A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified.This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic.The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies.The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields. In 1989 Cutkosky [A taxonomy of hand movements is important for several scientific fields, including robotics, prosthetics, physiology and rehabilitation. In robotics, it can be useful to compare the functionality of robotic hands with real human hands. In prosthetics, very advanced myoelectric hands have been developed from a mechanical point of view but they are usually not well accepted by amputees –4. A taxGRASP taxonomy. This taxonomy is organized in a matrix, with the grasps divided into several columns and in two rows according to four main parameters including power type, opposition type, position of the thumb and virtual finger assignments. Starting from Feix’s work, Wolf et al. [Several attempts to build a complete taxonomy of hand grasps were published in the scientific literature during the last 30 years. However, all of the presented taxonomies were based on qualitative approaches and qualitative justifications. Most of the taxonomies of hand movements include a division between power and precision tasks. This idea was originally proposed by Napier et al. in 1956  and inflf et al.  considerQualitative methods can provide useful perspectives of nature. However, quantitative measurements are strongly related to the scientific method and to the concept of science itself. Quantitative methods provide practical control over the subject studied, they make possible a formulation of principles that are capable of unambiguous confirmation or refutation (depending on experiments and measurements) possible and therefore very few investigations can be carried out without them .A quantitative taxonomy of hand movements can therefore reduce ambiguity in the field, but it requires the measurement of specific biomedical data. Several parameters can be used to quantitatively characterize hand grasps, such as posture, muscular activity and force. Kinematic data are usually measured with two main techniques: visual or wearable systems. Visual systems can be affected by visual occlusion in the recording of hand grasps and the procedure to place the visual markers can be time consuming. Data gloves are a common alternative that is sufficiently precise  and extrMuscular data can be measured with Surface Electromyography (sEMG). The sEMG signal can be modeled as a superimposition of the Motor Unit Action Potentials (MUAPs) of the active Motor Units (MUs) , 27. TheThis paper presents the first quantitative taxonomy of hand movements. The relative variations between joint bending angles (measured with a data glove) allow a quantitative characterization of the hand movement kinematics. The sEMG signals allow a functional analysis of the muscles involved in each grasp. The taxonomy is organized in a hierarchical structure and it is based on a signal feature extraction procedure that is common in sEMG literature.The problem of computing phylogenetic trees with a hierarchical structure is common in biology. Phylogenetic trees represent the evolutionary relationships among sets of organisms or groups of organisms, representing the ancient idea of a “Tree of Life”. From a phylogenetic point of view, phylogenetic trees are nothing else than a particular kind of family tree. The tree structure groups organisms with unique and shared characters. The more the considered characters are similar the higher the possibility that two subjects belongs to the same group. The more the characters are diverse, the longer the distance between the individuals. If phylogenetics looks for common genetic characters among a group of organisms, then in our case, we are aiming to detect similar features in a set of grasps. In the former, the same parent node in the tree indicates the presence of a shared ancestor, in the latter being part of the same subtree strongly suggests a common underling behavior. We analyze the movements performed by 40 intact subjects to extract the common underlying patterns that characterize each grasp. We create hierarchical trees for each subject individually and subsequently merge them into supertrees to obtain a generalized taxonomy. The problem of merging phylogenetic trees into a single structure emerges often in biology, where this procedure is commonly used to compute a branching history of species. Many practical approaches have been proposed in the literature for fusing information from several trees or for mixing trees in a common structure. Kluge proposed the “total evidence” approach, which claims that the phylogenetic analysis should consider all the available information . The exaThis section describes how kinematic and sEMG data were recorded and analyzed to create a quantitative taxonomy of hand movements. The data analysis procedure can be summarized as data acquisition . The Ninapro databaseThe acquisition setup includes a data glove and a set of surface electromyographic electrodes with built-in accelerometer. Hand kinematics were measured using a 22-sensor CyberGlove II , 4217, 4In order to allow the creation of the sEMG based quantitative taxonomy, pre-processing and feature extraction were performed. First, data preprocessing was performed to assure good data quality. This phase included filtering and synchronization. Second, the information of the sEMG signals was made usable by extracting a set of signal features using a moving window technique , 44. FinThe CyberGlove data were analyzed with a procedure that takes inspiration from window based time series analysis and in particular from the literature in EMG data analysis , 43. TheThe Delsys electrodes are not shielded against power line interference, so the power line interference was removed using an Hampel filter at 50 Hz .A high-resolution timestamp based on the Time Stamp Counter (TSC) of the CPU was assigned to each sample recorded for both the sEMG and joint angle data. The timestamp was used in the post-processing phase to synchronize the devices. To do so, all the modalities were up-sampled at the sampling frequency of the fastest device (2 kHz) using linear-interpolation. This is a well-known technique to increase resolution, avoid aliasing, and reduce noise . InterpoSignal feature extraction was performed applying the method described by Englehart et al. . Each mox on each time window w of T samples in length.The features were chosen according to use in the previous literature and include Root Mean Square (RMS), Mean Absolute Value (MAV), Integrated Absolute Value (IAV), Time Domain Statistics (TD)  and Wavew was obtained as xt is the tth sample in the window w.Root Mean Square (RMS) is arguably one of the most common features to represent sEMG signals. RMS provides a useful measurement of signal amplitude and, under ideal conditions, it has a quasi-linear relationship with the force exerted by a muscle . The RMSw they are defined as [The Mean Absolute Value (MAV) and the Integrated Absolute Value (IAV)fined as , 53: \dw and w+1 [x in a given window w, ZCw(x), is increased by one if, given two consecutive samples xt and xt+1, { xt>0 and xt+1<0} or { xt<0 and xt+1>0} and |xt−xt+1|≥ threshold.The Mean Absolute Value Slope (MAVS) is defined as the difference between the MAV value of two adjacent time windows,  and w+1 : \documngs (ZC)  feature x in a given window w, SSCw(x), is incremented if, given three consecutive samples xt−1, xt and xt+1, { xt>xt−1 and xt>xt+1} or { xt<xt−1 and xt<xt+1} and { |xt−xt+1|≥ threshold or |xt−xt−1|≥ threshold}.Another feature related to the frequency of the signal is the Slope Sign Changes (SSC)  which isw it is defined as [Waveform Length (WL) returns a single parameter providing a measure of the waveform complexity and given a time window fined as : \documThe quantitative taxonomy of hand movements is based on a hierarchical structure in order to highlight dependencies and relationships between the different motions. For each subject, one hierarchical tree was computed for each modality-feature combination, thus leading to five hierarchical trees for the EMG data and five trees for the kinematic data. We adopted this approach instead of building only one large hierarchical tree containing all the subjects in order to achieve a higher control of intermediate results and to be able to check the similarity across subjects. For each subject modality-feature combination, the hierarchical trees were computed by performing one-way Multivariate Analysis of Variance (MANOVA)  between edit distance, that is the minimal-cost sequence of node edit operations that transforms one tree into another [The capability to merge several highly specific trees is the key idea in obtaining a unique hierarchical structure. This part of the data analysis is fundamental, since it allows us to create a general and global quantitative taxonomy that takes into account inter- and intra-subject variability. Inter-subject variability is due to the highly specific way in which each person performs hand movements. Intra-subject variability is due to the small differences between repetitions of the same movement by the same subject. Despite the inter- and intra- subject variability, each hand movement has common underlying kinematic and muscular patterns that can be extracted by analyzing several repetitions of the same movement performed by different subjects. The variability between subjects can be measured as  another , 57  distance . The calFinally, the quantitative muscular and the kinematic taxonomies of hand movements were merged into the general quantitative taxonomy of hand movements. While the EMG tree gives a vision of muscular involvement in the movement and the kinematic tree shows the actual physiological movement performed by the subject, mixing the two allows a general analysis of the movement from both the muscular and the kinematic perspective.Evaluating the similarity between the quantitative muscular and the kinematic taxonomies of hand movements can yield fruitful insight, particularly to measure whether the two agree or not. While a reasonable agreement between the two taxonomies may enforce their representativeness, a strong disagreement may lead to a limited acceptability. Intermediate situations can be interesting to highlight differences in the data acquisition modalities or highlight differences between the muscular activation and the actual performed movement. The tree edit distance is frequently used in the comparison of hierarchical trees , 57. TheThis work presents a quantitative taxonomy of hand grasps based on muscular and kinematic data, described in detail in “large diameter and medium wrap grasps are linked at the first level in both the EMG and the kinematic taxonomy. The same happens for several other groups of movements, such as the small diameter and fixed hook grasps, the prismatic pinch and tip pinch grasps. Other movements change from first level connections in one tree to second level connections in the other. This is the case, for instance, of parallel extension and the lateral grasp, prismatic four fingers and writing tripod, precision sphere and tripod. An interesting change happens considering the power sphere, precision sphere, tripod and three finger grasps. These grasps are strongly linked (i.e. they are very similar) considering the kinematic taxonomy. In the EMG taxonomy the precision sphere and the tripod grasps are closer to the quadpod movement, while power sphere and three finger grasps are closer to the prismatic pinch and tip pinch grasps. Few movements change the grasp group depending on the considered taxonomy. This is the case of the power disk, the index finger extension and the parallel extension grasp. The power disk is grouped with the prismatic four fingers, the writing tripod and the stick grasp in the EMG based taxonomy. On the other hand, only in the penultimate level in the kinematic taxonomy is linked to them. The index finger extension grasp is isolated in both trees. In the EMG tree, this grasp is in a single branch, close to the majority of graspings but linked at the higher level to the grasp group including the parallel extension, lateral and the extension type grasps. In the CyberGlove tree on the other hand, it is completely isolated from the others movements. A strong difference between the two taxonomies occurs for the parallel extension grasp. In the EMG tree, the grasp is isolated from the other movements but grouped with the lateral and the extension type grasps. In the kinematic taxonomy it is close to the prismatc four fingers and the writing tripod grasp. There are two possible reasons to explain this difference. First, the EMG taxonomy considers the activation of wrist flexors/extensors, while the taxonomy based on the data glove does not consider them. The EMG signals measure all the muscular activity in the forearm, including the activity related to wrist movements while the Cyber Glove on the other hand is sensitive only to finger movements. Second, the difference can be due to variations in the force used to accomplish the movements, since the EMG signals are sensitive to it. In any case, except these few situations, the differences between the EMG and the kinematic taxonomy of hand movements are limited, confirming the validity of the proposed approaches and thus the validity of both taxonomies.The quantitative hand movement taxonomies based on EMG Fig. a and kinindex finger extension is clearly distant from all the other movements, while small diameter, fixed hook, large diameter, and medium wrap are very similar grasps. As a further prove to sustain the idea of merging trees built from different sensors, Table As previously said, the final EMG and glove taxonomies are assembled from supertrees built from single features. We measured how much the grasps are similar one to each other by using MANOVA starting from those features. The comparison involved the same features for different movements, so we computed a metric able to cope with the entire distribution in a multi-dimensional space as described in “The general quantitative taxonomy of hand grasps Fig.  is compu1) flat grasps; 2) distal grasps; 3) cylindrical grasps; 4) spherical grasps; 5) ring grasps. Flat grasps are well separated from all the others and are characterized by an elongated (or "cupped") positioning of the palm with an abducted or adducted thumb. Parallel extension can be added to this group considering its similarity with the extension grasp and that the grasp is included in the same group in the quantitative muscular taxonomy of hand grasps. Distal grasps are usually characterized by the strong involvement of distal phalanxes, thus of the flexor digitalis profundus. Cylindrical grasps are strongly linked to the shape of the object. They usually involve palm opposition with both adducted or abducted thumb and virtual fingers 2-5. Spherical grasps are strongly linked to the shape of the object as well. They involve both pad and palm opposition with virtual fingers 2-3, 2-4 and 2-5. Ring grasps are almost entirely in accordance with the GRASP’s taxonomy grasps with virtual fingers 2. This category includes as well the three finger sphere grasp, which is the only power, pad opposition grasp with virtual fingers 2-3 in the GRASP taxonomy. The three finger sphere grasp is grouped differently within the muscular and the kinematic taxonomy. These facts suggest that in static conditions the middle finger may have an accessory function in the grasp.The general quantitative taxonomy of hand grasps presents a division into five categories that correspond to real finger positioning and muscular activation, reflecting the shape of the grasped object and balanced combinations of parameters rather than the force used or other specific single parameters. The categories were named as follows according to specific properties of each group: Cylindrical and spherical grasps can also be grouped into a macro-sub group at the third level. Qualitative comparison of the results with the kinematic hand grasping synergies  highlighpower sphere, the precision sphere and tripod grasps are grouped together, similarly to what happens in the kinematic one. The three finger sphere is closer to prismatic pinch, tip pinch and ring grasp. Similarly to the kinematic taxonomy, the extension type, lateral and quadpod grasps are grouped separately from all the other movements.The main differences between the general and the muscular taxonomies concern the grasps targeting spherical objects. In the general taxonomy, the parallel extension and index finger extension. The parallel extension grasp is grouped with the lateral and extension grasp in the EMG taxonomy, while it is grouped with the prismatic four fingers and the writing tripod grasp in the kinematic taxonomy. In the general taxonomy, the parallel extension grasp is located alone in its own branch, separated from almost any other grasp and separated from the extension, lateral and quadpod grasp. The index finger extension grasp is represented as completely separated from the others in the kinematic taxonomy. In the EMG and in the general taxonomy of hand movements on the other hand, the grasp is still quite isolated but it is grouped with classical grasps  at a very high level.Two more important differences between the general taxonomy and the modality-specific ones are related to two movements that have different connections in EMG and glove trees: As previously mentioned, the differences between the muscular and the kinematic taxonomies are due to the properties of the movements that the different sensors can highlight. Thus, the general taxonomy of hand grasps provides a unified and general description of all of them.GRASP taxonomy [Comparing the general quantitative taxonomy of hand grasps with previous taxonomies allows evaluating the considerations used to create the previous taxonomies and to better interpret the results of this paper. Among the taxonomies presented so far, the taxonomy  is a a wGRASP taxonomy considering the sub-groups determined by the intersection of the GRASP parameters. However, it differs in the fact that the parameters considered in the GRASP taxonomy are differently  represented by the quantitative taxonomy  that strongly influenced the scientific literature in the past is not well supported by the general and modality-specific quantitative taxonomies of hand movements . This result is also confirmed considering only the quantitative muscular and kinematic taxonomies, that are more similar to the general quantitative taxonomy. The subdivision according to opposition type  is partially supported by the general quantitative taxonomy of hand grasps . The subdivision according to thumb position (thumb abducted or adducted) is not well supported by the general taxonomy of hand grasps, even if the index finger extension grasp is constantly well separated from all the other grasps and the lateral and parallel extension grasp are grouped. The subdivision according to virtual finger assignments is well supported by the general quantitative taxonomy of hand grasps. The index finger extension grasp is constantly well separated from the other grasps, coherently with the fact that it is the only grasp with virtual fingers 3-5 in the GRASP taxonomy. The general quantitative taxonomy category "ring grasps" is almost entirely in accordance with the GRASP taxonomy groups having virtual finger 2. This category includes as well the three finger sphere grasp, which is the only power, pad opposition grasp with virtual fingers 2-3 in the GRASP taxonomy. Two grasps of the virtual finger 2-4 category in the GRASP taxonomy (extension type and quadpod) are grouped in the category “flat grasps” of the general quantitative taxonomy. However, grasps that are grouped with virtual fingers 3, 2-3 and 2-5 are often mixed within the general quantitative taxonomy of hand grasps.The thumb-abducted, palm opposition, power grasps of the GRASP taxonomy are grouped together in the general quantitative taxonomy . Coherently with the GRASP taxonomy, the fixed hook is grouped at the second level with the thumb abducted palm opposition power grasps. The index finger extension grasp is separated from almost all the others, somehow corresponding to the GRASP taxonomy, in which this movement is alone in the power palm thumb adducted group having virtual fingers 3-5.The GRASP taxonomy, the ring and the sphere three finger grasps are grouped in the quantitative taxonomy. However, these grasps are grouped with the prismatic  pinch and the tip pinch, which previously were represented as part of the precision grasps. In the GRASP taxonomy, the thumb-adducted, side opposition, intermediate grasps include the lateral and stick grasps. These two grasps are not grouped in the general quantitative taxonomy of hand grasps. In fact, the lateral grasp is grouped with the extension type and the quadpod grasp. The stick grasp is grouped at the second level with the writing tripod and the prismatic four fingers and, at the third level, with the power disk. In the quantitative taxonomy, the thumb abduction, precision group of the GRASP taxonomy is divided into several sub-groups. The sub-groups are often grouped  according to the shape of the object rather than on the properties previously identified. The prismatic four finger grasp is grouped with the writing tripod at the first level, the tripod and the precision sphere are grouped at the second level, the prismatic  pinch and the tip pinch are grouped at the first level. This result shows that, on average, the shape of the object influences the positioning of the fingers and the muscular activity more than the usefulness for power or precision tasks and opposition type.Coherently with the GRASP taxonomy is obtained for the parallel extension grasp, that is separated from most of the other movements in both of them.Finally, accordance between the general quantitative and the This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on muscular and kinematic measurements of the hand Fig. . SeveralThe modality-specific taxonomies provide very similar representations of the hierarchical organization of hand movements (edit distance = 33), thus validating each other. The similarity between the muscular and the kinematic taxonomy confirms the existence of strong relationships between the muscular activity and the actual motion of the hand, as expected by anatomy. Small differences between the muscular and the kinematic taxonomies exist. Such differences can be due to the differences in the techniques used to record the data. The EMG based taxonomy considers the force exerted and the motion of the wrist, while the taxonomy based on the data glove does not consider these parameters. Nevertheless, these differences are in general small compared to the similarities and they can possibly be reduced by considering weights when merging hierarchical trees, as explained in “Several parameters can be used to quantitatively characterize hand grasps. This work considers kinematics and muscular activity in order to target the posture of the hands and due to practical data availability. The consistency between the muscular and kinematic taxonomy enforces the usefulness and reliability of the results. The analysis of other parameters can definitely be interesting and should be considered in follow-up work. The edit distance boundaries depend on the number of nodes (thus on the number of considered grasps). Intuitively, the larger the number of classes, the higher the possible number of variations that can occur between different trees. This fact can be one of the reasons behind the discrepancy obtained between the modality trees. Future work should address this fact in detail, by applying additional or alternative operations, measures or approaches.Depending on the domain, one specific taxonomy may be more useful than the other. While the kinematic taxonomy may be more useful for robotics, the muscular taxonomy may be more suitable for applications in prosthetics. Additionally, both taxonomies can have applications in rehabilitation, physiology and neuroscience. The general taxonomy aims at providing a solution that is intermediate to the different fields, allowing (and hopefully fostering) the collaboration among them on the basis of the first set of quantitative results in this challenging domain. The general quantitative taxonomy provides a comprehensive quantitative representation of hand grasps, overcoming the subjectivity of the taxonomies previously presented in literature and the limitations of the muscular and the kinematic taxonomies presented in this paper. The general quantitative taxonomy suggests a division into five groups of grasps that were named after specific properties of each group: 1) flat grasps; 2) distal grasps; 3) cylindrical grasps; 4) spherical grasps; 5) ring grasps. Cylindric grasps and spherical grasps can also be grouped into a macro-sub group at the third level.The division in categories is arbitrary, made in order to facilitate the comparison with previous taxonomies and to provide a further synthesis of the taxonomy. Future work could benefit from including quantitative approaches to perform the division in categories.GRASP taxonomy [GRASP taxonomy), while it enforces movement groups defined on the basis of real finger positioning and muscular activation, reflecting often the shape of the grasped object and balanced combinations of parameters rather than specific single qualitative parameters. The intersections of different parameters in the GRASP taxonomy are partially similar to the general quantitative taxonomy of hand grasps. However, there are still important differences , is not well supported by the general quantitative taxonomy of hand movements, while the subdivision into opposition and virtual finger assignments are usually better represented in the general quantitative taxonomy . We offer two possible interpretations of these results. First, human intuition and perception enrich previous taxonomies with alternative perceptions of the grasps, such as their usual aim, that is separated from a strictly kinematic or muscular representation of the grasps. Second, it can be important that the parameters of previous taxonomies are considered but it is not easy to balance and weigh the parameters in each movement and category properly only on a qualitative basis.The comparison of the general quantitative taxonomy of hand movements with previous taxonomies is important because it allows to validate the parameters on which the previous taxonomies were based and to better interpret the results of this paper. The taxonomy  represences Fig. . ConsideThe hierarchical model of human manipulation and grasping described in this paper improves several fields  with the quantitative analysis of relationships that were previously widely described in literature on the basis of qualitative parameters. In robotics, the five categories of movements defined in Fig. In conclusion, this work presents the first quantitative taxonomy of hand grasps based on muscular and kinematic data. The taxonomy clarifies with a solid quantitative approach what was proposed in the field so far based mainly on qualitative assumptions, thus unifying the diverse perspectives presented and offering a scientific reference for the taxonomies of hand grasps. The results were compared with previously presented taxonomies of hand grasps, improving them and clarifying the parameters used to define them. They appear at a first qualitative inspection in accordance with hand synergy studies.


Daphnia pulex.With up to millions of nearly neutral polymorphisms now being routinely sampled in population-genomic surveys, it is possible to estimate the site-frequency spectrum of such sites with high precision. Each frequency class reflects a mixture of potentially unique demographic histories, which can be revealed using theory for the probability distributions of the starting and ending points of branch segments over all possible coalescence trees. Such distributions are completely independent of past population history, which only influences the segment lengths, providing the basis for estimating average population sizes separating tree-wide coalescence events. The history of population-size change experienced by a sample of polymorphisms can then be dissected in a model-flexible fashion, and extension of this theory allows estimation of the mean and full distribution of long-term effective population sizes and ages of alleles of specific frequencies. Here, we outline the basic theory underlying the conceptual approach, develop and test an efficient statistical procedure for parameter estimation, and apply this to multiple population-genomic datasets for the microcrustacean  Moreover, it remains to be seen whether simpler, more intuitive approaches might yield results that perform to a comparable (or even greater) degree of accuracy.Because polymorphisms with different allele frequencies arise at different average times in the past, information on the amount of variation associated with different allele-frequency classes in a population sample can provide insight into the history of population-size change. This is especially true for neutral variants, whose temporal dynamics depend only on stochastic sampling effects. This simple idea has led to the development of several technical and computationally demanding approaches for estimating historical changes in the sizes of populations, either from patterns of segregating variation at the single-nucleotide level or from information on linkage disequilibrium between nucleotide sites . All of n be the number of sampled haploid genomes , the number of polymorphic genomic sites with r copies of the derived allele is denoted G is the total number of monomorphic and polymorphic sites evaluated across the genome. In the following, a mutation in class r will be referred to as rth order, with The approach taken here is conceptually straight-forward, the main biological assumptions being that the sites underlying the analysis have evolved in a neutral fashion for a considerable number of generations , and that there be no substantial population structure. All aspects of the analysis are based on samples of the site-frequency spectrum (SFS) for such sites. Letting u)  , so if qDaphnia pulex.The goal here is to use these principles to determine the long-term series of effective population sizes most compatible with the SFS, and given the measures of interval-specific n samples (assumed to be The re known .k and ending at level n) and can end as deeply as level 1. This means that internal branches starting at level k can span up to Here, we are concerned with the average features of alleles within different frequency classes (the tree . For anyn) and the allele class (r). Only the branch lengths are functions of the population size. As described below, the full set of coefficients (derived in the Appendix) provide the basis for analytical expressions for various useful statistical features of SNPs. Here, we adhere to the infinite-sites model, so that each new mutation arising on a genealogy is assumed to appear at a unique site, with the mutation rate to a novel SNP being equal to u per site per generation.A key point is that the n sequences have been sampled randomly (from n haploids) at each of L nucleotide sites known to behave in an effectively neutral manner. Under neutrality, for a population with constant effective size, the expected number of sites occupying frequency class r in an unfolded site-frequency spectrum isu being the mutation rate per nucleotide site per generation  experiences k we assign an effective population size k, the expected time (in generations) to the next coalescence event into the past isi.e., We start with the assumption that neration . Here, wr at each relevant level of the coalescent, averaged over all possible random genealogies in a sample of size n. These are derived in the Appendix.Given knowledge of the expected internal features of the coalescent, for each SNP frequency class, the expected value of n but may end at any level in the genealogy from n) not having coalesced by level i.e., as As an example, consider the simplest case of the singleton class. All singleton mutations must be present on external branches, which always start at level k, allowing for variable ending points, is k and descending down to the base of the tree. From Equation (A7), the expected number of order-r branches starting at level k is r present at level Things are more complicated for the higher-order site-frequency classes because internal branches no longer initiate at the same levels. However, by extension from Equation (3b), one can infer that the probability of a mutation arising on a single branch starting at level n is even.Before proceeding, recall that there are two forms of a site-frequency spectrum. The unfolded distribution, described above, requires information on the ancestral allelic states of each SNP site, ideally inferred from at least two suitably distant outgroup species . Such a rth-order SNP in terms of the historical effective population sizes isWhereas the previous results are concerned with the demographic history of the population, an alternative viewpoint considers the average ages and demographic features of SNPs of various frequencies. Once the interval-specific estimates of i is inversely proportional to i. Thus, for r will be derived alleles.Although the preceding expressions apply to an unfolded SFS, where the designated alleles are known to be derived , studies without such a luxury must rely on a folded SFS. In this case, each frequency class will be a mixture of derived and ancestral alleles with different average ages. For low-frequency alleles in large samples, almost the entire set of sampled SNPs will consist of derived alleles, and the preceding expressions can still be used to obtain reasonably precise estimates. This follows from Equation (1), which shows that the expected frequency of SNPs of order r and ancestral alleles of order r in a folded SFS can then be written asA more precise approach is to explicitly treat the frequencies of the folded distribution as mixtures of classes of derived alleles of order s in the past be r isr can be obtained from the theoretical results on the mean time spent in different intervals. Weighting of the interval-specific durations by their associated For a population experiencing temporal changes in size, alleles of different order will generally experience different long-term effective population sizes from birth to the present. Letting the population size at time i is inversely proportional to i.see Appendix. With a folded site-frequency spectrum, the weighting approach used for the age of an allele in the preceding section can be applied using the definitions of u, and one or more of the interval-specific effective population sizes, i.e., solving a set of The results summarized in Equation (4b) amount to a series of L sampled sites, the number of monomorphic sites is i.e., assuming that the elements of the sampled SFS are all essentially independent and Poisson distributed with parameters equal to the frequency expectations times L, the likelihood function is given in Supplemental Material.It then becomes necessary to pool adjacent population sizes so as to reduce the number of parameters to be estimated. Consistent with https://github.com/EvolBioInf/epos. The starting point of the estimation procedure assumes a constant population size throughout the entire history of the sample. The maximum-likelihood estimator of k flanked by two different We have implemented the above procedure in the program epos (Estimating POpulation Sizes), which runs under the UNIX command line. The C sources of epos and tutorial-style documentation are available from github at github.com/EvolBioInf/bootSfs) implements the bootstrap to estimate the sampling variance of the estimated demographic history. Third, it is possible to exclude classes from the SFS, if for example the singleton class is deemed unreliable owing to sequencing errors. Fourth, the user can specify the AIC stopping criterion. Fifth, all possible combinations of breakpoint locations can be re-evaluated at each iteration, as opposed to sequentially adding fixed breakpoints; to accomplish this, there are two versions of the function nextConfig in epos: a fast, greedy version, which adds one new level at a time and a slow, exhaustive version, which goes through all possible combinations of levels. This flexibility is provided because the the number of possible sets of breakpoints increases exponentially as the stepwise estimation procedure advances.Several additional features are built into epos. First, it is possible to analyze folded as well as unfolded SFSs. Second, the auxiliary program bootSfs  predicted consistent increases in population size to the present . From ions ago . AssuminDaphnia populations do not exhibit major demographic shifts across allele-frequency classes, with the population average Finally, the relationships between the mean https://doi.org/10.25387/g3.10265867.The details on data acquisition, curation, and deposition appear in i.e., at the times of average occurrence of coalesence events. This, of course, will never happen precisely in any natural population. However, as the times of coalescent events vary widely among genealogies, such granularity can be expected to average out. Moreover, the approach taken does provide an increasingly fine dissection of the overall time scale under evaluation with increasing sample size (n). So as shown by The methods developed herein provide a model-independent means for estimating the past demographic history of a sample, using information on the frequency distribution of nucleotide sites assumed to behave in a neutral to nearly neutral manner. The approach taken assumes that changes in population size occur only at specific points in a genealogy, vs. internal intron sites, both of which behave in a nearly neutral fashion). SFS sample-size variation will generally be the rule in low-coverage population-genomic sequencing data, where some individuals will have inadequate sequence at random sites.In the proposed method of estimation, Epos simply starts with an assumption of constant The theoretical basis of the methods described herein is the same as that adopted by One potential concern with our method is its reliance on a composite-likelihood approach that ignores the nonindependence of linked SNPs. There are two reasons to believe that this is a minor issue with respect to the final analyses. First, most organisms have ten or more chromosomes, so only a minor fraction of pairs of loci are even on the same chromosome, and even a smaller fraction are within the de novo mutation appearing in a sample.Like all polymorphism-based methods, our approach is expected to become increasingly unreliable at very distant times in the past, owing to the increasing granularity of the coalescent process, and the fact that few polymorphisms are expected to survive for more than


Mesenchymal stem cells (MSCs) have immune-modulatory and tissue-regenerative properties that make them a suitable and promising tool for cell-based therapy application. Since the bio-chemo-mechanical environment influences MSC fate and behavior, the understanding of the mechanosensors involved in the transduction of mechanical inputs into chemical signals could be pivotal. In this context, the nuclear pore complex is a molecular machinery that is believed to have a key role in force transmission and in nucleocytoplasmic shuttling regulation. To fully understand the nuclear pore complex role and the nucleocytoplasmic transport dynamics, recent advancements in fluorescence microscopy provided the possibility to study passive and facilitated nuclear transports also in mechanically stimulated cell culture conditions. Here, we review the current available methods for the investigation of nucleocytoplasmic shuttling, including photo-perturbation-based approaches, fluorescence correlation spectroscopy, and single-particle tracking techniques. For each method, we analyze the advantages, disadvantages, and technical limitations. Finally, we summarize the recent knowledge on mechanical regulation of nucleocytoplasmic translocation in MSC, the relevant progresses made so far, and the future perspectives in the field. Over 10 years, thanks to their unique properties and multiple clinical benefits, mesenchymal stem cells (MSCs) have been studied and used as suitable and promising tools for cell-based therapy applications. MSCs are adult multipotent cells with the great potential to self-renew and to differentiate into multiple cell lineages mainly derived from the mesodermal layer but also from the ectoderma and endoderma under specific conditions Fig. .Fig. 1GrMSCs can be easily harvested from different mesenchymal tissues that are bone marrow, adipose tissue, umbilical cord, and dermis. Cell isolation is a simple procedure based on the collection of a heterogeneous population of plastic-adherent cells screened by the expression of specific surface antigens  is transduced into a cell biochemical signal leading to gene transcription. This mechanism is defined as mechanotranscription. Among all the transcription factors involved in MSC differentiation pathways, few factors have been studied in the context of mechanotranscription. In Table Matrix microenvironment  and external mechanical stimuli clearly influence both in vitro and in vivo cell growth. Substrate and matrix stiffness regulate cell properties, such as differentiation, proliferation, and cell shape  is a non-deforming mechanical stimulus able to increase chondrogenic gene expression in MSCs  induces shearing stress and it is found to promote both osteogenic than myogenic differentiation. The method commonly used to induce this type of stress involves perfusion bioreactors with a steady, pulsatile, and unidirectional flow  is the first element acting in the mechanotransduction mechanism. Some cell membrane elements capture chemical and mechanical signals from the extracellular environment and transmit them through the cell to the nucleus. These membrane components are integrins, cadherins, cilia, and ion channels. Integrins bind cytoskeleton through focal adhesion complexes and functioning as the direct linkage between ECM and intracellular environment. Thus, thanks to integrins, external stimuli could be transmitted and translated into a structural rearrangement of cytoskeleton organization. Other sensors of external inputs are ion channels, Piezo proteins, which respond to stimuli altering the intracellular cationic flow and triggering intracellular mechanisms , and the lamina. As focal adhesions connect ECM to the cytoskeleton, LINC (LInker of Nucleoskeleton and Cytoskeleton) complexes connect the cytoskeleton to the nucleoskeleton, leading to the transmission of an external stimulus to the internal environment of the nucleus  is the other structural linkage between the cytoplasm and the nucleus. Recently, researchers have shown that LINC complexes and NPC are directly connected /TAZ  localization is strictly related to the rigidity and cell shape  composed by approximately 30 nucleoporins (Nup), giving to the NPC a cylindrical structure that spans from the cytoplasmic to the nucleoplasmic side of the cell Fig.  Garcia . In the The spoke ring is the NPC structure localized in the nuclear envelope (NE) lipid bilayer. It is a cylinder of 50-nm length and width between 20 and 80 nm, filled with protein filaments, and the phenylalanine–glycine repeats nucleoporins (FG-Nups). FG-Nups have the role to block big inert molecules (> 70 kDa) and facilitate cargo passage to the nucleus. The exchange of ion and small molecule is also allowed from a group of secondary channels (around 4 nm in diameter), placed around the spoke ring. The transmembrane ring located between the two NE lipid membranes confers stability to the NPC cylinder.Finally, the nuclear and the distal rings are in the nucleoplasmic side and are connected by Nup153 and Tpr proteins forming the nuclear basket (50–75-nm length) Gu .Transports through the NPC could be passive or facilitated. They are bidirectional and share the central diffusion channel located within the central pore: molecules smaller than ~ 70 kDa in size (corresponding to a maximum diameter ~ 10 nm) can passively diffuse across the central part of the pore and their translocation capability is function of size ). The carrier is aided to translocate principally by FG-Nup filaments with cell energy expenditure. In fact, the release of molecules into the nucleus or into the cytoplasm is driven by the state of the Ran nucleotide that cycles between the GDP and the GTP bound states.NPC molecular machinery is so efficient that in a single pore could translocate up to 1000 molecules/s corresponding to a mass flow nearly 100 MDa/s  and single pore (nanometers), allowing visualization and analysis of molecular dynamics through a single NPC. The main techniques used to evaluate fluorescent molecule and protein diffusion or fluxes between different cell compartments are photo-perturbation, correlation spectroscopy, and single-molecule tracking Fig. .Fig. 5ScMf), representing the protein fraction diffusing in the selected ROI, and the characteristic time of the diffusion t1/2. Small and highly diffusing proteins show fast recovery, while no fluorescence recovery is observed with an immobile molecule. The speed of the recovery is dependent on the molecular size, the environment viscosity, or the interaction degree between the protein of interest and other molecules  is a powerful method used to investigate protein mobility into the cells . It could be used in place or paired with FRAP to investigate proteins diffusion. This technique is based on the cell image acquisition between bleaching pulses on a fixed ROI. In this case, if the protein is free to move and able to enter in the bleaching region, a fluorescent signal decay is measured over the entire cell , a small volume of the sample is illuminated and specimen fluorescent fluctuations are acquired over time. Using the autocorrelation function G, fluorescence fluctuations provide information about molecules concentration and diffusion. In fact, G amplitude is inversely proportional to the average number of fluorescent molecules and the decay time represents the diffusion capability Elson . FCS is Using photo-perturbation approaches or FCS, it is possible to measure the mobility properties corresponding to the average behavior of the observed molecules, but the data need to be fitted with the right diffusion model to avoid inaccurate interpretation  technique allows to detect and track in time and space individual fluorescent particles. It provides rich data sets that describe diffusion and binding kinetics of the protein of interest . However, nowadays, the measurements supporting this theory are not completely reliable because they are carried out with a standard transmission electron microscopy procedure. This means that the sample must be fixed and included in resin, then cut using the microtome to obtain the slices imaged by the TEM. The use of a sliced plane does not allow to know the effective direction and depth of microstructures and, therefore, involves a systematic error in the measure. To overcome this limitation, it would be necessary to use a scanning EM tomography (STEM) or a focused ion beam combined with scanning electron microscopy (FIB-SEM) that allows a three-dimensional imaging of the sample and the measure of the effective dimension of the NPC without parallax errors. Using the STEM microscopy, we measured the nuclear ring area in non-adherent MSCs (which take roundish shape) and spread MSCs finding no significant differences . YAP is a mechanosensitive transcription factor, notoriously involved in cancer, regeneration, and organ size control. They analyzed several elements related to the NPC mechanotransduction by using the FRAP technique. They demonstrated that applying forces to the cell nucleus, the YAP nuclear translocation increased by decreasing the restriction of NPC to protein transport. Moreover, modulating the stiffness of the substrate or using drugs to depolymerize the cell cytoskeleton, they have proven that YAP translocation was mediated by forces transmitted to the nucleus and that the import is related to the force transmission via actin cytoskeleton and not microtubules organization.In the last years, we are working on the TF nucleocytoplasmic transport in MSCs. We evaluated the inert green-fluorescent protein (GFP) nucleocytoplasmic passive diffusion in MSCs grown on flat substrates or in three-dimensional substrates able to modify cell morphology  techniques, removed any doubt. They used AFM to apply a constant force to the cell nucleus and observed that force application increased the nuclear/cytosolic YAP ratio and that the YAP localization returned to the cytosol upon force release Fig. . TherefoDespite MSCs being extremely sensitive to mechanical stimuli and therefore an optimal candidate to investigate mechanotransduction mechanisms, to the best of our knowledge, in literature, there are no other articles investigating the TF nucleocytoplasmic transport in MSCs. This is probably due to the difficulty of achieving MSCs expressing fluorescent molecules. In fact, standard DNA transfection procedures result less efficient in the case of stem cells Maurisse . On the Nowadays, as well as understanding the effective mechanisms of the nuclear pore opening, the other important questions still open in the field of nucleocytoplasmic translocation are related to the characterization of TF-facilitated transport in relation to the cell environment and the nuclear shape.Currently, we are studying the facilitated transport of a transcription factor involved in MSC differentiation towards the myocardial phenotype: MyoD (Vandromme et al. We are considering two methods of investigation, FRAP and SMT, and facing their technical challenges. Apart from the difficulty of achieving MSCs expressing fluorescent transcription factors, each of these two methods has specific complexities. In the case of FRAP, for example, the extensive cellular three-dimensionality, induced by the cell growth in the Nichoid, complicates the measurement. In fact, what occurs during the bleaching phase of a nuclear ROI is that also many fluorescent proteins in the cytosol are bleached out. Moreover, as shown in Figs. Regarding SMT, the major problems with this technique concern the three-dimensionality of the investigated systems and the sample fluorescence of the Nichoid. In fact, using a HILO microscope, the highly inclined beam does not allow the observation of the entire sample. Acquiring only a small part of the sample, the three-dimensionality is partially lost. This limitation could theoretically be overcome using a light sheet microscope. Instead, the Nichoid fluorescence problem is more complicated to solve. In fact, as explained in the previous paragraph, to make good SMT measurements, it is very important to maximize the SNR to contrast the brightness of the single molecules with respect to the background and to be able to make an accurate tracking. Since our sample fluoresces in the same wavelengths as the fluorophore, the application of this technique on cells grown in the Nichoid is still challenging Fig. . We are Mechanosensors and mechanotransduction mechanisms have a key role in modulating MSC fate by controlling the master switch between stemness maintenance and differentiation in these cells. As it has clearly emerged in some recent studies, the NPC plays a determinant role as a mechanosensor by regulating the nuclear import of transcription factors likely based on a stretch-activation mechanism. Despite the huge advancements in fluorescence microscopy to measure nucleocytoplasmic shuttling and to deepen the involvement of NPC in mechanotransduction, the optimal acquisition method is still to be defined, mainly due to the difficulty of transferring the existing techniques to 3D cell models.The understanding of these mechanisms will allow the development and design of more performing substrates to mechanoguide and control cellular fate. These innovative systems could be used to improve cell-based therapy in regenerative medicine and in the field of personalized medicine.


This paper presents a new antenna proximity sensor for mobile terminals based on the measured reflection coefficient using a bidirectional coupler which is positioned between the main antenna and the front-end module. Using the coupled forward and reverse long-term evolution signals by the bidirectional coupler, the reflection coefficient looking into the antenna was calculated in the base-band processor. The measured reflection coefficients showed clear differences for both the types of objects, and the distances between the terminal and the objects. The proposed antenna proximity sensor showed a recognition distance that was approximately 5 mm longer than that of a conventional capacitive proximity sensor. As the technologies for mobile terminals evolve, many types of sensors have been deployed in the terminals ,15,16,17A proximity sensor can be classified as either an ultrasonic proximity sensor, an inductive proximity sensor, or a capacitive proximity sensor according to the detection principle used. An ultrasonic sensor contains an ultrasonic transceiver, which transmits an ultrasonic signal and receives the reflected signal back from objects within a limited distance. The distance between the sensor and the object can be obtained by multiplying the velocity of the ultrasonic wave by the time the wave takes to make a round-trip .The inductive proximity sensor is mainly used to detect metals. An oscillator in the sensor generates a sinusoidal current that has a fixed magnitude. As the distance between the metal surface and the sensor decreases, the eddy current becomes larger and, as a result, the amplitude of the oscillating current decreases. If the amplitude of the oscillating current becomes lower than the threshold, the trigger circuit will determine that a metal object is present .The capacitive proximity sensor senses not only metals but also non-metals by measuring capacitance. As an object approaches the proximity sensor, the sensor can detect the object using the measured capacitance from the sensor. Currently, capacitive proximity sensors are widely used to recognize the hand-gripping condition of the mobile terminals. This is done by connecting a sensing probe from the sensor integrated circuit to the main antenna, which could deteriorate the radiation performance . The capIn this paper, a new proximity sensor is proposed based on the measured reflection coefficient using a bidirectional coupler between the front-end module (FEM) and the antenna. Some object types can also be recognized using the phase information of the reflection coefficient. The overall performances will be presented to validate its sensing ability as a proximity sensor.n-th port of the coupler, respectively. The 4-port S-parameter of the bidirectional coupler is used to find the reflection coefficient looking into the antenna. The 4-port S-parameter of the bidirectional coupler is given by:Since the forward and reverse ports of the bidirectional coupler are in good match to the transceiver through 50 Ω, By replacing d signal .An antenna proximity sensor, which is based on the measured reflection coefficient using a bidirectional coupler between the FEM and the antenna, is proposed for use on mobile terminals. The operational principles and the sensing procedures have been explained. The proposed proximity sensor was implemented and was experimentally verified using an LTE signal.The experimental results show that the proposed sensor can recognize the objects in the distance range of 0 to about 8 mm using the object identification function and can recognize the objects at a distance of up to about 10 mm using the distance identification function. The proposed sensor was experimentally proven to be superior to the conventional capacitance sensor in performance, function, cost, and size.


This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces. Surface size, geometry and texture are some of the most influential subjects in the field of precision engineering . The devExtensive research has been conducted on developing various measurement instruments to fulfill a wide range of metrological needs ,8,9,10, Considering the characteristics of the multi-sensor measurement, the modelling process can generally be divided into two steps, including data registration and fusion . In the Recently, Gaussian process (GP) has been used to address the multi-sensor data fusion problem . GP is kThe remaining sections are outlined as follows. Due to the limited resolution and the error of a measurement instrument, surface measurement can generally be formulated as a regression problem of the form, where ess (GP) , a non-pGP is basically a collection of random variables, any finite subset of which obeys joint Gaussian distribution. It is generally be defined by a mean function x K (1)GP(X)Hence, according to the Bayesian theory, a prediction of It is seen that the GP inference is determined by the covariance forms which represents the covariance of the points on the model. Hence the construction of appropriate covariance kernel function is the core of GP modelling. Taking the information of the preprocessing as input, multi-feature classification module extracts and classifies the geometric features of the designed models so as to design an appropriate covariance kernel functions for GP modelling. An initial measurement strategy is also loaded from a pre-established class database. The class database is responsible for collecting appropriate class of measurement strategies for specific tasks with respect to certain types of surface features based on the prior knowledge. If the contained surface features and the required specifications are successfully classified, the measurement strategy will be automatically loaded. Otherwise, manual design of the sampling strategy is required and a new class will be uploaded to the database so that they can be successfully classified in future. A certain class in the database would contain necessary information for the setup of the measurement instruments, including the types of the sensors and the associated parameters which will be used during measurement. For contact types of sensors, such as trigger probe, the initial sampling patterns may be also included for further optimization in GP modelling process. It is emphasized that the sampling strategies produced in this stage are only initial plans which require further optimization. Multi-dataset regression module is responsible further optimizing the initial measurement strategy. This is carried in two steps. Firstly, taking the designed covariance kernel function as input, GP model is established to fuse the datasets obtained from different sensors to obtain a unique measured surface. Secondly, both the bias and the uncertainty of the fused surface model is used to qualify the reliability of the measured surface, and is served as critical sampling criteria to perform adaptive sampling until reaching desired reliability. During the process, a virtual instrument simulator  can alsoCovariance kernel is a function used to describe the spatial correlation of a geometric feature in GP modelling, and hence the design of a covariance kernel should be in accordance to the characteristics of the measured surface. For a machined surface with known CAD model, the characteristics of the surface topography, such as smoothness, roughness, and periodicity can be specified and classified easily. In GP-BIS, such knowledge is the key to design covariance kernel functions and would significantly improve the measurement efficiency and accuracy. There already exists various covariance kernel functions which possess variety of geometric characteristics ,34. TablFor some surfaces containing multiple features at different scales, a single covariance kernel may not be flexible enough to describe the spatial correlation. In such case, the listed kernel functions can be served as base functions to form various composite kernel functions by combining them together. One of the important technical merits of multi-sensor measurement is capability in improving the quality of the measurement results via data fusion process. Considering that all the measured datasets are from a common surface, they should be inherently correlated. Hence, besides the spatial correlation within each dataset, the correlation across the datasets should also be taken into account. In GP modelling, this can be formulated by modelling the auto-covariances and cross-covariances among the measured datasets . For arbOne of immediate advantage of above multi-dataset regression is that it preserves the full Bayesian inference during the fusion process so that credibility can still be assigned to the fused model using the covariance of the established GP model as given in Equation (7). Hence, the measurement process can generally be considered as a time-series learning process that the credibility of the fused model can be served as critical sampling criterion to perform on-line sampling adaptation. As shown in The proposed GP-BIS has been implemented in MATLAB 2018a  and tested on several surfaces with variety of complexities. Firstly, case studies have been conducted on multiple features enriched complex surfaces to study the capability of the GP modelling. A set of 121 × 121 grid points are extracted from the designed surface in each case study, and are added Gaussian noise with 2 μm standard deviation to simulate the measurement error. Then, a sinusoidal wave with amplitude 0.4 mm and period 5 mm in both two X and Y directions is superimposed to the parabola to make the surface more complex. The designed surface now has two radically different topographies in two different scales. Hence, in the multi-classification process, the GP-BIS identifies two different types of topographies and a new composite kernel functions has been designed using SE and PER see . The meaThe proposed GP-BIS has applied to an instrument equipped with a touch trigger probe and a laser scanner. Trigger probe is one of typical sensors which can achieve high accurate measurement with generally very low efficiency. The laser scanner, on the other hand, is capable of realizing high efficient measurement while with relatively low accuracy. For the measurement of complex surfaces, more intelligent multi-sensor measurement strategy is required to achieve both high accurate and high efficient measurement. The probing error and the linearity of the laser scanner are identified to be 0.9 μm  and 15.2 μm (uniform). Measurement has been conducted on a machined workpiece which is designed by a peak function defined by Equation (10).Although laser scanner has extremely good measurement efficiency, the accuracy is very low comparing with touch trigger probe. With the aid of the GP-BIS, the trigger probe can perform adaptive sampling, and the efficiency has been dramatically improved and the accuracy has been preserved at the same time. With the aid of the laser scanner, multi-dataset regression is used to further improve the measurement efficiency around 40% comparing with pure triggering measurement. The results demonstrated the capability of the proposed GP-BIS in enhancing the performance of the multi-sensor instrument in measuring complex surfaces.This paper presented a GP-BIS for realizing automatic and intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a consecutive learning process and makes use of Gaussian process to model the process based on two key modules including multi-feature classification and multi-dataset regression. The system has been implemented and applied to produce multi-sensor measurement strategy for an instrument which is equipped with a trigger probe and a laser scanner. The results shows that the performance of the multi-sensor instrument has been dramatically improved around 40% in terms of measurement efficiency while maintains the measurement accuracy at the save level. The proposed study should provide new insight for the realization of intelligent measurement of complex surfaces. One of the problem of GP modelling is the high computation complexity which would lead huge time for the computation when large number of data is involved in the inference process. Future work considers the implementation of approximated GP inference with the aid of graphics processing unit (GPU) for the practical use of the proposed method especially in manipulating vision based measurement dataset.


We thank Dr. Guo and coworkers for their interest and comments  on our aSecond, unfortunately, the serum creatinine (SCr) concentration used for the diagnosis of AKI in our study had not been corrected according to fluid balance because of the inconsistent data recorded. Adjustment of the SCr concentration was proposed according to the assumption that the SCr concentration may be diluted by positive fluid balance . HoweverFinally, the C-index observed following the addition of both L-FABP and NT-proBNP showed the improvement beyond that of the baseline model alone Table . On perf


In automotive radar systems, a limited number of antenna elements are used to estimate the angle of the target. Therefore, array interpolation techniques can be used for direction of arrival (DOA) estimation to achieve high angular resolution. In general, to generate interpolated array elements from original array elements, the method of linear least squares (LLS) is used. When the LLS method is used, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In addition, through the transformation matrix obtained from the LLS method, the phases of the interpolated array elements are not precisely generated. Therefore, we propose an array transformation matrix that generates accurate phases for interpolated array elements to improve DOA estimation performance, while maintaining constant amplitudes of the array elements. Moreover, to enhance the effect of our interpolation method, a power calibration method for interpolated received signals is also proposed. Through the simulation, we confirm that the array interpolation accuracy and DOA estimation performance of the proposed method are improved compared to those of the conventional method. Moreover, the performance and effectiveness of our proposed method are also verified using data obtained from the commercial radar system. Because the proposed method exhibits better performance when applied to actual measurement data, it can be utilized in commercial automotive radar systems. In recent years, radar systems have been installed in automobiles to detect targets located in multiple directions. Typically, automotive radar systems use frequencies in the 24-GHz or 77–81-GHz band. Since such a high frequency band is used, the miniaturization of the radar antenna system has become possible. In the automotive radar system, the number of receiving antenna elements is gradually decreasing to reduce the manufacturing cost of the radar. Therefore, various techniques have been proposed for accurately estimating the direction of arrival (DOA) of the target with the limited antenna elements as much as possible.Array interpolation is one of the methods for improving the DOA estimation accuracy using a limited number of antenna elements. Several studies on enhanced array interpolation methods have been conducted ,2,3,4,5.Among the various array interpolation methods, an array interpolation method that moves array elements from an original location to a desired location using a transformation matrix is widely used ,7,8. To Thus, in this paper, we propose a transformation matrix in the logarithmic domain for the array interpolation. We focus on minimizing the phase differences between the original and the interpolated array elements. First, we take logarithms for the array elements and extract the phase information from them. We then apply the LLS method to the logarithmic-domain matrices to find an appropriate transformation matrix. Finally, the interpolated array elements are generated by the new matrix, and the DOA estimation is conducted. Based on a comparison of interpolation errors of the proposed and conventional transformation methods, our array transformation method successfully interpolates newly-produced array elements with more elaborate phases. In addition, the proposed array transformation does not affect the amplitudes of the interpolated array elements; they are conserved even after the transformation. Moreover, for a given antenna array, the proposed transformation matrix for that array is calculated and stored (offline) in advance. Thus, we do not have to calculate the transformation matrix in real time.We also extend the proposed array interpolation scheme to received signal interpolation. When we use the transformation matrices obtained by the LLS method and our proposed method, the powers of the interpolated received signals are not uniform over all array elements. In this case, the effect of the array interpolation and the performance of the DOA estimation are not fully ensured. Thus, to mitigate this problem, we also propose a calibration method for the interpolated received signal powers. The simulation results confirm that our proposed method performs DOA estimation better than the conventional array interpolation method. In addition, based on actual measurement data acquired using an automotive radar, our method shows improved angular resolution and estimation performance. The proposed method can be effectively used in a radar system using a small number of array elements.The remainder of this paper is organized as follows. In K directions of N linearly placed array elements. The location of the array elements is t can be expressed as:kth signal source at time t. These amplitudes are assumed to be zero-mean complex Gaussians and uncorrelated with each other. In addition, the noise vector We assume that signals, coming from moothing .In this section, we briefly introduce the conventional array interpolation technique using the transformation matrix derived from the LLS method. For the field of view (FOV) of an automotive radar, which is expressed as:To find the proper transformation matrix In addition, the Through this transformation matrix, received signals for the interpolated array elements can also be generated, which are defined as:By utilizing these interpolated received signals, the authors of ,10,11,12When we use the conventional transformation matrix for array interpolation, a major problem occurs. Based on the transformation matrix obtained from the LLS method, interpolated array elements are generated by linear combinations of original array elements. In this case, the amplitudes of the interpolated array elements may not be equivalent to those of the original array elements. In other words, degraded . In addiFor the elements of the steering matrices on both sides of (6), we take logarithms such as:As in the original domain, the appropriate matrix This matrix The conventional transformation matrix in (8) formulates the interpolated array elements with linear combinations of the original array elements. However, this new transformation matrix generates only the phase information of the interpolated array elements using combinations of the phases of the original array elements. In other words, based on our transformation, it conserves the amplitudes of the original array elements in the interpolated array elements because:Therefore, the proposed array transformation affects only the phases of the interpolated array elements and generates more accurate phases for the interpolated array elements. The transformation matrix Similar to the received signal interpolation in (11), received signals of the interpolated array elements with the transformation matrix Using For a much better DOA estimation, we also consider the power of the received signals. When we use the interpolated received signal vectors, ,M})see  where ·¯timation . In our In other words, the powers of the interpolated received signals are nearly equivalent among the array elements. Thus, if we use the received signal vector Many studies have been conducted on the location in which to interpolate array elements to improve the accuracy of DOA estimation algorithms. In ,8, the aIn the simulation, we used four array elements , the more accurate the array interpolation was conducted. For both transformation matrices, Using these transformation matrices, we formulated the received signals and conducted the DOA estimation. As shown in For the statistical performance evaluation, we calculated the resolution probability We also conducted a simulation for a case in which three targets existed in the FOV of the radar. The simulation was conducted while maintaining the same simulation conditions given in lgorithm . To appllgorithm ,21. If t method) ,23. More method) . Thus, fUnder the same simulation conditions, we also applied the total least squares estimation of signal parameters via rotational invariance techniques (TLS EPSRIT) . The TLSlgorithm  with thaFurthermore, simulations were conducted not only for the four array elements, but also for three and five array elements. When the number of array elements was three, the original location of the array elements was given as 0,1λ,3λ] ,16. In aTo verify the performance of our proposed method, we also conducted actual measurements on a testing ground of the Mando Corporation using its automotive LRR. In the measurement, a single-element transmit antenna and four-element receiving uniform linear array antenna (Using the same automotive radar, the measurements were conducted on the expressway, as shown in In this paper, we proposed a logarithmic-domain transformation matrix used for array interpolation to improve the accuracy of DOA estimation. Our transformation matrix was obtained by minimizing the differences between the phases of the original array elements and the interpolated array elements. Our proposed method identified a more accurate transformation matrix with less phase distortion, and the amplitudes of the array elements were maintained after the transformation. In addition, to improve the accuracy of the DOA estimation algorithm, we proposed a method for adjusting the powers of the interpolated received signals to a similar level. Finally, from the simulation and the measurement results, we verified that our new method showed much better angular resolution and estimation accuracy than did the DOA estimation using the conventional transformation matrix derived from the LLS method. The proposed method can be effectively applied to radar systems using a small number of antenna elements, such as automotive radar systems.


Grasping movements are typically performed toward visually sensed objects. However, planning and execution of grasping movements can be supported also by haptic information when we grasp objects held in the other hand. In the present study we investigated this sensorimotor integration process by comparing grasping movements towards objects sensed through visual, haptic or visuo-haptic signals. When movements were based on haptic information only, hand preshaping was initiated earlier, the digits closed on the object more slowly, and the final phase was more cautious compared to movements based on only visual information. Importantly, the simultaneous availability of vision and haptics led to faster movements and to an overall decrease of the grip aperture. Our findings also show that each modality contributes to a different extent in different phases of the movement, with haptics being more crucial in the initial phases and vision being more important for the final on-line control. Thus, vision and haptics can be flexibly combined to optimize the execution of grasping movement. For instance, even without vision, we can easily grasp the lid of a marmalade jar, while holding the jar with the other hand. Thus, haptic information  is sufficient to select and perform the proper action by specifying both the direction and amplitude of the movement as well as the preshaping of the hand. While the planning, execution and control of grasping movements under visual guidance have been extensively studiedreaching movements by asking participants to move one hand to a visual target or to their other unseen hand16. The end points of reaching movements based only on haptic information are generally more variable than those directed toward visual targets. However, even if haptic information might seem to be unreliable, it still plays an important role. When haptic information about the target position is complemented by the visual information, the end points of reaching movements are more accurate and more precise than in the conditions based on a single sensory modality, supporting the idea that multisensory information can be efficiently combined during sensorimotor processing20.Several studies have compared the use of visual and haptic information in 26. These properties can be acquired also through haptics. Proprioceptive inputs from the extended arm and from the fingers enclosing the object inform the central nervous system about the object location in space and about its size, respectively27. These inputs are enriched by the additional tactile inputs induced by different skin stretch patterns of the arm and the hand28. Haptic inputs are indeed sufficient to guide grasping movements29. For instance, the maximum grip aperture of the contralateral hand consistently scales with the haptically perceived size of the object. However, how does grasping under haptic guidance compare to visually guided grasping? And, does haptic information support vision when both are available during the execution of a grasping action?In contrast to reaching, grasping actions toward a haptically sensed object need to be based not only on extrinsic properties , but also on intrinsic properties of the object 30. When grasping was based only on haptic information, the maximum grip aperture was usually wider and movements took longer compared to vision. Surprisingly, when grasping was under combined visuo-haptic guidance, movements were very similar to those based on only visual information30 which raises the question what the reason may be that multisensory information is not exploited during grasping movements. One reason may be that vision provides us with sufficiently accurate and reliable information and thus grasping is already maximally optimized. A simpler reason may be that the haptic and visual information were considered as not belonging to the same object32 given that both the haptically-explored object and the hand feeling this object were hidden below a platform on which the seen object was attached30. Thus, haptic information may have been completely ignored and movements were planned and executed by relying on visual information alone.Only a handful of studies have directly compared grasping actions under visual only and haptic only guidance36. In fact, haptic feedback obtained after grasping an object is considered to play an important role in calibrating goal directed actions44. Hence, blending the haptic with the visual information when both are available even prior to movement start would be the best strategy to minimize the risk of misestimating the position and/or the size of an object.The failure of observing multisensory integration in grasping movements is particularly surprising in light of the following considerations. Humans can effortlessly judge the size by enclosing an object between the digitsIn the present study, we compared grasping actions under haptic only, visual only or visuo-haptic guidance to further investigate the unisensory and multisensory processes in prehension. In the visual condition, participants had full vision of the object and the workspace. In the haptic condition, vision was prevented and participants used their left hand to continuously feel the object that was the target of the grasping action performed with their right hand. Finally, in the visuo-haptic condition, participants could make use of both visual and haptic information throughout the movement. Importantly, in the visuo-haptic condition, the object that was the target of the grasping action was the exact same object that the participants felt with the other hand.To understand how unisensory and multisensory information is used to plan and execute grasping movements we have monitored the movements of the hand and the digits as the reach and grasp unfolded throughout the entire movement trajectory instead of relying only on specific kinematic markers . There are important advantages of this approach. First, movements that are indistinguishable in terms of kinematic markers might turn out to fundamentally differ in other important aspects. And, second, the relative role of visual and haptic information in multisensory integration is not necessarily fixed and might instead dynamically develop along the movement.We tested a sample of 20 participants , recruited at New York University Abu Dhabi. All had normal or corrected-to-normal vision and no known history of neurological disorders. All the participants were naïve to the purpose of the experiment and were provided with a subsistence allowance. The experiment was undertaken with the understanding and written informed consent of each participant and experimental procedures were approved by the Institutional Review Board of New York University Abu Dhabi in compliance with the Code of Ethical Principles for Medical Research Involving Human Subjects of the World Medical Association (Declaration of Helsinki).y-axis could slide out to change the size of the stimulus46  was used to prevent vision of the workspace in the haptic condition and between trials. A pure tone of 1000 Hz, 100 ms length, 65 dB of intensity was used to signal the start of the trial, while another one of 600 Hz with the same length and intensity to signal its end.Index digit, thumb and wrist movements were acquired on-line at 200 Hz with sub-millimeter resolution by using an Optotrak Certus system . Markers were attached on the first phalanx of the thumb and index digit onto the, respectively, lateral and medial fingernail top side, and on the styloid process of the radius. The Optotrak system and the occlusion goggles were both controlled by a custom Matlab program.y-axis of the stimulus in three different conditions: Visual (V): Participants were allowed to see the object (goggles open); Haptic (H): Vision was prevented, participants were allowed to touch the object along its y-axis with the left hand (goggles closed); Visuo-haptic (VH): Participants were allowed to both see and touch the object (goggles open).Participants sat comfortably in front of the table, with the center of their torso positioned between the object, located on their left side, and the home position, located on their right side Fig. . ParticiAll the trials started with the participants’ thumb and index digit of the right hand positioned on the home position Fig. , the lefAfter a variable period, the start tone was delivered and participants had to perform a reaching and grasping action at their natural speed towards the object Fig. . No reacTo introduce some variability in the task, three different configurations of the cube’s sides were used: one where both the expandable sides were in, one where the thumb side was slid out and one where the index digit side was slid out. All the analyses will consider the latter two configurations in which the object was of the same size (6 cm). These data were collapsed for all further analyses. Each modality condition was recorded in a separate block of trials. The order of these blocks was randomized across participants, while sides configurations were randomized within each modality. We ran ten trials for each configuration which led to a total of 90 trials per participant . Before the experiment, a training session was performed in which ten trials were run in each modality to accustom the participants with the task.47. The raw data were smoothed and differentiated with a third-order Savitzky-Golay filter with a window size of 21 points. These filtered data were then used to compute velocities and accelerations in three-dimensional space for each digit and the wrist. Movement onset was defined as the moment of the lowest, non-repeating wrist acceleration value prior to the continuously increasing wrist acceleration values48, while the end of the grasping movement was defined on the basis of the Multiple Sources of Information method49. We used the criteria that the grip aperture is close to the size of the object, that the grip aperture is decreasing, that the second derivative of the grip aperture is positive, and that the velocities of the wrist, thumb and index digits are low. Moreover, the probability of a moment being the end of the movement decreased over time to capture the first instance in which the above criteria were met48.Kinematic data were analyzed in RTrials in which the end of the movement was not captured correctly or the missing marker samples could not be reconstructed using interpolation were discarded from further analysis. The vast majority of the trials with missing marker samples was among the same four participants. The exclusion of these participants left us with 960 trials from which another 61 trials had to be removed from further analysis.For each trial, we calculated the movement duration (MD), defined as the time from the movement onset to the end of the trial, the maximum grip aperture (MGA), defined as the maximum Euclidean distance between the thumb and the index digits, the maximum wrist velocity (MWV) and the maximum wrist deceleration (MWD) Fig. . MoreoveFor each kinematic variable we performed a repeated measures ANOVA considering as within factor the three modalities: Visual, Haptic and Visuo-haptic. Bonferroni correction was applied to the follow-up pair-wise comparisons.48. Each wrist, thumb and index digit trajectory was resampled in 201 points evenly spaced along the three-dimensional trajectory in the range from 0 (movement onset) to 1 (movement end) in 0.005 steps using cubic spline interpolation.To analyze how the movements of the hand and the digits unfolded throughout the movement, we have spatially normalized each grasping movement using the total length of the movement, i.e., the length of the trajectory that the midpoint between the digits covered from the starting position to the end of the movementt-tests at each step of the space-normalized trajectories  we ran a Bonferroni corrected paired ies Fig. .Figure 553. Interestingly, this movement rapidity did not hinder performance, quite the contrary, the MGA reduction suggests that the joint visuo-haptic information actually optimized grasping movements.Table The above analyses did not show striking differences between the H and V modalities. However, based on this evidence we cannot conclude that the actions were actually performed in a similar fashion. In fact, the analysis of pMGA and pMWD showed that the MGA and MWD occurred earlier along the movement in H compared to V and VH Fig. . On the The comparison between H an V modalities showed that under visual guidance the grip aperture was significantly smaller only from the 96% to the 99.5% of the trajectory Fig. , H-V. WhThe wrist velocity in H was significantly lower than in V from the 73% to the 94% (and for the last 1%) of the trajectory Fig. , H-V andThe aims of the present study were twofold. First, we compared grasping movements toward an object that was only seen  and toward an object that was continuously felt by the other non-grasping hand (haptic input based on both proprioception and touch) to unravel the differences and the similarities between these grasping categories. Second, we tested whether the motor system makes use of the multisensory information when grasping movements are under both haptic and visual guidance. Our results show that grasping movements under haptic or visual guidance share some similarities but also exhibit some critical differences. In addition, the combined visuo-haptic information optimizes grasping by speeding up the movement and, at the same time, by reducing the grip aperture that is necessary to successfully grasp the object.30. Wider grip apertures are normally found when vision of the hand is prevented60, but, interestingly, the same strategy was also used in H, even though the haptic on-line feedback from the non-grasping hand was potentially available. From this we may infer that haptic information about either the size or the position of the object, or both, is less reliable than in vision and it thus promotes a different grasping behavior.The similarities based on kinematic parameters  between haptically- and visually-guided grasping might suggest that haptics is as good as vision in providing the motor system with reliable information about the position and shape of the object. But this was not the case. In the H condition, the main kinematic parameters  occurred earlier along the trajectory of the movement than in V. This pattern is even more explicit when we look at how the grasping actions under H or V evolved along the whole trajectory. These differences indicate that, in H, hand preshaping was initiated earlier, the digits closed on the object more slowly, and the final phase was more cautious, most likely, to keep a sufficiently wide safety margin to avoid an accidental collision with the object30 who observed a similar MGA in V and VH conditions. A possible explanation could be that visual and haptic signals were not combined because they were treated as coming from two different objects; a strong evidence against the unity assumption32. Instead, participants in our study could see that their left hand was holding the object while performing the reach-to-grasp action with the right hand, thus making it obvious that what they were feeling was the same object that they were looking at.When actions were under both haptic and visual guidance, we observed a very different grasping behavior, which showed clear evidence of visuo-haptic integration. The grasping actions in the VH condition benefited from multisensory inputs in several ways: movements were performed in a shorter amount of time compared to the V condition, and the grip opened less than in H and V conditions. These results are at odds with those found by Pettypiece and colleagues48.Interestingly, haptics and vision seem to each play different roles along the movement trajectory. Whereas the VH grip aperture started to differ from V already in the initial phases of the movement, the differences between VH and H appeared only later. On the contrary, the differences in terms of wrist velocity arose earlier between H and VH, than between V and VH conditions. The advantage of the VH condition with regards to the unisensory conditions seems to rely on a more predominate role of H in the initial phases which gives place to V in the final phases when on-line visual feedback becomes more crucial67. On the other hand, two signals supply us with independent haptic information. The fingers enclosing the object provide information about the object size, and, the flexion-extension of the arm provides information about the position of the object. Thus, the visual and haptic signals could be integrated in several ways to plan the grasping action.Taken together, these results reveal a clear advantage of visuo-haptically guided grasping. However, one open question concerns the contribution of the different haptic and visual signals. Visual estimates of size are tightly linked to visual estimates of distance: we can properly scale retinal information only if our visual distance estimates are approximately correctIn the case of full integration, the haptic position estimate would be integrated with the visual position estimate to promote a better visual size estimate which, in turn, would be integrated with the haptic size estimate. Instead, in the case of partial integration, two options exist. In one case, the estimate of distance would be based only on vision and thus only the haptic size estimate would be integrated with the visual size estimate. In another case, only the haptic and visual distance estimates would be integrated to provide a multisensory distance estimate used to scale the visual size information. In addition, once the movement has started, the sensory feedback from the visual and haptic modalities needs to be integrated also in real-time along the movement trajectory. The current data do not allow to distinguish between these scenarios and they will thus needed to be addressed in future studies.In summary, we showed that the simultaneous availability of both visual and haptic information of the target object substantially improves reach-to-grasp actions. Thus, the nervous system combines visual and haptic information to optimize movement execution.