly increased during a wildfire smoke event that occurred during the study. During the wildfire event, EC homes offered little to no protection from outdoor PM2.5 (Fin = 0.96, 95% confidence interval (CI) 0.85, 1.07), while AC homes offered significant protection (Fin = 0.23, 95% CI 0.15, 0.32). We recommend additional research focused on cooling pad design for the dual benefits of cooling efficiency and particle filtration. [ABSTRACT FROM AUTHOR]

The article features Commander Rick Husband of Texas and how he became a member of the American Astronaut Corps. Also cited are the tragic accident involving the Space Shuttle Columbia that led to the deaths of Husband, Willie McCool, David Brown, Laurel Clark, Ilan Ramon, Mike Anderson, and Kalpana Chawla, and how members of the astronaut corps are selected.

Two-mirror telescopes possess an agreeable tradeoff between performance and complexity that suits the needs of many scientific tasks, and recent developments in manufacturing technology have enabled unobscured two-mirror designs with freeform surfaces. We illustrate how structural aberration coefficients reveal the fundamental aberration characteristics of such systems and provide a summary table of general solutions that prescribe freeform surface shapes and tilts. Two unobscured design forms with remarkable aberration cancellation, inspired by well-known on-axis anastigmatic solutions, are presented. [ABSTRACT FROM AUTHOR]

There are many excellent descriptions of mirativity in various language grammars, and more recently there has been a flurry of research refining mirativity to include how languages linguistically realize surprise and related concepts such as 'unexpectedness' and 'new information'. However, there is currently no commonly accepted set of independently motivated diagnostics for testing mirativity that utilizes the best practices and first principles of semantic and pragmatic investigation. As such, the goal of this paper is to go back to basics and examine mirativity from the point of view of a field linguist who has been given the task of discovering and documenting how a speaker of a language linguistically expresses her surprise. This approach rests on two premises: first, mirativity is about surprise in the psychological sense. The second premise is that we take seriously that mirativity involves a kind of meaning, and that all languages have the linguistic resources for communicating mirative (surprise) meaning. The outcome is a set of tests that can be used to probe mirative meanings in any language. [ABSTRACT FROM AUTHOR]

Freeform imaging systems are a subject of current interest. To aid in the design of such systems, we introduce structural aberration coefficients for plane symmetric imaging with XY-polynomial freeform surfaces that may also be tilted. Further, we extend the coefficients to systems with an arbitrary number of components and specifically provide analytical equations for two-mirror systems. Distinguishing between system configuration and structural parameters enhances clarity, which we highlight by deriving new conditions for minimum root-mean-square (RMS) spot size and minimum RMS wavefront error in aplanatic two-mirror telescopes. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate how evidentials are used to reflect the unprepared mind of the speaker. Lexical evidentials are typically used by speakers to encode the kinds of evidence they have for making statements about states, events and actions they did not personally witness or were a part of. However, under certain conditions evidentials can be used to express the surprise of the speaker. This is known in the literature under the label mirativity. The empirical factors that condition the mirative use of an evidential are first determined, and then using an information and schema-theoretic analysis it is shown that mirativity is the linguistic reflex of a series of mental events involving the processing of new information, coupled with other contextual factors involving speaker knowledge. [ABSTRACT FROM AUTHOR]

To meet the increasing need for low-cost, compact imaging technology with cellular resolution, we have developed a microLED-based structured light sheet microscope for three-dimensional ex vivo and in vivo imaging of biological tissue in multiple modalities. All the illumination structure is generated directly at the microLED panel-which serves as the source-so light sheet scanning and modulation is completely digital, yielding a system that is simpler and less prone to error than previously reported methods. Volumetric images with optical sectioning are thus achieved in an inexpensive, compact form factor without any moving parts. We demonstrate the unique properties and general applicability of our technique by ex vivo imaging of porcine and murine tissue from the gastrointestinal tract, kidney, and brain., Competing Interests: R.L. is the founder of Light Research Inc., which didn’t support this research., (© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Human milk oligosaccharides (HMOs) are an unconjugated class of glycans that have been implicated for their role in promoting the healthy development of the brain-gut axes of infants. Production of HMOs is ever-changing and specifically tailored for each infant in response to various biological factors ( e.g. , cognitive development, diseases, or allergies). While every HMO consists of up to only five monosaccharides, their structures can be composed of many possible glycosidic linkage positions and corresponding α/β anomericities, linear or branched chains, and potential fucosylation/sialylation modifications, thus leading to a tremendous degree of isomeric heterogeneity. With limited availability of authentic standards for every putative HMO structure (estimated to be >200 total), new analytical methods are needed for their accurate characterization. Complete sequencing of the human milk glycome would enable a better understanding of their infant-specific biological roles and potentially lead to their widespread incorporation into infant formula. Herein, we explore the use of our high-resolution cyclic ion mobility spectrometry-mass spectrometry (cIMS-MS)-based platform for the separation of core disaccharide and trisaccharide isomer building blocks as a first step toward the sequencing of larger HMOs. By utilizing the flexible capabilities of the cIMS array, separation pathlengths were extended up to 40 m, thus enabling the resolution of all seven sets of sialylated, fucosylated galactosyllactose and lactosamine HMO building block isomers. Additionally, we assessed the utility of pre-/post-cIMS tandem mass spectrometry (MS/MS) and tandem cIMS (cIMS/cIMS) for the characterization of HMOs based on their diagnostic fragmentation patterns and mobility fingerprints. We anticipate that our presented cIMS-MS-based methodology will enable the better characterization of larger, unknown HMOs when incorporated into an overall workflow that also includes online liquid chromatography and enzymatic hydrolyses.

In this Letter, a microLED-based chromatic confocal microscope with a virtual confocal slit is proposed and demonstrated for three-dimensional (3D) profiling without any mechanical scanning or external light source. In the proposed method, a micro-scale light-emitting diode (microLED) panel works as a point source array to achieve lateral scanning. Axial scanning is realized through the chromatic aberration of an aspherical objective. A virtual pinhole technique is utilized to improve the contrast and precision of depth reconstruction. The system performance has been demonstrated with a diamond-turned copper sample and onion epidermis. The experimental results show that the microLED panel could be a potential solution for portable 3D confocal microscopy. Several considerations and prospects are proposed for future microLED requirements in confocal imaging.

For the first time in the history of the modern era smoking tobacco is not the most popular inhaled product. After a flurry of legislature, cannabis has come to the forefront of both medicinal and recreational drug use. A confluence of evidence suggests, however, that marijuana consumption may confer a particularly worrisome cardiovascular risk profile. While combustible forms still contain many of the same harmful chemicals found in tobacco such as aromatic amines, polycyclic aromatic hydrocarbons (PAHs), and nitric oxide, some in even greater concentrations than tobacco, edible preparations have been evidenced to cause more cardiovascular-related emergency department visits. Importantly, this body of evidence suggests that cannabis use may be placing a younger, healthier population at risk of suffering major cardiovascular accidents particularly in the moments immediately following consumption. With males in their 30's apparently bearing the brunt of this burden, cannabis consumption has been associated with an increase in ischemic stroke-a blockage in the cerebral or cerebellar vasculature-and almost a fivefold increase in myocardial infarction. THC containing compounds have also been linked to vascular complications ranging from mild plaques to total arterial occlusion resulting in claudication, rest pain, ischemic ulceration and gangrene-recently termed cannabis arteritis. While this research remains in a nascent stage, marijuana consumption seems to be predisposing a youthful, traditionally low health risk cohort to a variety of major adverse cardiovascular events., (Copyright © 2020 Elsevier Ltd. All rights reserved.)