To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Rumination has been shown to play a part in post-traumatic stress disorder (PTSD), but its relation to the intrusions characteristic of PTSD has mainly been investigated experimentally. This proof-of-concept case study explored the occurrence, personal experiences, and possible relation between rumination and intrusions in two PTSD patients in their daily living using a mixed method approach. A novel wearable self-tracking instrument was employed which provided fine-grained temporal resolution of observation data and could eliminate recall bias. Furthermore, quantitative and qualitative data were collected on participants’ symptoms, rumination and experiences of using the self-tracking instrument. First, without distinguishing between the two phenomena, the participants tracked both for a week. After receiving psychoeducational training for distinguishing between rumination and intrusions, the differentiated phenomena were tracked for a week. Both participants reported being subjectively able to distinguish between rumination and intrusions and made observations with high adherence during the project. Data hinted at a possible temporal relation between the phenomena in line with theories posing rumination as a maladaptive coping strategy as well as an exacerbator of PTSD symptoms. However, relations to mood were inconclusive. Furthermore, by using the self-tracking instrument, participants gained a heightened awareness of the characteristics of rumination and intrusions and contextual cues for occurrence, as well as a greater sense of momentary agency. Results reveal promising prospects in using the wearable self-tracking instrument for further investigation of the relation between rumination and intrusions in the lived lives of PTSD patients, as well as potential for incorporating this method in clinical treatment.
Key learning aims
(1) Self-tracking with the One Button Tracker is a novel symptom registration method, particularly suited for use in psychotherapeutic treatment and research.
(2) Rumination and intrusions appear to the participants as distinct cognitive phenomena and treatment targets in PTSD.
(3) Registering rumination and intrusions in real-time could reveal important temporal relations between them and the contexts in which they occur.
(4) The data obtained with this self-tracking method can potentially be used as a tool in, and for the further development of psychotherapy for PTSD.
Each summer, surface melting of the margin of the Greenland Ice Sheet exposes a distinctive visible stratigraphy that is related to past variability in subaerial dust deposition across the accumulation zone and subsequent ice flow toward the margin. Here we map this surface stratigraphy along the northern margin of the ice sheet using mosaicked Sentinel-2 multispectral satellite imagery from the end of the 2019 melt season and finer-resolution WorldView-2/3 imagery for smaller regions of interest. We trace three distinct transitions in apparent dust concentration and the top of a darker basal layer. The three dust transitions have been identified previously as representing late-Pleistocene climatic transitions, allowing us to develop a coarse margin chronostratigraphy for northern Greenland. Substantial folding of late-Pleistocene stratigraphy is observed but uncommon. The oldest conformal surface-exposed ice in northern Greenland is likely located adjacent to Warming Land and may be up to ~55 thousand years old. Basal ice is commonly exposed hundreds of metres from the ice margin and may indicate a widespread frozen basal thermal state. We conclude that the ice margin across northern Greenland offers multiple opportunities to recover paleoclimatically distinct ice relative to previously studied regions in southwestern Greenland.
Quality of life (QoL) measures are increasingly recognized as necessary parts of outcome assessments in psychosis. The present paper is a comprehensive study of patients with first-episode psychosis where QoL is measured by the commonly used Lehman Quality of Life Interview (L-QoLI). The aim is to examine if the L-QoLI maintain its original structure when used in a group of patients with first-episode psychosis, and to investigate what determines global subjective QoL with a specific emphasis on premorbid adjustment, duration of untreated psychosis (DUP) and clinical symptoms. The study indicates that the psychometric properties of the L-QoLI do not change significantly when used in first-episode samples. The patients report subjective and objective QoL in the fair to good range, with only a moderate association between the objective and subjective measures. Poor global satisfaction is predicted by being single, abusing drugs, being depressed, having a diagnosis of psychotic affective disorder, having poor premorbid social adjustment and DUP over 10 weeks. The study supports the notion that patients with first-episode psychosis construct QoL in the same way as other groups, and that longer durations of compromised function at this stage produces poor satisfaction with life rather than a downward readjustment of expectations.
During the last decades we have seen a new focus on early treatment of psychosis. Several reviews have shown that duration of untreated psychosis (DUP) is correlated to better outcome. However, it is still unknown whether early treatment will lead to a better long term outcome. This study reports the effects of reducing DUP on 5-year course and outcome.
During 1997-2000 a total of 281 consecutive patients aged > 17 years with first episode non-affective psychosis were recruited of which 192 participated in the 5-year follow-up. A comprehensive early detection (ED) program with public information campaigns and low-threshold psychosis detection teams was established in one health-care area (ED-area), but not in a comparable area (No-ED area). Both areas ran equivalent treatment programs during the first 2 years and need-adapted treatment thereafter.
At the start of treatment ED-patients had shorter DUP and less symptoms than No-ED-patients. There were no significant differences in treatment (psychotherapy and medication) for the 5 years. Mixed-effects modeling showed better scores for the ED-group on PANSS negative, depressive and cognitive factors and for GAF social functioning at 5 year follow-up. The ED-group also had more contacts with friends. Regression analysis did not find that these differences could be explained by confounders.
Early treatment had positive effects on clinical and functional status at 5 year follow-up in first episode psychosis.
Two common approaches to identify subgroups of patients with bipolar disorder are clustering methodology (mixture analysis) based on the age of onset, and a birth cohort analysis. This study investigates if a birth cohort effect will influence the results of clustering on the age of onset, using a large, international database.
The database includes 4037 patients with a diagnosis of bipolar I disorder, previously collected at 36 collection sites in 23 countries. Generalized estimating equations (GEE) were used to adjust the data for country median age, and in some models, birth cohort. Model-based clustering (mixture analysis) was then performed on the age of onset data using the residuals. Clinical variables in subgroups were compared.
There was a strong birth cohort effect. Without adjusting for the birth cohort, three subgroups were found by clustering. After adjusting for the birth cohort or when considering only those born after 1959, two subgroups were found. With results of either two or three subgroups, the youngest subgroup was more likely to have a family history of mood disorders and a first episode with depressed polarity. However, without adjusting for birth cohort (three subgroups), family history and polarity of the first episode could not be distinguished between the middle and oldest subgroups.
These results using international data confirm prior findings using single country data, that there are subgroups of bipolar I disorder based on the age of onset, and that there is a birth cohort effect. Including the birth cohort adjustment altered the number and characteristics of subgroups detected when clustering by age of onset. Further investigation is needed to determine if combining both approaches will identify subgroups that are more useful for research.
Few studies have examined rate and predictors of self-harm in discharged psychiatric patients.
To investigate the rate, coding, timing, predictors and characteristics of self-harm induced somatic admission after discharge from psychiatric acute admission.
Cohort study of 2827 unselected patients consecutively admitted to a psychiatric acute ward during three years. Mean observation period was 2.3 years. Combined register linkage and manual data examination. Cox regression was used to investigate covariates for time to somatic admission due to self-harm, with covariates changing during follow-up entered time dependently.
During the observation period, 10.5% of the patients had 792 somatic self-harm admissions. Strongest risk factors were psychiatric admission due to non-suicidal self-harm, suicide attempt and suicide ideation. The risk was increased throughout the first year of follow-up, during readmission, with increasing outpatient consultations and in patients diagnosed with recurrent depression, personality disorders, substance use disorders and anxiety/stress-related disorders. Only 49% of the somatic self-harm admissions were given hospital self-harm diagnosis.
Self-harm induced somatic admissions were highly prevalent during the first year after discharge from acute psychiatric admission. Underdiagnosing of self-harm in relation to somatic self-harm admissions may cause incorrect follow-up treatments and unreliable register data.
Evidence based treatment of schizophrenia as well as antipsychotic drug utility patterns have changed considerably in recent years and the present study aims to investigate the current level of unplanned hospital readmissions in a cohort of patients with schizophrenia, and to determine the risk-reducing effects of current antipsychotic drug treatment.
An open cohort study included all consecutively discharged patients with schizophrenia in a 3-year period (n = 277). The treatment-dependent variables were entered in a multivariate Cox survival analyses with time to unplanned readmission as the dependent variable.
11.2% of patients were readmitted within 30days of discharge, and 44.8% were readmitted within 12months. Antipsychotic monotherapy reduced the risk of readmission by 74.9%. Treatment in CMHC also had a risk-reducing effect. The prescription rate of clozapine in this sample was 10.1%.
The over-all level of unplanned readmissions was in correspondence with the findings of others. Current antipsychotic drug treatment independently offers strong protection against unplanned readmissions. There may be a potential for further optimalizing antipsychotic drug treatment according to treatment guidelines.
Unplanned readmissions are very common for patients with schizophrenia but antipsychotic drug treatment is associated with a strong risk-reducing effect in this regard.
Antipsychotic maintenance treatment for patients with schizophrenia has been demonstrated to be the single most important modifiable factor to prevent unplanned readmissions. Effectiveness studies have indicated different risk for drug discontinuation between current antipsychotics.
Objectives & aims
To evaluate time until discontinuation of antipsychotic treatment, and specifically to investigate if differences between the prescribed antipsychotics could be detected.
396 patients with schizophrenia were included in an open cohort study and followed through treatment until all antipsychotics prescribed at inclusion were discontinued, predictors for time to discontinuation were analysed with univariate and multivariate Cox survival analyses with time until discontinuation as the dependent variable and antipsychotic monotherapy as the predictor variable. The analysis was controlled for common confounders.
65.7% of the patients were men, mean age was 42.4 years, and 12.9% were first-episode patients. 287 were prescribed antipsychotic monotherapy. In the multivariate Cox analysis with time to all-cause discontinuation only clozapine was significant different from olanzapine, Adjusted Hazard Rate (AHR) 0.17 (0.07,0.45) (p = 0.0003), this was also the case for the prediction of time to clinician-decided discontinuation, AHR (0.20 (0.06,0.70) (p = 0.012). In the analysis with time to patient-decided discontinuation as the dependent variable also Second Generation Antipsychotic Long-Acting Injectables (LAI) (AHR 0.26 (0.09,0.77) (p = 0.015) and First Generation Antipsychotic (LAI) (AHR 0.35 (0.16,0.80) (p = 0.013) had significant lower risk for discontinuation.
Clinicians and patients discontinued clozapine at a lower rate than olanzapine, patients also discontinued LAI formulations at a lower rate than olanzapine.
The impact of cream processing on milk fat globule membrane (MFGM) was assessed in an industrial setting for the first time. Three creams and their derived MFGM fractions from different stages of the pasteurization procedure at a butter dairy were investigated and compared to a native control as well as a commercial MFGM fraction. The extent of cross-linking of serum proteins to MFGM proteins increased progressively with each consecutive pasteurization step. Unresolved high molecular weight aggregates were found to consist of both indigenous MFGM proteins and β-lactoglobulin as well as αs1- and β-casein. With regards to fat globule stability and in terms of resistance towards coalescence and flocculation after cream washing, single-pasteurized cream exhibited reduced sensitivity to cream washing compared to non- and double-pasteurized creams. Inactivation of the agglutination mechanism and the increased presence of non-MFGM proteins may determine this balance between stable and non-stable fat globules.
Both blood- and milk-based biomarkers have been analysed for decades in research settings, although often only in one herd, and without focus on the variation in the biomarkers that are specifically related to herd or diet. Biomarkers can be used to detect physiological imbalance and disease risk and may have a role in precision livestock farming (PLF). For use in PLF, it is important to quantify normal variation in specific biomarkers and the source of this variation. The objective of this study was to estimate the between- and within-herd variation in a number of blood metabolites (β-hydroxybutyrate (BHB), non-esterified fatty acids, glucose and serum IGF-1), milk metabolites (free glucose, glucose-6-phosphate, urea, isocitrate, BHB and uric acid), milk enzymes (lactate dehydrogenase and N-acetyl-β-D-glucosaminidase (NAGase)) and composite indicators for metabolic imbalances (Physiological Imbalance-index and energy balance), to help facilitate their adoption within PLF. Blood and milk were sampled from 234 Holstein dairy cows from 6 experimental herds, each in a different European country, and offered a total of 10 different diets. Blood was sampled on 2 occasions at approximately 14 days-in-milk (DIM) and 35 DIM. Milk samples were collected twice weekly (in total 2750 samples) from DIM 1 to 50. Multilevel random regression models were used to estimate the variance components and to calculate the intraclass correlations (ICCs). The ICCs for the milk metabolites, when adjusted for parity and DIM at sampling, demonstrated that between 12% (glucose-6-phosphate) and 46% (urea) of the variation in the metabolites’ levels could be associated with the herd-diet combination. Intraclass Correlations related to the herd-diet combination were generally higher for blood metabolites, from 17% (cholesterol) to approximately 46% (BHB and urea). The high ICCs for urea suggest that this biomarker can be used for monitoring on herd level. The low variance within cow for NAGase indicates that few samples would be needed to describe the status and potentially a general reference value could be used. The low ICC for most of the biomarkers and larger within cow variation emphasises that multiple samples would be needed - most likely on the individual cows - for making the biomarkers useful for monitoring. The majority of biomarkers were influenced by parity and DIM which indicate that these should be accounted for if the biomarker should be used for monitoring.
The main purpose of this study was to find several early factors affecting stayability in rabbit females. To reach this goal, 203 females were used from their first artificial insemination to their sixth parturition. Throughout that period, 48 traits were recorded, considered to be performance, metabolic and immunological indicators. These traits were initially recorded in females’ first reproductive cycle. Later, removed females due to death or culling and those that were non-removed were identified. A first analysis was used to explore whether it was possible to classify females between those reaching and those not reaching up to the mean lifespan of a rabbit female (the fifth reproductive) cycle using information from the first reproductive cycle. The analysis results showed that 97% of the non-removed females were classified correctly, whereas only 60% of the removed females were classified as animals to be removed. The reason for this difference lies in the model’s characteristics, which was designed using early traits and was able to classify only the cases in which females would be removed due to performance, metabolic or immunologic imbalances in their early lives. Our results suggest that the model defines the necessary conditions, but not the sufficient ones, for females to remain alive in the herd. The aim of a second analysis was to find out the main early differences between the non-removed and removed females. The live weights records taken in the first cycle indicated that the females removed in their first cycle were lighter, while those removed in their second cycle were heavier with longer stayability (−203 and +202 g on average, respectively; P < 0.05). Non-removed females showed higher glucose and lower beta-hydroxybutyrate concentrations in the first cycle than the removed females (+4.8 and −10.7%, respectively; P < 0.05). The average lymphocytes B counts in the first cycle were 22.7% higher in the non-removed females group (P < 0.05). The females removed in the first reproductive cycle presented a higher granulocytes/lymphocytes ratio in this cycle than those that at least reached the second cycle (4.81 v. 1.66; P < 0.001). Consequently, non-removed females at sixth parturition offered adequate body development and energy levels, less immunological stress and a more mature immune function in the first reproductive cycle. The females that deviated from this pattern were at higher risk of being removed from the herd.
Economic pressures continue to mount on modern-day livestock farmers, forcing them to increase herds sizes in order to be commercially viable. The natural consequence of this is to drive the farmer and the animal further apart. However, closer attention to the animal not only positively impacts animal welfare and health but can also increase the capacity of the farmer to achieve a more sustainable production. State-of-the-art precision livestock farming (PLF) technology is one such means of bringing the animals closer to the farmer in the facing of expanding systems. Contrary to some current opinions, it can offer an alternative philosophy to ‘farming by numbers’. This review addresses the key technology-oriented approaches to monitor animals and demonstrates how image and sound analyses can be used to build ‘digital representations’ of animals by giving an overview of some of the core concepts of PLF tool development and value discovery during PLF implementation. The key to developing such a representation is by measuring important behaviours and events in the livestock buildings. The application of image and sound can realise more advanced applications and has enormous potential in the industry. In the end, the importance lies in the accuracy of the developed PLF applications in the commercial farming system as this will also make the farmer embrace the technological development and ensure progress within the PLF field in favour of the livestock animals and their well-being.
This paper reviews the effects of extended lactation (EXT) as a strategy in dairy cattle on milk production and persistency, reproduction, milk quality, lifetime performance of the cow and finally the economic effects on herd and farm levels as well as the impact on emission of greenhouse gas at product level. Primiparous cows are able to produce equal or more milk per feeding day during EXT compared with a standard 305-d lactation, whereas results for multiparous cows are inconsistent. Cows managed for EXT can achieve a higher lifetime production while delivering milk with unchanged or improved quality properties. Delaying insemination enhances mounting behaviour and allows insemination after the cow’s energy balance has become positive. However, in most cases EXT has no effect or a non-significant positive effect on reproduction. The EXT strategy sets off a cascade of effects at herd and farm level. Thus, the EXT strategy leads to fewer calvings and thereby expected fewer diseases, fewer replacement heifers and fewer dry days per cow per year. The optimal lifetime scenario for milk production was modelled to be an EXT of 16 months for first parity cows followed by an EXT of 10 months for later lactations. Modelling studies of herd dynamics indicate a positive effect of EXT on lifetime efficiency (milk per dry matter intake), mainly originating from benefits of EXT on daily milk yield in primiparous cows and the reduced number of replacement heifers. Consequently, EXT also leads to reduced total meat production at herd level. For the farmer, EXT can give the same economic return as a traditional lactation period. At farm level, EXT can contribute to a reduction in the environmental impact of dairy production, mainly as a consequence of the reduced production of beef. A wider dissemination of the EXT concept will be supported by methods to predict which cows may be most suitable for EXT, and clarification of how milking frequency and feeding strategy through the lactation can be organised to support milk yield and an appropriate body condition at the next calving.
To assess the prevalence of prediabetes and metabolic abnormalities among overweight or obese clozapine- or olanzapine-treated schizophrenia patients, and to identify characteristics of the schizophrenia group with prediabetes.
A cross-sectional study assessing the presence of prediabetes and metabolic abnormalities in schizophrenia clozapine- or olanzapine-treated patients with a body mass index (BMI) ≥27 kg/m2. Procedures were part of the screening process for a randomized, placebo-controlled trial evaluating liraglutide vs placebo for improving glucose tolerance. For comparison, an age-, sex-, and BMI-matched healthy control group without psychiatric illness and prediabetes was included. Prediabetes was defined as elevated fasting plasma glucose and/or impaired glucose tolerance and/or elevated glycated hemoglobin A1c.
Among 145 schizophrenia patients (age = 42.1 years; males = 59.3%) on clozapine or olanzapine (clozapine/olanzapine/both: 73.8%/24.1%/2.1%), prediabetes was present in 69.7% (101 out of 145). While schizophrenia patients with and without prediabetes did not differ regarding demographic, illness, or antipsychotic treatment variables, metabolic abnormalities (waist circumference: 116.7±13.7 vs 110.1±13.6 cm, P = 0.007; triglycerides: 2.3±1.4 vs 1.6±0.9 mmol/L, P = 0.0004) and metabolic syndrome (76.2% vs 40.9%, P<0.0001) were significantly more pronounced in schizophrenia patients with vs without prediabetes. The age-, sex-, and BMI-matched healthy controls had significantly better glucose tolerance compared to both groups of patients with schizophrenia. The healthy controls also had higher levels of high-density lipoprotein compared to patients with schizophrenia and prediabetes.
Prediabetes and metabolic abnormalities were highly prevalent among the clozapine- and olanzapine-treated patients with schizophrenia, putting these patients at great risk for later type 2 diabetes and cardiovascular disease. These results stress the importance of identifying and adequately treating prediabetes and metabolic abnormalities among clozapine- and olanzapine-treated patients with schizophrenia.
Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.
To achieve functional but also productive females, we hypothesised that it is possible to modulate acquisition and allocation of animals from different genetic types by varying the main energy source of the diet. To test this hypothesis, we used 203 rabbit females belonging to three genetic types: H (n=66), a maternal line characterised by hyper-prolificacy; LP (n=67), a maternal line characterised by functional hyper-longevity; R (n=79), a paternal line characterised by growth rate. Females were fed with two isoenergetic and isoprotein diets differing in energy source: animal fat (AF) enhancing milk yield; cereal starch (CS) promoting body reserves recovery. Feed intake, weight, perirenal fat thickness (PFT), milk yield and blood traits were controlled during five consecutive reproductive cycles (RCs). Females fed with CS presented higher PFT (+0.2 mm, P<0.05) and those fed AF had higher milk yield (+11.7%, P<0.05). However, the effect of energy source varied with the genetic type and time. For example, R females presented a decrease in PFT at late lactation (−4.3%; P<0.05) significantly higher than that observed for H and LP lines (on av. −0.1%; P>0.05), particularly for those fed with AF. Moreover, LP females fed with AF progressively increased PFT across the RC, whereas those fed with CS increased PFT during early lactation (+7.3%; P<0.05), but partially mobilised it during late lactation (−2.8%; P<0.05). Independently of the diet offered, LP females reached weaning with similar PFT. H females fed with either of the two diets followed a similar trajectory throughout the RC. For milk yield, the effect of energy source was almost constant during the whole experiment, except for the first RC of females from the maternal lines (H and LP). These females yielded +34.1% (P<0.05) when fed with CS during this period. Results from this work indicate that the resource acquisition capacity and allocation pattern of rabbit females is different for each genetic type. Moreover, it seems that by varying the main energy source of the diet it is possible to modulate acquisition and allocation of resources of the different genetic types. However, the response of each one depends on its priorities over time.
The spread of African swine fever virus (ASFV) threatens to reach further parts of Europe. In countries with a large swine production, an outbreak of ASF may result in devastating economic consequences for the swine industry. Simulation models can assist decision makers setting up contingency plans. This creates a need for estimation of parameters. This study presents a new analysis of a previously published study. A full likelihood framework is presented including the impact of model assumptions on the estimated transmission parameters. As animals were only tested every other day, an interpretation was introduced to cover the weighted infectiousness on unobserved days for the individual animals (WIU). Based on our model and the set of assumptions, the within- and between-pen transmission parameters were estimated to βw = 1·05 (95% CI 0·62–1·72), βb = 0·46 (95% CI 0·17–1·00), respectively, and the WIU = 1·00 (95% CI 0–1). Furthermore, we simulated the spread of ASFV within a pig house using a modified SEIR-model to establish the time from infection of one animal until ASFV is detected in the herd. Based on a chosen detection limit of 2·55% equivalent to 10 dead pigs out of 360, the disease would be detected 13–19 days after introduction.