The identification of the sound sources present in the environment is essential for the survival of many animals. However, these sounds are not presented in isolation, as natural scenes consist of a superposition of sounds originating from multiple sources. The identification of a source under these circumstances is a complex computational problem that is readily solved by most animals. We present a model of the thalamocortical circuit that performs level-invariant recognition of auditory objects in complex auditory scenes. The circuit identifies the objects present from a large dictionary of possible elements and operates reliably for real sound signals with multiple concurrently active sources. The key model assumption is that the activities of some cortical neurons encode the difference between the observed signal and an internal estimate. Reanalysis of awake auditory cortex recordings revealed neurons with patterns of activity corresponding to such an error signal. Auditory scenes are generally composed of sounds produced by multiple sources. The observed complex auditory signal is a superposition of these sources, making the identification of the individual sound elements a non-trivial problem Fig. 1A.In one family of computer algorithms, the blind source separation algorithms Fig. 1B,An alternative family of computer algorithms assumes that the elements that are present in a scene belong to a large, but finite, dictionary of known sounds Fig. 1C A particular auditory scene activates only a few from the large number of neurons available in the auditory cortex Corrected Projections Algorithm (CPA). It only uses the minimization of the difference between the sensory representation of the incoming sound and an internal estimate to identify the sources present in the auditory scene. CPA does not explicitly minimize the number of active dictionary elements; the sparse representation is a direct consequence of the model design. The estimated parameters indicate the presence or absence of a particular dictionary element and its behavior matches certain aspects of the psychophysics of auditory stream perception. Here, we propose the hypothesis that the architecture of the corticothalamic circuit matches an efficient circuit implementation of CPA, and we show cortical recordings that are consistent with the proposed role of auditory cortex in the implementation of CPA.In this paper, we propose a new dictionary-based algorithm, the k has a characteristic frequency spectrum ), that is at any instant t, the superposition of several of these sources creates the observed signal CPA uses similar assumptions as previous source identification algorithms Fig. 1D,n possible sound elements n>>f, where f is the number of features of the signal f will be the number of frequency bands of the spectrum) that includes the sources T samples of the observed signal i = 1..n Similar to previous algorithms ). The second difference is in the way that multiple observations are processed. In the standard algorithms, the estimated parameters T observed samples . This uniqueness property of CPA contrasts to other algorithms that require additional constraints to find unique solutions The inclusion of the similarities  = 1 for each one of the few sources participating in the scene and  = 0 for the large number of dictionary elements that are not part of the scene . This typical binary behavior of the presence parameters f of the input signal is large enough, potentially allowing the use of very large dictionaries . If the sources in a scene can be represented by orthogonal elements, a common approach is to estimate them using Principal Component Analysis (PCA). However, PCA might require larger amounts of input data than CPA because PCA does not incorporate prior information from a dictionary .If signals that match the sources in the scene are part of the dictionary Fig. 3A,Fig. S3).The presence parameters of CPA indicate the presence or absence of learned sound objects that are already part of the dictionary. An element that has not been encountered before will not be recognized, as there won't be a single presence parameter with a value of 1 indicating its participation in the scene. Instead, it will appear as small values over multiple presence parameters . K(T) represents the uncertainty about the stored presence parameters K(T), which depends on the dictionary elements This efficient implementation of CPA, which we call  or iCPA Fig. 4, f by f matrix (see ), where f is the number of features of the input signal. Therefore, iCPA can handle very large dictionaries  because as the number of features of the dictionary elements increases, the dictionary elements will be closer to being orthogonal and iCPA presence parameters will be closer to the ideal estimate, i.e. ones and zeros . This widespread low activity profile impairs the detection of the known elements if the new element is too loud . The widespread low level activity profile could be used to indicate the presence of a new sound source that needs to be acquired ICPA is also robust to the presence of unknown dictionary elements in an auditory scene because an element that is not part of the dictionary and, hence, is not represented by a large activity of a single presence parameter, shows up as a low activity profile that is spread across multiple dictionary elements .ICPA also requires a massive feedback signal K should have a large value if the input K. Therefore, the behavior of K matches the time course of the uncertainty about which sources are present; in the silence preceding a scene there is a large uncertainty about which sources are present. As the scene continues, there is more information and the uncertainty diminishes. We therefore labeled the elements of K the uncertainty associated elements.The elements of K but also on the estimation error equation 5), which causes a difference in behavior between K. In order to illustrate this behavior, we have simulated the responses of a train of clicks  we argue how this problem could be resolved.CPA implies that a single auditory source is represented by a single dictionary element, which is in contrast to other sparse representation approaches where a single source can be represented by more than one dictionary element Fig. S3) providing an indication that something unfamiliar is being presented which could be added to the dictionary. In order to create the dictionary, the animal should be continuously acquiring the sources that it is exposed to. Although there are multiple algorithms that are capable of learning these sparse overcomplete representations In order to identify a source, CPA also requires that each single element that corresponds to a source should already be part of the dictionary. If such element is not yet part of the dictionary, CPA will, of course, fail to recognize this sound in that the source won't be assigned a single presence parameter. However, an unknown source evokes small values over multiple presence parameters , and these features have not been reported for other brain areas. However, this all is far from being a proof that the algorithm is implemented in the corticothalamic system. Specifically feedback is a quite general feature across different levels of the auditory system, including inferior colliculus Concerning the implementation of iCPA, we have argued in favor of the hypothesis that the auditory cortex is the place where the conversion from a signal estimation error into a presence parameter error occurs. We base this hypothesis on the sparseness of the response and the massive corticothalamic feedback. Also, other implications of iCPA for activity signaling errors in the presence parameters seem to coincide with reported features of auditory cortex .Although responses in auditory cortex are sparse eterssee . SecondlThe iterative implementation of CPA, as well as other frameworks of cortical function ICPA makes a prediction about two types of behaviors in cortical cells, with one type encoding the error in the presence parameters and a second type encoding the associated uncertainty. We postulate that these two populations could be distinguished by their levels of spontaneous activity and we found that the spontaneous activity can be used to determine the level of adaption, which according to iCPA differs for the two cell populations. There are two pairs of known candidates. One pair consists of the fast spiking interneurons and the regular spiking neurons in which fast spiking interneurons have higher firing rates than regular spiking neurons The proposed model provides a mechanism for how the corticothalamic system solves the source identification problem in agreement with the observed physiology of stream segregation in auditory cortex We have used the spectrogram to characterize the auditory sources In order to be able to identify sources in a complex auditory scene, CPA requires that all the sources present are already stored as dictionary elements. Therefore, the auditory system should be learning samples of all the sounds that it encounters to improve its performance of sound identification in complex scenes. Although we do not know how extended this learning of sound is, a recent study has shown that humans, without being aware, learn a random spectro-temporal modulations of noise We note that the presence parameters  to zero Fig. 6. Analyzing scenes composed of amplitude modulated sources is a problem that also appears in other modalities, such as olfaction i = 1..n that will minimize the average minimal square error between the signal t = 1..T, and the estimate of that signal k = 1,..,n,k = 1,..,n, are unit vectors. This problem can be solved as linear least square minimization n by n matrix.The estimation of the presence parameters in CPA was done by finding the set of presence parameters The auditory scene consisted of 180 samples of a 10 dimensional mixture signal generated by the linear combination of two vectors, given by:The dictionary consisted of additional 16 vectors, whose elements were taken from a normal distribution of mean zero and variance one. All the 18 dictionary elements were normalized to have unit value and zero mean.The dictionary of elements used was the same as n-dimensional presence parameter column vector f-dimensional column vector n dictionary elements, creating an f by n matrix n by f matrixn by n matrix tr symbol.ICPA consists of calculating a new As an initial value for We evaluated iCPA with T = 10 observations of the auditory scene. There, the estimated presence parameters reached a steady state. The components of dictionary elements were taken from a uniform distribution between zero and one. Afterwards, each dictionary element had its mean subtracted and were normalized to unit length. The auditory scenes were generated by taking a few elements from the dictionary and modulating them using a normally distributed amplitude modulation with zero mean and variance 1. The matrix f = 111 features for the dictionary elements. The spectrograms were calculated using the Matlab function SPECGRAM with each window smoothed using a Hanning window.The sound produced by the cricket and the violin, playing a C4 were sampled at a frequency of 22050 Hz. In order to generate the spectrograms, a window of 10 ms was used, yielding 111 frequency bands. This would correspond to The dictionary elements that represent the violin and the cricket were calculated by taking the average spectrum of the presentation of the cricket and the violin. These average spectra were incorporated as two of the 400 dictionary elements.In order to generate the other 398 dictionary elements, a collection of music files were resampled to a frequency of 22050 Hz and the spectrograms were calculated using the same parameters as the violin and the cricket sound. Spectrograms were calculated for 30 seconds segments and the Principal Component that captured 95% of the variance of the spectrogram was incorporated into the dictionary, yielding a dictionary with elements that matched the spectra of natural sounds. The matrix f = 100 features and a dictionary of 150 elements. Each element was a 100-dimensional column vector. The elements of dictionary elements were taken from a uniform distribution between zero and one. Afterwards, each dictionary element had its mean subtracted and it was normalized to unit length. The observed signal 100-dimensional column vector and consisted of a train of 9 pulses. The pulses were generated by applying one of the elements of the dictionary with unit amplitude for one time step. The silence period between pulses was equal to 30 time steps. Constant additive Gaussian noise with zero mean and standard deviation of 0.002 was added to the pulse train. The simulation initial parameters P (0) matrix was a diagonal matrix with the diagonal elements equal to 0.5.We used a signal of We assumed the cortex to receive two types of inputs. The first one is the error in the estimation The other elements required for the implementation of iCPA are the matrices n by f matrix h = 1..n, i = 1..f is considered as the response of one particular neuron or group of neurons.Cortical cells have a preferred frequency n dimensional parameter error signals f frequencies is not one-to-one. However, we can naturally decompose the error signal In the case of the h = 1∶150, i = 1∶100, were combined together to calculate the correlations. The Spearman correlation between the spontaneous activity and the normalized response of the last pulse was r = 0.51, p<1e-12, with the 95% confidence interval between 0.50 and 0.52. The correlation between the evoked activity and the normalized response of the last pulse was smaller, r = 0.34, p<1e-12, with the 95% confidence interval being between 0.33 and 0.35.Both groups of elements, We used previously published data of 32 awake rat auditory cortex neurons in response to trains of clicks. For more details, see The correlation coefficients were calculated using the Spearman's rank correlation coefficient. The correlation between the spontaneous activity and the normalized response of the last pulse was r = 0.74, p = 2.81e-5, with the 95% confidence interval of the correlation coefficient between 0.48 and 0.88. There was no significant correlation between the evoked response to the first click of the train and the normalized response of the last pulse .Figure S1Sources that have some spectral overlap are still separable using CPA. (A) Two elements that show some degree of overlap are mixed. The figure has the same structure as (TIF)Click here for additional data file.Figure S2ICPA can identify sources present with less observations of the auditory scene than Principal Component Analysis. We would like to compare the capability of iCPA in identifying the sources that generated a signal with the capability of Principal Component Analysis (PCA) as a function of the number of observations of the auditory scene. We generated an auditory scene by using 10 vectors of f = 500 features, selected from a dictionary of n = 1000 possible elements, amplitude-modulating them and combining the amplitude modulated signals. The amplitude modulation of each source, at each time step, is given by is a number, uncorrelated across the 10 sources and uncorrelated in time, taken from a lognormal distribution of log mean value of zero and log standard deviation of 2. At each time step, the combination of the 10 signals created the auditory scene. Besides, at each time step, a 500 dimensional uncorrelated noise, taken from a Gaussian distribution with zero mean and 0.5 standard deviation, was added to the auditory scene. We evaluated iCPA and the Principal Component based method by assessing its performance in identifying the sources present for different number of observations of the signal. The performance of iCPA was given by how many of the 10 largest (TIF)Click here for additional data file.Figure S3Estimated parameters for a source that is not part of the dictionary is distributed across multiple dictionary elements. ICPA was presented with a source of f = 300 features that was not part of a dictionary of n = 1000 possible sources, with an amplitude of 1. The new element appears as low level activation on multiple presence parameters.(TIF)Click here for additional data file.Figure S4CPA can identify dictionary elements even in the presence of unknown elements. (A) ICPA identifies the two random non-orthogonal sources of f = 300 features using a dictionary of n = 1000 possible sources. The mean amplitude of these known dictionary elements was one. (B–D) Adding an extra source that is not part of the dictionary with increasing standard deviation amplitude of 0.25, 0.5 and 1 causes the larger level of background activation in the presence parameters. However, the iCPA is robust to the presence of this “non-dictionary” element.(TIF)Click here for additional data file.Figure S5There was a correlation between spontaneous activity and normalized response of the last click for a 20 click per second train. (A) There is a significant correlation between the spontaneous firing rate and the normalized response of the last click of a 20 click/sec (B) There was no significant correlation between the normalized response of the last pulse and the response evoked by the first click (see ).(TIF)Click here for additional data file.Figure S6Correlations between normalized response of the last click and spontaneous activity was maintained after subtracting the spontaneous activity. There was a significant correlation between the spontaneous activity and the normalized response of the last click of (A) the 5 click/sec train and (C) the 20 clicks/sec train. The responses to the clicks were calculated by subtracting the spontaneous activity from the evoked response. There was no significant correlation between the spontaneous subtracted normalized response of the last click and the evoked activity for neither the 5 clicks/sec train (B) nor for the 20 clicks/sec train (D). (See )(TIF)Click here for additional data file.Text S1Definition of auditory scene.(DOC)Click here for additional data file.Text S2Corrected projections algorithm.(DOC)Click here for additional data file.Text S3Proof that CPA detects the elements present in a mixture.(DOC)Click here for additional data file.Text S4Effects of auditory scene complexity and dictionary size on CPA performance.(DOC)Click here for additional data file.Text S5Recurrent implementation of CPA.(DOC)Click here for additional data file.Text S6Statistics.(DOC)Click here for additional data file.Audio S1Cricket only.(WAV)Click here for additional data file.Audio S2Violin only.(WAV)Click here for additional data file.Audio S3Cricket+violin.(WAV)Click here for additional data file.


Every year approximately 3.7 million neonatal deaths occur worldwide plus an additional 3.3 million stillbirths. Thirty-eight percent of all under 5-year-old deaths are concentrated in the first 28 days of life, 75 % of them in the first 7 days, making the first week the most dangerous period of a lifetime. Sub-Saharan Africa remains the most dangerous region to be born, here 1.16 million babies do not survive more than 28 days and every year, half a million babies die within the first 24 h of life , and place of birth .As reported in Table Even when we restricted our analyses to the neonatal population of VLBW, we also observe a large and constant increased number of treated patients (>400 pt/year) with a significant increase in survival from 23 to 50 %. These samples were not selected since the birth weight and the gestational age did not change much in the study period. This result is obviously much less encouraging in ELBW infants who require mechanical ventilation, path still not viable in our hospital  and oxygen, and cost due to increased consumption of electricity.The improved level of care and the observed increase in survival came at a reasonable price. Hiring and training of the personnel was the major spending issue, while drugs and electricity were less expensive than expected. However, most families could not pay even the modest fee charged by the hospital and thus almost 80 % of the patients were treated at no cost for the parents.To get more state funds for the Neonatal Pathology Unit, the Direction of the CMSC is trying to implement educational activities for students and staff of the other Birth Centers as well as using patronage of foreign organizations.We were able to show that it is possible to increase survival of newborns, and especially premature newborns, in a developing country with low cost measures. In particular, survival of VLBW babies almost doubled from 23 % in 2005 to 50 % in 2010. This success was achieved mainly through training highly motivated staff in basic newborn care, including fluid management, positioning, monitored oxygen supply, and correct nutrition together with a dedicated patient chart system. Assigning each nurse her individual patients for whom she would be responsible during her shift, and assuring complete hand-over of all relevant information during shift change also contributed to strengthen responsibility and accountability of the operators. Improved hygiene and constant availability of supplemental oxygen were additional factors for this success. To motivate staff of the Pathology Neonatal Unit has been the main difficulty. A constant accompaniment of the nurses during our mission in the clinical examination and evaluation of the newborn, drafting procedures, educational programs, and preparation of medical records allows to improve the quality of neonatal assistance.Further decrease in death rate can be achieved if mechanical ventilator could be available. Mechanical respiratory support remains a pivotal intervention for ELBW but without dedicated personnel trained in intubation and mechanical ventilation, and a guaranteed continuous supply in electricity, oxygen, and pressurized air, this much more expensive intervention was not possible. In this group of patients, delivery room procedures and a seamless transfer from the delivery room to the neonatal care unit are further critical steps which need improvement.We now aim to implement emergency procedures with clear task assignment (who does what) to tackle this issue. The availability of additional drugs (like aminophylline and caffeine citrate) and low intensity ventilator support  may improve the outcome for ELBW in the near future.It is interesting to note, how the percentage of outborn patients increased over the years to over 80 %. It shows the excellent reputation of the CMSC, with birth clinics not only in the capital, but all over the country referring newborn babies for specialized care, making it the reference center for neonatal care in the country. On the other hand this also underlines the need to improve levels of care [For the time being, sustainability of this project for the coming year is provided by a generous donation from a private foundation. This will allow further visits that will aim not only to perpetuate achieved success and introduce further treatments to increase survival , but alsIn extreme settings, even simple solutions can dramatically improve survival among premature newborns, the most at risk among those most at risk. Long lasting support is essential to reach durable improvements. Successful training of local staff  is not aThus external sources of funding and support from the local health system  are important for the development of a functioning health system. It is necessary to specifically identify companies and hospitals that offer a partnership for a fixed period, with the possibility to verify the clinical progress and welfare  are to reduce by two-thirds, between 1990 and 2015. The child mortality under 5 years  is expected to reduce by three quarters, between 1990 and 2015, the maternal mortality rate (MMR). The indicators are: immunized against measles (%) within the first year (IMR), assisted deliveries (%) by qualified personnel, U5MR and MMR. According to the current trend of annual improvement, the fourth Millennium Development Goal (MDG 4) will be reached in 2045 rather than in 2015. Without an important reduction in the mortality during the first 7 days of life, we will not reach goal 4 of the “United Nations’ Millennium Development Goals” .


Posttraumatic neglected Achilles tendon ruptures in a young patient have not been described in the literature to our knowledge; indeed, neglected ruptures of the Achilles tendon have only rarely been described in adults. We present the case of a 7 year old girl with posttraumatic neglected rupture of the Achilles tendon that was operated on 8 weeks after the trauma. Achilles tendon rupture is defined as being neglected if the diagnosis is made at least 4 weeks after the trauma . NeglectThe authors report the case of a 7 year old girl with posttraumatic neglected rupture of the Achilles tendon treated 8 weeks after the trauma with a tenorrhaphy using the Bunnell technique augmented with the plantaris gracilis tendon .A 7 year old girl was seen at our clinic for pain at the right calf with limping and weakness during plantar flexion of the right foot. Her mother stated that she had started to limp about 8 weeks before after a swinging door hit her on the back of the leg while she was running. At that time she complained of a small cut on the skin that was over her Achilles mid-tendon. She was then taken to the emergency department of the closest hospital, where the skin was sutured. She was allowed to walk but she started to limp on her right lower limb and to feel a kind of weakness during plantar flexion. She was seen again by a pediatrician after 6 weeks, who then referred her to us.Physical examination showed an Achilles tendon gap under the skin at inspection and upon palpation of the area Fig.  and an iShe was then taken to the operating room. Under general anesthesia and in the prone position, the right foot showed slight dorsiflexion due to the prevalence of the extensor muscles Fig. . After tThe patient was completely asymptomatic at 3 years follow-up.The parents of the young patient gave informed consent prior to their daughter being included in the study.Acute traumatic rupture of the Achilles tendon of a child has rarely been described in the literature. More frequently, children are affected by spontaneous avulsion of the Achilles tendon due to the effects on the calcaneus of various conditions such as inflammatory and autoimmune diseases, infectious diseases, or neurological and genetically determined collagen abnormalities . To our Our patient was also a 7 year old patient. She was struck across the mid-tendon sideways by a swinging door, but the lesion at the Achilles tendon was not diagnosed immediately. Neglected traumatic Achilles tendon ruptures in children are not reported in the literature. Nonoperative treatment can be successful only if the tendon is immobilized in the full equinus position. Surgical treatment is often recommended for older and relatively athletic patients, or in cases where treatment has been delayed . In thisSeveral different techniques have been described for the treatment of chronic or neglected Achilles tendon ruptures in adults, such as bridging approximation procedures, V–Y slide lengthening of the tendon, gastrocnemius advancement, fascial turndown flaps, local tendon transfers, free tissue transfer, and the use of synthetic grafts –8. The rComplete healing of the scar was obtained after 20 days. A physical therapy program was needed after cast removal to recover the full range of ankle motion. Excellent functional results were observed by 3 months after the surgery. The incision had healed completely and the foot was plantigrade, the patient could walk on tip toes, and she was able to perform 20 repetitive single-leg heel-raise maneuvers. The AOFAS score for the ankle and hindfoot was 96 points (out of a maximum possible score of 100 points), due to moderate restriction of the sagittal motion. At follow-up 3 years after surgery, the patient had recovered full range of motion of the ankle and full ankle function.To our knowledge, this is the first neglected rupture of the Achilles tendon in a child described in the literature. We believe that in patients under the age of 10 years, nonoperative treatment of acute lesions is safe and offers satisfactory results after a relatively short period of immobilization . In negl


Slipped capital femoral epiphysis (SCFE) is commonly treated with in situ pinning. However, a severe slip may not be suitable for in situ pinning because the required screw trajectory is such that it risks perforating the posterior cortex and damaging the remaining blood supply to the capital epiphysis. In such cases, an anteriorly placed screw may also cause impingement. It is also possible to underestimate the severity of the slip using conventional radiographs. The aim of this study was to describe and evaluate a novel method for calculating the true deformity in SCFE and to assess the interobserver and intraobserver reliability of this technique.We selected 20 patients with varying severity of SCFE who presented to our institution. Cross-sectional imaging [either axial computed tomography (CT) scans or magnetic resonance imaging (MRI) scans] and anteroposterior (AP) pelvis radiographs were assessed by four reviewers with varying levels of experience on two occasions. The degree of slip on the axial image and on the AP pelvis radiographs were measured and, from this, the oblique plane deformity was calculated using the method as popularised by Paley. The intraclass correlation coefficient (ICC) was calculated to determine the interobserver and intraobserver reliabilities between and amongst the raters.The interobserver reliability for the calculated oblique plane deformity in SCFE ICC was 0.947 [95 % confidence interval (CI) 0.90–0.98] and the intraobserver reliability for the calculated oblique plane deformity of individual raters ranged from 0.81 to 0.94. The deformity in the oblique plane was always greater than the deformity measured in the axial or the coronal plane alone.This method for calculating the true deformity in SCFE has excellent interobserver and intraobserver reliability and can be used to guide treatment options. This technique is a reliable and reproducible method for assessing the degree of deformity in SCFE. It may help orthopaedic surgeons with varying degrees of experience to identify which hips are suitable for in situ pinning and those which require surgical dislocation and anatomical reduction, given that plain radiographs in a single plane will underestimate the true deformity in the oblique plane.Level II diagnostic study. When assessing a patient with slipped capital femoral epiphysis (SCFE), several factors need to be addressed. These include the stability of the slip as defined by Loder et al. and the severity of the slip . Most auBased on the anteroposterior (AP) radiograph, Wilson defined a severe slip as one with a slip greater than half the metaphyseal diameter . The Soux  and an image of the hip in the AP position . Using these two images, the deformity can be calculated in the oblique plane. The head–neck angle is measured from the AP pelvis film to give angle x Fig. . The hea. y Fig. . From thx and y is as follows. Angle x is taken from the AP pelvis radiograph.To the best of our knowledge this technique has not been applied in this fashion previously. The technique for measuring angles The method of calculating the oblique plane deformity is explained in Figs. z = arctan√(tan2x) + (tan2y), but it can be more simply extrapolated to the calculation of x2 = y2 + z2 by means of Pythagoras’ theorem  for the axial image. All images were obtained using a standardised imaging protocol. For the purpose of review, all patient information was removed and each hip assigned a study number. A total of 20 hips were included in the study. Approval of the clinical audit department was obtained.Measurements were made independently by four different orthopaedic surgeons after agreeing the method to be used . The observers received minimal training regarding the measurement methods. Training consisted of a demonstration of the angles to be calculated. No written training was received and no further training was given for the re-test element of the study. Observers with differing levels of experience were selected to see if the level of agreement varied among less experienced surgeons. The observers were blinded regarding patient history, physical examination findings and subsequent treatment.For deformity in the coronal plane, the head–neck angle was measured from the AP radiograph. The deformity in the transverse plane was measured from the suitable axial image from the CT series (or MRI if not available). For each patient, the magnitude of the true or oblique plane deformity was calculated as previously described.The intraclass correlation coefficient (ICC) was used to measure the intraobserver and interobserver reliabilities, with 1.0 being perfect agreement and 0 indicating agreement by chance alone. Excellent reliability was defined as an ICC >0.9, good 0.8–0.9 and fair 0.7–0.8. The intraobserver reliability was calculated for each variable  and the same images were re-examined by the same observers in order to calculate the intraobserver reliability for the oblique plane calculation between the first reading and the second reading.p-value >0.05) and Q–Q plots of normality. Box and whisker plots of spread were calculated.Statistical analysis was performed using SPSS v19 . The data were assessed for normality using both the Shapiro–Wilk test . There were 12 unstable slips and 8 stable slips. The measurements for all patients for both the initial test and re-test can be found in the The interobserver reliability for all measurements performed during the first round of testing was excellent, with an ICC of 0.947 for the oblique plane calculation between all four observers Table .Table 2Ip < 0.001) for all values and a range from 0.814 to 0.941 (p < 0.001) in the oblique plane  . For theHaving calculated the oblique plane deformity, the management plan can then be made with more confidence. If the true magnitude is higher than first appreciated on initial radiographs, the decision may be made to perform open reduction or a corrective osteotomy. In addition, the degree of the true magnitude in the oblique plane may be used as a guide to prognosis.We chose to use the AP pelvis rather than a coronal CT reconstruction to measure the deformity in the coronal plane because of our aim to identify a method which is readily available. The rationale for this was that some authors recommend urgent fixation of acute slips  and, as There are several limitations to our study. The number of cases was relatively small; however, given that they all represented moderate or severe SCFE, there was not a significant heterogeneity of values in our samples.Although it was not part of the original aims of the study, we found that several patients were seen on CT or MRI to have a marked curve to the femoral neck itself, rather than a straight neck and a sharp deformity at the physis Fig. . These rThe magnitude of the mean angles in our series also gives important information. The mean deformity  in the axial plane was twice that of the mean coronal plane deformity . The mean oblique plane deformity was even higher at 75° (range 43–98). This shows that the true deformity can be significantly underestimated when relying on one image, particularly the AP radiograph. This study demonstrates that the true magnitude of the deformity in the oblique plane can be accurately and reliably measured with little inter- and intraobserver variability.


Attention is preferentially deployed toward those stimuli which are threatening and those which are surprising. The current paper examines the intersection of these phenomena; how do expectations about the threatening nature of stimuli influence the deployment of attention? The predictions tested were that individuals would direct attention toward stimuli which were expected to be threatening (regardless of whether they were or not) and toward stimuli which were surprising. As anxiety has been associated with deficient control of attention to threat, it was additionally predicted that high levels of trait anxiety would be associated with deficits in the use of threat-expectation to guide attention. During fMRI scanning, 29 healthy volunteers completed a simple task in which threat-expectation was manipulated by altering the frequency with which fearful or neutral faces were presented. Individual estimates of threat-expectation and surprise were created using a Bayesian computational model. The degree to which the model derived estimates of threat-expectation and surprise were able to explain both a behavioral measure of attention to the faces and activity in the visual cortex and anterior attentional control areas was then tested.As predicted, increased threat-expectation and surprise were associated with increases in both the behavioral and neuroimaging measures of attention to the faces. Additionally, regions of the orbitofrontal cortex and left amygdala were found to covary with threat-expectation whereas anterior cingulate and lateral prefrontal cortices covaried with surprise. Individuals with higher levels of trait anxiety were less able to modify neuroimaging measures of attention in response to threat-expectation. These results suggest that continuously calculated estimates of the probability of threat may plausibly be used to influence the deployment of visual attention and that use of this information is perturbed in anxious individuals. ► Attention is directed toward stimuli which are expected to be threatening. ► Attention is also captured by surprising stimuli. ► The OFC and amygdala track threat-expectation. ► The ACC tracks the surprise associated with a stimulus. ► Anxious participants are less able to use threat-expectation to control attention. Emotionally salient stimuli, particularly those which are threatening, attract attention . HoweverExpectation based processes have been employed to account for a wide variety of behavioral and neural phenomena, most prominently in the field of reward based learning , but alsIn order to test these predictions we presented participants with a succession of pictures of faces which indicated the presence of threat (a fearful expression) or no threat . Expectation of threat was experimentally manipulated by varying the relative frequencies of the two expressions  throughout the course of the study. Variation in blood oxygen level dependent (BOLD) signal in the visual cortex  was used29 participants  who had been screened to exclude current or previous axis I psychiatric disorder or alcohol/substance misuse using the Structured Clinical Interview for the DSM-IV  were recParticipants completed a single scanning session during which both behavioral and neuroimaging data were collected. Before the scan participants completed the trait subscale of the Spielberger State-Trait Anxiety Inventory .The model was provided with information about whether a threatening (fearful) or neutral face was presented on each trial in a block of the task and used this information to update its expectation that a threatening face would be presented on the following trial. The surprise associated with a trial was defined as the negative logarithm of the conditional probability of the observation, given the expectation . A total of 45 slices were acquired using a voxel resolution of 3 × 3 × 3 mmIn order to test the ability of the model derived regressors to predict a behavioral measure of attention, separate general linear models were fitted to each participant's reaction time data. Before performing these analyses the positive skew of the reaction time distributions was reduced by removing extreme responses  and log transforming the data. As described above, increased attention toward the facial stimuli should be indexed by increased reaction time on the bars task and decreased on the gender task. Therefore the critical explanatory variables in these analyses coded for the interaction between the model derived regressors and the task demands (threat-expectancy × task and surprise × task). Importantly, the analyses controlled for all other relevant aspects of the task by including additional regressors which coded for: a) the emotion of the face presented , b) the task being performed , c) the model derived expectancy and d) surprise signals, e) whether the participant made an error on that trial and f) the interaction between the emotion of the face and the task being performed. These multiple regression analyses, performed individually for each participant, resulted in estimates of the regression coefficients for each explanatory variable, for each participant. A second level analysis then tested whether the two critical regression coefficients significantly differed from zero across the group of participants, and whether this difference was in the predicted direction . Analysis was performed using the statistical toolbox of Matlab .www.fmrib.ox.ac.uk/fsl). Image pre-processing employed the default options for FEAT: motion correction was applied using rigid body registration to the central volume; Gaussian spatial smoothing was applied with a full width half maximum of 5 mm; brain matter was segmented from non-brain using a mesh deformation approach; high pass temporal filtering was applied with a cut off of 100 s.Functional magnetic resonance imaging analysis was carried out using FEAT  Version 5.91 . Different durations for the expectancy and surprise regressors were chosen to reflect the roles of the two forms of information within the model. Specifically, in order for learning to proceed, the model must carry forward the estimate of threat-expectation from one trial to the next, in contrast the surprise signal is involved in the transient updating of threat-expectation. The effects of other aspects of the task were controlled for by including regressors  coding for the presentation of each of the four basic trial types  as well as for trials where an error was made. The addition of these extra regressors ensures that activations attributed to the model derived regressors cannot be accounted for by participants' responses to stimuli presentation or to the emotion presented on the faces.p of 0.01) and correction over the whole brain using p < 0.05 was used. For the identified clusters, MNI co-ordinates provide the location of the peak voxel within the cluster, Z-max is the z score for this voxel and the p value is corrected at the cluster level.A random effects analysis was employed at the group level to assess for regions in which the BOLD signal covaried with the model derived threat-expectation and surprise regressors across all participants. Trait anxiety, as measured by trait-STAI scores, was added as a covariate in this analysis to assess whether varying levels of trait anxiety were associated with varying use of expectancy and surprise to control attention. Note that, in contrast to the behavioral analysis, the effect of attention to the faces was not expected to interact with task instructions; in other words, increased attention to the faces was expected to cause increased visual cortical activity regardless of whether the task required participants to respond to the gender of the faces or the orientation of the bars. Cluster based multiple comparison correction with an initial Z-threshold of 2.3  = 2.24, p = 0.03] and on trials in which the face presented was surprising . In addition (t(28) = 5.0, p < 0.001] and when completing the bars rather than the faces task . There was no correlation between levels of trait anxiety and either the threat-expectation × task  or surprise × task regressors . Average reaction time did not significantly alter across successive blocks for the faces task  or bars task .Both the model derived threat-expectancy and surprise signals had the predicted effect on the deployment of attention as assessed using reaction times. Specifically, reaction times were increased in the bars relative to the gender task on trials in which the model expected the face to be threatening . The whole brain analysis also identified a number of attentional control regions in which activity also covaried with the threat-expectation signal. These included bilateral orbitofrontal cortex  and a left sided fronto-temporal cluster  which extended into the left amygdala. Additionally a posterior parietal cluster was identified, on the left hand side . There was no effect of the task completed by participants (i.e. gender vs. bars) on the relationship between activity and threat-expectation in any of these clusters.Consistent with the behavioral analysis, a neuroimaging measure of the deployment of attention, activity in the visual cortex, was increased when threat-expectation was high a. A clusx y z = − 12 −88 −12, Z-max = 3.71, p-corrected < 0.001] extending forward into the left thalamus, an extensive frontal midline cluster incorporating both dorsal and rostral ACC , left sided dlPFC  and precentral gyrus . These activations were not significantly modified by the task completed by participants.An extensive network of regions was found to covary with the surprise regressor b. This ix y z = − 18 −104 0, Z-max = 3.38, p-corrected = 0.02]. This cluster overlapped with the visual cortical activations of both the threat-expectation and surprise regressors . Thus increased attention to the faces induced by task instruction produced increased activity in the same regions of visual cortex as that associated with both the threat-expectancy and surprise regressors.As the facial stimuli are significantly more complex than the bars, the visual cortical activations reported above are likely to reflect increased attention to the faces. This assumption was further tested using a conjunction analysis which compared the results reported above with the effects of task instruction. A simple comparison of the trials from the gender-task blocks (which require attention to be directed to the face) with those from the bars-task blocks (which require attention to be directed away from the face) across the whole brain revealed a single cluster of activation in the occipital pole , right side dlPFC , right  and left vlPFC , medial PFC including the dorsal and rostral ACC  and the posterior cingulate/precuneus . These regions overlapped with those identified as responding to threat-expectancy generally in the right vlPFC (32 26 −10), left temporal lobe abutting the amygdala (− 32 −8 −18) and precuneus (10 −56 16). A similar negative correlation was found when analysis was limited to the visual cortical cluster (see r(29) = − 0.4, p = 0.03]. No regions displayed a positive correlation with trait anxiety.A number of regions displayed a negative correlation between trait anxiety and utilization of the threat-expectancy signal. These included the right posterior parietal and lateral occipital cortex  with no regions displaying a negative correlation. However, this region was not identified in the initial analysis as responding to either expectancy or surprise.Consistent with the decreased use of threat-expectancy to guide attention in high levels of anxiety, trait anxiety was found to correlate positively with surprise in a single cluster in the left superior temporal gyrus cortex [t(27) < 1.8 p > 0.09] and the neuroimaging analysis also revealed no significant effects.We did not expect the prescan manipulation of attention to negative stimuli  to interThe current study demonstrates that a simple expectancy based model is able to account for a significant amount of variance in both the behavioral and neural signatures of attention to emotional faces. Specifically, markers of participants' attention to the faces increased when the computational model expected the faces to be threatening or when it found the emotional content of the faces surprising. The predictive validity demonstrated by the computational model in the current study suggests that similar expectancy based calculations may be instantiated by neural systems in order to influence the deployment of attention to threat. The degree to which individuals employ threat expectancy to control attention may vary as a function of anxiety as increased levels of self-reported trait anxiety was associated with a decreased correlation between the model derived threat-expectancy signal and neural activity in a number of attentional control and visual cortical regions. However, this relationship was not observed in the behavioral data. Lastly, the current results are unlikely to be accounted for by task or stimulus related confounding factors such as the valence of the presented stimuli as both the behavioral and neuroimaging analyses performed were careful to control for such factors.As well as demonstrating the predicted influence of threat-expectation and surprise in the visual cortex the current study was able to assess other regions of the brain which may be involved in generating and tracking such expectancies. The threat expectation signal itself was found in bilateral OFC and the amygdala on the left, both of which have previously been identified as being involved in the control of attention to emotional stimuli . InteresAs described above, a negative correlation was found between individual self reports of trait anxiety and the threat-expectation signal in a range of attentional control and visual cortical structures. There was also evidence of a positive correlation between trait anxiety and the surprise signal, although this was in an area which had not been identified in the main analysis. Anxious individuals, therefore, appeared less able to utilize the statistical regularities of their environment to predict the occurrence of threatening stimuli. Interestingly, previous work has demonstrated that exposure to unpredictable environments increases anxiety . This raThe regressors used in the current paper were generated by a computational model which had been presented with the same information on face valence as the participants. It is possible to create similar regressors without the input of a computational model, by using knowledge of the task structure; specifically, the model derived threat-expectancy regressor is similar to a task derived regressor which codes for whether a trial occurs in a high or low threat block and the surprise regressor is similar to the absolute difference between this and a regressor coding for the face valence on each trial. Analyzing the data using such task derived regressors produces similar results to those reported above. Crucially, however, the participants had no prior knowledge about task structure. In this context, the advantage of using the computational model is that it demonstrates, formally, how participants may have generated the estimates of threat-expectancy. An example of the insights provided by this approach is the surprise signal found in the data; surprise is not an obvious parameter of the experimental task, its relevance being suggested by the expectancy based model. The finding that activity in an extensive neural circuitry covaried with surprise suggests that the brain may instantiate a similar computational process to that described by the model.Finally some limitations to the current study should be acknowledged. Firstly, our task did not allow a conclusive examination of the degree to which activity in the visual cortex reflected processing of the facial stimuli. While the conjunction analysis was able to demonstrate that manipulation of attention to the faces produced by task instructions altered activity in the same region of cortex as that influenced by both threat-expectancy and surprise, alternative task designs may have facilitated a finer grained analysis of face processing. For example future studies may benefit from pairing face stimuli with pictures of houses, which are thought to invoke activity in distinct regions , and thuIn the current study, a computational model of threat-expectancy was used to predict the behavior and neural activity of non-clinical participants who were presented with emotional stimuli. Attention to the faces was significantly predicted both by model derived estimates of threat-expectation and by the surprise elicited when these expectations were violated. Individuals with higher levels of trait anxiety were less able to modify neuroimaging measures of attention in response to threat-expectation. These results suggest that expectancy based processes may plausibly be involved in the control of attention to threatening information and that these processes are perturbed in anxiety.


The first stages of the crystallization of polycaprolactone (PCL) were studied using several techniques. The crystallization exotherms measured by differential scanning calorimetry (DSC) were analyzed and compared with results obtained by polarized optical microscopy (POM), rheology, and atomic force microscope (AFM). The experimental results suggest a strong influence of the observation scale. In particular, the AFM, even if limited on time scale, appears to be the most sensitive technique to detect the first stages of crystallization. On the contrary, at least in the case analysed in this work, rheology appears to be the least sensitive technique. DSC and POM provide closer results. This suggests that the definition of induction time in the polymer crystallization is a vague concept that, in any case, requires the definition of the technique used for its characterization. The phenomenon of crystallization of polymers has been studied since the start of the macromolecular science. Despite the great efforts spent by the scientific community on this topic, the formation of crystalline structures between high-entangled polymer chains is still debated.A complete investigation of the crystallization process requires the use of several analytical techniques, since a single technique does not supply sufficient understanding of the complex multiscale transformation. Heat transition involved in the process is typically measured by differential scanning calorimeter (DSC-DTA), structures formation is generally monitored by microscopy , and order alteration is observed by X-ray diffraction and infrared spectroscopy, whereas changes in flow properties are commonly measured with rheological tests , 2. HoweThe application of atomic force microscopy (AFM) to polymer system is often devoted to the observation of a solidified sample after quenching the crystal structure, as a static subsequent step. Most dynamic hot stage studies of morphological development in situ have been performed using optical microscopy, with its limited resolution.Recently, the possibility to follow real-time dynamics of processes at nanoscale, even if limited on time scale, is opened to AFM. Since the initial in situ observations of the polymer crystallization reported by Pearce and Vancso –5 and byPearce and Vancso , 4 firstIn situ AFM studies gave the opportunity to observe developing morphologies at a lamellar scale, from the generation of the primary stable nucleus  to the dThese observations highlighted the roughness of the growth front, seen as being smooth at the larger scale of optical microscopy .In particular for the investigation of material transformations that are associated with changes in rheological and thermal properties, such as melting and crystallization, an accurate comparison between these techniques is crucial. In the literature, a few attempts for combining morphology, calorimetry, and rheology can be found .Martins et al.  developeNagatake et al.  modifiedIn this paper, the investigation of the morphology development of melt-grown crystal lamellae of polycaprolactone by real-time hot stage AFM is reported. The morphological insights are also investigated by using polarized optical microscopy (POM), differential scanning calorimetry, and rheology tests.Mn 70000–90000 by GPC, Mw/Mn < 2, density 1.145 g/mL at 25°C), supplied by Sigma Aldrich.The polymer investigated in this work is polycaprolactone , in order to prevent oxidative degradation.μm, is sandwiched between two cover glasses and this is observed in light transmission using an optical Olympus BX51 Microscope equipped with digital camera. To apply the same thermal protocol to the glass/polymer system a special calorimeter  was adoμm and the other one a square surface having a side of 30 μm. In both experiments, the scan rate was 2 Hz and 256 sample lines. The Linkam hot stage THMS600 is used for conditioning the sample during the in situ AFM characterization. The equilibrium melting temperature of PCL is relatively low, about 60°C, so that it was possible to perform the AFM acquisition without altering the linearity of the AFM piezo.In order to characterize the local morphology of the samples a Veeco Dimension 3100 AFM with Nanoscope III controller was adopted. All the sample images were kept using the tapping mode in order to minimize the interaction with the sample surface. Two tests were conducted with the same thermal history, but analyzing different surfaces: one test explored a square surface having a side of 10 σ = 100 Pa, and constant frequency, ω = 0.1 rad/sec. The choice of these values is crucial in order to reduce, as much as possible, the disturbance during the crystallization and ensure that the whole crystallization process occurs in the range of sensitivity of the equipment. To verify whether these parameters could affect the experimental results, tests with stress values ranging from 1 to 1000 Pa have been performed.Rheological experiments were performed using a stress controlled rotational rheometer (HAAKE RheoScope) equipped with 25 mm parallel plates and using a gap thickness of 0.1 mm. To follow both the cooling process and the isothermal crystallization, the evolution of the mechanical properties has been monitored by an oscillatory test at the constant stress, Optical observations were made directly in the rheometer by a CCD camera (HAAKE RheoScope) equipped with a 5x magnification objective.T = 120°C for 15 min. The cooling step from the annealing temperature (Ta) to the crystallization temperature (Tc) has been performed in two steps. The first step from Ta to the melting temperature Tm = 60°C was performed at a rate compatible with the limitations of the different experimental techniques  whereas the second step from Tm to Tc was performed at a rate of −0.5°C min−1. The temperature profile has been verified by an independent thermocouple placed inside the molten sample. The temperature sensors of the rheometer, the calorimeter, and the hot stage were carefully calibrated to minimize temperature differences among the instruments.The same thermal history has been applied to all the experiments. In particular, an annealing treatment has been conducted at A preliminary calorimetric test was performed in order to determine the relevant temperatures for the crystallization phenomena of PCL. The sample was heated to 120°C at 30 C/min, kept at this temperature for 15 minutes, and then cooled down to 60°C at −10 C/min and from 60 to 25°C at −0.5 C/min. The result of this test is reported in The calorimetric plot evidences an exothermic peak due to the crystallization of PCL, which starts at 44°C, has a maximum at 39°C, and ends at 34°C.A series of isothermal crystallization tests were thus carried out at temperatures between 50°C and 43°C. The corresponding exothermic peaks are reported in αDSC(t)) by the division of the heat that develops at each crystallization time t (ΔHt) by the total area under the exothermic peak, that is, the total heat (ΔH0) generated up to the complete crystallization:The crystallization heat, obtained by the measurement of the area under the exothermic peak, can be transformed into the relative degree of crystallinity , where t0.5  is the time at which αDSC(t) approaches the value of 50%. This parameter is proportional to both the primary nucleation rate and the crystal/spherulite growth [t0), that is, the time at which αDSC(t) starts to increase, is also clear from 0 increases exponentially with the crystallization temperature.An approximation of the crystallization rate can be made by the overall crystallization rate . The micrographs were acquired at suitable time intervals with crossed polarizer during the same isothermal tests performed during the DSC tests.n is the measured birefringence at a given time, Δn0 is the birefringence at the start of the isothermal test (which is nearly zero), and Δn∞ is the birefringence at the end of crystallization (when a plateau is reached). Being birefringence essentially proportional to crystallinity, αPOM(t) defined by =ΔαPOM is plotted as a function of time for a series of isothermal tests similar to those presented in In αPOM as a crystallization characteristic per se, information about structure formation (induction time) and structural changes can be obtained.It is important to point out that evolution intensity expressed by  does notG′), the loss modulus (G′′) and the complex viscosity (η*) versus time during both the cooling  and the isothermal crystallization is reported together with the corresponding temperature profiles for two different quiescent crystallization tests.In Figures G′ and G′′) are susceptible to structural changes in the polymer. As an example, G′ after a first increase during the cooling step, which can be ascribed to the effect of temperature, shows a plateau at T = Tc, followed by a second increase representative of the crystallization process. Finally, a second plateau is found when crystallinity reaches a maximum value.The experiment shows that both moduli (αRHEO(t) has been estimated from the time dependent G′(t) values according to the following equation [Go′, G′(t), and G∞′ are the melt stiffness values at time 0, t, and infinity, respectively.From the rheological experiment, the induction time for the isothermal crystallization can be detected at the time when the storage modulus abruptly increases. The transformed crystallized fraction equation :(3)αRHEαRHEO to the isothermal crystallization at 45°C and 47°C, respectively, is reported.The results for the structure development during the isothermal quiescent crystallization have been obtained at different crystallization temperatures. In Figures The time scale starts at the end of the cooling step for both the rheological and the morphological tests. The times read from the figures are, then, directly related to the evolution of the crystallization in isothermal condition.G′ trend . This result is in agreement with the results reported by Acierno et al. [G′ during time after the impingement is still related to the crystallization process itself.The morphological investigation shows that spherulites develop and grow until the impingement occurs. As expected, the number of nuclei increases as the isothermal crystallization temperature decreases. What is interesting is the comparison between the structure development and the  Figures . Indeed,o et al. , Bove ano et al. , and Pogo et al. . The incT < 60°C). In contrast to that observed with the calorimetric experiments, the first signal of structures formation was observed during the second cooling step, at temperatures just below 50°C. For that, it was impossible to carry out isothermal AFM tests at temperature lower than 50°C.The same thermal protocol adopted before was replicated during AFM measurements. In that case, however, the recording of the data starts in parallel with the second cooling step (The evolution of the samples surface morphology was recoded in terms of distance from a reference position (height) and the characteristic oscillation (amplitude and phase) of the AFM cantilever during the observation times. The three acquired signals are strictly related to the structure formation on the sample surface during the crystallization phenomena. However, in our case, the amplitude signal was found the most sensible to the change in surface morphology.μm and another with wider area having a side of 30 μm. As shown in For the present analysis, two different tests were considered: one with an observed square area with a side of 10 The crystalline structure development was followed by AFM while growing and it was found that the growth front propagates by the advancement of primary arms, with the regions between the arms filled in by secondary arms growing behind the primary front .During the isothermal step new crystal aggregates were not formed but just the development of the previously formed structures was evident. This observation is coherent with the hypothesis of nucleation dependence on temperature and thus during the isothermal step no new stable nuclei are formed.The development of new crystalline structures on the sample surface was followed by means of the evolution of the average value of the amplitude signal, αAFM(t)):In particular, by assuming as initial value of the averaged amplitude αAFM(t) as function of the isothermal crystallization time.In At early stage of the crystallization process, a small part of the investigated surface is covered by crystalline structure, so the variations of the averaged amplitude signal are very low. As the crystallization proceeds, the variations of the averaged amplitude become larger until a large part of the surface is covered by the crystalline structure and therefore the variations of the averaged amplitude are constant.As expected, even on the same sample, the morphology appears very different by changing the surface analyzed. This is clearly shown in All the techniques adopted in this work were analysed in such a way to obtain sigmoidal curves representative of the evolution of the crystallization. From all the curves, it was possible to identify an induction time as the time at which the measured signal deviates from the value that it presents at the start of the isothermal step when the sample is still molten, without any crystallinity. A comparison between the results obtained with the different techniques presented is shown in Results clearly reveal that the induction time found on the entire range of crystallization is dependent on the technique used. As expected, the observation scale plays an important role in its determination. Rheology appears to be the least sensitive technique, probably because in order to measure an increase in rheological parameters, an interaction among crystals is necessary. On the contrary, AFM tests that are able to resolve the surface sample at dimensions comparable to the primary nuclei can detect crystallization at initial stages.The morphological development taking place during the crystallization of polycaprolactone (PCL) was studied using several techniques.Results show that the induction time is dependent on the technique used. As expected, the observation scale plays an important role in its determination. In particular, despite its limitations, the AFM that is able to resolve the surface sample at dimensions comparable to the primary nuclei is able to detect crystallization at initial stages. On the contrary, rheology appears to be the least sensitive technique, probably because in order to measure an increase in rheological parameters an interaction among crystals is necessary. DSC and POM techniques supply closer results. It suggests that the definition of induction time in the polymer crystallization is a vague concept that, in any case, requires the definition of the technique used for its characterization.


Predicting population extinction risk is a fundamental application of ecological theory to the practice of conservation biology. Here, we compared the prediction performance of a wide array of stochastic, population dynamics models against direct observations of the extinction process from an extensive experimental data set. By varying a series of biological and statistical assumptions in the proposed models, we were able to identify the assumptions that affected predictions about population extinction. We also show how certain autocorrelation structures can emerge due to interspecific interactions, and that accounting for the stochastic effect of these interactions can improve predictions of the extinction process. We conclude that it is possible to account for the stochastic effects of community interactions on extinction when using single-species time series. One of the most important modelling applications in conservation ecology is predicting future population abundances. The practice of population viability analysis (PVA) connects stochastic population models to data and is used to understand the factors limiting population growth and to assess risk of extinction or falling beneath a certain abundance  and other indigenous microorganisms, whereas complex communities were composed of the consumer–resource system plus additional grazers . While D. pulicaria and the S. vetulus are filter feeders and have been shown to compete in microcosm experiments . More experimental details are provided in Grover et al. . In what follows, Nt denotes the random population abundances at time t.Our analysis was based on a derivation for the mean and variance of a discrete-time abundance model with demographic and environmental variance. A full derivation of the mean and variance for abundances is provided in Appendix A, although some details are presented here. Throughout the text we use the convention that random variables are denoted using capital letters, such as λ offspring per generation with a variance given by ϕ2, the demographic variation in reproduction (see Appendix A for more details regarding the assumptions of this variance model). Next, we assume that λ can vary randomly over time with variance given by φ2, the environmental variation in reproduction. After reproduction offspring and parents survive with probability pt−1 = p0p(nt−1), where p0 is the density-independent survival and p(nt−1) is the density-dependent survival. We note that the individual parameters of survival and reproduction in the product λp0 are non-identifiable (as shown in Appendix A), therefore we define r = λp0. Following these assumptions, the expected value and variance for current abundances as a function of the previously observed abundances are as follows:We start by assuming that every individual gives birth on average to nt−1 and is referred to as the demographic stochasticity. This expression comes from computing the average variability in the reproduction and survival process (see Appendix A). Thus, this variance component corresponds to the overall contribution of demographic variance to the total population variance, always scaling like nt−1 regardless of the particular form of the reproduction and survival. Note that only in the case of density independence, when p(nt−1) = 1, the demographic variance of the population process is exactly proportional to nt−1. When survival is density dependent, the demographic stochasticity will scale as a more complex function of nt−1  ≡  ln Nt) can be written as:Previous work has shown that autocorrelation can be induced through population interactions that are subjected to variability  terms represent demographic and environmental stochasticity. This system can be viewed as a linearised approximation to more complex functional responses  or X2(t), obtaining an ARMA model (see Appendix B for derivation). In this work we refer to the AR(1) component as the density-dependence model, whereas we refer to the AR(2) component as simply an AR autocorrelation model, consistent with an extensive literature on the effects of environmental autocorrelation  model  and growth and intraspecific interactions (b11b22) in the system. In contrast, the moving-average (MA) component that is generated is a function of how well the interspecific interaction propagates perturbations. The MA parameter is maximised when the species interacting with the focal species follows a random walk, such that perturbations from equilibrium are propagated by the interacting species, rather than being dampened by a density-dependent response . In our model formulation, the autocorrelation components effect both the demographic and environmental variances, and we limit ourselves to fitting one AR component (in addition to the density dependence) and one MA component based on time-series diagnostics presented in Appendix C.This approach can be further generalised such that the univariate stochastic dynamics for a species in a community of We considered a number of possible assumptions that might be made when conducting a PVA to construct a realistic but tractable set of models. The effects of each model assumption identified were assessed independently. These assumptions are as follows: 1) the form of the density dependence, (2) the distributional form of the transition pdf, (3) the form of the autocorrelation and (4) the variance model of the process error. For each of the assumptions, we considered a handful of explicit alternative specifications and evaluated the inferential consequences of considering each of these components, one at a time. Thus, four sets of independent comparisons were done. The comparisons are summarised in Table the formp(nt−1) in eqs p(nt−1) = 1 − bnt−1) models, as well as the undercompensatory Beverton–Holt (p(nt−1) = 1) model of population growth as a ‘null hypothesis’. Although primarily associated with the mean response of the population, this term also affects the variance  similar to a test performed by Drake  from the distribution of fpts predicted by model τ(n)'s were obtained for all observed abundances, and 105 simulations were used to obtain the distribution, τ(n), ith microcosm we used the likelihood of all si observed fpts for each community type. Because there were three microcoms per community type this likelihood is given by ni,j.After parameters were estimated, we used simulations to examine the consistency of our models with the observed extinction process. The metric we used to characterise extinction was the first passage time (fpt), defined as the time it takes for a population to first reach a quasi-extinction abundance k, used in the AIC calculation was the number of parameters fit in the one-step transition likelihood. Models with lower AIC values were interpreted as doing a better job of explaining the extinction process. Using the FPT, rather than the raw abundances, can lead to different predictions as the FPT is a different summary of the data and may provide different information. We also calculated the BIC criterion for all models and found that all our conclusions were robust to the form of criterion used.The fpt likelihoods for each model and each data set were used to calculate values of the Akaike information criterion (AIC), which were then used to select between models for each model-set comparison. The number of parameters, Finally, to obtain an absolute measure of the predictive error we calculated the root mean square error (RMSE), defined as et al. We tested the impact of measurement error on parameter estimates to determine if the observed MA parameter estimates were due purely to measurement error. The effects of measurement error were tested by simulating 100 time series using parameters from the Ricker density-dependent, demographic and environmental variance model with no autocorrelation for each of the three simple microcosm experiments. We rescaled the observations by 1/0.58 to account for the unobserved volume in each microcosm  by simulating until the time series reached 1 individual or less. We repeated this procedure 10 000 times to obtain stable estimates of the MTE.In the simple community experiment 2 of 3 populations persisted the entire duration of the experiment suggesting that these populations were stable, but subject to stochastic extinction events over longer timescales . In contet al. The form of density dependence had a moderate effect on predictions, and removing density dependence greatly worsened predictions. We found that the Ricker model of density dependence minimised the ΔAIC value in simple communities though the Beverton–Holt also performed well (ΔAIC = 0.91), whereas in the complex communities the Gompertz model performed best overall Table. The undτ(n) best in the simple communities, whereas the log-normal was best in the complex communities . Removing the density-dependent term in the demographic variance did not appear to lead to a difference in the simple communities (ΔAIC = 1.64), although it did in the complex communities (ΔAIC = 3.41). Interestingly, when looking at AIC values for the abundances , rather than τ(n), our results are consistent with previous work that suggests that including the density dependence in demographic stochasticity is important (Drake τ(n)'s and abundances may be due to the fact that the density-dependent term in the demographic variance becomes approximately 1 at low abundances. For abundance time series, low population sizes are relatively rare and the estimates will tend to be dominated by the region where most abundance observations occur, whereas in the τ(n)'s low abundances will tend to have a larger relative effect because observations are more evenly dispersed across the range of observed abundances.Comparing model variances, we found that demographic and environmental stochasticity were both important for predicting nt Drake . The difτ(n)'s rather than the whole distribution as in the AIC. Despite these differences, the overall best RMSE models were the same as the AIC comparison. The simple community had a RMSE in τ(n) of 19.58 days, predicted by the Ricker density-dependence model with demographic and environmental variability and a MA autocorrelation model. The minimum RMSE for the complex community was better than the simple communities at 5.91 days, predicted by the Ricker density-dependence model with demographic and environmental variability and an ARMA autocorrelation model. The inclusion of a suitable autocorrelation model improved the overall RMSE by about 20% in the simple community and by about 30% in the complex community.Finally, the results from the RMSE calculation Table differedWe tested the impact of a binomial sampling model on MA parameter estimates with data simulated from the simple community experiment. We found that the magnitudes of the MA parameter estimates due to measurement error were less  than the MLE estimates of the MA terms in the simple community experiment microcosms . These results suggest that the MA terms present in the data are not due to sampling error and that another mechanism was likely responsible.We found that incorporating MA models into population growth processes can dramatically affect the MTE of a species . As the sensu Ziebarth et al. Given the potential importance of MA models in predicting abundances it is worth further understanding the impacts of these models on dynamics. In our formulation, the moving-average affects the growth rate through the log-scale abundances, and the magnitude of MA components is proportional to magnitude of perturbations from the expected value of the one-step transitions. The contribution to the growth rate by the moving-average component can then be interpreted as a rescaling of the log ratio of the observed and expected population abundances, In this study, we assessed the effects in the predicted extinction risks of four different population modelling assumptions , but much remains to be done. Despite a well-developed body of literature describing the properties of population extinctions, very little has been done to quantitatively test these predictions , implied by the Ricker model, was found to be best in the simple community, whereas in the complex community a weaker form of density dependence, embodied by the Gompertz model, resulted in better predictions. The discrepancy in optimal density-dependent models for otherwise similar populations reflects community-level differences between a strongly coupled consumer–resource system and a more complex community with a number of biotic and abiotic interactions. This result implies that the best model for one population may simply not translate to other populations of the same species when ecological forces differ between communities  of ecol


Delay in the diagnosis of colorectal cancer (CRC) may have important clinical and medico-legal implications. This study identifies the claims made on the basis of delay in the diagnosis of CRC to the Swedish insurance agency  and the impact and consequences of the delay on prognosis, treatment and survival for patients who reported the claims. The Company handles claims of medical malpractice where claimants seek compensation for alleged suffering and/or negative clinical impacts of diagnostic delays.Between January 1, 1995 and December 31, 2008, a total of 80 patients filed claims for negative effects resulting from delays in the diagnosis of CRC. Review of the claims led to identification of delay for 62 patients. The clinical symptoms that were overlooked and other causes of delay that had any relation to therapy, prognosis and economic compensation were evaluated.. The prognosis was postulated to have been adversely affected for 15 % of the patients. The delay was mainly caused by incomplete consideration of the symptoms hematoschisis or anaemia, changed bowel routine, or incomplete clinical or radiological examination and by misinterpretations of the results. No impact of duration of delay on survival was identified. The importance of identifying concomitant metastatic disease at diagnosis was overwhelming. Economic compensation was given in 79 % of the cases.The median delay in the diagnosis of CRC was six months. This delay was considered to have had an impact on the therapy in 20 % of the casesThis study found that claims for compensation for delay in diagnosis of CRC are rare. The delay in the diagnosis of the primary tumour was considered to have had an impact on the magnitude of therapeutic measures for a fifth of the patients who filed claims. Economic compensation for the patients´ injuries was given in almost 80 % of the cases. Health-care related injuries and adverse events are always harmful for patients. In international analysis the incidence of adverse advents varies between 2.9-16.6 % . In SwedIn Sweden colorectal cancer (CRC) is common and about 6200 individuals annually will get the diagnosis of CRC. In Sweden there are no available data about the consequences or impact of delay in diagnosis and treatment of CRC. A delay in the diagnosis of any cancer causes the patient to believe that she/he has lost the chance of cure or improvement. There are also undoubtedly deplorable psycho-social consequences associated with diagnostic delay. It is also important to consider any medico-legal implications of health-care related injuries and adverse events, which in the individual case can have very serious consequences.Sweden has had a patient insurance system to compensate patients for health-related injuries since 1975. Patients who have been treated within the medical service system financed by the county medical councils (Landstinget in Swedish) can report their claims to the malpractice insurance review board referred to here as LÖF . The patients may request economic compensation for the suffering and/or negative impacts on the treatment and the prognosis of the disease they have experienced. The insurance covers both physical and psychological injuries. The insurance company LÖF processes more than 90 % of all patient injury claims, mainly as a consequence of the county councils being responsible for almost (more than 90 %) all medical care in Sweden. In this health-care system it is uncommon for patients to request a second medical opinion.The aim of this retrospective study was to identify the cause of the claims made to LÖF for diagnostic delay of CRC and to explore the consequences of the claimed delay on treatment, prognosis and survival. It also identified the extent to which economic compensation was given to the claimers.In Sweden  there are annually 6200 individuals who will get the diagnosis of CRC. Between January 1, 1995 and December 31, 2008 altogether 80 claims to LÖF were identified under the heading CRC-delayed diagnosis. Nine of the 80 claims could be excluded as they were related to treatment complications, six were registered under the wrong heading, and in three cases no cancer was diagnosed. The remaining 62 claims were recognized as involving delayed diagnosis and the charts and histopathological reports of these claims were scrutinized.The alleged diagnostic delay was calculated in months from the date given in the patient´s report to LÖF to the date of clinically established diagnosis of cancer. Each author judged separately the impact of delay on prognosis and consequences on therapy without reference to the statements by the reviewers at LÖF. In the cases where there were differences between the authors´ judgments the terms supposedly or probably were added. There was no analysis of the psychological harm the delay could have caused the individual patient.Data concerning survival were received from Swedish Cancer Register. Survival time was calculated from date of established diagnosis of cancer.The survival probability was analyzed using the Kaplan-Meier method (Stat-view® 5.0). Anova factorial (Stat-view® 5.0) was used for analyzing differences between groups.It was calculated that LÖF receives 4.4 claims dealing with CRC annually corresponding to 0.7 per 1000 new cases of CRC. Between 1995 and 2005 there were 62 claims of diagnostic delay of CRC. In 51 claims there was a delay in diagnosing the primary tumour and in two diagnosing recurrent/metastatic disease. In nine cases the delay was attributed to systemic errors made by the providers, in 7 delays in remitting the patient to the X-ray or CRC care unit. In one case the reply from the X-ray unit disappeared and in one an exchange of X-ray answers was found. In 34 cases the diagnostic delay concerned colon cancer, in 27 cases rectal cancer and in one case the diagnosis was squamous cell cancer. At the time for diagnosis 11 patients with colon cancer had metastases and in cases with rectal cancer this figure was 12. The two cases with a delay in recognizing recurrent/metastatic disease were cases of rectal cancer. Of these 23 patients with metastatic disease the grading of the primary CRC cancer according to the classical Dukes’ method was Dukes’ B in 1, Dukes’ C in 10 and in 12 cases no grading could be identified as no histomorphological analyses had been performed.The sex and age distribution in six cohorts of the claimers on the date of missed diagnosis is depicted in Table Haematoschisis or anaemia was the predominating symptom in 33 cases (53 %), changed bowel habits in six, and pain in five cases. In 14 patients the different symptoms overlapped Table . In two Localisation of the cancer in the colon or the rectum and staging of CRC according to Dukes’ classical criteria and the duration of diagnostic delay of more or less than 6 months are shown in Table There were 23 patients who had metastases (Dukes’ D) at the time of diagnosis. Eleven of these patients had liver metastases, three lung metastases, one both liver and lung metastases, two skeleton metastases, one ovarian, one suprarenal and four generalized cancer.The median alleged delay in diagnosis of CRC was 6.0 months (range 0.7-43). In five claims no delay was identified and in one the delay was less than one month. The majority of patients who had a delay of less than 6 months (24/32) were without metastatic disease. For 17 claimants the diagnostic delay was more than one year, eight of them had metastatic disease. The claimed diagnostic delay was considered to have had an impact on the extent of therapy concerning the surgical, radiation or chemotherapeutic procedures in 12 of 60 patients (20 %). Five of these patients had metastases and eight of them had a delay of more than 6 months Table .In seven patients and probably in an additional two, in total 9 of 62 (15 %) the diagnostic delay was considered to have had an impact on prognosis. Three of the patients had metastatic disease at diagnosis. In eight of these cases the diagnostic delay was more than 6 months and in one less than 6 months. Of the 29 patients with a delay of more than 6 months, 8 were considered to have experienced a worse prognosis. Of the 31 cases with a delay of less than 6 months, this figure was one (p = 0.0158).The Kaplan-Meier curves showed no significant difference in survival related to duration of diagnostic delay of more or less than 6 months Figure  or 12 moEconomic compensation for diagnostic delay was approved for 49 of 62 patients (79 %). The rules stated for economic compensation are that there should be a causal relationship between the claimed injury and the care and that it could be established that the injury could have been avoided.This retrospective study identified a low number of claims for compensation for patients who reported a diagnostic delay of CRC related to the clinicians (53/62) or to the providers (9/62). This number covered a period of 14 years, i.e. approximately five claims annually compared with an overall rate of 6200 new cases of CRC reported each year to the Swedish Cancer Register. More than half of the claims (52 %) were reported by patients younger than 60 years, who account for only 20 % of the age distribution of patients with CRC. The majority (53 %) of diagnostic errors were related to a history of gastrointestinal bleeding or anaemia and in these cases no diagnostic procedures were carried out or incorrect procedures were undertaken. At diagnosis more than one third (37 %) had an advanced cancer with metastatic disease.From the present study it is not possible to draw any conclusion of the total rate of diagnostic delay of CRC in Sweden. It can only be stated that during the period studied the malpractice insurance company (LÖF) processed about five cases annually dealing with diagnostic delay of CRC. This may be an underestimate because some patients may refrain from filing claims. However, these data correspond rather well with those reported by the Physician Insurers Association of America (PIAA). PIAA, which was started in 1985, has received about 2500 colorectal claims between 1985 and 2006; e.g. about 120 claims per year or one per 1000 new cases of CRC in clinical care in the US (150.000 new cases are diagnosed yearly). There was, however, a big difference between the two insurance systems regarding economic compensation. In the Swedish system almost 80 % were compensated, in the US system somewhat more than 25 % [Eurocare 4 the 5-year survival is 61.5 % for patients diagnosed between 2000 and 2002 [According to It is a dogma that early diagnosis of cancer before the onset of symptoms improves survival ,8. HowevThis study indicated that the delay of some kind affected the therapy since the delay resulted in a need for more extensive surgery or for down-sizing X-ray or chemotherapy. For patients with metastatic disease 5 out of 23 were not subjected to liver surgery and/or down-sizing chemotherapy due to too advanced disease. It was suggested that the diagnostic delay had an influence on the prognosis in only 15 % of the patients; all these were referred to stage Dukes’ C (n = 6) or metastatic disease (Dukes’ D) (n = 3). These figures are based on personal experience of the authors and are open for debate. It can, however, be considered obvious that diagnostic delay has a negative influence on a cancer disease but the analysis made here on the basis of our study provides support for those who question this statement ,14. It iIn the present study there was no significant impact on survival of a delay, whether the delay was more than 6 months or less than 6 months. The most important impact on survival in this selected group of patients with CRC was that metastatic disease of any location significantly  reduced survival . These findings have to be seen against in the context of those patients who claimed diagnostic delay had metastatic disease in 37 %.The relation between diagnostic delay and survival is complex. Findings that raise questions about standard cancer kinetics come from studies that have found that longer delay has resulted in improved survival . The mosIn this study the diagnostic delay was in 71 % of the cases related to the inability of the doctor who first met the patient to identify adequate signs of CRC, i.e. bleeding, anaemia and changes in bowel habits. The diagnostic guidelines established in Sweden were clearly not followed. Colonography or colonoscopy , must be considered as routine examinations with a diagnostic predictive value of above 90 and 80 %, respectively . In a feThe insurance company (LÖF) showed a generous attitude to the claimers, since it is documented that in 79 % of the case the patients were economically compensated. The high rate can be explained by the fact that LÖF covers injuries where it can be proved that the injury is related to the medical care and could have been avoided. There are no regulations requiring disciplinary actions in LÖF´s system but the insurance company is working on developing objective grounds for determining the right for the patient to receive compensation according to the provisions of The Swedish Patient Injury Act.The strength of this analysis is related to the condition that the data were collected from a well-established patient national insurance system in Sweden covering a long period (14 years). The information was based both on the hospitals’ records and on patients´ reports to LÖF. The survival data were obtained from an authoritative organization on a national basis. The limitations of this study arise because of the nature of retrospective analyses associated with sometimes incomplete data and a limited number of patients. Furthermore, the evaluation of impact concerning prognosis and consequences of the treatment and course was based on the joint judgments of the authors independent of the primary statements made by the insurance company.This study indicated that patients with CRC rarely file claims for economic compensation for diagnostic delay. The delay of diagnosis was a median of six months and was mainly the result of clinical errors. The delay was considered to have had an impact on treatment in a fifth of the patients who filed claims and the prognosis was postulated to have been adversely affected in 15 %. No impact on survival was identified. Economic compensation was given to the claimers in almost 80 %.Two of the authors  are medical experts in the Swedish Patient Claims Panel and one (JA) is permanently employed by The County Council´s Mutual Insurance Company as medical director. There are no competing financial interests.All three authors designed the study design. Two authors (LOH and HJ) scrutinized the claims and the charts. The analysis of data, drafting of first manuscript was done by the three authors. The final version of this manuscript was read and approved by the three authors.