Experimental Characterization of Polarized Light Backscattering in Fog Environments.

This paper focuses on the experimental characterization of the polarization behavior of light backscattered through fog. A polarimetric orthogonal state contrast imager and an active, purely polarized white illuminator system are used to evaluate both linear and circular polarization signals. The experiments are carried out in a macro-scale fog chamber under controlled artificial fog conditions. We explore the effect of backscattering in each imaging channel, and the persistence of both polarization signals as a function of meteorological visibility. We confirm the presence of the polarization memory effect with circularly polarized light, and, as a consequence, the maintenance of helicity in backscattering. Moreover, the circular cross-polarized channel is found to be the imaging channel less affected by fog backscattering. These results are useful and should be taken into account when considering active polarimetric imaging techniques for outdoor applications under foggy conditions.

Impact of the Warhead of Dipeptidyl Keto Michael Acceptors on the Inhibition Mechanism of Cysteine Protease Cathepsin L.

Cathepsin L (CatL) is a lysosomal cysteine protease whose activity has been related to several human pathologies. However, although preclinical trials using CatL inhibitors were promising, clinical trials have been unsuccessful up to now. We are presenting a study of two designed dipeptidyl keto Michael acceptor potential inhibitors of CatL with either a keto vinyl ester or a keto vinyl sulfone (KVS) warhead. The compounds were synthesized and experimentally assayed in vitro, and their inhibition molecular mechanism was explored based on molecular dynamics simulations at the density functional theory/molecular mechanics level. The results confirm that both compounds inhibit CatL in the nanomolar range and show a time-dependent inhibition. Interestingly, despite both presenting almost equivalent equilibrium constants for the reversible formation of the noncovalent enzyme/inhibitor complex, differences are observed in the chemical step corresponding to the enzyme-inhibitor covalent bond formation, results that are mirrored by the computer simulations. Theoretically determined kinetic and thermodynamic results, which are in very good agreement with the experiments, afford a detailed explanation of the relevance of the different structural features of both compounds having a significant impact on enzyme inhibition. The unprecedented binding interactions of both inhibitors in the P1' site of CatL represent valuable information for the design of inhibitors. In particular, the peptidyl KVS can be used as a starting lead compound in the development of drugs with medical applications for the treatment of cancerous pathologies since sulfone warheads have previously shown promising cell stability compared to other functions such as carboxylic esters. Future improvements can be guided by the atomistic description of the enzyme-inhibitor interactions established along the inhibition reaction derived from computer simulations.

Unveiling the Potential of BenzylethyleneAryl-Urea Scaffolds for the Design of New Onco Immunomodulating Agents.

This work focuses on the development of thirteen benzylethylenearyl ureas and one carbamate. After the synthesis and purification of the compounds, we studied their antiproliferative action on cell lines, such as HEK-293, and cancer ones, such as HT-29, MCF-7 or A-549, on the immune Jurkat T-cells and endothelial cells HMEC-1. Compounds C.1, C.3, C.12 and C.14 were selected for further biological studies to establish their potential as immunomodulating agents. Some of the derivatives exhibited significant inhibitory effects on both targets: PD-L1 and VEGFR-2 in the HT-29 cell line, showing that urea C.12 is active against both targets. Some compounds could inhibit more than 50% of cancer cell proliferation compared to non-treated ones when assessed in co-cultures using HT-29 and THP-1 cells. In addition, they significantly reduced CD11b expression, which is a promising target for immune modulation in anticancer immunotherapies.

Impact of the Recognition Part of Dipeptidyl Nitroalkene Compounds on the Inhibition Mechanism of Cysteine Proteases Cruzain and Cathepsin L.

Cysteine proteases (CPs) are an important class of enzymes, many of which are responsible for several human diseases. For instance, cruzain of protozoan parasite Trypanosoma cruzi is responsible for the Chagas disease, while the role of human cathepsin L is associated with some cancers or is a potential target for the treatment of COVID-19. However, despite paramount work carried out during the past years, the compounds that have been proposed so far show limited inhibitory action against these enzymes. We present a study of proposed covalent inhibitors of these two CPs, cruzain and cathepsin L, based on the design, synthesis, kinetic measurements, and QM/MM computational simulations on dipeptidyl nitroalkene compounds. The experimentally determined inhibition data, together with the analysis and the predicted inhibition constants derived from the free energy landscape of the full inhibition process, allowed describing the impact of the recognition part of these compounds and, in particular, the modifications on the P2 site. The designed compounds and, in particular, the one with a bulky group (Trp) at the P2 site show promising in vitro inhibition activities against cruzain and cathepsin L for use as a starting lead compound in the development of drugs with medical applications for the treatment of human diseases and future designs.

Exploring BenzylethoxyAryl Urea Scaffolds for Multitarget Immunomodulation Therapies.

Thirteen benzylethoxyaryl ureas have been synthesized and biologically evaluated as multitarget inhibitors of VEGFR-2 and PD-L1 proteins to overcome resistance phenomena offered by cancer. The antiproliferative activity of these molecules on several tumor cell lines (HT-29 and A549), on the endothelial cell line HMEC-1, on immune cells (Jurkat T) and on the non-tumor cell line HEK-293 has been determined. Selective indexes (SI) have been also determined and compounds bearing p-substituted phenyl urea unit together with a diaryl carbamate exhibited high SI values. Further studies on these selected compounds to determine their potential as small molecule immune potentiators (SMIPs) and as antitumor agents have been performed. From these studies, we have concluded that the designed ureas have good tumor antiangiogenic properties, exhibit good inhibition of CD11b expression, and regulate pathways involved in CD8 T-cell activity. These properties suggest that these compounds could be potentially useful in the development of new cancer immune treatments.

Generalized multi-cavity laser self-mixing interferometry based on scattering theory.

We present a generalized mathematical model and algorithm for the multi-cavity self-mixing phenomenon based on scattering theory. Scattering theory, which is extensively used for travelling wave is exploited to demonstrate that the self-mixing interference from multiple external cavities can be modelled in terms of individual cavity parameters recursively. The detailed investigation shows that the equivalent reflection coefficient of coupled multiple cavities is a function of both attenuation coefficient and the phase constant, hence propagation constant. The added benefit with recursively model is that it is computationally very efficient to model large number of parameters. Finally, with the aid of simulation and mathematical modelling, we demonstrate how the individual cavity parameters such as cavity length, attenuation coefficient, and refractive index of individual cavities can be tuned to get a self-mixing signal with optimal visibility. The proposed model intends to leverage system description for biomedical applications when probing multiple diffusive media with distinct characteristics, but could be equally extended to any setup in general.

A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection.

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7% and a training mean intersection over union of 99.5%. Also, the testing mean of the IoU was 94.4%, and the testing pixel accuracy was 96.2%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.

Polarization calibration assessment for a broadband imaging polarimeter based on a division of aperture architecture.

This article intends to provide all the experimental insights and analyze the best polarimetric calibration method for a division of aperture polarimetric imager considering the different implications it has on the experimental set-up and its performance. Polarimetric cameras require careful calibration for the correct measurement of polarization information. The calibration methods are introduced, intermediate results are presented, and the ability of the set-up to estimate Stokes vectors and Mueller matrices of the samples in passive and active imaging modes is evaluated. Polarization information recovery achieves accuracy errors below the 10% for all polarization modes when the Data Reduction Matrix or the Eigenvalue Calibration Method are used. Such performance, however, degrades significantly when using the Polarizer Calibration Method. To the best of our knowledge, this is the first time such a detailed comparison of calibration methods is presented in the literature, and it is also the first time the Polarizer Calibration Method is applied to a division of aperture polarimeter.

Analysis of the performance of a polarized LiDAR imager in fog.

This paper focuses on exploring ways to improve the performance of LiDAR imagers through fog. One of the known weaknesses of LiDAR technology is the lack of tolerance to adverse environmental conditions, such as the presence of fog, which hampers the future development of LiDAR in several markets. Within this paper, a LiDAR unit is designed and constructed to be able to apply temporal and polarimetric discrimination for detecting the number of signal photons received with detailed control of its temporal and spatial distribution under co-polarized and cross-polarized configurations. The system is evaluated using different experiments in a macro-scale fog chamber under controlled fog conditions. Using the complete digitization of the acquired signals, we analyze the natural light media response, to see that due to its characteristics it could be directly filtered out. Moreover, we confirm that there exists a polarization memory effect, which, by using a polarimetric cross-configuration detector, allows improvement of object detection in point clouds. These results are useful for applications related to computer vision, in fields like autonomous vehicles or outdoor surveillance where many variable types of environmental conditions may be present.

Overview of Biofluids and Flow Sensing Techniques Applied in Clinical Practice.

This review summarizes the current knowledge on biofluids and the main flow sensing techniques applied in healthcare today. Since the very beginning of the history of medicine, one of the most important assets for evaluating various human diseases has been the analysis of the conditions of the biofluids within the human body. Hence, extensive research on sensors intended to evaluate the flow of many of these fluids in different tissues and organs has been published and, indeed, continues to be published very frequently. The purpose of this review is to provide researchers interested in venturing into biofluid flow sensing with a concise description of the physiological characteristics of the most important body fluids that are likely to be altered by diverse medical conditions. Similarly, a reported compilation of well-established sensors and techniques currently applied in healthcare regarding flow sensing is aimed at serving as a starting point for understanding the theoretical principles involved in the existing methodologies, allowing researchers to determine the most suitable approach to adopt according to their own objectives in this broad field.

Quantitative demonstration of the superiority of circularly polarized light in fog environments.

The polarization behavior of light transmitted through scattering media is studied quantitatively. A division of focal plane (DOFP) imaging polarimeter modified with a wideband quarter-wave plate (QWP) is used to evaluate the linear and circular depolarization signals. This system allows the measurement of the linear and circular co-polarization and cross-polarization channels simultaneously. The experiments are carried out at CEREMA's 30 m fog chamber under controlled fog density conditions. The polarization memory effect with circularly polarized light is demonstrated to be superior in forward transmission compared to the same phenomena with linearly polarized light when imaging inside a scattering medium. This paves the way for its use in imaging through scattering media for hazard detection in different applications.

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review.

The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

Improvement of the signal-to-noise ratio in a low power self-mixing interferometer using a coupled interferometric effect.

We present experimental results of a low-emission self-mixing interferometer that uses a coupled interferometric effect to improve the signal produced by a vibrating target. This method is intended to be useful in applications where the target is prone to be damaged by high-intensity laser sources. The beam of a Fabry-Perot laser diode is split and ∼21% of the original emission is used to measure the harmonic micro-displacements of the target using the self-mixing effect. A portion of the residual beam, which also carries the interferometric information related to the target displacement, is reinjected back into the laser cavity by means of a fixed reflector, causing a second interferometric phenomenon that improves the signal-to-noise ratio of the measurement by up to ∼13 dB. A theoretical description of the phenomena is also proposed. Further, we apply this technique to the two most common self-mixing sensing schemes: internal photodiode and junction voltage. The reported results show good agreement with theory and prove the capability of the method to enhance the SNR in SMI schemes.

Geometric Model and Calibration Method for a Solid-State LiDAR.

This paper presents a novel calibration method for solid-state LiDAR devices based on a geometrical description of their scanning system, which has variable angular resolution. Determining this distortion across the entire Field-of-View of the system yields accurate and precise measurements which enable it to be combined with other sensors. On the one hand, the geometrical model is formulated using the well-known Snell's law and the intrinsic optical assembly of the system, whereas on the other hand the proposed method describes the scanned scenario with an intuitive camera-like approach relating pixel locations with scanning directions. Simulations and experimental results show that the model fits with real devices and the calibration procedure accurately maps their variant resolution so undistorted representations of the observed scenario can be provided. Thus, the calibration method proposed during this work is applicable and valid for existing scanning systems improving their precision and accuracy in an order of magnitude.

Confocal flowmeter based on self-mixing interferometry for real-time velocity profiling of turbid liquids flowing in microcapillaries.

We propose a new confocal device for flow profiling in microcapillaries. A viewfinder system is developed using a visible light microscope, allowing focusing with high precision an 830 nm Fabry-Perot laser diode on a microchannel. By means of a novel confocal approach, the Doppler shift produced by the particles of a turbid liquid moving in the focal plane can be measured in real time using the well-known self-mixing effect. The resolution of this device is characterized in function of the full width at half maximum of the Gaussian frequency peak related to the self-mixing signal in the frequency domain. Velocity measurements for flow rates from 0.2 to 1.6 mL/min are presented, and the results demonstrate that the method reduces the phase noise and the effects of the out-of-focus particles, allowing straightforward flow profiling in microchannel structures.

Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning.

The effective and non-invasive diagnosis of skin cancer is a hot topic, since biopsy is a costly and time-consuming surgical procedure. As skin relief is an important biophysical feature that can be difficult to perceive with the naked eye and by touch, we developed a novel 3D imaging scanner based on fringe projection to obtain morphological parameters of skin lesions related to perimeter, area and volume with micrometric precision. We measured 608 samples and significant morphological differences were found between melanomas and nevi (p<0.001). The capacity of the 3D scanner to distinguish these lesions was supported by a supervised machine learning algorithm resulting in 80.0% sensitivity and 76.7% specificity.

Synthesis and biological evaluation of cyclic derivatives of combretastatin A-4 containing group 14 elements.

Several tricyclic compounds inspired by the structure of combretastatin A-4 and bearing group 14 elements have been synthesized by homocoupling lithiated aryl fragments followed by ring-closing metathesis. These tricyclic compounds and their diolefin precursors were evaluated for their antiproliferative action on the tumor cell lines HT-29, MCF-7, HeLa and A-549 and on the non-tumor cell line HEK-293. In addition, their effects on the cell cycle were also measured. The tricyclic compounds show antiproliferative activity similar to that of combretastatin A-4, even though they are not so active in arresting the cell cycle. However, some diolefin precursors are able to cause accumulation of cells in the G2/M phase in a higher percentage than combretastatin A-4 itself. Inhibition of endothelial tube formation and VEGFR-2 phosphorylation of some selected compounds is comparable to that of combretastatin A-4, particularly those of tin-containing compounds 23c and 26c, whose actions exceed those of sorafenib, a clinically used VEGFR-2 inhibitor.

Antiprotozoal and cysteine proteases inhibitory activity of dipeptidyl enoates.

A family of dipeptidyl enoates has been prepared and tested against the parasitic cysteine proteases rhodesain, cruzain and falcipain-2 related to sleeping sickness, Chagas disease and malaria, respectively. They have also been tested against human cathepsins B and L1 for selectivity. Dipeptidyl enoates resulted to be irreversible inhibitors of these enzymes. Some of the members of the family are very potent inhibitors of parasitic cysteine proteases displaying k2nd (M-1s-1) values of seven orders of magnitude. In vivo antiprotozoal testing was also performed. Inhibitors exhibited IC50 values in the micromolar range against Plasmodium falciparum, Trypanosoma brucei, Trypanosoma cruzi and even more promising lower values against Leishmania donovanii.

Visible and Extended Near-Infrared Multispectral Imaging for Skin Cancer Diagnosis.

With the goal of diagnosing skin cancer in an early and noninvasive way, an extended near infrared multispectral imaging system based on an InGaAs sensor with sensitivity from 995 nm to 1613 nm was built to evaluate deeper skin layers thanks to the higher penetration of photons at these wavelengths. The outcomes of this device were combined with those of a previously developed multispectral system that works in the visible and near infrared range (414 nm⁻995 nm). Both provide spectral and spatial information from skin lesions. A classification method to discriminate between melanomas and nevi was developed based on the analysis of first-order statistics descriptors, principal component analysis, and support vector machine tools. The system provided a sensitivity of 78.6% and a specificity of 84.6%, the latter one being improved with respect to that offered by silicon sensors.

Multispectral imaging system based on light-emitting diodes for the detection of melanomas and basal cell carcinomas: a pilot study (erratum).

This erratum corrects the error of an omitted author in doi: http://dx.doi.org/10.1117/1.JBO.22.6.065006