Hypercycles proposed as a chemical basis for the selection of biological replicators may lead to homochirality when fed from achiral resources. Chiral hypercycle replicators  formed from achiral or racemizing resources may lead to spontaneous mirror symmetry breaking (SMSB) without the need for additional heterochiral inhibition reactions, such as those of the Frank-like models, which are an obstacle for the emergence of evolutionary selection properties. The results indicate that the chemical models for the emergence of primordial autocatalytic self-reproducing systems, of and by themselves, can also explain naturally the emergence of biological homochirality. This means that the emergence of biological homochirality8 is supposed to occur in processes either separated from, or in parallel with, the formation of the replicators. Furthermore, the emergence of biological homochirality is frequently considered to be merely an accidental singular process of chemical evolution, and not as a collective emergent phenomenon arising from the increase of complexity during chemical evolution. We show here how spontaneous mirror symmetry breaking (SMSB) emerges necessarily from hypercyclic dynamics when the chirality and enantioselectivity of the replicators are taken into account, and in conjunction with their formation from achiral resources. Common chemical transformations involve mono- and bimolecular reaction mechanisms, therefore autocatalysis is itself an uncommon transformation, and it is difficult to imagine how it could be higher than first order .The emergence of catalytic functionalities and autocatalytic systems form the basis for models of abiotic chemical evolution leading to self-reproducing systems: for example, autocatalytic sets,10 In the case of enantioselective autocatalysis ,i.e. the survival of only one of the two degenerate enantiomers,8 takes place only in low-order enantioselective autocatalysis when this is coupled to a heterochiral reaction: for example [3] such as in the Frank-like systems11 and [4] in the limited enantioselective reaction model.12However, the growth rates leading to the survival of one replicator at the expense of another one must be greater than those provided by first-order autocatalysis [1].14 on SMSB, the models for this are not employed except as models for the evolution of self-reproducing species. This is because inhibition reactions, such as those in [3] and [4], present serious obstacles for the coexistence of self-reproducing chemical species. Furthermore, this has also prevented researchers from considering enantiomeric mixtures in replicator models, principally due to the experimental reports on strong heterochiral interactions (i.e. reactions such as [3]) in enantiomeric mixtures of oligonucleotides.15 However, a recent report on D- and L-RNA mixed systems shows that heterochiral inhibiting interactions occurring in RNA should not be taken as dogma.16 In this respect, it is certainly significant that the old report15 on heterochiral inhibitions in oligonucleotides refers to structures built by only one type of residue. Note that their tertiary structures, for example helices, are chiral but are of a higher order symmetry  than those of actual biopolymers composed of different residues (with point group C1). This fact most likely hinders strong heterochiral interactions.Despite the dramatic experimental reports4 and in systems leading to SMSB rather than the racemic mirror-symmetric mixture.8 In all the simulations only transformations , since our primary objective is to indicate how SMSB may occur in hypercyclic replicators.We consider here the kinetics of chiral hypercycles, in various system architectures which maintain the reaction network out of thermodynamic equilibrium with its surroundings, that is, systems leading to non-thermodynamic final states as the most stable state of the system, as occurs in living biological systemsions see  involvinThe ability of cross-catalytically aided replicators to achieve a final dynamically stable chiral state (a non-thermodynamic state) instead of a racemic one, was simulated by deterministic kinetic rate equations (see the ESID(2RL) + X ⇆ 2RD(2RL) + Y] the final state is always a racemic stationary state; (b) in the case of a chemo-driven second-order autocatalysis (cubic non-linearity: A + 2RD(2RL) + X ⇆ 3RD(3RL) + Y) the backward reaction pathway of the direct transformation [1] is a necessary condition for SMSB, as in the case of hypercycle autocatalysis. Such similar behavior between second order autocatalysis and hypercyclic autocatalysis is in fact the main characteristic of the hypercycle model, namely, to convert a first-order autocatalysis into a second order one thus determining selectivity effects affecting the entire set of cross-catalytically coupled replicators.The reverse reaction of the direct transformation [1] that leads to the racemization of the chiral replicator in a thermodynamically isolated system is a necessary condition for SMSB in the thermodynamic system in e.g., open flow) but which also prevent the system from equilibrating with its surroundings.The dynamics of chiral hypercycles were also studied in other thermodynamic architectures not driven by external reagents .The principal trend of life is not only the presence of a common chiral sign , but the resilience to racemization of systems supporting continuous chiral enantioselective processes transferring chirality. This can only be supported on the basis of reaction networks leading to SMSB. Notice that any small error in chirality transfer during an elementary reaction will be additive, and a decrease of the ee value would occur in the absence of SMSB despite the high enantioselectivity of enzyme-catalyzed processes. When death occurs, such a system cannot be maintained and racemization begins. Therefore, the signature of life would not only be homochirality, but also the resistance to racemization. Notice that the example in The chemical assumption implicit in the simple hypercyclic replicator systems discussed here is that the transformations correspond to a reaction type for a family of compounds which possess the same reactant functional group, but different carbon atom backbones: the differences between the reaction rate constants would be due to the effect of the backbone structure on the reaction rate of the functional group. In this respect, homochiral cross-catalysis is the more reasonable chemical assumption, because enantioselective autocatalysis is in fact a homochiral catalysis, and therefore, a catalytic aid for the same type of reaction in the same structural family of chiral replicators should exhibit the same chiral configuration. This means homochirality between autocatalysis and cross-catalysis. However, when the cross-catalysis operates between replicators involving different types of chemical reactions and families of replicators, then the terms homo- and heterochiral cross-catalysis between the replicator families are no longer meaningful. What is important is if the enantiomer acting in one replicator family is the same or the opposite of that acting in the cross-catalysis between the two replicator families. 21 Therefore, any weak chiral induction on the enantiomeric transition states, sufficiently weak so that it has no chemical relevance for common asymmetric synthesis, selects one of the two SMSB bifurcation branches deterministically for one of the two chiral signs. There are recent dramatic experimental examples of this for the Soai reaction.13 Thus, for example 22 and experimental evidence strongly suggests that a net chirality of these compounds was present to some ee value during the stages of chemical evolution on Earth.23 The effect of permanent and natural chiral forces, acting over vast spatial regions, would also suffice to convert the stochastic final chiral signs of SMSB processes into a coherent deterministic one. All these chiral polarizations would act to transform the stochastic SMSB outcomes into deterministic ones. Homochirality would arise from competitive and cooperative chiral recognition events and not at as singular nor as an accidental event. In summary, the emergence of homochirality in hypercyclic replicators would be an expression of Darwinian evolution at the abiotic chemical level: in a SMSB process only the fittest of both enantiomers will survive. The environment, through the action of spatially extended chiral physical forces, selects the chiral sign. Whether biological homochirality is an accident that occurred during chemical evolution is a separate question that lies beyond the scope of this communication. However, in our opinion only chirality, by virtue of informational entropic reasons, can drive the evolution of chemical systems towards more specific and complex catalytic functionalities.SMSB processes yield a stochastic distribution of chiral signs between successive experiments or outcomes. This means that for a large number of experiments, or in a scenario of a large number of independent working reactors, as in the abiotic scenario of hydrothermal vents in the Hadean Ocean, the average over all the individual stochastic outputs must be the racemic mixture. However, a characteristic of SMSB bifurcations is that a very weak external chiral polarization can select one of the two degenerate chiral branches.e.g., in the RNA-world). Furthermore, such a hypothesis also implies that the actual chiral machinery present in extant living beings, capable of transferring chirality and which is highly resistant to racemization, is a complex SMSB network which is a descendant of the primordial ones.Enantioselective hypercycles enable quadratic (first-order) autocatalysis to achieve the enantioselective behavior of cubic (second-order) autocatalysis and therefore may lead to SMSB  for specific reaction rate constants in systems of thermodynamic architectures impeding the system from equilibrating with its surroundings. The significance of such a SMSB reaction network is that it does not imply heterochiral inhibiting reactions such as those of the Frank-like models and, as a consequence, the emergence of biological homochirality could already be included, both theoretically and experimentally, in the current models of the selection and evolution of biological replicators. These results suggest an abiotic scenario of a simultaneous emergence of biological homochirality during the formation of replicator networks with catalytic activity (


A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation. A number of methods have been developed for the challenging issue of autonomous navigation of mobile robots ,2,3,4,5.One snapshot method, the warping method ,15, distThe Descent in Image Distance (DID) method extends the concept from the warping method. The DID method was introduced by Zeil et al. and was investigated from various perspectives ,17. WhilOne of the simplest homing navigation methods is the Average Landmark Vector (ALV) model . At eachThe Distance-Estimated Landmark Vector (DELV) model is another landmark-based homing navigation algorithm . While tThe DELV model was initially tested using vision sensors, so an additional distance estimation process is required for selected landmarks ,22. BaseThe localization issue is a challenging issue, even in modern Simultaneous Localization And Mapping (SLAM) technology. Information about the environment can also be obtained from several types of sensors. The global localization map can be obtained with a global positioning system (GPS) and a laser-ranging sensor. The laser range sensor can also be tested for human robot interaction  or indusVisual information has been most widely applied to navigation algorithms, since it can provide a lot of information about the position, brightness, color, and shape of objects. Many navigation algorithms are developed with feature extraction using vision ,27,28, oThe main contribution of this paper is in suggesting a holistic approach with the omnidirectional depth information including the background for homing navigation in a real environment, which is called H-DELV. It is a landmark-based navigation with distance information, but an effort of extracting landmark features can be omitted with the suggested approach. We assume that the environment roughly has an isotropic landmark distribution where the frequency of landmarks are independent of the viewing direction and the majority of landmarks are commonly observed irrespective of the view. We explore the efficient and robust landmark-based navigation of a mobile robot without prior knowledge of landmarks such as the colors or shapes of landmarks. We will show that one-dimensional panoramic distance information is sufficient to localize the agent in the environment to guide the homing performance. We suggest a holistic approach of reading landmarks using omnidirectional depth information for homing navigation in a real environment. In our experiments, we test the homing navigation based on landmark segmentation over the depth information, as well as that based on a holistic view of the depth information.We analyze the effect of landmark vector strategies using data from various experiments, changing the weight of landmark vectors, using the reference compass or not, varying the number of landmark vectors, and adding the feature extraction or clustering process. Our paper seeks answers to the following questions: What are important factors that influence the homing performance in the snapshot model as a bio-inspired model? What kind of landmark vector strategies will be useful if the distance sensor readings are available? What strategies will be helpful in an environment assuming an isotropic landmark distribution? Which strategy, a holistic view of landmarks or a landmark distribution involved with the feature extraction, will be more effective in real environments? Is the robotic homing model similar to the model for insect navigation?Initially, we explain the robotic configuration for homing navigation. From the depth sensor, omnidirectional depth information as the depth snapshot is obtained by following the snapshot model. How landmarks are extracted in the snapshot determines which landmark vector strategies can be used. We will introduce two new approaches based on the landmark distance vectors, C-DELV and H-DELV and compare those with the conventional DELV method. In this section, we will explain the three methods one by one.x marks with an interval of angular resolution indicate detection points. In addition, one object makes some occlusion area on the wall which is marked in pink. The depth sensor has a distance accuracy of 5 mm (within a range of 60–1000 mm) or 30 mm (within a range of 1000–4095 mm) and an angular resolution of about 0.36For our navigation experiments, a mobile robot was used with a distance sensor as shown in The computer processes a landmark-based algorithm and transfers the appropriate motor actions to the robot. For a panoramic image (depth map) of the surrounding environment, the mobile robot takes two snapshots at a given location using the laser sensor, since the laser sensor has a limited viewing angle, To check the performance in terms of the homing direction, we used a regular grid of points on the floor as well as additional random positions to see the performance, where small markers were placed for easy detection. The robot was manually positioned at each of those points to sense the surrounding environment and the robot estimated the homing directions, using the suggested snapshot model algorithm.The experimental environment is a 3 m × 3 m area containing objects such as a desk, drawer, and trash bin. The panoramic depth information obtained with a mobile robot is shown in In our experiment, the DELV method  will be An example of depth snapshot and the corresponding landmark extraction results are shown in For the snapshot navigation, we initially tested the Distance-Estimated Landmark Vector (DELV) model , which iThe DELV method collects landmarks by assigning one representative landmark vector for each landmark area or segmented region. The centroid of the angular positions of points in the landmark area and the average of the depths of the points are the angle and the distance for a landmark vector. In this way, we obtain one landmark vector for each selected landmark area. For example, in Now, we consider another type of landmark representation from a depth snapshot. The landmark representation defines every point in the landmark area as the individual landmark vector, but the background area is ignored. Only dominant landmarks near the robot are collected, and we will use this set of landmark vectors for homing navigation. We call it the C-DELV method . More detailed evaluation will be given in the following subsection along with the angular error of the homing vectors and the success rate of homing at each location.For each task over three different environments, we can evaluate the homing performance based on the angular error of the decided homing vectors and evaluate the success rate of homing based on the homing vector sequence. The angular errors of the homing vectors are defined as the difference between the decided homing direction and the ideal homing direction. From each point on a mobile robot, we can draw a straight line to the home location, and the angular difference between the line and the homing vector from the point is considered to be the angular error. The performance of the homing navigation algorithm may vary depending on the distance from the home location. We thus assess the angular error by dividing the environment into two regions. Region 1 is the region close to home location, and Region 2 is the zone that surrounds the landmark but is farther from home than Region 1. The two regions for each environment are shown in The angular errors for each method are displayed in In The angular errors greatly depend on a set of landmarks that have been selected from the depth image. At some test points, the homing directions were estimated incorectly. So far, we have tested the methods in a restricted zone near the home position. If the robot moves far away from the home position, the landmarks in the environment can be changed, and the landmark matching process may not be effective. Similar results can be expected in some environments with occlusions.The success rate is the ratio of starting points that could finally reach the target location. That is, it corresponds to the percentage of the catchment area to the goal position. We calculate the distance error between the current position and the home position, and if the distance is smaller than a threshold (10 cm), then we take the trial as a success. If the robot does not reach the target zone in less than 30 s, it is counted as a failure. As with the angular error assessment, the area is divided into two regions and the homing rate is evaluated for each region. The results are shown in Among those three methods, a holistic approach, that is, the H-DELV method was the most effective in the performance evaluation. The DELV method needs a feature extraction process in which the landmarks are identified from the depth information. The C-DELV method also needs a process of disciminating landmarks from the background. At some robot positions, landmarks that are found at the home location may not be selected in the filtering process, which significantly affects the performance of the landmark arrangement process. In addition, the number of landmarks may be changed depending on the position of the robot or a different set of landmarks may be selected from the depth image. In contrast, the H-DELV method uses more information regarding the background distances. A holistic view of the whole omnidirectional information regards all of the depth information as a set of landmarks with a landmark for each sensor reading, which is why the H-DELV method is more powerful and more robust than the DELV and C-DELV methods.In the above experiments, we assume an isotropic landmark distribution where most of landmarks available at the home location are observed at other locations, although the viewing direction tends to change the landmarks and the size of landmark objects. If a robot is positioned outside the Regions 1 and 2, the robot sees very different landscapes and the snapshot model cannot be applied, since it violates the isotropic condition. Environmental changes, such as a new object added or one of objects missed, severely influence the landmark distribution. Here, we consider that kind of change in env1 as shown in The homing errors become large when landmarks change in the environment, but robots can return home at many positions and the suggested method has a potential to return home without further sophisticated algorithm. More knowledge about the environment or landmarks could possibly improve the performance rather than only memorizing a snapshot at a target position. In many non-isotropic environments, the snapshot model may experience landmark mismatch at a pair of locations, which degrade the homing performance. A holistic view of landmarks covering the background area has a higher probability to compensate for missing or added objects. Interestingly, some insects show homing errors when the landmark distribution is changed or landmarks disappear ,19. The We tested three landmark-based methods with the omnidirectional depth image. The difference of the methods depends on how to extract candidate landmarks from the depth image. The original DELV (distance-estimated landmark vector) method as a landmark-based method is based on feature extraction over visual images. The DELV method chooses a small number of landmarks based on a clustering process, and the C-DELV method selects objects near a given robot position, and regards the depth pixels of those objects as landmarks. The H-DELV method uses a holistic view of the omnidirectional depth information, including the background depth. As a result, the H-DELV method takes a given environment as an isotropic environment by collecting all of the surrounding depth information. In the isotropic environment, the suggested H-DELV approach is very effective. If the background depth is not available and only isolated landmarks are available, then the H-DELV method becomes similar to the C-DELV method. From If there are measurement errors with the depth information, it may degrade the performance. The DELV and C-DELV methods are more heavily influenced by the errors. Some missing landmarks or no mismatched landmarks degrade the performance. In contrast, the H-DELV method uses uniformly distributed samples over the omnidirectional depth information, and some small errors in a part of samples do not influence the homing performance significantly. According to the DELV method , there in samples in the one-dimensional depth image, and an arrangement matching process over m landmarks, if m landmark features are selected from n samples. Then, the computational complexity can be represented as The DELV and C-DELV methods have a feature extraction process over Our experiments show that the laser sensor information is more powerful than a vision sensor. The laser sensor has a high cost compared to a vision camera. Many SLAM methods use laser sensors with high accuracy. The methods focus on localization of a mobile robot in a complex cluttered environment. Most of the researchers use Kalman filtering of laser sensor readings to obtain highly accurate localization information. Actually, it requires the computationally intensive process of probability estimation. Our approach has a goal of reaching the home position at an arbitrary position. The method shows that the robot can return home without localizing the current position and the home position in a given environment. The algorithm simply compares the landmark vectors and determines the homing direction in a short time, without accumulating the map information of the environment in memory. This is an advantage of the suggested approach for homing navigation. In a complex cluttered environment including many landmarks or objects, the suggested approach may need more computation using the landmark vectors for an accurate homing direction.The above homing navigation approaches follow the snapshot model, which compares the two snapshot images at a pair of positions. The model assumes that the majority of landmarks are observed commonly to guide the navigation and the environment is almost isotropic. Complex cluttered environments with many occlusion points no longer follow the assumption, and the method would need several waypoints as a set of target positions . For eacAs mentioned in the Introduction section, bio-inspired homing navigation methods can be roughly divided into two categories: holistic methods such as the warping and DID methods, and landmark-based methods such as the ALV and DELV methods. The holistic method uses the information in the snapshot as a whole, and does not require any pre-processing on the snapshot information whether it is from vision or depth sensors. However, the landmark-based navigation methods require a collection of selected landmark information from the snapshot, and therefore the performance heavily relies on the landmark extraction. The landmark-based methods, if landmarks are correctly identified and matched, usually provide much better homing results than the holistic methods, but performance degradation may happen in real environments, due to errors in landmark identification and matching. The H-DELV method as a holistic approach actually possesses nearly all the environment information, including the background area. There would be some mismatch points between depth snapshots, but the majority of samples point to the same landmark area, which can reduce the homing error.The holistic approach is more useful in a static environment that has no symmetries, but has a sufficient number of landmarks. If a set of distance vectors are all regarded as landmarks, more landmarks would be helpful to guide homing. Omnidirectional depth information can lead to more successful homing. That is, in the environment where landmarks are distributed over the whole angle directions, better estimation of homing direction is possible. If landmarks on one side are observed and other landmarks are too far (out of the sensor range), the environment may experience relatively large homing errors.We note that ants can memorize key objects in their visual field using features of the skyline, for example, tree tops as a navigation guide, and, on later trips, they can compare the snapshot memories with the currently observed view ,32,33,34In this paper, we tested homing navigation based on the snapshot model in real environments, where the mobile robot is positioned at an arbitrary position in isotropic-like environments and is supposed to return home. Here, two new methods, C-DELV and H-DELV, were applied to a homing navigation model and compared with the DELV method. In addition, we tested weighted landmark vectors in the homing navigation approach, where objects that are closer to a given position have larger weights for estimating the current position. To evaluate the performance, we measured the angular error of the homing direction and the success rate of the homing.The H-DELV algorithm, a holistic approach with the omnidirectional depth information, showed the smallest angular error and highest success rate. Even a low resolution of depth samples covering the background estimate the homing directions more accurately. With the H-DELV method, the reference compass is greatly helpful for reducing homing errors. We argue that a holistic view of landmark distances without landmark segmentation process provides better homing performance than a landmark distribution processed with the feature extraction process. In natural landscapes of the environment, the holistic approach can lead to robust homing navigation. The above results may indirectly support the hypothesis that the insect navigation might use the snapshot model with a holistic view of distance vectors ,32. InteOur homing navigation method can be applied to cleaning robots or exploration robots in industry that need a battery recharging system or that have a goal to move towards a target position. For successful homing navigation in large areas with many objects or in a complex non-isotropic environment with many occlusions, the suggested method needs several waypoints like homing positions. Robot homing toward a sequence of waypoints may guide the agent to the home position. We leave that study as a future work.


Optical interrogation of suspicious skin lesions is standard care in the management of skin cancer worldwide. Morphological and functional markers of malignancy are often combined to improve expert human diagnostic power. We propose the evaluation of the combination of two independent optical biomarkers of skin tumours concurrently. The morphological modality of reflectance confocal microscopy (RCM) is combined with the functional modality of laser Doppler flowmetry, which is capable of quantifying tissue perfusion. To realize the idea, we propose laser feedback interferometry as an implementation of RCM, which is able to detect the Doppler signal in addition to the confocal reflectance signal. Based on the proposed technique, we study numerical models of skin tissue incorporating two optical biomarkers of malignancy: (i) abnormal red blood cell velocities and concentrations and (ii) anomalous optical properties manifested through tissue confocal reflectance, using Monte Carlo simulation. We also conduct a laboratory experiment on a microfluidic channel containing a dynamic turbid medium, to validate the efficacy of the technique. We quantify the performance of the technique by examining a signal to background ratio (SBR) in both the numerical and experimental models, and it is shown that both simulated and experimental SBRs improve consistently using this technique. This work indicates the feasibility of an optical instrument, which may have a role in enhanced imaging of skin malignancies. Melanoma and keratinocyte skin cancers (KSCs) are the most common types of cancers affecting fair-skinned populations ,2,3 withMost of the technical developments in non-invasive skin cancer detection are based on imaging reflected light from the skin surface or deeper tissues and examination of the image by an expert clinician. These images are interpreted on the basis of structural or functional changes manifest in the optical properties of skin tissue due to neoplastic changes. Changes in optical properties (scattering and absorption coefficients) of neoplastic tissue are due to morphological, molecular and ultrastructural changes . OpticalOther biological traits can be used in early detection of cancers. One of the hallmarks of cancer is angiogenesis , as vascIn brief, all of the aforementioned techniques suggest that optical biomarkers of malignant changes in skin, used for detection purposes, can be roughly separated into two groups: one related to morphological changes in the skin tissue  and the other related to functional changes, such as blood flow . The purpose of this work is to combine these two modalities to improve the efficacy of the imaging system. The combination of multiple imaging modalities to improve imaging efficacy has been explored previously in the literature. Chen et al. proposed a method that combines the optical coherence tomography and LDF to measure the blood perfusion and tissue structure at the same time . YaroslaOur approach to combine RCM and LDF is to employ a special configuration for RCM, which is able to pick up the Doppler shift in the frequency of the laser beam. We propose to use a laser feedback interferometry (LFI) system for this purpose. This technique is based on interferometry, which is one of the methods that can be used to extract the Doppler signal. By using LFI in a confocal configuration using a vertical-cavity surface-emitting laser (VCSEL), both confocal reflectance and Doppler flowmetry signals can be acquired concurrently in the same scan without changing the hardware. In the system, the aperture of the VCSEL acts as an RCM pinhole and defines the sensing volume . The back-reflected beam then interferes with the intra-cavity field to give rise to the Doppler signal. An LFI system is a compact sensing tool which is based on the self-mixing effect ,33,34,35We examine the proposed technique numerically by applying it to typical simulated models of KSCs, carried out by means of Monte Carlo algorithms specifically adapted for this application, and experimentally by conducting an experiment using an LFI system providing in-depth images of a dynamic turbid medium flowing in a microfluidic channel. We define a signal to background ratio (SBR) parameter to quantify the improvement in the contrast of an image resulting from the multiplication of the RCM and LDF images. There is consistency between simulated and experimental results, and it is shown that SBR for combination images improves significantly in both simulated and experimental studies. The common sensing platform for both measures being LFI, provides an imaging technique where both the confocal reflectance and Doppler flowmetry images can be acquired concurrently during one single scan. Such an image can be used to discriminate between changes in the blood content and optical properties of skin tumours.The paper is structured as follows: We discuss the methodology in Our approach to study the technique is through Monte Carlo simulation of photon-tissue interactions and experimental validation by examining a microfluidic channel containing a dynamic turbid medium. Our numerical configuration consists of tumourous skin models and a confocal LFI optical system ,38,40,41In the following subsections, we discuss confocal LFI, LDF and also the specifications of our specifically-adapted Monte Carlo algorithm to examine the multi-layer tumourous skin model.One of the most widely-accepted non-invasive modalities used to provide in vivo images inside living organisms is RCM . The ideω is the angular frequency and Laser Doppler flowmetry has been extensively used in microcirculatory blood flow measurements, as it is a non-invasive method. In laser Doppler perfusion imaging, a collimated laser beam is raster scanned on the skin area of interest to create the perfusion map . The oped signal :(2)PerfIn the proposed technique, we can neglect technical difficulties, such as measuring the blood perfusion in absolute value and distinguishing between the contributions of RBC’s concentration and velocities to the Doppler signal, which commonly exist in LDF systems . That is−1, respectively, and melanin has an absorption coefficient of 15 mm−1 and no scattering coefficient. The phase function of a scatterer determines the scattering direction of the photons. Here, all of the non-blood scatterers have a Henyey–Greenstein phase function with an anisotropy factor of 0.85, and RBCs have a Gegenbauer kernel phase function [Monte Carlo is the gold standard in the study of photon-tissue interaction and has been used extensively in the field ,53,54. Ifunction  with αGkof 0.991 . We usedof 0.991 .2 on the skin tissue model with 30 steps at each side and created 30 × 30 pixel images. We discussed the photon-tissue interactions in We numerically raster scanned an area of 10 × 10 mmTumorous regions in the numerical model are roughly defined as thin cylindrical volumes with diameters of 5 mm and thicknesses of 0.3 mm, laying within the second and third layers of papillary dermis and superior blood net, situated at a depth of 0.075 to 0.375 mm, as illustrated in Stücker et al. used a commercial laser Doppler perfusion imaging device to measure perfusion in different types of skin cancer ,24. Theyλ, respectively, To use ex vivo measurement results of tissue optical properties in an in vivo numerical model, we need to add the effect of average blood contents of the tissue to the measured ex vivo optical properties using the following expression :(3)μin Parts (i) and (ii) of i. Furthermore, T and BG denote the set of pixel indices associated with the tumourous and background areas, respectively. In the case of KSC1 shown in Part (a) of In order to quantify the numerical results, we defined an SBR parameter as the ratio of the average signal from the tumourous area to the average signal from the normal surrounding skin area obtained from the simulations. This parameter can be formulated as follows:To more clearly see the SBR improvement, we plotted the signals along the center-crossing horizontal axis in Parts (a) and (b) of To examine the feasibility of an experimental device for concurrent RCM and LDF, which is applicable for biomedical imaging purposes, we used a confocal LFI system to image a dynamic turbid medium . The pur2 and a channel thickness of about 1 mm. The top view of this microfluidic channel can be seen in 2 in 61 × 61 steps, at a 200-µm pitch, was scanned. In the proposed set up, the laser beam is perpendicular to the target, which is different from conventional LDF systems. This geometry is more common in biomedical imaging applications, and the Doppler power spectrum, which is a broadened signal around the frequency of zero, was extracted from the focused part of the beam with a high numerical aperture. At each step of the scan, the LFI signal was acquired from the VCSEL junction voltage and was fed into a 16-bit DAQ card after amplification. The time domain voltage signal was sampled at a rate of 60 kS/s, and 3 kS was used as the signal time sequence, which corresponds to 50 ms. To convert the time domain signal into the frequency domain, we applied fast Fourier transform to the time sequence and used a Savitzky–Golay filter in MATLAB  to filter out the frequency domain signal.As illustrated in Because semiconductor lasers are noisy at DC or low frequencies , we AC-cScan results are illustrated in The Doppler flowmetry and confocal reflectance parts of the LFI signal can be extracted from both the frequency  or the time domain signals. In order to show how the signal is processed to get both parts of the data out of it, frequency and time domain signals, acquired at a typical point inside the flow area, are shown in (a) and (b) of We investigated and characterized the proposed imaging technique by applying it numerically to two typical types of tumourous skin as mild and severe KSCs (KSC1 and KSC2) in this work. We derived the optical properties of KSCs from the literature and take into account the lowest level of changes in the optical scattering and absorption coefficients of the tumours in our simulations ,23, in oThe reduction in total reflectance can be attributed to a decrease in the ratio of ing form :(5)Rp=1We defined an SBR parameter as a touchstone to quantify the improvement in the sensitivity of the technique. Both simulation and experimental results concluded that combining two modalities increases the sensitivity dramatically. Numerical SBR values presented in Skin neoplastic diseases often develop from pre-existing skin lesions . ActinicOur assumptions in defining the geometry, location and properties of tumour models include generalization to make the study of the proposed technique easier. For instance, seborrhoeic keratosis, as probably the most common benign KSC, is different from SCC and BCC histopathologically. It usually occurs just as a thickened epidermis and no extension into the dermis layer, but still may rarely mimic KSC or MM clinically. Moreover, KSC can be thought of in the context of a field cancerization model. Therefore, care should be taken in determining where the healthy tissue is. Field cancerization theory states that there is a genomically-unstable area around the primary tumour, inclined to aberrant growth ,80. NeovSome obstacles to early skin cancer detection are the large skin surface area and its susceptibility to other types of skin lesions that can resemble a type of skin cancer, which are hard to differentiate. For example, differentiating between melanoma and dysplastic nevi is challenging clinically and histopathologically . OpticalOur proposed technique implemented in a hardware platform would afford a non-invasive, in vivo and non-contact skin investigation tool with high spatial resolution  area to the signal from the background area. The signal to background ratio of the combined images has increased remarkably in both simulated and experimental examinations with similar trends, which indicates the enhanced sensitivity of the technique.


The interrupted sampling repeater jamming (ISRJ) based on a digital radio frequency memory (DRFM) device is a new type of coherent jamming. This kind of jamming usually occurs as main-lobe jamming and has the advantages of low power requirements and easy parameter adjustment, posing a serious threat to the modern radar systems. In order to suppress the ISRJ, this paper proposes an adaptive transmitting scheme based on a phase-coded signal. The scheme firstly performs jamming perception to estimate the jamming parameters, then, on this basis, optimizes the waveform with genetic algorithm. With the optimized waveform, the jamming signal is orthogonal to the target echo, thus it can be easily suppressed with pulse compression. Simulation experiments are performed to verify the effectiveness of the scheme and the results suggest that the peak-to-side-lobe ratio (PSR) and integrated side-lobe level (ISL) of the pulse compression can be improved by about 16 dB and 15 dB, respectively, for the case where the jamming-to-signal ratio (JSR) is 13 dB. The application of digital radio frequency memory (DRFM) devices has greatly improved the performance and efficiency of radar electronic countermeasures (ECM) systems ,2,3. BasISRJ was first proposed by ,10 in 20In contrast, the electronic counter-countermeasures (ECCM) methods for ISRJ have not been fully studied. Some modern signal processing methods were attempted to filter out the jamming. Among them, the time-frequency (TF) analysis has been widely used. Especially, an ISRJ suppression method for dechirping radar was proposed in . By makiFor the above considerations, modern radar systems should have the ability of jamming perception ,18,19, tThe mechanism of ISRJ is shown in n’ is the number of signal samples. Then the intercepted signal Assuming the transmitted signal vector is Compared to the radar transmitting signal, the retransmitted signal has a time delay. By making use of the characteristics of Fourier transform, namely:FFT to the intercepted signal:Multiply IFFT to the phase shifted spectrum:The retransmitting of the intercepted signal can be described by the process of ‘FFT-phase shift-IFFT (inverse fast Fourier transform)’, and each of the steps can be expressed as:In summary, the jamming signal can be expressed as:According to previous analysis, the jamming signal is the output of jamming operator on radar transmitting signal. The process can be expressed as:According to the matrix theory, we may have:Therefore, in theory, the jamming signal can be suppressed by taking the eigenvector associated with some zero eigenvalue  as the transmitting signal.To analyze the characteristics of the eigenvectors associated with zero eigenvalues, we still take‘one retransmitting’ as an example. Then the jamming operator can be rewritten as follows according to Equation (6):Assuming that the jammer intercepts Thus the operator matrix Let obtained :(13)det(Equation (13) shows that the operator matrix of the jamming have zero eigenvalues. Therefore, we will analyze the characteristics of eigenvectors associated with zero eigenvalues.Firstly, since both Therefore, solving the matrix equation:The optimal waveform we want should meet:Substituting Equation (12) into Equation (15), we have the following equation group:For thevariables whose coefficient are not zero, the equation holds only if the variables satisfy While for the variables whose coefficient are zero, the equation holds for any value of variables It could be seen from Equation (16) that:According to the above analysis, the optimal waveform should be zero during the intercepting durations of the jammer; in other words, the optimal waveform is time discontinuous. Therefore, for convenience, we call it the time-discontinuous waveform (TDW).For the case of multiple retransmitting, the difference lies only in the structure of the sampling matrix Which means the jammer intercepts For this matrix, the above analysis also applies, hence the same conclusion can be drawn, i.e., the optimal radar waveform should transmit when the jammer is not sampling.In this section, an adaptive transmitting scheme for ISRJ suppression is presented. The flowchart of the scheme is shown in At the beginning, the radar transmits a predesigned waveform according to the mission;When the ISRJ jammer starts to work, it intercepts and retransmits the radar signal according to the set strategy;if there is no jamming, the radar maintains the transmitting waveform unchanged;if there is jamming in the echo, the jamming parameters estimation is performed;The radar carries out jamming perception to determine whether the jamming exists:The radar optimizes the waveform with the jamming parameters and transmits the optimized waveform in the next PRI.The detailed interpretations are as follows:According to the analysis of previous section, it is theoretically possible to achieve ISRJ suppression by transmitting TDW whose discontinuous positions are consistent with the jammer intercepting periods. However, a TDW may imply complex hardware and large radar operational bandwidth. Besides, the jammer will always detect the rising edge of the signal, so the simple TDW will cause the jammer to change the strategy, resulting in a failure of the jamming suppression ability of the designed waveform. Therefore, we shall insert some protection pulses at the discontinuous intervals of a TDW, making the whole waveform continuous in time. For the purposes of jamming suppression, the protection pulses should be orthogonal to the TDW. With this waveform, the jammer will sample and retransmit the protection pulses, thus the jamming signal can be easily suppressed with pulse compression. The structure of the transmitting waveform is shown in In order to effectively jam the radar, the energy of the jamming signal generally needs to be greater than the target echo. ISRJ can achieve some coherent processing gain, thereby reducing the requirement for transmitting power. But with respect to the target echo, the processing gain achievable is small. This means that the jamming-to-noise ratio (JNR) of ISRJ needs to be significantly larger than the signal-to-noise ratio (SNR) of the target echo. For example, if each jamming slice is retransmitted once , the peak amplitude of the jamming signal after pulse compression will be the same as that of the target when the JNR is two times of the SNR. To achieve better jamming effect, the JNR of ISRJ generally requires to be higher, which provides favorable condition for jamming perception.The purpose of jamming perception is to estimate the intercepting positions and durations of an ISRJ jammer, and provide the necessary prior knowledge for the antijamming waveform design. This is essentially a problem of target detection and parameter estimation in noise. Similar problems have been extensively studied in the fields of target detection ,22, edgeA phase-coded signal has the advantages of simple implementation and good clutter suppression performance ,29, therK is the number of discontinuous positions; kth discontinuous position.Assume that the discontinuous positions of TDW are defined by:Then the TDW can be expressed as:Similarly, the protection pulse can be expressed as:In waveform design research, the peak to side-lobe ratio (PSR) and integrated side-lobe level (ISL) of the autocorrelation function are usually used to evaluate the pulse compression performance of a waveform, while those of the cross-correlation function are usually used to evaluate the orthogonality of a pair of waveforms ,31. The The autocorrelation function of x and p:The cross-correlation function of However, for the situation considered in this paper, both the target echo and jamming signal are present in the received signal. Therefore, the autocorrelation characteristic of TDW alone cannot fully reflect the PSR and ISL of the signal after pulse compression. Considering that the jamming signal is essentially the retransmission of the protection pulse, therefore, it is necessary to consider the PSR and ISL characteristics of the summation of the autocorrelation and the cross-correlation functions. That is, the PSR of the sum function should be as large as possible while the ISL should be as small as possible.The summation of the autocorrelation and the cross-correlation functions is:Then the PSR and ISL can be defined as:The cost function can then be expressed as the weighted sum of the two indicators:Many modern optimization methods  have been used to solve such problems ,34,35,36In order to verify the effectiveness of the proposed scheme, simulation experiments are performed with the parameters shown in The system uses an m-sequence-based binary phase-coded signal as the transmitting signal. The code number is 100 and the code width is 0.1 μs. The jammer switches to ‘intercepting and retransmitting’ mode after detecting the rising edge of the radar signal. The intercepting duration is set 1 μs and each of the intercepted slice is retransmitted for twice. Therefore, the jammer can intercept 4 jamming slices in the whole pulse duration.Assuming the jamming-to-signal ratio (JSR) is 15 dB, then the real part and pulse compression result of the received signals are shown in The pulse compression result is shown in The simulations of jamming perception are shown in With the estimated jamming parameters, the cost function can be given with Equations (21)–(26) and solved with genetic algorithm (GA). Performing 100 Monte Carlo simulations, the average value of the cost function in each iteration step is shown in For different jamming scenarios, the typical designed waveforms can be seen from the target echo shown in The pulse compression results of the received signal are shown in However, because the protection pulse in the designed waveform cannot provide any processing gain for the target echo, there is a target energy loss relative to the conventional waveform. The loss is related to the jamming strategy. When each intercepted slice is retransmitted for only one time, the length of the total intercepted signal can be half of the transmitting pulse duration. At this point, the target energy loss will reach the maximum, i.e., 3 dB.The average jamming suppression performance of the obtained 100 waveforms with different JNR conditions is shown in Similarly, the ISL of the pulse compression result is shown in The results suggest that the designed waveform has an obvious ISRJ suppression effect.In order to suppress the ISRJ injected from the main lobe of the radar antenna, an adaptive transmitting scheme is proposed in this paper. Based on an ordinary single-channel radar, the scheme first estimates the intercepting positions and durations of the ISRJ with jamming perception technologies, then performs waveform optimization for ISRJ suppression (the designed waveform is named TDW). By inserting some protection pulses orthogonal to the TDW at the jammer intercepting positions, the processing gain obtained by the jamming signal can be greatly reduced, thereby suppressing the jamming performance. The simulation results show that the jamming parameters can be effectively estimated when the JNR is greater than 0dB. The designed antijamming waveform can achieve a JSR improvement of more than 10 dB when the input JSR is less than 20 dB, while the target energy loss less than 3 dB.


The condition of lubricating oil used in automotive and industrial gearboxes must be controlled in order to guarantee optimum performance and prevent damage to machinery parts. In normal practice, this is done by regular oil change intervals and routine laboratory analysis, both of which involve considerable operating costs. In this paper, we present a compact and robust optical sensor that can be installed in the lubrication circuit to provide quasi-continuous information about the condition of the oil. The measuring principle is based on non-dispersive infrared spectroscopy. The implemented sensor setup consists of an optical measurement cell, two thin-film infrared emitters, and two four-channel pyroelectric detectors equipped with optical bandpass filters. We present a method based on multivariate partial least squares regression to select appropriate optical bandpass filters for monitoring the oxidation, water content, and acid number of the oil. We perform a ray tracing analysis to analyze and correct the influence of the light path in the optical setup on the optical parameters of the bandpass filters. The measurement values acquired with the sensor for three different gearbox oil types show high correlation with laboratory reference data for the oxidation, water content, and acid number. The presented sensor can thus be a useful supplementary tool for the online condition monitoring of lubricants when integrated into a gearbox oil circuit. Gearbox oils are exposed to high mechanical loads, wide ranges of temperature, and the possibility of contamination with liquid or solid impurities. As these operating conditions lead to the degradation of the oil, it is exchanged at regular intervals in most applications to prevent damage to the machinery. The time between changes is usually chosen conservatively, which leads to unnecessary material and labour costs and increases environmental pollution.For large machinery with high oil volumes, regular laboratory analysis of the oil is carried out. Although this technique allows oil change intervals to be realized according to the actual requirements, additional costs arise from sample drawing and analysis. Especially in remote facilities like offshore wind turbines, regularly drawing samples requires high effort. Additionally, sudden deterioration of the oil—e.g., contamination with cooling water from a leaking seal—remains undetected, and can cause severe damage over a short period of time.Online sensors that are directly installed at a facility and which continuously monitor the oil condition may present a solution to this complex of problems. Different approaches have been made in this field, based on impedance spectroscopy ,2, oil dHowever, these oil types differ from gearbox oils with respect to the chemical properties of the base stock and additive composition. Moreover, the operating conditions, expected contaminants, and relevant condition parameters are different for gearbox applications. In this paper, we present a working NDIR sensor for the measurement of three gearbox oil condition parameters: oxidation, water content, and acid number (AN). Starting from a general analysis of the reasons for oil deterioration, we investigate the impact of oil aging on the infrared absorption spectrum of gearbox oils. We introduce a method based on multivariate partial least squares regression to select spectral regions suitable for monitoring the AN. Based on the findings of this regression analysis, we select optical bandpass filters to be implemented in the sensor prototype. The optical design of the sensor prototype is simulated with a ray tracing software. The presented sensor prototype is realized in a compact and rugged aluminum housing appropriate for use in industrial environments.Demanding operating conditions lead to a degradation of the gearbox oil used in industrial applications. Laboratory oil analysis examines different oil condition parameters to monitor the level of degradation and to estimate the remaining service life. Three such parameters that are relevant for gearbox oil are oxidation, water content, and acid number. This section gives an overview of the causes and consequences of the degradation processes, as well as common monitoring methods.The agitation experienced by the oil in a gearbox leads to increased contact between the oil and oxygen from the air. In conjunction with high temperatures, the oil can react with oxygen molecules, altering its chemical and physical properties. As a consequence, the viscosity of the oil increases. Further polymerization of the oxidation products can cause varnish, sludge, and hard compounds to form, which diminish the oil flow . A detaiIn laboratory practice, the oxidation of used lubricants is measured by infrared spectroscopy according to ASTM E2412 .Increased water content in the oil can occur because of leakage, pressure washing, improper storage conditions for fresh oil, or condensation in the gearbox housing. Too much water reduces the load-bearing ability of the oil, which leads to increased wear on highly-stressed surfaces. In addition, water promotes the oxidation process of the oil, causes the machinery parts to corrode, and may lead to the sedimentation of additives .In laboratory practice, the water content of used oils is often measured by Karl–Fischer titration according to ASTM D6304 . AdditioThe acid number (AN) describes the mass of potassium hydroxide (KOH) in milligrams that is needed to neutralize the acids contained in one gram of used oil. The AN is therefore a measure of the amount of acidic compounds in the oil, and is thus closely related to the risk of corrosion of machinery parts. As many different sources of oil degradation affect the AN, it has been established as a key parameter in oil condition monitoring.In laboratory analysis, the AN is measured by titrating the used oil sample with KOH as described in ASTM D664 . To incrc, and the length of the path d that the light takes through the material [As described in Equation , the absmaterial .(1)A regression to analyze the relation between the infrared absorption spectrum and the AN of used oil samples. The PLS algorithm constructs a regression model consisting of a scalar Equation . In our The regression coefficient vector enumbers . Thus, wWe performed PLS regression analysis on a data set consisting of infrared absorption spectra and the related AN of used wind turbine oil (WTO) and marine gearbox oil (MGO) samples. 2 absorption band from 2400 cmThe most prominent maxima of As small particles in the oil lead to scattering of the infrared light, higher absorption values are measured as the particle count increases. To accommodate for this fact, a baseline correction of the absorption value must be carried out. Because the scattering has a higher impact on shorter wavelengths and thus induces a sloping baseline, two baseline points at 3900 cm2) windows with a diameter of 25.4 mm and a thickness of 5 mm, separated by a polytetrafluoroethylene (PTFE) spacer of 0.1 mm thickness. One of the windows is drilled to provide a hydraulic connection to the lubrication circuit. In order to achieve sufficient pressure resistance, the windows are compressed with o-ring seals on either side when the cell is installed in the sensor housing. Two thin-film hot plate infrared sources with reflectors (Micro-Hybrid JSIR350) emit radiation in the mid-infrared range that travels through the windows and the oil filling between them. Depending on the condition of the oil, the light is absorbed to different extents at different wavenumbers. The transmitted light is then collected by two four-channel pyroelectric detectors (Micro-Hybrid PS4x2C1). The detector channels are fitted with optical thin-film bandpass filters to monitor different regions of the infrared absorption spectrum of the oil. When selecting optical bandpass filters to monitor the spectral ranges identified in To estimate the wavenumber of maximum transmission of thin-film bandpass filters when inserted in this optical setup, we use the approach described in . EquatioThe first column of Equation  to calcuTo ensure that the presented NDIR oil condition sensor remains economically feasible, we selected commercially available optical bandpass filters with parameters matching the targeted All optical and electronic components of the sensor were designed to fit in a rugged aluminum housing with a diameter of 55 mm and a length of 82 mm. A of an oil sample in each channel of the NDIR sensor, a background measurement of 2 windows of the measurement cell installed. The reinstalled measurement cell is then filled with oil, the measurement of To measure the absorption To compensate for the sloping behaviour of the infrared absorption spectrum, the baseline of the measured absorption values is corrected as represented in x is a vector containing the baseline-corrected absorption values y is the condition parameter reference value of the oil sample. The reference values for the oil condition parameters of the samples stated in A calibration model is used to derive the oxidation, water content, and acid number from the infrared absorption measured with the NDIR sensor. We use multivariate PLS regression to construct a linear calibration model according to Equation . In thisAll samples of the data set stated in We used leave-one-out cross-validation to construct and validate the calibration models for the sensor, as this method has been proven to perform well on small data sets . Figure In addition, the MGO performs more poorly in the prediction of oxidation and AN than the WTO, whereas the RMSE is lower or equal. These results also suggest that this deviation is caused by the relatively small change of oxidation and AN in the data set of the MGO as described in We took repeated readings from some oil samples in order to measure the repeatability independent of the absolute measurement inaccuracy introduced by the calibration model. Averaging over the WTO and the MGO samples, the achieved repeatability (95% confidence range) was 0.19 A/cm for the oxidation and 0.04 mgKOH/g for the AN using the appropriate calibration models for each oil type. Testing the repeatability of the measured water content in the AGO samples with the appropriate calibration model led to a confidence range of 0.23 A/cm or 18.6 ppm.The results presented in In this paper, we presented the development of a sensor for monitoring gearbox lubricants. The sensor can be integrated into the oil circuit of a gearbox in order to monitor the condition of the lubricating oil in use. The system works on the principle of non-dispersive infrared absorption measurement and uses a transmission setup consisting of two thin-film infrared emitters and two four-channel pyroelectric detectors. Seven different optical bandpass filters are used to monitor changes in the infrared absorption spectrum of the oil. To select the optical bandpass filters, a multivariate regression analysis of the used oil infrared absorption spectra was performed.We performed a ray tracing simulation to analyze and correct the influence of the optical sensor setup on the optical characteristics of the bandpass filters. The sensor prototype was implemented in a rugged aluminum housing to withstand the operating requirements of industrial environments.Calibration models for predicting the oxidation, water content, and acid number of different gearbox oil types were constructed by means of multivariate partial least squares regression. Cross-validation of the calibration models showed good correlation between the laboratory reference data and the values measured with the presented NDIR sensor. The measurement accuracy could be further improved by using customized optical bandpass filters and a larger set of used oil samples to generate the calibration model.Compared to oil condition sensors based on impedance spectroscopy ,2, partiBecause of its quasi-continuous data acquisition, the sensor is able to represent a supplementary tool to complex laboratory analysis for the condition monitoring of lubricating oil in gearbox applications. When integrated into a lubrication circuit, the sensor can immediately detect water ingress, and appropriate measures can be taken to prevent damage to the machinery. Oil change intervals can be adjusted to the actual demand by automatically monitoring the oil oxidation and the acid number over long periods of time, which reduces the operating and service costs.


Shigella is a highly adapted human pathogen, mainly found in the developing world and causing a severe enteric syndrome. The highly sophisticated infectious strategy of Shigella banks on the capacity to invade the intestinal epithelial barrier and cause its inflammatory destruction. The cellular pathogenesis and clinical presentation of shigellosis are the sum of the complex action of a large number of bacterial virulence factors mainly located on a large virulence plasmid (pINV). The expression of pINV genes is controlled by multiple environmental stimuli through a regulatory cascade involving proteins and sRNAs encoded by both the pINV and the chromosome. The primary regulator of the virulence phenotype is VirF, a DNA-binding protein belonging to the AraC family of transcriptional regulators. The virF gene, located on the pINV, is expressed only within the host, mainly in response to the temperature transition occurring when the bacterium transits from the outer environment to the intestinal milieu. VirF then acts as anti-H-NS protein and directly activates the icsA and virB genes, triggering the full expression of the invasion program of Shigella. In this review we will focus on the structure of VirF, on its sophisticated regulation, and on its role as major player in the path leading from the non-invasive to the invasive phenotype of Shigella. We will address also the involvement of VirF in mechanisms aimed at withstanding adverse conditions inside the host, indicating that this protein is emerging as a global regulator whose action is not limited to virulence systems. Finally, we will discuss recent observations conferring VirF the potential of a novel antibacterial target for shigellosis. E. coli, including Shigella, are able to live in complex environments and have evolved intricate control systems. The expression of their pathogenic phenotype is the result of a multifactorial process which requires the synthesis of a large set of virulence determinants that may not be simultaneously needed during all stages of the infection process. These determinants are controlled by global regulatory networks, integrating specific regulators with conserved housekeeping processes.Bacterial pathogens must often survive within fundamentally diverse habitats. Dynamic adaptation to the surroundings depends on the ability to sense environmental variations and to respond in an appropriate manner. This involves drastic changes in the transcriptional program of the cell. The capability to swiftly modulate gene expression requires investment by the bacterium in numerous gene functions that not only allow adaptation to different milieus but also enable the cell to co-ordinately rework its response to mutable conditions  is the synthesis of VirF which acts as an antisilencer, relieving H-NS mediated repression at the virB , for its early effectors and their chaperones (Ipa and Ipg proteins), and for MxiE, the last regulator in the cascade  associated with the expression of virulence  encompassing the RNA polymerase binding site . This temperature-dependence relies on the interaction of H-NS with two binding sites within the virF promoter, spaced by a DNA linker region. The accessibility of the target DNA to H-NS varies significantly with temperature and H-NS is able to recognize its binding boxes only at lower temperature: with increasing temperature H-NS binding decreases, and at a temperature higher than 30°C the virF promoter becomes insensitive to H-NS repression. The position of the two H-NS binding sites on the virF promoter has been investigated in detail  and −1 (binding site I) with respect to the virF transcription start site. Site I overlaps the canonical −35 and −10 regulatory elements, suggesting that this region is involved in the transcriptional repression of the virF gene by H-NS: the interaction of H-NS with DNA may prevent the −35 and −10 boxes to be accessed by RNA polymerase  endowed with decreasing intrinsic DNA curvature. The results of footprinting and in vivo transcription assays using these templates highlight a strict correlation between intrinsic DNA curvature and the capability of H-NS to bind to its target sites and repress virF transcription. Moreover, the reciprocal rotational position of the two H-NS sites appears crucial for the thermoregulation of virF: moving the sites by about a half helix turn further apart produces templates which, despite a comparable overall bending level, are almost insensitive to H-NS repression, likely because the protein can no longer form a tight, transcription-blocking nucleoprotein complex. Temperature does not only affect the amplitude of the DNA bend within the virF promoter but also the position of the bending center, identified by means of circular permutation assays , centered around positions +55 (site I), −1 (site II), −130 , and −200 (site IV), have been identified in the virF promoter region . The synthesis of these nucleosides depends on the products of the tgt  and miaA  genes, respectively. The intracellular concentration of VirF decreases in tgt mutants and in miaA mutants, inducing a low virulence phenotype and an avirulent phenotype, respectively  suggests that some of them may encode factors perturbing the invasive process which likely have been silenced during evolution in order to optimize bacterial fitness within the host. Most of these genes encode yet unknown products. In this respect the speG gene, encoding the enzyme spermidine acetyl transferase (SAT), is an exception. In E. coli, SAT prevents the accumulation of spermidine (and the consequent toxic effects) by catalyzing the conversion of this polyamine into its physiologically inert form, acetylspermidine , whose induction by VirF has been observed only at 37°C , was able to severely affect intercellular spreading and induce a significant inhibition of cell invasion , Sapienza Università di Roma, Consiglio Nazionale delle Ricerche, and Institut Pasteur (PTR-24-16).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The generation mechanism of meteotsunamis, which are meteorologically induced water waves with spatial/temporal characteristics and behavior similar to seismic tsunamis, is poorly understood. We quantify meteotsunamis in terms of seasonality, causes, and occurrence frequency through the analysis of long-term water level records in the Laurentian Great Lakes. The majority of the observed meteotsunamis happen from late-spring to mid-summer and are associated primarily with convective storms. Meteotsunami events of potentially dangerous magnitude (height > 0.3 m) occur an average of 106 times per year throughout the region. These results reveal that meteotsunamis are much more frequent than follow from historic anecdotal reports. Future climate scenarios over the United States show a likely increase in the number of days favorable to severe convective storm formation over the Great Lakes, particularly in the spring season. This would suggest that the convectively associated meteotsunamis in these regions may experience an increase in occurrence frequency or a temporal shift in occurrence to earlier in the warm season. To date, meteotsunamis in the area of the Great Lakes have been an overlooked hazard. Meteotsunamis are meteorologically generated water waves that have temporal and spatial characteristics similar to seismic tsunamis and can pose serious hazards to coastal communities34567913161121222327seefor a list of historic events). The disastrous effects of meteotsunamis in the Great Lakes were most vividly demonstrated in 1954, when a three meter meteotsunami killed seven people in Chicago, IL, USA11The Laurentian Great Lakes, which form the Earth’s largest freshwater system and include over 10,000 miles of coastline, are an example of a region with low seismic activity but a long history of impactful meteotsunami events, as illustrated in 41118293031The objective of this paper is to characterize the occurrence frequency of meteotsunamis in the Great Lakes, as well as the timing of and causative storms associated with these events. Long-term water level and radar records across the region are analyzed to quantify meteotsunami return levels, associated storm structure types, and monthly and annual occurrence distributions. Patterns in Great Lakes-wide regional meteotsunami characteristics are then analyzed in the context of the geographic, bathymetric, and atmospheric settings of the region. This is the first study that provides insight into the occurrence frequency of meteotsunamis throughout the Laurentian Great Lakes region, as well as the mechanisms which influence the regional meteotsunami characteristics.To reveal meteotsunami occurrence in all five Great Lakes, we first identified meteotsunamis from water level records of up to 20 years at 32 locations across the region. Specifically, meteotsunami events are defined in this study as water level oscillations with period less than 2 hours and a height above 0.3 m that are associated with strong surface wind and/or barometric pressure perturbations at a nearby weather stationThe average number of meteotsunamis observed per year at each station in the Great Lakes is illustrated in To calculate the occurrence frequency of extreme meteotsunamis, a Peaks Over Threshold (POT) approach was taken to fit the Pareto Type 1 distribution to the meteotsunami size data in each lake33To examine the origin of meteotsunamis in the Great Lakes, the storm types associated with identified events which exceed the one-year return level height are classified from radar reflectivity imagery. Storms are classified as one of seven categories363738To estimate when meteotsunamis are most likely to arise, monthly and annual distributions of meteotsunamis have been computed. The peak time of meteotsunami events within the year is calculated as the circular mean month of the observed events. For the first time, long-term water level records throughout multiple water bodies in a region (e.g. the Laurentian Great Lakes) are analyzed to identify spatial and temporal patterns in meteotsunami occurrence. The results reveal that meteotsunamis in the Great Lakes are much more common phenomenon than anecdotal historical reports indicate. Great Lakes meteotsunamis are associated primarily with linear and complex convective storms and generally come about from the late-spring to mid-summer. This meteotsunami timing coincides with beginning of the summer swimming and recreation season, putting many lake users at risk to the danger posed by meteotsunami. These findings suggesting that to date meteotsunamis have been an underrated hazard for the Great Lakes region.In the following, meteotsunami occurrence statistics are viewed on a regional scale to reveal patterns in frequency, causative storms, and seasonality, which are then compared with the physical and atmospheric setting of the region. In terms of meteotsunami occurrence frequency, water level stations which experience the greatest number of meteotsunamis per year tend to be located towards the southwest portion of the Great Lakes. This observed pattern is also true on a lake-by-lake basis , where LThe distribution of specific storm structures is also consistent with climatological patterns in storm distribution. Convective complexes are associated with the greatest number of meteotsunamis in each lake except for Lake Ontario, where linear convective structures dominate. These results are similar to the spatial distribution of mesoscale convective complexes in the region, which varies longitudinally across the Great Lakes with peak activity in the westInterestingly, while meteotsunamis in the Great Lakes are associated primarily with convective storms, the observed peak time of meteotsunamis in the Great Lakes precedes that of the mid-summer peak in convective activity39Under a changing climate, the patterns observed here in meteotsunami occurrence frequency, cause, and season may shift. The contribution of cyclones to Great Lakes meteotsunamis may decrease in the future, as the frequency of tropical and extratropical cyclones are projected to decrease under a changing climate4748Long-term water level data are obtained from 32 monitoring stations operated on the Great Lakes by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS), with gauge locations illustrated by yellow circles in 525311β and a local height threshold xm. To establish xm, a failure-to-reject method is employed by sorting the height observations and deleting the lowest values until the distribution is no longer rejected by the one-sided Anderson-Darling goodness of fit test (α = 0.1)β is estimated with the Maximum Likelihood Estimate. Meteotsunami event probabilities are represented in terms of mean recurrence intervals (RI), the inverse of which express the probability that specified return level (RL) magnitudes will be exceeded in any one year. The PT1 distribution was chosen among other extreme value distributions such as the Generalize Extreme Value, Weibull, Power, and Generalized Pareto Distributions based on the Anderson-Darling goodness of fit tests as well as the stability of parameter estimates during the failure-to-reject threshold selection processA Peaks Over Threshold (POT) approach of extreme value statistics is used to represent extreme meteotsunami size-frequency data in each lake33NEXRAD base reflectivity radar imagery made available by the Iowa Environmental Mesonet at 5 minute intervals is used to classify the structure of storms associated with meteotsunami events greater than the 1 year return level at each station. Storms are classified as one of seven categories: convective cluster, convective complex, linear convection, bow convection, extratropical cyclone, frontal, or atmospheric gravity wave (AGW)36373852The monthly and annual timing of meteotsunami occurrences is analyzed on a lake-by-lake basis. The meteotsunami events are binned into histograms by both month and year of occurrence. The monthly distributions of meteotsunami occurrence are expressed as the fraction of events in each lake normalized by the total number of observed meteotsunamis in the lake. The annual distributions of meteotsunami occurrence are expressed as the number of observed event per year normalized by the number of active water level stations in each year.How to cite this article: Bechle, A. J. et al. Meteotsunamis in the Laurentian Great Lakes. Sci. Rep.6, 37832; doi: 10.1038/srep37832 (2016).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Access to healthcare is inequitably distributed across different socioeconomic groups. Several vulnerable groups experience barriers in accessing healthcare, compared to their more wealthier counterparts. In response to this, many countries use resources to strengthen their primary care (PC) system, because in many European countries PC is the first entry-point to the healthcare system and plays a central role in the coordination of patients through the healthcare system. However it is unclear whether this strengthening of PC leads to less inequity in access to the whole healthcare system. This study investigates the association between strength indicators of PC and inequity in unmet need by merging data from the European Union Statistics on Income and Living Conditions database (2013) and the Primary Healthcare Activity Monitor for Europe (2010). The analyses reveal a significant association between the Gini coefficient for income inequality and inequity in unmet need. When the Gini coefficient of a country is one SD higher, the social inequity in unmet need in that particular country will be 4.960 higher. Furthermore, the accessibility and the workforce development of a country’s PC system is inverse associated with the social inequity of unmet need. More specifically, when the access- and workforce development indicator of a country PC system are one standard deviation higher, the inequity in unmet healthcare needs are respectively 2.200 and 4.951 lower. Therefore, policymakers should focus on reducing income inequality to tackle inequity in access, and strengthen PC (by increasing accessibility and better-developing its workforce) as this can influence inequity in unmet need. The socioeconomic conditions in which people live play a large part in influencing their chances of living a healthy life –4. AccesFor those in need, access to healthcare has a positive influence on self-perceived health and life expectancy , 18. MorThe most commonly used measure of access to healthcare is self-assessed unmet need . Carr anReported unmet need ranges from less than 1% in Slovenia and Belgium to 26% in Latvia . In addiAs mentioned above, this article intends to explore whether the strength of European PC is associated with inequality in unmet need. To the best of our knowledge, the present study is the first attempt to address this association with an international comparison. Nonetheless, the existence of this association is supported by previous studies that have provided evidence of the positive influence of PC strength on several other health(care)-related measures. For example, strong PC is associated with better population health –31, imprBesides the fact that previous literature on unmet healthcare need has not addressed the link between the strength of PC and socioeconomic inequalities in unmet need, it is also characterised by other limitations. Firstly, most of the existing literature on unmet need comprises single-country studies (conducted mainly in the US and Canada). In addition, few of these studies are based on general population groups , 37, whiIn the present study we build on and contribute to the mentioned body of literature by answering the following research question: is the strength of European PC systems associated with income-driven inequity in unmet healthcare need at the macro level? In other, more poetic words, do we reap social inequity in unmet need, when sowing weak PC systems?To answer the research question, data from two European databases were combined: (i) data on national unmet healthcare needs from the 2013 wave of European Union Statistics on Income and Living Conditions (EU-SILC) and (ii) data on the strength of the national PC systems from the Primary Healthcare Activity Monitor for Europe (PHAMEU) (2010).The EU-SILC, gathered under the coordination of Eurostat, is the EU reference source for comparative statistics on income distribution and social inclusion at the European level . EU-SILCAccess to healthcare was measured by asking participants: ‘Was there any time during the last 12 months when, in your opinion, you needed medical examination or treatment […] but you did not receive it?’ If participants answered ‘yes’ to this question, they were categorised as participants who suffered from unmet healthcare need. Inequity (or the gap) in unmet healthcare need was calculated by subtracting the percentage of participants in the lowest quintile of equivalised income reporting unmet need by the percentage of participants in the highest quintile of equivalised income reporting unmet need. Equivalised income is the total income of a household, after tax and other deductions, divided by the number of household members. To convert the household members into equalised adults, they were each weighted according to their age using the modified OECD equivalence scale . This apTable 1.Secondly, given the complexity of and variation in European PC, PHAMEU was used to determine the strength of the national PC systems, and by doing so, made the complex European PC landscape comparable. The seven strength indicators of PHAMEU capture a combination of PC functions both at the structure level  and at the process level  . A detaiThe data were analysed using SPSS . The distribution of the dependent variable  and the governance-indicator was highly skewed, and because they were rejected by the normal distribution hypothesis using the Shapiro-Wilk test, these two variables were logarithmic transformed.Firstly, the dependence between the seven aforementioned strength indicators  and the gap in unmet healthcare need between low- and high-income groups was tested using Pearson correlation coefficients. Secondly, multiple linear regression models were used to assess the relative and independent contribution of the seven strength indicators to the gap between low- and high-income groups in unmet healthcare need. In the second regression model, we additionally controlled for the unequal distribution of countries’ wealth by adding the Gini index of income inequality to the model. The World Bank  defines Table 2). According to PHAMEU, the countries that scored the highest (lowest) on the governance-indicator were the Netherlands and Spain (Switzerland and Luxembourg). Furthermore, concerning economic conditions, the United Kingdom and Spain (Bulgaria and Ireland) scored the highest (lowest). The United Kingdom and the Netherlands (Iceland and Luxembourg) had the best (weakest) developed workforce.Firstly, we provide the reader with a brief summary of European PC strength  accessibility was reported in Slovenia and Denmark (Ireland and Luxembourg). Regarding continuity, Denmark and Estonia  were the strongest (weakest). Sweden and the Netherlands (Austria and Germany) had the strongest (lowest) PC coordination. Countries that provided the best (weakest) comprehensive care were Lithuania and Bulgaria (FYR Macedonia and Slovakia). In short, although other countries often had the strongest (weakest) scores on several strength-indicators, the Netherlands, Spain and the UK primarily showed to be the strongest concerning PC, while Luxembourg scored the weakest in this respect.Fig 1 shows how unmet healthcare need differed by income level. In all European countries, people in the lowest income group reported the highest unmet need. The highest percentage of the population reporting unmet need was observed in Turkey (28.2%) and the lowest in the United Kingdom (0.1%). The country with the highest social inequity in unmet healthcare need between low- and high-income groups was Turkey. Consequently, Turkey reported the highest inequity in unmet healthcare need. The Netherlands had the lowest gap and therefore reported the lowest inequity in unmet healthcare need .Table 3 presents the results of this correlation matrix. We observed a significant correlation between unmet healthcare need and economic condition , workforce development , access  and coordination . Each of these correlations showed that the higher the score on the indicator, the lower the gap in unmet need. Furthermore, the matrix revealed a significant correlation between the Gini coefficient for income inequality and the gap in unmet healthcare need . There were no associations between the gap in unmet healthcare need and governance, continuity and comprehensiveness.To present the univariate association between the social gap in unmet healthcare need and the seven strength indicators of the PC system, Pearson correlation coefficients were calculated. . This model showed significant associations between two of the seven strength indicators and explained 29.5% of the variance in inequity in unmet healthcare need. Consistent with the correlation matrix, the association between access and unmet healthcare need remained significant in the regression model. Access was inverse associated with the gap in unmet healthcare need (p 0.020). The better the access to the PC system, the smaller the gap in unmet healthcare need within a country. More specifically, when the access-indicator is one standard deviation higher, the inequity in unmet need is about 4.371 lower. Secondly, we observed an inverse association between workforce development and the gap in unmet healthcare need (p 0.047). In other words, the better developed the PC workforce is, the lower the inequity in unmet healthcare need. Specifically, when the workforce development of a country is one standard deviation higher, the inequity in unmet need of this particular country is 3.967 lower. The significant correlation in the bivariate analysis for economic conditions and coordination disappears in the multiple regression model. Furthermore, the other three strength indicators  had no significant impact on the gap in unmet healthcare need.To determine the independent impact of the strength indicators on the observed gap in unmet healthcare need, an initial multiple linear regression model was estimated In the second and final model we controlled for the Gini index for income inequality. This model explained 46.0% of the variance. The association between access and the gap in unmet need on the one hand (p 0.018) and workforce development and the gap in unmet need on the other (p 0.008) remained statistically significant when the Gini index was taken into account. When the access- and workforce development indicator of a country are one standard deviation higher, the inequity in unmet healthcare needs are respectively 2.200 and 4.951 lower. A positive association between the Gini index for income inequality and the gap in unmet healthcare need was shown (p 0.011), indicating that the higher the income inequality, the bigger the gap in unmet healthcare need. Specifically, when the Gini index is one SD higher, the social inequity in unmet need will be 4.960 higher. Finally, the other five strength indicators  showed no significant associations with the gap in unmet need.In most European countries some social groups experience barriers in accessing (primary) healthcare and have therefore an inequitable disadvantage compared to their more wealthier counterparts , 8. ManyThe results of this study show the largest inequity gap in unmet healthcare need in Turkey. Moreover, according to PHAMEU, Turkey has a weak PC system. Bivariate analyses revealed a significant correlation between the social gap in unmet need and (i) the Gini coefficient for income inequality, (ii) the access-indicator of the strength of PC and (iii) the workforce development-indicator of the strength of PC. Furthermore, according to the estimation results of the multiple linear regression model, two indicators of PC strength predict inequity in unmet healthcare need. Firstly, an inverse effect between access and inequity in unmet healthcare need was observed. In other words, a more accessible primary healthcare system was associated with lower inequity in unmet healthcare need. This is consistent with recent literature, in which unmet healthcare need has been shown to be the most commonly used proxy to measure access to healthcare .Secondly, this study suggests that a better-developed workforce within PC and a more central role of PC professionals  within the healthcare system is associated with lower inequity in access to healthcare, thus lowering inequity in unmet need . FurtherFinally, in a recent research, Chaupain-Guillot and Guillot  found a To the best of our knowledge, this is the first study to describe an association between the strength of PC systems and inequity in unmet healthcare need at the macro level; however the authors stress the explorative nature of this study. Given the impact of context on the perception of unmet need, we invite further research exploring this association at the micro level .The operationalisation of unmet need in this study, consistent with the definition of Carr and Wolfe , has twoFrom a policy perspective, our results suggest that policymakers should focus on making PC more accessible and expanding the PC workforce in order to reduce the inequity in unmet healthcare need. Policymakers are therefore urged to develop multidimensional and differentiated legislation that will reduce barriers to care access . In orde