Quantitative melanoma diagnosis using spectral phasor analysis of hyperspectral imaging from label-free slices.

Introduction: Melanoma diagnosis traditionally relies on microscopic examination of hematoxylin and eosin (H&E) slides by dermatopathologists to search for specific architectural and cytological features. Unfortunately, no single molecular marker exists to reliably differentiate melanoma from benign lesions such as nevi. This study explored the potential of autofluorescent molecules within tissues to provide molecular fingerprints indicative of degenerated melanocytes in melanoma. Methods: Using hyperspectral imaging (HSI) and spectral phasor analysis, we investigated autofluorescence patterns in melanoma compared to intradermal nevi. Using UV excitation and a commercial spectral confocal microscope, we acquired label-free HSI data from the whole-slice samples. Results: Our findings revealed distinct spectral phasor distributions between melanoma and intradermal nevi, with melanoma displaying a broader phasor phase distribution, signifying a more heterogeneous autofluorescence pattern. Notably, longer wavelengths associated with larger phases correlated with regions identified as melanoma by expert dermatopathologists using H&E staining. Quantitative analysis of phase and modulation histograms within the phasor clusters of five melanomas (with Breslow thicknesses ranging from 0.5 mm to 6 mm) and five intradermal nevi consistently highlighted differences between the two groups. We further demonstrated the potential for the discrimination of several melanocytic lesions using center-of-mass comparisons of phase and modulation variables. Remarkably, modulation versus phase center of mass comparisons revealed strong statistical significance among the groups. Additionally, we identified the molecular endogenous markers responsible for tissue autofluorescence, including collagen, elastin, NADH, FAD, and melanin. In melanoma, autofluorescence is characterized by a higher phase contribution, indicating an increase in FAD and melanin in melanocyte nests. In contrast, NADH, elastin, and collagen dominate the autofluorescence of the nevus. Discussion: This work underscores the potential of autofluorescence and HSI-phasor analysis as valuable tools for quantifying tissue molecular fingerprints, thereby supporting more effective and quantitative melanoma diagnosis.

Decoupling between SARS-CoV-2 transmissibility and population mobility associated with increasing immunity from vaccination and infection in South America.

All South American countries from the Southern cone (Argentina, Brazil, Chile, Paraguay and Uruguay) experienced severe COVID-19 epidemic waves during early 2021 driven by the expansion of variants Gamma and Lambda, however, there was an improvement in different epidemic indicators since June 2021. To investigate the impact of national vaccination programs and natural infection on viral transmission in those South American countries, we analyzed the coupling between population mobility and the viral effective reproduction number [Formula: see text]. Our analyses reveal that population mobility was highly correlated with viral [Formula: see text] from January to May 2021 in all countries analyzed; but a clear decoupling occurred since May-June 2021, when the rate of viral spread started to be lower than expected from the levels of social interactions. These findings support that populations from the South American Southern cone probably achieved the conditional herd immunity threshold to contain the spread of regional SARS-CoV-2 variants circulating at that time.

Snapshot polarimetric imaging in multi-view microscopy.

Polarimetric imaging allows for the vector nature of optical information across a scene to be obtained, with recent applications ranging from remote sensing to microscopy. In polarimetric microscopy in particular, different polarization states are conventionally achieved under time-division multiplexing strategies and are mainly subject to static phenomena. In the present work, we propose a cost-effective technique for polarization sensing with the possibility of real-time imaging microscopy. By modifying a commercial camera and replacing the conventional lens with an optical system that integrates a microscope objective and a lenslet array with a polarization mask, linear Stokes parameters can be obtained in a snapshot. The proposed scheme is robust against misalignment and suitable for handling video sequences of microscopic samples. To the best of our knowledge, this is the first report on combining multi-view sensing and polarization imaging for applications to microscopy.

Kinesin 1 regulates cilia length through an interaction with the Bardet-Biedl syndrome related protein CCDC28B.

Bardet-Biedl syndrome (BBS) is a ciliopathy characterized by retinal degeneration, obesity, polydactyly, renal disease and mental retardation. CCDC28B is a BBS-associated protein that we have previously shown plays a role in cilia length regulation whereby its depletion results in shortened cilia both in cells and Danio rerio (zebrafish). At least part of that role is achieved by its interaction with the mTORC2 component SIN1, but the mechanistic details of this interaction and/or additional functions that CCDC28B might play in the context of cilia remain poorly understood. Here we uncover a novel interaction between CCDC28B and the kinesin 1 molecular motor that is relevant to cilia. CCDC28B interacts with kinesin light chain 1 (KLC1) and the heavy chain KIF5B. Notably, depletion of these kinesin 1 components results in abnormally elongated cilia. Furthermore, through genetic interaction studies we demonstrate that kinesin 1 regulates ciliogenesis through CCDC28B. We show that kinesin 1 regulates the subcellular distribution of CCDC28B, unexpectedly, inhibiting its nuclear accumulation, and a ccdc28b mutant missing a nuclear localization motif fails to rescue the phenotype in zebrafish morphant embryos. Therefore, we uncover a previously unknown role of kinesin 1 in cilia length regulation that relies on the BBS related protein CCDC28B.

Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression.

This work presents a wireless multichannel electroencephalogram (EEG) recording system featuring lossless and near-lossless compression of the digitized EEG signal. Two novel, low-complexity, efficient compression algorithms were developed and tested in a low-power platform. The algorithms were tested on six public EEG databases comparing favorably with the best compression rates reported up to date in the literature. In its lossless mode, the platform is capable of encoding and transmitting 59-channel EEG signals, sampled at 500 Hz and 16 bits per sample, at a current consumption of 337 A per channel; this comes with a guarantee that the decompressed signal is identical to the sampled one. The near-lossless mode allows for significant energy savings and/or higher throughputs in exchange for a small guaranteed maximum per-sample distortion in the recovered signal. Finally, we address the tradeoff between computation cost and transmission savings by evaluating three alternatives: sending raw data, or encoding with one of two compression algorithms that differ in complexity and compression performance. We observe that the higher the throughput (number of channels and sampling rate) the larger the benefits obtained from compression.

Efficient Sequential Compression of Multichannel Biomedical Signals.

This paper proposes lossless and near-lossless compression algorithms for multichannel biomedical signals. The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications. We make use of information theory and signal processing tools (such as universal coding, universal prediction, and fast online implementations of multivariate recursive least squares), combined with simple methods to exploit spatial as well as temporal redundancies typically present in biomedical signals. The algorithms are tested with publicly available electroencephalogram and electrocardiogram databases, surpassing in all cases the current state of the art in near-lossless and lossless compression ratios.

Wearable EEG via lossless compression.

This work presents a wearable multi-channel EEG recording system featuring a lossless compression algorithm. The algorithm, based in a previously reported algorithm by the authors, exploits the existing temporal correlation between samples at different sampling times, and the spatial correlation between different electrodes across the scalp. The low-power platform is able to compress, by a factor between 2.3 and 3.6, up to 300sps from 64 channels with a power consumption of 176µW/ch. The performance of the algorithm compares favorably with the best compression rates reported up to date in the literature.

Beef quality parameters estimation using ultrasound and color images.

BACKGROUND: Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. PROPOSAL: An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. CONCLUSIONS: The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat.

A confocal microscopy image analysis method to measure adhesion and internalization of Pseudomonas aeruginosa multicellular structures into epithelial cells.

Formation of multicellular structures such as biofilms is an important feature in the physiopathology of many disease-causing bacteria. We recently reported that Pseudomonas aeruginosa adheres to epithelial cells rapidly forming early biofilm-like aggregates, which can then be internalized into cells. Conventional methods to measure adhesion/internalization, such as dilution plating for total cell-associated or antibiotic protected bacteria, do not distinguish between single and aggregated bacteria. We report a procedure that combining double bacteria labeling, confocal microscopy and image analysis allows identification and quantification of the number of adhered and internalized bacteria distinguishing between single and aggregated bacterial cells. A plugin for Fiji to automatically perform these procedures has been generated.

Protein secondary structure determination by constrained single-particle cryo-electron tomography.

Cryo-electron microscopy (cryo-EM) is a powerful technique for 3D structure determination of protein complexes by averaging information from individual molecular images. The resolutions that can be achieved with single-particle cryo-EM are frequently limited by inaccuracies in assigning molecular orientations based solely on 2D projection images. Tomographic data collection schemes, however, provide powerful constraints that can be used to more accurately determine molecular orientations necessary for 3D reconstruction. Here, we propose "constrained single-particle tomography" as a general strategy for 3D structure determination in cryo-EM. A key component of our approach is the effective use of images recorded in tilt series to extract high-resolution information and correct for the contrast transfer function. By incorporating geometric constraints into the refinement to improve orientational accuracy of images, we reduce model bias and overrefinement artifacts and demonstrate that protein structures can be determined at resolutions of ∼8 Å starting from low-dose tomographic tilt series.

Simultaneous object classification and segmentation with high-order multiple shape models.

Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First, to automatically select the SM that best represents the object, and second, to accurately segment the image taking into account both the image information and the features and variations learned from the online selected model. A new energy functional is introduced that simultaneously accomplishes both goals. Model selection is performed based on a shape similarity measure, online determining which model to use at each iteration of the steepest descent minimization, allowing for model switching and adaptation to the data. High-order SMs are used in order to deal with very similar object classes and natural variability within them. Position and transformation invariance is included as part of the modeling as well. The presentation of the framework is complemented with examples for the difficult task of simultaneously classifying and segmenting closely related shapes, such as stages of human activities, in images with severe occlusions.