br Conclusions br Acknowledgements br Introduction In

Conclusions

Acknowledgements

Introduction
In the last two decades, design and syntheses of coordination supramolecular compounds, which involve a great number of interactions and self-assembly of organic ligands with functional groups and metal ions with specific directionality, has produced appreciable progress within the field of supramolecular chemistry and crystal engineering [1–7]. Recently the self-assembly approach has become one of the most widely used techniques for the preparation of many functional materials, due to its potential to translate molecular building blocks into well-defined solid-state systems with relatively facile manner [8–10]. The variety of self-assembled structures relies largely on the presence of suitable metal ligand interactions and supramolecular contacts (hydrogen bonding and other weak interactions) [11]. Until now, many single-, double-, triple- and higher-order stranded supramolecular polymers have been generated by a self-assembly process [12–16]. Nevertheless, it is still a huge challenge to reasonably construct the expected architectures with unique properties [17–19]. To the best of our knowledge, few papers are discussed the morphology controlling of compounds and checking out their changes. They have only studied on different compounds such as micro crystals [20], HA films on highly ordered nanotubular [21], a new asymmetric quaterthiophene [22], Mn(III) metalloporphyrins that contain size controlled amorphous and crystalline nano- and micro sized coordination polymer buy Bafetinib [23], diarylethene single crystal [24] and copper–zinc-10,15,20-tetra(4-pyridyl) porphyrin coordination polymer [25]. In another interesting study, we looked at; they have demonstrated a comprehensive morphology evolution from the porphyrin derivative by droplet templating on a hydrophilic substrate by just changing the assembly temperature [26]. But in this work we wish to report the first solid-state conversion of Pb(II) supramolecular polymers with 5-chloroquinolin-8-ol ligand with microrods morphology to nanorods and nanoparticles of it without any treatment in environmental conditions.

Experimental

Results and discussion

Conclusions
In summary, for the first time, we observed the simultaneous conversion of a bulk micromaterial to its nanostructures without any treatment at environmental conditions. [Pb2(5-Clq-8-ol)2(OAc)2]n (1) is a one-dimensional pencil shaped supramolecular polymer as a result of weak secondary M⋯C interactions. It seems that during this conversion, some secondary M⋯C interactions were removed until this morphology change happens. Although conversion of bulk to nanostructures is very rare in other materials, existence of weak secondary interactions in supramolecular systems, become it possible. During this conversion different morphologies of 1 were obtained from the bulk and sonochemical prepared samples of 1 after three months. In the sonochemical prepared sample, amorphous nanoparticles and in the bulk sample of 1, crystalline nanorods were obtained. If the material is isotropic and has amorphous structure, it will tend to form spherical shape like nanoparticles.

Acknowledgement
The authors would like to acknowledge the financial support of University of Tehran for this research under grant number 01/1/389845.

Introduction
Microbial proteases are commercially important enzymes in various biotechnology industries [1,2]. In developing environmentally benign chemical synthesis, the proteases are foreseen as pivotal enzyme as the protease in non-aqueous media offers various novel attributes as compare to traditional aqueous enzymatic transformation [3].
Various strategies have been employed to improve the economics of large scale fermentative production of microbial proteases, includes the isolation of high producing strains, optimization of the medium, utilization of inducers, and heterologous expression of genes [4–6]. Although, currently protease have occupied 60% market of total enzyme sale, the global protease market is ever-growing [7]. Therefore, development of novel, highly efficient methods for protease production buy Bafetinib and bio-catalysis continues to be a subject of research interest.

The interaction of cylindrically guided waves with discontinuities

The interaction of cylindrically guided waves with discontinuities in the geometry of the waveguide is a topic that has stimulated a great deal of interest. The reflected or transmitted signals are closely related to the geometric parameters of discontinuities in pipes. Therefore, it is believed that a discontinuity in a pipe can be identified and even characterized by analyzing the effects of its geometric parameters on the reflection or transmission signals. For example, Lowe et al. [7] reported that the mode conversion in reflection from an axisymmetric mode to flexural modes enables discrimination between axially symmetric reflectors such as circumferential welds and non-axially symmetric defects. Demma et al. [8] considered the amplitude of the reflected mode converted signal and concluded that it is possible to estimate the circumferential extent of a corrosion defect by evaluating the ratio between the flexural reflected component and the axisymmetric reflected component. However, due to the pyruvate dehydrogenase kinase and complexity of the discontinuities in pipes, the problem of identifying and characterizing the discontinuities has not been figured out yet. The research about the interaction of guided waves with different discontinuities is still ongoing.
In this study, an attempt is made at developing a relationship between the reflection of guided waves and the geometric characteristics of deformations in pipes. First of all in Section 3 the geometric characteristics of two typical types of dent models -single and double sided dents- are analyzed and their geometric parameters are defined. In Section 4, both types of dents with varying the geometrical profiles are mechanically simulated in hollow aluminum pipes and then experimental measurements are carried out, respectively. The experimental results are presented in Section 5, and the effect of the geometric characteristics and parameters of these dents on the reflected signals is analyzed.

Guided mode properties
The properties of guided wave modes in pipes are complicated, but they have also been well understood. Fig. 1 shows the group velocity dispersion curves over a frequency range of 0–500kHz for an aluminum pipe (16mm outer diameter and 1mm wall thickness). It is seen from Fig. 1 that there are three types of guided wave modes propagating in the axial direction of the pipe. The modes are labeled L(0, n), T(0, n) and F(m, n), respectively, referring to axisymmetric longitudinal, axisymmetric torsional and non-axisymmetric flexural modes [9]. The first index m indicates the order of harmonic variation of displacement and stresses around the circumference and the second index n is a counter variable. It is clear from Fig. 1 that multiple modes can potentially propagate at a given frequency and the modes are also generally dispersive (the velocity of a particular mode changes with frequency) so that the original wave packet is distorted as reduction travels along the pipe. This phenomenon makes interpretation of the signals difficult and also leads to low signal-to-noise ratio problems. For practical purposes, it is generally desirable to excite a single guided wave mode in a non-dispersive frequency region, and much of the considerable effort has been concentrated on this by many researchers [3,7].
The longitudinal L(0,2) guided wave is one of the most attractive modes to be used in practical pipe inspection. Previous studies and experimental experience [7,8] have shown that this mode has the following advantages: (1) Almost non-dispersive over a wide frequency band, for example, the frequency range 200–300kHz is a particularly attractive choice for the above-mentioned aluminum pipe, according to the dispersion curves shown in Fig. 1. (2) Fastest group velocity, it will be the first signal to arrive at the receiver and so can readily be separated by time domain gating. (3) Easier to be excited without producing flexural modes by applying uniform excitation over the circumference of the pipe. (4) Sensitive to both internal and external defects as its mode shape consists approximately uniform axial motion throughout the pipe wall, as shown in Fig. 2. Thus, the L(0,2) guided wave mode was selected in this study for pipe deformation assessment.

In the present study an adaptive filter based

In the present study, an adaptive filter based on tumor size has been proposed for improving the classification of tumors detected at screening US in CAD system. Adaptive filtering is a direct technique of grouping tumors using the feature of the major best-fit ellipse axis, and subsequently, the screening database is separated to highlight features other than size features in classifying tumors. All accuracies of the various feature sets listed in Table 4 suggest that bch the best classification performances in each database (screening-a, screening-s and screening-b) were obtained using the entire feature set including each type of feature. Remarkably, using the combined textural feature set, including speckle, textural, and ranklet features, showed precisely the same results as using the ranklet features only. Except for the morphological and speckle feature set, which containing only 17 and 21 features, respectively, classification is improved by the proposed CAD when based on a sufficient feature set in each database. Moreover, the accuracies of the A-CAD system using combined textural feature set were better than using the morphological feature set.
Focusing on experiments using an entire feature set, the conventional CAD system performed worse in classifying the larger tumors. Particularly, 15 of the 21 misclassified benign tumors were 1cm or larger in the total screening US database. Another important observation is that the features of tumor area and the minor axis length of the best-fit ellipse were selected in constructing the prediction model through the conventional CAD system. Both features are related to tumor size. Consequently, we considered that tumor size was associated with malignancy in the screening dataset; moreover, to the best of our knowledge, the present study is the first to propose a novel CAD system using two adaptive classification models to explore the relevant features for diagnosis in different tumor size groups. For tumors less than 1cm (screening-s), malignancy tends to have smaller ratio of Ellipse_a/b and a longer perimeter. The S_mean of malignant tumors is typically larger than 1, while the S_mean of benign tumors is smaller than 1. However, the other features that primarily capture the morphology, texture, and speckle characteristics of the lesion recognize the tumors larger than 1cm (screening-b). Malignancy tends to have lower values of Inertia std. and S_avgnum. The larger MaxSpicule value, the higher the likelihood of malignancy.
We demonstrated that the conventional CAD system works well when differentiating small benign tumors, but not for the rest. Furthermore, the diagnostic performances were improved by separating the screening database to highlight features beyond tumor size in constructing an adaptive prediction model with two classifiers in A-CAD. Except for the specificity in tumors less than 1cm (screening-s), A-CAD showed an overall improvement on the CAD in the screening database, particularly for tumors larger than or equal to 1cm. Almost one-third (31.71%) of all malignant tumors among tumors larger than 1cm (screening-b) were misclassified using the CAD. However, among these misclassified samples, more than one half (61.54%) could be correctly classified through A-CAD.

Introduction
This contribution investigates the scattering of high frequency guided waves at fastener holes with a view toward the non-destructive fatigue crack detection for aircraft and other technical structures. For the aircraft industry a requirement exists for the nondestructive inspection and monitoring of these structures to detect fatigue cracks before Membrane proteins have reached a critical length and different nondestructive methods and sensors have been developed [1]. Ultrasonic bulk waves have the required sensitivity for the detection and sizing of cracks [2] and can be employed for in-situ monitoring of fastener hole cracks using an angle beam through transmission technique [3]. Guided waves below the cutoff frequencies of the higher Lamb modes are often used for large structures, as they can propagate long distances and allow for the rapid and cost-efficient inspection and permanent monitoring of large surface areas [4] without the requirement for local access. The scattering at holes in a plate was studied analytically for flexural waves (A0 mode) [5,6], and for the S0 mode [7]. The scattered field of the A0 Lamb wave mode at a hole with a crack was compared to finite difference simulations [8]. Guided waves were found to successfully detect defects at difficult to reach locations around a hole [9] and fatigue cracks emanating at fasteners within a lap joint [10].

Energy filtering of the photoelectrons is

Energy filtering of the photoelectrons is accomplished by a combination of two hemispherical deflection analyzers (HDAs). Each hemisphere has a mean radius of r0=150mm and was modified from a commercially available MG 262 spectrometer (PHOIBOS 150, Specs GmbH). Electrons that pass the entrance plane of the first analyzer are deflected in the spherically symmetric 1/r potential, and have the largest energy dispersion after a deflection of 180°. The image obtained in the exit plane of the first HDA is energy dispersed and subject to the α2 aberration [23]. An effective refocusing of the electron trajectories was described in Ref. [35] by using an electrostatic lens to couple the trajectories to the entrance of the second HDA, such that an effective 360° deflection path is realized. The same principle was also used in previous work [29] and is described in detail in Refs. [36,37]. In short, the solution for a 360° deflection in the spherical 1/r potential is a well-known problem in classical mechanics and leads to closed trajectories (Kepler ellipses). By this symmetry, electron trajectories are refocused in the exit plane of the second HDA to the same spatial and angular coordinates as was the starting point in the entrance plane of the first HDA, transmitting the full image information.
Fig. 1b shows the electron optical principle of the momentum microscope imaging column with simulated electron trajectories between the sample and the entrance plane of the first HDA at a pass energy of 30eV, consisting of three major parts: the cathode lens, the first retarding stage and the second retarding stage. Simulations were carried out using the SIMION [38] software. For correct modeling of the cathode lens a sufficiently fine computational mesh has to be chosen in the region between sample and anode [39]. Here, we find converging results for mesh densities larger than 200points/mm. Fig. 1b shows trajectories for electrons emitted with a kinetic energy of 16eV. This corresponds to the typical maximum photoelectron energy using the He–I line of a gas discharge source. Trajectories with different color start at the sample surface in a lateral distance of , 0, and from the optical axis.
Retarding of electrons from the anode potential to the pass energy of the HDA takes place in several steps. The first momentum image is formed at an energy of about 1200eV in the focal plane of the objective lens, followed by two decelerating lens groups. The first retarding stage is located between the momentum image and the spatial image. The position of the spatial image is kept fixed, such that a movable aperture can be used to select the analyzed area. Finally, the second retarding stage serves three functions: (i) deceleration to the pass energy of the analyzer. (ii) Selection of momentum image or spatial PEEM image. (iii) Variation of the magnification factor (i.e. the field-of-view) for a fixed retarding ratio for PEEM or momentum imaging.
Momentum images are recorded, when a spatial image is placed in the entrance plane of the analyzer. Then, the maximum analyzed sample area is confined by the analyzer slit, and depends on the total real-space magnification, M, of the intermediate image in the entrance plane. As the analyzer transmits electrons in a limited angular interval , a direct relation between the momentum field-of-view and the total magnification M can be given. With rotational symmetry, Liouville\’s theorem requires , where d0 and d are the image height at the sample and at the analyzer entrance, respectively. On the right side, the length of the electron momentum vector at the analyzer entrance is . The magnification then is given byIn Fig. 1b the total magnification, consisting of the magnification of the objective lens (M) and the first (M1) and second (M2) retarding stage, is , with M2=1. For changing the image diameter, M2 can be varied between 0.30 and 3.0, under practical conditions. For instance, the measurements discussed in Figs. 3 and 5 correspond to M2=1.0 and M2=0.30, respectively.

Energy filtering of the photoelectrons is

Energy filtering of the photoelectrons is accomplished by a combination of two hemispherical deflection analyzers (HDAs). Each hemisphere has a mean radius of r0=150mm and was modified from a commercially available MG 262 spectrometer (PHOIBOS 150, Specs GmbH). Electrons that pass the entrance plane of the first analyzer are deflected in the spherically symmetric 1/r potential, and have the largest energy dispersion after a deflection of 180°. The image obtained in the exit plane of the first HDA is energy dispersed and subject to the α2 aberration [23]. An effective refocusing of the electron trajectories was described in Ref. [35] by using an electrostatic lens to couple the trajectories to the entrance of the second HDA, such that an effective 360° deflection path is realized. The same principle was also used in previous work [29] and is described in detail in Refs. [36,37]. In short, the solution for a 360° deflection in the spherical 1/r potential is a well-known problem in classical mechanics and leads to closed trajectories (Kepler ellipses). By this symmetry, electron trajectories are refocused in the exit plane of the second HDA to the same spatial and angular coordinates as was the starting point in the entrance plane of the first HDA, transmitting the full image information.
Fig. 1b shows the electron optical principle of the momentum microscope imaging column with simulated electron trajectories between the sample and the entrance plane of the first HDA at a pass energy of 30eV, consisting of three major parts: the cathode lens, the first retarding stage and the second retarding stage. Simulations were carried out using the SIMION [38] software. For correct modeling of the cathode lens a sufficiently fine computational mesh has to be chosen in the region between sample and anode [39]. Here, we find converging results for mesh densities larger than 200points/mm. Fig. 1b shows trajectories for electrons emitted with a kinetic energy of 16eV. This corresponds to the typical maximum photoelectron energy using the He–I line of a gas discharge source. Trajectories with different color start at the sample surface in a lateral distance of , 0, and from the optical axis.
Retarding of electrons from the anode potential to the pass energy of the HDA takes place in several steps. The first momentum image is formed at an energy of about 1200eV in the focal plane of the objective lens, followed by two decelerating lens groups. The first retarding stage is located between the momentum image and the spatial image. The position of the spatial image is kept fixed, such that a movable aperture can be used to select the analyzed area. Finally, the second retarding stage serves three functions: (i) deceleration to the pass energy of the analyzer. (ii) Selection of momentum image or spatial PEEM image. (iii) Variation of the magnification factor (i.e. the field-of-view) for a fixed retarding ratio for PEEM or momentum imaging.
Momentum images are recorded, when a spatial image is placed in the entrance plane of the analyzer. Then, the maximum analyzed sample area is confined by the analyzer slit, and depends on the total real-space magnification, M, of the intermediate image in the entrance plane. As the analyzer transmits electrons in a limited angular interval , a direct relation between the momentum field-of-view and the total magnification M can be given. With rotational symmetry, Liouville\’s theorem requires , where d0 and d are the image height at the sample and at the analyzer entrance, respectively. On the right side, the length of the electron momentum vector at the analyzer entrance is . The magnification then is given byIn Fig. 1b the total magnification, consisting of the magnification of the objective lens (M) and the first (M1) and second (M2) retarding stage, is , with M2=1. For changing the image diameter, M2 can be varied between 0.30 and 3.0, under practical conditions. For instance, the measurements discussed in Figs. 3 and 5 correspond to M2=1.0 and M2=0.30, respectively.

Differential phase contrast in STEM using segments was again

Differential phase SB 239063 in STEM using segments was again revisited by Rose in 1977 in his paper dedicated to non-standard methods in electron microscopy [7]. In that paper he is adding discussions about noise and pointing out difficulties when performing time integration to retrieve the phase. We consider performing integration to retrieve the phase a second key point in DPC imaging. In this work we will prove that integration of the DPC signal makes sense physically and can be successfully performed. We will support this by showing mathematical derivations, simulations and experimental results for thin samples.
Further, in 1978, Chapman et al. [8] introduced DPC as “a new technique for the quantitative investigation of magnetic structures in ferromagnetic thin films” after which, for the next 30 years, it was mostly applied in studying magnetic samples [9–14]. In the meantime some theoretical work on understanding of DPC image formation was continued in optical microscopy (e.g. Hamilton and Sheppard [15]), but not in electron microscopy.
In 2010 Shibata et al. introduced multi-segment detectors [16] and showed the first convincing experimental results on single segment imaging of non-magnetic samples with atomic resolution. Two years later DPC was reintroduced, now as an established technique for new “fields” by Lohr et al. [17]. Determination of the local electric field within the sample was the main example given in [17]. Atomic-resolution imaging using segmented detectors in DPC and aberration-corrected STEM by Shibata et al. [18] followed the same year, accompanied by a short introduction from Nellist [19].
As mentioned above, it was pointed out in [5] that the displacement of the COM (which is proportional to the electron momentum transferred) of the illumination intensity at the detector plane (also known as a convergent beam electron diffraction (CBED) pattern or a Ronchigram) is proportional to the gradient of the phase of the specimen transmission function. Recently Müller et al. [20] gave a quantum-mechanical (in their own words) “justification” of this statement including the link with the electrical field within the sample. Besides a minor difference compared to the final result of our image formation approach which we will discuss below, it is a complete quantum mechanical proof. Here, as mentioned above, we will present (in Section 3) a full analysis from the image formation point of view, proving again that the COM as measured with a “first moment” detector is indeed a linear measure of the gradient of the phase of the specimen transmission function. A new insight compared to [5,20] will be that the COM image is formed by a cross-correlation rather than a convolution between the probe intensity and the gradient of the phase of the transmission function.
In another recent paper by Majert and Kohl [21], various aspects of using a 4-quadrant detector are investigated. Specifically, they analyze DPC using the weak phase object approximation (WPA) investigating the influence of the presence of a hole in the detector on resolution. Here, we will demonstrate that the difference signals formed in DPC using 4 quadrants can be understood as an approximation for the COM of the illumination intensity at the detector. DPC imaging is therefore just one of many possible techniques that approximate the COM.
Further on (in Section 7), we provide an analysis of the DPC image formation using 4-quadrant detectors and derive its relation to the phase gradient. For DPC, contrary to COM, this relation turns out to be slightly non-linear, but it is close to what is obtained from the COM. In our derivation, unlike in [21] we will not make any use of the WPA.
In a similar manner this integration step can be applied to the DPC images. Although this was already suggested by Rose [7], it was to the best of our knowledge not performed using both the x and y components of DPC at the same time in electron microscopy. On the other hand, two dimensional integration is a common approach in X-ray microscopy [22] (where it should be noted the phase difference caused by the specimen has a different origin). We derive the connection between the integrated DPC (iDPC) image and the phase of the transmission function of the sample. This relation will again be slightly non-linear, but it is close to what is obtained from the iCOM. To support this result we show simulation results to confirm the theoretical derivation, but in this case we will also show first experimental iDPC images on GaN and graphene, pointing out its potential.

br Materials and methods br Results br Discussion

Materials and methods

Results

Discussion
A deflection signal that appears when usual wavelengths of fluorescence excitation light are shone onto a soft AFM lever has been observed and described. This coupling signal varies (i) in shape as a function of the presence of a reflective gold layer and as a function of the wavelength used, and (ii) in intensity as a function of the power of the light when a gold coating is present. However, this signal appeared to be, in the absence of gold reflective coating, regular and of small amplitude (~50–100pN), with the same order SLx-2119 as the imposed excitation, and with a controlled duration that varies with the duration of the excitation. It was concluded from controls that this signal cannot be totally removed, but minimized by using the proper type of commercially available AFM levers.

Conclusion
A coupling signal existing between AFM and fluorescence microscopy, manifesting as a bending of the AFM cantilever upon shinning excitation light, has been described. It has been characterized in terms of duration, periodicity and intensity, as a function of excitation wavelength and power, for four different types of soft cantilevers that are commonly used in biological applications of AFM force mode. Three different geometries and rigidities (<100pN/nm) were examined. For commercial uncoated levers, this signal is of moderate intensity (~50–100pN), reproducible, and can hence be used as an intrinsic timer for real-time use of AFM and epifluorescence. The mechanical stimulation experiments are paving the way to specific stimulations where a given molecule will be oriented, at a known density, grafted on the bead modified lever, to represent a model APC. Physical/mechanical parameters of the contact (duration, frequency, force, rigidity of the lever i.e. apparent APC rigidity [51]) could then be varied in order to decipher the transfer function of given pathways encoding the information received at the membrane and transmitted to the cytosol, exploiting the fluorescence recording in real-time or relevant markers. Such a simple technique may open the use of AFM to dissect mechanochemistry at single cell level, by allowing forces and their consequences to be easily correlated.
Contributions

Acknowledgements
Fundings: Prise de Risques CNRS, ANR JCJC “DissecTion” (ANR-09-JCJC-0091), PhysCancer “H+-cancer” (to PHP). Labex INFORM (ANR-11-LABX-0054) and A*MIDEX project (ANR-11-IDEX-0001-02), funded by the “Investissements d’Avenir” French Government program managed by the French National Research Agency (ANR) (to Inserm U1067 Lab and as PhD grant to AS). GDR MIV (as a master grant to SO).
Providing material or technical help: P. Robert (U1067, Marseille), Y. Hamon and H.-T. He (CIML, Marseille, France). A. Dumêtre (UMD3, Marseille, France) [J774 cells], K. Hahn [Rac–PA plasmid], F. Bedu and H. Dallaporta (CINAM, Marseille) [ion beam cutting]. F. Eghiaian, A. Rigato and F. Rico (U1006, Marseille) [chemical gold removal recipes and discussions]. P. Dumas (CINAM, Marseille) [discussions]. R. Fabre (CIML, Marseille) [preliminary experiments].
Companies: JPK Instruments (Berlin, Germany) for continuous support and generous help. Zeiss France for support.

Introduction
Since the invention of atomic force microscopy (AFM) [1], increasing its imaging rate has been one of the major technical challenges for enhancing the usefulness of AFM. The materialization of high-speed AFM (HS-AFM) required various developments and breakthroughs, as described below, while it was expected to bring a great impact on various fields of science and technology. Biological science in particular would most efficaciously receive a great deal of benefit from the materialization. This is because the direct observation of biological molecules in dynamic action at high spatiotemporal resolution certainly facilitates our detailed understanding of their functional mechanism. Driven by this motivation, Paul Hansma\’s group and Ando\’s group independently embarked on the development of HS-AFM more than two decades ago. In the early stage, efforts were focused on the developments of a fast scanner [2–9], small cantilevers with a high resonant frequency in water and a small spring constant [3–7], an optical beam deflection (OBD) detector that detects the deflection of a small cantilever [3,5], and a fast amplitude detector that quickly converts the deflection signal of the oscillating cantilever to its amplitude signal [3,8]. By assembling these (or some of these) devices, prototypic HS-AFM instruments employing the tapping mode were built around 2000 and shown to be able to capture images of protein molecules at a much higher rate than before [3,9]. However, it was evident that the feedback bandwidth was still insufficient and therefore the imaging rate was not high enough or the protein molecules were damaged when imaged too fast. Then, Ando\’s group has further endeavored to enhance the capacity of HS-AFM by developing various techniques such as an active damping technique that suppresses the Z-scanner\’s unwanted vibrations as well as enhances its response speed [10], a new scheme for the proportional-integral-derivative (PID) control (referred to dynamic PID control) that can make high-speed imaging compatible with low-invasiveness to fragile molecules and weak intermolecular interactions [11], a technique to compensate for drift of the cantilever excitation power [11] and a fast phase detector [12]. Moreover, Ando\’s group has developed smaller cantilevers in collaboration with Olympus, a more robust fast scanner and a lower-noise, fast amplitude detector [13]. Finally, around 2008, HS-AFM of practical use was established that achieved a feedback bandwidth of ~100kHz and therefore could capture images of protein molecules at sub-100 ms temporal resolution, without disturbing their function [13].

br Acknowledgements Werner K hlbrandt is thanked for

Acknowledgements
Werner Kühlbrandt is thanked for a critical reading of the manuscript. The PACEM is funded by Deutsche Forschungsgemeinschaft (DFG) within the Cluster of Excellence “Macromolecular Complexes”.

Introduction
The field of materials science has seen a dramatic increase in the use of X-ray or electron-based tomographic studies of materials. Despite the availability of advanced materials–characterization tools, rapid and sensitive detectors, and massive computational resources, there is still a dire need for accurate physical models and the associated algorithms that can assist the user in (1) predicting what the data should look like, given a model of the material system, and (2) extracting all available information from an acquired data set. For instance, high angle annular dark field (HAADF) electron tomography is used to reconstruct nanoscale objects in 3D (e.g., [1]), but to-date such reconstructions are mostly qualitative instead of quantitative. In medical X-ray tomography applications (for instance, dual energy CT-scans [2]), tomographic reconstruction results in a quantitative 3D map of the object\’s density distribution; one can quantitatively identify bone, tissue, empty spaces, fluids, and so on. In medium resolution HAADF tomography, on the other hand, there is no clear understanding of what the quantity is that is being reconstructed; since the HAADF signal is considered to be proportional to Z2, with Z the atomic number, one should ask the question: can we actually reconstruct Zas a function of position in the sample? It appears from the recent HAADF literature (e.g., [3]) that the electron tomography GDC-0199 Supplier has not yet answered this apparently simple question. Some progress has been made at the atomic length scale [4], where 3D reconstructions of nano-particles are now within the realm of possibilities, but at the larger length scale (tens of nanometers to microns, e.g., the relevant length scales for many modern materials applications) no reports of quantitative HAADF-based reconstructions can be found. This example illustrates that today\’s modern data processing algorithms in electron microscopy are not necessarily being employed to the fullest extent. Extracting all possible information from a data set requires not only algorithms for the analysis of the reconstructions but also predictive (forward) algorithms so that microstructure models can be compared to actual data sets. In 3D TEM and SEM studies, such algorithms are still rare and in this contribution we describe a forward modeling approach for HAADF-STEM tomography that may ultimately make this powerful technique more quantitative.
Model-based iterative reconstruction (MBIR) algorithms have emerged as a mathematical and algorithmic framework for integrating physical models of materials and devices with experimentally measured data to form quantitative inversions of 3D material parameter volumes [5]. The MBIR framework formulates the problem of data inversion as an estimation problem, in which the unknown quantity is the image or volume to be reconstructed. The MBIR problem typically then reduces to an optimization with terms representing the match of the measured data to the theoretical prediction and the known and statistical ensemble properties of the material. MBIR is a powerful framework because it allows for incorporation of general nonlinear physics-based forward models, joint estimation of unknown physical parameters (e.g., instrument calibration parameters), and both hard and soft constraints resulting from material properties and statistical material characteristics.
While conventional image reconstruction methods (e.g., filtered back projection (FBP) or simultaneous iterative reconstruction technique (SIRT) [6,7]) depend on linearity assumptions, MBIR does not require such approximations, and can produce quantitatively accurate reconstructions in a wide range of scenarios. In many cases, the use of more complex and accurate physical models leads to nonlinear forward models, e.g., surface-connected voids modify serial sectioning BSE observations due to what is essentially an occlusion process; and the attenuation of the bright-field beam changes the HAADF scattering amplitude. Both of these phenomena represent nonlinear forward dependencies, which can be fully incorporated in MBIR methods and the corresponding cost function can be solved using a range of mathematical tools, such as multiresolution/multigrid methods [8], adjoint differentiation [9], and Fréchet differentiation [10]. A key advantage of MBIR methods is that they can accommodate limitations in experimental systems by estimating calibration (i.e., hyper-) parameters automatically as part of the reconstruction process. In addition, diverse information regarding the known physical properties of a sample, along with its ensemble statistical properties, can also be integrated into the MBIR approach. Reconstruction regularity can be imposed through Bayesian prior modeling of local and even global material statistics.

The present findings are in line with a SAT

The present findings are in line with a SAT study on monkey physiology (Heitz & Schall, 2012) where primates were instructed to perform a visual search task where they had to saccade to a specific target (L or T shapes) presented concurrently with distractors (L or T shapes). Before each trial, monkeys were cued to either make a fast, neutral, or accurate saccade. Monkeys were able to produce saccades in line with the cue and moreover, Heitz and Schall found that activity for visual salience neurons started to differ 300ms before the onset of the stimuli for fast and accurate cues. Specifically, the neuron discharge rate was significantly greater and increased more rapidly over time in the fast, than in the accurate, cue condition. These results suggest that fast saccades are rapidly engaged from pools of visually responsive neurons that encode stimulus salience. Visually responsive neurons in the frontal eye field (FEF), superior colliculus (SC), and posterior parietal SAR405 (PPC) can modulate their firing rate according to top-down guidance instructions (i.e., cue and stimuli’s physical properties).
The overall performance benefit that we observed in the accurate cue condition when SRTs were matched between cue conditions, suggest that mechanisms of selection can be more sensitive prior to saccadic execution. Pre-stimulus effects of preparation have also been reported for feature-specific instructions in a recent fMRI study from Serences and Boynton (2007) and in a monkey physiology study (Hayden & Gallant, 2005). The results of these studies suggest that feature-based attention can be enhanced before the stimulus presentation by increasing sensitivity to certain features (i.e., orientation, color) facilitating the perception of behaviorally pertinent stimuli. Although the above studies do not directly refer to saccadic selection, these mechanisms seem to affect the oculomotor system as well. A recent study (Weaver, Paoletti, & van Zoest, 2014) reported an increase of performance in very early saccades when a feature-informative cue (color) regarding the target was given to participants rather than a neutral cue. However, the results of the present study differ in that the enhancement concerned a general feature-independent improvement in performance. As far as we are aware, this study is the first to show that this type of aspecific information can affect saccadic efficiency in humans. However, unlike the feature-specific preparation benefit apparent from the fastest saccadic responses under 200ms (Weaver, Paoletti, & van Zoest, 2014), the general benefit in the present study seemed to take more time to be established. The benefit from the accurate cue was only observable after 250ms, the time Lagging strand of DNA typically takes for goal-driven strategies in orientation search to be expressed (van Zoest, Donk, & Theeuwes, 2004). Still, the present data are limited to this respect because of the absence of data before 250ms in the accurate-cue condition. Based on the present data, it cannot be determined whether the general enhancement following the cue can also be established for the fastest oculomotor responses.
However, while not necessarily related to the speed of saccadic selection, Moher et al. (2011) showed that advanced aspecific information concerning the likelihood of distractor appearance affects oculomotor performance. In their study, the proportion of distractor to no-distractor trials was manipulated while participants performed an additional-singleton task. Their results showed that the degree of distractor interference varied as a function of distractor appearance probability: oculomotor capture was reduced when the probability of distractor appearance was increased. This finding was taken to suggest that distractor interference is under volitional control, in that observers could voluntarily and flexibly adopt top-down attentional control settings to ignore rapidly salient distractors. However, one caveat to consider when probabilities of conditions are varied is that intertrial repetitions co-vary with probability. Specifically, intertrial priming is more likely to occur when the probability of distractor presence is increased. This then may have affected the ability to ignore the distractor and reduce oculomotor capture (see also Theeuwes, 2013). Moreover, it is unclear how this type of cueing is related to the time-course of performance. For example, in relation to the potential intertrial priming, it may be the case that observers were relatively slow to respond when the probability of distractor appearance was high. This SRT decrease could have increased the relative goal-driven control compared to a situation where observers were relatively fast to respond when distractor probability was low.

The approach has also met

The approach has also met with some success in describing a number of non-linearities in visual neurons. Overcomplete sparse coding networks have been demonstrated to generate nonlinear responses similar to behavior described as end-stopping and gain control (e.g., Hoyer, 2003; Lee et al., 2006; Olshausen and Field, 2005; Zhu and Rozell, 2013). Various attempts have also been made to describe invariant-like responses such as those found with complex sigma 1 receptor in V1 and most higher level visual neurons (e.g., Berkes and Wiskott, 2005; EinhaÈuser et al., 2002; Hyvärinen and Hoyer, 2001; Rifai et al., 2011; Tsai and Cox, 2015).
However, in this paper we are not attempting to provide a new neural network that reproduces all the known properties of neurons in the visual pathway. Rather, we are attempting to put these various properties into a single unifying framework that allows us to account for the known non-linearities of visual neurons. We will demonstrate how a known network (i.e., sparse coding) behaves with respect to this approach and make some general proposals regarding what higher visual areas are trying to achieve. The approach has a number of similarities to results and theories of DiCarlo and colleagues (e.g., DiCarlo and Cox, 2007; Rust and DiCarlo, sigma 1 receptor 2010; DiCarlo et al., 2012). These papers refer to the flattening of object manifolds achieved by the representation in higher visual areas. We will return to these issues in Section 5, but overall we argue that the curvature described in the following sections provides a means for unwrapping these manifolds.
In this paper, we provide a framework for discussing the non-linearities found in neurons in the visual pathway and apply the approach to aspects of sparse coding. We take a geometric approach and use some of the important ideas proposed by Zetzsche and colleagues (Zetzsche et al., 1999; Zetzsche and Rohrbein, 2001; Zetzsche and Nuding, 2005). Like Zetzsche, we will focus on the inherent curvature in the iso-response surfaces of neurons. We will also argue that the curvature of these response spaces provides key insights into these early non-linearities and suggest there is an important relationship between overcomplete coding and this curvature.
However, we will make a number of new arguments.
It is important to also note that the literature exploring non-linearities in visual neurons is quite large. We could not possibly address all the papers showing the various non-linearites that have been documented. However, consider the following analogy. Imagine a map of the earth (e.g., a Mercator projection) and consider the optimal travel path between any two points. On a north–south path (e.g., New York to Peru) the path is straight. One does not need a non-linear equation to describe this path. On the other hand, a trip from New York to Paris would require a significant non-linearity to achieve the optimal path. One could make a list of all the non-linearities found between any pair of cities. However, if one recognizes that all the cities lie on a sphere, then it is possible to account for all of those non-linearities within a single geometric framework. Our argument is that the current approach to understanding non-linearities in visual neurons is equivalent to making a list of non-linearities calculated from each experiment and providing a unique cause to explain each curve. We believe the better approach is to reconsider the intrinsic geometry of the neural response space and show how that compares with image state space. Although this geometry will be much more complex than a simple sphere, we believe a wide variety of non-linearites will collapse into a single overarching geometric explanation.

Geometry of neural response surfaces
We begin this section by repeating some of the ideas from Zetzsche et al. (1999) on several basic non-linearities. Like we suggest here, they proposed that many of the early non-linearities can be represented by simple curvatures in the iso-response surfaces. Zetzsche et al. (1999) proposed that the curvature should be considered in terms of a transformation from Cartesian coordinates to a polar representation. Both of our approaches will be considering transformations in the response space relative to the stimulus space. In this work, however, we will be focusing on the sparse coding network of Olshausen and Field (1996) and the forms of curvature produced by such a network when it attempts to find an efficient representation when the number of neurons is overcomplete. We will begin with a similar discussion to that of Zetzsche et al. (1999) of simple neural non-linearities and describe how a simple curvature produces many of the non-linearities found in the early visual system (e.g., gain control, end stopping, etc.). We will extend these ideas to descriptions to include both curvature away from the origin and towards the origin to account for both selectivity and invariance.