Implementation of the Quality by Design (QbD) approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE) as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM) is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN) in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms. The recent regulations from federal agencies, to apply the Quality by Design approach in pharmaceutical development, have compelled researchers in pharmaceutical industry to employ design of experiments (DoE) during product development. The benefits of DoE in product and process optimization are evident; it is the most convenient way to link the drug and excipient attributes and the process parameters to the Critical Quality Attributes (CQAs). Furthermore, its usage is necessary in defining and controlling Design Space . Design In the 1980s the use of DoE, especially factorial design, was generalized in the development of solid dosage forms. Statistical tools allowed the determination of critical process parameters of complex processes, as well as improvement and optimization of formulations .y is response and x1, x2, …, qx are inputs. Among diversity of DoE techniques, Response Surface Methodology (RSM) is the most frequently used optimization technique , which ai.e., mechanistic) link between inputs and outputs, describe their relationship. However, since the relationship between inputs and outputs is often more complicated, the observed model sometimes results in poor estimation of optimal conditions [This equation presents a mathematical description of a relationship between inputs and output. The advantage of this empirical model is that one can easily, without knowing true  and the output(s) often requires non-linear modeling techniques that are advanced in comparison to DoE approaches. DoE models are polynomial, whereas machine learning offers a diverse variety of classification and modeling techniques. i.e., synaptic weights), are used to store the knowledge [Artificial neural networks (ANNs) are computer programs, which recognize patterns in a given collection of data and produce model for that data. It emulates brain in two respects: (1) knowledge is acquired by the network through a learning process ; and (2) interneuron connection strengths  or “unit”), which takes one or more inputs and produces an output. At each neuron every input has an associate weight  and learning algorithm .In etc. [ANN is defined by the following parameters: (1) interconnection pattern between different layers of neurons; (2) the learning process for updating the weights of the interconnections; and 3) activation function that convert a neurons input to its output activation. Activation functions that are used in ANNs are sigmoidal, piecewise linear, step, Gaussian, exponential, logistic sigmoidal,  activatiTraining of the network is in fact the “learning process”. It could be divided into: (1)supervised learning: when network weights are adjusted using the known data, split into input-output pairs. Supervised learning is the most commonly used training method for ANNs and will be further explained in this paper;(2)unsupervised learning: it refers to the problem of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution;(3)reinforcement learning: which differs from standard supervised learning in that correct input/output pairs are never presented.i.e., network training), some of the interconnections are strengthened and some are weakened, so that a neural network will output a more correct answer [pio is the output predicted by the network of ith output node from pth sample; pit is the training  output of the ith output node from pth sample; N is the number of the training samples and M is the number of the output nodes. Training of an ANN is actually adjustment of weight values in order to obtain the best nonlinear relationship between parameters used as inputs and outputs of the network. At the beginning of the training process, weights between the neurons have random values. During the training phase input/output data pairs are presented to the network, and the network searches for the input-output relationships. One cycle of input-output presentation to the network is called an iteration (epoch). Process of the network training can be thought of as search for the optimal weights values that successfully convert inputs to outputs through sometimes numerous iterations. This process is often called convergence . During t answer . Once tht answer :(2)wheSSE decreases indefinitely with increasing number of hidden nodes or training iterations (epochs) [SSE for the training set decreases rapidly due to learning, whereas its subsequent slower decrease is attributed to memorization or overfitting (if the number of training cycles or hidden nodes is too large). In the case of test data set, SSE decreases initially but subsequently increases due to memorization and overfitting of the ANN model [In the process of the network training, the progress of change of error for both training and test data sets are evaluated simultaneously. In the case of training data set, (epochs) . InitialNN model . It is tNN model . Steps iIt is important to emphasize that ANNs work as “black boxes”. It is not possible to see the mathematical relationship between inputs and the output. Generally, ANNs could be divided into static ANNs and dynamic ANNs. Differences between static and dynamic networks are in the way in which signals are transmitted through the network.In order to obtain relevant and reliable results by using ANN models, it is recommended that the number of experimental runs is 10 times greater in comparison to the number of inputs; or, if this is not feasible, at least two to three times the number of inputs . It is, There are numerous types of static neural networks. The most frequently used static neural network in pharmaceutical development is the multilayer perceptron network.feedforward artificial neural network model that maps sets of input data onto a set of appropriate outputs. An MLP consists of multiple layers of nodes in a directed graph, with each layer fully connected to the next one  are the weights of the connections between the input and hidden units, and between the hidden and the output units, respectively. hb and ob are biases of hidden units and output units, and hf(·)and of(·)are hidden and output functions respectively [i.e., they can be thought of as function intercepts). Multilayer perceptron (MLP) is the simplest neural network architecture, that is a next one . Except lgorithm ,8. For aectively . Biases Generalized regression neural network (GRNN) was first introduced by Specht . It modei.e., radial units (neurons) can be optimized and usually it corresponds to the number of the training samples. Regression layer has one unit (neuron) more than the output layer and their activation function is linear. One extra unit (in comparison to the output layer) is used for calculation of the probability density whereas remaining units are used for calculation of outputs. In the output layer a specialized function is applied where the calculated outputs of the regression layer are divided by the probability density [Radial layer is used to perform clustering on the known, training data. Clustering algorithms usually used for training of this layer are sub-sampling, K-means or Kohonen . The num density .D between the training sample and the point of prediction, is used as a measure of how well the each training sample can represents the position of prediction, using the standard deviation or the smoothness parameter, σ [Fundamental equation of GRNN can be presented as:meter, σ .(5)The advantage of the GRNN is that its training is much faster compared to MLPs.Dynamic (or recurrent) networks have the ability to store data and elaborate it in time. That is the advantage of dynamic neural networks (compared to the static networks). Every input is analyzed as a function of the previous input. The network remembers past inputs; therefore, the current output is integration of past inputs and current response of the system .Connections between the neurons can be set up to have a memory which is very important for dynamic networks. The order of the memory says how many time steps the signal will be delayed. Treatment of dynamic data requires this kind of temporal dependencies of signal channeling. Network topology (architecture), together with the control system for time delay of the signal, forms a complete system. Correction of weights in the dynamic neural network is somewhat more complicated, in comparison to static neural networks. It is possible to use the technique called backpropagation through time where the backpropagated signal is buffered and reversed which enables getting forward and backpropagated signals synchronized in time . μ can be adapted using the usual algorithms. An important class of memory filters is based on the (non-dispersive) delay operator. Gamma memory is actually a combination of Tapped-Delay-Line (TDL) and a simple feedback neuron. So, the signals kg(t) at the taps k in time t of the Gamma memory are convulsions of the impulse response kg tap k [.Gamma memory is a specific short memory recurrent structure which preserves temporal information about the system. Distinctive feature of gamma memory is the number of taps-number of signal delays. For a given number of taps memory remembers previous system states and integrates them with current ones. From the point of view of signal transmittance Gamma memory can be seen as recursive low-pass filter  which acts as an infinite response filter. It is ideal for adaptive systems since its interpolation weight gk tap k .K-th order tapped delay line, the memory structure for Time Delay Neural Network (TDNN) [K-output filter with impulse responses gk(t) = d(t − k). A k (TDNN) , can be i.e., −1). Over time, the network stores long term memory structure in its feedback (recurrent) and regular connections, whose weights are adjusted during the training [Depending on the way that signals are sent back in the neural network, partial or full recurrence exists. Partial recurrence occurs, for example, when recurrent connections are sent from the hidden layer back to itself. In the fully recurrent networks the final output (of the network) is sent back into the network. If connections between the neurons are set up to have a memory then the order of the memory says how many time steps the signal will be delayed  is considered as a special kind of feed-forward networks that has additional memory neurons and local feedback ; therefoi.e., its outputs are equal to 0. Then, when the activation function in the hidden layer is applied for the first time its outcomes are sent to output and copied layer. During the second training cycle, the hidden layer receives inputs from both input and copied layer which are then combined and analyzed simultaneously. The process is repeated for the next training cycles so that each time the copied layer contains values of the outputs of hidden layer from the previous training cycle. This type of signal recurrence is recognized as a one-step time delay [kx and ky are networks primary input and output; ihw, jhw and how  are the weights of the connections between the input and hidden units, between the copied and the hidden units and between the hidden and the output units, respectively. hb and ob are biases of hidden units and output units, and hf(·)and of(·)are hidden and output functions respectively. The weights of the copied layer determine the impact of signal recurrence on the networks output. Essential characteristic of Elman neural network  is the fme delay . The alget al. in 1991. They modeled the in vitro release characteristics of drugs in matrices prepared from various hydrophilic polymers [A pioneer work in the field was a paper published by Hussein polymers . Neural Chen and coworkers  studied et al. [In modeling of a roller-compaction process, Turkoglu et al.  varied fet al. [f2).Optimization of theophylline release from controlled release tablets was performed according to desirable drug release profiles, based on the plasma concentration profiles of theophylline predicted by the pharmacokinetic analysis in humans . Experimet al.  with optIn a number of papers, ability of ANNs to predict controlled release profiles of a drug was compared with the response surface methodology ,26. In gGeneralized regression neural network was applied in optimization of formulation of aspirin matrix tablets with Eudragit RS and Eudragit L, as matrix forming materials ,27. The Dynamic neural networks were for the first time applied in the prediction of drug release profiles from controlled release formulation in 2002 . Drug re2 factorial design for network training and two for network testing). Network topology was chosen according to the flow diagram presented in Eleven formulations were prepared . The next step was to change the network type and to evaluate the drug release prediction using dynamic neural networks. Gamma memory one layer dynamic neural network was applied (GMDNN)  . Gamma mFrom GM signal is transferred to the first function layer, then to the weight layer and subsequently to the second function layer. Delta terminator compares two signals: one comes from the data source and represents real, observed outputs; whereas the second signal comes from the second function layer and represents outputs predicted by the dynamic system. The number of inputs and outputs in weight and function layers, as well as tapped delay line, were optimized using Monte Carlo simulations. As in the case of model developed by MLP, the obtained model does not predict drug release successfully. GM predicted shifting of profiles towards higher percentage of drug released after second hour of study, which was not confirmed experimentally. Topology of the recurrent one layer dynamic neural network (OLDNN), which was next used in the training, is presented in Most of the networks elements were the same as in previously described GMDNN. From the first data source inputs are transferred to the first weight layer. The first weight layer has 2 inputs and 17 outputs. The number of its outputs was optimized using Monte Carlo simulations. OLDNN is a partially recurrent network since the recurrent connections are sent from the hidden layer back to itself (first function layer). In the fully recurrent networks the output is sent back into the network. From the first, recurrent function layer, signal goes to the second weight layer that has 17 inputs and 10 outputs. Then the signal goes to the second functional layer, which is an ordinary function layer. Once again, the network was trained and afterwards tested. The model obtained using OLDNN enabled adequate prediction of drug release profiles, with similarity factors for test formulations 64.01 and 77.54, respectively. Even though results obtained using this network were better, there were still shifting values of predicted drug release profiles compared to experimentally observed values at later time points. Therefore, in the next study , the staIn INPUTS) in production of modified release preparation (regardless of whether they were matrix tablets or multiple unit systems) and drug release profiles or dissolution rate constants (OUTPUS). In the training session, prediction capability of the trained networks was tested comparing predicted and experimentally observed drug release profiles, using the similarity factor as parameter and/or correlation coefficient.The mutual idea of presented studies could be described as follows: they attempted to create a relationship between formulation and/or process variables (in vitro dissolution profiles of controlled release tablets. The ANNs developed from the four software programs were validated by predicting the in vitro dissolution time-profiles of each of the 19 formulations, which were excluded from the training process. Although the same data set was used, the optimum ANN architectures generated from the four software programs was different. Nevertheless, it was concluded that the four programs provided reasonable predictions of in vitro dissolution profiles for the data set employed in this study.To date, there are many commercial, as well as freely available computer programs that can be used for ANN modeling. It should be pointed out that modeling of the same data through different software will give a little bit different results. An interesting study has been published by Chen and coworkers . They coetc. Decision tree methodology, a nonparametric statistical technique, used for classification problems, could be successfully used in characterization of controlled release problems, as was reported. The main constraint of this methodology is that it requires a large data set, because inductive generalization given in the form of a decision tree is dependent on a sufficient amount of data [So far, a lot of work has been done concerning application of ANNs in optimizing drug release profiles from controlled release formulation. As neural computing is progressing, current trends and recently published articles ,41,42,43 of data . There have been very few attempts to use genetic programming in the optimization of pharmaceutical formulations ,43,44. Pi.e., understanding of Design Space and Quality Risk Management for modified release formulations. However, ANN models are well established and could be used in implementation of Quality by Design concept,


Textile-based transducers are an emerging technology in which piezo-resistive properties of materials are used to measure an applied strain. By incorporating these sensors into a sock, this technology offers the potential to detect critical events during the stance phase of the gait cycle. This could prove useful in several applications, such as functional electrical stimulation (FES) systems to assist gait.We investigated the output of a knitted resistive strain sensor during walking and sought to determine the degree of similarity between the sensor output and the ankle angle in the sagittal plane. In addition, we investigated whether it would be possible to predict three key gait events, heel strike, heel lift and toe off, with a relatively straight-forward algorithm. This worked by predicting gait events to occur at fixed time offsets from specific peaks in the sensor signal.Our results showed that, for all subjects, the sensor output exhibited the same general characteristics as the ankle joint angle. However, there were large between-subjects differences in the degree of similarity between the two curves. Despite this variability, it was possible to accurately predict gait events using a simple algorithm. This algorithm displayed high levels of trial-to-trial repeatability.This study demonstrates the potential of using textile-based transducers in future devices that provide active gait assistance. Foot drop is currently estimated to affect approximately 20% of stroke survivors . With thThere are a number of assistive devices which are designed to minimise the effect of foot drop by maintaining the foot in appropriate orientation during gait. These approaches can be considered as either passive devices, such as ankle foot orthoses, or active devices, such as functional electrical stimulation (FES)  or intelMost current FES systems obtain gait phase information from a footswitch located in the heel region of the shoe. Although, with appropriate signal conditioning, accurate detection of heel strike and heel lift is possible, footswitches can be time consuming to set up, are prone to false event detections when the user weight shifts and reports have suggest that users dislike them . FurtherAnecdotal reports suggest that, despite training, a number of users of conventional FES devices locate their electrodes incorrectly, resulting in a poor foot response during gait. With the advent of electrode array-based stimulation systems ,18 that An alternative to either footswitches or inertial sensors is to derive information on gait phase from a sock incorporating a textile-based transducer. This approach offers a number of advantages. Firstly, it would be possible to integrate the stimulation unit into the sock and therefore eliminate the need for connecting wires and increase the ease of use to the patient. Secondly, the system would work without footwear and so would be more suited to indoor use. Finally, as the sensor measures the joint motion itself, rather than inferring events from foot loading or shank motion, it may be possible to obtain more detailed information on gait.Textile-based transducers consist of either yarns made from conductive elastic fibers or conductive coatings applied to elastic base materials. Both approaches utilise piezo-resistive properties to sense strain. Most previous work in this area has focused on either knitted fibre transducers  or smearTextile-based transducers exhibit a high degree of non-linearity in the relationship between resistance and deformation. One of the primary causes of this non-linearity is the viscoelatic properties of the textiles which results in a number of phenomena, such as velocity dependent resistance peaks, delayed recovery after rapid stretching and hysteresis. In previous applications, these effects have been overcome using either complex mathematical models  or machiThe textile-based transducer investigated in this study was a knitted resistive strain sensor. Knitted structures consist of stitches which are arranged in rows and columns and which are bound to the stitches above and below as shown in Figure In order to understand how the resistance of the knitted sensor changed in response to an applied strain, we measured the resistance of a sample undergoing repeated stretching and relaxing. Figure For the main experimental work each subject wore an instrumented sock on their left leg Figure . To ensust and 5th metatarsal heads. In addition, tracking markers were placed on the lateral aspect of the shank, calcaneous and dorsal aspect of the midfoot. Although previous studies have recommended using a shank marker plate with underwrapped bandage [In order to derive kinematic signals during walking, 3D data from a number of reflective markers Figure  were col bandage , pilot wTwenty subjects  were recruited into the study. The mean (SD) age of the subjects was 43 (18), mean (SD) height 171 (8) cm and mean (SD) weight 72 (12) Kg. Each subject provided written consent to participate and ethical approval was granted by the institutional ethics committee. Each subject performed ten walking trials, at their self selected walking speed, in both a sock-only condition and in a shod condition. Each trial consisted of approximately 15 steps, with trials being separated by approximately 40 seconds. The sock was not removed between the different trials and for the shod condition subjects wore their normal shoes. Synchronised kinematic and digitised sock voltage data was collected for each set of ten trials. Although gait event timings from consecutive gait cycles can be collected using footswitches, they can only be used during shod gait. For this study we wanted to investigate sensor output in both a sock-only and shod condition. Therefore force-plate data from two AMTI force platforms was used to collect kinetic data, allowing for identification of a single gait cycle for each trial.Kinematic data was processed by using the static calibration to calculate ankle joint centre and define segmental coordinate systems for the shank and foot. The 3D coordinate data for each trial was then used to calculate using Cardan angles. All kinematic calculations were implemented using Visual3D (C-Motion Inc) and the data for both sets of trials for all twenty subjects exported to Matlab for further processing.th order Butterworth filter) to remove measurement noise. The minimum in the kinematic signal corresponds to the point at which the ankle begins to plantarflex in preparation for toe off.In order to compare the kinematic data with the sensor data, the kinematic data was upsampled to 1500 Hz, matching the collection frequency of the sensor data. Two consecutive heel strikes were then identified from the two force platforms as the point at which the vertical component of the ground reaction force exceeded 5N. These points were then used to define the gait cycle data for both the kinematic and the sensor signal. HL was then identified as the minimum in the kinematic signal occurring just before toe off. In order to locate this minimum, the raw 3D coordinate data was low pass filtered at 6 Hz  was applied to both the sensor and the kinematic signal using a FFT filter. This allowed the variation between the two signals to be compared, irrespective of the signal means. Finally, to remove the effects of signal amplitude, the filtered sensor signal was scaled so that the peak-to-peak range matched that of the filtered kinematic signal. Example plots for two sock-only and two shod trials are shown in Figures In order to address our first research question, which was aimed at understanding the match between the kinematic and sensor signal, we used two separate measures to quantify signal similarity. These measures have been used previously to evaluate the accuracy of wearable sensors in the prediction of lower limb kinematics  and are 1. Pearson's correlation coefficient, r.ith angle and ith voltage data points in the kinematic and sensor curves after both have been scaled to have a peak-to-peak range of unity. The number of data points across the whole gait cycle is given as n.2. The normalised mean absolute deviation, Separate measures of signal similarity were obtained for the sock-only and shod conditions by averaging across the ten gait cycles .t = 0 (the first HS), to determine the time of HL, TO and the second HS. Although this was tested on the ten separate trials, it could potentially be implemented on a continuous sensor signal to identify consecutive gait events.For this proof of concept study we aimed to investigate whether a relatively simple algorithm could be used to identify the three gait events from the sensor signal. Although sensor data from each subject displayed similar features, these features occurred at different points in the gait cycle. It was therefore necessary to adjust the algorithm parameters separately for each individual subject. Our data showed that sensor output could sometimes be modified when shoes were worn. Therefore, algorithm parameters were adjusted separately for the sock-only and the shod conditions. As our dataset consisted of a single gait cycle for each trial, the algorithm was implemented as a forward search, staring from 1-a5a). Each of these parameters is depicted in Figure The final algorithm operated using the stages outlined below and required adjustment of five parameters (1) where the sensor signal is increasing and exceeds a preset threshold (1a). HL was then identified to be a fixed time offset (2a) from this point.1. Identify the first point (P1. TO was then identified to be a fixed time offset (3a) from this maxima.2. Find the first maxima after P4a) then find the next maxima. HS was then identified to be a fixed time offset (5a) from this maxima.3. Advance by a fixed time (1-a5a). were obtained from the first five trials of each subject/condition using an automated search algorithm. This analysed the maximal values of the signal over the initial stages of the gait cycle in order to determine the threshold 1a. It then calculated the mean values of 2-a5 arequired to accurately identify the three gait events. To ensure that the algorithm would work effectively with a raw voltage signal the four time offsets (2-a5a) were fixed in seconds, rather that gait cycle time. Once the values for the five parameters had been set using data from the first five trials, they were used to predict the three gait events for the final five trials. Algorithm accuracy was then calculated as the mean absolute deviation (in %gait cycle) between the predicted time and true time across the five trials. In addition, the standard deviation of the difference between the true and predicted time (%gait cycle) was used to capture the trial-to-trial repeatability in event prediction.The five parameters . For these data a more complex algorithm would be required.The algorithm developed to predict gait events was found to be accurate for HL and TO for both sock-only and shod conditions with mean errors across subjects ranging from 1-1.6% gait cycle Tables  and 3. EThis study was designed to establish the possibility of using a textile-based transducer to extract the salient features of ankle joint motion and derive information on gait phase during walking. The results demonstrated that the output of the sensor displayed the same features as the ankle joint kinematic signal. However, the exact match between these two signals varied considerably between individuals. Despite this variability, it was possible to accurately predict gait events using a simple algorithm which also showed good levels of trial-to-trial repeatability.The textile-based transducer examined in this study exhibited a number of nonlinear characteristics. Although it was possible to remove the effect of baseline drift using high pass filtering, preliminary characterisation demonstrated hysteresis in the relationship between resistance and strain. Despite this non-linearity, data from some subjects demonstrated a very close match between sensor output and the ankle joint kinematic signal Figures  &7. HoweTo investigate the possibility of using a textile-based transducer in future FES applications, we developed an algorithm for gait event detection which was based around two specific signal features. These were a rapid increase and peak around TO and a subsequent peak at the end of the gait cycle. The first of these two features corresponds to the rapid ankle plantarflexion which occurs just prior to TO. Our analysis showed that this feature exhibited high levels of step-to-step repeatability as demonstrated by the low standard deviations in the prediction accuracy of HL and TO. However, the larger standard deviations found for HS showed that the second feature, the peak at the end of the gait cycle, exhibited slightly lower levels of step-to-step repeatability.Previous studies have investigated the accuracy of using footswitches, accelerometers, gyroscopes and even neural sensors  to prediThere are a number of limitations to the current proof of concept which will need to be addressed if systems using textile-based transducers are to be used in future FES or other active gait assist devices. Firstly, the proposed algorithm locates HL and TO at specific times behind the occurrence of events in the sensor signal. This means that, although the algorithm works well for off-line processing, it would not be effective in a real-time system. Furthermore, the proposed algorithm requires a number of individual-specific thresholds to be set. Future work must now focus on algorithms which can automatically adapt to individual differences in sensor output and predict gait events from signal characteristics which occur before the required gait events. With more advanced approaches it should be possible to eliminate the need for manual adjustment of thresholds whilst still maintaining a level of computational complexity which could be implemented within an embedded controller. Another limitation of the study was that it was performed on individuals with normal gait patterns in a controlled laboratory environment. Clearly, future work must focus on patients with drop foot and establish the feasibility of using an instrumented sock in a real-world setting.In summary, our data showed considerable inter-subject variability in the match between the signal from an instrumented sock and ankle motion in the sagittal plane during normal walking. However, using a relatively straight-forward algorithm, we were able to predict three gait events to a high degree of accuracy with good trial-to-trial repeatability. Although more complex algorithms would be required, our results demonstrate the potential of using a textile-based transducers in future FES applications.The authors declare that they have no competing interests.SJP was involved in the design of the study, data collection and writing of the manuscript. LPJK and TD conceived the original idea, contributed to the study design and helped to draft the manuscript. MJM was involved in data collection, processing and in some aspects of the experimental design and EL and BF were involved in the development of the experimental set up including the design and manufacture of the instrumented socks All authors read and approved the final manuscript.


The authors wish to retract this article for the following reasons: On re-examination of the data, some errors were identified which contradicted some of the main conclusions of the work. In particular, an error in the calculation of ∆(-) values for miR-193a-3p and miRPlus E1245 was identified, which affects the results reported in Figure 6, and hence the data does not support the conclusions originally drawn regarding the E1245. In addition, the interpretation of the presented PCA results is inaccurate since there is no convincing evidence of large or consistent variation related to treatment effect.Since the experiments were performed only twice, there is no statistical support for the reported miRNA expression level changes during XMRV infection of human cell, and therefore the conclusion that XMRV infection modulates miRNAs in host cells cannot be reported with confidence unless supported by additional experimental validation.


Systematic numerical investigations of conformational motions in single actin molecules were performed by employing a simple elastic-network (EN) model of this protein. Similar to previous investigations for myosin, we found that G-actin essentially behaves as a strain sensor, responding by well-defined domain motions to mechanical perturbations. Several sensitive residues within the nucleotide-binding pocket (NBP) could be identified, such that the perturbation of any of them can induce characteristic flattening of actin molecules and closing of the cleft between their two mobile domains. Extending the EN model by introduction of a set of breakable links which become effective only when two domains approach one another, it was observed that G-actin can possess a metastable state corresponding to a closed conformation and that a transition to this state can be induced by appropriate perturbations in the NBP region. The ligands were roughly modeled as a single particle (ADP) or a dimer (ATP), which were placed inside the NBP and connected by elastic links to the neighbors. Our approximate analysis suggests that, when ATP is present, it stabilizes the closed conformation of actin. This may play an important role in the explanation why, in the presence of ATP, the polymerization process is highly accelerated. Actin is one of the most abundant proteins of the living cell. It builds part of the cytoskeleton and is involved in cell motility, transport inside the cell and muscle contraction In enzymes, binding of ligands and catalytic conversion reactions are often accompanied by conformational changes, so that pronounced mechanochemical motions take place inside these proteins  simulations. The difficulty encountered is that such motions are typically on the scale of milliseconds or longer, whereas, in full MD simulations, only the dynamics on much shorter time scales can be resolved. To overcome this limitation, various acceleration methods have been proposed  in many proteins There is one property of elastic-network models which makes them particularly appealing. Since all residues are pictured as identical point particles, irrespective of their actual chemical structure, and because interactions between the particles do not depend on the chemical nature of the corresponding residues, EN models turn out to be stripped of almost all chemical details. In this rough approximation, a protein is viewed as a mechanical object, i.e. as a complex elastic network. Therefore, by studying such models, one can investigate purely mechanical aspects of conformational protein dynamics, distinct from the chemical aspects. By using the EN approach, we have previously analyzed large-scale conformational responses of myosin-V to the application of forces to individual residues in its various functional regions, including the NBP Previously, conformational dynamics of G-actin was investigated through the normal-mode analysis using an all-atom description for the protein and its ligands Our aim was to systematically probe mechanical responses of the actin macromolecule and to understand internal organization of its dynamics. As we have found, two mobile actin domains are able to perform well-defined large-scale motions. They can come so close to each other that additional interactions between the residues from different domains develop; thus the actin can get locked in a metastable closed state. Furthermore, our detailed study of mechanical sensitivity of the molecule to application of various perturbations in the nucleotide-binding region has revealed that characteristic global domain motions can also be easily induced by application of only local perturbations to some selected residues in the NBP. Binding of nucleotides and the hydrolysis reaction lead to local mechanical perturbations in the NBP and, in this way, can induce characteristic large-magnitude motions of mobile domains. In the coarse-grained EN models, chemical aspects of ATP binding cannot be adequately resolved. In the future, more elaborate investigations combining the EN approach with local MD simulations may be needed to systematically study such processes. In the present study, such effects were approximately considered by placing a fictitious ligand dimer into the actual nucleotide binding pocket and by introducing elastic links between the ligand and the nearest residues. We have found that binding of a model ligand, imitating the ATP, can stabilize the closed state of actin and induce a transition to this state from the equilibrium open state of the molecule. This observation can be important for understanding the role of ATP in the polymerization process.In this study, actin is described in terms of the coarse-grained anisotropic network model As a reference state for the construction of the elastic network, the uncomplexed G-actin in the ADP-bound state (PDB ID: 1J6Z) was used In our simulations, positions of all residues were determined at each integration step and, therefore, complete information about conformational motions was available. This full data was used, e.g., when conformational snapshots were constructed or videos of characteristic conformational motions were generated. For concise characterization, we have additionally used a set of three order parameters followed in the simulations. Specifically, distances In the first part of our study, characteristic global motions of actin domains, shown in Generally, application of a static force induces rigid translations and rotations of the entire protein. To eliminate such effects, additional balancing forces were computed at each integration step and applied to the network. They were chosen in such a way that only global translations and rotations could be caused - and thus compensated - by them, without any internal deformations arising. This immobilization procedure was the same as in Ref. To generate static forces, for each residue a direction was randomly chosen and the force magnitude was randomly selected from the interval between When external forces were switched off, the network was undergoing relaxation back to its equilibrium conformation. The relaxation trajectories starting from different initial deformed states are displayed in Thus, we see that the two principal mobile domains of actin are able to perform large-magnitude motions characterized by substantial changes of the distances between S2 and S4, as well as of the dihedral angle between the two mobile domains. The displacements of residues, accompanying such motions, are large and the linearized description and the normal-mode analysis are not justified in this case . The slowest normal mode of the elastic network corresponds to the propeller-like twist of the two mobile domains which can be well characterized by the dihedral angle see . The secOur analysis based on full nonlinear equations of the elastic network indicates that, for some large-amplitude motions, the cleft separating subdomains S2 and S4 almost disappears, so that the residues belonging to opposite domains could come near one to another. In such situations, the EN model needs to be modified, as explained below.When an elastic network for a protein is constructed, distances between all pairs of residues in the equilibrium reference state are checked and elastic links are introduced whenever the distance between a pair is shorter than the cut-off length. Suppose now that some residues, well separated at equilibrium, come close when a perturbation is applied. If we want to follow the concept of the EN approximation, additional links connecting such residues would need to be introduced once they are close one to another. Such emergent (and breakable) links cannot be elastic, instead they should be described by a pair interaction potential which becomes flat as the distance between the particles increases. Hence, they would be effectively present only when the two particles are close one to another - and would disappear when the particles are far apart.To allow such bonds to emerge, we add into the EN model a set of breakable links between those G-actin residues from opposite domains which are connected by elastic links in the EN model of F-actin. Thus, the original EN model of G-actin is expanded by us through the introduction of five additional breakable links connecting pairs of residues 62–204, 63–203, 63–204, 66–203 and 67–203. The new links are described by truncated Lennard-Jones potential (6) with the interaction parameters given in the Taking the expanded EN model, global mechanical responses of the elastic network were examined and its relaxation trajectories were explored using the same procedure as described above for the original network. The results are displayed in Actually, not one, but two closed metastable states of actin can be discerned in Summarizing the results of our investigations, we conclude that large-amplitude propeller and scissor motions of the principal mobile domains can take place in the elastic network of G-actin. These motions are generic and the protein responds by them when random globally distributed perturbations are applied. Moreover, we find that, in the expanded version of the EN model, the protein can also be found in the closed stationary conformation which represents its new metastable state. A transition from the equilibrium reference conformation to this metastable state can be induced by applying appropriate perturbations to the network nodes.When nucleotides (ATP or ADP) are bound to actin, this leads to local mechanical perturbations in the NBP. This pocket is located at the bottom of the cleft separating subdomains S2 and S4 see . It inclOur attention was focused on the perturbations corresponding to the transition from the ADP- to the ATP-bound states of G-actin. The nucleotide-free state is less relevant in the context of actin polymerization and it was not analyzed here. As the reference conformation, the ADP-bound state (PDB ID: 1J6Z) was always taken. When ATP is instead bound, this means that the phosphate Our analysis was performed similar to the previous investigation for myosin-V To probe mechanical sensitivity of each residue, 200 simulations have been performed for each probed residue. In each of these simulations, a static force with randomly generated orientations and the magnitude The results of the sensitivity analysis are presented in In the above sensitivity analysis, the original EN model was employed. As we have shown in the previous section, this model can be, however, expanded by including a set of breakable links which become effective when subdomains S2 and S4 come close one to another. Domain motion responses to the application of forces to sensitive residues in the NBP region have been further analyzed in the framework of the expanded EN model.For the detailed analysis, only one sensitive residue in each of the three loops was chosen. Similar behavior could be expected if its neighbor in the same loop was instead selected. Thus, we focused on the responses induced by application of perturbations to the group of three residues: 16 (in G-loop), 73 (in H-loop) and 159 (in G-loop). Static forces were applied, at the same time, to all three residues in the group. The magnitude of each force was randomly chosen between Comparing Thus, a small number of sensitive residues lying in the NBP region and belonging to three different loops could be identified. Applying appropriate static perturbations to a group of three such residues, each from a different group, a transition from the open to the closed state of G-actin could be reproduced. Remarkably, local deformations in the NBP region were able to spread over the elastic network and become transformed into large-amplitude global motions of mobile domains.Since EN models are coarse-grained and entire residues are replaced by point-like particles, the detailed atomic structure of ligands (ATP or ADP) cannot be resolved in this approach. In this section, the ligands will be treated as additional particles. As it turns out, even this greatly simplified phenomenological description allows us to understand some important aspects of ligand-induced conformational changes.The structure of G-actin with ADP is experimentally known and it was already used by us to construct its elastic network. Below, ADP is explicitly included into the EN description. We treat it as a single particle and put this particle into the equilibrium nce  see . The natWhen an ATP molecule is bound to actin, we model it as a dimer consisting of two particles . The firIn contrast to ADP, residing entirely on one of the mobile domains, the phosphate interacts with the residues from different mobile domains cf.  and, thuBinding of the ATP, imitated in our simple phenomenological model through the introduction of an additional The hydrolysis reaction and the release of phosphate are roughly imitated in our model by cutting all links which connect the Binding of the artificial ligand leads to a new, unique equilibrium position. 100 relaxation trajectories in the presence of the ATP ligand are shown in So far, effects of thermal fluctuations have been excluded from our analysis. Such effects may become, however, important if metastable states are possible. If thermal fluctuations are strong enough, they can induce transitions between stable and metastable states, so that all of them can be visited by the system.The effects of thermal fluctuations can be taken into account by introducing additional random forces with appropriate intensities into the equations of motions see . IntegraAlready the rough modeling employed in this section reveals some important effects of the nucleotides. Binding of ATP can directly lead to flattening of the protein and closing of the cleft between its inner and outer domains. While the ATP-free actin shows the tendency to switch between its two equilibrium states, the ligand can stabilize the closed conformation of actin and, furthermore, stiffen the macromolecule. Note that the structural details of the ligand-induced closed conformational state may depend on the parameters of interactions between the ligand particle and the NBP residues. Moreover, the dimer model of ATP used in the above simulations represents only a simple approximation for the actual ATP molecule. Therefore, the results of our numerical investigations including the ligand should be viewed as only providing a demonstration that a transition to a stable closed conformation can be induced by ATP binding. This prediction can be further tested by experiments and special MD simulations.In our study, the attention was focused on purely mechanical aspects of actin dynamics. With this purpose, a greatly simplified dynamical model of this molecule was considered where all residues, independent of their chemical differences, were treated as identical particles connected by identical elastic links. In addition to the elastic links, the mechanical model also included a small number of breakable links that become established when pairs of residues come sufficiently close and break down at large separations. The information about the chemical structure of G-actin was effectively encoded only in the architecture of the elastic network, determined by the experimentally known equilibrium conformation of the molecule.Remarkably, this greatly simplified model has allowed us to understand various aspects of intramolecular conformational motions in actin monomers. The model shows that the mobile inner and outer domains of actin are able to perform large-amplitude propeller twist and scissor-like motions, earlier revealed by the normal-mode analysis for small deviations from the equilibrium state We have found that, similar to myosin To demonstrate that ligand (i.e. ATP) binding can indeed induce large-scale conformational changes, we have imitated the ligand by a dimer; one of the particles, corresponding to phosphate, has attractive interactions with the sensitive residues inside the NBP. Previously, a similar ligand description was employed when cyclic operation of the molecular motor hepatitis C virus helicase was analyzed In the hierarchy of coarse-grained models proposed to describe actin monomers and filaments see , the empThe ATP-induced transition to the closed conformation of actin can play an important role in the explanation why, in the presence of ATP, the growth of actin filaments is strongly accelerated. The closed conformation of G-actin, stabilized under ATP binding, is not identical to that of the filamentous F-actin. However, in both of these conformations the cleft separating the upper mobile subdomains S2 and S4 is strongly reduced, so that a better fit and higher affinity to the actin filament may result. Another effect of binding of ATP, observed in our model, is the increased stiffness as compared to the ADP state see . This isIn recent experiments In the present study, conformational dynamics in actin monomers, known as G-actin, is investigated in the framework of the anisotropic EN model The total elastic energy of the network is given by the sum of the energy stored in its elastic linksIn our study, the cutoff length of G-actin in the anisotropic EN has been determined by following the arguments by Atilgan et al. On the considered time scale of milliseconds, inertial effects are negligible and conformational dynamics is purely dissipative The dynamics of the EN is followed by integrating Integration of relaxation trajectories has been continued until a stationary state was reached. The numerical criterion was that the sum of the distances between the centers of mass of four actin subdomains and the dihedral angle have ceased to change by more than Generally, application of external forces can induce rigid rotations and translations of the entire elastic object. To eliminate such global motions, some immobilization procedure needs to be employed. The easiest immobilization solution would have been to pin three network particles. However, the disadvantage of such simple method is that, depending on the choice of the pinned particles, various internal deformations of the network may arise. As an alternative, we have previously proposed a computational immobilization method which does not lead itself to network deformations The equations of motions (3) of the EN model are nonlinear because distances are nonlinear functions of the coordinates. EN models are often used for the normal-mode analysis Assuming that only one of the normal modes is excited, the motions of network particles are given by The two slowest normal modes for the elastic network of G-actin have been computed and the respective motions are displayed in  domains . The secThe linearized The equations of motion (3) depend only on relative distance changes. Therefore, they are always invariant against rigid translations and rotations of the entire elastic network. This implies that the linearization matrix Studying the response to external forces, we notice that the distance ably see , i.e. reIn contrast to regular elastic links, breakable links are described by the truncated Lennard-Jones potentialWhen breakable bonds are added, equations of motion (3) should be modified by including additional forcesOur choice of the interaction parameters for breakable links was based on the requirements that (i) this potential becomes flat when distances between the residues exceed the cutoff length of 2 the interaction potential (7) near the equilibrium distance was by a factor of Comparing the experimentally known conformations of the globular G-actin and the filamentous F-actin 2. Supporting We have also repeated some of the simulations using soft breakable links with An important actin binding protein is the bovine pancreatic deoxyribonuclease (DNase I). Complexed with DNase I, a structure of the actin monomer with ATP bound (PDB ID: 1ATN) was experimentally resolved In our numerical investigations, a greatly simplified phenomenological description of ligands ADP and ATP as a point particle and a dimer has been employed. ADP was modeled by introducing an additional node in the network whose equilibrium coordinates were chosen to coincide with the position of the omain S4 . The natWhen ATP was bound, it was modeled as a dimer which consists of two particles, ADP and work see . It is kIn the final part of our study, some effects of thermal fluctuations are considered. Neglecting hydrodynamic effects, this is done by introducing appropriate random forces into the dynamical equations, i.e. by writing them asOur simulations with thermal noise are used to obtain a statistical distribution of the interdomain distances Figure S1Patterns of relaxation trajectories for the elastic network of G-actin with the soft breakable links of strength 2 in absence (A) and in the presence (B) of the ATP ligand. Å 100 relaxation trajectories starting from the initial conditions obtained by application of random static external forces to three sensitive residues in the NBP region are shown. The orientations of forces are random and their amplitudes are drawn at random from the interval between (TIFF)Click here for additional data file.Movie S1Visualization of the motion corresponding to the lowest eigenmode of the EN model of G-actin: the propeller-like twist of outer and inner domains with respect to each other.(MOV)Click here for additional data file.Movie S2Visualization of the motion corresponding to the second lowest eigenmode of the EN model of actin: the scissor-like opening and closing of the cleft between subdomains S2 and S4.(MOV)Click here for additional data file.Movie S3Ligand-induced conformational motions. The initial state is the equilibrium conformation of G-actin with ADP bound. Introducing an additional network node which corresponds to (MOV)Click here for additional data file.


Energy-based surgical scalpels are designed to efficiently transect and seal blood vessels using thermal energy to promote protein denaturation and coagulation. Assessment and design improvement of ultrasonic scalpel performance relies on both in vivo and ex vivo testing. The objective of this work was to design and implement a robust, experimental test matrix with randomization restrictions and predictive statistical power, which allowed for identification of those experimental variables that may affect the quality of the seal obtained ex vivo.The design of the experiment included three factors: temperature (two levels); the type of solution used to perfuse the artery during transection (three types); and artery type (two types) resulting in a total of twelve possible treatment combinations. Burst pressures of porcine carotid and renal arteries sealed ex vivo were assigned as the response variable.The experimental test matrix was designed and carried out as a split-plot experiment in order to assess the contributions of several variables and their interactions while accounting for randomization restrictions present in the experimental setup. The statistical software package SAS was utilized and PROC MIXED was used to account for the randomization restrictions in the split-plot design. The combination of temperature, solution, and vessel type had a statistically significant impact on seal quality.The design and implementation of a split-plot experimental test-matrix provided a mechanism for addressing the existing technical randomization restrictions of ex vivo ultrasonic scalpel performance testing, while preserving the ability to examine the potential effects of independent factors or variables. This method for generating the experimental design and the statistical analyses of the resulting data are adaptable to a wide variety of experimental problems involving large-scale tissue-based studies of medical or experimental device efficacy and performance. Blood vessel transection and sealing requires that tissue segments be held together with an immediate and temporary bond that prevents leakage and holds long enough to allow the adjacent tissue margins to re-grow, heal, and regenerate structural connections through the natural healing process. Energy-based vessel sealing methods use no exogenous materials to accomplish vessel closure, but rely on acute vessel compression, thermally induced tissue injury, and tissue healing to generate the seal. This process limits inflammation and eliminates concerns with biocompatibility and immunogenicity associated with using an exogenous material such as sutures, staples, or bonding adhesives. In addition, energy-based methods are easier and faster to use than materials-based methods, especially for endoscopic surgeries, resulting in a decrease in operative time and surgical invasiveness, and reduced infection rates. Ultrasonic scalpel technology has been used extensively in fine tissue dissection, hemostasis, vessel closure and transection -3. The o® software was used to record the data.Porcine vessel sealing and burst pressure testing were performed as previously described . BrieflyThe design included three factors: The temperature of the solution  or 37°C), the type of solution that was utilized ; "Plasma protein" enriched buffer ) and the type of vascular tissue used .A number of preliminary experiments (n = 4-12) were carried out as described above. The data were used to perform basic sample size computations to aid in determining the sample size for the main experiment. All computations were based on comparisons of two means. To determine the sample size necessary to detect a significant difference between the means of two factors at a desired significance level and with given power, the observed sample means and the pooled standard deviation corresponding to the two factors from the pilot were computed. The Analyst application in SAS was utilized to compute the sample sizes. The input consisted of the two observed sample means, the pooled standard deviation, the desired significance level and the desired power for the test.The statistical package SAS was used to carry out the data analysis. The factors temperature, solution and vessel type, as well as "Session" (with 48 levels) were declared as class variables for the analysis of variance. To incorporate the information about the randomization restrictions discussed above, PROC MIXED was used, and Session was declared as a random effect as suggested in   and their interactions. Using the significance level of 0.05, a p-value of less than 0.05 indicates a  significant main effect or interaction effect.The objective of this work was to design and implement a robust, experimental test matrix with randomization restrictions and predictive statistical power, which allowed for identification of those experimental variables that may affect the quality of the seal obtained ex vivo. A number of pilot studies using small sample sizes were initially performed to determine which variables likely contribute to seal quality and therefore should be included in the test design. The factors initially studied included temperature of the solution, type of infusion solution, vessel type, and infusion flow rate during blood vessel transection/sealing. The response variable in each case was the burst pressure (mmHg) at which the seal failed.In addition, the pilot data was used to estimate the sample size needed to obtain significant results (at a significance level of 0.05) with a power of 0.9, or the probability that a test with the specified assumptions  would correctly reject the null hypothesis when the alternative hypothesis was true. Given the minimum desired detectable difference, as well as (an estimation for) the standard deviation and the desired significance level of the test, the sample size that was necessary for a two-sample test to have a given power was computed. The sample size computations were based on pairwise comparisons, in which each factor level was examined as to its potential effect on burst pressure. In some cases, it was clear that these comparisons were not trending towards showing significant differences, and the corresponding factors (or factor levels) were therefore not included in the experimental design. However, those comparisons that were not statistically significant in the pilot study due to small sample size but showed a trend toward predicted statistical significant differences with an increased sample size  were included in the experimental design. In addition, the interaction effects of the included factors (comparisons between combinations of two or three factors) were deemed important to assess.Based on the pilot studies, the effects of solution temperature, solution type, and vessel type were chosen to be studied. Taking into account the sample size computations made from the pilot study and the practical limitations imposed by the experimental test setup, the size of the matrix and experimental design was determined. A balanced design for the test matrix was selected where each treatment combination was observed the same number of times. The experiment included three factors, with a total of 12 possible treatment combinations. While the vessel type could be varied at random from run to run, the temperature and solution were hard-to-change factors. This physical reality forced randomization restrictions on the design. The experiment consisted of 480 runs (40 per treatment combination), divided into 48 sessions of 10 runs each. For each session, one of the six combinations of levels for temperature and solution was chosen (each combination was used for eight different sessions) and held fixed. The assignment of the treatment combinations to the sessions was carried out at random. This assignment was done in Excel. Using a column for temperature and a column for solution, eight copies of the treatment combinations of temperature and solution were generated. A third column contained random numbers. The data in the three columns was then sorted in ascending order of the random numbers to create a random order of the treatment combinations. The treatment combinations were then assigned to the 48 sessions in that order. Each session contained ten runs (five runs per vessel type) chosen in random order using the same procedure in Excel. The procedure to generate a random order of the sessions is shown in Table An analysis of variance was conducted to determine the effects of the factors and their interactions. The resulting ANOVA table is shown in Table Assessing the efficacy of surgical device performance ex vivo is dependent upon the quality of the experimental design, and the ability of the test to control or measure those variables that contribute to device performance. Careful design of the experimental test matrix is an important first step in this process. Failure to appropriately consider practical limitations on randomization can substantially compromise the utility and significance of the data generated. The biostatistical methods described in this report provide a procedural model for the development of the design of factorial experiments that have randomization restrictions. Since the impact of more than two factors on a response variable were assessed in this experiment, it was appropriate to carry out an analysis of variance. However, an underlying assumption for the validity of the usual analysis of variance for a complete factorial design is that the runs are observed in a completely randomized order . This usAcute collateral damage, although expected, was not examined in this study. Histopathology is commonly used to estimate the extent and degree of thermally-induced coagulative necrosis and protein denaturation incurred in relation to the distance from the point of energy application . HoweverFrom this investigation, it was determined that the design and implementation of a split-plot experimental test-matrix provided a mechanism for addressing the existing technical randomization restrictions in a statistically meaningful way, while preserving the ability to examine the potential combinatorial effects of independent factors or variables that can be assessed in ex vivo testing. This type of approach would likely be useful to a broad spectrum of medical device researchers who must attempt to recapitulate in vivo conditions, ex vivo.This research was funded by an industrial research contract from Ethicon Endo-Surgeries, Blue Ash, OH, to the University of Dayton. Publication costs are being defrayed by the University of Dayton through an unrestricted research account (CMK). The authors do not hold any stocks or patents related to this work.WSD carried out the design of the experiment, generated the split plot design with randomization restriction, and performed the statistical analyses.CMK jointly conceived of the study (with MFP), participated in the design and implementation of the experimental test set up, trained student research assistants, conducted vessel burst pressure testing, contributed to the interpretation of the data analysis, and jointly oversaw project coordination.MFP jointly conceived of the study (with CMK), participated in the design and implementation of the experimental test set up, trained student research assistants, conducted vessel burst pressure testing, performed the data analysis, and jointly oversaw project coordination. All co-authors contributed to the preparation of the manuscript, and have read and approve the final manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/11/75/prepub


Hydra magnipapillata have opened the way for the molecular analysis of an ancient nervous system, which includes stinging cells, an unusual neurosensory and neurosecretory cell type. They hold some surprises for the mechanisms and evolution of sensory transduction that could not have been anticipated from what has been learned from flies and vertebrates. Research in BMC Biology now implicates the ancient opsin-mediated transduction pathway in the neuronal control of stinging cell discharge.Genome and proteome data from http://www.biomedcentral.com/1741-7007/10/17See research article  Hydra. Cnidarians diverged from bilaterians early in metazoan evolution, and the study of their stinging cells and its neuronal control brings insights of relevance to the evolutionary origins of sensory perception.For swimmers close to Pacific coastlines, a slight touch by the tentacles of a box jellyfish can be dangerous or even fatal. One of the most sophisticated cellular inventions in animal evolution, the cnidarian stinging cell is responsible for this toxicity. These cells  are characteristic of cnidarians, a group of animals that include jellyfish, sea anemomes and freshwater polyps such as g and a pressure of up to 7 GPa, sufficient to penetrate even the cuticles of crustaceans [Hydra magnipapillata [Each nematocyte contains one giant vesicle that is derived from the Golgi apparatus, which is packed with neurotoxic and hemolytic venoms and a coiled spiny tubule inside a capsule, the nematocyst  . The 'ststaceans . This ulapillata  providesHydra magnipapillata, but we do not yet know whether it is also involved in the control of nematocyst discharge in Hydra. The hair-bundle-like sensory apparatus of Hydra is formed by the nematocyte itself, and it surrounds the kinocilium (known as the cnidocil) and the docking site of the nematocyst vesicle  rescued this inhibition.The work of Plachetzki and colleagues  now showHydra has been observed previously, but this is the first study to provide clear evidence that it can be traced to a cellular receptor. The surprising new findings are that sensory neurons in the battery-cell complex of Hydra tentacles exhibit this photosensitivity and, more importantly, that light information is used to control nematocyte discharge. The authors present several hypotheses that might explain light-regulated nematocyte discharge. Of these hypotheses, one is particularly intriguing as it assumes a further optimization of discharge: a shadow being cast by the prey on a battery complex could enhance the likelihood that stenoteles hit their target [Photosensitive behavior of r target .Hydra, and they are maintained by a stem cell system that also gives rise to nerve cells. It is therefore parsimonious to assume that the observed integration of light information by opsin-based signaling into the control of nematocyte discharge is under strong selective pressure. This selective pressure might even have led to the formation of ocelli in hydrozoan medusae  or even to the complex lens eyes in box jelly fishes .Nematocytes are the major cell population in a Pax6 controls eye and Pax2 ear formation. In addition, the basic helix-loop-helix (bHLH) gene achaete-scute is co-utilized in hair and retina cell differentiation [Pax2/6 gene in Hydra and Nematostella, and the specific expression of achaete-scute in nematocytes and in the sensory neuron of battery cells [During development, the formation of vertebrate light- and mechanosensors (eyes and ears) is tightly coupled. In vertebrates, the transcription factor ntiation . The idery cells   from a protist. Alternatively, the first multicellular animals directly derived from protozoans exhibiting extrusive organelles and signal transduction cascades for photic and stress perception. Although none of the currently sequenced protist genomes contains a homolog of the nematocyst-specific proteins with biophysical properties that might account for the high speed discharge of such organelles , this si


Complex diseases are often the downstream event of a number of risk factors, including both environmental and genetic variables. To better understand the mechanism of disease onset, it is of great interest to systematically investigate the crosstalk among various risk factors. Bayesian networks provide an intuitive graphical interface that captures not only the association but also the conditional independence and dependence structures among the variables, resulting in sparser relationships between risk factors and the disease phenotype than traditional correlation-based methods. In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 simulated data. We use the Bayesian network as a tool for the risk prediction of disease outcome. Recent genome-wide association studies have identified many DNA variants  that affect complex human diseases. However, because currently identified genetic variants collectively explain only a small proportion of disease phenotypic variance ,2, it isInferring the dependency structures for multiple interacting quantities is a challenging task, however. Without sophisticated analysis tools, it is difficult to discern conditional independence from dependence of two variables in the data. Bayesian networks are a promising tool for this purpose. First, they provide useful information that describes processes composed of locally interacting components. Second, statistical foundations for learning Bayesian networks from observations and computational algorithms to do so are well developed and have been used successfully in many applications. Finally, although Bayesian networks are mathematically defined strictly in terms of probabilities and conditional independence statements, a connection can be made between this characterization and the notion of direct causal influence -6.E, which is a directed acyclic graph (DAG) whose vertices correspond to the random variables X1, …, nX; and θ, which describes a conditional distribution for each variable, given its parents in E. Together, these two components specify a unique distribution on X1, …, nX. The graph E represents conditional independence assumptions that allow the joint distribution to be factorized, economizing the number of parameters. The graph E encodes the Markov assumption, which states that each variable iX is independent of its nondescendants, given its parents in E[By definition, a Bayesian network is a representation of a joint probability distribution, which consists of two components: ents in E.X and its parents U1, …, KU as continuous variables, and a natural choice for multivariate continuous distributions is Gaussian distributions. These can be represented in a Bayesian network by using linear Gaussian conditional densities. In this representation the conditional density of X given its parents is given by:To fully specify a joint distribution, we also need to specify each of the conditional probabilities in product form. In this paper we treat the variable When including both quantitative traits and genetic variants in the network analysis, Bayesian networks provide a natural platform for the mining of quantitative trait loci (QTLs). As a result of the small effect size of causal SNPs (mean OR < 1.4 for most common human diseases) and the multiple testing burden, many SNPs identified through genome-wide association studies are false positives if multiple comparisons are not properly taken into account. Because SNPs often exert their effects on quantitative traits, such as gene expressions, which in turn leads to the manifestation of downstream disease phenotypes, the QTL signals are enriched in the true disease causal variants, as suggested by emerging evidence. Therefore QTLs identified for disease-associated quantitative traits are more likely to be true risk factors for the disease and are natural candidates for disease risk prediction.Functionally, not all SNPs are equally important in causing the disease. Because nonsynonymous SNPs produce a different peptide sequence, they are more likely to be disease causal variants than synonymous SNPs are. Therefore, by incorporating functional annotations of SNPs into the association analysis, we can reduce signal dilution and improve the power of detection of disease variants. In our analysis, we integrate the functional annotation of SNPs by adopting a weighted average approach to generate gene-level scores. We then use data to determine the appropriate weight or contribution of synonymous or nonsynonymous SNPs to the disease phenotype. We present more details in the Methods section.In this paper, we apply a Bayesian network to dissect the complex regulatory relationships among disease traits and various risk factors for the Genetic Analysis Workshop 17 (GAW17) data and use a Bayesian network as a tool to predict the risk of disease outcome.The effective sample size for rare variants is quite small, and association analyses performed at the single-SNP level for these rare SNPs often lack sufficient power. To address this issue, we systematically explored several grouping methods published in the literature for rare variants, including the collapsing method , the weiIn the weighted-sum method, the gene-level genetic variable is the sum of minor alleles of all the variants within a particular gene, but each variant is weighted by its minor allele frequency in order to put more emphasis on rare variants.i, we obtain two gene level scores iS and iNS using only synonymous SNPs and nonsynonymous SNPs, respectively. We then generate a combined gene score:To incorporate the functional annotation of SNPs into the analysis, for each gene w is the weight of nonsynonymous SNPs in causing the disease phenotype. Note that w is the same for all the genes in the data. Let sP and nsP denote the proportions of true positive genes using synonymous and nonsynonymous gene scores, respectively. Then w can be estimated by sP/(sP + nsP). For real data, the functional annotation of SNPs can be obtained from public databases such as SIFT. The R package locfdr is used to calculate the proportion of true positive genes.where p-value cutoff threshold of 0.1, and those top genes that appear in greater than 100 replicates are retained. Recognizing that replicates may not be available in real data, in a separate analysis we combine 200 replicates into one pooled sample and generate 200 bootstrap samples from the combined sample. The 200 bootstrap samples are then treated as the 200 replicates, and we find that the gene list obtained from the bootstrap approach agrees closely with the genes selected from the replicate-based approach (96% overlap). These steps are repeated for each of the response variables of interest , and the union of the marginally associated genes for each response variable is taken, resulting in the selection of 548 genes.To lessen the computational burden, we first perform variable selection to reduce the number of genes to be included in the network analysis. To accomplish this task, we construct simple regression models in which the weighted gene score (described in the previous subsection) and the smoking status are the explanatory variables. For each of the 200 simulated GAW17 replicates, we obtain a list of top genes passing the The nodes fed into the Bayesian network contain the following variables: environmental variables , disease phenotype, quantitative traits , and the gene-level scores for the genes selected in the previously described step.The conditional likelihood of the variables given their parents is represented in a Bayesian network by using linear Gaussian conditional densities. To avoid biologically uninterpretable directional edges in the network, we ban the following edges from appearing in the network: (1) edges that point from traits  to genes, (2) edges that connect genes to environmental variables, and (3) edges among genes.We optimize the Bayesian network using a Monte Carlo Markov chain. The steps are as follows: First, a random network structure using all the variables is initialized. Next, a node from the network is randomly selected. Then, one of the following three operations is performed on the selected node: (1) adding an edge between the selected node and a potential parent node if the selected node has no parents; (2) deleting the edge from an existing parent; or (3) reversing the direction of the edge between the selected node and one of its existing parents. Finally, the post-operational likelihood for the selected node is calculated. To do this, a random number from the uniform distribution  is chosen; if the random number is smaller than the Metropolis-Hasting criterion, then the new network configuration is accepted; otherwise, we revert back to the original configuration. After the initialization step, the process is repeated many times until the network likelihood stabilizes.We estimate the confidence of the edges in the constructed Bayesian network by counting the number of times they appear among the 200 replicates. More formally, the confidence score for an edge in the network is calculated as:f(iG) = 1 if and only if edge f can be extracted from the network constructed from replicate data set iG. In our analysis, a cutoff of 5  is applied to the confidence score to select the final network. In real data, where replicates may not be available, the confidence score can be obtained from bootstrap samples generated from the original data.where We use half of the data (randomly selected 100 replicates) as the training cohort to obtain a Bayesian network following the steps described earlier. Using the features selected by the Bayesian network, we use a support vector machine to build the risk prediction model in which the response variable is the binary disease outcome and the environmental variables  and the QTLs (which are connected to the quantitative traits in the Bayesian network) are the predictors. The performance of the prediction model is then averaged over the remaining 100 replicates.In Figure To quantify the advantage of using a joint approach  in which multiple traits are considered simultaneously versus a marginal approach  in which only one trait is considered, we tabulate the area under curve (AUC) value of both methods in Table We assess the importance of the functional annotation of SNPs in disease risk prediction by separately building Bayesian networks using only nonsynonymous SNPs and synonymous SNPs. The prediction performance in terms of the AUC value is summarized in Table By examining the regulatory mechanism of genetic factors on various traits, we find that no direct edges in the network connect genes to the disease phenotype. This result suggests that SNPs exert their effects on disease risk indirectly by affecting other quantitative traits that are disease related. This result agrees closely with the true simulation model. In addition, note that in order to most optimally draw inferences about the conditional independence relationships among the nodes in a Bayesian network, we assume that no hidden nodes are missing from the network. However, this assumption is violated in the GAW17 data because there is a latent component of the disease liability that is unobserved. Despite this imperfect setup, the Bayesian network still performs rather well. By comparing the true simulation network to our derived network shown in Figure Our results suggest that the functional annotation of SNPs should not be overlooked in both association signal detection and disease risk prediction. In our analysis, we estimated the relative contribution of nonsynonymous SNPs versus their synonymous counterparts and found that the disease phenotype is predominantly driven by nonsynonymous SNPs, which closely agrees with the released simulation model. Therefore it is not surprising that the Bayesian network constructed with only synonymous SNPs fails to recover any of the true gene-trait relationships (shown in Figure Finally, we find that genetic variants collectively explain only a small proportion of the disease phenotype. The risk prediction model constructed with only genes as predictors gives an AUC value of only 0.61. However, after the environmental variables are added to the model, the AUC value is dramatically improved to 0.83. This result suggests that although the genetic variants may play an important role in disease etiology, because of their rare nature and because only a small proportion of the population carries these disease variants, their utility as disease risk predictors is limited.The authors declare that there are no competing interests.JK conceived of Bayesian network study and performed the network based analyses outlined in this paper. WZ, LL, JSL, and XY pre-processed the data and compared different gene score methods. HZ supervised the study.


E. coli as bacterial artificial chromosomes, and foreign genes have been inserted using a transposition-based system. However, with recent advances in molecular biology techniques, particularly targeting reverse engineering of the baculovirus genome by recombineering, new frontiers in protein expression are being addressed. In particular, BACs have facilitated the propagation of disabled virus genomes that allow high throughput protein expression. Furthermore, improvement in the selection of recombinant viral genomes inserted into BACS has enabled the expression of multiprotein complexes by iterative recombineering of the baculovirus genome.The baculovirus expression system is one of the most successful and widely used eukaryotic protein expression methods. This short review will summarise the role of bacterial artificial chromosomes (BACS) as an enabling technology for the modification of the virus genome. For many years baculovirus genomes have been maintained in  Autographa californica multiple nucleopolyhedrosis virus(AcMNPV) and Bombyx mori nucleopolyhedrosis virus(BmNPV) and both are in the Alphabaculovirus genus. Both viruses are pathogens of the larval stages of lepidopteran species and have an infection cycle that involves ingestion and infection of the cells of the mid-gut, followed by systemic infection. In contrast to vertebrate herpesviruses, which have a similarly sized, circular DNA genome, and which are largely cryptic infections except in immunocompromised individuals, infection of caterpillars with AcMNPV and BmNPV results in a short duration, acute, systemic infection with liquefaction of the host  within the virus genome was also demonstrated [Early experiments in protein expression using the baculovirus genome as a vector were based on the replacement of the coding sequence for the polyhedrin protein with another gene. This approach has two advantages; firstly, it capitalises on the very high expression level usually observed for polyhedrin. Secondly, as polyhedra are readily observed under a light microscope in cells that express native polyhedrin, recombinant viruses can be selected based on a polyhedra negative phenotype, allowing for selection of pure recombinant virus stocks. As the virus genome was too large for routine direct manipulation at that time, early protein expression studies took advantage of the fact that homologous recombination in baculovirus-infected insect cells is relatively efficient. Replication of the baculovirus genome involves expression of proteins that promote homologous recombination , 39. Tra viruses –43. The nstrated –46.Perhaps the best demonstration of the utility of recombinant protein production using the baculovirus system is by its widespread use. A search of PubMed with the terms “((baculovirus OR AcMNPV) AND recombinant protein)” returns 7281 peer-reviewed publications, between the initial description of human interferon expression in 1983, and December 2011 . There aE. coli as a bacmid [α  fragment, allowing selection of recombinants in E. coli by standard blue-white screening. This removed the need for plaque selection of recombinant viruses, which made the system more accessible to non-virology laboratories. The bacmid system was successfully marketed by Invitrogen as “Bac-to-Bac” and has been adopted by many laboratories as a method for the efficient production of recombinant baculovirus. Notable variations on this core technology include the introduction of an antibiotic resistance gene expressed in E. coli with the transposed element, and the inclusion of the sacB gene on the transfer vector, to select for recombinant bacmid and counter-select the transfer vector which has not recombined, respectively [While the selection of viruses produced by homologous recombination in insect cells was a highly effective way to recover recombinant viruses, it required a reasonable level of training in virology in order to be able to complete routinely. The development of baculovirus genomes in which a bacterial artificial chromosome (BAC) sequence and transposon target site were inserted at the polyhedrin locus in the virus genome allowed the propagation of the virus genome in a bacmid . This syectively . These vSince the initial description of application of the bacmid technology to AcMNPV, the approach has since been applied to other related baculoviruses –64. In mE. coli has also enabled the use of advanced reverse engineering approaches. In particular, the use of the recET/lambda-red-based recombineering approach [In addition to facilitating insertion of foreign coding sequences into the baculovirus genome, maintenance of the virus genome as a bacmid in approach  has beenapproach , 67 or rapproach , 68, 69.approach .E. coli  strain BJ5183 [The great advantage of the BAC platform as a basis of mutagenesis is that recovery of the mutant genome does not rely on the ability of the virus to replicate in insect cells, only on the viability of the BAC sequence. Therefore, there is a similar potential for mutagenesis by homologous recombination as in insect cells, but it is possible to separate out failure of the resulting virus to replicate from failure of the recombination reaction. Bideshi and Federici first reported application of recET-based recombination to generate deletion mutants in the AcMNPV bacmid  using thn BJ5183 . This muE. coli, including herpesviruses [To date, ~25% (40 out of an estimated 156) AcMNPV genes have been disrupted using the ET recombination method, demonstrating its utility for the routine modification of viral genomes maintained as bacmids. Similar approaches have been used with BmNPV , 74 and sviruses –81. Thervp80 null mutant [One of the problems with using baculovirus-based systems for the production of biopharmaceuticals has been the consequence that a live virus is used in the protein production process and must be purified away from any recombinant protein produced. Although baculoviruses are naturally present at high quantities in the environment and AcMNPV is easily inactivated with nonionic detergents and binary ethylenimine  and doesl mutant . As VP80l mutant , the nulIn addition to improving understanding of the virus biology, the bacmid platform, coupled to recET mutagenesis, has also been applied to improving the baculovirus system for protein production. Although many recombinant baculoviruses capable of expressing recombinant protein have been generated with the BAC-Tn7 transposase-based system , there alacZ expression. However, although it was a significant improvement on previous approaches, there was still a residual background of replicating viruses. The problem was finally solved by Zhao et al. [orf1629) for virus replication in insect cells that is located next to the polyhedrin locus. This mutation was only possible because the defective viral genome could be maintained in E. coli as a bacmid. As the virus genome could only replicate in insect cells once recombination with the transfer vector had occurred, the problem of background virus replication was eliminated. In practice, there is probably a low level of replication of the defective genome through complementation unless virus genomes are purified by plaque-cloning methods. However, this is not significant enough to interfere with most high-throughput applications. Recent developments to this approach have combined the knockout deletion in orf1629 with deletions of chiA, v-cath, and p10 genes, with the aim of improving expression levels and trafficking of recombinant proteins [The problem of selection of recombinant viruses was partially solved by Kitts et al. , 90, whoo et al. , who useproteins , 93. TheAlthough many protein expression experiments focus on the production of one specific recombinant protein, there is an increasing recognition that many, if not most, biological processes are completed by complexes formed of several interacting proteins. In response, there is an increasing focus on the expression and purification of these complexes using recombinant systems. The AcMNPV based-baculovirus expression system has proved to be particularly useful for the expression of functional complexes , 122–124The most straightforward approach to the production of protein complexes using the baculovirus system is the co-infection of the same batch of cells with two or more recombinant baculoviruses, each expressing a different foreign protein. The principle is that cells coinfected with the two viruses will express all the foreign proteins, allowing assembly of the complex. This approach has been used successfully for the production of many complexes and is often used to assess the potential for proteins to interact to form complexes –128. HowLoxP recombination target site was inserted at the position of the chiA and v-cath genes that are adjacent in the baculovirus genome in the bacmid, using the recET recombineering approach. As the bacmid already contained the Tn7 transposase target site, the LoxP site allowed insertion of foreign genes at a second locus by Cre-mediated recombination. The resulting system is referred to as “Multibac” in the literature. The advantage of this system is that it is possible to “mix and match” the sequences inserted at the polyhedrin and chiA-v-cath loci [in vitro passages in 90% of cells [The first attempt to address this problem, based on the manipulation of bacmid DNA was by Berger et al. . In thesath loci , 136. Byath loci , 135. Thof cells . Overallof cells . An alternative system for the production of multiprotein complexes from baculovirus genomes engineered to co-express protein subunits avoids both the transfer vector size problem and the potential for genetic instability in insect cells . InsteadIn order for the BAC-based recET approach to be viable for the routine insertion of genes for protein expression, it was necessary to significantly improve the ease with which recombinants could be selected. To achieve this, a bipartite selection system that allowed at least a 21-fold increase in the efficiency of selection of recombinants was developed . By coupThe use of bacterial artificial chromosomes continues to have a major impact on the development of studies for baculovirus and other large DNA viruses. Coupled to the recET recombineering approaches , 138, thIn terms of the applied use of baculovirus systems, BACs have facilitated protein expression for a number of years. The new recombineering approaches have also opened up new ways to produce virion-free biopharmaceuticals , and for