Dictyostelium discoideum, a model organism for eukaryotic chemotaxis. A large number of cell trajectories in stationary, linear chemoattractant gradients is measured, using microfluidic tools in combination with automated cell tracking. We describe the directional motion as the interplay between deterministic and stochastic contributions based on a Langevin equation. The functional form of this equation is directly extracted from experimental data by angle-resolved conditional averages. It contains quadratic deterministic damping and multiplicative noise. In the presence of an external gradient, the deterministic part shows a clear angular dependence that takes the form of a force pointing in gradient direction. With increasing gradient steepness, this force passes through a maximum that coincides with maxima in both speed and directionality of the cells. The stochastic part, on the other hand, does not depend on the orientation of the directional cue and remains independent of the gradient magnitude. Numerical simulations of our probabilistic model yield quantitative agreement with the experimental distribution functions. Thus our model captures well the dynamics of chemotactic cells and can serve to quantify differences and similarities of different chemotactic eukaryotes. Finally, on the basis of our model, we can characterize the heterogeneity within a population of chemotactic cells.Chemotaxis, the directed motion of a cell toward a chemical source, plays a key role in many essential biological processes. Here, we derive a statistical model that quantitatively describes the chemotactic motion of eukaryotic cells in a chemical gradient. Our model is based on observations of the chemotactic motion of the social ameba  Directional movement of cells in response to chemical cues is ubiquitous in nature. It is essential for many biological processes ranging from embryogenesis The chemotactic performance of a cell line is commonly investigated using gradient methods like micropipette assays or diffusion chambers. To date, chemotaxis in such assays has been characterized based on averaged quantities taken over a population of cells as well as over time. Typical examples are the average velocity in gradient direction a priori modeling assumptions Processes that exhibit both deterministic and stochastic components are commonly described by Langevin-type stochastic differential equations. This approach has a long-standing tradition in the study of random (non-chemotactic) cell motion. The first random walk models that describe the motion of microorganisms date back to the early 20th century Dictyostelium discoideum, a common model organism for cell motility and chemotaxis Here, we introduce an analogous statistical concept to describe the directional movement of chemotactic cells in a chemical gradient. Earlier Langevin-type chemotaxis models were based on the assumption that random cell motion can be described as an Ornstein-Uhlenbeck process In summary, it is the overall objective of our work to advance our understanding of eukaryotic chemotaxis beyond a description in terms of averaged values. In particular, we will characterize the deterministic and stochastic components of chemotactic motion along with their dependence on external parameters. Our primary goal is thus to phrase a detailed statistical description of chemotactic motion that captures also the distribution functions of fluctuating quantities. At this stage, it remains a purely descriptive approach. In future studies, it will serve as a basis for the detailed comparison of different mutant cell lines. This will enable us to identify the molecular players that determine specific features in the motion patterns of eukaryotic cells and link our model parameters to the underlying signaling events. Ultimately, this will lead to a detailed understanding of how eukaryotic cells move in response to a chemical gradient, a long-term aim of quantitative biology.D. discoideum cells in stable linear gradients of cyclic AMP (cAMP). The gradients were generated using a pyramidal microfluidic network that provides well-defined concentration profiles with high temporal stability. The layout of our gradient device can be seen in D. discoideum chemotaxis, for details see Ref. We studied the chemotactic motion of starvation developed D. discoideum shows directional responses In our experiments, cAMP gradients were linearly extending over a distance of about 320Dictyostelium cells increases over the first hours of development After considering the chemotactic index as a classical average measure of the chemotactic performance, we moved on to analyze the fluctuations in various motion parameters by extracting the probability distribution functions of these quantities from the data. The results are summarized in It is our aim to model chemotactic motion based on the generalized Langevin equation 1. To phrase a specific model equation of this type, we need the explicit functional dependencies of the deterministic and stochastic parts on the cell speed and direction. We determined these expressions from our experimental data by conditional averaging Let us first consider the deterministic and stochastic parts in a fixed cAMP gradient of 1.5 nM/For the stochastic part, we found multiplicative noise that can be approximated by a linear dependence on the cell speed. The noise parallel to the direction of motion could be fitted, at each angle, by a first-order polynomial D. discoideum was obtained,Thus, by conditional averaging, the following Langevin equation for the chemotactic movement of The model incorporates quadratic damping and multiplicative noise.In the previous section, we have derived a probabilistic model of chemotactic motion in one given gradient of We used an Euler-Maruyama scheme to simulate the model equations 2 and 3 based on the parameters that were retrieved from the experimental data. For details of the numerical scheme see In To illustrate how the model parameters reflect different types of cell motion within a population, we have divided the data set of Dictyostelium chemotaxis in linear gradients of cAMP using microfluidic tools. Different steepnesses were systematically explored, covering the full range of gradients, in which chemotactic behavior was observed We have recorded large data sets of Thus, when considering only the chemotactic index, many details of the cellular motion patterns are lost. For example, when comparing mutant cell lines with deficiencies in different cytoskeletal regulators, the character of the cell trajectories may change considerably without substantial changes in the chemotactic index. Such differences in the structure of the cell trajectories may yield interesting information about the role of the respective proteins in the regulatory network of the cytoskeleton and cannot be resolved by the chemotactic index alone.Here, our stochastic model of chemotactic cell motion will make a contribution. Using this more detailed description, it is possible to capture subtleties that go far beyond the information that is contained in the chemotactic index. We based our model on the assumption that chemotactic cell motion contains both deterministic and stochastic contributions. Such processes can be typically described by a Langevin-type equation. By applying angle-resolved conditional averaging to the experimental data, we obtained the deterministic and stochastic parts of the underlying Langevin equation and analyzed the dependence on the external gradient. To date, similar data-driven stochastic modeling has been only applied to non-directional, random cell motion in absence of external stimuli, see e.g. Refs. The stochastic equation of motion showed quadratic damping and multiplicative noise, similar to non-directional random ameboid motion . In the data presented here, the cells are exposed to a constant gradient, while the midpoint concentration increases when the cells are moving up the gradient. Our data thus presents a global average over a range of midpoint concentrations for each gradient investigated. In order to also resolve the dependence on the midpoint concentration, the cell trajectories would need to be divided into small intervals along the gradient direction to perform the stochastic data analysis within each interval, i.e., for each midpoint concentration, separately. However, even though we have collected a substantial amount of data, much larger data sets would be required in order to obatin statistically meaningful results from this type of analysis. This is primarily because the stochastic data analysis requires an additional division of the data also according to angle and speed. In Dictyostelium chemotaxis, the parameter Dictyostelium cells on surfaces with different coatings.In previous studies, it has been shown for human dermal fibroblasts that the damping parameter Furthermore, the noise term was found to be independent of the external gradient. Together with our earlier observation that the stochastic components of non-directional motility are not affected by development or ambient cAMP Our model can be considered as a description that captures the behavior of a representative, average individual from the chemotactic cell population. The mean values and fluctuations of various motion parameters are correctly captured for this average chemotactic cell. However, by subdividing the cells into subpopulations of different motility and chemotactic performance, we demonstrated that a considerable cell to cell variability exists and that the parameters of the Langevin equation are different for each subpopulation.Also the form of the stochastic part is influenced by the heterogeneity of a typical cell population. While the slopes We can also relate the multiplicative noise in our Langevin equation to the stochastic processes that occur in the cell during gradient sensing. To the best of our knowledge, the only purely stochastic model of gradient sensing was presented by Gamba et al. Dictyostelium chemotaxis in exponential gradients, the average cell speed did not depend on the direction of propagation. This led to a description based on a Langevin equation for the angle of propagation only In a recent study of In future work, we will apply our analysis to mutant cell lines that carry deficiencies in various components of the chemotactic signaling pathway. The objective is to relate the specific parameters of our stochastic description to the individual molecular players in a chemotaxing cell. Such relations between microscopic molecular components and macroscopic dynamical observables are an essential building block for a comprehensive model of eukaryotic chemotaxis, the central aim of this field.Dictyostelium AX3 wild type cells, kindly provided by Günther Gerisch . Cells were grown in HL5 medium . The culture was renewed from frozen stock every four weeks. Cells were starved in shaking suspension of phosphate buffer . Pictures were taken with a 10x plan apochromat  objective every 40 seconds during 50 minutes using a Photometrics CoolSnap CCD camera  at a resolution of 1024x1024 pixels. Differential interference contrast (DIC) was used to enhance cell contour visualization. About 120 cell tracks were recorded for each experiment. The cell contours were automatically detected using a method inspired by Kam For each cell track, the velocity and acceleration of the cell was calculated at each point by finite differences from the cell positions. The deterministic and the stochastic parts of motion were separated according to equation 1. We determined the functions vely see . We denoThe cross-correlation of the acceleration components was found to be neglectible as compared to the autocorrelation of each individual component. We could therefore conclude that there were no mixed stochastic terms, so that the stochastic contributions in the parallel and perpendicular directions could be computed according toFurthermore, because no angle dependence was found in either of the stochastic components, we re-evaluated them without angular binning and fitted the results by a first order polynomial An Euler-Maruyama scheme was used to simulate the equations 2 and 3 with the parameters obtained from our experimental data Figure S1Directional dependence of the friction coefficient The friction coefficient is shown as a function of (TIFF)Click here for additional data file.Figure S2Effective force term  To establish a relation between the amplitudes (TIFF)Click here for additional data file.Figure S3Noise perpendicular to the direction of motion. (A) Stochastic component of the perpendicular acceleration. Black dots show the experimental data, the red lines show a linear fit (TIFF)Click here for additional data file.Figure S4Gradient dependence of the stochastic part. Constants (TIFF)Click here for additional data file.Figure S5Stochastic part of subpopulations. Constants (TIFF)Click here for additional data file.Figure S6Conditional averaging for the non-directional case. Left: The parallel acceleration (red datapoints) can be fitted by a quadratic term (red line) while the perpendicular acceleration is zero and independent of the velocity (green datapoints and constant fit). Right: The stochastic components in the parallel (red) and perpendicular direction (green) can be fitted by a first-order polynomial.(TIFF)Click here for additional data file.Figure S7Stochastic components of the Langevin equation. Stochastic component of parallel acceleration, for (a) (TIFF)Click here for additional data file.Figure S8Testing the influence of flow forces. Histograms of the velocity components in x- and y-direction in absence of a chemoattractant gradient (the x-direction corresponding to the direction of fluid flow). Both histograms superpose closely, indicating that the fluid flow does not induce any preferred direction of cell motion.(TIFF)Click here for additional data file.Figure S9Testing the influence of cell position in the chamber. Scatter plot displaying each cell according to its mean speed and chemotactic index as a dot in the ((TIFF)Click here for additional data file.


With the relatively high rate of injuries to the lower extremity due to jump-landing movement patterns and the accompanied high costs, there is need for determining potential preventive programs. A program on the intervention of jump-landing technique is possibly an important preventative measure since it appeared to reduce the incidence of lower extremity injuries. In real life situations, amateur sports lack the infrastructure and funds to have a sports physician or therapist permanently supervising such a program. Therefore the current prevention program is designed so that it could be implemented by coaches alone.The objective of this randomized controlled trial is to evaluate the effect of a coach supervised intervention program targeting jump-landing technique on the incidence of lower extremity injuries.Of the 110 Flemish teams of the elite division, 24 teams are included and equally randomized to two study groups. An equal selection of female and male teams with allocation to intervention and control group is obtained. The program is a modification of other prevention programs previously proven to be effective. All exercises in the current program are adjusted so that a more progressive development in the exercise is presented. Both the control and intervention group continue with their normal training routine, while the intervention group carries out the program on jump-landing technique. The full intervention program has a duration of three months and is performed 2 times a week during warm-up (5-10 min). Injuries are registered during the entire season.The results of this study can give valuable information on the effect of a coach supervised intervention program on jump-landing technique and injury occurrence. Results will become available in 2011.Trial registration number: NTR2560 Different studies have pointed out that injuries in jump-landing sports are of frequent occurrence -4. In voSports injuries often are accompanied by pain or other physical discomforts, which in turn result in use of healthcare resources and possibly also in absenteeism from work. The severity of sports injuries can be described based on different criteria including sporting time lost, permanent damage and cost . In a stArguably the most important injury mechanism for these lower extremity acute and overuse injuries is the jump-landing maneuver ,3,5. ProWith the relatively high rate of injuries of the lower extremity in jump-landing sports and the severity of these injuries, there is a need for practical preventive programs ,29. A prThe objective of this randomized controlled trial is to evaluate the effectiveness of a coach supervised intervention program targeting jump-landing technique on the incidence of lower extremity injuries.- What is the effectiveness of a coach supervised jump landing intervention program on the incidence of acute and overuse injuries to the lower extremity?- What is the effect of a coach supervised jump landing intervention program on jump landing technique of individual athletes?The CONSORT statement was followed to describe the design of this study. This statement is a checklist intended to improve the quality of reports of randomized controlled trials .st and 2nd regional divisional basketball competitions.A two-way cluster randomized controlled trial with a follow-up period of one season (6 months) is used. The participation subjects are athletes from basketball teams of the national divisional, and, 1The study protocol is accepted by the local ethical committee of the Free University Brussels (B.U.N. B14320071963) and a trial registration number was requested (NTR2560).This study is financially supported by the Flemish Government through the establishment of the Policy Research Center Sports, Youth and Culture.st and 2nd regional basketball teams in Flanders, Belgium, are eligible for inclusion in the study. Athletes are excluded if they do not master the Dutch language or have a current injury to the lower extremity at time of inclusion.Athletes who are actively participating in the national division and, 1Written informed consent is obtained from each athlete and the study is conducted in accordance with the ethical institutional rules for human research and in regulation with the Declaration of Helsinki for Medical Research involving human subjects.A power calculation is carried out for the main outcome variable lower extremity injury incidence. A difference of 50% in the incidence of lower extremity injuries between the intervention and control group after a follow up of one season is considered to be clinically relevant.The prevalence of lower extremity injuries in basketball in Flanders being about 78% in one season , 34 subjOf the 110 Flemish teams of the elite division, 24 teams are included and equally randomized to two study groups  trainers are provided with a DVD containing general information on the study. Trainers of the intervention teams receive a different DVD with general information on the study and specific information on the intervention program and the individual exercises. Also a written manual including the complete program and a detailed explanation for each individual exercise is provided.Both research groups continue their regular training routine. Teams of the intervention group (IG) receive an additional intervention program to be carried out during the regular warm-up Table . This prThe program is a modification of two prevention programs previously proven to be effective for the prevention of lower extremity injuries ,32. The During the study period there are two fixed measurements, at baseline (T0) and after 3 months (T1). At T0 a questionnaire is completed by the athletes, and at both occasions the athletes' jump landing technique is assessed. Additionally, exposure hours and lower extremity injuries are continuously registered during the entire 2010-2011 basketball season (6 months).At T0 all athletes complete a questionnaire on demographic data, previous injuries, sports history and current sports participation .®. In addition, all jumps are evaluated through a predefined list of criteria, the JLS-System (Jump-landing Scorings system) . The researchers collect the jump-landing data at T0 and T1. These jumps are all analyzed through the computer program Darttrainerm) Table . This toExposure is recorded by the coach on an exposure form. Coaches note the total duration of each training session and match, and classify the level of participation of each player . If the player does not participate fully, the coach note the reason--that is, being injured, ill, or absent for other reasons. Completed exposure forms are returned on a weekly basis. Every week trainers are contacted by phone and urged to fill out the exposure sheets, and, if necessary, the injury forms. In this, we followed the example of Verhagen et al. who applTo determine the effectiveness of the program for the prevention of injuries, all injuries that occur during the entire season are registered in both the IG and CG. Injuries are divided into two main categories: acute or overuse injuries. An acute injury is defined as a sports accident, with a sudden, direct cause/onset, which requires at least minimal  care (e.g. ice or taping) and which causes the injured subject to miss out on at least 1 training session. Exclusion criteria involve muscle cramps and mild bruises. A subject sustains an overuse injury if he or she experiences a physical discomfort, with an insidious onset, that caused pain and/or stiffness of the musculoskeletal system, and that is present before, during and/or after the sports activity. The overuse injury is only registered if present for at least three consecutive sessions. Illness and fatigue are not registered as overuse injuries .Coaches receive injury forms at the start of the study. In case of injury, the coach completes in concordance with the injured player injury registration forms, which have to be completed within one week after injury onset. On this form the player is asked to provide information on the injury location, injury type, diagnosis of the injury, direct cause of the injury, preventive measures used at the time of the injury, first aid given, and subsequent medical treatment. If an injury is noted on the exposure form and no injury registration form is received within two weeks after the injury is logged, the coach is contacted and urged to let the player complete the injury registration form.Coaches are contacted on a weekly basis by means of a telephone call. The goal of this call is to inquire about the ongoing of the study and to follow-up on any missing exposure or injury registration forms. In addition, IG coaches are asked whether they followed the prescribed exercises for that particular week. If not, the exercises that are carried are noted in order to gain insight into the compliance to the program.At T1 coaches are required to complete a questionnaire inquiring on their opinion of the intervention program, their compliance to the prescribed exercises, and to inquire about any difficulties in executing the program.The injury incidence density (IID) is calculated as the number of new injuries per 1,000 hours of participation, using exposure time of each individual participant until the first injury. The number of injuries divided by the total time at risk is the preferred measure of incidence because it can accommodate variations in the exposure time of individuals ,35. If aBecause the unit of allocation was teams, we perform a multilevel Cox proportional hazard regression analysis, using the computer based software SPSS 17.0 , to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). Teams will be used as cluster levels .U test (p < 0.05) for data which are not normally distributed.The difference between the CG and IG regarding the JLS-system is calculated using Repeated Measures ANOVA (p < 0.05) for normally distributed data and the Mann-Whitney-This study evaluates the effect of a coach supervised injury prevention program. If successful this will provide valuable information for sports injury prevention practice. As the majority of injuries are sustained in amateur sports where medical guidance is limited, effective coach supervised prevention has a great potential impact on public health.The authors declare that they have no competing interests.IA, EC, EV, RM were involved in the conception and design of the study. IA is involved in study data collection. All authors were involved in drafting the manuscript and revising it for critically important content. All authors have read and approved the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2474/11/281/prepub


Recent studies on the medical treatment of Parkinson's disease (PD) led to the introduction of the so called Deep Brain Stimulation (DBS) technique. This particular therapy allows to contrast actively the pathological activity of various Deep Brain structures, responsible for the well known PD symptoms. This technique, frequently joined to dopaminergic drugs administration, replaces the surgical interventions implemented to contrast the activity of specific brain nuclei, called Basal Ganglia (BG). This clinical protocol gave the possibility to analyse and inspect signals measured from the electrodes implanted into the deep brain regions. The analysis of these signals led to the possibility to study the PD as a specific case of dynamical synchronization in biological neural networks, with the advantage to apply the theoretical analysis developed in such scientific field to find efficient treatments to face with this important disease. Experimental results in fact show that the PD neurological diseases are characterized by a pathological signal synchronization in BG. Parkinsonian tremor, for example, is ascribed to be caused by neuron populations of the Thalamic and Striatal structures that undergo an abnormal synchronization. On the contrary, in normal conditions, the activity of the same neuron populations do not appear to be correlated and synchronized.To study in details the effect of the stimulation signal on a pathological neural medium, efficient models of these neural structures were built, which are able to show, without any external input, the intrinsic properties of a pathological neural tissue, mimicking the BG synchronized dynamics.We start considering a model already introduced in the literature to investigate the effects of electrical stimulation on pathologically synchronized clusters of neurons. This model used Morris Lecar type neurons. This neuron model, although having a high level of biological plausibility, requires a large computational effort to simulate large scale networks. For this reason we considered a reduced order model, the Izhikevich one, which is computationally much lighter. The comparison between neural lattices built using both neuron models provided comparable results, both without traditional stimulation and in presence of all the stimulation protocols. This was a first result toward the study and simulation of the large scale neural networks involved in pathological dynamics.Using the reduced order model an inspection on the activity of two neural lattices was also carried out at the aim to analyze how the stimulation in one area could affect the dynamics in another area, like the usual medical treatment protocols require.The study of population dynamics that was carried out allowed us to investigate, through simulations, the positive effects of the stimulation signals in terms of desynchronization of the neural dynamics.The results obtained constitute a significant added value to the analysis of synchronization and desynchronization effects due to neural stimulation. This work gives the opportunity to more efficiently study the effect of stimulation in large scale yet computationally efficient neural networks. Results were compared both with the other mathematical models, using Morris Lecar and Izhikevich neurons, and with simulated Local Field Potentials (LFP). PD is a degenerative disorder of the central nervous system that often impairs motor skills, speech, and other functions. It is cAt present, there is no cure for PD, but medications or surgery can provide relief from the symptoms. The most widely used form of treatment is L-dopa in various forms. However, only 1-5% of L-DOPA enters the dopaminergic neurons. The remaining L-DOPA is often metabolized to dopamine elsewhere, causing a wide variety of side effects. Due to feedback inhibition, L-dopa results in a reduction in the endogenous formation of L-dopa, and so eventually becomes counterproductive. In the There are a few sites in the brain that can be targeted to achieve different results, so each patient must be assessed individually, and the particular site (or concurrent sites) to be stimulated is chosen based on the particular needs. Traditionally, the two most common sites are the Subthalamic Nucleus (STN) and the Globus Pallidus internus (GPi), but other sites, such as the CM-Pf  and NuclA increase of the neuronal synchronization causes a increase of power spectral density (PSD) of the Local Field Potential (LFP) taken from STN and GPi, whereas, the neuronal desynchronization causes a decrease of the PSD.Such a desynchronization in the β band takes place also when the patient is subjected to DBS, sending a pulse train with frequency larger than 70 Hz . The DBSThe first step toward the understanding of the dynamic effect of the DBS on the brain nuclei, is to design a model of these areas and simulate the effects caused by the stimulation. At this aim, after a deep scanning of the state of the art, in , a modelIn the second part of this paper, this new reduced order model gave us the possibility to increase, where needed, the population size using roughly the same computation time. The positioning of the electrodes within the population so built revealed the expected effects i.e. a desynchronization within the neurons, which led to a spreading of the power spectral density over a wide frequency range. This effect even increases in the presence of multiple stimulating sites within the population.In this paper the population targets simulated represented the (STN) and Globus Pallidus Externus (GPe) but recent researches have a new target like the PPn .The patients series and selection criteria, surgical planning and stereotactic technique are described elsewhere. In thisj vdenoted the membrane potential of the th jneuron and j wwas an auxiliary variable Eq. 4 describes the evolution of the parameter s denotes synaptic), relevant for the synaptic coupling and modulating the dynamics of the current jv. The other parameters denote:where C: membrane capacitance;ca, gk, glg: leak, Ca++, and K+ conductances through membranes channel;ca, vk, vl v: equilibrium potential of relevant ion channels;1, v2, v3, v4,v: tuning parameters for steady state and time constant.The dynamics of the calcium and potassium ion channels, responsible for the neurons spiking were controlled by a set of three equations:The neuron model is dimensionless, and we modulated the parameters to obtain realistic bursting patterns ,27.The dynamics of the neuron was controlled by external currents: a slowly varying current mentrout  as a soujv, and after a time delay τj the activity in the GPe, in turn, led to an inhibition of the STN neurons mediated by recurrent pathways s. In this case the network desynchronized after applying the stimulation signal.This shows that the neuron bursting was strongly synchronized in-phase and R(t) = 1 for t ∈ 0[3]s. The mean synchronization measure was R(t) = 0.6 for The DBS with four electrodes resulted in a good desynchronization for excitatory stimulation, indicated by a vanishing synchronization measure R. In this case,  acts as a pacemaker and drives another population (population 2), which gets synchronized only because of the driving. This structure resembles the case in which the pacemaker-like population in the BG and thalamus drives cortical motor areas which induce the peripheral shaking . ConsequTo study the challenging situation of strong driving, we assume that the mean coupling within the driving population is equal to the mean coupling between the two populations. The excitatory coupling between the pacemaker and the driven system is given by The local separation of stimulation and recording sites guarantees that the feedback signal is not corrupted by stimulation artifacts. Figure The stimulation in population 1 causes an instantaneous desynchronization of population 2, which shows a decrease of the synchronization measure R.In this case we use only one electrode to stimulate the coupled networks Figure  and 14.We can see that, when stimulated, the first population acts as peacemaker for population 2 (driven population), Figure The stimulation is realised via four electrodes located within the network. So we studied how the level of desyncronization of the driven population changes with a number of electrodes applied Figure  and 16.We can see in this simulation how the increasing the number of electrodes causes an increasing in the desynchronization effect. Stimulating the first population Figure  acts as For the first population we have R(t) = 0.2 where t ∈ 3,6.5]s, moreover for the second population the value of R change from R(t) = 0.5 with t ∈ 0 Hz range there is a high activity.Subsequently we applied the traditional stimulation via four electrodes in a population , computationally much lighter, was demonstrated to lead to the same results as other neural networks already introduced in the literature. This gives the opportunity to more efficiently study the effect of stimulation in large scale neural networks. Results were compared with the other mathematical models, i.e. Morris Lecar neurons, analyzing both parameters related to the neural level (the synchronization effect) and indexes related to the macroscopic level (LFP). The results agree in terms of the positive effect of the stimulation in terms of power spectral density. We consider these results very promising: they open the way to the possibility of simulating large scale networks in pathological conditions at the aim to design new control strategies for the PD effect mitigation.PD: Parkinson's Disease; DBS: Deep Brain Stimulation; BG: Basal Ganglia; FDA: Food and Drug Administration; STN: Sub-Thalamic Nucleus; GPi: Globus Pallidus internus; PPTg: Nucleus Tegmenti Peduncolopontini; PSD: Power Spectral Density; LFP: Local Field Potential; GPe: Globus Pallidus externus; VIM: Ventral Intermediate Nucleus; CM-Pf Centrum Medianum-Parafascicular.The authors declare that they have no competing interests.AL, as the first author, designed and realized the simulation tool, carried out the simulations, refined the models and analysed the results; PA contributed in designing of the neural networks architectures and in revising the obtained results, further improving the neural models used; PM is the Nerurosurgeon who performed the surgical stereotactic procedures and contributed in revising the paper contents, according to his experience in the Deep Brain Stimulation field.All authors read and approved the final manuscript.


In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30–0.45 and a recall of 0.10–0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed  Research on link prediction for social networks has been actively pursued Recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people Link prediction for face-to-face behavioral networks should be useful for developing novel services and performing sociological analyses. Link prediction is promising for predicting communications that are likely to occur; this can be viewed as potential communication demands. Hence, link prediction in face-to-face behavioral networks may contribute toward realizing novel services such as friendship recommendation in real-world environments, which is already common in online environments. Link prediction techniques may also be useful for analyzing the evolutionary dynamics of social networks in real-world environments, an important topic in social science.et al.However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been fully explored. Our research group has been investigating the effectiveness of link prediction techniques for face-to-face behavioral networks using a publicly available dataset called the SocioPatterns dataset To clarify the effectiveness of link prediction techniques for face-to-face behavioral networks, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the SocioPatterns dataset The main contributions of this study can be summarized as follows.We investigated the prediction accuracy of conventional link prediction techniques for face-to-face behavioral networks by using publicly available datasets.We showed that in order to achieve high link prediction accuracy for face-to-face behavioral networks, it is essential to incorporate temporal information and to appropriately tune the length of the training period, the period spanned by the records analyzed; good tuning results in increased link prediction accuracy.decaying weight method can lead to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified. The prediction accuracy of the decaying weight method is comparable to the accuracy achievable with an appropriately tuned training period.We showed that the proposed Among various link prediction techniques proposed in the literature, the common neighbor (CN) For each node pair In the following paragraphs, we briefly introduce definitions of the link prediction score The common neighbor predicts new link formation from the idea that the existence of many common adjacent nodes between two nodes implies a high probability of new link formation between those two nodes The Adamic/Adar predicts new link formation from the idea that many common adjacent nodes with small degree between two nodes implies a high probability of new link formation between the nodes The preferential attachment predicts new link formation from the idea that a high-degree node has a higher chance of forming new links Similarly to CN, the Jaccard coefficient predicts new link formation from the number of common adjacent nodes, but the link prediction score is normalized The resource allocation predicts new link formation from an idea similar to that in AA We evaluate the effectiveness of link prediction techniques through experiments using the SocioPatterns dataset For comparison purposes, we also use the Enron email dataset training period and a testing period, and investigate how accurately the occurrence of communications in the testing period can be predicted using the history of communications in the training period. In our experiments, we divide the history of communication into a First, as an input for link prediction techniques, an undirected graph Next, we calculate the link prediction score As a final step, we examine how accurately we can predict which node pairs will have at least The link prediction problem can be formulated as ranking or classification problems. Following prior work Precision evaluates the correctness, and recall evaluates the completeness of link prediction. Generally, there is a tradeoff between precision and recall, whereby a larger threshold We perform experiments by changing Before evaluating the effectiveness of link prediction techniques, we analyze the characteristics of datasets used in this paper. To investigate the accuracy of link prediction techniques for a face-to-face behavioral network, we find the precision and recall of ten link prediction techniques while varying the threshold Unsurprisingly, this result suggests that link prediction techniques cannot perfectly predict future link formation, but can nevertheless achieve considerable accuracy. It is therefore expected that conventional link prediction techniques can be applicable to services such as friendship recommendation that require a moderate level of prediction accuracy. For instance, it is reported in et al.Focusing on the differences among link prediction techniques, we find that RA and PA achieve the highest accuracy. ee nodes . RA succTo investigate the effects of link prediction technique parameters on the prediction accuracy, we obtain the AUC scores of WCN, WAA, WPA, WJC, and WRA while varying the parameter on score .This figure shows that appropriate link weights improve the accuracy of link prediction. However, as reported in We next obtain the AUC score while varying the threshold weights) . In thisThis figure shows that the accuracy is higher when predicting only the formation of strong ties than when predicting link formation including weak ties. This suggests that link prediction techniques are more effective at predicting the formation of strong ties than weak ties. This result is considered natural since weak ties may include noisy links, which are difficult to predict. Note that this tendency is observed in different face-to-face behavioral networks In this section, we examine the effectiveness of using temporal information about communication in addition to the network topology for link prediction. By utilizing temporal information, we can change the contribution of each communication event to the link prediction score, which may result in more accurate prediction. A simple way to utilize temporal information is to change the training period length. By changing the onset time of the training period, we can evaluate the prediction accuracy when the contribution of old communications are ignored. The effect of the length of the training period on the prediction accuracy has not been fully explored in existing work We obtain the AUC scores of the link prediction techniques while varying the training period length for several starting times of the testing period We next determine the training period length not by time, but by the number of face-to-face contacts used for link prediction, and perform a similar experiment to investigate the effect of training period length. Since the number of face-to-face contacts recorded in the dataset is different from hour to hour, this may explain why the optimal training period length depends on the starting time of the testing period. These results show that in most cases, the prediction accuracy is highest when using approximately 3,000 contacts. However, when the starting time of the testing period is 36 [h], the prediction accuracy increases together with the number of contacts used for prediction is increased. We carefully investigated the results for other starting times in the testing period, and found that prediction accuracy increases as the number of contacts used for prediction increases when the testing period includes the third day. In other cases, using approximately 3,000 contacts achieves the highest accuracy.These observations suggest that prolonged observation of face-to-face contacts for link prediction often degrades the prediction accuracy. We cannot draw general conclusions from our results alone, but in this experiment, using approximately 3,000 contacts (30 contacts per person) is optimal in many cases. Analysis using other datasets is required in order to determine the optimal training period length in the general case.linear and exponential. Let contacts between time We next investigate the prediction accuracy when using a decaying weight method, which changes the contribution of each contact on the link prediction score by weighting each face-to-face contact. In the above experiments, all contacts used for link prediction are weighted equally for link prediction score calculation. However, our experimental results suggest that simply using all contacts often degrades the prediction accuracy. We therefore change the contribution of each contact on the link prediction score calculation by assigning a heavy weight to new information. In this experiment, we use two types of decaying weight methods, These results show that the decaying weight method achieves higher accuracy than the flat weight method with The effectiveness of the decaying weight method is due to the spatiotemporal locality of face-to-face communication. The mobility of people is restricted by the space and their moving speed. Hence, if we know that person We next compare the results for the SocioPatterns dataset and those for the Enron email dataset to investigate the characteristics of link prediction techniques for face-to-face behavioral networks. From This result shows that conventional link prediction techniques achieve higher precision and recall for email networks than for face-to-face networks. Moreover, we should note that the number of nodes in the network obtained from the Enron email dataset is greater than that obtained from the SocioPatterns dataset. Hence, link prediction techniques have higher performance for email networks than for face-to-face networks. The topological structure of a network may affect the accuracy of link prediction techniques for the network.Moreover,  dataset  and 5. TWe next investigate the AUC score of CN for networks obtained from the Enron email dataset by changing the number of email messages used for prediction . In thisWe lastly compare the prediction accuracy for a face-to-face behavioral network with that for other networks. To clarify the effectiveness of link prediction techniques for face-to-face behavioral networks, we have investigated the accuracy of conventional link prediction techniques for such networks under various conditions. We have performed several experiments utilizing the SocioPatterns dataset containing a history of face-to-face interactions among participants at an academic conference.The experiments showed that conventional link prediction techniques predict new link formation in a face-to-face behavioral network with a precision of 0.30–0.45 and a recall of 0.10–0.20 through the use of an appropriate threshold. Comparison of the results for the SocioPatterns dataset and other datasets showed that the accuracy of link prediction techniques for face-to-face behavioral networks is relatively higher than that for non-social networks, but not particularly high among other types of social networks. These results suggest that conventional link prediction techniques are applicable to services, such as friendship recommendation, which require a moderate level of prediction accuracy.The experiments also reveal a unique characteristic of link prediction for face-to-face behavioral networks whereby prolonged observation of social networks often degrades the prediction accuracy. For an email network, it is sufficient to use a naive method that observes all records of email messages and simply uses them unmodified. In contrast, for face-to-face behavioral networks, it is essential to determine an appropriate training period length to ensure high prediction accuracy. In most cases in our experiments, observing approximately 30 communications per person provided high prediction accuracy. However, further analysis is necessary in order to determine the optimal training period length in the general case.One possible method that can be used to avoid the need for tuning the training period length in link prediction for face-to-face behavioral networks is using the decaying weight method. Our results showed that an exponential decaying weight method achieves comparable accuracy with the method using the optimal training period length, suggesting that the decaying weight method can be useful when the optimal length of the training period is unknown.We plan to investigate the causes of differences in accuracy between different types of social networks. It is also important to investigate the optimal training period length in other face-to-face behavioral networks in future work.


A striking feature of vascular plants is the regular arrangement of lateral organs on the stem, known as phyllotaxis. The most common phyllotactic patterns can be described using spirals, numbers from the Fibonacci sequence and the golden angle. This rich mathematical structure, along with the experimental reproduction of phyllotactic spirals in physical systems, has led to a view of phyllotaxis focusing on regularity. However all organisms are affected by natural stochastic variability, raising questions about the effect of this variability on phyllotaxis and the achievement of such regular patterns. Here we address these questions theoretically using a dynamical system of interacting sources of inhibitory field. Previous work has shown that phyllotaxis can emerge deterministically from the self-organization of such sources and that inhibition is primarily mediated by the depletion of the plant hormone auxin through polarized transport. We incorporated stochasticity in the model and found three main classes of defects in spiral phyllotaxis – the reversal of the handedness of spirals, the concomitant initiation of organs and the occurrence of distichous angles – and we investigated whether a secondary inhibitory field filters out defects. Our results are consistent with available experimental data and yield a prediction of the main source of stochasticity during organogenesis. Our model can be related to cellular parameters and thus provides a framework for the analysis of phyllotactic mutants at both cellular and tissular levels. We propose that secondary fields associated with organogenesis, such as other biochemical signals or mechanical forces, are important for the robustness of phyllotaxis. More generally, our work sheds light on how a target pattern can be achieved within a noisy background. in planta. We further propose a generic mechanism of noise regulation through a secondary signal and predict its parameters for an optimal efficiency. More generally, our work suggests that noise can have visible macroscopic effects on developmental phenotypes, and that different layers of control are required to modulate these effects.How living organisms affected by natural, stochastic variability achieve regular developmental patterns is a challenging question in biology. A fitting field of investigation is provided by phyllotaxis, the regular arrangements of lateral organs such as leaves or flowers on the stem of vascular plants, as visible on a pinecone or a sunflower head. In spiral phyllotaxis, the most frequent amongst higher plants, these arrangements can be described using spirals, numbers from the Fibonacci sequence and the golden angle, which has led to an ideal, deterministic view of phyllotaxis. Nevertheless, organ initiation can be influenced by cellular and organismal noise. In order to investigate the effect of such noise, and how it might be regulated, we developed a stochastic dynamical model of the inhibitory interactions between organs. Our model predicts stereotypical alterations of phyllotactic patterns, recapitulating disparate observations. Comparing simulations and experiments, we identified the main source of noise affecting phyllotaxis  The shoot apex is a major organizer of the aerial architecture of vascular plants. Lateral organs (leaves and flowers) are successively initiated as primordia at the shoot apex yielding a regular arrangement on the stem known as phyllotaxis. Two main categories of phyllotactic patterns are observed: whorled – many primordia emerge simultaneously, and spiraled – a single primordium is initiated at a time. Spiral phyllotaxis features two sets of conspicuous spirals (the parastichies) rotating either clockwise or anti-clockwise, see Arabidopsis thalianaAltogether, this body of work is underpinned by an ideal, deterministic view of phyllotaxis, in which perfectly regular patterns can be reproduced by theoretical models. Nevertheless, living organisms are affected by a natural, stochastic variability. Along with a variability among species Arabidopsis thaliana apex.We used the dynamical system introduced in A typical simulated spiral sequence is shown in We next questioned whether this abstract tissue level model could be used to interpret observations at the cellular level. To do so, we re-considered cellular models of auxin polar transport Accordingly, we subsequently used the abstract dynamical system. In order to investigate the origin of variability of phyllotactic patterns, we incorporated two different sources of noise in this dynamical system  and 3.Organs are initiated when the inhibitory field falls below the threshold Examples of sequences of angles and plastochrons are shown in Helianthus annuus) Arabidopsis thaliana) This raises an issue about organ sorting and more precisely about defining which of the concomitantly initiated organs is the older so as to compute the divergence angle. In view of the equivalence in age between these concomitant organs, we pick their order at random. The concomitant initiation of two organs leads therefore half of the time to characteristic M-shaped patterns, i.e., sequences close to en angle . Consequto  and  . It is nplt3 plt5 plt7 multiple mutant of Arabidopsis Consequently, the simple addition of noise in the model can reproduce observed alterations. Another visible alteration is a number of divergence angles close to 180 degrees, corresponding to a transient distichous phyllotaxis. This type of alteration was recently observed in the We then investigated more systematically the space of parameters also see  & S4. ThA second possible source of stochasticity lies in the organogenetic activity of the apex, which could be reflected either in the size of the competent circle or in the range of inhibition. As only the ratio, Examples of sequences of divergence angles and plastochrons are shown in ld noise , an impopatterns . At low It has been previously argued clusions  and founAs permanent reversals of the handedness of spirals have apparently not been observed Available experimental data demonstrate that some mutations make phyllotaxis more irregular than in wild type plants We categorized the level of controls into three classes. (i) Before initiation. (ii) During initiation. (iii) Post-initiation. In the first class, the outcome would be a reduction in the level of noise introduced in the dynamical system. Auxin signalling could play such a role by filtering fluctuations So far, auxin is the only hormone that have been shown to be necessary and sufficient for organ formation at the periphery of the shoot apex, see after it has been intiated. In our framework, each primordium is endowed with a spatial position and an age (the time since its initiation). Correcting  the position would only have a small effect on the precision of divergence angles and whence would not remove M-shaped sequences, which were the major alterations in the noise on threshold. Moreover, the M-shaped sequences are due to the incorrect sorting of 2 organs of the same age, so that it is natural to seek whether the age of primordia can be corrected. We focused on a model where the physiological age is changed post-initiation according to the influence of closer primordia. More precisely, we considered a secondary field of range We then turned to the third class, where noise correction must act on an intrinsic property of a primordium ectively . Indeed The sequences and histograms of angles were similar to the threshold noise model with no secondary field. However the proportion of alterations differed. We investigated quantitatively the parameter space see  and founWe investigated the impact of noise on phyllotaxis starting from a deterministic model whereby primordia are sources of an inhibitory field loration , S4 onlyMost previous theoretical research on phyllotaxis focused on its mathematical regularity. Nevertheless, observations indicate a variability across species and genotypes We simulated two sources of noise, on the threshold for initiation and on the size of the generating circle. We found that noise leads to stereotypical alterations, in addition to fluctuations of the divergence angles and plastochrons around their deterministic values: (i) transient distichous pattern with angles of We then investigated how the noise on threshold might be corrected. A pre-filter simply corresponds to a modulation of the level of noise. Other filters require the propagation of information from older primordia to an initium or to a primordium. Such a transfer of information might be provided by other hormones As we have shown that our model properties can be translated into cellular properties, our results can guide specific cellular simulations that address aspects of stochasticity. Conversely, if the fluctuation of a cellular property can be measured in experiments, it can be translated onto our model using the mapping from cell-based models . Thus ouWe reimplemented the model of Douady and Couder optimize library of scipy (http://docs.scipy.org/doc/scipy/reference/optimize.html). The time of initiation is selected whenever this minimum reaches the threshold value. Then an initium is created and the process is repeated. We chose to achieve a precision on time of The dynamical system was implemented in Python. The time of initiation is found using a standard dichotomic solver. At each step of this process, the minimum of the inhibition on the circle is calculated using the We considered the concentration-based model, as formulated by We added two sources of noise in the model, on the threshold of initiation and on the radius of the competent circle. In each case, the threshold (resp. the radius) is re-defined, following each initiation, from a random variable of Gaussian distribution of mean We investigated the effect of initialization of the simulations by changing the initial values of the divergence angle, turning on noise before or after convergence to a stationary state. We found our results to be unaffected by the type of initialization . TherefoWhen noise was large enough, we frequently observed a vanishing plastochron. Initia were considered as concomitant when the plastochron is smaller than the time-precision of the simulation. In this case, these initia are equivalent and so we defined their order of apparition at random. In order to separate the subsequent M-shaped patterns from other features of variability, we ignored M-shaped patterns when computing the standard deviation of divergence angles, i.e. the standard deviation was computed from the symmetrised distribution of angles with values in .We investigated the effect of a second inhibitory field that is turned on with a delay after initiation and has similar properties to the first inhibitory field.After a delay After a delay Figure S1Emergence of the abstract model from cell-cell communication. An arrow, or a bar, indicates that the cellular parameter have a positive, or respectively a negative, effect on the inhibitory field parameter. Auxin parameters concern production, degradation and ‘passive’ diffusion (or non-polar transport), and polar efflux. A different auxin production/degradation serves to define the central zone. Polar efflux parameters concern PIN1 level, PIN1 polarisability (how easily a polar distribution is obtained in response to flux/concentration cues), and efflux efficiency. In the flux-based model, the differentiation of new primordia occurs when an auxin threshold is reached, which directly maps to the threshold of initiation in the abstract model. The parameters of the abstract model are defined in the Main (PDF)Click here for additional data file.Figure S2Effect of noise initialization on convergence of simulations. (A) Examples of sequences initiated from a distichous state, i.e. a first divergence angle of (PDF)Click here for additional data file.Figure S3Role of noise. Exploration of the parameter space of the inhibitory field, as a function of the field steepness. The data plotted are averaged over values of the ratio (PDF)Click here for additional data file.Figure S4Role of noise. Exploration of the parameter space of the inhibitory field, as a function of the value of the ratioof the inhibition range to the radius of the competent circle. The data plotted are averaged over values of the steepness (PDF)Click here for additional data file.Figure S5Deterministic model: Role of the decrease incorresponding to the transition from vegetative to reproductive stage. A varying (PDF)Click here for additional data file.Figure S6Two fields acting at the same level; redundant interaction. (A) Phase diagram showing three possible behaviors following the parameters (PDF)Click here for additional data file.Figure S7Two fields acting at the same level; synergetic interaction. Divergence angles (in stationary regime) as a function of the range (PDF)Click here for additional data file.Figure S8Exploration of the parameter space of the secondary field. (A) Number of concomitant initiations as a function of the age shift (PDF)Click here for additional data file.Text S1Mapping of cellular-based models on the abstract model.(PDF)Click here for additional data file.Video S1A temporal sequence of the deterministic model corresponding to . Opens in VLC media player.(AVI)Click here for additional data file.


Embryophyta group from a set of features extracted solely from their primary amino acid sequence.Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the High predictability of several GO terms was found for Molecular Function and Cellular Component. As expected, a lower degree of predictability was found on Biological Process ontology annotations, although a few biological processes were easily predicted. Proteins related to transport and transcription were particularly well predicted from primary structure information. The most discriminant features for prediction were those related to electric charges of the amino-acid sequence and hydropathicity derived features.Embryophyta plants.An analysis of GO-slim terms predictability in plants was carried out, in order to determine single categories or groups of functions that are most related with primary structure information. For each highly predictable GO term, the responsible features of such successfulness were identified and discussed. In addition to most published studies, focused on few categories or single ontologies, results in this paper comprise a complete landscape of GO predictability from primary structure encompassing 75 GO terms at molecular, cellular and phenotypical level. Thus, it provides a valuable guide for researchers interested on further advances in protein function prediction on  Molecular Function ontology refers to biochemical activities at the molecular level, no matter what entities accomplish that function or the context where it takes place; Cellular Component ontology refers to the specific sub-cellular location where a gene product is active, describing different parts of the eukaryotic cell; Biological Process ontology refers to a series of events with a defined beginning and end, to which the gene product contributes. Currently, as of February 2013 there are 38137 defined GO terms, distributed over 9467 molecular functions, 3050 cellular components and 23928 biological processes. However, in spite of such variety of functions, all proteins share a common basic configuration: a linear polypeptide chain composed by different combinations and repetitions of the twenty amino acids encoded by genes. Although, currently there are almost 8 million sequences in non-redundant databases, for most, we know just that amino acid sequence deduced from the DNA chain[The universe of protein functions can be summarized through the use of the Gene Ontology (GO) project, which aimed to construct controlled and structured vocabularies known as ontologies, and apply them in the annotation of gene products in biological databases. MoleculMany approaches have been developed in this matter . One Embryophyta). First, from the previously described methods, only Blast2GO[There are, however, several aspects that must be discussed about current performances in prediction of GO terms, when applied to non-model organisms such as land plants . MoreovBlast2GO, or beloBlast2GO.In order to improve the performance of current GO term predictors for land plants, it would be useful to have a better understanding of the underlying relationships between primary structure information and protein functionality. However, the structure of the machine learning models behind high-accuracy predictors often makes difficult to understand why a particular prediction was made. In thisSince most of the current GO prediction methods are limited to a few arbitrary functional classes and single ontologies, they cannot provide any information about relationships on the predictability at the various levels of protein functionality , which could be another key element for determining how the information of the primary structure is related to protein function.Embryophyta group of organisms, which is composed by the most familiar group of plants including trees, flowers, ferns, mosses, and various other green land plants. The analysis provides the following key elements: predictions are made by using features extracted solely from primary structure information; analysis comprises most of the available organisms belonging to the Embryophyta group; biases due to protein families are avoided by considering only proteins with low similarity among them and a strong evidence of existence; predictions and analysis are made over a set of categories belonging to the three ontologies; proteins are allowed to be associated to several GO terms simultaneously.This work presents an analysis on the predictability of GO terms over the Results from this work answer whether it is possible to predict most GO-slim terms from primary structure information, what categories are more susceptible to be predicted, which ontology is most related to the information contained in the primary structure and what relationships among ontologies could be influencing the predictability at different levels of protein functionality in land plants.Embryophyta proteins comprises the following parts: (i) selection of the protein sequences conforming the database in order to cover the highest number of available plant proteins, while ensuring high confidence annotations and avoiding possible biases; (ii) categories describing positive and negative samples associated to each GO term are determined in order to minimize the impact of hierarchical relationships; (ii) protein sequences are mapped into feature vectors that encode a number of attributes of varied nature; (iii) computed features are clustered into groups of similar information content; (iv) one binary classifier is learned for each GO term and each feature cluster in order to evaluate the prediction power of individual clusters, and finally (v) one binary classifier is learned for each GO term using the whole set of features in conjunction with an automatic feature selection strategy in order to determine the global predictability of each GO term.The implemented methodology for assessing predictability of GO terms in R environment for statistical computing[seqinR package[GNU General Public License.The following subsections describe the methods employed for each part of the methodology. All simulations were implemented on the omputing. Additioomputing, and the package, all of Embryophyta proteins at UniProtKB/Swiss-Prot database . The third group is analogous to the second one, but applying such characterization to the predicted secondary structure of the protein. Predictions were computed with the Predator 2.1 software[Protein sequences are mapped into feature vectors by extracting three types of attributes: physical-chemical features, primary structure composition statistics and secondary structure composition statistics  be the linear correlation computed between any pair fi and fj and ciy=cor be the linear correlation between fi and y. Defining this, relevance of features can be quantified by computing ciy for all features and then, redundant ones can be identified by analyzing the n×n feature correlation matrix. In order to speed up the calculations, an algorithm based on the Fast Correlation-Based Filter of[The feature selection procedure is carried out in the second part of the Results and discussion section, where the global predictability of each GO term is evaluated by using the whole feature set. Since redundant features would possibly overfit the training data, an analysis of relevance and redundancy was applied. Let ilter of was usedIn order to allow samples to be associated to multiple categories, decision making was implemented following the one-against-all strategy. The method produced a strong class imbalance since it trains a number of binary classifiers, each one intended to recognize samples from one class out of the whole training set. To overcome the problems that imbalanced classes commonly produce in pattern recognition techniques, the Synthetic Minority Over-sampling Technique (SMOTE) was employed.A support vector machine (SVM) with Gaussian kernel was used for running all the classification tests. This SVM is trained with the ’kernlab’ package, available in R-CRAN. DispersIn order to estimate the performance of the predictive model, a 5-fold cross-validation strategy is implemented. In such strategy, the test procedure is repeated five times, and each time an 80% of the data is used for adjusting the SVM parameters and training the model, while the remaining 20% is used as testing samples. This strategy also allows providing an estimation of the reliability of the model by computing the variability of the results through the five repetitions.Classification results with individual feature clusters, for an identity cutoff of 30%, are condensed in FigureReceptor binding achieved the highest classification scores. This category is intended to comprise proteins that interact selectively and non-covalently with one or more specific sites on a receptor molecule. About 63% of the proteins associated to this category in the database are proteins involved with binding of serine/threonine kinase receptors, which turned out to be easily predicted from most of the defined features.FigureTranscription factor activity achieved was easily predicted from the feature groups 1, 3, 6, 8 and 14. Not so surprising is the fact that DNA binding also presents a similar behavior since most transcription factors must interact with DNA molecules and consequently they are also included in this category. However it is worthy to note that several other proteins also perform DNA cleavage, such as polymerases, nucleases and histones, and they were also well predicted from the same feature groups. The conclusion from these results becomes more evident by observing the results of the DNA metabolic process in Figurenucleus* in FigureTransporter activity refers to proteins that enable the directed movement of substances into, out of, within or between cells. Most of them are integral transmembrane proteins, that are distinguished by their high content of hydrophobic residues[transporter activity were reached with the groups 3 and 6, which include GRAVY index as well as monomer and dimer frequencies of three out of the four most hydrophobic residues: leucine, isoleucine and phenylalanine. Additionally, predictability of this molecular function is reflected, while in a minor degree, on the transport biological process, which reaches its highest values for the same feature groups  from another, rather than from predictive value of certain sequence-derived features. However, in order to provide further information on the robustness of the proposed methodology when the identity cutoff changes, FigureFigureplastid, which was not easily predicted with any feature set independently, but reached medium to high classification results when the complete set was used. Such behavior is a clear example of the multivariate associations that could be missed when analyzing only individual feature sets.Note that GO terms were ordered again from top to bottom according to their predictability, but this order is not strictly the same as in Figuretransporter activity, transcription factor activity, and DNA binding in molecular functions; ribosome, nucleus*, nucleolus and thylakoid in cellular components; regulation of gene expression, epigenetic, Cell cycle, Photosyntesis and DNA metabolic process in biological processes.Other results were consistent with the insights provided by the previous analyses, showing that some of the best predicted GO terms were A reduced number of categories had performances under 50%, most of them from the biological process ontology and a few form the molecular function ontology. It is important to note that the majority of those categories achieved very high specificities and low sensitivities, pointing out to a high dispersion of such categories over the feature space, which yields to a very high number of false negatives. Also, the high dispersions observed in the boxplots for some of the worst predicted classes demonstrate that there is a high variability among repetitions of the experiment which means that those low performances are not confident. Conversely, the categories with high performances show also low dispersions associated to them, hinting consistency in the predictors.Although the main purpose of this work is not to design a highly accurate GO term predictor, but to provide a comprehensive analysis of the predictability of GO terms from primary structure information, it is important to mention how this method compares with currently used prediction tools. The blue and green lines in FigureFinally, Figurecytoplasm, and intracellular, and Figurecellular process and metabolic process. Conversely, a few classes that were lacking sensitivity were favored by the contributions of their descendants, as it is the case of the root nodes of the ontologies.It is notable how categories with the major number of descendants have been negatively affected by their false positives. This is especially observed in FigureAn analysis of GO terms predictability in land plants proteins was carried out in order to determine single categories or groups of related functions that are more related with primary structure information. For this purpose, pattern recognition techniques were employed over a feature set of physical-chemical and statistical attributes computed over the primary structure of the proteins. High predictability of several GO terms was observed in the three ontologies. Proteins associated to transport activities showed high correct prediction rates when using hydropathicity related features. Also, proteins involved with transcription (and therefore associated to the nucleus) presented high discriminability from the extracted features. Ribosomal and other proteins involved with translation, proved to be highly predictable from features related to electric charges of the amino acid sequence. At the biological process level, proteins related to regulation of gene expression and nucleic acid metabolic process were easily predicted, while some other biological processes showed low predictability from the extracted primary structure features. The information derived from this study could be used to get further improvement in prediction performances by combining the information from SVM classifiers with annotation transfer methods.The authors declare that they have no competing interests.JAJG wrote the R scripts, designed and developed the workflow of the experiments and analyses. JJGC contributed to the biological analysis of the results. CGCD revised and approved the manuscript. APL suggested the main ideas and guided through the overall process. All authors contributed with the final manuscript.Data Set. The main set comprises a total of 3368 protein sequences, from which 1973 sequences are annotated with molecular functions, 2210 with cellular components and 2798 with biological processes. Automatically-assigned annotations were not included in the analyses.Two files are provided, both of them in character separated value (CSV) format; comma is used for the decimal point and a semicolon as column separator, according to the Excel convention for CSV files in most Western European locales.features.csv: Contains the feature matrix of 3369 rows (3368 proteins + header) and 439 columns (438 features + header). The first column contains the Uniprot identifiers of proteins and the first row contains the feature names. Position  is an empty field.labels.csv: Contains the membership matrix of 3369 rows (3368 proteins + header) and 76 columns (75 GO terms + header). The first column contains the Uniprot identifiers of proteins and the first row contains the GO keys of functional categories. Position  is an empty field. Each position in the matrix contains the number zero or the number one to establish whether or not the protein of that row is associated to the GO term corresponding to that column.Click here for fileFeature clusters description. Table describes the feature clusters derived from correlation analysis. For this purpose, the Ward clustering algorithm was used, with absolute Pearson correlation distance as metric. A single letter stands for a single amino acid frequency according to their one-letter code, while letter pairs stand for dimmer frequencies. Also, symbols α, β and −, stand for frequencies of alpha helices, beta sheets and coils respectively.Click here for fileDetailed results for several identity thresholds. Figures depicting detailed results for each class with the full feature set, for several identity cutoffs. Left plots show sensitivity, specificity and geometric mean (green line) achieved with the five-fold cross-validation procedure, while right plots depicts boxplots for analyzing performance variation throughout the five repetitions. Left plots also depicts the performance of the BLASTP algorithm for comparison purposes (blue line).Click here for file


ACCOMPLISH  targets both individual and environmental determinants of hand hygiene. This study aims to evaluate the cost-effectiveness of a multicomponent implementation strategy aimed at the reduction of healthcare associated infections in Dutch hospital care, by promotion of hand hygiene.Public health authorities have recognized lack of hand hygiene in hospitals as one of the important causes of preventable mortality and morbidity at population level. The implementation strategy ACCOMPLISH package will be evaluated in a two-arm cluster randomised trial in 16 hospitals in the Netherlands, in one intensive care unit and one surgical ward per hospital.The A multicomponent package, including e-learning, team training, introduction of electronic alcohol based hand rub dispensers and performance feedback.ACCOMPLISH package from a healthcare perspective will be performed.The primary outcome measure will be the observed hand hygiene compliance rate, measured at baseline and after 6, 12 and 18 months; as a secondary outcome measure the prevalence of healthcare associated infections will be measured at the same time points. Process indicators of the intervention will be collected pre and post intervention. An ex-post economic evaluation of the Multilevel analysis, using mixed linear modelling techniques will be conducted to assess the effect of the intervention strategy on the overall compliance rate among healthcare workers and on prevalence of healthcare associated infections. Questionnaires on process indicators will be analysed with multivariable linear regression, and will include both behavioural determinants and determinants of innovation. Cost-effectiveness will be assessed by calculating the incremental cost-effectiveness ratio, defined here as the costs for the intervention divided by the difference in prevalence of healthcare associated infections between the intervention and control group.This study is the first RCT to investigate the effects of a hand hygiene intervention programme on the number of healthcare associated infections, and the first to investigate the cost-effectiveness of such an intervention. In addition, if the ACCOMPLISH package proves successful in improving hand hygiene compliance and lowering the prevalence of healthcare associated infections, the package could be disseminated at (inter)national level.NTR2448 Public health authorities have recognized lack of hand hygiene (HH) as one of the important causes of preventable mortality and morbidity at population level -3. Both Guidelines stipulating when HH is required have been in place for many years, but are often not adhered to. Two thirds of the studies included in a systematic review reported compliance rates below 50% . Grol anIt is increasingly recognized that the observed failure to achieve large and sustained effects is due to the absence of well-designed implementation strategies using insights from the behavioural sciences ,9. Such The knowledge gained from our previous behavioural research has therefore been used to develop ACCOMPLISH : a multicomponent implementation strategy to improve the hand hygiene behaviour of physicians and nurses. ACCOMPLISH has the potential to substantially increase compliance and reduce HAI in Dutch hospitals, but experimental implementation and evaluation of this package is necessary before large-scale dissemination in healthcare practice can be advised.ACCOMPLISH package and will test the effects of this implementation strategy on HH compliance and prevalence of HAI. Furthermore, an economic evaluation will investigate the cost-effectiveness of this strategy from a healthcare perspective.This study aims to evaluate the cost-effectiveness of the Research questions:ACCOMPLISH on HH compliance rates?1. What is the effect of the multicomponent implementation strategy 2. Which process indicators contribute most substantially to observed changes in HH compliance?ACCOMPLISH on the prevalence of HAI?3. What is the effect of the multicomponent implementation strategy ACCOMPLISH package?4. What is the balance between costs and health effects (costs per prevented HAI) of the ACCOMPLISH will be introduced, while eight control hospitals will maintain normal HH practices. All infection prevention activities as well as any large outbreaks or other major events which might influence HH behaviour will be documented in a process evaluation. The primary outcome measure will be the observed hand hygiene compliance rate, measured at baseline (T1) and after 6 (T2), 12 (T3) and 18 (T4) months. As a secondary outcome measure the prevalence of HAI will be measured at the same time points. At each time point data identifying process indicators will be collected. The process evaluation will also include determinants of implementation, namely the social-political context, the organization, the adopting person (i.e. the HCW), the innovation itself and the facilities needed for implementation [The ACCOMPLISH package will be tested in a two-arm cluster randomized trial in 16 hospitals in the Netherlands (eight intervention and eight control) in one intensive care unit (ICU) and one surgical ward per hospital. In eight hospitals entation . See figA multicenter study of 16 hospitals in the Netherlands.The sample of 16 hospitals will include university teaching hospitals, large general hospitals, small general hospitals and non-academic teaching hospitals and will de randomised at hospital level. Target population: nurses and physicians with direct patient contact working in the ICU or surgical ward at the participating hospitals in the Netherlands.ACCOMPLISH is evidence and theory based, by following a planned stepwise behavioural research approach to intervention development . It focuThe ACCOMPLISH package includes changes to the physical and social environment, performance feedback & goal setting, training and action planning , as part of the training in which HCW will be assisted in making concrete plans and formulating solutions to the everyday problems they encounter concerning HH, using if-then plans. Concretely, it will involve formulating plans how and when they intend to perform hand hygiene , as has been successfully used in other areas of public health  and moreIn order to increase the chances of successful implementation, an extensive communication strategy will be put in place prior to the start of the intervention period.In each hospital contact persons will be appointed: the Infection Control Practioner (ICP), who will act as a coordinator for all activities, and also as the primary contact between the researchers and hospital. Other contacts will include medical staff, nurses and managers form each hospital, to stimulate active participation in the study by all groups.At the start of the intervention a festive kick-off meeting will be planned in the participating units, in which HCW will be informed about the intervention activities. Special attention will be paid to the reasons for a process evaluation during the study. HCW will be instructed as to how they can signal when a component of the intervention is interfering with their work activities, so that effective measures to solve these problems can be set in motion swiftly. Twelve months after the start of the intervention post intervention measurements will commence.After the post-intervention measurements, the ACCOMPLISH package will be adjusted for all process evaluation information gathered and will be offered to the control hospitals. Based on experience gained from the study, they will receive instruction on how to implement the package.The compliance rate is operationalised as the number of HH practices divided by the number of opportunities for HH according to the international guidelines of the WHO , which sAlthough some HCW may be aware of the true nature of the study, most will revert to their routine quickly, reducing the Hawthorne effect . In addihttp://www.prezies.nl/). This module is comparable to CDC surveillance methods (http://www.cdc.gov/HAI/surveillance/monitorHAI.html). Point-prevalence will be measured at T1, T2, T3 and T4 in all experimental and control units, in a given time slot (two week period). All prevalence data will be collected by a qualified ICP, who will be trained before commencement of the study. At each data collection point two point-prevalence measurements will collected, and the data of the two measurements averaged to adjust for fluctuations.The prevalence rate is operationalized as the number of HAI per 100 patients. This will be assessed with the PREZIES module of the national surveillance system of HAI during the 18 month intervention period , costs  and savings  will all be collected in this study. Data on treatment  and follow-up consultations will be collected retrospectively from (electronic) patient charts and hospital administrations in a case-control study . From each of the 4 strata  1 hospital will be selected for the case control study, and from this hospital 15 cases will be selected by the local ICP. Fifteen controls will be matched by characteristics such as age, sex and diagnosis on admittance. Since cost data per patient (but not per day care) are typically highly skewed, nonparametric bootstrap techniques will be used for calculating the differences in distributions of the direct medical costs. The data will be extracted from the patient files by a researcher using an anonymous data-collection form. Information will be collected on, among others, length of hospital stay, length of ICU stay, number of days on artificial ventilation, number of intravasal or urinary catheters placed and on antibiotic use.To measure the economic impact of improved hand hygiene cost-effectiveness will be assessed by calculating the incremental cost-effectiveness ratio, defined here as the costs for the intervention (minus savings) divided by the difference in prevalence of HAI between the intervention and control group. Costs and effects will both be discounted with a 3% ratio. A sensitivity analysis will be performed to demonstrate the sensitivity of the results to changes in improved compliance under influence of process indicators (barriers and facilitators). Prognostic modelling will be used for a limited set of predictors of outcome .Compliance: Compliance rates are expected to increase from 20% at baseline to 50% twelve months after the intervention. Assuming 120 observed opportunities for compliance per ward per measurement moment and a 10% heterogeneity between wards, a power of 93% was calculated with two-sided alpha = 0.05 for this design. In each hospital one ICU and one surgical ward will be included in the study.Prevalence of HAI: 8568 patients are expected to be included in this study. Based on PREZIES data a baseline prevalence of HAI of 12% in the surgical ward and of 25% in the ICU is estimated . ApproxiRandomisation will take place at hospital level by means of computer generation. Therefore in each hospital both wards will be included in the same arm of the trial.Process indicator questionnaires: These will be analysed with multivariable linear regression analysis, with self-reported compliance  as primary outcome variable. The determinants included in the questionnaire will include the socio-political context, the organization, the adopting person or HCW, the innovation and the facilities needed to implement the innovation . FurtherRandomised controlled trial: The effect of the intervention strategy on the overall compliance rate among HCW , and on prevalence of HAI will be assessed. Multilevel analysis will be performed to compensate for the clustered nature of the data (HCW are clustered within wards), using mixed linear modelling techniques.In this paper the ACCOMPLISH intervention study has been outlined. This study is the first RCT to investigate the effects of a HH intervention programme on the number of HAI, and the first to investigate the cost-effectiveness of such an intervention. Furthermore, this is one of the first interventions tailored to the different behavioural determinants of physicians and nurses.Strengths of the study are number of participating centres, the cluster randomized controlled trial design and the length of follow-up . Furthermore, the inclusion of not only a process measure (compliance) as outcome, but also the number of HAI and the cost-effectiveness study add to the strength of the study design. The limitations of the study are the use of prevalence rather than incidence data and the fact that that not all process evaluation instruments have been validated.The results of the study will contribute to the body of evidence on influencing HH and shed light on the cost-effectiveness of such measures from a societal perspective. In addition, if the ACCOMPLISH package proves success in improving HH compliance and lowering the prevalence of HAI the package could be disseminated at national and international level.The study protocol has been approved by the Medical Ethics Board of the Erasmus University Medical Centre in Rotterdam. In the Netherlands is it not necessary to collect informed consent from healthcare workers before observing their behaviour, although all participating wards were informed about the study prior to commencement.The authors declare that they have no competing interests.VE, EB, and MV conceived of the study and its design and drafted the manuscript. ES, PE and SP participated in the design of the study and revised the manuscript. AH, AO, MB, EB and JR revised the manuscript for important intellectual content. All authors read and approved this final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/11/721/prepub