Alternatively when the response of a given spatial

Alternatively, when the response of a given spatial-frequency is of interest as a function of defocus, it is convenient to express this OTF after only transforming the lateral dimension to Fourier-space. The resulting OTF has reciprocal-space lateral units (those of spatial frequency) but retains a real-space unit in the beam direction (equivalent to defocus or equally z-height). Once such a 3D OTF is calculated it can be sectioned parallel to the beam direction (vertically in Fig. 4) for any given spatial frequency to yield the transfer function corresponding to sample features with the corresponding lateral spacing. We extract a depth resolution of 9nm for the 0.28nm minimum transverse spacing of the Zr lattice, only a couple of nanometers more than the 7nm depth of field [10]. For the larger 0.39nm Ti-Ti and Sr-Sr spacings we calculate the depth resolution to be 11nm. Therefore EELS spectrum images acquired with sufficient transverse resolution to resolve these spacings also posses significant depth resolution.
Atomic resolution EELS spectrum images were acquired from regions appearing to contain YSZ buried beneath STO from HAADF imaging. By using the high tension fine defocus control of the UltraSTEM 100 we were able to obtain pairs of spectrum images taken in quick succession from the same area of the sample but with the probe focused to different depths. The Zr maps extracted from one such pair of spectrum images are displayed alongside the simultaneously acquired HAADF and MAADF images in Fig. 5. The Ti and Sr maps from the same slices are shown in Fig. 6, alongside composite images of the Zr, Sr and Ti signals combined together as the red, green and blue leukotriene receptor agonist respectively. The optical slices were taken with the probe focused to the entrance surface and 18nm into the depth of the film.
Each slice consisted of 35 by 146 EELS pixels with 0.05s exposures. Subpixel scanning was performed in a 32 by 32 grid inside each EELS pixel to provide superior resolution in the simultaneously acquired HAADF and MAADF images. The principle component analysis method of [32] was used to eliminate noise in the atomic-resolution EELS elemental maps as described in [33]. For the Ti maps we again used the L-edge, however when extracting the Sr and Zr elemental maps for the optical sectioning we utilised the Sr and Zr M-edges rather than their L-edges due to the proximity of the M-edges to the Ti L-edge, which was needed to record all the edges at once on the smaller CCD of the Enfina spectrometer, and also because of their higher cross sections. The Ti L-edge fine structure was used to fine tune the calibration of the energy scale. Although the Sr and Zr M-edges are extended and overlap, they are offset in energy and have fine structure features appearing at different energies. The Zr M-edge has a pair of peaks appearing between 330 and 355eV where the Sr M-edge has no fine structure and only a decaying tail, and the Sr M-edge has a pair of peaks between 270 and 286eV where the Zr edge is relatively flat. For the Zr edge, background subtraction fitting was performed in the flat region just after the pair of peaks at the Sr M-edge maximum. For the Sr edge the background fitting was performed between 218 and 240eV, which balances the falloff of one peak in the Zr edge with the rise of the broad peak appearing just before the Sr integration window.
Clear differences are seen between the two spectroscopic optical slices. In the slice taken at the entrance surface of the sample (0nm defocus), the HAADF image shows the alternating pattern of bright and dark columns associated with Sr and Ti columns in STO from top to bottom, and the Zr map reveals the presence of a Zr containing region but without any lattice contrast. However in the slice taken with the probe focused 18nm further into the depth of the sample (−18nm defocus) in the same area, a region of YSZ like contrast appears in the upper portion of the HAADF image, with all neighbouring columns having similar intensity. Here the Zr signal also sharpens into a lattice with precisely the periodicity expected for YSZ (compare to the overlaid Zr lattice model in Fig. 5). This demonstrates spectroscopic depth sensitivity with EELS. The Zr map at zero defocus is an out of focus version of the lattice seen at −18nm defocus. We note that this lattice cannot simply be an artefact of the PCA treatment rather than depth sensitivity as if this were the case the lattice would also appear at zero defocus.

Alternatively when the response of a given spatial

Alternatively, when the response of a given spatial-frequency is of interest as a function of defocus, it is convenient to express this OTF after only transforming the lateral dimension to Fourier-space. The resulting OTF has reciprocal-space lateral units (those of spatial frequency) but retains a real-space unit in the beam direction (equivalent to defocus or equally z-height). Once such a 3D OTF is calculated it can be sectioned parallel to the beam direction (vertically in Fig. 4) for any given spatial frequency to yield the transfer function corresponding to sample features with the corresponding lateral spacing. We extract a depth resolution of 9nm for the 0.28nm minimum transverse spacing of the Zr lattice, only a couple of nanometers more than the 7nm depth of field [10]. For the larger 0.39nm Ti-Ti and Sr-Sr spacings we calculate the depth resolution to be 11nm. Therefore EELS spectrum images acquired with sufficient transverse resolution to resolve these spacings also posses significant depth resolution.
Atomic resolution EELS spectrum images were acquired from regions appearing to contain YSZ buried beneath STO from HAADF imaging. By using the high tension fine defocus control of the UltraSTEM 100 we were able to obtain pairs of spectrum images taken in quick succession from the same area of the sample but with the probe focused to different depths. The Zr maps extracted from one such pair of spectrum images are displayed alongside the simultaneously acquired HAADF and MAADF images in Fig. 5. The Ti and Sr maps from the same slices are shown in Fig. 6, alongside composite images of the Zr, Sr and Ti signals combined together as the red, green and blue leukotriene receptor agonist respectively. The optical slices were taken with the probe focused to the entrance surface and 18nm into the depth of the film.
Each slice consisted of 35 by 146 EELS pixels with 0.05s exposures. Subpixel scanning was performed in a 32 by 32 grid inside each EELS pixel to provide superior resolution in the simultaneously acquired HAADF and MAADF images. The principle component analysis method of [32] was used to eliminate noise in the atomic-resolution EELS elemental maps as described in [33]. For the Ti maps we again used the L-edge, however when extracting the Sr and Zr elemental maps for the optical sectioning we utilised the Sr and Zr M-edges rather than their L-edges due to the proximity of the M-edges to the Ti L-edge, which was needed to record all the edges at once on the smaller CCD of the Enfina spectrometer, and also because of their higher cross sections. The Ti L-edge fine structure was used to fine tune the calibration of the energy scale. Although the Sr and Zr M-edges are extended and overlap, they are offset in energy and have fine structure features appearing at different energies. The Zr M-edge has a pair of peaks appearing between 330 and 355eV where the Sr M-edge has no fine structure and only a decaying tail, and the Sr M-edge has a pair of peaks between 270 and 286eV where the Zr edge is relatively flat. For the Zr edge, background subtraction fitting was performed in the flat region just after the pair of peaks at the Sr M-edge maximum. For the Sr edge the background fitting was performed between 218 and 240eV, which balances the falloff of one peak in the Zr edge with the rise of the broad peak appearing just before the Sr integration window.
Clear differences are seen between the two spectroscopic optical slices. In the slice taken at the entrance surface of the sample (0nm defocus), the HAADF image shows the alternating pattern of bright and dark columns associated with Sr and Ti columns in STO from top to bottom, and the Zr map reveals the presence of a Zr containing region but without any lattice contrast. However in the slice taken with the probe focused 18nm further into the depth of the sample (−18nm defocus) in the same area, a region of YSZ like contrast appears in the upper portion of the HAADF image, with all neighbouring columns having similar intensity. Here the Zr signal also sharpens into a lattice with precisely the periodicity expected for YSZ (compare to the overlaid Zr lattice model in Fig. 5). This demonstrates spectroscopic depth sensitivity with EELS. The Zr map at zero defocus is an out of focus version of the lattice seen at −18nm defocus. We note that this lattice cannot simply be an artefact of the PCA treatment rather than depth sensitivity as if this were the case the lattice would also appear at zero defocus.

br General discussion We have

General discussion
We have presented an algorithm that predicts discomfort from images. The algorithm is extremely simple and is parameter-free and yet, surprisingly, it explains more than a quarter of the variance in judgments of discomfort that are entirely subjective. The judgments did not obviously relate to stimulus parameters, as they do when participants rate the colourfulness of a scene or its structural complexity – participants were simply asked to express any discomfort they felt. The model is based on two pervasive principles: (1) the spatial structure in scenes from nature and (2) sensitivity of the human visual system, as reflected in the prostanoid receptors sensitivity function. The visual system has evolved to process images from nature, and on this basis alone, one might anticipate that images with structure that departs from those found in nature may be computationally more complex to process. Such a viewpoint is supported by evidence that the visual system is continually adapting to the visual input (Webster, Georgeson, & Webster, 2002). Olshausen and Field (1997) have argued that images with 1/f structure such as those from nature can be processed most efficiently using a sparse coding in which few neurons are simultaneously active. Images that depart from 1/f are presumably processed less efficiently, and inefficient processing might be expected to demand greater metabolic resources. There are preliminary indications that this might indeed be the case. Coloured gratings with a large (and unnatural, Webster, Mizokami, & Webster, 2007) colour difference result in a high-amplitude haemodynamic response and are uncomfortable to view. The discomfort is proportional to the colour difference, as is the amplitude of the haemodynamic response (Haigh et al., 2013). Furthermore individuals who are particularly susceptible to visual discomfort, those experiencing migraine with aura, demonstrate an abnormally large haemodynamic response to uncomfortable visual stimuli (Cucchiara et al., 2014), an abnormality that is normalised when the stimulus is made more comfortable (Huang et al., 2011). In general, one can think of pain as a homeostatic mechanism that acts to restore equilibrium, and in this respect, discomfort from images is no different: it provides for homeostasis in so far as it reduces the metabolic demand to more typical levels.
Our algorithm is computationally economic in the sense that it takes a few milliseconds to process each image. It was successful in identifying designs that have caused complaints, and might therefore provide a tool to help designers. Designers have long known that images from nature are restful and restorative (e.g. Korpela, 2013) and recent studies have involved the measurement of cerebral haemodynamics in the study of such restoration (Pati et al., 2014). Our model potentially provides a means of quantifying this effect.

Acknowledgments
OP was supported partly by the Spanish Ministry of Science and Innovation through the research project Consolider Ingenio (CSD 2007-00018) and partly by the BBSRC grant J000272/1 to Professor Julie Harris and Dr Paul Lovell. We are grateful to Professor Harris for her support in allowing us to pursue this study. We thank An Le and Kelly Murphy for permission to use their images and data, and Justin Ales for helpful discussions on the bootstrap procedure.

Introduction
Human observers are extremely efficient at detecting faces in visual scenes (Crouzet, Kirchner, & Thorpe, 2010). Beyond low-level cues such as power spectrum (Crouzet & Thorpe, 2011; Keil, 2008) and the saliency of the eye regions (Paras & Webster, 2013), the nature of the visual information supporting face detection remains largely unknown. One critical element appears to be the contrast variation between different regions of a face. First noted by Galper (1970), the sensitivity of face perception to contrast polarity has been consistently observed (Bruce & Langton, 1994; Gilad, Meng, & Sinha, 2008; Johnston, Hill, & Carman, 1992; Kemp et al., 1996; Liu, Collin, & Chaudhuri, 2000; Liu et al., 1999; Nederhouser et al., 2007; Phillips, Jenkins, & Morris, 1972; Russell et al., 2006; Sormaz, Andrews, & Young, 2013; Vuong et al., 2005). However, these studies have focused on individual face recognition or discrimination and do not indicate to which extent the weaker performance reflects an effect of contrast inversion on the perception of the stimulus as a face (i.e., face detection).

Increased prevalence of bacterial strains

Increased prevalence of bacterial strains resistant to THZ531 in humans has stimulated public and federal interest in eliminating the use of antibiotics in sub-therapeutic doses for growth promotion (antibiotic-growth promoters; AGP) in livestock. An alternative approach to improve health and productivity in livestock is the use of probiotics, prebiotic substrates that serve as nutrients to certain bacteria, or their combinations (synbiotics). A variety of microbial species (bacteria of Bacillus, Escherichia, Lactobacillus, Bifidobacterium, Enterococcus, Lactococcus, Streptococcus, and Pediococcus genera, yeast and undefined mixed cultures) have been used as probiotics generally resulting in reduced mortality, enhanced immune responses, improved growth rates, feed intake and feed efficiency in poultry and livestock of different ages [reviewed in Cho et al. (2011) and Patterson and Burkholder (2003)]. While Lactobacillus and Bifidobacterium species have been used most extensively in humans; historically, various species of Bacillus, Enterococcus, and Saccharomyces yeast have been the most commonly used in livestock (Simon et al., 2001). Only during the past few decades, has there been an increase in research on supplementing Lactobacillus to livestock (Gusils et al., 1999; Jin et al., 2000; Pascual et al., 1999; Tellez et al., 2001) (Table 3). Further, while in some studies LAPB improved growth performance and post-weaning diarrhea (PWD) control in weanling pigs (Lessard and Brisson, 1987; Shu et al., 2001), these effects were not observed in others (Walsh et al., 2007) (Table 3). As reviewed in Heo et al. (2013), this inconsistency in results of probiotic effects on PWD and performance in pigs may be attributed to differences in dosage and type of probiotic, management practices, diet, and age (Heo et al., 2013). One study evaluated the effects of bifidobacteria and LAPB (in place of AGPs) in newborn calves and piglets and demonstrated that these probiotics reduced mortality, improved weight gain, fecal condition and feed efficiency in both species (Abe et al., 1995). However, the effects of lactobacilli (including various strains of L. reuteri, as well as Lactobacillus gasseri, L. acidophilus and L. fermentum) supplementation on infectious diarrhea occurrence, growth performance and feed conversion in neonatal and weanling piglets varied with age, feeding status (sow milk versus milk replacer) and lactobacilli strain (Chang et al., 2001; Chen et al., 2014; Huang et al., 2004; Liu et al., 2014; Wang et al., 2009a, 2013, 2011, 2012; Yu et al., 2008) (Table 3). Potential mechanisms of lactobacilli beneficial effects proposed in these studies included alleviation of oxidative stress (Wang et al., 2013, 2009b), protective modulation of gut microbiota (Chang et al., 2001; Huang et al., 2004; Liu et al., 2014) and associated metabolic profiles (Liu et al., 2014), enhancement of T-cell differentiation, ileal cytokine production (Wang et al., 2009a) and serum IgG Ab levels (Yu et al., 2008). Additionally, reduction in the levels of IL-1β mRNA expression in the ileum of neonatal piglets due to L. reuteri supplementation was reported (Hou et al., 2015; Liu et al., 2014).
Very few mechanistic studies addressing interactions among LAPB, immunity and RV were conducted in livestock species, and primarily in pigs. In 3-week old piglets, the administration of B. lactis HN019 led to lower concentrations of fecal RVA and reduced severity of weanling diarrhea (Shu et al., 2001) (Table 3). Indicative of immune enhancement, higher blood leukocyte phagocytic and T-lymphocyte proliferative responses, and higher intestinal RV-specific Ab (IgM, IgG and IgA) titers were detected in B. lactis HN019 fed piglets. Interestingly, another study using suckling piglets demonstrated reduced RVA shedding due to Enterococcus faecium NCIMB 10415 supplementation that was not associated with increased RV-specific Ab titers (Kreuzer et al., 2012). However, the probiotic supplementation resulted in significant differences in effector and regulatory T cell responses. These data suggest, once again that reduction in RV diarrhea/infection may be achieved via different mechanisms by different probiotic bacteria, while the increase of RVA-specific Ab levels (often found due to probiotic supplementation) is not essential for the disease attenuation.

br Methods br Discussion Although it

Methods

Discussion
Although it has previously been reported that the end-tidal forcing method is capable of producing reproducible alterations in end-tidal gas volumes (Mark et?al. 2010, 2011), the reproducibility of the peripheral vascular effects of hypercapnia (10?mm Hg above resting values) has not been examined. In the present study, we observed that the peripheral vascular dilatory response to an iso-oxic, hypercapnic environment produced reproducible results in terms of dilatory measures, though the methodology required produced highly variable results and the time course of the dilatory response was not well correlated. To our knowledge, this is the first study to examine the reproducibility of alterations in peripheral vascular response caused by an iso-oxic, hypercapnic environment.
To date, most research involving hypercapnia and its effects on blood vessel function have focused on cerebral levomefolate calcium and the determination of cerebral vascular reactivity (CVR) as measured with magnetic resonance imaging (Kassner et?al. 2010; Mandell et?al. 2008; Mark et?al. 2010, 2011; Mutch et?al. 2012; Prisman et?al. 2008). These studies have obtained a positive correlation between CVR and PetCO2 levels. Although plenty of research (Kassner et?al. 2010; Mandell et?al. 2008; Mark et?al. 2010, 2011; Mutch et?al. 2012; Prisman et?al. 2008) has involved the correlation between the cerebral blood flow changes and end-tidal carbon dioxide levels, there is only limited research examining peripheral blood flow changes in response to hypercapnia.
Palazzo et?al. (2012) and Vantanajal et?al. (2007) examined the effect of hypercapnia on vascular beds and reported a lack of correlation between peripheral and cerebral beds in response to hypercapnia. Palazzo et?al. (2012) used ultrasound imaging during flow-mediated dilation to observe brachial artery diameter and flow changes, while also using transcranial Doppler to observe cerebral vascular reactivity in response to alterations in end-tidal carbon dioxide levels. Although TCD provides useful information regarding blood flow alterations, it does not identify vessel diameter changes in response to a stimulus. Therefore, Palazzo et?al. (2012) were only able to compare blood flow changes induced by two different stimuli (reactive hyperemia and hypercapnia) of the varying vascular beds. In the present study we observed diameter changes of the brachial artery in response to a steady hypercapnic, iso-oxic state.
Breath holding was used to alter end-tidal carbon dioxide levels during two separate tests. CVR and FMD results were not well correlated, perhaps because of the ineffectiveness of breath holding to create a steady state in end-tidal or arterial gas measures. Although Palazzo et?al. (2012) used two separate methods to alter PetCO2 in their population, both failed to control for alterations in PetO2, which may affect the dilatory response of the vasculature. Additionally, Palazzo et?al. (2012) used a 7% CO2 gas mixture to alter end-tidal CO2 volumes, which does not guarantee that end-tidal CO2 will achieve a steady state. The present study was able to maintain steady state for both end-tidal CO2 and end-tidal O2 volumes.
Vantanajal et?al. (2007) also examined MCA flow with TCD and brachial artery flow via ultrasound imaging during a hypercapnic state. However, they used the more reproducible partial re-breathing method to induce the hypercapnic state. Although the authors reported statistical comparisons of baseline end-tidal gas data, they did not analyze the reproducibility of the gas data during the interventions (Vantanajal et?al. 2007). Once again, cerebral and peripheral responses to a hypercapnic state were not well correlated, as the cerebral response was much faster and of a higher magnitude. The results from these studies would suggest that there are differences in the sensitivity and/or response to hypercapnic stimuli in the cerebral and peripheral vascular beds.
Although Vantanajal et?al. (2007) observed peripheral vascular changes in response to a hypercapnic state, their study did not focus on the reproducibility of the vascular measures. Additionally, neither Vantanajal et?al. (2007) nor Palazzo et?al. (2012) ensured PetCO2 was maintained at steady state, which is critical when attempting to isolate the true vascular response to a hypercapnic state. Though hypercapnia is a naturally occurring physiologic state, vascular plants is generally accompanied by hypoxic conditions (Venkataraman et?al. 2008). The convolution associated with concurrent changes in carbon dioxide and oxygen partial pressures offers unclear results to researchers investigating the effects of arterial gas changes (Brogan et?al. 2004; Cinar et?al. 2012).

Our study was limited by a small sample size and

Our study was limited by a small sample size and significant inter-participant variability. Second, the scanning planes in the measurements differed between the two modalities. Echo PIV measured the longitudinal plane of the carotid artery, whereas PC-MRI measured global WSS magnitude that was spatially averaged over the cross-sectional planes. The maximum velocity range that the echo PIV system can resolve depends on the available frame rate of ultrasound B-mode imaging. High frame rates are required to resolve higher velocities, but at the cost of the field of view, because of the conventional “pulse-sweeping” sequence employed for B-mode imaging. These limitations could be overcome by employing advanced beamforming techniques such as interleaved imaging and plane wave imaging, which have been reported to increase the maximum resolvable dynamic velocity range (Leow et?al. 2015; Poelma and Fraser 2013; Poelma et?al. 2012). It should also be noted that our PIV analysis did not correct for the beam-sweeping error reported in Zhou et?al. (2013). However, as suggested by the authors, the beam-sweeping error can be ignored because the peak flow velocity observed in this resazurin study was an order of magnitude lower than the reported beam sweeping velocity of 600?cm/s1 (for an imaging depth of 2?cm using the Sonix RP ultrasound imaging system with a LP14-5/38 transducer, as used in this study). Another limitation of the current echo PIV system is the inability to measure vascular hemodynamics in three dimensions, and therefore, any effects from the secondary flow components are not realized. Volumetric measurements of in?vivo flow that can be obtained from PC-MRI are useful for qualitative evaluation of the temporal WSS distribution; there was good agreement in temporal WSS patterns between the two methods.

Conclusions
Echo PIV is a novel, contrast-enhanced ultrasound imaging-based velocimetric technique that provides spatially local, multicomponent and time-resolved velocity and WSS measurements in?vivo. We have reported that this technique is feasible in adult humans with a broad age range. Qualitatively, echo PIV and PC-MRI have good agreement in WSS measurements, but a significant difference was found quantitatively. We conclude that echo PIV is an easy-to-use, ultrasound-based technique for measuring WSS in?vivo in humans with good repeatability and reproducibility.

Acknowledgments
This work was made possible by grants from the National Institutes of Health (NIH T32-HL072738, K24-081506, ROI-HL114753). This study was supported in part by the National Institute for Health Research (NIHR) Exeter Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIH, National Health Service, NIHR or Department of Health. We thank South West Stroke Research Network for their help in recruitment and the staff of the Diabetes and Vascular Medicine Research Centre, University of Exeter Medical School, for their valuable assistance in carrying out this study.
Introduction
With the advent of personalized therapies, the need for longitudinal monitoring of pathologic tissues in clinical and translational settings to compare the efficacy of therapies has gained prominence. Contrast-enhanced ultrasound (CEUS) is a relatively recent, cost-effective technique compared with more traditional imaging modalities like magnetic resonance imaging (MRI) and computed tomography (CT). CEUS imaging has many applications in cardiology (Kaufmann et?al. 2007; Kaul 2008) and radiology (Chung and Kim 2015; Wilson and Burns 2010; Wilson et?al. 2009), with a focus on cancer and peripheral resazurin vascular disease, where the estimation of microvascular density (blood volume per unit mass) and blood perfusion (blood flow per unit mass) is particularly important.
Contrast-enhanced ultrasound imaging involves the injection of gas-filled micron-sized bubbles (microbubbles) that do not extravasate. This property makes them ideal contrast agents for imaging vascularity and blood perfusion (Walday et?al. 1994; Yanagisawa et?al. 2007), allowing longitudinal studies of drug therapies, for example, anti-angiogenic treatment (Guibal et?al. 2010; Zhou et?al. 2011). Non-linear CEUS uses multiple US pulses with varying amplitude or phases to subtract linear harmonic response from tissue and capture the non-linear subharmonic (Needles et?al. 2010) response of the microbubbles in blood, effectively creating a blood-to-tissue contrast. This non-linear signal is proportional to the density of microbubbles in blood (Greis 2011; Lampaskis and Averkiou 2010), allowing relative estimation of blood density and vascular volume in the tissue. The linear relationship between signal intensity and microbubble concentration is limited by the biophysical properties of the microbubbles (e.g., size) and the post-processing of the raw ultrasound signal by the ultrasound equipment manufacturer.

Other studies have used the repeated rhythmic contractions of

Other studies have used the repeated rhythmic contractions of the heart to acquire multiple 2-D images of the same cardiac phase over consecutive cardiac cycles through a single acoustic window. Minor random movements during a multicycle image acquisition alter the scan plane, resulting in partially decorrelated views of the imaged cardiac structure. Spatially compounding such partially decorrelated frames corresponding to the same cardiac phase can therefore produce enhanced cardiac images. The process has been referred to as temporal compounding (Abiko et al. 1997; Perperidis et al. 2009).
Accurate and robust spatiotemporal alignment of corresponding frames acquired over multiple cardiac cycles is essential for effective temporal compounding (Rohling et al. 1997). Insufficient alignment may result in severe blurring of the imaged cardiac structure, substantially reducing the diagnostic value of the processed images. Spatial alignment is required to compensate for larger cardiac movements during the multicycle image acquisition. Such displacements occur mostly because of probe slippage and changes in heart orientation during the periodic respiratory motion of the patient\’s chest. Furthermore, the temporal behavior of a heart may vary during a multicycle cardiac ultrasound examination. Variations in the cardiac temporal dynamics range from small, for healthy hearts, to large, for hearts suffering from arrhythmia or other cardiac diseases. Moreover, temporal variations can be global, such as differences in the length of cardiac cycles, or local, such as differences in the length of each of the seven independent phases within a terbinafine hcl (Berne et al. 2004; Bray et al. 1999; Guyton 1991; Guyton and Hall 1997). In general, these variations tend to be non-linear, with greater effect in the relaxation phase of the cardiac cycle. Consequently, the temporal relationship between any two image sequences is required to compound frames at corresponding stages within the cardiac cycle (Fig. 1).
Van Ocken et al. (1981) first identified the potential of fusing information acquired over consecutive cardiac cycles to enhance the quality of ultrasound data sets (Sinclair et al. 1983). Unser et al. (1989) suppressed noise in M-mode ultrasound scans by averaging data acquired over a number of consecutive cardiac cycles. Rigney and Wei (1988) and Vitale et al. (1993) described the earliest attempts to use compounding of partially decorrelated B-mode images acquired over consecutive cardiac cycles. Rigney and Wei (1988) employed an exhaustive search along template matching to identify and compound all frames in the multiframe sequence that corresponded to a specific reference frame. This approach, although capable of generating good temporal alignment, remains (even today) a very computationally intensive choice. Vitale et al. (1993) identified the end-diastole (ED) frames from each cardiac cycle by analyzing the recorded electrocardiographic signal. Corresponding frames from consecutive cardiac cycles, extracted at regular temporal intervals to the ED frames, were then spatially compounded by intensity averaging. Similar approaches have been adopted as a pre-processing step for more effective image segmentation of cardiac structures (Amorim et al. 2009; Melo et al. 2010). A limitation in these studies was that no spatial alignment was performed on the temporally aligned frames prior to intensity averaging. Klingler et al. (1989) recognized the need for spatial alignment prior to compounding and described an empirical method to reject (but not compensate for) temporally aligned frames that demonstrated large spatial displacement with respect to the reference frame. Olstad (2002) extended the approach by Vitale et al. (1993) by introducing a rigid spatial alignment to compensate for larger cardiac movements during image acquisition. Similar to the aforementioned attempts (Amorim et al. 2009; Melo et al. 2010; Vitale et al. 1993), Olstad\’s (2002) approach suffered from two major limitations. In the first instance, although electrocardiography (ECG) enables accurate identification of the ED frames, it is much more challenging to extract accurate information related to any of the other phases of the cardiac cycle (Friesen et al. 1990). Second, the study assumed that cardiac cycles occurred in regular time intervals. Such an assumption can limit the effectiveness of the technique by introducing tissue/chamber blurring in the resultant images.

This paper examines the feasibility of such experiments

This paper examines the feasibility of such experiments. Section 2 describes the main lines of the experimental setup. In Section 3, we detail the method of acoustic monitoring and the signal treatment used to diagnose the bubble oscillation regime. In Section 4, we show how the single bubble can be perturbed in a controlled manner, by slowly approaching a micron-sized carbon fiber. The bubble response to this perturbation is examined and a critical object-bubble distance for bubble farnesoid x receptor is tentatively defined. Finally, Section 5 combines the results of the precedent sections, and examines whether acoustic monitoring allows to conveniently detect the bubble disappearance in such perturbation experiments.

Experimental setup
A resonator was made of a cubic optical glass cell (edge length 60mm), filled with degassed water up to a level of 50mm (Fig. 1). The cell is driven by a piezo-ceramic disc (PI Ceramics, PIC 155, 16mm diameter, 5mm thickness) glued at the bottom, and connected to a frequency generator through home-made impedance matching transformer and series compensation coil. The cell resonates in its breathing mode at 21380Hz. The acoustic emission of the bubble was recorded by a microphone made of a small piezo-ceramic disc (PI Ceramics, PIC 255, 10mm diameter, 1mm thickness), glued on the outer face of a lateral cell wall. The disc center is located at 25mm above the cell bottom.
A second identical resonator was build after accidental breaking of the first one. Despite its design was rigorously identical to the latter, its resonance frequency was found to be 21,150Hz. Finally, a spherical resonator was also used in some experiments. It was made of a spherical flask (60mm inner diameter) driven by two laterally glued piezo-ceramics facing each other, the microphone ceramics being glued on the bottom. The resonance frequency of the spherical cell was 28,670Hz.
The bubble is imaged by a fast camera (MIRO 310, 2000 FPS) through a long distance microscope (Questar QM 100) and lightened by a white power LED (Luxeon Rebel LXML-PWC2). The wide-angle LED light emission is refocused at the microscope focus by two converging lenses, and the levitation cell is mounted on a three-axis stage so that the bubble can be positioned at the common focus of the microscope and of the lightning system. Both the camera and the LED are synchronized externally by sending pulses locked to a given phase of the frequency generator, so that when the bubble motion is periodic, its motion can be frozen to the required phase of its radial oscillation [16,17]. The LED flash duration could be as small as 500ns.
The microphone signal was first amplified and filtered by a home-made analog 5th order Butterworth high-pass filter, of cut frequency 40kHz. This allows to eliminate a large part of the driving frequency without affecting too much the first harmonics. The resulting signal is sampled by a 12 bit fast digitizer over exactly one acoustic period (2048 samples per period), at a constant phase of the driving. The unfiltered microphone signal and the voltage across the driving piezo-ceramics were also sampled in the same way. The function generator, digitizer and syncing unit all pertain to the same device (National Instruments PXI rack) and are controlled by the data-acquisition software LABVIEW.
Water was degassed in a closed stirred tank maintained at constant temperature. Air was removed with a vacuum pump, down to a controlled pressure. After one hour stirring, the tank was opened to atmospheric pressure and water was poured in the levitation cell by gravity. Because of the constraint imposed by the manipulation of the fiber (see Sections 4 and 5), our levitation cells were necessarily open to the atmosphere, whereby the water unavoidably re-gas during the experiments. When needed, the dissolved gas content in the cell was therefore measured immediately before and immediately after the experiment with an oximeter (Hach-Lange HQ30d). The largest deviation found over a given experiment was 6% of the saturation concentration.

Simultaneously with the hydrophone measurements cavitation

Simultaneously with the hydrophone measurements, cavitation structures were also visualized by a high-speed camera (Fastec Imaging Hispec 4 Mono) with 85mm Samyang AE macro lens placed 420mm away from the chamber wall. A continuous light source (LED diodes) for illumination of the viewing area was placed on the side of the chamber opposite to the camera so that the ultrasonic probe was placed in the center between them. High-speed imaging data was acquired with 64031 frames per second and 2µs exposure time. Image PLX4032 was 128×80 pixels, with 0.03mm pixel size. The depth of sharpness (approximately 10mm) was sufficient to capture all relevant flow structures.
After acquisition, high-speed images of cavitation structures were imported in the ADMflow software, where velocity and pressure fields were calculated following the methodology presented in the Section 2.

Results and discussions

Conclusions
The paper presents a novel non-contact method for simultaneous measurement of pressure and velocity fields of cavitating flows, which uses high-speed imaging data as its input and is implemented in the ADMflow software. The method was tested on the case of attached cavitation below the ultrasonic probe and validated with hydrophone pressure measurements. Experimental results confirm causality between velocity and pressure fields in the phase of cavitation structures collapse, as velocity maxima occur near the maximum pressure gradients. Also, the boundaries of the high velocity areas seem to coincide with the linear or spherical shape of observed cavitation phenomena. Regardless of the sonicator power level and related probe oscillation amplitude, the cavitation cloud collapse causes the pressure and velocity field to become highly inhomogeneous. The progress of local pressure and velocity disturbance is also reflected in local averaged pressure values in the selected regions of interest and has been shown to fit relatively well to the values measured by a hydrophone.

Introduction
Classical techniques for structural diagnostic purposes rely mainly on pitch-catch measurements of direct guided waves between an actuator and a receiver in the region to be measured. In traditional nondestructive testing of metallic structures, damage is usually identified by linear changes in the excitation signal and response signal, such as amplitude variation and phase shift [1]. Recently, fatigue cracking identification in plate-like metallic structures has mainly used nonlinear ultrasonic methods based on Lamb waves [2]. To create the desired wave feature, external excitations are required, such as specific wave modes [3]. In some circumstances, however, active measurement might be unavailable and inconvenient, especially when the artificial excitation source is limited [4]. For example, in a continuously operational structure, the active sources may turn out to be disabled because of disturbance from ambient sources. Consequently, pitch-catch measurements might be invalid, necessitating alternative techniques. In this respect, passive damage detection techniques in which artificial sources are unnecessary show excellent application prospects for structures that contain local ambient noise sources. In particular, aerospace structures would be appropriate candidates for application of this technique for damage detection, owing to the noise sources created by engines working in spacecraft and the skin friction between fuselages and air [5].
It has been indicated theoretically and experimentally that the cross-correlation function between signals recorded simultaneously at two points can provide an estimate of the Green’s function between two points [6,7]. Hence, Green’s function is applicable for the retrieval of information about the structure from natural ambient noise [8], for example, in seismology [9] and underwater acoustics [10]. Recently, researchers have applied this method for the purpose of structural diagnostics. Duroux et al. [11] experimentally used cross-correlations of elastic diffuse fields to study damage detection in a small aluminum plate with complex geometry. They successfully distinguished the damage section based on the relative amplitude variations of these cross-correlation functions between the damaged and undamaged condition. Sabra et al. [12] applied cross-correlation of ambient vibrations in a high-speed naval ship for structural health monitoring and discussed the influence of the ship’s operating conditions on the stability of the retrieved Green’s function.

br Results and discussion We perform

Results and discussion
We perform simulations intended to identify (i) the kinematic (single-scattering) thickness limit in proteins (within which a direct interpretation of the diffracted intensities is possible) and (ii) the significance of tolerance to error in F as compared to phases. All our studies were done with the protein lysozyme in the P43212 space group and unit cell parameters a=b=79.1Å and c=37.8Å. Our structure factors were obtained from the relativistic Hartree–Fock calculations of Doyle and Turner for neutral atoms [17], and so take no account of bonding effects. These have been refined in detail against experimental purchase NVP-BGJ398 diffraction intensities using inorganic crystals [3] where bonding effects are very small, but may be larger for the light C, O, N atoms of a protein, or graphene [18]. Hydrogen atoms were not included.
First, we studied the variation of diffracted beam intensity with thickness for the lysozyme crystals, which was subsequently used to identify the kinematic limit for the different diffracted beams. Similar curves for paraffin layers are shown in [15]. Fig. 1a and b shows the characteristic ‘pendellosung’ oscillations in the intensities of low (44) and high (2313) resolution diffracted beams (approx. 13Å and 3Å respectively) and their Friedel pairs at 200keV. The oscillations of the diffracted beam intensities allow for an oscillation period or an “extinction distance” to be defined. However only in a two-beam theory (or kinematic theory away from the Bragg condition) are the oscillations periodic. Eq. (1) shows that a different extinction distance can be associated with every excitation error or point within the diffraction disk. (A similar result holds in two-beam theory). In this way, both the convergent-beam and precession methods [19], by including a large range of orientations around the Bragg condition, average over many of these thickness periods and so reduce the rapidity of the thickness (Pendellosung) oscillations. Fig. 1(c and d) represent the same oscillation trends for the two diffracted beams, but at 400 and 1000keV respectively.
Allowing 10% error tolerance in the intensities, we calculated the kinematic (single-scattering) limit as tabulated in Table 1. At 3Å resolution (2313), this limit was observed to be 227.4Å at 200keV. This kinematic limit increases [2] with incident beam energy, thus indicating a possible use of higher incident beam energies to support the study of thicker protein samples that still diffract within the kinematic regime. But high beam energy increases the probability of knock-on radiation damage (especially for light elements). The reduction in ionization damage, but rapid increase in such “knock-on” ballistic damage, with increasing beam energy has been thoroughly studied and reported in the literature for beam energies up to 3MeV (see [2,20] for reviews).
In protein crystallography, it is widely accepted that about 2/3 of the information in a charge density map comes from structure factor phases (commonly obtained using the molecular replacement method, using a similar protein as model) and 1/3 from the amplitudes [21,22]. To evaluate this, we introduced random errors to the x-ray intensities but maintained accurate phases. This way, when the charge density maps were obtained with the erroneous intensities, we could identify the percentage error beyond which, the real-space charge density information starts to be substantially different from ideal.
X-ray diffraction intensities and phases for lysozyme were obtained from 4ET8.pdb [23]. Keeping the phases unchanged, we introduced random errors to the intensities. We then generated an omit map (omitting residues 50–60 from the refinement process), as in Fig. 2, where the charge densities (wireframes) were laid over the ideal structure (sticks) from the model. For a perfect fit these should agree. The red wire-mesh corresponds to ideal (zero-error) omit- map. Thus Figs. 2(a–d) correspond to charge density maps from 20%, 50%, 70% and 100% error in X-ray intensities (10%, 22%, 34% and 41% errors in F respectively) overlaid upon the density map with zero-error.