The recognition of required treatments for cerebral palsy (CP) patients, including orthopedic surgery, differs according to region. This study was performed to identify factors associated with satisfactory changes in physical function after orthopedic surgery.358 patients were selected for the questionnaire survey. The following information was collected: gender, primary disease, age of initial surgery, total procedural count, operated sites, satisfaction of postoperative rehabilitation frequency, ideal amount of postoperative rehabilitation sessions per week, frequency of voluntary home training per week, satisfaction of the timing of surgery and the current satisfaction with the changes in physical function after the orthopedic surgery. We classified the patients into the satisfied and dissatisfied group according to satisfactory changes in physical function after the surgery. We performed unpaired t-tests and chi-square tests to determine the variables that differed significantly between the groups. Variables with a p value of <0.2 were included in the multivariate logistic regression analysis.The logistic model was revised and summed up to two potential predictors of postsurgical satisfaction with physical function: satisfaction with the frequency of postoperative rehabilitation sessions and the orthopedic surgery of the hip .This study demonstrated that the frequency of postoperative rehabilitation and history of hip surgery seemed to be related to the satisfaction with the changes in physical function after orthopedic surgery. The orthopedic surgery for cerebral palsy (CP) has progressed over the years, conducted with various techniques to lower the rate of patient morbidity –4. FamilOrthopedic surgeries have several purposes such as pain relief, improvement of motor function and deformity. A study evaluating the satisfaction of surgical treatment performed on CP children with musculoskeletal dysfunction, reported that improvement of motor function led to improvement of quality of life and increased the opportunity for participating in outdoor sports activities ,8. AnothThe purpose of this study was to identify the factors associated with satisfaction with the changes in physical function after orthopedic surgery in CP patients.A total of 745 patients who underwent surgery at an Orthopedic Hospital in Tokyo from April 2005 to December 2009 were included in this study. 440 questionnaires were collected from the participants, and patients with CP indicated as the primary disease were selected. 38 patients with apoplexy, 17 with congenital diseases besides CP, and 27 participants with uncompleted questionnaires were excluded from this study. Based on the responses of the questionnaire, a total of 357 patients with CP as the primary disease (response rate: 48.1%) were included in study.We developed the original questionnaire based on previous studies to investigate postoperative rehabilitation and satisfaction concerning changes in physical function –8. The pThe questionnaire . Each patient was classified into the satisfied group (very satisfied and satisfied) or dissatisfied group .t-tests. On the other hand, gender, surgery sites, satisfaction with the frequency of postoperative rehabilitation sessions, and timing of the surgery were used as independent variables, while satisfaction with the changes of physical function was used as a dependent variable. The associations were assessed by chi-square tests. Secondly, those variables with a p value of <0.2 were used as independent variables and the current satisfaction with changes in physical function were used as a dependent variable. These associations were assessed by multivariate logistic regression analyses with forward stepwise selection (likelihood ratio). Statistical analyses were performed using SPSS . A P value of ≦0.05 was considered to imply statistical significance.In order to investigate the factors contributing to the satisfaction with changes in physical function with as few variables as possible, we initially performed the unpaired t-tests and chi-square tests to determine which variables differed significantly between the satisfied and the dissatisfied group. The number of procedures, age at the time of the initial surgery, ideal number of postoperative rehabilitation sessions and frequency of voluntary training at home per week were used as independent variables and satisfaction with the changes in physical function was used as the dependent variable. The presence of association with the variables were assessed by the unpaired t-tests and chi-square tests. According to the satisfaction of changes in physical function, there were 253 participants in the satisfied group and 105 participants in the dissatisfied group. The results of unpaired t-tests presented age to be a significant factor at the time of the first surgery. The patients in the satisfied group had a lower average age than the dissatisfied group (P = 0.005). The results of chi-square tests suggested that, satisfaction with the frequency of postoperative rehabilitation sessions and the surgery site at the hip joint were also significant factors. The frequency of surgery at the hip joint (P = 0.012) and satisfaction with the frequency of postoperative rehabilitation sessions (P = 0.000) were significantly higher in the satisfied group than the dissatisfied group. And p values of surgery sites at the neck, knee joint and ankle were lower than 0.2.The purpose of this study was to clarify contributing factors to the satisfaction with changes in physical function after orthopedic surgery in CP patients. We first performed bivariate analysis to determine which variables differed significantly between the satisfied and dissatisfied groups. The following variables with p values set at <0.2 were included in the multivariate logistic regression analysis: age of initial surgery, satisfaction with the frequency of postoperative rehabilitation sessions and surgery sites, which were the neck, hip joint, knee joint and ankle. Consequently, the frequency of postoperative rehabilitation sessions and surgery site at the hip joint were found to be predictive factors of positive outcomes in patients’ satisfaction.One study examining the outcome of a single-event multilevel surgery in ambulant CP children reported that parental concerns over insufficient rehabilitation might undermine parental satisfaction . BecauseIn general, the purposes of conducting orthopedic surgeries on the lower extremity are to improve standing and gait function ,2. Uppert-test, but was not significant after the multivariate logistic regression analysis. When we performed the multivariate logistic regression analysis to analyze the satisfaction of the post operation intervening frequency and the factor of conducted surgical area of the hip, by observing the difficultly for some of the patients in maintaining a relatively functional physical condition (maximum of 5 years post-operation) until the time of the conducted questionnaire, we comprehended that it might have been a contributing factor to influence the satisfactory results of the surgery. For the Japanese interdisciplinary knowledge to lead to satisfying outcomes, it was important to acknowledge that it might be difficult for patients to maintain a relatively functional physical condition on a long-term basis.The mean age of the participants in the satisfied group at the time of the first surgery was significantly lower than in the dissatisfied group. The purposes of orthopedic surgery has a tendency of improving or achieving crawling, standing and being able to walk because infants and children grow and develop. For adults, on the other hand, orthopedic surgery is aimed at relieving pain for secondary disabilities such as cervical spondylotic myelopathy, low back pain and improving gait functions. The satisfied group included many infants and children who experienced improvements of physical ability after surgeries. However, the dissatisfied group included many adults with whom the surgeries did not yield desired outcomes. The age of initial surgery was a significant factor according to the unpaired S1 Questionnaire(PDF)Click here for additional data file.


The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity. MMG represents the mechanical manifestation of the neurophysiological phenomena underlying muscle contraction and the mechanical counterpart of the electrical activity of the motor units . Motor urequency ,8, and trequency ). This frequency ,11. Consrequency ,13. Thusrequency .i.e., muscle torque and power) used as indices of the muscle performance with implications in prosthetics design [MMG is useful in various areas of interest because its characteristic parameters can be delineated in the time domain including the root mean square (RMS), peak to peak (PTP) amplitude and mean average value (MAV), and in the frequency domain which includes the mean power frequency (MPF), median frequency (MDF), center frequency (CF), and frequency variance (FV). Recently, the MMG parameters have been characterised and classified in the joint time-frequency domain including time scale variables, wavelets, and energy distributions among other representations. These features provide valuable information about the contractile properties of skeletal muscles' motor units used to estimate the force capacity and fatigability . Measures design , orthopes design , the asss design , and in s design . MMG offet al. [As a growing area of research, investigators have conducted comprehensive reviews on the feature extraction methods of MMG. Beck et al.  undertooet al.  substantet al. . In esseet al. ,23.However, despite the ease of collection due to the sensing flexibility and the validated significance of the signal parameters to estimate various aspects of skeletal muscle activities, to the best of our knowledge, there has not been any documented effort that has presented a comprehensive point of view on the classical and modern techniques used to extract MMG parameters with highlights on the clinical applications. Thus, the objective of this study was to conduct an appraisal of the effectiveness of common MMG features' extraction methods, their recent derivatives and other representations during skeletal muscle activities with implications in clinical practices. This review focuses specifically on the methods used to extract the temporal, spectral, and the joint time frequency parameters of MMG signals underlying the static and dynamic skeletal muscle during voluntary and non-voluntary activities. However, the review does not relate, except for a brief note, to more comprehensive methods and technical exploratory information as could be found in the previous reviews ,24–27 de2.The considerable variation of the transducing technology in the available MMG sensors has been validated . Howeveri.e., 2 Hz up to 100 Hz and low sensitivity to random signals (noise); (b) ease and standardization of the sensor attachment; (c) biocompatibility and applicability in a clinical environment; and (e) cost effectiveness when compared with other clinical assessment techniques [Typically, the characteristics of a reliable MMG transducer include the following: (a) high sensitivity in the muscle vibrational frequency range, chniques  to mentichniques .Further, to obtain MMG signals with acceptable integrity, the established technical guidelines used for electromyograms (EMGs) are often adopted . The uniAnother consideration during MMG acquisition is ensuring the sensor firmness to the skin surface and contact pressure standardization to reduce the variability of the signal during repeated recordings . The conIn experimental designs where more than one subject are recruited, investigators may seek a comparison between muscles, tasks and/or individuals, thus, a normalization of the acquired signal is recommended. There is inter individual variability that may adversely influence the signal including differences in muscle mass, muscle length and the thickness of the tissue between the sensor and the muscle. To eliminate the effect of the muscle variations, normalization to a reference level is often prescribed.i.e., m/s2 for the contraction (since sensors' sensitivity is often presented in mV/g where g is the acceleration due to gravity when accelerometer sensor is used) the need to normalize may be unnecessary [i.e., to match/convert the voltage level to the physical measurements). In any case, a meticulous attention to sensor placements and sensor selection based on the purpose of acquisition and the type of muscle action should be a priority.Normalization constitutes a means of adjusting data to conform to a common scale for an objective averaging and analysis in order to validly compare between muscles, task and individuals . Normaliecessary . This prSince MMG signals are inherently mechanical, the signal acquisition task may be facilitated because the signal can be collected without the need for a separate circuitry to eliminate the electrical noise interfaces especially 50 Hz noise. Equally, the signal acquisition may be realised with a single uniaxial electrode configuration unlike the simplest monopolar configuration of EMG (often unused) with a separate reference electrode whereas bipolar and multipolar configuration of EMG require even more electrodes. These facts collectively inform the few hardware requirements  in MMG ai.e., the retraining straps on the dynamometers isolate only the relevant muscle), and through high pass filtering [Current efforts to mitigate the effect of motion artifacts include: the application of Fourier truncation , the useiltering . The cutiltering . Althougiltering . The majiltering  and pieziltering , especiailtering .et al. [Theoretically, methodological and physiological concerns may dictate the signal sampling rate based on the site of the muscle of interest. A common compromise is to sacrifice the storage space for high sampling rate during the signal acquisition exercise. The rule of thumb based on the Nyquist-Shannon sampling theorem is that; “for a reliable reproducibility and representation, a signal should be sampled at least twice the highest frequency content of the signal” . This suet al. . This apet al. . HoweverCollectively, as the reliability of a recorded MMG signal is influenced by the relative size of the sensor and the firmness to the skin surface, so are locations and orientation of the sensor on the muscle, the thickness of the subcutaneous fat layer, and the noise and artifact reduction technique adopted. Reliable MMG signals require an appropriate selection of signal analysis methods . Equally3.Applications of MMG signal parameters in muscle research for experimental decisions have been well documented. Diverse methods of time domain and frequency domain marked the beginning of the investigation into the signal analysis. Different types of joint time-frequency domain analysis were proposed lately to extract MMG parameters because of the non-stationarity of the signal. Selection of an appropriate signal processing technique is crucial for the objectivity of the analysis of MMG signals obtained during muscle activities. During an isometric contraction, where the signal may be assumed to be stationary, the application of the traditional parameters extraction methods is widely accepted. However, in a dynamic muscle action, changes in muscle length, number of active motor units, and the thickness of the tissue between the muscle and MMG sensors affect the MMG amplitude and frequency, resulting in non-stationary signals ,41,62. TIn order to characterize the signal responses, the time domain's PTP, RMS, MAV or average rectified values (ARV), and the frequency domain's MDF and MPF  and the The renewed interest in MMG signal relies on the use of the well-established EMG theory and signal processing techniques to investigate MMG signal characteristics . Therefovice versa. However, the complex relationships between the time and frequency (TF) content of the signal have been examined by using techniques such as WT, STFT, and Wigner-Ville Distribution (WD) to compute the TF representations. While most recently, various linear wavelet analysis algorithms were proposed in muscle research with reliable classification performance, the need for continuous reassessment of these methods is warranted [The traditional approach to MMG signal power spectra computation is Fourier based transforms used to convert from the time domain to the frequency domain or arranted  as the iarranted . Althoug3.1.There are several time domain processing techniques used to extract information using MMG amplitude. The estimation of the muscle effort, monitoring of muscle fatigue and the examination of neuromuscular disorders have been delineated by the changes in the time domain features of the MMG signal which generally signifies changes in motor unit recruitment during muscle contractions ,71–76. Bi.e., the time windowed mean of the absolute value of the signal. The ARV is characterised with a lower coefficient of variation when compared with RMS during MMG analysis [Other common representations of MMG amplitude features are the mean amplitude value (MAV) or average rectified value (ARV) which is the average of the absolute value of the MMG signal over a specific time interval T in seconds, analysis . Furtheranalysis  and elecanalysis . The conanalysis , sustainanalysis ,85. Thisanalysis .ix represents the i th sample of the MMG signal and N is the number of samples in the segment considered.The commonly used MMG amplitude representations can therefore, be defined by the following Accordingly, the assessment of the level of muscle contraction can be obtained by the time series analysis of MMG signal . However3.2.i.e., dynamic (eccentric or concentric) or static . The measure of the magnitude and pattern of the spectral compression has been demonstrated by the frequency features of the signal with significant clinical implication [There are diagnostic and assessment features of MMG signals that are more evident in the frequency domain. Different patterns in the spectral analysis during muscle contraction have been reported. The common experimental practice includes the use of a shift in the frequency feature of MMG to track muscle fatigue. The magnitude of the changes is dependent on the type of muscle action, lication . The mealication  and is alication . Generallication .3.2.1.vice versa by projecting the data set in a space of sine and cosine. The algorithm has been described as the most important numerical algorithm and characterises with a high computational efficiency [Practical signals can be delineated in frequency domain by Fourier transform representation. In order to investigate the frequency domain behavior, the frequency component of MMG must be characterized. The standard practice involves the application of the fast Fourier transform algorithm to compute the power density spectrum of MMG , from whficiency . To obtaet al. [et al. [S (f) and fs are the power density spectrum (PDS) of the signal and the sampling frequency respectively. MPF represents the moment of the PDS and the major indicator of spectra changes:Because not all the features of signals are evident in time domain, investigators have always estimated the frequency content of the signal based on the recommendation of Diemont et al.  and as c [et al.  to estimThe MMG signal's MDF, though not commonly used due to the reported relatively low reliability , it is hThe frequency variance (FV) shown in Equally, as the foremost frequency domain estimator with pronounced limitations during non-static contraction due to changes in the motor unit recruitment and firing rates and the underlying muscle length changes, the trend of recent studies have continued to uphold the suitability of the FFT algorithm to compute the frequency parameters of MMG signal in isometric muscle contractions . EssentiThe FFT algorithms assume the stationarity of an input signal and may not be suitable to analyse dynamic muscle actions. There are other classical model based techniques which have not been widely used including maximum entropy spectrum estimation ,90,98 an3.3.During the analysis of dynamic contractions of muscles, there is need to rapidly track the frequency changes (in time) during certain muscle activities that reflect the motor unit recruitment and firing rate to estimate the torque generation across the range of motion considered . HoweverThe major compromise associated with a high TF resolution is the considerable amount of time required for computation . Neverth3.3.1.x(t) is as shown in w(t − τ) represents the window function, τ variable slides the window across the waveform, x(t).The short time Fourier transform theoretically involves the breaking down of the waveform into a number of short segments . The anai.e., in time) in MMG signal frequency with force development during muscle contractions [A spectrogram can be obtained from the square magnitude of the STFT. Thus the energy distribution of the signal along the frequency direction at a given time can be computed . Howeverractions . Howeverractions ,102.3.3.2.i.e., functions that oscillate for a short period, localized in time and frequency and their integral over time must be zero). The wavelet transform can be used to examine changes in the spectral density of a signal using an enhanced frequency resolution derived from wavelets. The wavelet transform reflects discontinuity better than FT and is deemed more suited to decompose signal to its fundamental form and reveals transient characteristics of muscle action which may not be evident in Fourier transform algorithms [x(t) is the input signal, ψ is the probing function, and a is the variable that varies the time scale of the probing function, while b acts to translate the function across the input signal x(t).Unlike Fourier based methods, wavelet methods do not assume signal stationarity and project signals into orthogonal space function called wavelets  representing the signal of interest [The Wigner Ville transform (WVT) method adopts the TF energy distribution method to estimate the power spectral function of a non-stationary signal . As a spinterest :(11)W was initially proposed in the field of fluid mechanics , but hasCollectively, there are a number of important considerations in the selection of methods for MMG signal analysis. These factors may include; the nature of contraction (stationary or dynamic), the information to be extracted (embedded in time (T), frequency (F) and/or joint TF domain representations), the frequency band of the information content of the signal, and perhaps the speed of computation and the precision of representation. 4.4.1.In rehabilitation and exercise sciences where clinicians and exercise physiologists may wish to prevent injury and improve performance, the direct measurement of the muscle force may not be feasible  and the et al. [The estimation of muscle force during dynamic muscle action is more relevant during the assessment of skeletal muscle functional activities . There aet al. , demonstet al.  includinMotor unit recruitment and firing rates are the major mechanisms on which the production of muscle force depends. These mechanisms have been used to assess muscle force at different levels of contractions . TherefoAnother approach to muscle force assessment is through isokinetic dynamometry (ID). The ID technique represents a convenient and an objective means to study the force-velocity characteristic of muscle groups ,133. TheThe current trends indicate that the pattern of MMG frequency parameters during isometric and isokinetic voluntary contraction may discriminate the task and be muscle specific . For ins4.2.The characteristics of MMG parameters do not only depend on the muscle being studied, but are also related to the muscles' state of health , stiffness, temperature and the depth of the subcutaneous tissue . There ai.e., as modulating signals) including EMG and MMG to mention the most common [i.e., force, torque, joint angle, etc.) [An efficient NMES rehabilitation can be achieved by a stimulation protocol that optimizes muscle force  to enhant common . During t common  especialt common , there ie, etc.)  and the e, etc.) .i.e., on the application of MMG as a control signal for an externally powered prosthesis) [Alternatively, the application of MMG signal as a NMES modulating biopotential in electrically stimulated muscles has been recently proposed and has since received some clinical attentions . Impetussthesis)  as advansthesis) ,115,144 sthesis)  to demonCurrently, the most effective stimulation parameter is yet to be agreed upon. The reason for this inconsistency may be due to the lack of consensus regarding “what constitutes the appropriate stimulation parameters” because the stimulation patterns available are not equally effective in different candidates with varying degree of lesions. If the muscle activation pattern can be reliably discerned by MMG signals there may be a justification for its application as an outstanding biopotential NMES modulating signal as proposed by Gobbo and colleagues , since t5.We have presented various methods used to extract MMG parameters and different studies confirming the wide application of those parameters to examine various aspects of muscle activities with experimental and clinical applications. The limitations of each technique have been enumerated aiming at limiting the erroneous interpretation and conflicting report often encountered in the literature. A number of domains where MMG parameters have been demonstrated to describe the basic muscle activation pattern and areas of potential application were highlighted herein.Contrary to the traditional signal modality used as the control signal in human machine interfaces, including EMG, which is described as an index of superficial muscle activities  and charWithin the limitations of the studies investigated, the wavelet analysis method a time scaled method used to estimate the joint time frequency features of MMG is significantly more robust in terms of its applicability in the real time muscle activity classification. This fact lends more support to the joint TF approach in the analysis of stochastic and non-stationary MMG signals as previously suggested by Alves and Chau . HoweverUnderstanding the pattern of muscular events embedded in MMG features during voluntary and stimulus evoked contraction would better inform the design of exercise and rehabilitation to prevent injuries, enhance performance and promote healthy living. This will facilitate flexibility in the treatment modalities and provides enormous support to the projected therapeutic and functional outcome of NMES rehabilitation intervention in disabled individuals. As a signal modality that is rich in the information on the state of neuromuscular systems, MMG signal features have been well explored to examine various aspects of skeletal muscle activities. However, there are still wide knowledge gaps in the sensor technology to manage the muscle tremor. Attempts to reduce the gap include integration of two different sensors to improve sensing performance and signal integrity . Issues i.e., contraction and relaxation). However, the direct relationship between the MUs activation strategy and the MMG signal's parameters remains debatable [It has been established that MMG signals are related to MU activation rate and the muscle fiber contractile properties . Further experimental validation and clinical assessment of the available parameters extraction methods in such population may be informative.There is need to consistently examine the reliability of signals if their clinical application is envisaged . The relAlthough the MMG signal parameters have been fairly applied to quantify muscle performance, it is pertinent for the investigators considering its incorporation into clinical assessment to be aware of the strength and limitation of each standard technique described to forestall erroneous estimations and interpretations. The unification of the patterns highlighted will give a common framework nurtured by researchers' consensus. Avoidance of factors that may affect the reliability of the signal is equally of immense importance. Our knowledge of the mechanical behavior of the muscle signal during activation has been improved . In some6.An appraisal of the standard practice in MMG signal parameters extraction with implications in clinical practice has been conducted. The recent advances in the applications of the signal features as control signals in muscle machine interfaces, as practical proxies of muscle force and the proposition of the signal as a modulating signal in NMES devices  bespeak the signal relevance in muscle research. Notwithstanding the fact it represents a non-invasive signal to assess the muscle performance, a marked deficiency in the available methods to mitigate the tremor artifact in MMG is equally evident in the literature. This query remains a valid research question for which the signal modality may continue to be subjected to challenges mitigating against its projected potentials in muscle research and rehabilitation. Nevertheless, the current level of research outputs serves as impetus for the development of robust and reliable muscle MMG linear and non-linear models. The need for rehabilitation researchers to consistently consider the nature of the signal before deploying the parameter extraction methods has been highlighted. It is hoped that this review will be a useful point of reference for future studies in the application of MMG signal. Collectively, more clinical evaluations of muscles during different tasks are encouraged, supported by further development of computational tools and researches into new algorithms for muscle performance analysis and their validations using MMG signal. The results of this review are relevant to experimental and clinical domains where MMG parameters may be of immense use.


Cell line generation (CLG) in the scope of bioproduction can be defined as a method to isolate a single cell expressing a recombinant protein of interest. The standard method of CLG often involves the introduction of the transgene into a cell in an attempt to use the cellular machinery for transcription, translation and secretion. The use of random genomic integration via auxotrophic selection markers creates different layers of complexity, which leads to significant variations in growth, productivity and stability among the subsequent population. The useThe changing landscape of CLG has resulted in the inclusion of robotics and high throughput technologies such as flow cytometry into development pipelines. The standard CLG method may include (but not limited to) transfection, selection and stable pool generation followed by a number of rounds of clonal enrichment using the new technologies to isolate cells with the necessary quality attributes for product manufacturing. However we present a single step method to isolate CHO cells for the expression of monoclonal antibodies (mAb). The method employed here uses semi solid cloning as well as the ClonePix FL (Molecular Devices) to isolate mAb producing CHO cells. The method examines directly seeding transfected cells into a semi-solid matrix for selection, propagation and subsequent isolation. By combining the selection with the isolation in a single step, this direct approach allows for a more efficient process in identifying a "serendipity event", i.e. a single cell that has been transfected with the vector containing the gene of interest, undergone random integration/s at a non-essential locus and now has the ability to express the recombinant protein of interest. Transfected cells were seeded into semi-solid matrix at different seeding densities and then the positive colonies were isolated using the ClonePix FL. The clones were assessed for growth and productivity between the single step and the standard methods.To assess the single step method, we examined the growth, productivity and interclonal diversity from the isolated clones. For colony formation in the semi solid matrix, initial seeding density was increased from 500-1000 cells/mL as recommended  to 40,00The single step method presents a simple change in methodology for increasing the probability of isolating a mAb expressing CHO line without changing the fundamental process. In fact, by directly seeding the transfected cells into the semi solid matrix, the method presents a more robust process eliminating the need for bulk selection and a resistant pool, while also reducing current timelines for CLG. With the rapid changes in the biopharmaceutical industry, especially in biosimilar developments, being aThe authors acknowledge the Australian Commonwealth Government's NCRIS Program and co-support from the Queensland State Government.


To overcome this issue, we introduce the pairwise interaction model (PIM), a generalization of the PWM model. The model is based on the principle of maximum entropy and explicitly describes pairwise correlations between nucleotides at different positions, while being otherwise as unconstrained as possible. It is mathematically equivalent to considering a TF-DNA binding energy that depends additively on each nucleotide identity at all positions in the TFBS, like the PWM model, but also additively on pairs of nucleotides. We find that the PIM significantly improves over the PWM model, and even provides an optimal description of TFBS statistics within statistical noise. The PIM generalizes previous approaches to interdependent positions: it accounts for co-variation of two or more base pairs, and predicts secondary motifs, while outperforming multiple-motif models consisting of mixtures of PWMs. We analyse the structure of pairwise interactions between nucleotides, and find that they are sparse and dominantly located between consecutive base pairs in the flanking region of TFBS. Nonetheless, interactions between pairs of non-consecutive nucleotides are found to play a significant role in the obtained accurate description of TFBS statistics. The PIM is computationally tractable, and provides a general framework that should be useful for describing and predicting TFBSs beyond PWMs.The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair contributes independently to the transcription factor (TF) binding. However, this description ignores correlations between nucleotides at different positions, and is generally inaccurate: analysing fly and mouse  Central effectors of this regulation are Transcription Factors (TFs), which bind on short DNA regulatory sequences and interact with the transcription apparatus or with histone-modifying proteins to alter target gene expressions in vitro study of 104 TFs using DNA microarrays has revealed a rich picture of binding patterns in vitro specificity of several hundred human and mouse DNA-binding domains was investigated using high-throughput SELEX. Correlations between nucleotides were found to be widespread among TFBSs and predominantly located between adjacent flanking bases in the TFBS in vivo are certainly determined in multiple ways besides in vitro measured TF binding affinities and the extent of correlations between their nucleotide positions remains to be fully assessed.Despite the widespread use and success of PWMs, there is mounting evidence that its central hypothesis of independence between positions is not always justified. Several works have reported cases of correlations between nucleotides at different positions in TFBSs On the computational side, a number of probabilistic models have been proposed to describe nucleotide correlations in TFBSs, generally based on specific simplifying assumptions, such as mutually exclusive groups of co-varying nucleotide positions in vivo TFBS statistics for a majority of TFs. This calls for a refinement of the PWM description that includes interdependence between nucleotide positions.Here, using a variety of ChIPseq experiments coming both from fly and mouse, we first show that the PWM model generally does not reproduce the observed in vivo.To this purpose, we propose the general Pairwise Interaction model (PIM), which generalizes the PWM model by accurately reproducing pairwise correlations between nucleotides in addition to position-dependent nucleotide usage. The model derives from the principle of maximum entropy, which has been recently applied with success to a variety of biological problems where correlations play an important role, from the correlated activity of neurons in vivo TFBS statistics and predictability are significantly improved in this refined model. We consider, for comparison, a model that describes the statistics of TFBSs as a statistical mixture of PWMs We find that the in vitro binding results The difference between the PIM and the PWM model lies in the pairwise interaction between nucleotides. Surprisingly, despite significant differences in prediction accuracy between the two models, these interactions are fairly weak, sparse and found dominantly between consecutive nucleotides, in general qualitative agreement with The PIM only requires a modest computational effort, and the refined description of TFBS that it affords should generally prove useful when enough data is available.D. Melanogaster and from mouse embryonic stem cells (ESC) and a myogenic cell line (C2C12). For a given TF, the ChIPseq data consist of an ensemble of DNA sequences, the ChIPseq fragments, each a few hundred nucleotides long. The exact positions of the TFBSs are unknown on each of these sequences. They are determined as Methods for a detailed description). This process ensured that the frequency of different nucleotides at a given position in the considered set of binding sites (the high-scoring L-mers) was exactly accounted for by the PWM.We first tested how well the usual PWM model reproduced the observed TFBS statistics. Specifically, we asked how well the frequencies of different TFBSs were predicted using only single nucleotide frequencies. For this purpose, we used a collection of ChIPseq data available from the literature We then enquired whether the frequencies of the different binding sites in the set agreed with that predicted by the PWM, as would be the case if the probabilities of observing nucleotides at different positions were independent. Methods). The DKL is zero i f the two distributions are identical and is positive otherwise. We found that for most TFs (22 out of 28), the DKL divergence was significantly larger than expected just from finite-sampling error noise , between the two distributions oise see . In the The discrepancy between the observed statistics of TFBSs and that predicted by the PWM model calls for a re-evaluation of the model's main hypothesis, namely the independence of bound nucleotides. To account for the correlative structure of TFBS statistics, we wish to construct a model that assigns a frequency to each of the possible the frequency counts of the 4 nucleotides at each position in the sequence data, (e.g. 40% of nucleotides at the third position are C and 60% at the fifth position are T), as the PWM does,the frequency counts of each pair of nucleotides in the sequence data ; that is a C at the third position is always associated to a T at the fifth position and not only in 60% of the cases as would be expected for independently bound nucleotides).Methods) that the model distribution has the following form, referred to as the Pairwise Interaction model (PIM) hereafter:There are many models that can achieve these two requirements. In order to precisely specify a single one, we ask that the model probability distribution exactly reproduces the frequency counts of single nucleotides and pairs of nucleotides in the sequence data but is otherwise as unconstrained as possible. This is the principle of maximum entropy. This provides a natural generalization of the PWM model since the PWM model is the maximum entropy model that reproduces the frequencies of single nucleotides (condition i) above). Specifically, call TACATATGTATA. As written under the form (1), the quantity The PIM binding energy The first sum comprises the binding energies of the individual nucleotides, with Methods). However, trying to fit each of these frequencies would carry the risk of over-fitting the data with an unrealistically large number of parameters. To avoid this, we instead maximized the likelihood of the data under the model, but with a penalty proportional to the numbers of parameters involved, as provided by the Bayesian Information Criterion (BIC) Methods). In addition, just as in the procedure described previously for the PWM model, the PIM and the collection of TFBSs for a given factor were iteratively refined together, as schematized in This is an example of an inverse problem, where energies are devised from observed frequencies. As mentioned in the introduction, such problems have recently been studied in a variety of biological contexts Precise predictions of TFBSs are one important output of ChIPseq data. They condition further validation experiments such as gel mobility shift assays or mutageneses. Therefore, we assessed the difference in TFBS predictions between pairwise (PIM) and independent (PWM) models.First, we compared the set of ChIP sequences retrieved by the two models at the cutoff of e.g for Fosl1, Max, N-Myc, USF1). Conversely, the overlap between the models was decreased when the fragments typically contained one or less TFBS .Second, using the set of ChIPseq peaks on which the PIM was learned, we looked for the best predicted binding sites on each ChIPseq bound fragment using both the PWM model and the PIM . The oveIn conclusion, we found that the TFBS predictions made by the two models could differ significantly both in the rank of ChIPseq fragments and in the rank of binding sites on these fragments.Methods for details). The results are shown in An underlying assumption of the PWM model is that there exists a preferred consensus sequence, of which other sequences are close variants. Some authors have instead analyzed the binding specificity of transcription factors by introducing multiple preferred sequences The best description of Twi ChIPseq data is, for instance, provided by a mixture of 5 PWMs, which corresponds to 184 independent parameters. The mixture model yields a significant improvement compared to the single-PWM model, and milder ones for Essrb and MyoD. In the three cases however, it does perform as well as the PIM.As in the PWM case, the finite size of the datasets leads us to expect fluctuations in the estimation of the DKL. In order to assess the magnitude of these finite-size fluctuations, we computed the average DKL between the best-fitting PIM and a finite-size artificial sample drawn from its own distribution, as shown in Methods for details). For each local minimum, we constructed a PWM model from the sequences in its basin of attraction, and associated a weight proportional to its size. We compared these PWMs to those of the mixture model, by calculating their DKL. This gave an effective distance which allowed us to associate each local minimum to the nearest PWM of the mixture model.The connection between the PIM and the PWM-mixture model can be further explored by considering the binding energies of all possible L-mers. This can be viewed as the “ energy landscape” of the PIM in the space of all possible binding nucleotide sequences. The “energy” of a sequence is defined in term of its probability (i.e. how frequently it appears in a set of binding sites) by Using this procedure, we computed the set of PWMs and weights corresponding to the PIM inferred from the 22 TFs for which the PWM did not offer a satisfying description. Examples are shown in This representation allows one to identify interesting features captured by the PIM. For example, in the case of Twist, most of the correlations are coming from the two nucleotides at the center of the motif, which take mainly The inference of the PIM yields explicit values for the interaction parameters Methods). Heatmaps displaying the results for the representative Twist, Esrrb and MyoD factors are shown in Methods). This analysis yielded typical values of Methods). As seen in To estimate the strength of interaction between two positions, we used the tool of Direct Information, originally introduced to predict contacts between residues from large-scale correlation data of protein families on Ratio , meaningMethods). The interaction strength can also be used to measure the typical distance between interacting nucleotides. To that purpose, we computed the relative weight of the Direct Information as a function of the distance between nucleotides  are shown in and MyoD . The majand MyoD . Althougand MyoD .Methods):We also analysed the interaction matrix This form is reminiscent of the Hopfield model We wondered how many patterns were necessary to approximate the full interaction matrix in vitro TF binding studies The availability of ChIPseq data for many TFs is an opportunity to revisit the question of nucleotide correlations in TFBSs, and to propose alternative descriptions of TFBS ensembles beyond the PWM To refine the PWM description, we have proposed and analyzed a model with general pairwise interactions (the PIM), as well as a model using a mixture of PWMs. While the mixture model somewhat improves over the PWM description, the PIM achieves a much more significant and general improvement, and can even be shown to be optimal given the amount of available data. The PIM could account for higher-order correlations than pairwise, superseding explicit descriptions in terms of multiple motifs such the one provided by the PWM-mixture model.Several other approaches have previously been proposed to describe nucleotides correlations in TFBS, usually based on computationally-friendly approaches such as a Markov models or Bayesian networks The PIM derives from the principle of maximum entropy, with the constraint that pairwise correlations are accurately described by the model. This approach has already been applied in a variety of biological contexts. The determination of amino acid interactions in protein structures in vitro TF binding data from extensive high-throughput SELEX experiments The inferred parameters of the PIM provide insight into the location and strength of the effective interactions between nucleotides without potential biases coming from model simplifying assumptions. The dominant pairwise interactions are found mainly between consecutive nucleotides in the TFBS flanking regions, in agreement with in vitroin vivo binding data may help to disentangle the different possible origins of the found correlations, and seems worth pursuing. It appears similarly interesting to study how much of the found pairwise correlations can be explained on the basis of structural data. Finally, the role of nucleotide interactions in TFBS evolution However, the physical interpretation of the effective interactions is not clear, since these may combine real physical interactions with genomic correlations. This is similar to the case of protein families, where structural and functional contraints are hard to distinguish from phylogenic correlations or other observational biases Independently of these future prospects, we have found that the TFBSs predicted from ChIPseq data depended significantly on the model used to extract them. Since the PIM and the developed workflow significantly improve TFBS description and require a modest computational effort, they should prove worthy tools in future data analyses.Drosophila Melanogaster and ChIPseq data from Mus Musculus. Data was retrieved from the literature http://hgdownload.cse.ucsc.edu/goldenPath/mm9/encodeDCC/wgEncodeCaltechTfbs/, for a total of We use both ChIP-on-chip data from e bp see . DNA seqi.e. the PWM background model).It is important to discriminate the statistics of the motifs proper from that of the background DNA on which motifs are found. Besides particular nucleotides frequencies, the background DNA can exhibit significant nucleotide correlations, for instance arising from CpG depletion in mammalian genomes  could be improved or was consistent with the finite number To estimate whether the description of the data by a model  and to Information theory offers a principled way to determine the probabilities of a set of states given some measurable constraints. It consists in maximizing a functional known as the entropyFinally, using the constraint The normalization constant The split of the energy Local binding energies. The probability is invariant with respect to the following global shift of the local binding energies To uniquely prescribe the This condition can be imposed on any set of energies Pairwise interactions. Another invariance stems from the fact that contributions can be shifted between local binding energies and pairwise interactions. Namely, the following change of variables does not affect the probability:These can be imposed on any set of pairwise interactions The parameters of the model in Eq. (9), giving the energy of an observed sequence of length To build the model, we start from the PWM description, characterized by the set of initial i.e. it is not predictive. Such a case where the number of parameters used to estimate a distribution becomes of the order of the size of the sample is known as overfitting. The BIC allows to overcome overfitting by penalizing high dimension parameters. Using Bayes Rule, and a uniform a priori distribution on the models, we haveConsider a sample That is, the probability of the model given the data can be inferred from the probability that the data is generated by the model. The latter is obtained by marginalizing the joint distribution of the data and the parameters over the space of parameters For a unidimensional parameter In the present case, the sample The interpretation of Eq. (21) is clear: adding new parameters improves the fit, but also adds new sources of uncertainty about these parameters due to the finite size of the data. This uncertainty disappears as Finally, Eq. (21) is a functional over models, the chosen model We investigated an approach based on a mixture of PWMs. For that purpose, we used a comparable setup as for the PIM. However, instead of adding correlations to a given PWM, new PWMs were added to a mixture model. More precisely, a mixture of We defined the basins of attraction of a PIM energy landscape, in the following fashion. Let We computed local-energy-minimum sequences and their basins of attraction for the final set of bound sites obtained with the best PIM. A PWM was learned on each basin of attraction, leading to a set of representative PWMs, with different weights representing different proportions of bound sites in their basins.We wanted to build a quantity based solely on direct interactions The The normalization of the probabilities The Direct Information As there is no upper bound for this direct information, we built a normalized version of the direct information:The Mutual Information was defined as:For each TF, an interaction weight was defined for each pair of nucleotides asSimilarly, a ‘correlation weight’ can be defined by replacing i.e. all possible interactions are represented. Conversely, if only one interaction accounts in the distribution budget, then The interpretation is simple: if all weights are equal, then The previously defined interaction weights were averaged over all possible pairs of nucleotides at a given distance whereIn the PIM energy, shown in Eq. (1) from the main text, only Since the matrix Denoting by Finally, the full PIM energy is given by:https://github.com/msantolini/PIM. It allows one to build the different models given sequence data (ChIP-chip or ChIPseq for example) and an initial PWM. Data files used for Twist in the present study are also provided as test examples.The source code used in the present paper is available at File S1Supporting figures and tables. Figure S1. Dependence of the fit on the number of ChIP sequences. For each TF, the number of available ChIP sequences is plotted vs. the improvement in the description of its TFBS statistics, provided by the he PIM as compared to the PWM model. The latter is quantified by the ratio of DKL between the respective model probability distributions and the experimental ones provided by the ChIP data, Figure S2. Comparison of the different methods to define the basins of attraction. We compare two methods that allow to define the basins of attraction of the PIM model. Given an initial sequence, the attractor is found by changing iteratively either the nucleotide providing the strongest decrease in energy (deterministic method) or a random nucleotide providing a strict decrease of energy (random method). We show for the Figure S3. Same as Figure S4. Same as Figure S5. Background correlations.  Heat maps showing the correlations between nucleotides in the ChIP data of the Figure S6. Variable spacer length. We learned a PIM for Esrrb including the Table S1. Comparison between initial PWMs and  PWMs. Bottom rows correspond to the 6 factors that are satisfactorily described by the PWM model. Information content is in bits.(PDF)Click here for additional data file.


Baumannia, Blochmannia, Portiera, and Sulcia). The main results are the observation of a positive correlation between both rates with differences among lineages of up to three orders of magnitude and that the substitution rates decrease over long endosymbioses. To explain these results we propose three mechanisms. The first, variations in the efficiencies of DNA replication and DNA repair systems, is unable to explain most of the observed differences. The second, variations in the generation time among bacterial lineages, would be based on the accumulation of fewer DNA replication errors per unit time in organisms with longer generation times. The third, a potential control of the endosymbiont DNA replication and repair systems through the transfer of nuclear-encoded proteins, could explain the lower rates in long-term obligate endosymbionts. Because the preservation of the genomic integrity of the harbored obligate endosymbiont would be advantageous for the insect host, biological mechanisms producing a general reduction in the rates of nucleotide substitution per unit of time would be a target for natural selection.The availability of complete genome sequences of bacterial endosymbionts with strict vertical transmission to the host progeny opens the possibility to estimate molecular evolutionary rates in different lineages and understand the main biological mechanisms influencing these rates. We have compared the rates of evolution for non-synonymous and synonymous substitutions in nine bacterial endosymbiont lineages, belonging to four clades ( The main problem to estimate and compare the rates of sequence evolution among bacterial lineages is the difficulty to obtain the times of divergence of the compared species and strains. Although for culturable bacteria, in very short periods of time, an experimental approach in laboratory may be applied , the estBuchnera aphidicola), mealybugs, whiteflies, and tse-tse flies in comparison with close free-living relatives (Buchnera genes than in their corresponding orthologs in enterics (Buchnera) and the free-living relative were small , Buchnera and free-living enterics, respectively  of the genes ectively . Rates oectively , 2008.Although the analysis of a reduced set of genes may give clues about the rates of molecular evolution, the availability of the complete endosymbiont genomes may contribute to get a whole view of the rates of evolution in each symbiont lineage, not affected by the random or selective processes of each gene.Candidatus Sulcia muelleri” and “Candidatus Baumannia cicadellinicola,” hereafter Sulcia and Baumannia, respectively) of two sharpshooters species (Hemiptera:Cicadellidae:Cicadellinae) of the tribes Proconiini  and Cicadellini  were analyzed. For both symbionts, the start of the symbiosis took place before the divergence of both insect lineages, but while Sulcia has a long-term coevolutionary history with Auchenorrhyncha, the relation of Baumannia with sharpshooters is much more recent, because it replaced another ancestral symbiont in the subfamily Cicadellinae, probably as a consequence of a diet change from phloem to xylem   at genomic scale and the comparison among different endosymbiont lineages have been performed . In the to xylem . Because89-fold) . This clCandidatus Portiera aleyrodidarum” (hereafter Portiera), the endosymbiont of whiteflies , leading with time to the endosymbiotic genera “Candidatus Carsonella” and Portiera, respectively  and Sulcia (SUL) dN and dS gene estimations were obtained from the Supplementary Material of a previous publication  using twice the time of divergence of the lineages. We used a time of divergence between H. vitripennis and G. atropunctata of 66.2 My, an average of the estimations of 69 My and 63.4 My obtained for their Sulcia and Baumannia endosymbionts , whose period of evolution was estimated at 32.6 My (B. floridanus (BFL) and B pennsylvanicus (BPN), with a period of evolution of 18 My each  were plotted . A significant positive correlation (r2 = 0.54)  was observed under the linear regression model When the average values for dN/t and dS/t in each lineage were plotted , a positive correlation (r2 = 0.98) was more clearly observed. Sulcia was the slowest evolving lineage followed by three out of the four Portiera lineages. Portiera from B. tabaci and Baumannia evolved with intermediate values. Finally, the rates of evolution of the three Blochmannia lineages were very high with the values of B. floridanus around 140-fold and 600-fold higher than Sulcia for dN/t and dS/t, respectively. On the contrary, the average dN/dS ratios were similar in most of the lineages, except in Sulcia which showed a value of around 0.30 .Gene dN/t and dS/t values in the nine lineages (Sulcia and Portiera) evolve more slowly than the recent ones (Baumannia and Blochmannia). The exception is one of the lineages of Portiera, but it may be explained by the genome instability associated with this lineage . To explain this plot we need to consider mechanisms that alter simultaneously the rates of synonymous and non-synonymous substitutions. Both rates will not be the same because natural selection will favor the removal of most of the non-synonymous substitutions avoiding their transfer to the next generation but, in theory, it will have almost no effect over the synonymous ones, by its neutrality for most of the changes. However, natural selection may affect both rates simultaneously, if its action consists of selecting general mechanisms that either increase or decrease the mutation at any position of the DNA sequence. In this regard, we might predict that mechanisms lowering mutation rates would be favored in primary endosymbionts , although the observation of a lower number of genes in this category does not mean automatically a high substitution rate since it may be compensated by better performances of other components. Only within one clade, we can observe some changes that may be associated with an increase of the mutation rate based on the loss of DNA repair and replication enzymes. This is the case of Portiera from B. tabaci B and Q biotypes (Portiera strains (Baumannia and Blochmannia), are those displaying the largest substitution rates (Table 1).The first possible mechanism we considered is the efficiency of DNA replication and repair systems. The lower the efficiency, the higher the rates of mutation and substitution. The overall efficiency of these two systems involves many components. Not only is it difficult to analyze these components, but to determine how they are combined to generate an overall efficiency. Many mechanisms for DNA repair have been described in living beings including direct reversal, base excision repair, nucleotide excision repair and recombination . Their ebiotypes  which ha strains . HoweverFrom the first theoretical studies, generation time was considered as one of the main factors contributing to explain the variability of the rates of molecular evolution among lineages. The generation time hypothesis was based on the idea that species with shorter generation times would have larger rates of mutations per year considering that larger numbers of DNA replications per unit of time generate larger numbers of mutations. Initially, the effect of generation time in vertebrates was only clearly observed in synonymous rates . The difIn spite of the difficulties of study the effect of generation time in bacteria, it has been recently reported that, in Firmicutes, the DNA mutation and the protein evolutionary rates are smaller in spore-forming lineages than in non-spore-forming lineages . BecauseB. floridanus and B. pennsylvanicus could be explained by the behavior of their hosts (Camponotus floridanus and Camponotus pennsylvanicus) with the former displaying year-round activity and the latter winter dormancy (In our analysis with endosymbionts, generation time may explain the observed differences, although no information over the number of DNA replications per unit of time may be easily estimated. However, the differences between dormancy . These hSulcia and Portiera by one way and Baumannia and Blochmannia by the other are related to generation time, we cannot discard that it was natural selection, which selected that the former replicate their genomes with slower rates per unit of time than the latter. By this way, the degenerative effects associated with intracellular life will be weakened avoiding its replacement by a novel and more efficient endosymbiont (If the large differences among symbiont .B. aphidicola (Candidatus Evansia muelleri” may be compensated by the import of the insect nuclear-encoded protein (-10.The third proposed mechanism is based on the recent demonstration that an insect protein was transferred to an endosymbiont, in the aphid endosymbiont hidicola  and the  protein . After, The huge variation among the rates of synonymous substitution among lineages of endosymbiotic bacteria cannot be explained by differences in the genetic arsenal required to avoid the production of mutations. The fact that this variation also affects to the non-synonymous rates requires that one or several general mechanisms decreasing the number of substitution by unit of time were involved. The generation time effect could explain this variability but, it is not necessary that the hosts display differences in their generation times. A slow rate of DNA replication and cell division in the endosymbionts will be sufficient to reduce the number of mutations per unit of time. The idea that the hosts may take the control of the endosymbiont rate of DNA replication or decrease the number of mutations by the transport of nuclear-encoded proteins within the endosymbiotic bacterial cell should be explored.FS conceived research. FS and DS-G designed the analyses. DS-G performed the experiments. FS wrote the manuscript with inputs from DS-G. Both authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


It is now well documented that critically ill patients are exposed to stressful conditions and experience discomforts from multiple sources. Improved identification of the discomforts of patients in intensive care units (ICUs) may have implications for managing their care, including consideration of ethical issues, and may assist clinicians in choosing the most appropriate interventions. The primary objective of this study was to assess the effectiveness of a multicomponent program of discomfort reduction in critically ill patients. The secondary objectives were to assess the sustainability of the impact of the program and the potential seasonality effect.We conducted a multicenter, cluster-randomized, controlled, single (patient)-blind study involving 34 French adult ICUs. The experimental intervention was a 6-month period during which the multicomponent program was implemented in the ICU and included the following steps: identification of discomforts, immediate feedback to the healthcare team, and implementation of targeted interventions. The control intervention was a 6-month period during which any program was implemented. The primary endpoint was the monthly overall score of self-reported discomfort from the French questionnaire on discomforts in ICU patients (IPREA). The secondary endpoints were the scores of the discomfort items of IPREA. The sample size was 660 individuals to obtain 80 % power to detect a 25 % difference in the overall discomfort score of IPREA between the two groups (design effect: 2.9).The results of this cluster-randomized controlled study are expected to confirm that a multicomponent program of discomfort reduction may be a new strategy in the management of care for critically ill patients.NCT02442934 , registered 11 May 2015.ClinicalTrials.gov  It is now well documented that critically ill patients in intensive care units (ICUs) are exposed to stressful conditions and experience discomforts from multiple sources –7. TheseQuantification of discomforts is commonly performed using standardized methods as well as objective or subjective measures. Quantification of noise  or lightDifferent actions have previously demonstrated effectiveness in correcting and reducing specific discomforts. The optimization of alarm management and monitoring, the control of the sound level during verbal transmissions between the teams, and the implementation of music sessions, cycling light, and unrestrictive visiting policies have been shown to be efficacious in experimental and/or controlled studies –27. ThesAn alternative intervention can be proposed. The "assessment-feedback" approach, which combines systematic assessment with feedback to the healthcare teams, previously demonstrated its effectiveness by sensitizing the healthcare team. For example, patient satisfaction assessment combined with feedback has emerged as an important source of information for screening specific problems and developing effective plans of action for quality improvement in healthcare organizations . Also, aThese observations prompted us to establish a multicenter, cluster-randomized, controlled study with the objective of assessing the effectiveness of a multicomponent program of discomfort reduction in critically ill adult patients. This program combines three main components: assessment and identification of discomforts; feedback to the healthcare workers; and implementation of corrective actions. We hypothesized that this program may have a positive effect on the discomfort level self-reported by critically ill patients.The primary objective was to assess the effectiveness of the multicomponent program of discomfort reduction in the ICU. The secondary objectives were to assess sustainability and a potential seasonality effect of this program.The study design was built to achieve both the primary and the secondary objectives. To achieve the primary objective, a multicenter, cluster-randomized, controlled, single (patient)-blind, two-parallel group study was designed using the recommendations of the Consolidated Standards of Reporting Trials  statement . The cluTo assess the sustainability of the impact of the program, the study was completed with a second 6-month period during which the program was no longer applied in the ICUs randomized to the experimental group. During this second 6-month period, the program was applied in the ICUs randomized to the control group. We hypothesized that the multicomponent program of discomfort reduction is considered attractive for caregivers in the participating ICUs, leading to an initial preference for the investigators to be randomized to the experimental group. The inferred risk is better adherence to the study in the ICUs randomized to the experimental group in comparison with the other ICUs randomized to the control group and, therefore, an imbalance in the number of included patients in the two groups. That is the reason why the study was designed to offer the opportunity to all the ICUs to implement the program, no matter when the cluster randomization may occur (whether during the first 6-month period or during the second 6-month period).Moreover, the prolongation of the study for a 6-month period  allows assessment of the potential seasonality effect of the program (because the program will be applied during another part of the year) and the ability to obtain eventual confirmation of the impact of the program in other ICUs.Thus, five periods are differentiated and illustrated in Fig. Unité Aide Méthodologique à la Recherche Clinique, AP-HM, France) and the Self-Perceived Health Assessment Research Unit . A steering committee includes the coordinating investigator, six French ICU experts, and three nursing managers, each of them working in one participating ICU center, and the head of the Clinical Research Unit , is responsible for the study. The methodological support will be provided by the Clinical Research Unit . On the day of ICU discharge, the bedside nurse will find the patient, whose information has already been entered; the nurse will connect to the electronic-specific database and will test eligibility and exclusion criteria by following a step-by-step approach (screening process). If the patient presents eligibility criteria and has no exclusion criteria, then the bedside nurse will have to assess the discomforts perceived by the patient. To ensure adequate training of the nursing staff, the application (access to the specific website and use of a preproduction database) will be used in each ICU for a training period of several weeks with technical and educational support provided by the coordinating investigator and the coordination team before the first patient is included.Discomforts assessment: a web-based systematic measurement of the patient discomforts will be performed using IPREA by the bedside/institutional caregiver (including nurses and assistant nurses) in charge of the patient  will be displayed on the screen before the application is closed by the nurse or the assistant nurse. Moreover, the overall discomfort score will be automatically calculated and presented on the screen to the nurse at the end of the administration of IPREA. Second, monthly and cumulative discomfort scores and their relative ranking compared to other ICUs assigned to the experimental group will be forwarded each month to the local champions by the coordination team.Targeted interventions: an implementation of targeted interventions based on the monthly feedback will be planned in each ICU to reduce discomforts. Once the local champions have received the results of their ICUs, they will organize a meeting with their ICU team to present and comment on their results. At the end of these monthly meetings, the local champions will consult with the staff  and select appropriate actions. These actions may correspond to the most important perceived discomforts in the ICU, those perceived at a higher level in comparison with the other ICUs randomized in the experimental group, or those that appear most easily preventable. During these meetings, local champions will also assess the effectiveness of previously decided measures, identify any barriers to implementation, and suggest solutions to resolve these barriers. It is important to note that the measures implemented in the ICU are not decided by the steering committee or by the coordination team. The local champions consult with their staff to define the best targeted measures for their ICUs. Therefore, these measures will be different from one ICU to another. After each monthly meeting, local champions will send the meeting report with the list of participants to the coordination team. Beyond this role of meeting moderators, the local champions will raise the awareness of the staff about discomfort prevention and disseminate and facilitate the ongoing measures for discomfort prevention on a daily basis.The program comprises the following three stages:During a 6-month period, the ICUs will apply any specific program. The control period will be based on usual care management. However, during the last month of this 6-month period, the caregivers will have to assess discomfort among the eligible patients without receiving any type of feedback.Active consultation is conducted by the coordinating investigator among ICU heads in each potential participating ICU to ensure agreement regarding implementation of the program on the ICU level. Computer-generated randomized lists will be drawn up before the beginning of the study using a permuted block design, for which the clinical research unit (AP-HM) will be responsible. The randomization will be stratified by the average monthly number of patients not deceased during ICU discharge during the year preceding the start of the study . The randomization will assign each ICU  to either control or experiment. The investigators and caregivers will not be blinded, but all participants will be unaware of the ICU allocation.The following ICU-specific parameters will be recorded: type of ICU ; number of beds; mean of stay duration; annual mortality rate.Data related to the patient will be recorded from an electronic case report form (eCRF) specifically elaborated for the study. The following data will be collected: demographics ; health status before the ICU stay using the Knaus score  and the The endpoints pertain to the individual level. Patient discomfort will be assessed using the French self-reported discomfort IPREA questionnaire ("Inconforts des Patients de REAnimation": Discomforts perceived by ICU patients) . IPREA iThe primary endpoint is the monthly overall discomfort score of the 16-item IPREA .The secondary endpoints are the monthly discomfort scores of the 16 items included in IPREA.The sample size was determined to detect a 25 % difference in the overall score of IPREA between the two groups. This 25 % difference, considered to be clinically significant, was based on a previous report indicating a mean overall discomfort IPREA score of 22 (standard deviation of 14). The intracluster correlation coefficient (ICC) was estimated from the database of the IPREA questionnaire validation study. With a design effect calculated to be 2.9 , a two-sided significance level of 5 %, and 80 % power, 630 IPREA scores are needed (315 in each group) to detect this difference. To prevent 10 % of the questionnaires being unusable or invalid, a total of 700 individuals should be included.The data will be analyzed using SPSS version 17.0 software and SAS/STAT® version 9.2.The primary  and secondary (16-item scores of IPREA) endpoints will be analyzed on the individual patient level using cluster level summaries, which will account for the correlation between patients in the same center. Analysis methods based on individual patient-level data perform well when the number of clusters is high . The oveThe primary analysis will be performed on the intention-to-treat population corresponding to all included individuals in each participating ICU. A secondary analysis will be performed on the per-protocol population corresponding to all included individuals who complete the IPREA questionnaire. Supplementary sensitivity analyses will be performed on a subgroup of ICUs corresponding to ICUs with a recruitment ratio (ratio of number of collected questionnaires to eligible patients) higher than 0.7, 0.8, and 0.9.t-tests or Mann–Whitney tests for continuous variables.Descriptive analyses will provide ICU and patient characteristics for each group . The normality of the parameters will be estimated using frequency histograms and the Shapiro test. Comparisons between the two groups will be performed using the main individual characteristics using chi-squared tests or Fisher exact tests for qualitative variables and Student All analyses will account for clustering to ensure correct type I error rates and confidence intervals . Approprt-tests or Mann–Whitney tests according to the variable distribution. A set of secondary analyses will be adjusted for covariates of clinical interest using generalized linear mixed regression models including the group , ICUs and individuals (as random variables), a priori covariates , and other co-variables selected with a threshold p-value <0.05 during univariate analysis. These models will be performed using the MIXED SAS procedure (version 9.2). The same procedure will be performed on the 16 IPREA scores for each discomfort item , and the International Conference on Harmonization (ICH) E6 Guideline for Good Clinical Practice. Regulatory monitoring will be performed in accordance with the French law requiring the approval of the French ethics committee . All records and subjects’ identities will remain confidential in accordance with the following French regulations: the French National Committee of Informatics and Liberties  and the French Consultative Committee for Data Processing in Health Research . Oral consent will be collected for each participant.The study will be conducted in accordance with the Helsinki Declaration, French laws and regulations .We hypothesize that routine assessment of patient discomforts will be useful for further investigation and implementation of targeted preventive/curative actions. A previous study using the IPREA questionnaire for patient discomforts assessment has shown that most of the identified discomforts may be relatively easily relieved by interventions conducted by the ICU team. This program is in accord with the message of the 2009 French consensus conference centered on “a better life in ICU,” which lists recommendations to reduce stressors and discomforts. This program is based on the "plan-do-check-act" (PDCA), which is a management method for the control and continuous improvement of widespread processes and products in business and contributes to quality management and continuous quality improvement in healthcare . The PDCHowever, some issues related to this specific protocol study should be developed. First, the choice of a cluster design should be discussed. Because the experimental program is applied to the whole ICU and affects all the individuals within the ICU, the individual randomization will not be appropriate . To fit Second, the option of parallel design may be discussed. Although the large differences in patient populations between ICUs should lead us to prefer a crossover design  over a Third, the fact that the healthcare team will not be blinded should be considered in the interpretation of the final results. The implementation of a program of discomfort reduction associated with systematic assessment of patient discomforts may have a direct influence on healthcare behaviors. The caregivers could make more of an effort than usual, leading to better attention to the patient’s discomfort and the potential sources of discomfort. This phenomenon is well described and is known as the Hawthorne effect, and it may involve underestimation of the intervention effect , 50. ThiDespite the acknowledged need to consider discomfort issues, self-reported assessment remains underutilized in clinical practice. Environmental barriers have been described to explain why self-reported measures have not been routinely implemented in clinical practice , 52. TimIn conclusion, the results of this cluster-randomized controlled study are expected to confirm that a multicomponent program of discomfort reduction may be a new strategy for the care of critically ill patients.At the time of first manuscript submission, the trial was currently recruiting participants. The study completion date was October 2015.


The complexity of shoulder mechanics combined with the movement of skin relative to the scapula makes it difficult to measure shoulder kinematics with sufficient accuracy to distinguish between symptomatic and asymptomatic individuals. Multibody skeletal models can improve motion capture accuracy by reducing the space of possible joint movements, and models are used widely to improve measurement of lower limb kinematics. In this study, we developed a rigid-body model of a scapulothoracic joint to describe the kinematics of the scapula relative to the thorax. This model describes scapular kinematics with four degrees of freedom: 1) elevation and 2) abduction of the scapula on an ellipsoidal thoracic surface, 3) upward rotation of the scapula normal to the thoracic surface, and 4) internal rotation of the scapula to lift the medial border of the scapula off the surface of the thorax. The surface dimensions and joint axes can be customized to match an individual’s anthropometry. We compared the model to “gold standard” bone-pin kinematics collected during three shoulder tasks and found modeled scapular kinematics to be accurate to within 2mm root-mean-squared error for individual bone-pin markers across all markers and movement tasks. As an additional test, we added random and systematic noise to the bone-pin marker data and found that the model reduced kinematic variability due to noise by 65% compared to Euler angles computed without the model. Our scapulothoracic joint model can be used for inverse and forward dynamics analyses and to compute joint reaction loads. The computational performance of the scapulothoracic joint model is well suited for real-time applications; it is freely available for use with OpenSim 3.2, and is customizable and usable with other OpenSim models. The human shoulder is a complex mechanism that enables a wide range of motion and provides the maneuverability and structural support necessary to perform day-to-day activities and athletic feats . While t1) evaluate the accuracy of scapular kinematics from the model of the scapulothoracic joint against “gold standard” bone-pin data measured during shoulder flexion, abduction, and internal rotation tasks; 2) test whether the model can reduce kinematic error due to systematic and random noise in marker trajectories; and 3) assess the computational performance of the model for inverse kinematics, inverse dynamics, and forward dynamics simulations.We have developed a biomechanical model of the scapulothoracic joint capable of fast and accurate kinematic and dynamic analyses. The scapulothoracic joint model is based on an internal coordinate joint formulation  that capSeveral shoulder models have characterized the mechanics of the scapula in the study of upper-extremity function –13, as sAnglus Acromialis) of the scapula relative to a frame fixed to the clavicle, assuming a ball joint between these segments. Although ISB-recommended angles have been the standard description for scapular kinematics for nearly a decade, a consistent set of “normal” scapular kinematics for a standardized set of tasks has yet to be established. In contrast, clinical gait analysis routinely utilizes deviation from normative data [The International Society of Biomechanics (ISB) recommended standard for describing the motion of the upper-extremity including the scapula  has beenive data  as a tooThe rotations (Euler angles) and translations of the scapula are coupled . As the The motion of the scapula is influenced by muscle forces and joint reaction forces, which arise from the thoracic surface as well as the acromioclavicular and glenohumeral joints. Muscle forces are dependent on scapular kinematics, which determine the length, velocity and effective moment arms of the large muscles that attach to the scapula. Joint reaction forces, especially between the scapula and thorax, are primarily a consequence of muscle loads. The more the structure of the scapulothoracic contact restricts the motion of the scapula in the direction of muscle forces, the greater the reaction forces will be.To solve for the acceleration of a constrained multibody system, both applied forces and constraint reaction forces must be accounted for and the mass matrix must be inverted. Here a fundamental numerical problem arises since applied forces are large and the mass of segments are small, thus the solution for system accelerations approaches a singularity. This problem has been widely ignored. Several models of the upper extremity  were noThe inability to accurately and reliably describe the kinematics of the scapula and/or to enforce physiological scapula motion during dynamic simulations  necessitmobilizer [We developed a model of the scapulothoracic joint using a new formulation of the internal coordinate joint, called a obilizer . We thenabduction-adduction [elevation-depression [upward rotation [internal rotation or “winging” [Angulus Inferior of the scapula to lift off the thoracic surface. The origin of the joint frame on the scapula is specified as the centroid of the Angulus Acromialis (AA), Trigonum Spinae (TS) and Angulus Inferior (AI) markers, which are the anatomical landmarks recommended by the ISB [We characterized the scapulothoracic joint by the translation and rotation of the scapula on the surface of the thorax modeled as an ellipsoid , 10. Therotation , and 4) winging” , 28. The the ISB . The oriInternally, the scapulothoracic joint is composed of two mobilizers: an ellipsoid mobilizer  and a piIn Simbody, the kinematics of a body, B, with respect to its parent, P, are fully described by the following four mobilizer equations:PXB, composed of the rotation matrix, R, and translation vector, p, of a mobilizer frame, B, fixed in the body (Bo frame) with respect to a parent mobilizer frame, P, fixed in the parent body (Po)  in Eq , if we do not assume H(q) to be constant, H(q) can represent curvilinear paths in the 6-dimensional spatial kinematics basis of a body with respect to its parent. For instance, H(q) can represent movement about a helix or slider path that is curved in space (curvy slot), or other coupled motions, which are typical of biological joints [Eq  describev) in Eq , and accAB in Eq , of B winship Eq  with thel joints .u, as a ball-and-socket, which are the components of the angular velocity of the child (scapula) joint frame with respect to the parent joint frame on the thorax (center of the ellipsoid). However, rather than grant 3 additional mobilities for spatial translations, which would then have to be constrained, the translations are coupled to the orientation of the scapula:q is the scapula-fixed Euler angle sequence relating to abduction (θ1), elevation (θ2) and upward rotation . The vector p(q) describes the translation of the scapula’s joint frame, S, on the surface of an ellipsoid, fixed in the thorax joint frame, T:n = n(q) is the surface normal expressed in the thorax, and h, w, d are the ellipsoid radii along the axes of T that correspond to height, width and depth, respectively. The angular velocity remains the same function of u as for a ball-and-socket joint, so N isThe ellipsoid mobilizer of the scapulothoracic joint enables the abduction (adduction), elevation (depression) and upward (downward) rotation of the scapula with respect to the thorax. The scapulothoracic ellipsoid mobilizer has the same 3 mobilities, Since there are coupled linear velocities as a consequence of the rotating normal vector, the ellipsoid mobilizer matrix has the following form:In this case THS and its derivative The mobilizer matrix th dof of the scapulothoracic joint, which is internal rotation of the scapula about a winging axis in the plane of the scapula that enables the medial border and the inferior angle (Angulus inferior) to raise off the thoracic surface  and recurse inward towards the ground to form the complete set of system accelerations, M is the system mass matrix in the mobility space; G is the Jacobian matrix of the kinematic constraints; b contains the coordinate and mobility (speed) terms of the differentiated constraints; λ are the constraint Lagrange multipliers corresponding to components of the constraint forces; fapplied is the net applied generalized force, and finertial is the net velocity-based generalized force due to rotating frames. Given system accelerations  to compute the trajectory of coordinates and mobilities forward in time .Eqs –4 definequations . When poquations  of this quations :Mu˙+GTλMs is the spatial inertia matrix of the scapula and fi is an applied spatial force on the scapula; TRGS is the unknown thoracic reaction load applied to the scapula expressed in ground; λac) on the scapula expressed in ground. The system accelerations  in the rigidly secured sensor frame, which had a measurement accuracy < 1mm [We evaluated the kinematic accuracy of the scapulothoracic joint by comparing thorax, scapula and humerus model marker locations to measured marker data from bone-pin motion-capture experiments . Ludewigcy < 1mm . CombiniWe scaled upper-extremity segment dimensions to obtain a match between model markers and corresponding experimental marker locations identifying the same bony landmarks. Scaling functionality was provided by the Scale Tool in OpenSim , 31.We customized the location and orientation of the thoracic ellipsoid surface, with respect to the thorax origin and the location and orientation of the joint reference frame with respect to the scapula origin on the acromion according to the ISB recommendations , to consOnce the upper extremity model was appropriately scaled, we performed inverse kinematics to determine the joint coordinates of the model that minimized the weighted-least-squares error between model marker and corresponding experimental marker locations at each frame of the movement trial , 30. We We evaluated the robustness of kinematics determined from the scapulothoracic joint model by introducing errors in the experimental bone-pin marker locations to simulate the effects of using surface markers attached to the skin. The purpose of the noise model was to test the effect of random error and bias in the markers movements relative to the bone, such as stretching, translation and rotational offsets, as well as warping. The noise model combines a systematic bias (as a function of the movement) and a random component (for the offset direction and white noise), and we tested a range of error magnitudes. We specified the mean and standard deviation of the noise distribution for each marker at each instant in the movement. We used skin marker errors (location on skin surface to corresponding fixed bony landmark location) measured by MRI  to speciWe computed Euler angles from the noisy marker data according to the ISB standard  after loWe next performed an inverse dynamics analysis to calculate the generalized forces necessary to produce the reconstructed shoulder motions  coordinates, velocities, and accelerations). Coordinates were determined from inverse kinematics of bone-pin measurements. A third order lowpass Butterworth digital filter with a 2Hz cut-off was used to filter the coordinate data and a generalized cross-validated quintic spline  was usedThe multibody dynamic equations of motion were used to determine the acceleration of the joint coordinates in response to applied forces. The velocities and accelerations were integrated forward in time to yield the position and velocity of the coordinates to generate a simulation of passive arm swing. Passive force elements prevented the scapula and humerus from reaching non-physiological positions. We initialized the simulation to be at rest at the maximum humerus elevation angles calculated from the bone-pin trial of each task, then allowed the arm to swing passively for a duration of 2s. The total system energy was computed and monitored during each simulation above. We used a variable step Runge-Kutta-Merson integration algorithm  with a rAfter scaling the shoulder model, including the dimensions of the scapulothoracic joint, the inverse kinematics of the joint were in excellent agreement with bone-pin measurements. On average, the model was accurate to within the measurement error  of the bone-pin markers from the experimental data collection procedure .The scapular kinematics resulting from the scapulothoracic joint in ISB standard rotation sequence  the problem is exacerbated . AlthougThe mobilizer formulation  of the sIn addition to greater accuracy and faster computational performance, the scapulothoracic joint yields scapular kinematics that may also be easier to interpret and compare across subjects and populations. The coordinates of the scapulothoracic joint uniquely span the functional kinematics of the scapula while the recommended ISB angles parameterize a (Y-X-Z) gimbal joint at the clavicle. It is difficult to interpret the location and orientation of the scapula from Euler angles of the gimbal joint without visualization of its spatial location. To illustrate this point, consider winging of the scapula , a measure of clinical importance , 44. TheAlthough the results of the model are promising, there are important limitations to consider. First, the shoulder model employing the scapulothoracic joint was scaled to one healthy subject’s experimental bone-pin data for three shoulder tasks. The small RMS error between the model and experimental markers is, in part, a function of how well the thoracic surface of the scapula is represented by an ellipsoid. For other subjects and particularly those with pathologies, the smooth ellipsoid surface assumption may not be as accurate. Second, skin-surface markers were not measured in conjunction with bone-pin measurements; therefore, we are unable to directly assess the ability of the scapulothoracic joint to attenuate skin movement and other soft-tissue artifacts. The application of systematic and random noise may overestimate the effect of measurement noise on kinematic accuracy, particularly in the direct measurement (no model) case. In practice, the ellipsoid parameters will not be optimized to fit high accuracy data (as performed with the bone-pin measurements) which would also impact reconstruction accuracy. Despite these limitations, for an extensive set of added noise, the model-based reconstruction was at least twice as accurate as without the model, which assures us that under typical circumstances the scapulothoracic joint will not increase the effect of measurement noise. We urge others to test the scapulothoracic joint model for their study population and movements.The scapulothoracic joint will not alleviate all the difficulties of characterizing scapular kinematics. Particularly, defining the joint frames on the thorax and scapula bodies from clinical data remains challenging. Applying the ISB-recommended frames defined by anatomical landmarks on the thorax and scapula (including stylus measurements) can serve to define the scapulothoracic joint that connects the scapula to the thorax and specify the thorax (ellipsoid) and scapula dimensions. To be effective, we must test the sensitivity of reconstructed kinematics to the joint frame definitions and surface dimensions across multiple subjects. Developing a standard set of poses from which to prescribe reference angles for the humerus (relative to the thorax) and identify the thorax position and orientation in ground , could enable automatic scaling and registration of experimental markers into their model counterparts, for example a rigid acromion marker cluster , 45, to https://simtk.org/home/scapulothoracic) to enable widespread exploration, testing, and adoption.We are poised to automate shoulder movement reconstruction from existing marker placement recommendations  or to ap