We present an image postprocessing framework for Scanning Tunneling Microscope (STM) to reduce the strong spurious oscillations and scan line noise at fast scan rates and preserve the features, allowing an order of magnitude increase in the scan rate without upgrading the hardware. The proposed method consists of two steps for large scale images and four steps for atomic scale images. For large scale images, we first apply for each line an image registration method to align the forward and backward scans of the same line. In the second step we apply a “rubber band” model which is solved by a novel Constrained Adaptive and Iterative Filtering Algorithm (CIAFA). The numerical results on measurement from copper(111) surface indicate the processed images are comparable in accuracy to data obtained with a slow scan rate, but are free of the scan drift error commonly seen in slow scan data. For atomic scale images, an additional first step to remove line-by-line strong background fluctuations and a fourth step of replacing the postprocessed image by its ranking map as the final atomic resolution image are required. The resulting image restores the lattice image that is nearly undetectable in the original fast scan data. The invention of the scanning tunneling microscopy (STM) revolutionized the study of nanoscale and atomic scale surface structures and properties , 2. Howexy plane). Changes in the surface height z or in the density of states cause a change in the tunneling current. The change in current with respect to position can be measured itself, or alternatively the height of the tip corresponding to a constant current can be measured. These two modes are called the constant height mode and the constant current mode, respectively. In the constant current mode, feedback electronics adjust the height by a voltage to the piezoelectric height control mechanism. In the constant height mode, the voltage and height are both held constant while the current changes to keep the junction voltage from changing. The constant current mode is usually used in STM because surface features can easily exceed a predefined tip-sample separation  and can crash the tip in a constant height mode. But the constant current mode is slow, due to more time required by the piezoelectric movements to register the height change. The time to complete a measurement for each pixel position is about 2 msec for a typical equipment and approaches 0.1 msec for a top-of-line setup [The common practice for STM measurement is by bringing the tip close to the sample surface and applying a voltage bias to generate a tunnel current between the tip and sample. The tip is moved across the sample parallel to the surface  nearly matches the quality of the image of the same sample from a slow scan rate of 2 msec per pixel and also eliminates the scan drift that is apparent in the slow scan image (2) where the raw image is overwhelmed by noise and tip crash streaks  surface. Cu surface is a prototypical platform for atomic resolution STM studies on electron scattering , nanostr1 surfacean image . For atoan image  and to ran image  from a feaks see .The paper is organized as follows: we introduce our methods of background removal in The intensity fluctuation in the atomic resolution images seriously degrades the image quality and impedes the registration and image restoration method that we will apply later. Because the image intensity varies strongly from one scan line to the next, but stays nearly constant over long segments within each line, this fluctuation can be treated as a background for each scan line. Therefore, the first step in processing the atomic resolution images is to remove this background in a line-by-line manner.Fa ∈ ℝm×n and Ba ∈ ℝm×n, respectively. Consider one of the images, say Fa, whose ith line is represented by f ∈ ℝn1×. The background is considered a slow varying function along the line. We wish to remove the background and keep the fast varying features. To achieve this, we estimate the value of the background at point x, gx, by a weighted least mean-square linear fit to a segment of the line centered at x with length L:w(x′ − x) is to reduce the impact of noise in the data. It is a numerically efficient alternative to robust least mean-square fits. The corrected image is given byx. We found numerically The forward and the backward scans are represented by An additional minor improvement to the background removal process is to remove the small remnant slope at the two edges by fitting a small part of the line at two ends to a linear background with slopes of the same magnitude but opposite sign (so that the two lines meet at the same height in the middle) and removing this background from the corresponding halves of the line. This step makes the intensity somewhat more uniform across the whole line.L is determined by searching for the maximum correlation coefficient after image registration, a process that we will describe below.The value of The effect of the background removal step using linear regression allows the subsequent two steps to be carried out which produced Our goal is to combine data from forward and backward scans to obtain a more accurate image. Because there is always a small registration difference between the forward and backward scans, a simple combination of the unprocessed data  will yield a blurred image . Indeed,The method for image registration is improved significantly from the one used in . The maiFa and Ba. In the registration procedure, each pair of corresponding lines from Fa and Ba are adjusted by a line shift to minimize their difference. The line shift is calculated for the ith lines of Fa and Ba, which are represented by f ∈ ℝn1× and b ∈ ℝn1×, respectively, as a minimization problem. The objective is to find a constant c, such that f(x + c) is as close to b(x − c) as possible. In [c in practice allows one to solve this minimization through a heuristic search. However, this method is susceptible to noise, especially for atomic resolution data in which presence of noise signal may have the same magnitude as images of atoms. Therefore we need a more robust approach to image registration.Although the images are two-dimensional, the data is acquired in a line-by-line manner. Therefore the image registration method is applied separately to each line of the forward and the backward scans, ible. In , the conFa and Ba as linearly correlated data and find the constant c such that the correlation coefficient is maximized:favg = (1/N)∑xf(x + c) and bavg = (1/N)∑xb(x − c), N is the number of points in each line, and f(x).The improved approach is to treat the pair of data sets Fa and Ba. The average image is much sharper compared to Fa − Ba| shown in The benefit of image registration is clearly visible in Fa and backward scan Ba, we wish to find an approximate image that is as close to the true image S as possible. Our main goal is to eliminate any noise and spurious signals in the image, while keeping all features in the image. We consider any feature that exists only in one (forward or backward) scan but is not matched in the other scan as spurious and must be removed. To remove such spurious signals, we propose a “rubber band” model, in which a “rubber band” is inserted between the curves of the two scanned signals and is pulled tight, as illustrated in s denote one row of S while f and b represent the corresponding rows of Fa and Ba. The associated model for this pair of lines is given byFa and Ba via a constrained signal filtering that maintains the filtered value within the bounds set by Fa and Ba until convergence. The existence of the solution is guaranteed as the average of the forward and backward scans satisfies the constraint and we use it as the initial candidate signal. For the filtering process, the value of each point on the line is replaced by the average value of its two nearest neighbors, subject to the constraint that it is bounded by Fa and Ba. The algorithm is outlined in Using the aligned forward scan F represents the rth row of the 2D matrix F and the operators min{·, ·} and max{·, ·} generate vectors of element-wise minimum or maximum value of the two input vectors, respectively.In The first three postprocessing steps described above on atomic resolution fast scan data yield images that clearly show surface lattice structure, as in Ωx,y the square consisting of n × n pixels centered at  and define a ranking function R for S on Ωx,y as follows: If S is the ith smallest one among all S for  ∈ Ωx,y, define R = :i − 1. Then, we replace S by R. Clearly, we have n2 − 1. The best value for n is the number of pixels that covers more than 1-2 atomic distances.The first part of this algorithm is to remove all large scale topography information while retaining the local height information that is needed to distinguish the atoms. To achieve this, we denote by The ranking map obtained this way is a ragged image. We apply a 2D median filter  to obtaiThe idea of using the ranking map for feature enhancement can be viewed as the reverse of ranking based smoothing techniques such as median filtering. However the ranking map algorithm presented here is unique in that it does not merely use the ranking to help determine the image intensity as in the median filtering method. In the ranking map method, the final image intensity is the ranking itself, and the original image is discarded once the ranking map is constructed.−11 Torr). Electrochemically etched tungsten tip was cleaned by in situ electron bombardment heating.To establish the effectiveness of the algorithm, it is tested on STM topography scans of a clean copper(111) single crystal surface at room temperature (299 K) using a tungsten tip under conventional scan speed (about 2 msec per data point) as well as fast scan speed of 0.1 msec per data point with multiple scans for the same region on the sample surface. The STM scans are acquired by using a variable temperature STM (Omicron) with Nanonis (SPECS) controller in constant current mode. Clean Cu(111) single crystal was prepared by cycles of Ar+ sputtering and postannealing (550°C) in ultrahigh vacuum condition (8 × 10As already discussed in previous sections, the original unprocessed topography data from a fast scan with a scan rate of 0.1 msec per pixel are shown in Figures For the atomic resolution fast scan data, the raw image in Figures We have demonstrated an algorithm that can greatly reduce the noise and error of STM images by combining forward and backward scan data in a line-by-line manner. This allows us to push the scan rate for a conventional STM setup to beyond its normal limit, up to 10 times faster. An order of magnitude increase in the scan rate will greatly reduce systematic errors such as the scan drift and environmental noise and will also improve research productivity. Furthermore, this increase represents a first step towards the goal of real-time observation of dynamic processes on surface by STM.Using the ranking map as the atomic resolution image, as described in this work, is a general algorithm for postprocessing STM images beyond fast scans. Any images obstructed by significant surface topology variations can be treated by this algorithm to yield sharp, atomic resolution results. This algorithm is also useful as an alternative way to enhance image features and can be used as a general tool in image processing technology.


The equilibrium volume of a thermoresponsive polymer gel changes dramatically across a temperature due to the coil–globule transitions of the polymers. When cofacially oriented nanosheets are embedded in such a gel, the composite gel deforms at the temperature, without changing the volume, and the response time is considerably shorter. We here theoretically predict that the deformation of the composite gel results from the fact that the nanosheets restrain the deformation of some polymers, while other polymers deform relatively freely. The unrestrained polymers collapse due to the coil–globule transitions and this generates the solvent flows to the restrained regions. The response time of this process is rather fast because solvent molecules travel only by the distance of the size of a nanosheet, instead of permeating out to the external solution. This concept may provide insight in the physics of composite gels and the design of thermoresponsive gels of fast response. Composite polymer gels with embedded nanosheets deform discontinuously without volume expansion during temperature changes, but the reason behind it is unknown. Here the authors predict theoretically that the deformation results from restrictions which the nanosheets set on the polymer gel. Gels of specific polymers  show the volume phase transition, in which the volume of the gel at the thermodynamic equilibrium changes by the factor of tens or more across a temperature6. Thermoresponsive polymer gels have potential applications to soft actuators8, resizable colloids11, drug delivery systems12, etc. The volume phase transition is driven by the coil–globule transition, in which polymers changes from swollen random coil to collapsed globule in a small window of temperature14. The dynamics of the volume phase transition is very slow, typically in the order of hours (for a gel of ~1 mm size)17. The dynamics is accelerated for gels with heterogeneous structures19, dangling bonds20, amphiphilic conetwork21, and mobile crosslinks23. However, the response time of the volume phase transition is not shorter than the order of minutes (for a gel of ~1 mm size)21, because to change the volume of the gel, solvent molecules must travel by the length scale of the size of the gel through the fine mesh of the polymer network3.A polymer gel is composed of a polymer network, which is swollen in a solvent and changes its volume by taking in (or squeezing out) solvent moleculesN-isopropylacrylamide) (PNIPA) gels show the volume phase transition at a temperature, above which these gels collapse. In recent experiments, Aida and coworkers prepared a composite gel, in which solid nanosheets are immobilized in a PNIPA gel25. These nanosheets are cofacially oriented and aligned in a stack of layers, which are parallel to the nanosheets. Above the transition temperature, the composite gel shows elongation in the normal to the layers and contraction in the parallel to the nanosheets. The volume of the gel does not change during the deformation and the response time of the deformation is in the order of seconds for the gel of ~1 mm size25. These features are in contrast to the volume phase transition, with which gels change their volume with a long response time. Understanding the physical mechanisms involved in the volume-conserving deformation may open a new avenue of researches of thermoresponsive gels that respond in a reasonably short time.Poly the solvent from the external solution27. Experimentally testing our predictions may thus open a new avenue of the applications and designs of composite thermoresponsive polymer gels that respond in a reasonably short time.Here we theoretically analyze the deformation of the composite gel by taking into account the fact that the gel is locally restrained by the nanosheets. Our theory predicts that the composite gel swells in the normal to the layer and shrinks in the lateral direction above the transition temperature. The direction of the deformation is indeed in agreement with experiments25. We use a simple model of a polymer gel with which the elastic energy of the polymer network, the mixing entropy, and the interaction energy are taken into account3 (see Methods and 15). Moreover, there are no long-range interactions (such as electrostatic interactions) between the nanosheets. The magnitudes of the attractive monomer–monomer interactions, relative to the monomer–solvent interactions, are represented by a dimensionless interaction parameter χ14. The thermodynamic state of the gel depends on the temperature via the interaction parameter χ. We thus analyze the deformation of the gel when one changes the interaction parameter χ.We treat a composite gel in which solid nanosheets are immobilized in the polymer network , but cannot in the lateral direction; the lateral deformation is suppressed by the elastic energy with respect to the shear deformation of the polymer network for cases in which the aspect ratio rin/h0 is large, see also Supplementary Figs. λ||) and it can deform in the normal direction with the same extension ratio λ⊥ as the central region, see Fig. For an ideal case in which solid nanosheets are aligned periodically in the layers Fig. , the com3. In a typical experiment, solvent molecules travel through a unit cell (of ~1 μm size) in ~0.1 s and permeate out from the gel (of ~1 mm size) in ~1 day . We here treat a timescale in which solvent molecules flow between the central and surrounding regions, but do not permeate out from the gel. With this timescale, the volume of each unit cell is constant, see Eq.  when the parameter χ is larger than a threshold value χsp1 . Because of the volume conservation of the unit cell, when the surrounding region collapses in the radial direction to decrease the free energy, the entire unit cell extends in the normal direction. The latter process increases the elastic energy of the polymer network in the central region. This elastic energy plays a role in the energy barrier between the undeformed and deformed states, making the deformation discontinuous  is relatively small because the initial volume of the surrounding region is smaller than the initial volume of the central region.The volume-conserving deformation of the composite gel is driven by the coil–globule transition, where the polymer network shrinks greatly to decrease the free energy for a large interaction parameter see Fig. . Our theχ, the extension ratio λ⊥ in the normal direction has an asymptotic forms (=(rex − rin)/rin) is the ratio of the volumes of the central and surrounding regions  increases with increasing the volume ratio s  as a function of the radius rin of the nanosheets, the lateral distance 2(rex − rin) between the nanosheets, and/or the number of cross-links per unit volume.For large values of the interaction parameter also Eq. ). ϕeq isalso Eq.  and Suppalso Eq. . The volχ > χsp2 (see Supplementary Note Dgel is the diffusion constant with which solvent molecules permeate through the polymer network (defined by Eq. (rin of the nanosheets and does not depend on the size of the gel. At the moment when the interaction parameter is changed to χ(>χsp2), the osmotic pressure is uniform in the entire region of the gel. A small deformation, driven by the thermal fluctuations, develops the osmotic pressure difference between the central and surrounding regions of a unit cell. This generates the flow of solvent molecules from the surrounding region to the central region. With this mechanism, the solvent molecules travel only by the distance rin during the deformation. This fact is responsible for the fast response of the composite gel. The response time τgel decreases with increasing the interaction parameter χ . Equatiχ is changed very slowly, the composite gel shows the discontinuous deformation at a value χtr. The interaction parameter χtr at the transition is a function of the volume ratio s and the rescaled shear modulus g0 of the polymer network. The interaction parameter χtr at the transition increases monotonically with increasing the volume ratio s  are probably specific to cases in which the composite gel is composed of polymers that show continuous coil–globule transitions, such as polyacrylamide gels in acetone–water mixture28 .When the interaction parameter sp1 Fig. . These trin, corresponding to the radius of the nanosheet. These features are in contrast to the volume phase transition of gels, with which the volume of the gels changes discontinuously and the response time is very long6. Recent researches showed that a bilayer of the thin films of composite gels is bent or twisted by changing the temperature because the two films change their volume with different ratio27, analogous to bimetal stripes29. The latter mechanism is very different from the mechanism of the deformation of the composite gel with layered nanosheets. With our mechanism, the solvent does not permeate through the entire length of the gel and thus the response time is very fast even for a bulk gel.Our theory predicts that the composite gel shows discontinuous deformation by changing the temperature. The deformation is driven by the fact that the surrounding regions locally collapse to minimize the free energy and thus solvent molecules flow from the surrounding regions to the central regions. The volume of the composite gel is constant during the deformation and the response time is relatively short because solvent molecules travel only by the distance 28) with which the polymers shrink continuously with decreasing the temperature. For cases in which the coil–globule transitions are discontinuous, the polymer gel may show phase separation even without embedded nanosheets and the composite gel made from the polymers thus may show the discontinuous deformations at the temperature range, where coil and globular states are both stable.Our theory predicts that the composite gel shows the discontinuous deformation even for cases in which the coil–globule transition of the polymers is continuous. The discontinuity of the deformation results from the fact that the volume of the unit cell is constant in the relevant timescale. This implies that the discontinuity of the coil–globule transition of PNIPA is not essential for the composite gel to drive the discontinuous deformation. This prediction may be accessible by using the combinations of polymers and solvent  and the rescaled shear modulus g0 may be changed by changing the number of crosslinking per unit volume. The extension ratio λ⊥ decreases with decreasing the aspect ratio rin/h0, but only slightly .24). In some cases, this condition may be dispensable; the polymer network in the surrounding region is not likely to slide into the central region because the entire region of the gel is connected by the polymer network and the elastic energy of the polymer network suppresses the lateral deformation of the central region  and the normal distance h0 between the two nanosheets are both shorter than the radius rin of the nanosheet. These conditions ensure that the nanosheets locally restrain the deformation of the gel and produce restrained regions and relatively free regions. Fourth, the deformation is measured in a timescale of the order of the response time τgel, in which solvent molecules travel by the distance of the radius of the nanosheet. In a very long timescale, solvent molecules flow out from the gel and change the volume of the gel, eventually reaching the equilibrium state15. The process towards the equilibrium indeed corresponds to the volume phase transition, where the lateral contraction (or extension) of the central regions of the unit cells is still restrained by the nanosheets. With a typical value of the diffusion constant of synthetic gels, Dgel ~ 1 × 10−7 cm2 s−11, the timescale of the volume change is ~1 day (for a gel of ~ 1 mm size) and the response time τgel is ~0.1 s (for nanosheets of ~1 μm radius). The two timescales are well separated and thus there is a relatively long time period in which our theory is effective. One can also control the above two timescales by designing the radius of the nanosheets, the size of the gel, and the pore size of the polymer network. Fifth, in the swelling process, the gel is swollen in a good solvent . The jump of the extension ratio λ⊥ at the threshold of the discontinuous deformation decreases with increasing the interaction parameter of the solvent used in the swelling process and, eventually, the deformation becomes continuous, see Supplementary Fig. Although our theory is relatively generic, this theory is ideally tested by experiments that satisfy the following conditions: First, the solid nanosheets are strongly attached to the polymer network, preferably covalently bonded to the polymers . Aida and coworkers prepared a composite gel of L-shape to demonstrate that the composite gel shows uni-directional procession when one changes the temperature back and forth, switching between the deformed and undeformed statesχ is zero, see Eq.    , where rα is the derivative of the positional vector r with respect to xα. Here and after, the indices α and β represent either 1, 2, or 3 and x1 = x, x2 = y, and x3 = z (SIn the reference state (before the network is swollen in a solvent), the position of a material point is represented by using the positional vector lso Eqs. –11) bel belr0 =  x3 = z S.3fela is the volume density of the elastic energy of the polymer network and fsol is the volume density of the mixing free energy. ϕ0 is the volume fraction of the reference state. ϕ is the volume fraction after the deformation and has the form g is the determinant of the metric tensor gαβ. dV0 represents the volume element of the reference state.The free energy density of a gel has the form2. With this model, the elastic energy density fela has the formG0 is the shear modulus of the polymer network and is proportional to the absolute temperature T for cases in which the elasticity of the polymer network is entropic13. gαα is the trace of the metric tensor gαβ. Here and after, we use the Einstein convention, with which repeated subscripts in the same term imply the sum over possible values of the indices. It is straightforward to extend our theory to cases in which the elastic energy has a more elaborate form for quantitative agreement with experiments34.The neo-Hookean model treats a polymer network, which is composed of randomly crosslinked Gaussian chainsfsol has the formχ is the dimensionless interaction parameter that represents the magnitudes of the monomer–monomer and solvent–solvent interactions, relative to the monomer–solvent interactions. kB is the Boltzmann constant and T is the absolute temperature. v0 is the size of a monomer.The mixing free energy Fgel does not predict the volume phase transition when the nanosheets are removed15.With Eqs.  and 6),,6), the χ = 0). After the swelling process, the material point at r0 =  is displaced to the positionλeq is the swelling ratio. We calculate the free energy of the gel, Eq.  and ϕeq is a function only of the rescaled shear modulus g0 that is defined by the formG0 of the polymer network is proportional to the absolute temperature T2, the rescaled shear modulus g0 does not depend on the absolute temperature T.For simplicity, we treat cases in which the polymer network is swollen in an athermal solvent . The polymer network in the surrounding region can deform in the normal direction with the same extension ratio λ⊥ as in the central region and deforms in the radial direction with an extension ratio λ|| = zs) of the central region, where t) (=λ⊥ − 1) is the strain and zs is the distance from the nanosheet at the bottom of the unit cell  of the solvent in the central region is derived by using Darcy’s lawϕ is the volume fraction of the polymer network, κ is Darcy’s constant, and p(r) is the hydrostatic pressure. Henceforth, the dot above a physical quantity (such as Ki + 4Gi/3 is the elastic modulus of the gel (Ki is the osmotic modulus and Gi is the shear modulus) and is defined by the Supplementary Eq.  diffusion constant Dgel of the gel has the form3. Equations (The flow field t) as a function of time for cases in which the interaction parameter is changed from zero to χ at t = 0. Equation (36. The right-hand side of Eq. (t) and omitting the higher order terms. The asymptotic form of the response time τgel for small values of the rescaled shear modulus g0 is shown in Eq. (∞ is the strain in the deformed state and the dimensionless parameter α∞ is defined below the Supplementary Eq. (t) − We analyze the strain Equation  is analoe of Eq.  is zero e of Eq.  has an ae of Eq.  in the pentclass1pt{miniman of Eq.  has an atary Eq. . Equatiotary Eq.  in the pThe Mathematica file (Aidageltheoryver20(revision).nb) used to derive the data that support the findings of this study are available in figshare with the identifier (10.6084/m9.figshare.6083357).Supplementary InformationPeer Review File


The existence of input plasticity in the deep cerebellar nuclear (DCN) cells of the adult cerebellum could have profound implications for our understanding of cerebellar function. Whereas the existence of plastic changes in mossy fiber (mf) synaptic responses in DCN neurons has been demonstrated in juvenile slices, there has so far been no direct demonstration of this form of plasticity in the adult cerebellum in vivo. In the present paper, we recorded from neurons in the anterior interposed nucleus (AIN) and stimulated the spinocerebellar tracts (SCT) directly or via the skin to obtain mf activation and the inferior olive to activate climbing fibers (cfs) in the nonanesthetized, adult, decerebrated cat. We used three different types of protocols that theoretically could be expected to induce plasticity, each of which involved episodically intense afferent activation lasting for 10 min. These were conjunctive mf-cf activation, which effectively induces plasticity in cortical neurons; mf and cf activation in a pattern resembling the protocol for inducing classical conditioning; and conjunctive activation of two excitatory mf inputs. None of these protocols had any statistically significant effect on the evoked responses in the AIN neurons. We conclude that the input plasticity for excitatory mfs in the AIN cells of the adult cerebellum in vivo is likely to be less effective than that of parallel fiber synaptic inputs in cerebellar cortical cells, at least in the timespan of 1 h. Plasticity in the neurons of the deep cerebellar nuclei as a substrate for behavioral learning has been debated for a long time, and theoretical predictions and circumstantial evidence have been used to argue that it may be required in some situations of presumed cerebellar-dependent adaptation , 29, 34.From a limb control point of view, an important source of information to the neurons of the interpositus nuclei comes from the spinocerebellar and spinoreticulocerebellar systems (SCTs). These are among the few mf systems that have been shown to directly innervate DCN neurons through collaterals of their axons that pass by the nuclei before they form mf synapses in the cortex –23, 33. In the present study, we addressed the issue of mf-DCN neuron plasticity using direct electrical activation of the SCTs or skin stimulation to activate the cutaneously activated components of the SCTs. These inputs were combined with each other to address the possible induction mechanism described above. To also explore protocols known to be effective at the cortical level, we combined the SCT input with conjunctive cf input  or, alteThe experimental procedures were approved in advance by the Malmö/Lund Animal Research Ethics Committee . Initial surgery was performed under propofol anesthesia, and all efforts were made to minimize suffering. Our EEG recordings were characterized by a background of periodic 1–4 Hz oscillatory activity, periodically interrupted by large-amplitude 7–14 Hz spindle oscillations lasting for 0.5 s or more. These forms of EEG activities are normally associated with deep stages of sleep. The pattern of EEG activity and the blood pressure remained stable and did not change with noxious stimulation throughout experiments.N = 14) were prepared as previously described. Briefly, following an initial anesthesia with propofol , the animals were decerebrated at the intercollicular level and the anesthesia was discontinued. The animals were artificially ventilated and the end-expiratory CO2, blood pressure, and rectal temperature were continuously monitored and maintained within physiological limits. Mounting in a stereotaxic frame, drainage of cerebrospinal fluid, pneumothorax, and clamping the spinal processes of a few cervical and lumbar vertebral bodies served to increase the mechanical stability of the preparation. The dorsal part of the pars intermedia of the left cerebellum was exposed to allow microelectrode access to the AIN. An additional exposure was made of the brainstem/spinal cord junction between the base of the skull and the first cervical vertebra. All exposed areas were covered in paraffin oil to prevent tissue drying.Adult cats  were advanced to target the AIN as previously described , 4. All In order to stimulate the spinocerebellar and spinoreticulocerebellar tracts, which provide direct mf synaptic inputs to the interpositus nuclei, we placed a tungsten-in-glass microelectrode (exposed tip 50–150 μm) for stimulation laterally at the border between the spinal cord and brainstem. Using this stimulation microelectrode, mf field potentials recorded inside the AIN were routinely evoked at threshold intensities of <20 μA (single stimulus pulse of 0.1 ms duration), suggesting an effective recruitment of directly and synaptically activated  mf synapses. In addition, we used electrical skin stimulation  to recruit another putative pool of spinocerebellar mfs. Cutaneous input is known to activate parts of the spinocerebellar neuron population, and since the other pathway mediating cutaneously activated mf input, the main cuneate nucleus does not terminate in the AIN ; potent In order to activate cfs, a second stimulation electrode was placed in the inferior olive, where low-threshold cf responses (evoked at <10 μA) could be evoked in the pars intermedia of the cerebellar cortex and in the AIN , 15.The combined SCT and skin burst stimulation protocol. The SCT electrode was stimulated with 15 pulses at 200 Hz, and the skin was stimulated 10 times at 333 Hz. With this configuration, the two inputs evoked largely overlapping time windows of excitation. The SCT stimulation intensity was typically 30–70 μA, in a couple of cases 100 μA.The skin burst and simultaneous, single inferior olive (IO) stimulation protocol. The IO was stimulated once, and a skin burst stimulation of 50 pulses at 333 Hz was started 10 ms in advance in order for the first mf input to arrive at the same time as the cf input . We also moved the SCT stimulation electrode to recruit a different set of mfs, and also the skin stimulation used to evoke mf inputs was moved to a distinctly different location. The three different protocols that we used were as follows:For all three protocols, the bursts were repeated at 0.33 Hz for 10 min, i.e., for a total of 200 repetitions.We quantified the responses obtained from a single-pulse stimulation, either to the SCT or to the skin, before and after a burst stimulation protocol. For the protocols involving skin bursts and the simultaneous or delayed IO stimulation, respectively, the responses were quantified using peristimulus histograms of raw spike time data (5 ms bin width). For the display and analysis of the combined skin burst and SCT stimulation protocol, we used a kernel density estimation (KDE) plot, i.e., each spike was replaced by a Gaussian distribution with standard deviation of 0.5 ms. The averaged sum of all Gaussian distributions transforms a discrete spiking pattern into a continuous function describing the spiking probability on a continuous time scale. The standard deviation of the kernels was set so that the total spiking probability function was smooth across neurons. This was done as the responses to the SCT stimulation were brief, which reduced the total number of spikes and made the responses more sensitive to chance distributions of single spikes. The KDE helped in reducing this problem. See Hoebeek et al.  for a moIn all cases, the response was quantified as the mean firing frequency during the time window of the response, with the firing frequencies being obtained either from the KDE plots or the peristimulus histograms. To smooth the signal used in the analysis, the peristimulus histograms were filtered with a moving average of width 15 ms. The response onset was counted from the first occurrence of at least two consecutive bins with an activity that exceeded the baseline activity by at least two standard deviations. The end of the response was defined as the bin where the activity decreased to the threshold. For each cell, the time window for the response was initially calculated individually for every peristimulus histogram . Then the median start and end points of the responses were used to define the response time window for the cell, in which the response was quantified. The response was quantified as the mean net activity during the defined time window. For responses evoked by SCT, the responses obtained were typically evoked between 1.5 to 4.0 ms after the onset of the stimulation. The KDE in itself did not allow a rigorous setting of the time limits, but histograms of the raw data provided a support for the chosen time limits in a similar fashion as above. For responses evoked by the skin, the quantified data was typically evoked within a response latency time window of 10–30 ms after the onset of the skin stimulation.Subsequently, the relative response for each set of single-pulse stimulations was compared to the relative response before onset of the burst protocol and the change in response from each cell was analyzed in separate consecutive time spans of 10 min (time points). The null hypothesis that there was no net change in the response was tested using Wilcoxon signed-rank test. The signed-rank test was computed for each time point by comparing the total number of cells with the number of cells with a positive response. The probability for the outcome is calculated, assuming there is a 50% probability for each cell having a positive change in response.DCN neurons were recorded Fig.  in the lActivation of SCTs can also be obtained using skin stimulation, as a proportion of the spinal neurons that projects through the SCTs are activated by skin afferents and the AIN neurons can be prominently excited by input from the skin  Fig. 1)1). The dN = 11 cells. For each time point , there was experimental data from seven to ten out of N = 11 neurons. As can be seen in Fig. After the termination of the burst protocol Fig. , the resIn the cerebellar cortex, simultaneous burst stimulation of the excitatory parallel fiber (PF) synapses and a single shock stimulation of the cfs by a stimulation electrode in the IO are highly effective to induce long-term potentiation of PF inputs in Purkinje cells and their afferent interneurons . We applN = 5 AIN neurons . On basis of the absence of any trend, we could hence not defend extending the data acquisition in these very time consuming and difficult experiments. At the same time, the scientific community has repeatedly realized that it is important that also negative findings are published , 18, eveFirst, it is important to point out that these findings do not imply that mf-interpositus plasticity does not exist in the adult cerebellum. It is of course possible that other protocols that we did not try would have been more effective. Perhaps a more likely possibility is that more long-term protocols and longer duration AIN cell recordings could have provided a different answer. In experiments of classically conditioned eyeblink responses, for example, effects in the Purkinje cell responses start to emerge at the same time scale as we were looking at here but they evolve substantially for hours after . It shouAn interesting aspect is the contrast to the effects observed in cerebellar cortical neurons using similar protocols and, in at least one case, comparable recording times , 16. Thi


Adjusting the mechanical strength of a biomaterial to suit its intended application is very important for realizing beneficial outcomes. Microfluidic spinning fiber have been attracting attention recently due to their various advantages, but their mechanical strength has unfortunately not been a subject of concentrated research, and this lack of research has severely limited their applications. In the current work, we showed the mechanical properties of microfibers can be tuned easily and provided a mathematical explanation for how the microfluidic spinning method intrinsically controls the mechanical properties of a microfluidic spinning fiber. But we were also able to adjust the mechanical properties of such fibers in various other ways, including by using biomolecules to coat the fiber or mixing the biomolecules with the primary component of the fiber and by using a customized twisting machine to change the number of single microfiber strands forming the fiber. We used the bundle fiber as an ophthalmology suture that resulted in a porcine eye with a smoother post-operative surface than did a nylon suture. The results showed the possibility that the proposed method can solve current problems of the microfibers in practical applications, and can thus extend the range of applications of these microfibers. For example, nylon or mersilene, each of which is commonly used as a suture material in keratoplasty and is much stronger than the cornea, could cause post-keratoplasty astigmatism due to the resulting mismatch between a suture and the cornea. This mismatch may lead to a structural distortion in the cornea, specifically the formation of an oval opening, which causes astigmatism5. In this regard, the mechanical properties of a biomaterial should be designable according to the mechanical properties of the body part with which it is meant to interact. (Table 16).A poor match between the mechanical properties of a biomaterial and those of a body part  as a sample flow and methanol as a sheath flow into a core inlet and sheath inlet, respectively, to form a laminar flow. Then, solvents of each solution  diffused into each other, which caused the precipitation of PLCL since PLCL is insoluble in methanol. As shown in Fig. In order to demonstrate the ability of a microfiber-spinning chip imparting various mechanical properties onto the microfibers, we first fabricated the microfiber using microfluidic spinning chip as done beforel) of PLCL molecules in the microfiber and the initial Young’s modulus (iYm). iYm was determined from the slope of the Stress-Strain (SS) curve between 0% and 20% of the strain. The equation isPLCL is the core flow rate, QMeOH is the sheath flow rate, and Ak is the cross-sectional area of the microfiber. The derivation of this equation is described as followed:To explain these phenomena, we suggested a simple equation for the relationship between length density  in condition k.In our experimental conditions, the number of PLCL molecules is proportional to mass and the volume of the microfiber, so equation  can be mk and the speed at which the fiber is extruded from the outlet in condition k , which could be expressed asl can be expressed as a function of tk is the cross-sectional area of the microfiber in condition k.In the case of microfluidic fiber spinning, since the sample flow rate is much less than the sheath flow rate, equation  can be mSubstituting equation  into equBased on our hypothesis that the distribution of PLCL molecules in the axial direction of the microfiber affects the iYm of the microfiber, we compared length density with iYm. The derivation of the equation (1) for the length density of the microfiber resulted from the following steps.k.That is, the length density of a PLCL molecule of a microfluidically spun microfiber is proportional to the ratio of sample flow rate to sheath flow rate and is proportional to AExperimentally determined iYm values and length densities calculated according to above equation were plotted for microfibers made using core flow rates of 6, 7, 8, 9, and 10 μL/min with sheath flow rates of 20 mL/h, 30 mL/h, and 40 mL/h for the (5:5)PLCL microfibers. .Bundle fibers were constructed from single microfibers whose mechanical properties differed from one another using the CTM. Immediately after single microfibers were extruded from the microfiber-spinning chip, we collected them around an acryl spool (20 mm width) to form a ring of overlapping PLCL microfibers Fig. . The len2 solution (in methanol) through a drop of a 2% alginate solution three times  with nitrogen as a purge gas. Microfiber samples each with a mass of about 5–10 mg were prepared. The results were obtained from conditions including a heating rate of 20 °C/min and temperature range of −20 °C to 150 °C.ex vivo ophthalmology surgery experiments were performed on pig eyes itself, not with eyes of pigs alive. Excised pig eyes were obtained from a local slaughterhouse. They were washed clearly and transported in phosphate buffer saline at a temperature of 5 °C. Based on the conditions of the system developed for controlling the mechanical properties of biomaterials, we performed cornea surgery using a fabricated bundle fiber whose mechanical properties relatively well matched the mechanical properties of the porcine cornea , to use it as a surgical suture. An incision was made on the cornea and ex vivo surgery performed with the bundle PLCL suture and a nylon 10–0 suture  as a control. We injected saline through the incision site to verify that no leakage happened after surgery. One hour after surgery, we used CirrusTM HD-OCT (ZEUS) of the cornea to observe the effect of the suture on the anastomosis and, in turn, the morphology of the cornea.In order to verify the advantages of being able to determine the mechanical properties of PLCL fibers, nea Fig. . The YouFeasible conditions were found that core flow rates from 5 μL/min to 10 μL/min were for the 5% (5:5)PLCL solution when sheath flow rates were 20 mL/h and 30 mL/h, while 6 μL/min to 10 μL/min for 5% (5:5) PLCL solution of sheath flow rates of 40 mL/h. The diameters of fibers fabricated under each feasible conditions were shown in Fig. 2 = 0.96). The effect of pulling rates on mechanical responses of bundle fiber was shown in Figure Bundle PLCL fibers of a twisted group and a wound group were well constructed using a CTM to see the effect of the number of single microfiber strands included in the bundle fiber. Fig.  Since puex vivo surgery, the incision line on the surface of the cornea was verified. , and was the main factor controlling the mechanical properties of the microfibers. The mechanical properties of a microfiber may thus be designed by simply using conventional spinning microchips as follows: changing the base materials to induce changes in certain mechanical properties such as elasticity, and changing the spinning flow condition to induce changes in the UTSS and Young’s modulus without significant changes in elasticity and UTSH. Thus, we concluded that when the conventional microfiber-spinning chip was used, we found the base material to be critical for determining the overall characteristics of the fibers; thus choosing the appropriate base material should be the first step in designing biomaterials that are intended to meet the need of the target tissues. Even with the same material, fine and independent control of the mechanical properties was shown to be possible using the microfluidic spinning chip.For a single PLCL microfiber, we have confirmed that not only the composition ratio but also the core flow rate change could vary the mechanical properties of microfluidic spinning microfiber. This difference shown between (5:5)PLCL microfibers and (7:3)PLCL microfiber may have been due to an increase in the amount of rigid L-lactic acid relative to caprolactone, but crystallization associated with the precipitation of (7:3)PLCL was apparently also an important factor. Also, we observed the plots of experimentally determined iYm and the length density in the fiber according to the equations we presented have consistent tendency. Since the equation  implies 43. While we originally might have expected hdECM to help harden the microfiber by either being a crystal nucleus or filler, we actually found that hdECM did not act as crystal nucleus and only acted as a filler to enhance the strength of the microfiber. As shown in Figure For a bundle PLCL fiber, we were able to use a CTM to make a bundle fiber with different UTSH values, by changing the number of strands, while keeping the UTSS and iYm value relatively constant. Given that, of the twisted group, the angle of the internal microfiber with respect to the direction of the bundle fiber was found to have no significant relationship with measured mechanical properties, (Figure The observation of OCT images of the porcine cornea surface after surgery suggested that bundle PLCL fiber, applied as a suture, did not induce unmatched anastomotic strength and not induce the distortion of the surface structure while nylon suture could. This observation indicated that there was less scar formation, less of a delay in the wound healing process, or a lower concern that astigmatism developed when bundle PLCL fiber was used as a suture than when the nylon was used. These results implied that designing biomaterials to have mechanical strengths matching the mechanical strength of the target tissue is important when using biomaterials to maintain or restore tissue structure or function. Noting that the nylon10–0 surgical suture is one of the most commonly used sutures in ophthalmology surgery, the bundle PLCL fiber can be utilized as a suture and developed into a commodity. One hurdle of this method was that it was difficult to connect a bundle fiber to a fine surgical needle in the laboratory, and we had to inevitably attach the bundle fiber to a thick needle, which could increase needle-induced damage to tissue. However, this issue can be easily solved with a help of suture fabrication company in the near future.In summary, we developed a system which consists of a microfluidic spinning chip and a CTM, and where quite easily, we could independently control different mechanical properties of fibers. First, using a spinning chip, we were able to change the UTSS and iYm values while keeping the UTSH relatively constant. This ability was presumably due to the molecular arrangement of the base material in the fiber depending on the flow environment. Secondly, the relationship between the unit microfiber and bundle fiber in this bottom-up fabrication system can be summarized as follows: we were able to change the elongation at break, iYm, and UTSS of the bundle fiber by changing the properties of the unit microfiber itself, while we changed the UTSH of the bundle fiber mainly by changing the number of single microfibers in the bundle fiber. In other words, we have constructed bundle fibers whose mechanical properties can be tuned independently. We also conducted experiments in an applied case, and have shown once again the importance the of the mechanical design of the biomaterial for either case of being applied or being implanted. Overall, the current work has suggested an easy and novel method to control the mechanical properties of fiber-form biomaterials, which increased the range of potential applications of fibers by having developed a way to better control their mechanical properties.Supplementary Information


Elevated skin temperature at the body/device interface of lower-limb prostheses is one of the major factors that affect tissue health. The heat dissipation in prosthetic sockets is greatly influenced by the thermal conductive properties of the hard socket and liner material employed. However, monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used which requires consistent positioning of sensors during donning and doffing. Predicting the residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. To predict the residual limb temperature, a machine learning algorithm – Gaussian processes is employed, which utilizes the thermal time constant values of commonly used socket and liner materials. This Letter highlights the relevance of thermal time constant of prosthetic materials in Gaussian processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant, the model can be optimised and generalised for a given prosthetic setup, thereby making the predictions more reliable. This was followed by skin irritation caused by the prosthesis, which was considered moderate or worst in 62% of the group. Hoaglund et al.  is taken and plotted with respect to time. Using regression, a line of best fit is generated and the slope is computed. The slope is a measure of the thermal time constant.The natural log of the exponentially decaying data as computed in step 2 ln is plotted with respect to time and the slope of line is indicative of the thermal time constant of the liner-socket material combination measured at the liner interface. Figs. 4a and 5a illustrate the temperature profile of the polyurethane liner when used individually and in combination , respectively, which is then utilised to compute the thermal time constant by the method described above.As in step 3, ln[τ so computed when the polyurethane liner is used individually is 2.8 min, whereas when it is tested in combination with a thermosetting socket material it is ∼5.4 min.Thermal time constant is essentially the same for all starting temperatures. The process to determine the thermal time constant as described above is repeated for different heat source temperatures . This is to confirm the accuracy of time constant value of the liner when used individually or in combination of a socket material and rule out any experimental errors. Hence the value of The thermal time constants for the prosthetic materials  as listed in Table 5K-type thermocouples via a data logger  were used. One thermocouple was taped on the lateral side of the limb and the other on the medial. The other two thermocouples were taped on the corresponding point on the liner-socket interface. Data from four channels was recorded at 0.5 Hz at a defined ambient temperature (dataset A). This was repeated again after two months to confirm the influence of ambient temperature on the residual limb skin temperature (dataset B). The temperature profiles of the liner and the residual limb skin were recorded in a climate controlled chamber with zero wind velocity and 40% humidity levels for ambient temperatures of 10°C, and then the same protocol was repeated for 15, 20 and 25°C. The results for both the subjects indicated that for any given ambient temperature, the liner temperature profile follows that of the in-socket residual limb temperature. This suggested a possibility to apply supervised machine learning algorithms to model the residual limb temperature of the amputee as a function of liner temperature. Time averaging of 5 s is done on the recorded data to help in identifying the trend better and smooth out the fluctuations. Since, the temperature profiles of the residual limb are almost similar for the ambient temperature pairs of 10°C, 15°C and 20°C, 25°C, the individual predictive model for both the subjects at ambient temperatures of 10 and 25°C are only discussed in this Letter. Hence experimentation for each amputee subject was used to develop individual predictive models using GPML [In our study, two transtibial traumatic amputees with the details listed in Table ®, Mathworks). The model designed takes the liner temperature as the input x and the predicted output is the residual limb skin temperature y. The Gaussian process technique is a supervised learning algorithm which infers a continuous function f(x) from a training set of input–output pairs. The key assumption in this method is this collection of random variables, any finite number of which have joint Gaussian distributions [The GPML model aims to determine the liner temperature as a function of skin temperature and use it for predictive analysis. Processing was performed with custom developed software , the Gaussian model is defined by N-dimensional covariance matrix CN which indicates the degree of closeness of outputs for varying inputs. Each element of CN is defined by covariance function Cf, which is a function of inputs and hyperparameters [ij in covariance matrixCij = Cf. The covariance function can be defined by user depending on the nature of the input–output response. The squared exponential covariance function was the best fit for our studyθ1, l, σn} and δij which is a delta function whose value is zero for all i≠j. The length scale l for an input parameter indicates how much the output will vary relative to changes in an input. This, if correlated to the heat transfer in prosthetic material, implies that the thermal time constant is a measure of how quickly the in-socket temperature changes with the change in residual limb temperature. Hence, if the length scale in the covariance function is set to the thermal time constant of the materials used in the prosthetic limb, it would optimise the Gaussian process model and generalise it for amputee subjects with similar prosthesis setup. For Subject 1, the value of length scale is specified as 5.4, which is equal to the time constant of the materials used in his prosthesis setup. Similarly, for Subject 2, the length scale is defined to be 6.7. When l is specified, less number of iterations are required for the computation of other hyperparameters. This in turn minimises the log marginal likelihood function to give the best predictions. The results from this model  lie in 95% confidence interval which translates to an accuracy of ±0.5°C. With the introduction of the thermal time constant as the length scale in the covariance function, the physical properties of the prosthetic material are accounted for in the model as opposed to in [For rameters , 15. Forur study\documented to in  which is6The accuracy of the model developed to non-invasively monitor the residual limb temperature of an amputee has been improved to ±0.5°C. It is indicated from the study that the residual limb temperature depends on the ambient temperature and the activity level of the subject. Also, a major factor is the thermal time constant of the prosthetic materials used. Owing to the low thermal conductivity of the prosthetic materials, it can restrict the heat transfer from the residual limb and create a warm microenvironment within the prosthesis. Hence, it becomes all the more imperative to build in the existing GPML model the thermal time constant so obtained from the thermal studies. It was also found that different prosthetic materials transfer heat logarithmically at different rates and that they also transfer different amounts of heat. Further to this it was found that placing these materials in combinations slowed the rate of heat transfer and also decreased the maximum amount of heat transferring through the materials.Thus, this Letter highlights the relevance of thermal time constant of prosthetic materials in Gaussian processes technique which would be useful in addressing the challenge of non-invasively monitoring the residual limb skin temperature. With the introduction of thermal time constant in the model, the accuracy increases, thereby making predictions more reliable. Also, this approach is quite useful in extending the model to a wider amputee population to define a generic behaviour. Future scope of the work includes studying the interplay between temperatures and sweating response in prosthesis of amputees with different pathologies.


The decomposition process of human remains can be used to estimate the post-mortem interval (PMI), but decomposition varies due to many factors. Temperature is believed to be the most important and can be connected to decomposition by using the accumulated degree days (ADD). The aim of this research was to develop a decomposition scoring method and to develop a formula to estimate the PMI by using the developed decomposition scoring method and ADD.A decomposition scoring method and a Book of Reference  were made. Ninety-one cases were used to develop a method to estimate the PMI. The photographs were scored using the decomposition scoring method. The temperature data was provided by the Royal Netherlands Meteorological Institute. The PMI was estimated using the total decomposition score (TDS) and using the TDS and ADD. The latter required an additional step, namely to calculate the ADD from the finding date back until the predicted day of death.R2 = 0.67 and 0.80 for indoor and outdoor bodies, respectively). When using the ADD, the R2 decreased to 0.66 and 0.56.The developed decomposition scoring method had a high interrater reliability. The TDS significantly estimates the PMI  contains supplementary material, which is available to authorized users. When human remains are found, the time of death has to be determined. The time between death and finding a body is called the post-mortem interval (PMI). Determining the PMI is important, because having a time frame can help with the identification of the human remains and contribute to investigate the possible causes of death . SeveralDecomposition is a continuous process and is influenced by multiple factors. The influence of these factors on the decomposition is not completely understood yet . ClimatiR2 value). The scoring method made by Megyesi et al. was validated by Dabbs et al. and Nawrocka et al. who both found a high reliability for the overall total body score, but also made suggestions for improvement [In multiple studies, the association between decomposition stadia and time was described –26. Gallrovement , 27, 28.Because a forensic autopsy is seldom performed on human remains in the Netherlands, the developed decomposition method had to be usable without performing an autopsy. Thereby, there was a need to develop a method which could lead to fast decision-making in the field. The first aim of this research was the development of a validated decomposition scoring method which is simple, practical and user friendly. The second aim was to develop a formula to estimate the PMI using the developed decomposition scoring method and ADD.A decomposition scoring method, consisting of a decomposition score and a Book of Reference, was made. The Book of Reference is a visual resource containing photographs of the decomposition phenomena. The decomposition scoring method developed by Megyesi et al.  was usedTwelve participants  were recruited for the validation test. The decomposition scoring method was explained using the Book of Reference. Forty-five coloured photographs, provided by the Public Health Service (PHS) IJsselland, were equally divided over FDS, BDS and LDS and were shown in a randomised order. The score representing the stage furthest along in the decomposition process had to be assigned to the photographs. Once the validation started, the Book of Reference was not shown again.The interobserver variability was measured using the Fleiss kappa test via the Online Kappa Calculator .From the years 2001 until 2016, there were a total of 2417 post-mortem investigations at the PHS IJsselland and PHS Drenthe. To be included, the personal data of the human remains had to be known and a post-mortem report had to be available. Cases had to be closed, which means there was no ongoing investigation. The human remains had to be completed and photographed. Children of 18 years and younger were excluded because of the effects on decomposition of smaller size , 17, 18 The cases provided by the PHS IJsselland were scored by a forensic physician from the PHS Drenthe and one from the PHS IJsselland. The cases provided by the PHS Drenthe were scored by another forensic physician from the PHS IJsselland. The forensic physicians had no knowledge of the cases they scored and a possible bias was prevented this way. The validated decomposition scoring method was explained to the participants using the Book of Reference before the scores were assigned to the photographs. After the scoring had started, the Book of Reference was not shown again. Photographs were assigned the score representing the decomposition stadia, visible to the eye, which is furthest along in the decomposition process.Temperature data was needed to determine the ADD. Temperature was not always known when individuals were found indoors. When unknown, an average temperature of 18 °C was filled in. This is considered a normal temperature indoors, with a maximum of 21 °C at day and a minimum of 15 °C at night. For human remains found outside, the temperature data was provided by the Royal Netherlands Meteorological Institute . WeatherThe statistical analysis was conducted using SPSS, version 23.0 (SPSS Inc.). The normality was tested using the Kolmogorov-Smirnov test. The Spearman correlation coefficient was used to test the correlation between the TDS and PMI. The PMI was estimated via two methods which were compared in this research. The first method consisted of using the TDS to estimate the PMI. The second method consisted of using the TDS to estimate the ADD. The latter required an additional step to estimate the PMI, namely to sum the mean actual daily temperatures from the finding date back until the estimated ADD is reached. For both methods, a linear regression analysis was used. Here, both the PMI and ADD were naturally log transformed to obtain a linear relationship between the TDS and PMI and between the TDS and ADD.not have the legal right to perform an autopsy in the Netherlands. This right is solely for the (forensic) pathologists. The reports are archived together with the available  photographs.This research was conducted at the PHS IJsselland and PHS Drenthe. A PHS is one of the multiple institutions where forensic physicians work. Forensic physicians perform external post-mortem investigations and do This study was approved by the PHS IJsselland and performed according to the ethical and legal standards in the Netherlands. All data were processed anonymously. For this type of study, formal consent is not required.Table The first stage is called no visible signs. An individual just passed away and there are no decomposition signs visible yet. The second stage is where the livor mortis, rigour mortis, algor mortis and vibices occur. Drying of the body can lead to tache noir (when the eyes are open) and brownish discoloration starting at the edges  expands to the rest of the body . Cloudy The putrefaction, caused by autolysis, is not visible in stage two and starts in stage three with grey to green discoloration caused by the bacteria . The samStage four starts when the putrefaction reaches an end and gasses are released and the tissue caves in, because tissues do not have the strength or the capability anymore, due to autolysis, to uphold connections. This gives the eyes, throat and abdominal cavity a hollow appearance . Due to Stage five contains gross skeletonization which means most of the skin is gone and no soft tissue is left. More than 50% of bone is visible in that area which means either in the jaw, cheekbones, skull or a combination for the face/neck area. In the trunk, it could be the ribcage, the thoracic or lumbar vertebrae, the pelvic bones or a combination. In the limbs, it is a combination of the humerus, ulna, radius, wrist bones, the bones of the hand, the femur, the tibia or fibula or the bones of the feet. Some joints can be disarticulated such as the jaw, the cervical, thoracic or lumbar vertebrae, the ribcage or pelvic joints. In the limbs, it could be the shoulder, elbow, wrist, knee and ankle.The last stage contains complete skeletonization (stage six) where only bones, cartilage, hair and sometimes a bit of skin are all that are left from the human remains , 39. TheThe Book of Reference is a visual resource containing photographic examples of all the decomposition phenomena with their corresponding scores of the stage they are in. It contains a total of 84 specifically selected photographs. The photographs are placed in a chronological order, following the point-based system of the decomposition scoring method. Before each region is visually explained, the decomposition scoring method is shown in a table. Figures Twelve participants scored 45 test photographs. Table n = 79, 86.8%) and 12 individuals were found outside (13.2%). Twenty-nine cases were found in the Spring (31.9%), 19 in the Summer (20.9%), 21 in the Autumn (23.1%) and 22 in the Winter (24.2%).Of the 91 cases with a known PMI, 28 individuals were female (30.8%) and 63 were male (69.2%)  of six to nine. A TDS of six is by far the most common score (44%), which means a lot of individuals showed signs of livor mortis or rigour mortis. The second most common score is nine (18.7%). These individuals were in the putrefaction stage.n = 76). The median ADD is 28.5 with an IQR of 18 to 90. Of the 76 cases, 59 cases had an estimated mean temperature of 18 °C (77.6%). For the outdoor cases (n = 12), the ADD ranged from 3 to 512 with a median of 14.5 and an IQR of 8.3 to 19. In the 12 cases where the individuals were found outdoors, the weather station was as close as 0 km and as far as 29 with an IQR of 14 to 19 km [Of the 79 indoor cases, two individuals were found in a shed and one individual was found in a garage. Here, it was definitely unknown what the temperature was. It could not be decided if it was a normal inside temperature or a temperature close to the outside temperature. The ADD ranged from 4 to 1080 . The correlation between LDS and PMI was second lowest  and the second highest correlation was between BDS and PMI  . Even though the correlation between FDS and PMI is the lowest, it is still considered to be a strong correlation. The remaining three correlations are considered to be ‘very strong’ [The correlation between the decomposition scores and PMI was the lowest between FDS and PMI , the TDS significantly estimated the PMI . The R2 indicated that 67% of the variation in the PMI was estimated by the TDS. The developed formula is PMI = 10^(− 1.18 + 0.22·TDS) . The developed formula is ADD = 10^(− 0.05 + 0.23·TDS) . The R2 indicated that 80% of the variation in the PMI was estimated by the TDS. The developed formula is PMI = 10^(− 0.93 + 0.18·TDS) . The developed formula is ADD = 10^(0.03 + 0.19·TDS) . The PMI and ADD were log transformed to achieve a linear relationship. After separating the indoor and outdoor cases, the following four formulas could be made with their associated standard errors. Starting with the indoor cases  pathologists in the Netherlands. The method can be used to score the decomposition of human remains found on land in all geographic regions with a similar climate as in the Netherlands (a temperate climate with a warm summer without a dry season (Köppen-Geiger classification ‘Cfb’)) –50.The developed decomposition scoring method in this research uses the new terminology derived from the terms introduced by De Kat et al., who developed an aquatic decomposition scoring method . The humα = 0.818 and absolute correlation coefficient of 0.990). Both researches made suggestions for improvement of the original decomposition scoring method so that even higher results could be achieved. We relate to some of these, for instance, the percentages used for skeletonization in stage four and five . Nawrocka et al. suggested that the percentages should be replaced by the determination of particular bones which are exposed [Multiple researchers studied the interrater reliability of the decomposition scoring method made by Megyesi et al. . Nawrock exposed . In our  exposed  noted co exposed  also fouParticipants in our study announced minor restrictions of the method developed in this research. Not everything from a body part was visible on the photographs. As a result, the decomposition could be further along than is visible on the photographs. Scoring the human remains could be more reliable when the body can be fully investigated. Second was seeing the difference between dried and leathery fingers and toes. It was said that the skin needed to be touched to make the optimal decision and to make the difference between score two and score four.The second aim of this research was to find a practical way to estimate the post-mortem interval. The developed method consists of the developed decomposition scoring method, a Book of Reference and a formula. Before the formulas were developed, the indoor and outdoor cases were separated, because outdoor bodies are influenced by more factors than indoor bodies, which alters the decomposition rate . It is bIn this research, temperatures below zero were not recorded as negative values, but as zero. It is unknown at which temperature putrefaction stops. Micozzi  states tA possible bias in this research is the post-mortem interval. This interval is based on bystanders who were present at the time of death (for example ambulance personnel), last used newspaper or tv-guide, the mail in the mailbox, entomology, the last time a person was in contact or was seen or the last time an individual used social media. In the Netherlands, the PMI is found based on multiple factors combined, but the estimated PMI will always have a margin of error.n = 48) with an estimated PMI were included in the original regression analysis. Moffat et al. [t distribution is much smaller in this research , the formulas do not give an accurate approximation of the PMI. Caution is advised when drawing conclusions based on the formulas developed in this research, especially when the PMI is long.The formulas developed in this research estimate the variation in PMI between 56 and 80%


The quality of CT slices can be drastically reduced in the presence of high‐density objects such as metal implants within the patients’ body due to the occurrence of streaking artifacts. Consequently, a delineation of anatomical structures might not be possible, which strongly influences clinical examination.The aim of the study is to clinically evaluate the retrieval of attenuation values and structures by the recently proposed Augmented Likelihood Image Reconstruction (ALIR) and linear interpolation in the presence of metal artifacts.A commercially available phantom was equipped with two steel inserts. At a position between the metal rods, which shows severe streaking artifacts, different human tissue‐equivalent inserts are alternately mounted. Using a single‐source computer tomograph, raw data with and without metal rods are acquired for each insert. Images are reconstructed using the ALIR algorithm and a filtered back projection with and without linear interpolation. Mean and standard deviation are compared for a region of interest in the ALIR reconstructions, linear interpolation results, uncorrected images with metal rods, and the images without metal rods, which are used as a reference. Furthermore, the reconstructed shape of the inserts is analyzed by comparing different profiles of the image.The measured mean and standard deviation values show that for all tissue classes, the metal artifacts could be reduced using the ALIR algorithm and the linear interpolation. Furthermore, the HU values for the different classes could be retrieved with errors below the standard deviation in the reference image. An evaluation of the shape of the inserts shows that the reconstructed object fits the shape of the insert accurately after metal artifact correction. Moreover, the evaluation shows a drop in the standard deviation for the ALIR reconstructed images compared to the reference images while reducing artifacts and keeping the shape of the inserts, which indicates a noise reduction ability of the ALIR algorithm.HU values, which are distorted by metal artifacts, can be retrieved accurately with the ALIR algorithm and the linear interpolation approach. After metal artifact correction, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image using the ALIR algorithm. Furthermore, the ALIR produced images with a reduced noise level compared to reference images and artifact images. Linear interpolation results in a distortion of the investigated shapes and features remaining streaking artifacts. Here, a commercially available phantom, which is utilized for clinical calibration, is used in order to evaluate the performance of the ALIR algorithm and the LI approach. Different tissue‐equivalent inserts are mounted between two metal rods and the reconstructed HU values are analyzed before and after metal artifact reduction. All values are compared to reference images that are acquired without metal rods. Furthermore, the retrieval of the shape of the inserts is analyzed based on the profile plots and a comparison with reference images. Since the present study is limited to phantom data, the reader is referenced to the expensive evaluation of the ALIR algorithm on clinical data in.22.AρeW. A wide range of tissue‐equivalent inserts, from cortical bone with ρeW=1.707 to lung 300 with ρeW=0.292 was used in order to investigate the reconstruction ability of the Augmented Likelihood Image Reconstruction algorithm and the linear interpolation approach.A commercially available heterogeneous phantom  was used in order to represent different human tissue classes. The main body of the phantom is composed of Solid Water and is equipped with 20 holes with a diameter of 30 mm each. The holes can be used to mount different tissue‐equivalent cylindrical rods with known mass density and an electron density relative to water, denoted by R1 and R2. At the position between the metal rods, where the strongest manifestation of the artifacts is presumed, the tissue‐equivalent inserts where alternately inserted (position is labeled AI). The original position of the insert that currently occupies position AI, is filled with an insert that has a density close to Solid Water. This setup results in ten different configurations where each of the ten tissue‐equivalent inserts occupies position AI. A CT scan with and without mounted metal rods is performed for each configuration. The resulting holes at R1 and R2 are again filled with Solid Water inserts for the metal‐free acquisition. The metal‐free images are used as ground truth for the evaluation of the metal artifact reduction and are further denoted as the reference images.Figure 2.B®, which secured the phantom against changes in position during table motion and replacement of the inserts.A 40‐slice Biograph mCT  was used for the acquisition of the images. The disk‐shaped phantom was positioned at the isocenter using a pedestal consisting of StyrodurScans were acquired sequentially with a slice thickness of 3 mm, a voltage of 120 kV, a field of view of 500 mm, and the flying focal spot setting enabled.2.CFor the reduction of metal artifacts, LI and the recently proposed ALIR algorithm is used.fk)   with a size of 16 × 16 pixels, which corresponds to an area of 16.38 × 16.38 mm2.D.2In order to analyze the behavior of the ALIR and LI algorithm regarding the smoothness of the resulting image and the preservation of the edge sharpness, a profile of a ROI within the images is examined. Due to the mounting procedure of the metal objects between the acquisitions of the images, small movements of the phantom can occur. To ensure an accurate overlap of the different image types, that is, metal artifact corrected images, image with artifacts, and reference, an affine registration is performed using the Insight Segmentation and Registration Toolkit (ITK).3AI and on position 7, a Solid Water insert is placed. In the first row, the reference image is shown, where the metal rods are replaced by Solid Water inserts. This reference image gives information about the exact HU values that should be reconstructed at this position. In the second row, images are shown, where metal rods are mounted at position R1 and R2. These images show strong streaking artifacts over the entire image. Especially, the very pronounced beam‐hardening artifact that connects the two metal rods prevents a differentiation of the different tissue‐equivalent inserts. It is almost not discernible that position AI shows a different structure than its surrounding. The third row shows the result from the linear interpolation (LI) approach. Although the strong beam hardening artifact could be reduced, a lot of streaking artifacts are remaining. The shape of the insert at position AI can be well spotted. However, the shape of the insert is rather oval than circular. Only after metal artifact correction with the ALIR algorithm, the adipose equivalent insert is clearly perceptible as are all other tissue‐equivalent inserts.In Fig. AI. Again a Solid Water insert is mounted at the original position of the insert at position 1. Due to the high ρeW value for the insert, the object is vaguely perceptible at position AI in the image with the metal rods. The LI result shows severe artifacts around position AI and many remaining streaking artifacts. Only after metal artifact reduction with the ALIR algorithm, the correct shape and HU values can be observed. However, some artifacts around the metal objects are remaining.A very similar situation can be observed in the case of the cortical bone equivalent insert at position μ, and the standard deviation, σ, of the analyzed ROI for the reference images, the uncorrected images, the LI results and the ALIR reconstructed images. Furthermore, the values are visualized within a boxplot in Fig. Table ρeW value, the attenuation values could be reconstructed in an accurate manner. The resulting error is mostly smaller than the standard deviation for the corresponding tissue class with a minimum error of 5.00 HU for cortical bone and a maximum error of 122.50 HU for lung 300.Reconstructed image resulting from the ALIR algorithm show mean values that come very close to the reference values. Especially for inserts with a high Furthermore, while the standard deviation for the images with metal artifacts are very high due to the amplified noise and pronounced streaking artifacts, the standard deviation for all tissue classes after metal artifact reduction is smaller than the reference values. This indicates that not only a reduction in streaking artifacts could be gained but also a reduction in the noise level is achieved while the mean values of the individual tissue class is preserved. However, for the LI images, this result comes with a drawback. Images show a much smoother overall appearance, that is, not only the standard deviation is reduced in homogeneous areas but the edges in the images are also smoothed as it can be seen in Figs. 3 equivalent tissue at position 5 and 6, respectively. The profile for the image with artifacts features an offset of approx. 300 HU, which is caused by the pronounced streaking artifacts. Furthermore, the perception of edges between phantom body and inserts is heavily affected by streaking artifacts and amplified noise ( and transported further in the newly calculated projection values that are used in the reconstruction for iteration k + 1.The reduced standard deviations after metal artifacts reduction, which are shown in Table The LI approach also results in a reduction of noise but with the important drawback of an overall smoothing of the image, which is generally not desirable. Compared to ALIR, edges of inserts are smoothed and show occasionally a very gradual transition as can be seen in Fig. 5An evaluation of HU values that are distorted by metal artifacts is presented and investigated. The results show that it is possible to retrieve these values accurately with the ALIR algorithm. After metal artifact correction with ALIR, structures, which are not perceptible in the original images due to streaking artifacts, are reconstructed correctly within the image. Furthermore, ALIR results in images with a reduced standard deviation compared to the reference and artifact images. This indicates a promising noise reduction ability of the recently proposed algorithm and will be researched intensively in the near future.The LI approach on the other hand results in a reasonable retrieval of HU values. However, images show an overall smooth appearance of structures, while the reduction of streaking artifacts is inferior compared to the ALIR algorithm.