Ascending thoracic aortic aneurysm (ATAA) is a multifactorial disease with a strong inflammatory component. Surgery is often required to prevent aortic rupture and dissection. We performed gene expression analysis (Illumina HumanHT-12 version 3 Expression BeadChip) for 32 samples from ATAA (26 without/6 with dissection), and 28 left internal thoracic arteries (controls) collected in Tampere Vascular study. We compared expression profiles and conducted pathway analysis using Ingenuity Pathway Analysis (IPA) to reveal differences between ATAA and a healthy artery wall. Almost 5000 genes were differentially expressed in ATAA samples compared to controls. The most downregulated gene was homeobox (HOX) A5  and upregulated cadherin-2 (FC = 12.6). Several other HOX genes were also found downregulated . 43, mostly inflammatory, canonical pathways in ATAA were found to be significantly  differentially expressed. The results remained essentially the same when the 6 dissected ATAA samples were excluded from the analysis. We show for the first time on genome level that ATAA is an inflammatory process, revealing a more detailed molecular pathway level pathogenesis. We propose HOX genes as potentially important players in maintaining aortic integrity, altered expression of which might be important in the pathobiology of ATAA. Ascending thoracic aortic aneurysms (ATAA) are a group of degenerative, often atherosclerotic, inflammation diseases in which the integrity of the aortic wall weakens due to changes in the extracellular matrix (ECM) and a part of the vessel starts to dilate and bulge out forming an aneurysm.1. The most important risk factor for ATAA alongside with genetic disorders is hypertension2. Other important risk factors of ATAA includes smoking, male gender, over 60 years of age3 and atherosclerosis. ATAA is caused by weakening changes occurring in the aortic wall, especially in the elastic tunica intima and media4, in which the amount of elastin decreases and elastin becomes fragmented5 thus making the wall more susceptible to bulging out i.e. forming an aneurysm. This type of change predisposing the aortic wall to bulge out is often associated with atherosclerosis6. In addition, changes occur in regard to collagen, especially the type IV collagen related to the vasa vasorum4. Multiple gene expression changes of important inflammatory and ECM-remodeling machinery genes have been demonstrated between healthy and diseased ascending aorta, for example in the expression of ECM remodeling matrix metalloproteases 2 and 9 (MMP-2 and −9)8 and a disintegrin and metalloproteinases (ADAMs) 8 and 159. Also, the expression of tissue inhibitors (TIMPs) of these metalloproteases has recently come in to the focus of research in aortic diseases, especially in ATAA research5.Degenerative aneurysms of the ascending aorta are the most common type of thoracic aortic aneurysms, with incidence of 6–10 per 100 00011) and of these only a minority involves the ascending aorta. Ascending aortic dissection is usually caused by a tearing of ATAA as is the case for the patients in our study. ATAA and aortic dissection can also be caused by rare genetic disorders  or Loeys-Dietz (LD), or Turner syndrome), a trauma, an inflammatory disease  or the cause may remain unclear1.The incidence of aortic dissection is less common 14. The multifactorial disease of ATAA is regarded as a “silent killer” due to its lack of clinical symptoms before late complications15. Several other conditions, including AAA, have been shown as predisposing factors to ATAA15. Hence it is crucial to research the processes, which are active and affect these, often lethal and requiring aortic surgery to prevent aortic rupture and dissection.To our best knowledge this work is the first whole genome wide expression profiling of ATAA. It is important to differentiate the ascending and descending aortic tissues since aorta is not an embryologically uniform structureIn this study we aim to describe differences in expression of individual genes and pathways between ATAAs and histologically healthy arterial wall obtained from left internal thoracic artery during cardiac bypass surgery from patients with coronary artery disease.9. Patient information was collected with questionnaires and from the patient database of the hospital. Of the ATAA samples  six (N = 6) had progressed to dissection. The mean age of our cases was 62.6 years and there were no patients with rare genetic disorders  or Loeys-Dietz (LD), or Turner syndrome) or a trauma. The control arteries (N = 28) were collected from atherosclerosis-resistant16 left internal thoracic artery (LITA) from patients undergoing coronary artery bypass surgery. LITA tissue was chosen, as it is a histologically healthy and is ethically accessible. Demographics of the study population are presented in Table The samples were collected from patients undergoing cardiac surgery in the Heart Center of Tampere University Hospital with informed consent17. The correlation between expression measurements from GWE and qRT-PCR methods was good , and the Bland-Altman plot showed that the fold changes (FCs) were in agreement with these two methods, although for highly up- or down-regulated transcripts, GWE yielding lower absolute FC values than in qRT-PCR.The fresh tissue samples obtained from surgery were soaked in RNALater solution  and RNA isolated with Trizol reagent  and the RNAEasy Kit . The quality as well as concentration of the RNA was evaluated spectrophotometrically . Over 23,000 known and candidate genes were analyzed with Illumina HumanHT-12 version 3 Expression BeadChip (Illumina Inc.), following the instructions provided by the manufacturer. . In brief, 200 ng aliquots of total RNA from each sample were amplified to cDNA using the Ambion’s Illumina RNA Amplification kit according to the instructions . Samples of cRNA (1500 ng) were hybridized to Illumina’s Expression BeadChip arrays (Illumina). Hybridized biotinylated cRNA was detected with 1 µg/ml Cyanine3-streptavidine . BeadChips were scanned with the Illumina BeadArray Reader. The accuracy of this array has been previously tested and in our TVS validation study in which the results of 192 differentially expressed genes were verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR)http://www.r-project.org/), log2 transformed and normalized by the locally estimated scatterplot smoothing normalization method implemented in the R/Bioconductor package Lumi (www.bioconductor.org). Locally estimated scatterplot smoothing (LOESS) normalization for the data was selected because it gave the best accuracy in comparison with qRT-PCR data for artery samples17. Data quality control criteria included detection of outlier arrays based on the low number of robustly expressed genes and hierarchical clustering.After background subtraction, raw intensity data were exported using the Illumina GenomeStudio software. Raw expression data were imported into R version 3.1.1  and then back transformed the log ratios to FCs. To make the interpretation easier, we replaced fold-change values that are <1 by the negative of its inverse. Statistical significance of differences in gene expression was assessed using the nonparametric Wilcoxon signed-rank test and the log-transformed data.www.qiagen.com/ingenuity) we analyzed the gene expression data to put it in context with the potential biological functions and known pathological mechanisms. The inclusion criteria for genes selected for the analysis was a FC >  ± 1.5, a p-value < 0.05 and an FDR < 0.05 leading to 4884 genes being selected for further analysis. Gene ontology using GO term analysis with GOrilla online tool18 was also performed.Using the “Core Analysis” function of QIAGEN’s Ingenuity Pathway Analysis . The genes with FC >  ± 5 and FDR < 0.05 are presented in Table In genome-wide analysis several homeobox (HOX) genes were amongst the most significantly downregulated in our comparison, especially HOXA5 and HOXC6. A total of 15 different HOX genes were down-regulated (FCs between −25.3 and −1.5) in the samples from ATAA, and one HOX gene (HOXD11) was upregulated with a FC of 1.8 in comparison to controls  differentially expressed in ATAA samples in comparison to control arteries . Our results seem to be fairly well in line with previous work of Seo D. et al., who have also shown some of the homeobox genes  to be dysregulated in atherosclerosis29. Nonetheless, most studies related to differential expression of HOX genes are of cancer pathogenesis30. There are only a few studies that have shown HOX genes to have a role in cardiovascular diseases29 such as abnormal vasculogenesis31, therefore further research in this area is imperative.HOX genes are a highly conserved33. The down-regulation of HOXA5 in our samples is in accordance with results from in vivo and murine models presented by Arderiu et al., which show normal HOXA5 expression to reflect a more stable vascular phenotype25. Suppression of HOXA5 has also been shown to be important in angiogenesis34 thus giving merit to our results as angiogenesis, induced by leukocytes35, is active in inflamed tissues such as atherosclerotic and aneurysmatic vessels. We suggest that the downregulation of HOXC6 is related to the apoptotic activity, as this association has been previously shown by Ramachandran S. et al. in human prostate36.When interpreting these results, we need to keep in mind the site-specificity of HOX gene expression, meaning that HOX gene expression is different in different parts of the healthy aorta, in a collinear manner typical for HOX genes38. Amongst the most upregulated were the genes CDH2, CYTL1 and SCG2. We consider the up-regulation of CDH2 to be merely an indicator of mesenchymal stem cells with the potential to differentiate into cardiac myocytes39. The upregulation of secretogranin 2 (SCG2) is a sign of transendothelial migration of leukocytes as shown by Kähler CM. et al.40 in the ascending aorta, and in this manner we believe it contributes to the loss of integrity in the aortic wall, making the aorta susceptible to aneurysm or even progression to dissection. CYTL1, along with other chemokines such IL6 and SPP141, attract leukocytes which in turn infiltrate the aortic wall and cause other inflammatory effects. This results in enhanced vascular permeability and loss of integrity in the aortic wall42, and thereby ensuing in aneurysm formation.Many chemotactic genes were differentially expressed in the ATAA samples, further demonstrating the inflammatory nature of aortic aneurysms45 and as both MMP 12 and 28 are macrophage associated, this further links leukocyte infiltration and function to the changes in the ascending aorta21. MMP28 has been implicated in the up keeping of tissue homeostasis by Balta S. et al.46 and even though it has mostly been investigated in association with various cancers, our results suggest it is related to aortic disease as well, through the same process of dysfunction in tissue homeostasis.Our results show MMPs 12 and 28 to be differentially expressed with FCs of 2.6 and −2.5 respectively. Many MMPs are associated with cardiovascular diseases, including ATAAs, and particularly ECM-remodeling19. Several similarities can be recognized between these previous works, many inflammatory genes we report here, such as osteopontin (SPP1) and interleukin-6 (IL6), are introduced in these previous works by Hinterseher I. et al.19. It is also noteworthy that the pathways described in these previous works such as Ca2+ -signaling13 and leukocyte transendothelial migration14 are also well in line with our results. Both of the aforementioned and many more similar pathways are also differentially expressed in our samples, but some have slightly different names as the pathway analyses have been conducted with different programs. These similarities in differentially expressed pathways further confirm the role of inflammation to be important in the pathobiology of aortic aneurysms.Aortic aneurysms are most commonly located in the abdominal aorta, and due to this, most of the previous similar research has focused on those. The important role of pathways of the immune system and cell adhesion have repeatedly been demonstrated in similar studies of whole genome expression and pathways analysis47, together with the differential expression of HOX genes26, to show that the processes necessary for the formation of neovessels and repair of the vascular wall are active in the aneurysmatic aorta48. Other pathways, that are differentially expressed, are likely to be a result of leukocyte-activity at the aortic site. Pathways such as “Integrin signaling” and “Leukocyte Extravasation Signaling” show that alterations of the arterial wall occur in order to allow leukocytes through, thus further compromising the integrity of the arterial wall49 through complex networks of cytokines and other inflammatory mediators, as has been previously studied in both mice50 and humans51. Our results regarding the p53-signaling in ascending aorta are in line with those of Leeper N. et al.52 relating this to the apoptosis of smooth muscle cells in the aortic wall20. The results we present here support the idea of inflammatory response as the most crucial characteristic of ATAA and show this disease to be an inflammatory condition53.The pathways we found differentially expressed suggest that inflammation, leukocyte accumulation and apoptosis are important events occurring in diseased ascending aorta. Pathways we found significantly differentially expressed consist mostly of inflammatory pathways with some hypoxia related signaling as well. We consider the differentially expressed hypoxia induced signaling pathways55. We acknowledge that especially the inflammatory pathways are to be expected to differ in LITA56, as has been shown in methodologically similar study by Ferrari G. et al.56, but this also does furthermore also support the crucial role of inflammation in ATAA. However, this means we must take in to account the bias caused by LITA control when interpreting these results, especially in regards to the inflammatory pathways being overestimated. We have also used it in our previous studies in comparison of different arterial beds including descending aorta as a control vessel23. Our LITA samples have been also collected and analyzed with exactly the same method than ATAA and thus are methodologically ideal controls as in our previous TVS works23. To control the effect of ATAA dissection to gene all analyses were performed in two ways with (n = 32) and without dissection (n = 26), with essentially same results. We also emphasize careful interpretation of the results regarding HOX genes, because HOX genes are important in developmental biology and our samples and controls are of different embryological origin.The histologically healthy non-atherosclerotic LITA was used as a control vessel. The use of LITA as the control vessel was based on the fact that ethical problems would arise from attempting to collect corresponding healthy aortic samples for gene expression studies during normal surgical operations. Due to LITAs many biologically analogous features we consider it to be the best control readily availableAs many cardiovascular patients have several drugs in use, we must also acknowledge that the use of pharmacologically active agents may affects the gene expression in vascular wall. And as there was a significant difference in statin usage between the cases and controls, this might have an effect on our results. Nonetheless, the differences in medication or in risk factors  are not sufficient to explain the significant differential expression of genes we have presented here and thus we consider our results to be of significance in furthering our understanding the pathobiology of ATAA.Our findings expand the current understanding of ATAA pathogenesis on the molecular level. Our results show for the first time at the genome level that ATAA is an inflammatory process, and pinpoint its detailed molecular and pathway level pathogenesis. We also propose cautiously HOX genes as possibly crucial players in maintaining aortic integrity, and altered expression of these transcription factors may lead to susceptibility to pathological changes in the ATAA and even lead to a need for aortic surgery. This however might, at least partially, be due to the different embryological origin of the compared vessels. Nonetheless our result well depict important molecular pathways that are significant in ATAA pathogenesis and offer a wide range of possibilities for future research on this serious, yet relatively little studied, disease.Supplementary informationSupplementary table 1Supplementary table 2Supplementary table 3Supplementary table 4


We have performed a systematic study on the growth of graphene as a function of time to identify the onset and gradual evolution of graphene shapes caused by etching and then demonstrated that the etching can be stopped by reducing the flow of hydrogen from the feed. In addition, we have found that the etching rate due to the in situ rise in hydrogen is strongly dependent on the confinement  of copper foil. Highly etched graphene with dendritic shapes was observed in unconfined copper foil regions while no etching was found in graphene grown in a confined reaction region. This highlights the effect of the dynamic reactant distribution in activating the in situ etching process during growth, which needs to be counteracted or controlled for large scale growth.Exposing graphene to a hydrogen post-etching process yields dendritic graphene shapes. Here, we demonstrate that similar dendritic structures can be achieved at long growth times without adding hydrogen externally. These shapes are not a result of a surface diffusion controlled growth but of the competing backward reaction (etching), which dominates the growth dynamics at long times due to an  in situ rise in hydrogen with time results in dendritic graphene.Growth to etching transformation following  Among the known methods, chemical vapor deposition (CVD) on the surface of metal catalysts such as copper and nickel appears as the most promising approach.5–9Graphene, a two-dimensional (2D) single-atom-thick crystal of sp10 the hydrogen/methane (H2/CH4) ratio11,12 and the growth temperature.13,14 Such intrinsic complexity of CVD growth leads to a large spread of experimental results, even when using similar nominal growth conditions. In particular, dendritic structures have been reported by several groups in the low CH4 flow rate regime.14–16 The microscopic mechanism that leads to these structures is a subject of debate. They were ascribed to the kinetics of carbon flux attachment to the carbon edges, the anisotropic surface diffusion due to the underlying crystallinity14 or to the degree of roughness15 of the copper foil and to a surface diffusion controlled growth mechanism arising from the limited amount of carbon source.16 Remarkably, similar dendritic shapes were achieved by etching the CVD grown graphene after growth (post-etching).17,18 In those experiments, graphene is first grown following a standard procedure using a CH4 in Ar/H2 gas mixture, then the CH4 is switched off and, finally, graphene is etched with Ar/H2. In other reports, gaseous oxidants were considered to be the main responsible for graphene etching, giving place to lines and holes with hexagonal shapes.19Despite continuous progress, CVD grown graphene, covering wafer size areas, is typically polycrystalline comprising small grain sizes which cause deterioration of the physical and chemical properties. To avoid the grain boundary created by merging of different domains, considerable efforts have been made to grow single-crystal isolated graphene flakes. However, isolated graphene flakes can adopt a large variety of shapes, including compact and dendritic structures, which are determined by the growth pressure,20 It is thus possible that the dendritic shapes might not be only the result of a surface diffusion controlled growth mechanism but also of an etching takeover over time without modification of growth conditions. Therefore, understanding the origin of the dendritic shapes is critical to gain insight into the dynamic processes involving growth and etching during deposition, which has often been ignored, and can lead to novel approaches to further develop CVD growth.All of the above reports, which focus primarily on either growth or etching of graphene, are seemingly disconnected. However, CVD growth of graphene actually involves a competition between growth and etching.in situ rise in H2 partial pressure during growth. We also show that the confinement of the copper foil impacts on the growth/etching balance. Etching is selectively observed in unconfined regions. This is a very important finding since it can be an additional source of heterogeneity that must be taken into consideration for explaining the wide dispersion of results among research groups. In addition, by tuning the amount of H2 after a standard growth period (before etching starts to dominate), we demonstrate that oxidative etching coexists with the reductive etching mediated by the in situ rise in H2 partial pressure. The previously observed oxidative etching19 becomes evident at low H2 flow rates and is characterized by the formation of hexagonal holes carved into the graphene flakes.Here we demonstrate that dendritic shapes can be obtained within a given growth process promoted by the etching takeover at long growth times. We have studied systematically the shape evolution the graphene flakes at shorter times under the same growth conditions. We have found that, indeed, after an initial growth, the competing backward etching starts dominating, shaping the graphene into dendritic structures. We have ascribed the etching and formation of dendrites to an 2 (450/50 sccm) for 1 hour at 1000 °C. The copper substrate was exposed to 1 sccm O2 flow during 2 min, while keeping the same Ar flow and H2 switched off, in order to decrease graphene nucleation density by passivating catalyst active sites. Growth was then performed in Ar/H2/CH4 (450/50/1 sccm) for various growth times at 1000 °C, after which the samples were fast-cooled down in Ar/H2 (450/50 sccm) atmosphere by taking the tube out of the furnace. Unless specified, these are the standard growth conditions selected throughout the paper. The growth was systematically studied with specific configurations of the copper substrate in order to analyze its influence on the concentration of reactants. To demonstrate the in situ increase of H2, and the transition to oxidative etching, we performed a series of experiments in which the H2 flow rate was reduced during an additional 10 min growth period. For further details, refer to the ESI.Graphene growth was performed under reduced pressure on copper foil using a 1 inch quartz tube reactor. Briefly, copper foil was annealed in Ar/HOptical microscope (Nikon Eclipse LV100 ND) characterization was done on as-grown graphene on copper foil. After growth, the graphene/copper substrate was heated upto 180 °C on a hot plate for 2 min for better optical visualization. The graphene-free copper surface changes color due to oxidation at such temperature and the graphene-covered copper remains unchanged which helps to identify graphene flakes by color contrast difference.Scanning electron microscopy SEM (SEM Magellan 400L XHR) operated at an acceleration voltage of 10 kV was used to characterize the morphology and shape evolution of graphene domain on copper foil. The crystalline structure of graphene was characterized with high resolution transmission electron microscopy (HRTEM FEi Tecnai G2 F20) operated at 200 kV after transferring the as-grown graphene to a TEM grid  have similarities with previous studies in which graphene was carved by post-etching with the addition of external doses of hydrogen.2. The purpose is to compensate for the in situ rise in the H2 partial pressure during growth and thus reduce the etching that is induced by it. In each of our experiments, we first grew graphene during 20 min with our standard parameters, and then we decreased the H2 flow rates from 50 sccm down to 30, 20, 10, 5, and 0 sccm for additional 10 min keeping the other parameters constant . For further comparison, we performed an additional experiment in which the H2 flow rate was increased to 60 sccm. 2 flow rate was kept constant for 30 min. For 60 sccm, we observe that the etching process is very aggressive leading to an almost complete disappearance of the flakes , with a high-resolution TEM (HRTEM)  Fig. S3. Additio) Fig. S3.21,26,27 It is, therefore, likely that confinement also impacts the growth/etching competition process. Considering this, we examined the inhomogeneities of the in situ H2 rise in different regions of the copper foil by observing the local graphene etching level. We bent the copper foil into two parts to make half part vertical (unconfined) and half part horizontal (confined). The horizontal part was sandwiched between a quartz glass slide and the quartz substrate holder (The confinement of the copper foil is known to play a significant role in determining the local partial pressure of gasses species.e holder . Due to e holder .2 to the copper foil is slow.21 Consequently, the growth rate and its accompanying H2 production become slower. However, on the unconfined region the boundary layer is thinner. There, the mass transport of the reactants to the copper foil is fast, facing a higher concentration of carbon promoting growth21 and a higher flow of H2. The H2 enrichment at the catalyst surface plus the local rise in H2 produced from the reaction as a byproduct favors the etching reaction. Consequently the etching process ends up dominating over the growth leading to dendritic graphene shape. Hence, these findings indicate that etching can be suppressed by subjecting the catalyst in a confined reaction region.In the confined region, the gas flow velocity is much slower due to the reduced space between the copper foil surface and the sample holder, resulting in a thicker boundary layer where mass transport of carbon and Hin situ hydrogen concentration increase. This contrasts with previous reports, which ascribed such dendritic structures to a surface diffusion controlled growth. The results are further confirmed by decreasing the external flow of hydrogen, which leads to a suppression of the etching. We have found however, that further growth at low hydrogen flows is slowed down due to the emergence of oxidizing etching, which indicates that the concentration of gaseous oxidants also increases. Moreover, the etching induced by the in situ increase of hydrogen concentration is also very dependent on the copper foil geometrical arrangement, yielding highly etched graphene on unconfined copper foil regions. These findings demonstrate that local fluctuations of the reactants during growth have a large impact on the resulting graphene morphology. They provide information on the etching process of graphene and highlight the critical importance of confinement aspects and the balance of gases as a source of heterogeneities, which can account for the wide dispersion of results among research groups. Overall, they also suggest that a careful balance of the local concentration of all reagents at any given time of the growth process is necessary to optimize the growth of graphene.We have demonstrated that the graphene morphology changes from a compact to a dendritic structure over growth time without modifying the external growth conditions. The dendritic shape is a consequence of a competing backward etching reaction, which starts dominating over the growth at long times due to an There are no conflicts to declare.RA-008-C7RA13169K-s001


Individuals in the indeterminate phase of Chagas disease are considered to have mortality rates similar to those of the overall population. This study compares mortality rates among blood donors seropositive for Chagas disease and negative controls in the city of São Paulo, Brazil.This is a retrospective cohort study of blood donors from 1996 to 2000: 2842 seropositive and 5684 seronegative for Chagas disease. Death status was ascertained by performing probabilistic record linkage (RL) with the Brazil national mortality information system (SIM). RL was assessed in a previous validation study. Cox Regression was used to derive hazard ratios (HR), adjusting for confounders. RL identified 159 deaths among the 2842 seropositive blood donors (5.6%) and 103 deaths among the 5684 seronegative (1.8%). Out of the 159 deaths among seropositive donors, 26 had the 10th International Statistical Classification of Diseases and Related Health Problems (ICD-10) indicating Chagas disease as the underlying cause of death (B57.0/B57.5), 23 had ICD-10 codes (I42.0/I42.2/I47.0/I47.2/I49.0/I50.0/I50.1/ I50.9/I51.7) indicating cardiac abnormalities possibly related to Chagas disease listed as an underlying or associated cause of death, with the others having no mention of Chagas disease in part I of the death certificate. Donors seropositive for Chagas disease had a 2.3 times higher risk of death due to all causes , 1.8–3.0) than seronegative donors. When considering deaths due to Chagas disease or those that had underlying causes of cardiac abnormalities related to Chagas disease, seropositive donors had a risk of death 17.9  times greater than seronegative donors.There is an excess risk of death in donors seropositive blood for Chagas disease compared to seronegative donors. Chagas disease is an under-reported cause of death in the Brazilian mortality database. Chagas disease is classified as one of the 17 most important neglected diseases by the World Health Organization (WHO). The majority of infected individuals live in 21 countries of Central and South America. Estimates of mortality attributable to Chagas disease vary considerably . To improve the measurement of disease burden and policy decision-making in regard to treatment availability, more accurate estimates of mortality, especially among otherwise asymptomatic seropositive individuals are needed,. This study compares mortality rates and causes of death of asymptomatic blood donors who tested seropositive for Chagas disease and seronegative for all screening tests conducted in Brazil. Mortality status was ascertained by linking donor names with the Brazilian national mortality information system (SIM). The study found that donors who tested Chagas disease seropositive had risk of death from all causes 2.3  times greater than seronegative ones. The data also suggest that the SIM may underestimate the total number of deaths attributable to Chagas disease in Brazil. Trypanosoma cruzi (T. cruzi). The parasite is transmitted by the reduviid bug, a member of the Triatomine subfamily, popularly known as the “kissing bug”. Transmission may also occur by blood or organ transfusion, contaminated food, from mother to child and due to laboratory accidents [Chagas disease was first described in 1909 by the Brazilian scientist Carlos Ribeiro Justiniano Chagas (1879–1934). The etiologic agent that causes Chagas disease is the protozoan parasite ccidents .Triatoma infestans peri and in-house habitat by the use of insecticides, housing stock improvement, sanitary education, and the adoption of screening tests among blood donors to detect the parasite and thus avoid transmission [T cruzi is mandatory in all Latin American countries with more than 92% of screening coverage of the blood supply.[T cruzi has declined among first time blood donors in Brazil, from 0.52% in 1996 [In Brazil, from 1980 to 2000 there was a great effort by health authorities to eradicate the insect and to stop transfusion transmission. The reduction in disease burden is predominantly a result of improved vector control. The main measures of disease control implemented were the elimination of smission . Donor sd supply. The prevIn humans, Chagas disease manifests in two phases, acute and chronic. The acute phase has mild symptoms that may last for approximately two months. In the chronic phase, the majority of cases are of the asymptomatic indeterminate form, which may last a lifetime. Individuals in the chronic phase of the disease with normal electrocardiogram (EKG) and normal chest and esophagus X-rays are considered individuals in the indeterminate form of the disease . WhereasT. cruzi transmitters and may even die without being diagnosed with the disease. Mortality-associated with Chagas disease is still inadequately estimated, particularly for those with the indeterminate form of the disease. Important unanswered questions include: do persons with indeterminate disease have an increased risk of dying? Studies that compare mortality among seropositive and seronegative individuals can help reduce this knowledge gap as described in a systematic review and meta-analysis published in 2016 [One of the critical challenges for Chagas disease control is that many individuals with the indeterminate form of Chagas disease are not aware that they are infected, so that these individuals are potential onward  in 2016 . The objThis is a retrospective cohort study. This study compared mortality rates and causes of death in blood donors at Fundação Pró-Sangue—Hemocentro de São Paulo (FPS), one of the largest blood banks in State of São Paulo, located in São Paulo city.FPS is a reference center in Latin America, and the proportion of the supply collected is approximately 33% of all donated blood used in the metropolitan region of São Paulo city. FPS is a reference center for the Pan-American Health Organization (PAHO),World Health Organization (WHO) and the Brazilian Ministry of Health, it collects and processes 90,000 blood units annually that are used by more than 100 health institutions .Blood donors constitute a particular population in which, if infected, seropositive individuals are mostly asymptomatic and therefore are more likely to be in the indeterminate form Chagas disease. Although donation eligibility assessment does not include EKG or X rays, blood donors go through an interview before donating and requirements to donate include being in good health with no evidence of or reported history of Chagas disease or cardiomyopathies.During the study period (1996–2000) FPS screened all donors with 3 serological assays (indirect immunofluorescence (IIF), indirect hemaglutination (IHA), and enzyme-linked immunosorbent assay (ELISA)) . Donors In order to ascertain the death status of the study population, a record linkage (RL) was performed to link records of the FPS database with the Brazil national mortality information system  records. As Brazil does not have a unique national identity number, a probabilistic RL was performed using open source software. Patients’ name, mother´s name and date of birth were the chosen linking variables. One of the advantages of the use of this software in our context is that it links names and other variables through the use of a Soundex system which is specific for Portuguese phonetics  .The RL procedure used here has been described in depth in a previous publication . In thisIn the SIM database, records containing the variables patients’ name, mother´s name, date of birth and address of residence were only available for the years 2001 to 2009 . FPS donors included in the study donated blood during the period of 1996 to 2000, this means that even though we were able to retrospectively follow all donors for at least nine years, up to first five years of follow-up were not available in SIM. For the cohort that donated blood in 1996, we missed the first five years, for the 1997 cohort, we missed the first four years and so forth. In other words, irrespective of their Chagas serology status, we do not know whether those who donated blood in 1996, for example, died in the period of 1996 to 2000. However, based on findings of seropositive and seronegative individuals who donated blood in 2000 and for whom we only missed deaths that might have happened during the year of their blood donation, we found that deaths were extremely rare in the first year after donation. Because of this finding and with a view of circumscribing this data limitation, we made the analytical assumption that nobody in our cohort died in the years 1996 to 2000.For the descriptive analysis, the underlying causes of death of seropositive and seronegative donors were grouped by chapters in the ICD-10.Confirmed deaths due to Chagas disease, B) Confirmed and possible deaths due to Chagas disease, and C) Death due to all causes. Group A is hierarchically a subgroup of group B, which is a subgroup of group C. “Confirmed deaths due to Chagas disease” refers to deaths recorded with ICD-10 codes B57.0—B57.5 listed on the underlying or the associated causes of death fields. “Confirmed and possible deaths due to Chagas disease” refer to deaths recorded with ICD-10 codes B57.0—B57.5 plus other codes indicating cardiac abnormalities that are possibly due to Chagas disease listed on the underlying or the associated causes of death fields: I42.0—Dilated cardiomyopathy, I42.2—Other hypertrophic cardiomyopathy, I47.0—Re-entry ventricular arrhythmia, I47.2—Ventricular tachycardia, I49.0—Ventricular fibrillation and flutter, I50.0—Congestive heart failure, I50.1—Left ventricular failure, I50.9—Heart failure, unspecified and I51.7—Cardiomegaly.For the multivariate analysis, causes of death were analyzed according to three groups: A) Comparisons of characteristics of donors that were seropositive and seronegative or that were alive or dead at the end of the follow-up period were performed using two-tailed Fisher Exact tests or Chi-squared tests, as appropriate. Mortality rates were calculated by dividing the number of donors that died (numerator) by the total person-years at risk of dying during the follow-up period, i.e. from donation to death or to the end of the study period (denominator). Survival of seropositive and seronegative donors over time was graphically assessed by Kaplan-Meier (KM) curves and statistical differences tested with the Logrank test.Age and sex were the only two demographic variables available for analysis in the FPS database that represented potential confounders of the association between the outcomes variables and the main explanatory variable under investigation, i.e. serological status. Age was analyzed a categorical variable .Multiple Cox Regression models were used to derive hazard ratios (HR) for the outcomes studied, adjusting for confounders and testing for interactions, where appropriate. Two sets of analyses were performed one for the whole cohort and another excluding Chagas disease positive donors co-infected with one or more of the following infections: syphilis, HIV, HTLV, HCV and HBV.Regression models containing all potential predictors were estimated. The predictor variables available for the regression models were age group and sex. Stepwise backward selection procedures were used to remove variables. Variables were removed with Wald test p-values ≥0.2. The remaining variables were then successively removed based on lack of evidence of confounding and their contribution to the models. Variables whose removal from the models caused substantial changes (defined as >10%) in the HRs were retained, as were variables whose removal incurred significantly reduced model fit based on likelihood ratio tests . For each outcome and potential risk factor, the proportional hazards assumption was tested statistically using Schoenfeld residuals and by examining for parallel curves in log-log plots. Schoenfeld residuals tests were not statistically significant and the curves were mostly parallel, indicating no evidence of violation of the proportional hazards assumption. In addition, the proportional hazard was assessed by including a time-dependent interaction term in the multivariable Cox models and by testing the statistical significance of this interaction using the likelihood ratio test. Consistently, results indicated no evidence of violation of the assumption. Statistical analyses were conducted using Stata version 13.1  software .Ethical approval for this study was obtained from the Ethics Research Committee of Clinical Hospital of the School of Medicine—University of Sao Paulo, HC-FMUSP (CAAE:03882512.4.0000.0065). HC-FMUSP Ethics Research Committee determined it unnecessary to obtain informed consent from each individual for reviewing medical records, given the difficulty of obtaining such consent and because the study was conducted using 2 existing databases. The identifiers were removed from database after the probabilistic RL was performed.Out of 1,354,569 blood donors to FPS during the study time period, 2,842 confirmed positive cases and 5,684 controls (matched by year of donation) were assessed for death status and are described on The RL method identified a total of 262 deaths, 159 (5.6%) among the seropositive blood donors and 103 (1.8%) among the seronegative. Mortality was significantly higher in seropositive donors.Out of the 2842 Chagas disease seropositive donors, 128 (4.5%) were also seropositive for at least one of the other tested infections. Syphilis (51 donors) was the most common co-infection, followed by HBV (35 donors) and HCV (26 donors), HTLV (21 donors) and HIV (2 donors). A total of 12 donors were co-infected with Chagas disease plus two of the other infections.The distribution of the underlying causes of death of seropositive and seronegative donors, as classified by ICD-10 chapters, is shown in Out of the 262 donors that died, 26 had codes B57 listed as the underlying cause of death and were therefore classified as "Confirmed deaths due to Chagas disease". All of them were seropositive for Chagas disease. There was no record with B57 listed as an associated cause of death, but there was one seropositive donor with code B57 listed on part II (co-morbidities) on the death certificate.A total of 49 donors were classified as "Confirmed and possible deaths due to Chagas disease". This number includes all 26 donors classified as "Confirmed deaths due to Chagas disease" plus 23 donors that had codes indicating cardiac abnormalities possibly related to Chagas disease, listed as an underlying or associated cause of death. Codes indicating cardiac abnormalities possibly related to Chagas disease were listed as associated causes of death in 16 (61.5%) of the 26 donors classified as "Confirmed deaths due to Chagas disease". Out of the other 236 donors that were not classified as "Confirmed deaths due to Chagas disease", only 23 (9.8%) had any of these codes listed as an underlying or associated cause of death (p<0.001). Moreover, these codes were listed in 35 (22%) of the 159 deaths of seropositive donors and in only 4 (3.9%) of the 103 deaths of seronegative donors (p<0.001).T. cruzi. While possible, it may also have been a coding mistake.The underlying causes of death of the 49 donors classified as "Confirmed and possible deaths due to Chagas disease" is shown in Donors’ cumulative survival according to serology, without excluding co-infected individuals, is shown in Confirmed deaths due to Chagas disease B) Confirmed and possible deaths due to Chagas disease and C) Death due to all causes  l causes . As therExcluding co-infected donors did not appreciably change the results. When multivariate analyses were performed excluding the 128 co-infected donors, seropositive donors had a HR 2.2  times higher than seronegative ones for all cause deaths and a HR 16.1  times higher when considering confirmed and possible deaths due to Chagas disease.In this study, we compared the mortality rates of blood donors identified as seropositive for Chagas disease and seronegative for all screening tests based on donation testing in the 5-year time period, 1996–2000. Our results show that seropositive blood donors had a risk of dying over the study time period 2.3 times greater than seronegative individuals. When considering only deaths due to Chagas disease or those that had underlying causes of cardiac abnormalities that could indicate Chagas disease, seropositive blood donors had a much higher risk of death than seronegative donors. In a recent systematic review, Cucunuba et al. analyzed 25 papers published on mortality of Chagas disease from 10,638 individuals and 53,346 persons-year of follow-up. Only six of those studies focused on asymptomatic patients , followiIn this study, we have been able to follow 8,526 individuals for 100,467 person-years of observation, doubling the data available and contributing to a more precise evaluation of the impact of Chagas disease-associated mortality among asymptomatic seropositive individuals.Classifying mortality for persons who are initially identified as being in the indeterminate form is difficult because disease progression is slow, studies need large sample sizes and very long follow-up periods. Population studies are expensive, and in areas with low prevalence such as the city of São Paulo, with prevalence rates of less than 1% , 6, screT. cruzi infection.A provocative finding is that cardiac causes of death with no mention of Chagas disease was significantly more common among seropositive donors. Indeed, of the 45 deaths due to cardiac disease among seropositive donors only 26 (58%) had Chagas disease listed on their death certificate as an underlying cause of death. This indicates that the impact of Chagas disease mortality in Brazil is underestimated when analyses of only death records that specifically mention Chagas disease as the underlying cause of death are conducted . The facThe use of the probabilistic RL method implies we might have missed some deaths . HoweverHowever, early deaths after donation were not frequent, as observed for those who donated in 1999 and 2000 for whom we were able to ascertain whether or not they had died after their first or second year of follow-up, respectively see . If we hT. cruzi infection are mostly poor, less educated, and from rural areas. This combination of factors could lead to increase mortality for reasons such as less healthy lifestyles or access to care. In turn, this could lead to increased mortality. Yet, these individuals presented to blood donate and at the time were eligible and donated following the extensive health history assessment covering a range of topics, including current physical health, substance abuse history, and sexual history. Our analysis might have generated increased accuracy in the HR results if we had been able to adjust for socioecomic status.Another limitation with greater potential impact on our results is the fact that data regarding socioeconomic status was not available in the blood bank database, so we could not control for this effect in our multivariate models. In general, persons with Although our study is focused in the city of Sao Paulo, we believe it well represent Brazil. Internal migration was an important factor for population increase in the city of Sao Paulo . Data reWe believe that most Chagas disease seropositive donors were in the indeterminate form of the disease. Individuals who present to donate blood have to report if they do not have any current serious conditions in a comprehensive questionnaire about their health status. Questions such as “Do you have a severe heart problem?” and “Do you have arrhythmia and use medication for this problem?” are part of the screening questionnaire. However, we do not know if any of the donors had abnormal EKGs or abnormalities identifiable by X-ray, in which case they would be classified in the clinical form of the chronic phase of the disease. The increased mortality findings have high relevance for common assumptions about Chagas disease morbidity. It is not uncommon to find articles in the literature that consider the indeterminate form of the disease as a benign disease not requiring close medical attention , 31, 32.In conclusion, this analysis shows that there is an excess risk of death in donors who test seropositive for Chagas disease compared to seronegative donors. Chagas disease is an under-estimated cause of death in the Brazilian National Mortality Information System.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


For many years initiation and termination of mRNA translation have been studied separately. However, a direct link between these 2 isolated stages has been suggested by the fact that some initiation factors also control termination and can even promote ribosome recycling; i.e. the last stage where post-terminating 80S ribosomes are split to start a new round of initiation. Notably, it is now firmly established that, among other factors, ribosomal recycling critically requires the NTPase ABCE1. However, several earlier reports have proposed that ABCE1 also somehow participates in the initiation complex assembly. Based on an extended analysis of our recently published late-stage 48S initiation complex from rabbit, here we provide new mechanistic insights into this putative role of ABCE1 in initiation. This point of view represents the first structural evidence in which the regulatory role of the recycling factor ABCE1 in initiation is discussed and establishes a corner stone for elucidating the interplay between ABCE1 and several initiation factors during the transit from ribosomal recycling to formation of the elongation competent 80S initiation complex. In the following chapters we will discuss possible implications of the ABCE1 involvement in ribosomal recycling and in translation initiation; i.e., in the role that has remained underexplored so far, as well as during the transition between these 2.Cryo-EM reconstructions of termination- or recycling-related complexes revealed that the NBD-containing core of ABCE1, when bound to ATP, occupies the GTPase-binding center and its FeS cluster domain binds and stabilizes the release factor eRF12+ concentrations, and its post-TC dissociation efficiency is enhanced by the presence of eIF6 (a well-described anti-association factor)The mRNA translation process is thought to be cyclic and thus the by-default pathway for a new round of protein synthesis after termination is the reuse of post-termination ribosomes.3637 and whose catalytic activity lacks nucleotide specificity.ABCE1 is one of the most conserved proteins in evolution that is encoded by all eukaryotic and archaeal genomes,2+ concentrations,in vivo.New mechanistic insights into the transition between termination, recycling and initiation suggest that recycling and initiation factors link these stages of eukaryotic protein synthesis.in vitro and in vivo,in vivo has already been suggested by others.Owing to the great complexity of the eukaryotic initiation process, it is possible that some important regulatory activities performed by known players have been overlooked due to redundancy and/or an unexpected breadth of their functional range of action. This may very well apply to ABCE1 thanks to its reported intrinsic ability to associate with the small ribosomal subunit in presence of NTP It is noteworthy that, after particle sorting, one of the cryo-EM reconstructions of the late-stage m48S ICper se but for its own release from the small subunit.Although NTP hydrolysis of ABCE1 promotes 70S dissociation in archaea, it has been demonstrated that it is not essential for ribosome splitting According to our model, the initiation cycle starts right after the ribosomal recycling, a stage where the 40S subunit is dissociated from the 60S subunit, de-acetylated tRNA and mRNA, the release of the latter 2 of which is supposed to be aided by eIFs 1, 1A and 3 , step 1.IN conformation in the closed state of the preinitiation complex .bona fide initiation factor with an important anti-association activity throughout the entire initiation phase. In addition to that we speculated that ABCE1 might also somehow ensure stable binding of several initiation factors while the 43S PIC is formed. Finally, we hypothesized a role for ABCE1 in regulating the initiation rates according to the energy metabolism of a cell. It is very tempting to speculate that the energy level in a cell closely coordinates all ABCE1 roles in initiation, termination and ribosomal recycling, which would make this highly conserved protein one of the main general regulators of the translational output in changing environmental conditions. In any case, it is for certain that our findings with ABCE1 presented here and with eIF3 published beforeIn this point of view we proposed a new hypothesis featuring ABCE1 as a


Human interaction involves the exchange of temporally coordinated, multimodal cues. Our work focused on interaction in the visual domain, using music performance as a case for analysis due to its temporally diverse and hierarchical structures. We made use of two improvising duo datasets—(i) performances of a jazz standard with a regular pulse and (ii) non-pulsed, free improvizations—to investigate whether human judgements of moments of interaction between co-performers are influenced by body movement coordination at multiple timescales. Bouts of interaction in the performances were manually annotated by experts and the performers’ movements were quantified using computer vision techniques. The annotated interaction bouts were then predicted using several quantitative movement and audio features. Over 80% of the interaction bouts were successfully predicted by a broadband measure of the energy of the cross-wavelet transform of the co-performers’ movements in non-pulsed duos. A more complex model, with multiple predictors that captured more specific, interacting features of the movements, was needed to explain a significant amount of variance in the pulsed duos. The methods developed here have key implications for future work on measuring visual coordination in musical ensemble performances, and can be easily adapted to other musical contexts, ensemble types and traditions. Many of the aspects of conversation described above, such as shared representations, a need for mutual understanding to coordinate common goals and turn-taking behaviours, are implicated in music performance )1 event synchronization (ES) [A variety of analysis techniques have been applied to the types of movement data discussed above, in order to provide an index of interpersonal movement coordination (i.e. stability of the relationships between co-performers’ movements) or synchrony . Analysis techniques that have been used with time-series data include cross-correlation,ion (ES) , cross-rion (ES) ) and theion (ES) ). The chet al. [CWT analysis was selected as the most appropriate method for the present study, as we sought to explore coordination, rather than exact synchrony, using continuous movement data from improvising duos. CWT analysis examines the shared periodicities of two time series across different frequencies and time. This technique thus permits the investigation of coordination at multiple timescales, by examining movements across different frequency bands, which allowed us to make use of information from different types of ancillary movements, from fast head nods to slower swaying motions. CWT analysis has also recently been proposed as an informative tool for quantifying movement coordination between improvising musicians . Specifiet al.  describe1.3.The overall focus of the present work was to test whether human judgements of visually apparent bouts of interaction between co-performers in improvising duos are influenced by body movement coordination at multiple timescales. Research on musical performance can add a new perspective here beyond work on many other types of human interaction (e.g. conversation), as music offers a rich and more diverse range of temporal patterns over which it is organized, from strictly beat-based music to free improvisation across a wide range of musical tempi. Music theory describes the organization of much music as relying on the perception of a regular pulse or ‘beat’ that can be inferred from auditory information ,57. BeatThe present work made use of two datasets of video recordings: one in which performances comprised a regular beat and metrical structure (standard jazz improvisations over the piece Autumn Leaves) and one in which the performance style is characterized by the avoidance of a regular, predictable beat and metre (free improvisations). This allowed us to investigate how the degree of predictability afforded by the metrical structure and pulsed versus non-pulsed nature of the music might differentially influence the types and timescales of movement coordination between co-performers.(1) How are visual bouts of interaction  between musicians characterized? In particular, we aimed to describe the characteristic movement types , duration and location within the musical form of the bouts of the interaction. We also aimed to characterize the range and commonalities between the periodicities of the movements.(2) Can human judgements of visually apparent bouts of interaction be explained by body movement coordination at multiple timescales? Specifically, the contribution of several variables that quantified the degree of shared periodicity between the movements of the performers, movement amplitude and phase relations between the movements to predicting visual bouts of interaction were examined. As a secondary point of interest, the contribution of audio features of the music were also explored, in order to test whether auditory aspects of the performances  explained additional variance that was not captured by the predictors derived from the visual aspects. If audio features were found to be key predictors despite the focus on the visual domain in the annotation of bouts of interactions, this could indicate that sound-producing movements, which are directly related to the audio signal, were also being interpreted as evidence of co-performer interaction. If audio features played a less prominent role in predicting visual bouts of interaction, this would suggest that sound-producing and sound-facilitating/ancillary movements serve different functions that can be differentiated by human raters.(3) How similar are movement coordination patterns in pulsed versus non-pulsed musical performances, and what differences emerge? In particular, we aimed to investigate whether the less predictable and more diverse range of temporal structures afforded by the free improvisations resulted in greater variation in movement coordination strategies in comparison to the more strictly hierarchical metrical structure of the standard jazz performances.In this work, three main research questions were outlined:These aims were explored in a first study (Experiment 1) by using quantitative measures of movement coordination to predict annotations of bouts of interaction made by four expert musicians. A second study (Experiment 2) then served to validate and expand upon several aspects of the procedures from Experiment 1, in particular by comparing predictions from the statistical models and musician annotators from Experiment 1 to ratings of visual interaction obtained from a naive sample of human participants. The outcomes of this work are important for developing computational methods that can approximate human judgements of meaningful coordination between co-performers, as the use of unsupervised  techniques for quantifying coordinated behaviours will allow for substantial scaling-up of the amount of data that can be processed when compared with, for instance, manual coding methods. Such approaches can potentially be applied in subsequent research on video footage from entire concerts or even large corpora of video-recorded musical performances.2.2.1.2.1.1.Improvising Duos corpus from the work of Moran et al. [t-test, t28=2.47, p<0.05). Analysis of the audio data from each performance using a pulse clarity algorithm [t-test, t28=4.85, p<0.001); the non-pulsed duos also exhibited fewer audio events per second on average than the pulsed duos . The duos comprised a variety of instruments, including saxophone, piano, double bass, electric bass, drums, trumpet, guitar, flute, clarinet, violin and cello. Performers in these duos were recruited on the basis of public performance experience of around 10 years in their respective styles. Some performers had played together before, but this was not a primary recruitment criterion. No performer played in more than one duo. In all duos, performers were situated in a similar position—in which both performers could see one another—and at a similar distance from the camera, having been advised by research team to face one another, and encouraged to perform in a standing position with their feet within a prescribed zone. For more information, these videos are available at http://datashare.is.ed.ac.uk/handle/10283/2840. All videos were recorded in the same room under similar performance conditions at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany using a Sony HDR-HC9 camera at 25 Hz. Audio was recorded at a sampling rate of 48 kHz to a separate audio track for each musician using two Audio Technica AT 2035 condenser microphones; in addition, digital instruments were recorded with a direct line in. However, performers were placed very close to each other, and therefore there was a significant amount of spill from the other instrument on most audio tracks.Each duo contributed two to three video recordings to the dataset. Video recording duration ranged from 98.3 to 336.5 s . In total, 72.5% of the annotation time series from different annotators overlapped. Consistency between the annotators was explored by calculating the overall interrater agreement across the three annotators. First, each pair of annotation time series was matched through dynamic time warping using a constrained, asymmetric window to adjust the small timing discrepancies between the annotators. These time-adjusted data were subjected to the analysis of interrater agreement, which resulted in an average κ (Cohen’s Kappa) of 0.797  across all 90 pairings . This result suggested a reasonable degree of interrater agreement, despite some variation in the annotations.This process produced 455 annotated bouts of interaction in total, with a median of five annotations per annotator for each video. The annotated segments varied in duration from 0.74 to 75.4 s  or solo sections (83 instances). Solos tended to be shorter than the joint sections ; this waFinally, the qualitative descriptions provided by the annotators within the 160 aggregated bouts of interactions were coded by one of the annotators in terms of the key body part(s) of the performers that were implicated in each bout. This revealed that upper body/torso movements dominated (73.0%) over head movements (23.9%), and any other body part  provided a relatively rare (3.1%) source of interaction .2.1.3.2 OF is a standard computer vision technique that performs two-dimensional movement tracking on video data by estimating the apparent velocities of objects. The EyesWeb implementation of OF that was used in this study is based on the algorithm of Farnebäck [et al. [x- and y-coordinates for the barycentre of the ROI for each frame of the video, where the video sampling rate was 25 Hz. To compute the barycentre coordinates, the image is converted to greyscale and the coordinates are calculated as a weighted mean of the pixel intensities within the ROI. The ROIs were constrained to the upper body because the manual annotations revealed that the head and upper body contributed to the vast majority of interactions (96.9%). The x- and y-coordinates of the performers’ movements were smoothed using a Savitzky–Golay filter with an order of three and length of five frames set heuristically to remove noise inherent in the OF output. The x- and y-coordinates were then converted into polar coordinates and reduced into radial coordinates (ρ), which were also detrended and normalized to a range of 0–1 for the analysis. In addition, an overall quantity of motion estimate was extracted from each ROI using a frame differencing (FD) method in EyesWeb. The implementation of FD was based on the Pfinder algorithm of Wren et al. [Automated movement quantification was implemented using dense optical flow (OF) estimation in EyesWeb XMI 5.7.0.0.arnebäck  and has  [et al. ; for an  [et al. . For eacn et al. , in whicn et al.  for furt2.1.4.λ=0.1) and Gaussian smoothing (σ=3) [To capture the auditory aspect of the performances, the envelope of the combined performance (performers 1 and 2) was extracted from the videos. As the performances consisted of diverse instrument combinations, there was no uniform recording set-up. For this reason, audio separation was not attained for each individual instrument, because such recordings would have created constraints that impaired communication between the improvisers . Nevertheless, the joint audio signal provides a continuous rather than discrete characterization of the note onsets and dynamics within the performances. This representation of the audio also does not attempt to define discrete onsets for cases in which they are virtually impossible to detect, that is, for smooth glides or long sustained sounds that are present in some of the performances in these datasets. The envelopes were extracted from the audio data with MIR toolbox 1.6.2 using a 100 Hz sampling rate and summing the half-wave rectified envelope with the non-differentiated envelope (ng (σ=3) . Figure 2.1.5.The CWT is a sophisticated method for analysing relationships between periodicities within time series ,66. ThisIn this study, we first examined the movement frequencies of the individual performers using WT analysis and then explored the relationship between the movements within each duo using CWT analysis. To capture the potentially wide range of variations in movements within the datasets, the CWT analysis was applied across a broad frequency range (0.3–2.0 Hz) to each duo’s movement data, and measures of CWT energy and phase were extracted from this analysis. A visualization of the process in given in For the audio data, the energy of the signal (root mean square of the envelope sampled at 100 Hz) was first extracted to describe the overall dynamics of the musical performance. Using this variable, the periodicity of the signal was estimated using a WT within the frequency range from 0.25 to 10 Hz. A wider frequency range was used for the audio data analysis than the movement data due to the higher sampling rate at which the audio data were collected as well as the faster periodicities over which audio events are likely to occur than ancillary movements . All wa2.2.2.2.1.χ2=67.9, p<0.05), but overall, no significant difference was found in relation to joint versus solo sections .We first examined the onsets of the annotated bouts of interaction relative to the annotated music structural sections (i.e. whether performers were playing together (joint sections) or soloing (solo sections)). One plausible function of the bouts of interaction could be to coordinate the transition involved in switching from one solo to another or between solo and joint sections (cf. ). To invet al. [As shown in et al. , who fou2.2.2.To diagnose the overall periodic patterns exhibited by the movement and audio data from both datasets, we first examined an overview of the wavelet-transformed signals obtained from the movements of the individual performers and the envelope of the combined audio data. These descriptive analyses were confined to the frequency range from 0.30 to 2.0 Hz; this choice of frequency range was dictated by the typical movement frequencies exhibited in music performances ,73 and tThe wavelet analysis results from the movement data, summarized in The audio data revealed a tendency towards faster frequencies in the pulsed duos in comparison to the non-pulsed duos, despite the opposite trend in the movement data. This naturally reflects the beat-driven nature of the moderate-tempo jazz standard that the pulsed duos performed (Autumn Leaves) that displayed faster tempi  itself. These descriptive results helped us to focus the subsequent analysis of interaction on plausible aspects of behaviours.2.2.3.3 To capture moments where both performers were moving in a periodic fashion but these periods were unrelated  influenced the judgements of bouts of interaction, despite the purely visual nature of the annotation task. The audio predictors were the root mean square energy of the audio envelope (Audio RMS), the WT energy computed from the Audio RMS variable across a broad frequency band from 0.25 to 10 Hz (Audio WT Energy (Broad)) and the clarity of the pulse sensation (Audio Pulse Clarity), which was computed with an existing model of pulse clarity  table 2table 2.z-scores for the analyses. No outliers were removed because we made use of classification models that are tolerant to violations of normality.The 12 predictors were extracted across the 30 performances and downsampled to match the video frame rate (25 Hz). For all analyses, we eliminated the first and the last 5 s of each performance to avoid artefacts from the annotations and CWT analysis. This left us with 110 250 video frames (73 min and 3 s), which we downsampled by a factor of 5 for the analysis. The resulting 22 050 observations  were converted into The analysis was completed in two stages. In the first stage, we identified the classification accuracy of all 12 predictors and trimmed the incidental ones. In the second stage, we assessed the classification accuracy of the remaining set of predictors using two complementary classification techniques: logistic regression and random forest classification. Logistic regression is an efficient and commonly applied technique for modelling the relationships between predictors and classes. The downside is that discovering potential interactions and nonlinear relationships between the predictors requires explicit postulation of such operations that is not feasible in explorative research. The random forest technique offers the benefits of decision trees, which are able to capitalize on interactions between the predictors and have good tolerance for noise and outliers ,78. The 2.2.4.We first assessed how well each of the 12 predictors was able to classify each frame in the sequence in terms of the interaction class (interaction/no interaction) using logistic regression. We used the area under the curve (AUC) from the receiver operating characteristic curve as an index of classification performance, because it offers a robust scheme against overfitting, especially when the observations are unevenly distributed . This anFor the second stage of analysis, we formulated two feature sets based on this initial screening stage. First, we eliminated the four lowest performing predictors that failed to predict the interactions in any dataset ), leaving us with eight features. Second, we compiled a feature set comprising only the five features that could predict interactions in the combined dataset , reaching good prediction rates for the 5-predictor models (AUC=0.884 for the combined dataset) and excellent rates for the 8-predictor models (AUC=0.967 for combined dataset). These results indicate that random forest models offer significant improvements in classification performance for these datasets, although with the trade-off of increased model complexity. In addition, the classification errors were unevenly distributed for most models. For the 1-predictor logistic regression model using the combined dataset, the confusion matrix indicates that 4.8% of the annotated interactions were misclassified by the model , whereas 26.3% of observations were predicted as interactions when they were not annotated as such . Similar asymmetric errors were revealed in other models using the combined dataset , which probably reflect the unbalanced distribution of annotated interactions (32.2%) and non-interactions (67.8%).A more elegant way of capturing both interactions and nonlinear patterns within the data is to use random forest models. These models are not strictly comparable to logistic regression for a 1-predictor model, in which they exhibited poor performance, because random forests are intended for use with multiple predictors. With added predictors, however, the classification rates improved significantly . To gain a more precise understanding of the predictor contributions to the more complex models, we computed the variable importance of each predictor from the 8-predictor random forest models and examined the decision tree splits as a way of characterizing the interactions between the predictors.2.2.5.To explore the contributions of the individual predictors to the models, we focused on the random forest method, because this method makes use of more sophisticated techniques than logistic regression for assessing the relative importance of the predictors. Specifically, random forest models make use of the mean decrease in the contribution to the classification rate, by calculating the decrease in prediction accuracy when the variable of interest is left out of the analysis using the out-of-bag data. This technique has repeatedly performed better than other candidates in large-scale simulation studies (e.g. ). We desr=0.47 between Movement Quantity and Movement CWT Energy (0.4 Hz)). A comparison of the predictor importance values between the pulsed and non-pulsed duos also reveals that these datasets differ markedly in terms of the key features implicated in the models, except that Movement Quantity is not a substantial predictor in either model. The interaction bouts in non-pulsed duo performances were best predicted by the cross-wavelet energy of the movements across a broad frequency range, followed by moderately fast co-occurring movements, as indexed by Movement CWT Energy in frequency bands centred around 0.9 Hz and 0.6 Hz. A different pattern emerged in the pulsed duo performances, in which interactions were characterized by slower shared periodic movements (Movement CWT Energy (0.4 Hz)) and periodic movements at unrelated frequencies (Movement WT Energy (Any)). These differences between the datasets are consistent with the patterns previously observed in the descriptive analyses —although relatively common in this dataset—were less indicative of interactions. It may be that these faster periodic movements are instead embodied expressions of pulse sensation, which do not necessarily serve a communicative function between co-performers.AUC=0.719). The 1-predictor classification rate was excellent in the non-pulsed duo dataset (AUC = 0.838), but poor in the pulsed duo dataset (AUC=0.510). Adding cross-wavelet energy measures within more specific frequency bands, the overall quantity of movement and a measure of the overall periodic movement without reference to joint periodicity did improve the classification rates across the datasets, but only when we modelled the relationships between the predictors with random forest models that are able to capture the interactions between the predictors and nonlinear patterns in the data. Simple additive logistic regression models failed to improve the model classification rates.Shared periodic movement of the performers within a broad frequency range (0.3–2.0 Hz) was the single most important predictor of interactions and delivered a satisfactory classification rate when the two datasets were combined  Whether interaction ratings obtained from a new sample of participants who were unaware of the purpose of the experiment were consistent with ratings obtained from the four musician annotators in Experiment 1. This was an important check, as the Experiment 1 annotators—who were among the authors of this paper—had some prior familiarity with both the visual and audio aspects of the video-recorded performances, and it is possible that they had certain unconscious biases in the interaction annotation task due to the fact that they were informed of the research aims.(2) How the classification successes and errors of the simplest (1-predictor logistic regression: Movement CWT Energy (Broad)) computational model from Experiment 1 related to interaction ratings obtained from the new participant sample.(3) Whether classification differences between the annotators and computational models in Experiment 1 could be due to the different amounts of visual information afforded to each. Specifically, the annotators had viewed the full videos, whereas the computational analysis was performed using movement data that were obtained from cropped versions of the videos, using constrained ROIs set around the upper body of each performer.(4) Whether participant ratings of co-performer interaction differed based on the musical style, by comparing data from the pulsed and non-pulsed duos datasets.In Experiment 2, we sought to validate the results and to understand further the potential limitations posed by the methods and modelling procedures implicated in Experiment 1. Specifically, we collected ratings of co-performer interaction using a subset of the duo recordings from Experiment 1 from a sample of naive participants. The aims of this work were to assess:In sum, we explored the degree to which participants’ judgements of interaction aligned with the annotators and computational model from Experiment 1 and whether these results varied as a function of amount of visual information (full/cropped videos) and dataset (pulsed/non-pulsed duos).3.2.3.2.1.t-tests between the four stimulus categories  or the two datasets .We selected 48 7.5-s excerpts from the video corpus that represented successes and failures of the 1-predictor model (Movement CWT Energy (Broad)) in predicting the manual annotations of interaction, while controlling for quantity of movement. Twelve excerpts represented each of the four possible model prediction categories . The predicted cut-off value for the 1-predictor model was established with the Youden index , and wasFinally, two versions of each of the 48 excerpts were produced. One version made use of the full visual information from each video excerpt, while the second version was cropped by making visible only the sections of the video (upper bodies of the two performers) that had been selected as ROIs for the optical flow movement tracking procedure. The median ROI size was 158×158 pixels, which is approximately one-fourth of the original video dimensions (700×576 pixels) and 6% of the area of the original video.Each video was edited to have a 320 ms fade-in and fade-out. All videos were produced without any corresponding audio information, as the task was to focus solely on visual cues of interaction. Rating data for the video excerpts were collected via an online experiment, which was hosted in the Qualtrics online survey platform, with videos hosted on YouTube.3.2.2.Mage=38.7 years, s.d.=14.3, age range=23–70, eight males) completed the experiment. These participants were an opportunity sample recruited via social media. Participants had 9.08 years of musical training on average ; most classified themselves as serious amateur musicians (N=6), amateur musicians (N=8) or music-loving non-musicians (N=7).In total, 26 volunteers . The experiment lasted 28 min on average.3.3.t-tests revealed that interaction ratings were significantly higher for true positives than true negatives (p<0.001), false positives (p<0.001) and false negatives (p=0.021). In addition, mean ratings of interaction for false negatives were significantly higher than false positives (p=0.003), suggesting ratings of the participants were more in line with the human annotators than the computational methods.The mean ratings of co-performer interaction for the four stimulus categories are listed in F1,24=2.98, p=0.097), indicating that the full videos that had been seen by the annotators and the cropped versions that matched the ROIs from the OF analysis provided generally equivalent information about the level of interaction in a duo. A main effect of stimulus category  indicated that ratings of co-performer interaction were significantly higher for the true positives/false negatives category (M=4.31) than for true negatives/false positives (M=3.69). A significant effect of dataset  revealed that interaction ratings were higher for the pulsed (M=4.36) than non-pulsed duos (M=3.70). The pattern of mean interaction ratings by stimulus category was similar between the pulsed and non-pulsed duos; mean interaction ratings for the true positives/false negatives category were higher in both datasets  than mean ratings in the true negatives/false positives category . However, a significant interaction between dataset and stimulus category did emerge , most probably explained by the greater difference in mean interaction ratings between the two stimulus categories for the pulsed duos (Mdifference=0.80) than the non-pulsed duos (Mdifference=0.46). No other significant interactions between predictors in the ANOVA were found.The primary aim of the analysis was to compare the degree to which the ratings from participants in Experiment 2 corresponded to the annotator’s and computational model’s categorizations of the video excerpts from Experiment 1. For comparison to the annotators, participant ratings for the true positive and false negative stimuli (cases in which the annotators identified interaction) were compared to ratings for the true negatives and false positives (cases in which the annotators identified non-interaction). We then tested the effects of these two aggregated stimulus categories, dataset (pulsed/non-pulsed duos) and visual information (cropped/full videos) on interaction ratings in a 3-way mixed ANOVA. No significant effect of visual information was found (M=4.07) than the ratings for the true negatives/false negatives category (M=3.93), no significant effect of the aggregated stimulus categories was found . A significant interaction  between stimulus category and dataset was also present. In the non-pulsed duos, mean ratings were higher for the true positives/false positives category (M=4.01) than the true negatives/false negatives category (M=3.43), but the opposite pattern of results emerged in the pulsed duos dataset . No other interactions between stimulus category, dataset and visual information were found in this analysis. Finally, the correlation was computed between the model predictions from the 1-predictor model from Experiment 14 and the average ratings from the participants in Experiment 2 for each video stimulus. The correlation across the full dataset was not statistically significant, r94=0.172, p=0.093. However, there was a significant correlation between participant ratings and the model predictions for the non-pulsed duos dataset, r50=0.320, p=0.021, but not for the pulsed duos, r42=−0.192, p=0.212.For comparison to the computational model from Experiment 1, ratings for the true positives and false positives (cases in which the model identified interaction) were compared to ratings for the true negatives and false negatives (cases in which the model identified non-interaction). An ANOVA was run with these new stimulus categories, dataset and visual information included as predictors. Although co-performer interaction ratings for the true positives/false positives category were higher on average  were rated significantly higher in co-performer interaction by naive participants than all other stimulus categories. The ANOVA results, as well as the pairwise comparison of co-performer interaction ratings between false negatives and false positives, indicate that the interaction ratings given by participants in Experiment 2 were significantly aligned with the coded bouts of interaction/non-interaction as identified by the human annotators from Experiment 1, but were less aligned with the predictions generated by the computational model, particularly for the pulsed duos. A significant, positive correlation between the model predictions from Experiment 1 and participant ratings from Experiment 2 emerged only for the non-pulsed duos dataset, which corresponds with the fact that the 1-predictor model performed significantly more accurately in predicting human ratings of musical interaction for the non-pulsed duos than the pulsed duos in Experiment 1. Overall, these results provide affirmation for the annotation methods employed in Experiment 1 but also suggest that there may be certain visual cues  that are picked up as interactive by human raters to which the computational methods used here are not sensitive.The absence of a significant effect or any interactions related to the ‘visual information’ variable indicates that the participants who were shown the cropped versions of the video did not perform in a noticeably different way from those provided with the full video. This result offers some evidence that discrepancies that arose between the annotators and computational models in Experiment 1 were not due to the different amount of visual information afforded to each. This is perhaps not entirely surprising, given that the annotators reported that their primary focus in coding bouts of interaction was on head and upper body movements (as coded in 97% of bouts), but rather provides additional support for this assumption.A somewhat unexpected result was that pulsed duos were consistently rated higher in co-performer interaction than non-pulsed duos. One potential explanation for this is that the performers in the pulsed duo videos were very often nodding/moving along to the beat of the music throughout the course of a performance. When the annotators in Experiment 1 viewed the entire video recordings they often did not annotate such ‘beat-marking’ movement as interaction, as this would result in some cases in entire performances being classified as bouts of interaction. As the participants in Experiment 2 were only exposed to 7.5 s of each video recording, this type of contextual information that was taken into account by the annotators was not necessarily available. As such, movements that were directly influenced by auditory aspects of the performance  may have served as more salient cues to the Experiment 2 participants. In addition, the musical expertise of the Experiment 2 participant sample varied quite widely compared to the expert musician annotators from Experiment 1; thus, this more musically heterogeneous group may have been less precise in differentiating between communicative, ancillary movements and sound-producing movement or movements that serve other purposes.To summarize, the results of Experiment 2 provide validation of several of the methodological decisions made in Experiment 1, and also highlight some areas for improvement in terms of developing computational tools that can approximate human judgements of interaction between musical co-performers.4.4.1.Human interaction involves the coordinated temporal exchange of information. Here we have investigated how interactions are coordinated in music performance, a domain which provides a particularly interesting object of analysis due to its diversity of temporal patterns and their hierarchical nature. In music, the ancillary movements of performers are assumed to relate to the coordination of phrasing and expressive intentions ,82 in anet al. [In Experiment 1, we contrasted two styles of improvized music to explore how interactions are visually coordinated in music. One dataset comprised performances relying on a steady pulse and one set avoided such a pulsed framework, providing a natural variation in the level of temporal regularity they contained. Visual interactions (as coded by expert musician annotators) in both datasets were generally related to joint performer movements, but the strength of this association varied according to the metrical regularity of the music. In non-pulsed music, visually apparent co-performer interactions were adequately predicted from the energy of the CWT of the movements of the two performers over a broad frequency range. In pulsed music, however, such a broad model failed to significantly account for the interactions. Only when a more complex model was specified, with energy calculated over specific frequency bands of CWTs nested with other predictors within a tree model, could a reasonable degree of visual interactions between the performers be predicted. This asymmetry of the success of the movement cues to predict interactions is consistent with the notion that visual cues provide complementary information in communication ,83,86; iet al. , who invet al. , our reset al. ,87–89.It is remarkable that here the simplest model consisting only of the broadband CWT energy could achieve a classification accuracy rate of above 0.80 for interactions in non-pulsed music, and the other predictors  did not make significant individual contributions to the model. Nevertheless, the low accuracy rate of the same model in predicting interactions in pulsed duo performances underscores the fact that both the cues for coordinating interactions and the importance of visual information in co-performer communication may vary drastically across musical styles. The ease with which the joint periodic movements across multiple timescales could predict interactions in non-pulsed music is perhaps unsurprising, because a hierarchical metrical structure is largely absent in such traditions and the performers cannot rely on predictable temporal patterns  to coordinate their performance. Instead, as in conversation, they provide gestural cues by swaying and nodding simultaneously, which typically use similar periods of movement. In pulsed duos, where the performers were following a loose musical script consisting of sections of joint performance separated by sequences of alternating solo sections that adhered to a metrical hierarchy, the ancillary movements were not particularly indicative of the interactions. Some of the annotated bouts of interaction could, however, be captured by a more complex account of the frequency ranges over which the joint movements were taking place. The reliance on more specific frequency bands (e.g. the band centred around 0.4 Hz) rather than the broadband CWT predictor in the pulsed duos may be due to the quite narrow musical tempo range (120–150 beats per minute) implicated in these performances of the same jazz standard, which could have afforded less diversity in terms of movement periods of the performers.4.2.The manual annotation method implicated in Experiment 1 has its drawbacks. It is a subjective task, requiring a careful operational definition of the concept of musical interaction, which will nevertheless be subject to different interpretations by different coders. It is also challenging to identify the precise onsets and offsets of the bouts of interactions, because interactions evolve over time and different coders may set different subjective thresholds for the amount of evidence that needs to be accrued before a bout can be identified as an interaction. In addition, when working from a single video recording, the perspective of the annotator is necessarily different from that of the individual performers involved in the musical interaction; in this particular case, the annotators were able to see the face of one performer more clearly than the other in each duo (due to the camera angle) and thus may have missed certain visual cues in terms of eye contact or facial expressions. Despite these challenges, we obtained high measures of interrater agreement between coders that were validated by ratings from an independent sample of participants (Experiment 2). However, we acknowledge that the operational definition and coding method used here is just one possible approach that could be expanded upon in future to include self-reports and interviews with the musicians themselves (cf. ), explorIt is probable that the movement cues that we focused on (periodic relationships between the two performers’ movements) are not sufficiently nuanced to pick up all visual interactions that were present in these performances. Although we explored the contributions of various additional predictors, several of these—such as the amplitude of the movements, measures of the musical qualities of the performances  and supplementary visual information (Experiment 2)—did not provide substantial further information in terms of identifying bouts of interaction. However, there are other, more subtle ways musicians could signal important events during a performance, such as by making unique, single gestures or eye contact, neither of which can be detected by the current approach. Series of studies have documented the different ways musicians coordinate their actions in duo performances with gestures and looking behaviours ,75,83. FThe majority (97%) of the ancillary movements that were coded as interactive by the annotators in Experiment 1 comprised the performers’ upper bodies—specifically, head and torso movements. The focus on upper body cues was further reinforced by the results of Experiment 2, which indicated that cropped videos containing only the upper bodies of the performers provided enough information for raters to discriminate interactive from non-interactive bouts. However, future studies should investigate whether such a finding is transferrable to other musical styles and instruments. For instance, singers have been noted to make use of hand gestures , which dThere are a host of other possible factors that might influence the frequencies of the movements produced by each performer. For instance, standing and sitting postures have different points of balance for movements, which is assumed to influence movement type and periodicity ,94. AlsoIn terms of the methodological choices, wavelet analysis proved to be a flexible tool for capturing time–frequency patterns from continuous movement data. Although WTs are routinely used in signal processing and in certain areas of behavioural sciences , they haThe notion that coordination takes place at different timescales was partially explored in the present study. The specific frequency bands of the CWT allowed us to explore a diverse range of ancillary movements, from rapid head nods to slow body sway and everything in between. Studying the synchronization of note onsets between the performers would be a natural extension of the study, to link the factors contributing to musical synchronization with the cues used to coordinate interaction . For insThe methodology adopted here allows an effortless expansion into other musical genres, or even ensemble performances from different cultures that have been documented on video. The emphasis on visual coordination cues in non-pulsed music that was revealed in the present study suggests that this approach might be particularly fruitful for studying other music that lacks a regular temporal structure, such as the works of numerous contemporary composers from Boulez  to Nanca4.3.The present work introduced a novel method for quantifying visually observed co-performer interaction in music ensemble performances using measures of the shared periodic movements of performers across multiple timescales. Statistical models incorporating these quantitative measures were successfully able to classify manually annotated bouts of visual co-performer interaction/non-interaction in improvising duos, as coded by expert musicians. The models were particularly effective in predicting such interactions in non-pulsed improvisations, which highlights the importance of the visual modality for coordinating ensemble performances in cases where the musicians cannot rely on a regular, predictable structure in the auditory domain. Models of interaction in pulsed music required a more complex combination of predictors, which highlights some discrepancies in the use of the visual communication channel for pulsed versus non-pulsed music and suggests that the narrow tempo range over which the pulsed performances were executed may have constrained the diversity of movement periodicities. The methods developed here can be easily transferred and extended for use with other musical styles, to explore potential commonalities and divergences in the types of movement cues and coordination that are used across different musical ensemble types and traditions.