Due to noisy motor commands and imprecise and ambiguous sensory information, there is often substantial uncertainty about the relative location between our body and objects in the environment. Little is known about how well people manage and compensate for this uncertainty in purposive movement tasks like grasping. Grasping objects requires reach trajectories to generate object-fingers contacts that permit stable lifting. For objects with position uncertainty, some trajectories are more efficient than others in terms of the probability of producing stable grasps. We hypothesize that people attempt to generate efficient grasp trajectories that produce stable grasps at first contact without requiring post-contact adjustments. We tested this hypothesis by comparing human uncertainty compensation in grasping objects against optimal predictions. Participants grasped and lifted a cylindrical object with position uncertainty, introduced by moving the cylinder with a robotic arm over a sequence of 5 positions sampled from a strongly oriented 2D Gaussian distribution. Preceding each reach, vision of the object was removed for the remainder of the trial and the cylinder was moved one additional time. In accord with optimal predictions, we found that people compensate by aligning the approach direction with covariance angle to maintain grasp efficiency. This compensation results in higher probability to achieve stable grasps at first contact than non-compensation strategies in grasping objects with directional position uncertainty, and the results provide the first demonstration that humans compensate for uncertainty in a complex purposive task. Optimal sensorimotor control models actions as decisions that maximize the desirableness of outcomes, where the desirableness is captured by an expected cost or utility to each action sequence. These models provide explanations for many aspects of our ability to compensate for uncertainty, but they have not been applied to understanding purposive movements—movements involving the application of forces to change the relative position of objects and the actor in the environment. Using time efficiency as a natural cost function, we present a statistical optimal control analysis of uncertainty compensation strategies in a purposive movement task, grasping an object with directional position uncertainty. In accord with the predictions of the analysis, the experimental results showed that people compensate for uncertainty by adopting grasp strategies that increase the chance to produce a stable grasp at first contact. Our findings suggest that visuomotor system plans for uncertainty even in complex purposive movements. Optimal sensorimotor control models actions as decisions that maximize the desirableness of outcomes, where the desirableness is captured by an expected cost or utility to each action sequence. These models provide explanations for many aspects of our ability to compensate for uncertainty force-closure graspingIn grasp planning, fingers must be targeted toward points on the object's surface that will allow the application of forces sufficient for lifting and dexterously manipulating the object. In particular, the finger-object contacts should permit forces that are capable of stably lifting the objects and counterbalance external forces and torques exerted on the object – termed Once people place their fingers on contact points supporting force-closure, they can begin to lift the object. Hence, the duration of a grasping task depends on the time to produce force-closure grasping, and this time is minimized by movement trajectories that produce force-closure at first contact. Failure to satisfy force-closure conditions at first contact requires subsequent adjustments to rearrange the contact points – a process that requires extra time and effort. Little is known about how people recognize contact points supporting force-closure or how this process is affected by uncertainty.The purpose of our work is to study uncertainty compensation in grasping and compare human performance against normative predictions. An illustration of precision grasping objects with position uncertainty is presented in We test what, if any, changes in grasping strategy occur as compensations for object location uncertainty. Using a robotic arm to generate oriented distributions of cylindrical object locations, we investigate whether people adopt grasping strategies that minimize the impact of uncertainty on grasp success in terms of force-closure grasping at first contact.no motion condition). In the other two, the object visibly moved through a series of randomly drawn positions. After the object was occluded, it either moved to a new random location (random-end location condition) or to a fixed location (fixed-end location condition). The chance that the reach trajectory will end with the fingers making object contact at points permitting force-closure depends on the path the index finger and thumb take to the object. The critical part of the trajectory occurs near the end of the movement, when the fingers approach possible object locations.A schematic representation of the apparatus is shown in Trajectory characteristics are illustrated for a single participant on the no-motion (left), fixed-end location (middle) and random-end location (right) conditions in x, the principal axis of uncertainty as vm, and the thumb and index finger contact surfaces are shown as line segments, with local position r and direction u. For each possible cylinder location ci, force-closure is only possible if the cylinder surface is in the approach path for both digits (wf is less than the finger surface width) and the angle between the index finger and thumb is sufficiently large.We investigated whether participants modified their index finger/thumb approach direction with the orientation of the object position distribution. We predicted a change in approach direction from an analysis of the conditions for force-closure at first contact. Force-closure occurs when the fingers make contact on the object at locations that permit the application of forces in the direction of both the surface normals and surface tangents that can potentially cage the object. A necessary condition for force-closure is that the required tangential forces are less than the force applied to the surface normals, scaled by the coefficient of friction. Geometrically, this relation produces friction cones at the contact points of the thumb and the index finger with cylinder, whose boundaries are determined by the surface tangent and normal vectors of both fingers (2) and the finger surface orientations (variance = 2.5 deg2). The results are shown for two orientations of object location uncertainty, 0 (gray) and 45 deg (black). The principal effect of additional variability is to narrow the range of approaches that produce high force-closure probabilities.On the basis of this analysis, the choice of a grasp strategy can be turned into a statistical decision problem, where the objective is to select an approach that maximizes the probability of force-closure at first contact see  section.Approach direction compensation: If participants exhibit the predicted compensation, we should observe approach direction vary to align with the axis of maximal uncertainty. We estimated the planned approach direction for each participant and condition by computing the covariance of sensor positions across trajectories, illustrated in >12.2905 and P<0.0393), and scales almost linearly for all participants. However, all participants' slopes were less than predicted by ideal compensation (shown by the black discontinuous line). In contrast, for the fixed-end location condition, where the final position was fixed across trials, the approach direction was near constant  across all covariance angles. Note that participants showed large differences in this measure but similar trends. To make these trends easier to visualize, we subtracted the mean across covariance angles of this measure from each participant's data. The results are shown in  = 40.4193 and P = 0.0079). In contrast, the inefficiency curves in the random-end location condition shows that by modifying approach direction, participants were able to maintain a low inefficiency rate for all covariance angles . Moreover, a test of the two regression results shows these trends are different .We can also gain insight into the benefits of compensation by focusing on the set of trajectories with low performance. We identified the set of trajectories that produced force-closure for less than 20% of sample locations . Because these trajectories will require post-contact adjustments in fingers positions before lift occurs for the majority of cases, we call this measure the >106.4595 and P-value<0.001), excluding Participant 5, shown in We also tested whether people modify their grip aperture profile as a function of condition and covariance orientation, by computing the mean value of the maximum grip aperture (MGA) across trials and regressed the results against covariance angles. For all conditions, we found no significant variation of MGA magnitude or time with covariance angle. However, there were significant differences across conditions for all participants . However, it may also indicate that uncertainty compensation strategies are relatively constant.In conclusion, the results show that people plan for the effects of uncertainty in selecting object contacts in purposive movement tasks.Five right-handed  participants with normal or corrected-to-normal vision participated in the study for monetary compensation. The appropriate institutional review board approved the study protocol and informed consent was obtained based on the Declaration of Helsinki.Participants were instructed to reach rapidly with and then use a precision grasp to lift a cylindrical object (2.2 cm diameter and 11.5 cm height) held in a cradle on a platform mounted on a robotic arm that precisely moved the object (<1 mm error), Participants selected a comfortable reference position by placing both fingers along the top edges of the reference block at the beginning of the experiment. At the start of each trial, participants viewed the cylinder for a period of time that depended on condition, and then vision was removed by shutting down the crystal liquid glasses. Thereafter, they were cued to rapidly reach, precision grasp and lift the object within 1200 ms while it was out of view. The trial was considered successful if the participant lifted the cylinder to trigger the switch within the timeout, however, none of the participants failed to grasp the cylinder within 1200 ms. The fingers had to be returned within 3 mm of their starting positions before the next trial was begun.Participants were familiarized with the task by running a number of training trials on the non-motion condition. Once they were ready and felt comfortable with grasping the cylinder, the real trials began. On the no-motion condition the cylinder was stationary and the view time was 1 sec before glasses occluded the vision of the object. In the fixed-end location condition, the robotic arm moved the cylinder over a sequence of 5 random positions (1 sec each) sampled from a strongly oriented 2D Gaussian distribution. The cylinder was returned to the based  position and the view was occluded after 1 sec. Thereafter, participants reached and grasped the cylinder, while it was out of view. Note that participants were told that the cylinder always returned to the base position. In the random-end location condition, the cylinder moved over a sequence of 5 positions (1 sec each) randomly generated by a strongly oriented 2D Gaussian distribution. Thereafter, the view of the object was occluded and the object moved to a new random position selected from the same Gaussian distribution. Finally, participants reached and grasped the cylinder, while it was out of view. In this condition, participants were told that the cylinder moved to a new position from within the same distribution of the 5 visible positions. For both fixed-end location and random-end location conditions, we used an 80 deg range of distribution orientations , designed to fit within, but strongly challenge participants' natural biomechanical reaching posture. The standard deviations of the major and minor axes of the covariance were 12 mm and 0.25 mm, respectively. Note that the covariance angle was defined with respect to the x-dimension of the coordinate frame. Trials from each covariance angle were blocked, and 100 trials of reaching, grasping and lifting the cylindrical object were performed for each block.Kernel density estimation was used to analyze the spatial distribution, velocities and orientations of the thumb and index finger trajectories as illustrated We computed approach direction for the average trajectory for each participant on the two conditions (fixed and random-end location). Because averages are strongly affected by outliers, we excluded trajectories that had substantially different temporal characteristics. Note that trajectory data were spatially variable, but timing was consistent for trajectories with similar velocity profile. From the histogram of the time of maximal x-velocities, we selected the trajectories that fell within 80% the histogram mean  and averaged them. From the average trajectory, we computed the approach direction of the main axis of the ellipse that describes the spatial variation of the fingers centroids, In addition, we measured MGA because it serves as a measure of position uncertainty To evaluate object-finger contact, we computed an estimate of the index finger and thumb contact surfaces relative to the sensor locations via a calibration procedure. The index finger and thumb were placed in grooves on a calibration block and the sensor locations were recorded. The orientation and position of the calibration block were also recorded by placing sensors in known locations on the calibration block. The groove angle, length and depth were precisely known due to the geometry of the block and the block required precision grip contacts similar to those made on the cylindrical object. We converted this information to approximate the index finger and thumb contact surfaces and computed a homogeneous transformation that converted between sensor and finger coordinates. In particular, finger coordinates had an origin at the center of the estimated region of potential contact, and had directions aligned so that one axis  was perpendicular to the calibration block surface , one axis pointed in the direction of the groove , and the other axis roughly corresponded to finger width. Once projected into the x-y plane the surface approximation became a line segment that has a 1–2 mm error from the actual index finger/thumb contact surface.According to an optimal statistical modeling approach, the goal is to select a visuomotor grasping strategy that chooses a desired movement trajectory by optimizing the expected gain. The gain function takes into account the costs and benefits of possible outcomes of the movement S is a random variable denoting an executed movement strategy, C represents cylinder locations, s. The conditional distribution s. In the “The optimal movement policy rf, directions th. With this representation, a reach plan is a desired trajectory S(t) = {rf, rth, uf, uth}, where time dependence is suppressed inside the brackets to make the notation less complex.To keep the analysis tractable, we made the following simplifications. Due to the shapes of both cylinder and fingers, and the post-contact lift direction of the cylinder we can safely neglect the spatial dimension along the cylinder's z-axis and focus on the perpendicular plane. Within the plane, the contact surfaces of the index finger and thumb can be approximated as line segments (see lower inset of ρ) centroid is modeled as a random vector ci sampled from a 2D Gaussian density P(c). To simplify the conditions for contact, we project the samples ci into a frame of reference defined by the index finger's (or thumb's) contact surface, forming contact coordinates. Contact coordinates are simply the position of points in the environment with respect to the index finger and thumb surfaces. Let Possible locations for the cylinder :Due to the cylinder geometry, the 4 conditions above are equivalent to the simplified condition, Eq. (3) that the angle between the surface normals is greater than 90 deg, for a coefficient of fiction H and δ denote Heaviside step function and the Delta Dirac, respectively  virtual “cylinder positions” drawn from a We estimated the expected probability of force-closure We tested the optimality of participants' grasp strategies and to provide a comparison, we also estimated the deterioration in grasping performance if participants do not compensate for uncertainty. To provide a baseline comparison for this grasp performance measure, we estimated the probability of force-closure if participants had adopted a non-compensation strategy for the random-end location condition (and hence would not have compensated for the cylinder location uncertainty). We simulated the non-compensation strategy by estimating the expected probability of force-closure for no-motion condition trajectories on the random-end location condition cylinder locations.Text S1Supplementary Materials(0.09 MB DOC)Click here for additional data file.


Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the CMA_L during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed. It is well known that the execution of even simple motor and/or cognitive tasks by the brain requires the participation of multiple cortical regions, which are mutually interconnected and exchange their information via plastic long-range synapses. Consequently, knowledge of brain connectivity is becoming an essential aspect of modern neuroscience, especially useful to understand how the brain realizes its basic functions and what the role of the different regions is. Connectivity, however, is an elusive concept, which can have different definitions depending on the emphasis of the investigators . In partAs an alternative method to study effective connectivity, a few authors in recent years have employed the so-called “neural mass models.” These models were originally proposed in the mid seventies , 10 and Application of neural mass models to estimate effective connectivity is, however, a very hard task, due to the elevated number of parameters involved and the presence of nonlinear terms, which preclude the use of analytical solutions. For instance, in a recent paper  we derivFor this reason, in the present paper we designed a new method, based on a genetic algorithm, to provide an automatic fitting between model and real data. The method tries to find absolute minima of alternative cost functions within the same procedure. Genetic algorithms have already been used to estimate the parameters of a neural mass model in order to fit real data . The alThe model of a single population was obtained by modifying equations proposed by Wendling et al. . It conshe, hi, and hg, are used to describe the synaptic effect of excitatory neurons , of slow inhibitory interneurons and of fast inhibitory interneurons, respectively. Model equations can be written as follows.Each neural group receives an average postsynaptic membrane potential from the other groups, and converts the average membrane potential into an average density of spikes fired by the neurons. This conversion is simulated via a static sigmoidal relationship. The effect of the synapses is described via second-order linear transfer functions, which convert the presynaptic spike density into the postsynaptic membrane potential. Three different kinds of synapses, with impulse response vi represent the average membrane potentials . These are the input for the sigmoid function which converts them into the average density of spikes  fired by the neurons. Then, these outputs enter into the synapses , represented via the second-order linear functions. Each synapse is described by an average gain  and a time constant . The outputs of these equations, which can be excitatory, slow inhibitory, or fast inhibitory, represent the postsynaptic membrane potentials . Interactions among neurons are represented via seven connectivity constants (Ci). Finally, p(t) represents all exogenous contributions, both excitation coming from external sources and the density of action potentials coming from other connected regions.In these equations, the symbols z0) affects the input p(t) in  in the ith ROI can be computed as follows:Wij is the weight of the synaptic link from the jth (presynaptic) ROI to ith (postsynaptic) ROI, T is the time delay , ni(t) represents a gaussian white noise with mean value mi and standard deviation σi, and the sum in the right hand member of  in  via a wefollows:pi(t)=ni while he was performing a right foot movement. A 58-channel EEG system  was used to record electrical potentials by means of an electrode cap, accordingly to an extension of the 10–20 international system. A/D sampling rate was 200 Hz. During motor task, subject was instructed to avoid eye blinks, swallowing, or any movement other than the required foot movements. Bipolar EMG was recorded from control and spinal cord injury (SCI) subjects, with surface electrodes from the right tibialis anterior muscle and orbicularis oris muscle to detect the onset of foot and lip movements, respectively. The electro-oculograms (EOGs) were recorded to avoid trials with artifacts due to eye-blink movements. The EMG was monitored throughout recordings from electrodes placed as described above to avoid poor quality of the recordings due to muscular artifacts. Artifact rejection was performed on a wide segmentation of the trials (from −4.0 s to +4.0 s) while a narrow segmentation (from −2.5 s to +0.5 s) was used as analysis period.A 3-shell Boundary Element Model (BEM) of the head was used to estimate the cortical current density (CCD) distribution in some regions of interest (ROI) of the cortex (the cingulate cortex (CMA_L), the primary motor area (M1F_L), and the supplementary motor area (SMAp_L) starting from activity measured on the scalp. The procedure used is described in previous works , 19, 23.Power spectra have been computed by using the Welch's average modified periodogram method . In partWe examined 5 subjects with spinal cord injury  and 5 healthy subjects . Informed consent was obtained from all the subjects. The study was approved by the local ethics committee. The SCIs were all of traumatic aetiology and were located at the cervical level ; at the time of the study, all the patients had a stabilized lesion (mean time since trauma 19.4 ± 7.2 months). Neurological status was assessed according to the American Spinal Injury Association (ASIA) standards on the basis of the patients' motor and sensory scores, neurological level, and neurological impairment. The completeness of the lesion was defined according to the concept of sacral sparing: sensory preservation of the peri-anal zone and/or motor function of the external anal sphincter . The lesion was complete in all 5 patients (ASIA-A: complete motor and sensory loss below the lesion level). None of the SCI patients had suffered a head or brain lesion in concomitance with the spinal injury. Neither uncontrollable spasticity-induced body movements nor dysaesthetic pain syndrome were reported by any of the patients. All subjects were right-handed as assessed by the Edinburgh inventory  by calculating the model output and comparing it to the real EEG signal: the better the fitting between the simulated signal and the real one, the higher the FC of the individual. Best-ranked individuals (higher FC) have higher probability to reproduce. During reproduction couples of parents are randomly selected according to their FCs. Each couple generates two new individuals whose chromosomes are obtained from applying genetic operators to the parents' ones. Typical genetic operators are crossover and mutation . CrossovThe major challenge in implementing a GA is to find an efficient fitting function for determining the FCs and rank the individuals, so that the algorithm is able to converge in reasonable time. To compare the simulated signal to the real one, we used their PSD. Actually, analysis of the peak frequencies and amplitudes in the PSD allows evaluations of the rhythms characterizing the signal, their frequencies, and the relative power associated to each frequency band.We introduced some changes to the original GA to improve its speed of convergence.melting pot) is the one characterized by the strictest requirements.The global population was divided in 4 tribes. Each tribe has its own fitting function. The algorithm allows migration between tribes, so that each individual may choose the tribe that consent its offspring to converge to the solution in the fastest way. In order to compute the FCs of each tribe, we first calculated three alternative cost functions. The first is the classic mean square error. The second aims at quantifying the similarity in the ratios between the local maxima and the local minima . The third focuses the attention especially to the position of the peaks . These three functions were then combined with different weights, in order to obtain four alternative FCs to be used in the four tribes. The fourth tribe  from the thalamus to the primary motor cortex and from the thalamus to the supplementary motor cortex. The differences in the connection weights are also significant (p < .05) from the high frequency region to some cortical ROIs.Second, from a separate parameter estimates, one can detect statistically significant differences in the synaptic strength between healthy and tetraplegic subjects. In particular, connectivity in tetraplegic patients is about 12% higher (on average) compared with that of healthy volunteers. Differences in connection weights between the two classes are very significant . The results are illustrated in The aim of this work was to derive patterns of connectivity among the main regions of interest (the cingulate cortex and the primary and supplementary motor areas) involved in simple motor tasks. To this end, we used neural mass models and electrophysiological data obtained with scalp EEG, propagated to the cortex. Moreover, we analyzed differences between normal and tetraplegic subjects. Although various attempts to derive connectivity from EEG, and to characterize EEG in pathological conditions are present in the literature, most works make use of empirical model . Just a few attempts to elucidate existing data via interpretative models can be found in the literature –17.In an interpretative model, parameters have a clear biophysical significance, and the model allows the formulation of hypotheses on the physiological mechanisms, the neural architecture, and the parameter changes responsible for data generation. Promising models assume the presence of interacting neural masses, which are reciprocally connected and generate the neural signals responsible for the measured electrical activity. Similar models integrated with Bayesian inference  were used by Friston and coauthors to estimate effective connectivity from neuroimaging data , 28, to Our work goes in the same direction as previous papers. However, three main innovative methodological aspects deserve a critical discussion: the kind of information used to validate the model, the structure adopted for the model, and the fitting procedure for parameter estimation.The first important issue concerns what kind of data the model is intended to reproduce, and so, which measurement is compared to model output. This is a crucial point, since the type and structure of a model are strictly dependent on the problem under study. In this work, as in previous ones –21, we fp are amongst the main generator sources of the early and late components of the motor-related cortical potentials  are primarily implicated in movement execution itself rather than in higher cognitive control of voluntary movements  which, view see , 36).In order to simulate EEG spectral patterns in these areas, including both alpha and beta as well as gamma rhythms, we adopted a simple model structure based on a few a priori assumptions. First we assumed that the cingulate cortex drives the primary motor area and the supplementary motor area during execution of the task, but it receives only negligible feedback from them. This assumption seemed justified by the attention that the cingulate cortex has received in the neuroscientific literature recently . In thesA further assumption is that the three ROIs under analysis, if stimulated, can oscillate with an intrinsic beta rhythm. This assumption agrees with present knowledge. Indeed, as traditionally described in the literature, a motor related activity in the beta range is frequently located close to the sensory motor area following finger movements  and is rBeyond this fundamental aspect, the model incorporates two other important assumptions, which are used to generate alpha and gamma rhythms, but have a less evident physiological and neural counterpart.First, model assumes that a low frequency alpha rhythm originates from an external area  and then propagates to the other regions of interest. Indeed, a classic idea on the genesis of alpha rhythms . A recenAn important simplification, which deserves a brief comment, is that we neglected any feedback synapse from cortical regions to the “thalamic area.” Of course, cortico-thalamic feedbacks exist in the brain and may have a role in the modulation of the alpha spectral content. Our choice has been adopted just to reduce the number of parameters in the fitting procedure, in order to avoid the problem of “overfitting.” In fact, increasing the number of unknown parameters improves the quality of fitting, but worsens the reliability of parameter estimates.A further important assumption is that also the gamma rhythm originates from an external area, that we supposed to be located in the frontal cortex. This hypothesis is corroborated by the observation that neurons in the frontal cortex shows the intrinsic capacity to oscillate at 40 Hz , 43. HowOnce this model structure has been designed, a fundamental point concerns what aspects of the spectra should be used to perform a best fitting between model predictions and real data. In previous works we used a least-square criterion function of the difference between model and measured spectra –21. AssuIn the present work we tried an innovative strategy, based on the idea that not all aspects of the PSD are of equal interest. In particular, we focused attention especially on the position and relative amplitude of the main peaks in the power spectra, thinking that these summarize the underlying mechanisms generating EEG rhythms. Moreover we tried different complementary “cost functions” in the implementation of the genetic algorithm (GA). Although GA are time consuming compared with other minimization techniques, they offer the possibility to try different alternative solutions for the problem (implementing different tribes) and to overcome the problem of local minima (which often makes the result of fitting procedures untenable) by generating different sons through mutations in the parameter space.Two main objectives have been pursued with this technique: to discover possible simple circuits, connecting the three aforementioned ROIs, able to explain the observed PSDs, and to detect possible differences in connectivity circuits between healthy subjects and tetraplegic patients. Results point out the existence of significant differences between the two classes, especially for what concerns the weights which link the LF  and HF regions to the primary and the supplementary motor cortices. In particular, these weights are stronger in tetraplegic patients compared with healthy individuals and these differences are statistically significant. Differences in connectivity weights might reflect a higher awareness (related with the gamma component) and a greater attention  in the tetraplegic patient than in the normal individuals, that is, greater concentration toward the task. The existence of larger and stronger connectivity weights in the cortical connectivity networks estimated in tetraplegic patients compared with those estimated in healthy volunteers has been previously observed by several authors , 47.A further interesting result of our work is that the greatest weights in the neural circuit are those which link the cingulate cortex to motor areas. This result underlines the importance of a feedforward signals from the frontal cortex in the initiation and planning of the voluntary movement.t test to avoid type II error. Of course, in order to “test the hypotheses” generated with our procedure, one needs to repeat the experiment with new “fresh” data, considering only the individual hypotheses to be verified, and using a correction. This may be the subject of future works.In the present work we performed 12 statistical tests, hence a possible objection is that the significance level should be corrected to account for multiple hypotheses. The problem of whether correction is appropriate or not is quite complex and depends on the objective of the work. As clearly stated in recent publications  if the mFinally, it is important to discuss the main limitations of the present preliminary work, and possible lines for future changes.A first aspect concerns the variability of parameter estimates within the same subject. Although this variability is less accentuate compared with that between the two classes, and between different subjects in the same class, it is still quite elevated. Analysis of how the connectivity pattern may vary in the same subject from one trial to the next still requires a deeper future analysis.In the present model we assumed that connectivity originates from pyramidal neurons, and reaches the input of excitatory interneurons, that is, we did not consider possible lateral connections from pyramidal neurons to inhibitory interneurons. Inhibitory interarea connections, however, may be important to reduce neural activity, to avoid instability, and to improve synchronization among rhythms. Lateral inhibitory synapses were considered by David et al. , and SteAnother important limitation of the present work is that the model is able to simulate PSD spectra only if the rhythms in the three ROIs  have almost the same frequency. In view of that, we excluded all trials which present different frequencies in the spectra from the best fitting procedure. The reason for this limitation is that the three ROIs receive the alpha and gamma oscillations from the same external ROIs  may be In conclusion, the present work represents a first attempt to explain the presence of multiple rhythms in three ROIs involved in motor tasks, and their variability, using a simple model of interconnected populations. Encouraging results concern the capacity to obtain reliable PSD spectra, by acting on a few parameters representing the connection weights, and to detect significant differences between the two classes. However, important limitations are still evident: they are especially concerned with a lack of inhibitory interactions among ROIs, with the dispersion of individual parameter estimates, and with the difficulty to generate more flexible peaks in the spectra. Overcoming these limitations deserve much future work.Nevertheless, despite their present limitations, we claim models of interacting neural mass may be of great value to gain a deeper insight into the mechanisms of rhythms generation in EEG, and to start the formulation of more quantitative hypotheses on the neural architecture and connectivity changes underlying motor/cognitive tasks.


Robotic systems are becoming increasingly common in upper extremity stroke rehabilitation. Recent studies have already shown that the use of rehabilitation robots can improve recovery. This paper evaluates the effect of different modes of robot-assistances in a complex virtual environment on the subjects' ability to complete the task as well as on various haptic parameters arising from the human-robot interaction.The MIMICS multimodal system that includes the haptic robot HapticMaster and a dynamic virtual environment is used. The goal of the task is to catch a ball that rolls down a sloped table and place it in a basket above the table. Our study examines the influence of catching assistance, pick-and-place movement assistance and grasping assistance on the catching efficiency, placing efficiency and on movement-dependant parameters: mean reaching forces, deviation error, mechanical work and correlation between the grasping force and the load force.The results with groups of subjects  showed that the assistance raises the catching efficiency and pick-and-place efficiency. The pick-and-place movement assistance greatly limits the movements of the subject and results in decreased work toward the basket. The correlation between the load force and the grasping force exists in a certain phase of the movement. The results also showed that the stroke subjects without assistance and the control subjects performed similarly.The robot-assistances used in the study were found to be a possible way to raise the catching efficiency and efficiency of the pick-and-place movements in subacute and chronic subjects. The observed movement parameters showed that robot-assistances we used for our virtual task should be improved to maximize physical activity. Loss of motor control is a common consequence of stroke  and resuStudies with the aforementioned devices showed that robot-assisted therapy can improve recovery in the long run for both subacute and chronic patients ,12-15. AMost of these studies focused on observing the effects of robotic assistance under controlled circumstances. Subjects performed repetitive, predefined arm movements in the robot workspace. Our study includes a complex virtual task: a dynamic environment where movements are subjective and not fully predictable, requiring the subject to be focused and perform considerable physical activity. The aforementioned studies were previously focused on reaching movements and pick-and-place movements while object grasping was not included. The grasping component is also implemented in our virtual task. The rehabilitation outcome of robot-aided therapy compared to classical therapy has already been investigated, so this is not the purpose of this study. The goal of our work was to implement different modes of robot-assistance in a complex virtual environment and evaluate how they affect the subjects' ability to complete the task. We were interested in the impact of various haptic parameters included in the human-robot interaction. Catching efficiency and pick-and-place efficiency are chosen as the indicators of the task performance. Mean reaching forces, deviation error, mechanical work and correlation between the grasping force and the load force are the observed parameters of the human-robot interaction.The MIMICS multimodal system with the HapticMaster robot (Moog FCS Inc.) was used in the study. This system has already been used in a study where psychophysiological responses were measured and evaluated in stroke subjects . It is aTwenty-three subacute hemiparetic subjects , ten chronic hemiparetic subjects  and a control group  participated in the study. As a result of the stroke, 13 subacute subjects suffered from hemiparesis of the left side of the body and 10 suffered from hemiparesis of the right side. All were right-handed before the stroke. Six chronic subjects suffered from hemiparesis of the left side of the body and 4 suffered from hemiparesis of the right side. They were also all right-handed before the stroke. The stroke subjects were undergoing motor rehabilitation at the University Rehabilitation Institute of the Republic of Slovenia in Ljubljana. The subjects in control group had no physical or cognitive deficits. All were right-handed. To better match the control group and the subacute stroke group, 13 controls performed the tasks with their left hand while 10 performed the tasks with their right hand.Before the study began, ethical approval was obtained both from the National Medical Ethics Committee of the Republic of Slovenia and from the Medical Ethics Committee of the University Rehabilitation Institute of the Republic of Slovenia. The rehabilitation task is a catch-and-place exercise. An inclined table is positioned in a room with several objects in the scene Figure . A smallThe task includes different options of robot-assistance. These include:Catching assistance. The catching assistance helps the subject to reach the catching point. It is realized by the use of an impedance controller that moves the subject's arm in the frontal plane. The assistance generates the forces when the ball reaches the center of the table, thus giving the subject enough time to reach the catching point by him/herself. The force increases as the ball gets closer to the robot end-effector.1. Grasping assistance. Instead of the manual grasping, the grasping assistance causes the ball to stick to the virtual gripper. When the subject reaches the basket, the ball is dropped automatically. If the grasping assistance is disabled, the grasping force produced by the subject needs to be higher than a reference force. The reference force can be changed during the task according to the subject's grasping ability.2. Tunnel assistance. The haptic trajectory tunnel enables movement from the catching point to the placing point along a predefined trajectory in a virtual haptic environment. An impedance controller prevents the subject from deviating largely from the desired trajectory. The bisector of the tunnel is generated using B-splines and control points. The control points are approximated by using B-splines from trajectories measured in healthy subjects' movements [3. ovements . The guiThe subjects first tested the virtual rehabilitation environment task for 2 minutes to familiarize themselves with it and find out if they were unable to perform a particular component of the task. They were instructed to try as hard as possible while avoiding extremely tiring or painful activity. The assistances were activated by a therapist based on the testing and stayed the same during the 6-minute training session. Therefore, 7 subacute subjects had grasping assistance, 5 had catching assistance and 7 had tunnel assistance. Seven chronic subjects had grasping assistance, 4 had catching assistance and 5 had tunnel assistance. The control group performed the task without any assistance. Several haptic parameters were measured during training including robot positions, interaction forces between the robot end-point and user, grasping force, position of the ball and a parameter which indicates the task states .The positions of the robot and the forces were smoothed with a weighted moving average filter  during the task. The control loop executed at 2500 Hz while the data were sampled at 100 Hz. To analyze performance of the subjects, we observed the following indicators:Efficiency. The catching efficiency is the percentage of caught balls divided by the number of all balls. The placing efficiency is the percentage of the balls which were successfully placed in the basket divided by the number of caught balls.1. Mean Reaching Forces. The mean reaching forces at the end-effector sensor can provide information about the direction of the intended movement. These forces were assessed from the time the ball reached the center of the table to the time the ball was caught. The sign of the force is set with respect to the position of the ball. The positive sign represents the force toward the ball, while the negative sign represents the force away from the ball. Only the horizontal component of the force was observed since this component represents the left-right movement of the subject's arm.2. Deviation Error. This is the percentage of the maximal deviation of the measured movement trajectory from a reference line normalized by the reference line length. The reference line is the central line of the tunnel.3. Mechanical Work. The mechanical work is computed from the measured forces at the end-effector and the end-effector positions. The computed work evaluates the interaction between the subject and the haptic robot. Therefore, it is not only the mechanical work performed by the subject. The interaction work toward the target and away from the target were distinguished. The work away from the target represents the resistive work when the guidance assistance is enabled.4. Correlation between the grasp force and the load force. The grasping forces measured during a single pick-and-place movement are divided into three phases: grasping phase, transport phase and release phase. The characteristic point of the grasping phase is when the grasp force reaches the rising time end-point. Rise time is the time required for the grasping force to change from 10% value to 90% value. The characteristic point of the transport phase time is the central point between the grasp and the release. The characteristic point of the release phase is the fall time end-point. Fall time is de fined as the time required for the grasping force to change from 90% value to 10% value. The load force is the vertical component of the end-effector force applied by the subject. Pearson correlation coefficients were computed between the grasping force and the load force for each grasping phase and for each trial. This measure is considered as a sensitive parameter for precision of the coupling between the grasping and load force [5. ad force . A tightad force .For each analyzed parameter, a one-way ANOVA was first used to compare the three groups without assistance . Then, a two-way ANOVA (assistance × group) was used to evaluate the effect of different modes of haptic assistance (enabled/disabled) on each parameter for both groups (subacute/chronic). Bonferroni corrections were used in post-hoc tests. The control group was not included in the two-way ANOVA since no controls used any kind of haptic assistance.Comparison of the three groups without catching assistance  revealed significant differences in both catching efficiency and mean reaching forces Table . For catComparison of the three groups without tunnel assistance  revealed significant differences in pick-and-place efficiency, deviation error and work toward the target Table . Post-hoTwo-way ANOVA (tunnel assistance × group) found significant main effects of tunnel assistance on pick-and-place efficiency (p = 0.011), deviation error (p < 0.001), work toward the target (p < 0.001) and work away from the target (p < 0.001). Significant main effects of group (subacute/chronic) were observed on work toward the target (p = 0.003) and work away from the target (p < 0.001). Significant interaction effects were observed on work toward the target (p = 0.003) and work away from the target (p < 0.001).Figure As expected, results showed that the stroke subjects had lower catching efficiency than the control group. The subjects reached the same level of efficiency when the catching assistance was applied. Therefore, the catching assistance is a promising tool in certain phase of rehabilitation to raise the efficiency even if it is realized by a simple impedance controller. On the other hand, the mean catching forces showed that the interaction force pointed in the opposite direction when the assistance guided the subject. In most cases, this means that the subjects let the assistance make the movement without making any effort themselves. The reason why control subjects had such low mean forces is that they usually reached the right spot before the ball came into the catching zone. A more complex adaptive assistance model could be the answer to decrease this parameter . AdaptivThe relationships between groups in placing efficiency are similar, except for the subjects with tunnel assistance who had close to 100% efficiency. The deviation error showed that the tunnel greatly limits the movements while the linearity error range in other groups was extended. The control, subacute dTA, and chronic dTA subjects chose the movements that strayed far away from the central line of the tunnel. These findings show that limiting the pick-and-place movement with a haptic tunnel is not the best type of aid at least for this virtual task. In Figure The grasping force parameters were examined for the subjects without the grasping assistance. The chronic group had longer rise and fall times than the other two groups. The results showed that the correlation between the load force and the grasping force exists in the release phase. The correlation is not evident in the other two phases. These results are specific for our dynamic task, while other studies showed high correlation along the whole movement ,18. Of cVarious clinical studies with robotic devices showed that robot-assisted therapy can improve recovery. Our study was aimed at studying the influence of robotic assistance in a dynamic virtual environment. Rehabilitation robots with their measurement possibilities provide objective performance information. The results of the observed evaluation parameters showed significant differences when different robot-assistive modes were applied to the subjects. Properly applied robot-assistive modes enabled the subject to focus on a particular function of the exercise, such as reaching or grasping, or coordinated actions that combine reaching and grasping. In clinical environments, it is important to appropriately customize the difficulty level in a way to a meet particular patient's performance capabilities. An interesting virtual environment might increase motivation and change the rehabilitation into a fun activity for some subjects as well. In the future, adaptive robot-assistance for pick-and-place movements as well as for grasping assistance will be implemented, to continuously adapt to patient's capabilities during the upper extremity rehabilitation.The authors declare that they have no competing interests.The overall design of the experiments was agreed by all the authors. JZ, AO and MMi developed all related programs and implemented the study. DN carried out the experiments and performed the statistical analysis. JZ and MMu analyzed the data and drafted the manuscript. All authors read and approved the manuscript.


Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes. One of the difficulties in medical and biological data analysis is the highly skewed class distribution of different sample types. This could happen when special cases or "positive" samples are of limited size, while control or "negative" samples are more abundant -4. SometThere are mainly two strategies in dealing with imbalanced data learning: via sampling and via cost-sensitive learning. Although cost-sensitive learning does not modify the data distribution or introduce duplicated samples, it requires the right cost-metric to assign different penalties for misclassification of different sample types. However, the correct cost-metric is often unknown a priori for a given dataset, and an improper cost-metric can significantly degenerate the classification accuracy . RecentlData sampling strategies can often be categorized into two groups: oversampling and undersampling. In oversampling, the samples in the minority class are increased to match the samples of the majority class, while in undersampling the samples in the majority class are decreased to match the samples of the minority class. The classical or "naive" method is to randomly select samples from minority class and use the selected samples to increase the size of the minority class for oversampling (random oversampling) or to randomly select samples from majority class and remove them so as to decrease the sample size for undersampling (random undersampling) . More adIn previous work, Zhang and Yang successfully applied a genetic ensemble hybrid system to the feature selection of high-dimensional data . If we cThe problem of using highly imbalanced dataset for pattern recognition is that the classification model built on the training data tends to be biased on preferring the majority class while ignoring the samples from the minority class. Data sampling method tries to remedy the skewed class distribution by either increasing the sample size of minority class or decreasing the sample size of majority class. However, algorithms that modify the sample distribution with greedy measures can introduce undesired bias. In this study we re-apply the techniques in feature selection to data sampling using a PSO based hybrid system. The schematic flow of sampling and evaluation processes in our hybrid system is illustrated in Figure As can be seen, the work flow can be divided into two steps, namely, sampling and evaluation. For a given dataset, an external 3-fold stratified cross validation is applied to partition the dataset into external training sets (sampling sets) and external test sets . Then, the external training sets are further partitioned with an internal 3-fold stratified cross validation, which gives the internal training sets and internal test sets. The internal training sets are used for sampling, while the internal test sets are used for guiding the optimization process. The external test sets are reserved for evaluation of the balanced dataset and is excluded from the sampling procedure.In the sampling procedure, the PSO hybrid system is used to evaluate the merit of each sample from the majority class in compensating the class imbalance. This is accomplished by generating different sample subsets of majority class and combining them with samples from the minority class for classification model construction and then for internal test fold classification. Those subsets that can create more accurate classification models are favored and optimized in each PSO iteration. When the termination criterion is met, selected samples from the last iteration are ranked by their selection frequency. After the sample selection frequency list is obtained, a balanced dataset can be created by combining the highly ranked samples of majority class with samples of minority class. In the evaluation step, different classification models are created using the balanced dataset generated by PSO hybrid system, and the external evaluation dataset is applied to evaluate the classification accuracy with different evaluation metrics. Such a training and evaluation process keeps the evaluation dataset for independent validation, which provides an unbiased evaluation.Particle swarm optimization (PSO) is a new group of population-based algorithms which uses the idea of social communication and historical behaviors to adjust the optimization process . It possFigure n particles, with i be the index of a particle in the swarm , j be the index of dimension in the particle , and t be the counter of iterations. The velocity of the ith particle vi, j(t) and the position of this particle xi, j(t) is updated by BPSO with following equations:Each sample of majority class in the training dataset is assigned an index in the particle space. The locus equals "1" if the sample is selected for building classification model or equals "0" if the sample is excluded from building the classification model. Suppose we have a population of pbesti, j and gbesti, j are the previous best position and the best position found by informants, respectively. random is the pseudo-random number generator that creates uniform distribution between [0-1].where pbesti, j and gbesti, j. It has been pointed out that in the imbalanced data evaluation a simple classification accuracy is not an indicative measure because the accuracy value is profoundly influenced by the large class [Fitness function is the optimization guide of the BPSO. It governs the update of ge class .Alternatively, metrics including Area Under the ROC Curve (AUC), F-Measure (FMeasure), and Geometric Mean (GMean) are often chosen as more appropriate measures ,12,26,27L is the number of classifiers integrated in the hybrid system and fitnessi(s) is formulated as follows:where s is the sample subset to be evaluated. This fitness function is essentially a weighted combination of the above three evaluation metrics, AUC(s) is calculated using Mann Whitney statistic [FMeasure(s) and GMean(s) are calculated as follows:where tatistic , while FFMeasure(s) and GMean(s) is further defined as follows:where each component in Precision:Sensitivity or Recall:Specificity:NTP is the number of true positive, NTN is the number of true negative, NFN is the number of false negative, and NFP is the number of false positive.where One limitation of previous efforts on imbalanced data analysis is that most studies only focused on Decision Tree as evaluation criterion . Insteadk-Nearest Neighbor (kNN), Naive Bayes (NB), Random Forest (RF) and Logistic Regression (LOG). J48 is a widely used decision tree classifier. It approximates discrete-valued functions and a group of favorite features selected by the algorithm are used as the test points at the tree nodes. Each path of the node is then created for partitioning the value of the feature. kNN classifier calculates the similarity, which is called distance, of a given instance with the others and assign the given instance into the majority class which the k most similar instances belong to. Such similarity can be defined as Euclidean distance, Manhattan distance or Pearson correlation. Naive bayes classifier bases its learning strategy on probability theory. It tries to estimate the distribution of the data and classify a sample by assigning the sample into a class with the highest probability. Random forest, as its name indicates, is a collection of decision trees [Specifically, the classification algorithms employed in the hybrid system composition includes Decision Tree (J48), on trees . InsteadPutting above components together, the BPSO based hybrid system can be summarized by pseudo-code in Figure Four typical medical datasets are obtained from UCI Machine Learning Repository  and a geFor the medical data, the first dataset named "Blood" was generated by Blood Transfusion Service Center in Taiwan. It has 568 samples denoted as not donating blood and 180 as donating blood in March 2007, and the prevalence of the dataset is 24.1%. The task is to classify these samples based on the information of blood donation frequency, recency etc. The second dataset, "Survival", was generated from the survey conducted on the survival of patients who had undergone surgery for breast cancer. It contains 225 patients who survived 5 years or longer and 81 patients died within 5 year. The prevalence of this dataset is 26.5%. The third dataset with the name of "Diabetes" is obtained from the study of diabetes in Pima Indian population. 500 samples were identified as negative while the other 268 samples were identified as positive, which gives the prevalence of 34.9%. The last dataset called "Breast" was created for breast tumor diagnosis. Within this dataset, 151 are benign samples and 47 are malignant samples, and the prevalence is 23.7%.The GWAS dataset contains 146 samples with each sample been described by more than 100,000 SNPs. Within the 146 samples 46 are labeled as geographic atrophy either central or non-central to the macula (CGA), 50 are labeled as uniocular choroidal neovascularization (Neov), and the rest 50 are the control samples. Therefore, the task is divided to classify CGA samples from the rest  and to classify Neov samples from the rest . In SNPs selection, we applied the selection procedure utilized by Chen et al. , and obtThe summary of each dataset is given in Table k-mean algorithm and randomly select samples of majority class according to the majority/minority ratio of each cluster and the cluster size. We used the k size of 10 for k-mean clustering and the Euclidean distance for similarity calculation.We compare our particle swarm based sampling strategy with random undersampling, random oversampling, and clustering based sampling. Random undersampling and random oversampling are implemented by decreasing samples of majority class or increasing samples of minority class to match the counterpart with a uniformed possibility, respectively. Clustering based sampling is implemented as the base version of those described in , that ism dimension space with m equals to the size of the majority samples in the training set. Different parameter settings of the particle swarm component are investigated empirically, and we fix the best combination : Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.Other papers from the meeting have been published as part of


Reverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.We propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.Our algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference. Reverse engineering is an interesting area of research currently receiving a lot of attentions from the Systems Biology community. In fact, reconstructed biomolecular networks may allow researchers to understand the molecular basis of complex traits and diseases Gene expression data have been prevailing over protein and metabolite activity data, because of the relative ease and unified way to measure RNA levels, and this disproportion will be further increased due to the appearance of gene expression measurements techniques based on novel sequencing technologies, e.g. As a precise definition of GNs is missing in current literature we here provide (one possible) formal definition.gene network is a mixed graph A Depending on the available measurements, different inference techniques can be employed. In case of experiments without targeted perturbations  the expression profiles can be analyzed to build a undirected graph whose nodes are the genes, and whose edges represent the presence of significant associations. Without targeted perturbations it is not generally possible to infer directions of the edges. A wide variety of techniques for constructing such undirected co-expression networks has been proposed, typically based on marginal associations On the other hand, targeted perturbations  are needed to enable causal inference, and the reconstruction of the directed structure of GNs. Many techniques for constructing GNs have been proposed of which the most popular techniques are based on ordinary differential equations a priori, i.e. “gold standard networks” in silico data. In this case the underlying networks are precisely known and thus allow for thorough evaluation and comparison of reverse-engineering algorithms in silico data strongly depends on the realism of the simulation system, e.g. the network topology, the type of mathematical model, the type of kinetic functions, the noise model, etc.The performance of these techniques needs to be evaluated and compared In Silico Network challenges, then explain the inference algorithm we devised and applied to the DREAM 4 data, followed by a description of the GN simulator we developed to generate additional synthetic networks and data. Then, we show the evaluations of variants of our algorithm on both the DREAM3 In Silico benchmarks and the additional simulated datasets. We also present the results of re-analysis of the DREAM4 In Silico benchmarks, which we were able to perform after the gold standard networks were released. We conclude with a discussion of the method, data and future steps to be made.The outline of the paper is the following: we first describe the DREAM4 In Silico Network challenge, which asked to infer directed GNs from simulated data. The challenge was, in turn, divided into three sub-challenges, respectively named InSilico_Size10, InSilico_Size100, and InSilico_Size100_Multifactorial.The Dialogue for Reverse Engineering Assessments and These sub-challenges differed, as their names suggest, in the network size and the type of data provided. In the first sub-challenge the partecipants had to predict the topology of five 10-gene networks, and were provided with steady state gene expression levels from wild-type, knockouts, knockdowns, multifactorial perturbations, and time series data. The second sub-challenge concerned instead five 100-gene networks, with the same type of available data except the multifactorial perturbations. The third sub-challenge involved five other 100-gene networks provided with multifactorial perturbations data only. The contestants were challenged to predict the network structures underlying the above data, i.e. assigning a level of confidence for the presence of each possible edge.wild-type file contained the knockout data .The network topologies to be inferred were generated by the organizers by extracting 10- or 100-node subnetworks from transcriptional regulatory networks of GeneNetWeaver 2.0 software The challenge description mentioned also that the data was simulated through a dynamical model describing both independent and synergistic gene regulation, which included both gene and protein expression (but only the gene expression data was provided to the partecipants). Internal noise was modeled through stochastic (Langevin) differential equations, and measurement noise was added to the simulated gene expression levels. Networks and data were generated by the The aim of these challenges was the prediction of the (directed and unsigned) network structures. How can we infer such gene regulatory networks? While the time-series data could be used for this purpose, the lack of protein measurements would make it difficult to infer relationships between gene activities from time dynamics: the protein dynamics causes delays between the gene expression dynamics. Therefore, we resorted to the steady state levels, in particular to the knockout datasets, where the perturbations and the relative responses were stronger.From this kind of data it is very easy to infer a so-called causal influence network: genes whose steady state values change as a result of a single-gene knockout are likely to be downstream of the perturbed gene However, some of the edges of a causal influence network may be indirect, i.e. mediated by other (measured) gene activities In the first step we quantify the importance of the responses of the gene activities toward single-gene perturbations and so how likely it is for each gene to be downstream of the perturbed genes. Let The confidence of edge As the absolute values of the steady state gene activities vary drastically . We recognize that certain edges can be removed without removing the causal influences: the edge from gene A to gene C could be removed if there is at least one additional path from gene A to C in the network Our down-ranking algorithm systematically checks for paths through the initial networks and recognizes which edges can be removed  and which edges can not be removed . Note that cyclic components in the networks are fully connected, as each gene in a cycle has a causal influence on all other genes in the cycle. Determining which edges in a cyclic component can be removed without removing causal paths depends on the order in which the edges are removed. Therefore, we decided not to touch any of the edges in cyclic components. We emphasize that we do not believe that the sparsest network possible is most biologically realistic. In fact, it is widely recognized that biomolecular networks are enriched in feed-forward loops The second step of our algorithm proceeds in the following way :Use a threshold value Calculate the condensation of network its endpoints belong to two different strongly connected components there is a path of length at least 2 between Obtain the subnetwork For all the remaining edges in network in silico data using our simulator developed in MATLAB.To be able to thoroughly evaluate and fine-tune the parameters of our algorithm we generated wild-type steady state. To simulate the single-gene knockout experiments we initialize In our model, the following nonlinear ordinary differential equation describes the evolution of the gene expression Method effectiveness was evaluated through the calculations of the Area Under the Receiver Operating Characteristic Curve (AUC(ROC)) and the Area Under the Precision versus Recall Curve (AUC(PvsR)) In Silico Network challenge in 2008 was very similar to the DREAM4 one. Here too GNs of different sizes  had to be inferred using steady states from wild-type, knockdown and knockout perturbations, and time series data. The kinetic equations were also similar, though in DREAM3 a deterministic model was used while in DREAM4 a stochastic one.In order to make informed decisions on the choice of the weight matrices to use and to fine-tune the threshold value for the second step of our algorithm, we practiced first on the DREAM3 benchmarks In order to choose which, amongst the confidence matrices We initially considered both the knockout and knockdown data, but since our algorithm consistently gave better results on the knockouts (data not shown), we will here further consider only the knockout steady states.By applying the aforementioned inferring techniques on these data, the matrices Then, given the confidence matrix obtained . We hereNegligible differences in the AUC(ROC), but more substantial improvements in the AUC(PvsR) measures were obtained for in silico data , and on data which should be more similar to the DREAM4 ones.By using our simulator, we generated 1000 100-gene networks with ER topology and average degree In a similar fashion, we applied the down-ranking algorithm on matrix reshold  , again iIn Silico Network challenge on the z-score on raw data confidence matrix  over which the performances were averaged, but it also indicates that the DREAM4 benchmarks consist of a set of networks with widely varying topologies.It should also be noticed that the the average node degrees in the DREAM4 networks are smaller  models Our algorithm is substantially different from the techniques used by the best performer teams of previous DREAM In Silico Network challenges. The predictions of this algorithm on the DREAM4 100-gene networks was very poor (average AUC(ROC) = 0.5722, AUC(PvsR) = 0.0313). Note that in DREAM2 there was no noise added to the in silico data, while for DREAM4 both biological and experimental noise were present. Since the internal noise is propagated through gene relationships, its effect on large networks make sophisticated models (like ODEs) much less reliable than our method based on simple cause-effect logic and graph inspection.To see how methods based on ODEs would perform on the DREAM4 data, we analyzed them with one of the best performer algorithm Further increases of the performance of our algorithm may be obtained by studying the possible relationships between the selected threshold et al.et al. approach to statistically identify the networks best supported by the data by adding edges, rather than removing edges from the causal influence network. Heuristic model search algorithms are strongly dependent on a good initial guess in the network space: we argue that networks which result from the algorithm described in this paper will provide a better initial guess than the initial causal influence network, as GNs are known to be sparse. In this sense, the resulting networks from our approach here should not be seen as the final prediction, but rather as inputs to more sophisticated methods involving thorough statistical testing. Nevertheless, as evidenced by its winning performance over 18 other participating teams in the DREAM4, this method can be considered state-of-the-art on its own.It has become unambiguously clear that systematic perturbations  are needed to establish the directed structure of GNs. However, systematic single-gene knockouts imply experimental requirements which are unrealistic and these experiments infeasible  for many organisms. It is unlikely that data such as considered here will become available from real experiments. Fortunately, “systems genetics” experiments may provide an alternative. In systems genetics experiments a population under study is genotyped and gene expression profiled are simultaneously collected (possibly even including metabolomics and proteomics data


Proceratophrys cristiceps belongs to the genus Proceratophrys within the family Cycloramphidae. These amphibians are found exclusively in South America in the morphoclimatic domain of the semi-arid depression zones in northeastern Brazil known as the Caatinga. We examined intrapopulational variation using univariate and multivariate statistics with traditional and geometric morphometrics, which supported the existence of two morphotypes of this species. Our results indicated significant degrees of variation in skeletal characteristics between some natural populations of this species. Careful analyses of variability levels are fundamental to avoid taxonomic errors, principally in populations that demonstrate characteristics intimately associated with their area of occurrence, as is the case of Proceratophrys cristiceps.The species  Proceratophrys cristiceps  are found exclusively in the inter-montane depression zones and inter-plain semi-arid regions known collectively as the caatinga (dryland) region Proceratophrys Miranda-Ribeiro, 1920, together with 18 other species distributed within different exclusively South American morpho-climatic domains.Individuals of the species P. cristiceps was originally described based on external morphological characteristics as well as some skeletal features The species P. cristiceps that always appear sympatrically throughout their range in the caatinga, a characteristic common to some other species of the same genus.Studies that deal with chromatism (whether in amphibians or other taxa) generally describe such variations as being characteristics of distinct species or treat it as a question related to taxonomic identification P. cristiceps beyond their general color patterns. The present work examines intraspecific variation within a population of neotropical toads and identifies two morphotypes of the species P. cristiceps through the use of morphometric studies and the morphology of their crania, and discusses polymorphism and its implication in the taxonomy of these amphibians as well as other taxa.There are currently no published reports of other visual characteristics that would allow the identification of the morphotypes of P. cristiceps demonstrated significant differences in terms of eye-nostril distance (END) by the Student t test, with this variable character being on the average 0.26 mm larger than the common morphotype. No other statistically significant differences were observed in relation to the other measurements between the two morphotypes  development in relation to the distance from the nostril to the most anterior extremity of the rostra (DNR) are presented in t test in relation to the logarithmic variable eye-nostril distance (END) was reinforced by allometric studies of the morphotypes . In the yellow morphotype, there was a shift to the left, with variations of landmarks 3, 5, 6, and 7. The principal differences between the common and yellow morphotypes can be seen in the occipital, supra-ocular, and rostral regions. The Hotelling's t-test of discriminate function.The analyses of the lateral configurations of the specimens were more uniformly distributed, and in spite of the fact that certain differences were observed they were not statistically significant by the Hotelling's Two basic types of variation can be considered from an evolutionary point of view P. cristiceps examined here, which can readily be classified into two color categories-a typical example of the intraspecific and intrapopulational variability. Additionally, as was demonstrated above, this same variation can be quantified using metric and shape attributes.In considering metric characteristics , a given animal population can demonstrate relative degrees of variability, especially when their shape is considered a valid character for analysis. This can be seen in the specimens of t test one of the measurements was actually statistically significant, showing a certain degree of cephalic variation.The animals of the yellow morphotype category generally have smaller dimensions that the common morphotype, and as demonstrated by the Student t test, in the eye-nostril distance (END), as well as in the linear equation of the distance from the nares to the most anterior extremity of the rostra (DNR). The morphotypes behave like differentiated elements in terms of the DNR criterion, as would be expected for representatives of disjoint populations Odontophrynus Reinhardt & Lütken, 1862 ProceratophrysReinforcing the observed differences between the color morphotypes, the cranial characters analyzed demonstrated that the yellow morphotype has a relatively short skull, which is supported by the statistically significant differences observed in the Ascaphus trueiVariations in bone structure among individuals of the same species are quite common, principally when considering the absence or presence of certain cranial structures. Intraspecific variability in the number and morphology of the lacrimal, sclerotic ossicles, and maxilla has been described in some species of lizards of Iberian Peninsula Erinaceus concolorThe absence of bony structures or even differences in the number of bones, principally when accompanied by chromatic variability, can generate problems for the systematics of some taxa-and in order to avoid taxonomic errors it is necessary to recognize certain characters as being valid and take into account the magnitude of those variations. In examining cranial variability in P. cristiceps are distinguished not only by a number of cranial characteristics but also by studies of cranial shape. Our analyses demonstrated that the principal variations were specifically located in the occipital, supra-ocular, and rostral regions, as well as in the prootic-squamosal length, giving rise to the two distinct morpho-shapes. The use of geometric morphometry identified valid differences between the animals and a more precise understanding of the magnitude of variation between the common and yellow phenotypes, completing the univariate analysis of the data. The utilization of this type of analysis is critical to understanding the interactions between phenotype, genotype, and environmental spaces The morphotypes of The identification of polymorphism in individuals of any species requires care in choosing the focal characters, as well as in defining the parameters of that polymorphism. Polymorphism is a variation within a species that is independent of ontogeny and of the sex of the animal  P. cristiceps. A good example is the similarity in the pattern of overall body development evident in the allometry analysis-which represents a quantitative phenomenon typical of the larger population, and whose distortions were only perceived when the animals were submitted to a much more profound analysis  that supported the differences between the morphotypes.Even though they demonstrated a certain degree of variability, many characteristics beyond those related to external morphology were observed to be held in common among the morphotypes of P. cristiceps were seen to have a series of variations beyond those normally recognized , and we therefore considered the morphological divergence of the coloration patterns in populations as being due to environmental or genetic fluctuations over time Individuals of the species P. cristiceps were collected using both active and passive capture techniques in the Pedra da Boca State Park  could be characterized as the “common” morphotype while the other 20 (25%) were of the “yellow” morphotype  according to established classification P. cristiceps (seven from the common morphotype and seven from the yellow variety) from Palmeirais (5°58′40″S×43°03′48″) in the Parnaíba River basin, Piauí State, and from areas within the municipalities of Patos (7°01′28″S×37°16′48″W) and Cabaceiras (7°22′50″S×36°17′51″W) in Paraíba State, were examined and compared with specimens collected in the Pedra da Boca State Park. Of the seven animals of the common morphotype, two were from Patos, two from Cabaceiras, and three from Palmeirais. Of the seven animals of the yellow morphotype, three were from Palmeirais, three from Cabaceiras, and one from Patos.Fourteen crania of Precision (0.05 mm) Veneer calipers were used to measure all of the 79 specimens collected. Nine different measurements were made: snout-vent length (SVL), head width (HW), internarial distance (IND), eye-nostril distance (END), eye length (EL), tibia length (TL), foot length (FL), length of internal metatarsal tubercle (IMT), as well as the distance from the snout to the most anterior extremity of the rostra (DNR). These measurements were chosen as being the most informative and the most frequently used by taxonomists working with this group.a priori to the Shapiro-Wilks normality test (data not shown) using the Statistica software package  in order to confirm their normal distribution. The Shapiro-Wilks test was used due to its adaptability to a wide range of problems related to evaluating the normality of univariate data t test applied to compare the measurement averages between morphotypes. When the absolute value between the two measurements was equal or greater than the minimal significant difference (MSD), those measurements were considered statistically different at a p<0.05 significance level All measurements were subjected t test to determine if the differences between the morphotypes were statistically significant.Regression analysis was also applied to the SVL (and other measurements) in order to compare the linear equations of the independent (SVL) and dependent variables through comparison tests of their regression lines ProceratophrysThe analyses of specimen shapes were undertaken considering the cephalic area in dorsal and left-lateral views. Head shape was chosen as this character is considered a determinant factor in studies of intra- and interspecific variation in the genus P. cristiceps from the Pedra da Boca State Park was based on diagnostic characters traditionally used for the different species of this genus The choice of the landmarks used with the specimens of Ten anatomical landmarks in the dorsal midline plane  were selFor the lateral cephalic region  the landWe used the method of point super-positioning following optimization criteria (Procrustes) during shape analyses to generate the smallest possible sum of the squares of the distances between corresponding points P. cristiceps we calculated a RWA with α = 0 and assigned equal importance to all of the spatial scales (as this is the best option for exploratory studies) In order to determine the relationships between the shapes of the two morphotypes of P-value was calculated using Hotelling's t-test function at a significance level of p<0.001. This latter method was applied as it represents a natural multidimensional extension of the t-statistic widely used in the biological sciences t-test and discriminate functions were generated using Statistica software .In order to determine if both of the distance shapes of the morphotypes were statistically significant, a discriminate function analysis was performed with the values transformed into Procrustes residuals by subtracting the mean shape, and a


The clinical presentation of carcinoma of the cervix as cervical lymphadenopathy has not been described before. We report a case of this unusual manifestation of cervical cancer.A 51-year-old woman presented to our Head and Neck department with cervical lymphadenopathy. A positron emission tomography scan revealed the primary tumour to be in the cervix and a cervical biopsy confirmed carcinoma of the cervix.Recurrences of carcinoma of the cervix presenting as lymphadenopathy have been described before but this is the first time a clinical presentation of carcinoma of the cervix as cervical lymphadenopathy has been described. Although metastasis from the cervix to the cervical lymph nodes is rare, this can be explained by outlining the drainage of the lymphatic system from the cervix. Carcinoma of the cervix commonly metastasizes by direct extension or lymphatic dissemination within the pelvis. Clinical presentation of carcinoma of the cervix as cervical lymphadenopathy has not been described before. We report a case of this unusual manifestation of cervical cancer.A 51-year-old woman was referred to the ENT department with a 2-week history of a lump on the right side of her neck. There was no history of change to her voice or dysphagia.She is a para 4 with all normal vaginal deliveries and has had normal cervical smears in the past. Her periods were regular and she gave no history of intermenstrual or post-coital bleeding. She smoked about 20–30 cigarettes per day.On further questioning in the clinic, she gave a history of increasing lethargy for the past 3 months and was also unable to report to work due to severe back pain.Five years before the present episode, she reported feeling unwell with significant weight loss and heavy periods. She was found to be anaemic and was given five units of blood. She was investigated for a possible colon cancer which proved to be negative. She was referred to a Menstrual Disorder Clinic but failed to attend the clinic twice.On examination, multiple cervical lymph nodes were palpable on both sides of the neck. Ultrasound scan of the neck revealed two large supraclavicular lymph nodes with several abnormal looking lymph nodes in the right carotid chain.An X-ray of the chest showed no abnormality. Fine needle aspiration of the lymph nodes yielded squamous carcinoma cells.Metastatic squamous cell carcinoma of an unknown primary tumour was suspected and investigations were performed to find a possible primary site. Clinical examination and endoscopy of the upper digestive tract did not yield an obvious primary tumour in the nasopharynx, larynx and hypopharynx.Computerised Tomography (CT) of the neck, chest and abdomen revealed marked mediastinal and para-aortic lymphadenopathy suggestive of spread of the known squamous cell carcinoma. There was evidence of dilatation of the collecting system bilaterally with dilatation of the proximal ureters suggesting an obstruction within the pelvis.A Positron Emission Tomography-CT (PET-CT) scan was performed which showed markedly increased uptake in the right cervical lymph nodes, as well as in the right paratracheal, anterior mediastinal, lower para-aortic, and bilateral iliac lymph nodes with an obturator node showing a photopaenic centre. In addition, there was a focal area of increased uptake in the pelvis, suggesting a lesion within the rectal wall or in the vaginal vault Figures  and 2.Given the histology of squamous carcinoma, the PET scan suggested that the uptake in the pelvis may represent a primary gynaecological problem rather than a second malignancy in the rectum. But given the distribution of the disease which was very unusual for cervical carcinoma, a review of the histology was suggested with a differential diagnosis of lymphoma to be considered. The histology from fine needle aspiration of the cervical lymph node confirmed it to be carcinoma cells of squamous origin.Our patient was then referred to the gynae-oncology team. On examination, the uterus was anteverted, mobile and bulky corresponding to about 14 weeks' size with no palpable adnexal masses. Her cervix appeared normal to the naked eye and a smear was obtained which was reported as normal.Magnetic Resonance Imaging (MRI) of the pelvis and abdomen was performed which revealed a highly abnormal cervix, diffusely infiltrated by an intermediate to high T2 signal intensity mass measuring approximately 3 × 4 × 3.5 cm. The mass involved the endocervical canal and the stroma with suspected early parametrial invasion anteriorly. There was no convincing evidence to suggest bladder involvement and the rectum was clear of disease. Several small intramural fibroids were demonstrated within the myometrium as well as a submucosal fibroid in the anterior body of the uterus F-fluorodeoxyglucose positron emission tomography with computed tomography (FDG-PET-CT) was done, which surprisingly suggested the possibility of a primary in the cervix.PET is a functional diagnostic imaging technique and has the advantage of being non-invasive and able to study the biological function of the tumour. Increased glucose metabolism has been observed in tumours  and F-18Guntinas-Lichius et al.  have shoOther studies have shown FDG-PET to have a sensitivity of 100% and sensitivity of 94% in the detection of unknown primary tumours. For the conventional diagnostic modalities , these values were 92% and 76% [On retrospective review of her past history, her admissions and blood transfusions for anaemia could have been related to underlying cancer of the cervix. But since she did not keep her appointments with the gynaecology clinics, that window of opportunity was lost.In keeping with her past history, investigation by the gynae-oncology team soon after the CT scan (which had suggested extensive lymphadenopathy) for a possible cervical cause for the lymphadenopathy, would have probably been more cost effective. Due to limited availability and higher cost of the PET scan, it is not routinely used as a primary tool of evaluation. A more thorough work-up and use of other less expensive modalities would have shown the primary to be in the cervix.It is very unusual for squamous cell carcinoma of the cervix to behave in an aggressive way with metastasis to extrapelvic lymph nodes. Small cell cancer of the cervix is known to be aggressive with early haematogenous and extrapelvic lymph node metastasis .The prognosis for metastatic carcinoma of the cervix is poor. Metastases to the neck signal a grave prognosis for the patient. Diddle , in his If left alone, cervical nodes grow rapidly with the attendant sequelae of ulceration and pain, making treatment difficult or impossible. Treatment is usually with local irradiation .Treatment of advanced cervical cancer is usually palliative. Several chemotherapy regimes have been described. Cisplatin has emerged as the most active single agent with overall response rates of 19% . Recent The comparison of cisplatin to cisplatin plus topotecan in GOG-179 has shown a statistically significant impact on the overall response rate, median progression-free survival, and median survival, with all outcome measures favouring the two-drug regimen .Our patient is presently undergoing palliative chemotherapy with a combination of carboplatin and paclitaxel. Her initial response has been encouraging with an anticipated improvement in quality-of-life scores.Recurrences of carcinoma of the cervix presenting as lymphadenopathy have been described before but this is the first time a clinical presentation of carcinoma of the cervix as cervical lymphadenopathy has been described.Although metastasis to the cervical lymph nodes is rare, this can be explained by outlining the drainage of the lymphatic system from the cervix. Prognosis in such patients is usually poor and treatment is mainly palliative.Although the management of our patient has not changed, this case report highlights an unusual presentation of carcinoma of the cervix and the investigative modalities which were needed to reach the final diagnosis.ENT: Ear, Nose and Throat; CT: Computerised tomography; PET: Positron emission tomography; MRI: Magnetic resonance imaging; FIGO: International Federation of Gynaecology and Obstetrics; HIV: human immunodeficiency virus; FDG: fluorodeoxyglucose.The authors declare that they have no competing interests.MM: Literature review, conceived and drafted the manuscript, DS, Helped in collecting records and preparing the manuscript, AJ, Department chair who provided general support. All authors revised and approved the final draft of the manuscript.Written consent was obtained from the patient for publication of the case report and any accompanying images. A copy of the written consent is available for review by the Editor-in-Chief of this journal.