A key limitation is that most tumours are communities of both tumour and non‐tumour cells. This problem is particularly pertinent for analysis of advanced invasive tumours, which are known to induce major changes and responses in both the tumour and the surrounding tissue. To identify bladder cancer tumour‐cell phenotypes and compare classification by tumour‐cell phenotype with classification by global gene expression analysis, we analysed 307 advanced bladder cancers (cystectomized) both by genome gene expression analysis and by immunohistochemistry with antibodies for 28 proteins. According to systematic analysis of gene and protein expression data, focusing on key molecular processes, we describe five tumour‐cell phenotypes of advanced urothelial carcinoma: urothelial‐like, genomically unstable, basal/SCC‐like, mesenchymal‐like, and small‐cell/neuroendocrine‐like. We provide molecular pathological definitions for each subtype. Tumours expressing urothelial differentiation factors show inconsistent and abnormal protein expression of terminal differentiation markers, suggesting pseudo‐differentiation. Cancers with different tumour‐cell phenotypes may co‐cluster (converge), and cases with identical tumour‐cell phenotypes may cluster apart (diverge), in global mRNA analyses. This divergence/convergence suggests that broad global commonalities related to the invasive process may exist between muscle‐invasive tumours regardless of specific tumour‐cell phenotype. Hence, there is a systematic disagreement in subtype classification determined by global mRNA profiling and by immunohistochemical profiling at the tumour‐cell level. We suggest that a combination of molecular pathology (tumour‐cell phenotype) and global mRNA profiling (context) is required for adequate subtype classification of muscle‐invasive bladder cancer. © 2017 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.Global  FGFR3 and CCND1, and frequently show 9p21 (CDKN2A) loss; GU tumours express FOXM1 but not KRT5, and frequently show RB1 loss; and basal/SCC tumours express KRT5 and KRT14, but not FOXA1 and GATA3. Apart from our efforts, UC gene expression subtypes have been described for muscle‐invasive (MI) cases et alGlobal mRNA expression analysis is an efficient method for phenotypic profiling of tumours, and has been used to define molecular subtypes for almost every major tumour type Formalin‐fixed paraffin‐embedded preoperative TUR‐B specimens reviewed by a uropathologist were collected from consecutive patients who underwent radical cystectomy in four hospitals in southern Sweden from 2006 to 2011. None of the included tumours received any treatment prior to sample‐taking ). The histological variants at pathological review are shown in supplementary material, Table n = 7031) with respect to variance.RNA samples were amplified and labelled by use of the SensationPlus kit , and raw data were generated with the human Gene ST 1.0 platform (Affymetrix). Batch effects adjustments were performed with the COMBAT algorithm Antibodies against CCNB1, CCND1, CDH1, CDH3, CDKN2A (p16), CHGA, E2F3, EPCAM, FGFR3, FOXA1, GATA3, KRT5, KRT14, KRT20, NCAM1, PPARG, RB1, RXRA, SYP, TP63, TUBB2B, UPK3, VIM and ZEB2 and antibodies for eight additional supportive markers were used . The full dataset  is available as supplementary material, Table A genomic circuit score α = 0.05 were used unless otherwise indicated. For group comparisons of gene expression data, false discovery rates were controlled at a maximum of q = 0.01.Statistical tests were performed with the R software environment for statistical computing. For group comparisons of IHC data, two‐sided non‐parametric test with significance threshold N = 238) reported by Aine et alurothelial‐like , genomically unstable , epithelial‐infiltrated , Squamous cell carcinoma-like/mesenchymal-infiltrated , SCCL/UroB (n = 46), and small‐cell/neuroendocrine‐like  , whereas we will use non‐italicized abbreviations for tumour‐cell phenotypes determined by IHC .We performed stepwise unsupervised hierarchical clustering using whole genome mRNA expression for 307 advanced bladder cancers, resulting in six consensus clusters of approximately equal sizes. To compare the clustering results with known molecular subtypes of UC, we used six gene expression signatures with subtype‐specific expression  mRNA signatures, but protein expression levels of the canonical Uro genes FGFR3, CCND1 and TP63 were not different in the Uro versus GU‐Uro groups of tumours  of both ZEB2 and VIM , all of which are markers for either a small cell or a neuroendocrine phenotype Sc/NE consensus cluster showed enriched expression of the markers. SAM revealed that several tubulin genes were among the top genes co‐expressed with markers for a neuroendocrine phenotype , the second most significant upregulated gene in this analysis, coincided with expression of the small‐cell/neuroendocrine differentiation genes   Figure A. CoordiKRT20 and UPK3 mRNA expression overlaps with the Uro‐diff signature, only subsets of the tumour cells are positive at the IHC level, and expression patterns are inconsistent with the proteins' structural functions in normal differentiation.Next, we investigated the relationship between relative mRNA levels and the tumour‐cell expression of the terminal differentiation markers KRT20 and UPK3. We hypothesized that not only marker levels but also subcellular expression patterns may reveal where and if urothelial differentiation takes place. KRT20 and UPK3 staining was remarkably inconsistent; a multitude of aberrant and heterogeneous expression patterns were observed for each marker, and, unlike that of the regulatory factors, KRT20 and UPK3 expression was hardly ever homogeneous in a tumour area 1 mm in diameter. Among the most conspicuous of these observations was the stochastic appearance of tumour‐cell KRT20 staining, and, even more, the aberrant subcellular localization of these markers Figure B, C. MorGU‐Uro, these tumours are grouped under the ‘Uro’ heading. The defining protein markers represent key genes in tumour biology central to the respective tumour cell phenotypes. We then assigned each tumour an mRNA phenotype based on the deconstructed consensus clusters, and grouped Uro‐Uro with GU‐Uro and SCCL/UroB‐UroB in one Uro class, combined GU‐GU and Sc/NE‐GU into one GU class, and combined all basal/SCC‐like cases into one class, and tested to what extent the IHC tumour phenotype definitions could recreate the data . The overall accuracy was 75%, being mainly reduced by the low sensitivity for the two small Mes‐like and Sc/NE‐like classes of tumour. The sensitivity was, however, high for Uro and GU tumours , and the specificity for the basal/SCC‐like tumour‐cell phenotype definition was excellent (0.93) . Hence, we see the presented tumour phenotype definitions as starting points for efficient classification of advanced bladder cancer into relevant and distinct molecular tumour‐cell phenotypes.On the basis of the extensive IHC analyses, we propose working definitions for five major tumour‐cell phenotypes in advanced bladder cancer: urothelial‐like (Uro), genomically unstable (GU), basal/SCC‐like, mesenchymal‐like (Mes‐like), and small‐cell/neuroendocrine‐like (Sc/NE‐like), as described in Figure Uro, GU and Epi-Inf clusters of gene expression. Indeed, our urothelial differentiation mRNA signature (Uro‐diff) includes both KRT20 and uroplakin genes normally expressed in luminal cells. Furthermore, the luminal category of tumours frequently express the transcription factors RXRA, PPARG, FOXA1, and GATA3, which are known to have a crucial role in urothelial differentiation. However, the full complement of factors is not always expressed in tumour cells . The basal/SCC‐like tumour samples also expressed ZEB2 and VIM, but only in surrounding stromal cells. Thus, the Mes‐like subtype defines a new entity of bladder cancer showing a tumour‐cell phenotype that is in stark contrast to previously defined subtypes, and cases of which are biologically very different from the basal/SCC‐like cases that they cluster together with.The definition of basal/SCC‐like urothelial tumours is established and conforms well to definitions of similar subtypes in other tumour types, i.e. basal‐like breast cancer and SCC of the lung. Nevertheless, frank basal/SCC‐like UC was grouped into two different global gene expression clusters. In this case, divergence is most likely attributable to the profiles of tumour‐infiltrating non‐tumour cells. One cluster, Sc/NE‐like turned out to harbour two very distinct tumour‐cell phenotypes. One‐half of these tumours expressed markers that are typical for neuroendocrine differentiation, e.g. SYP and ENO2, and, more robustly, TUBB2B. This and other tubulin isoforms have normal expression that is highly specific for tissues of neuroendocrine origin. This part of the Sc/NE consensus cluster also showed an absence of PPARG, FOXA1 and GATA3 expression, as well as of uroplakin and KRT20 expression. We name this tumour‐cell phenotype small‐cell/neuroendocrine like (Sc/NE‐like). The second half of the Sc/NE consensus cluster expressed PPARG, FOXA1, GATA3, and ELF3, as well as the Uro‐diff signature. This, in combination with the absence of FGFR3 and CCND1 protein expression, suggests that these tumours have a GU tumour‐cell phenotype. The forces driving Sc/NE‐like and GU tumours into one consensus cluster are most likely a high proliferation rate and frequent E2F3, CDKAL1 and SOX4 genomic amplification at 6p22, which are characteristic of both subtypes.The consensus cluster GU consensus cluster were of the Uro tumour‐cell phenotype; (2) basal/SCC‐like tumours were allocated to two different global mRNA tumour clusters; (3) the two small but distinct tumour subtypes Mes‐like and UroB grouped with typical basal/SCC‐like cases; (4) the Epi‐Inf consensus cluster was found to consist of cases with Uro and GU tumour‐cell phenotypes; and (5) the Sc/NE consensus cluster consisted of tumours with typical neuroendocrine and GU tumour‐cell phenotypes. We interpret these observations as signs of convergence/divergence at the global gene expression level that become evident when tumour phenotypes are measured both at the tumour‐cell level and by global mRNA profiling, as shown schematically in Figure The main finding of the present study is that global mRNA clustering and tumour‐cell phenotype analyses lead to different groupings of bladder cancer samples. We observed several instances where global mRNA clusters did not show the expected tumour‐cell phenotype: (1) approximately half of the tumours in the GS: performed IHC, including selection of antibodies, scoring of staining, and biological interpretation; PE: performed bioinformatic analyses, including hierarchical clustering and specific signature analyses; FL: responsible for data collection, including pathological re‐evaluation, clinical follow‐up, and ethical issues; MH: conceived the study, was responsible for final interpretations and conclusions, and was responsible for the final draft of the manuscript. All authors contributed to the drafting of the manuscript.Supplementary materials and methodsSupplementary figure legendsFigure S1. Consensus clustering of global mRNA expression from cystectomized patients (TUR‐B samples)Figure S2. Cases with Uro phenotype that cluster in the GU consensus cluster retain their Uro molecular profile, but proliferation, T‐cell and ECM signals are affectedFigure S3. Top 10 upregulated genes in Sc/NE–Sc/NE group (compared to Sc/NE–GU) frequently have a normal tissue expression pattern restricted to neuronal/endocrine tissuesFigure S4. Cases with GU phenotype that cluster in the Sc/NE consensus cluster retain expression of the Uro‐Diff signature (Mean mRNA) and key factors (IHC)Figure S5. Protein expression pattern of KRT20 in advanced urothelial carcinoma is de‐regulated and inconsistent with normal urothelial differentiationFigure S6. Protein expression of UPK3 in advanced urothelial carcinoma is de‐regulated and inconsistent with normal urothelial differentiationFigure S7. IHC data from 29 markers showing differential expression in tumour cells were considered in establishing tumour‐cell phenotype definitionsFigure S8. Concordance of tumour‐cell phenotype with consensus clusters and deconstructed subtypesTable S1. Histological variants included in the consecutive series of 307 tumours according to the 2016 WHO classification of invasive bladder tumoursTable S2. The pathological stage and grade based on uro‐pathologist re‐evaluation of TUR‐B specimens for the consecutive series of 307 tumoursTable S3. Differentially expressed genes between Mes‐Inf and Basal/SCC‐like, between UroB and Basal/SCC‐like, and between Sc/NE and GUTable S4. Comparison of tumour‐cell phenotypes (applied IHC definitions) to global mRNA consensus clusters (top)Table S5. Comparison of tumour‐cell phenotypes (applied IHC definitions) to deconstructed mRNA clusters (as described in text)Table S6. The complete IHC‐evaluation data setSupplementary Materials and MethodsClick here for additional data file.Supplementary figure legendsClick here for additional data file.Supplementary Figure S1 Consensus clustering of global mRNA expression from cystectomized patients (TUR‐B samples). Obtained tumour clusters are indicated on top. Subtype labels are adapted from Aine et al., 2015 Click here for additional data file.GU consensus cluster retain their Uro molecular profile but proliferation, T‐cell and ECM signals are affected.Supplementary Figure S2 Cases with Uro phenotype that cluster in the  For each plot, the y‐axis label indicates the data type . Cases with Uro phenotype in the GU consensus cluster (dark green) are shown to have increased CCNB1 staining (proliferation), T‐cell‐, and ECM‐Signatures, compared to their Uro ‐ Uro counterpart (light green). For canonical Uro markers , however, there was no difference, confirming their molecular profile as Uro tumours. Tumours with a GU phenotype lack expression of these canonical markers.Click here for additional data file.Sc/NE – Sc/NE group (compared to Sc/NE – GU) frequently have a normal tissue expression pattern restricted to neuronal/endocrine tissues.Supplementary Figure S3 Top 10 upregulated genes in  SAM analysis was perfomed comparing the two groups and the top 10 upregulated genes were queried in the gTEX database  and key factors (IHC).Supplementary Figure S4 Cases with GU phenotype that cluster in the  For each plot, the y‐axis label indicates the data type . Cases with GU phenotype in the Sc/NE consensus cluster (dark blue) are shown to have retained expression of Uro‐diff signature (left) and tumour cell expression of GATA3, FOXA1, and PPARG. Curiously RXRA (IHC) indicated high levels in the cases with Sc/NE phenotype, which was not the case for the RXRA values in the gene expression data set. Thus, Sc/NE ‐ Sc/NE cases lose urothelial differentiation signature, except for possibly RXRA protein expression, whereas the cases with GU phenotype in the same consensus cluster do not.Click here for additional data file.Supplementary Figure S5 Protein expression patterns of KRT20 in advanced urothelial carcinoma is de‐regulated and inconsistent with normal urothelial differentiation. (A) KRT20 positive strong cytoplasmic staining. (B) KRT20 positive strong membraneous staining. (C) KRT20 positive case where basal cells are negative. All cells distal to the tumour‐stroma interface are positive. (D) KRT20 positive case where few cells most distal to the tumour‐stroma interface are positive. (E) KRT20 positive case where bulk tumour, basal cells, and a lamina propria invading nest are positive. (F) KRT20 positive case with an infiltrative invasive growth pattern. (G) KRT20 positive case with a nodular invasive growth pattern. (H) KRT20 positive case with a trabecular invasive growth pattern. (I) Dysplastic urothelium with highly variable KRT20 staining. (J‐K) KRT20 positive mucinous tumours with signet‐ring morphology. Scale bars indicate 100 µm. (L) Sample including intestinal metaplasia, showing KRT20 positivity. (M) KRT20 positive case with heterogeneous expression . (N) KRT20 positive case with heterogeneous expression . (O) KRT20 positive strongly lymphocyte‐infiltrated Genomically Unstable tumour (Sc/NE ‐ GU). (P) CD3 staining of the same case as in (O), showing dense lymphocyte infiltration. Scale bars for (A‐I) and (L‐O) indicate 20 µm.Click here for additional data file.Supplementary Figure S6 Protein expression of UPK3 in advanced urothelial carcinoma is de‐regulated and inconsistent with normal urothelial differentiation. (A) UPK3 positive case showing strong nuclear staining. (B) UPK3 positive case showing cytoplasmic staining. (C) Strong apical UPK3 positivity in luminal cells that lack normal urothelial organization. (D) UPK3 positive case with an infiltrative invasive growth pattern. (E) UPK3 positive case with a nodular invasive growth pattern. (F) UPK3 positive case with a trabecular invasive growth pattern. (G) Tumour nests displaying a ‘pushing‐border’ growth pattern with UPK3 positivity along the nests' circumference, resulting in an inverted staining pattern (cells closest to tumour‐stroma interface positive). (H) UPK3 positive case with heterogeneous expression . (I) UPK3 positive case with heterogeneous expression . Scale bars indicate 20 µm.Click here for additional data file.Supplementary Figure S7, See also Supplementary Figure 8. IHC data from 29 markers showing differential expression in tumour cells were considered in establishing tumour‐cell phenotype definitions. Barplots showing the mean IHC scores for the deconstructed gene expression phenotypes including UroB, as we initially intended to produce a separate IHC definition also for UroB. Uro includes Uro cases from the Uro, GU, and Epi‐Inf clusters. GU includes GU cases from the GU, Epi‐Inf, and Sc/NE clusters. Basal/SCCL includes Basal/SCC‐like cases from the SCCL/Mes‐Inf and SCCL/UroB clusters.Click here for additional data file.Supplementary Figure S8, Concordance of tumour‐cell phenotype with consensus clusters and deconstructed subtypes. (A) IHC classification based on definitions in Figure Urothelial‐like (Uro), Genomically Unstable (GU), Epithelial Infiltrated (Epi‐Inf), SCC‐like/Mesenchymal Infiltrated (SCCL/Mes‐Inf), SCC‐like/Urothelial‐like B (SCCL/UroB), and the Small‐cell/Neuroendocrine (Sc/NE). (B) IHC classification based on definitions in Figure Click here for additional data file.Table S1. Histological variants included in the consecutive series of 307 tumours according to the 2016 WHO classification of invasive bladder tumours.Table S2. The pathological stage and grade based on uro‐pathologist re‐evaluation of TUR‐B specimens for the consecutive series of 307 tumours.Table S3. Differentially expressed genes between Mes‐Inf and Basal/SCC‐like, between UroB and Basal/SCC‐like, and between Sc/NE and GU.Table S4. Comparison of tumour‐cell phenotypes (applied IHC definitions) to global mRNA consensus clusters (top).Table S5. Comparison of tumour‐cell phenotypes (applied IHC definitions) to deconstructed mRNA clusters (as described in text).Click here for additional data file.Table S6 The complete IHC‐evaluation data set.Click here for additional data file.


The second author’s first name is incorrect. The full name should be Rocio Arco.The funding information incorrectly includes two grant numbers that did not support this research. The correct Funding Information should be: This work was supported by the Instituto de Salud Carlos III UEDR-FEDER, Ministerio de Economía y Competitividad UE/ERDF , Red de TrastornosAdictivos UE/ERDF (RD12/0028/0001 to F.R.F.), CIBERobn, Consejería de Economía, Innovación y Ciencia, Junta de Andalucía, UE/ERDF , Consejería de Salud, Junta de Andalucía (SAS111224 to F.R.F.). Ramírez-López has been funded by a FPU predoctoral fellowship of the Spanish Ministerio de Educación, Cultura y Deporte (AP-2009-0225); J.S. holds Miguel Servet research contract from the National System of Health, ISCIII (grant numbersCP12/03109).


Cancun, MexicoInformations: George SilvayPhone: 212 241 8346Fax: 212 876 2009george.silvay@mountsinai.orgE-mail: www.clinicalupdateinanesthesiology.orgSite: Toronto, CanadaInformations: Continuing Professional Development, University of TorontoPhone: (416) 978-2719info-sur1785@cpdtoronto.caE-mail: www.cpd.utoronto.ca/cardiactumoursSite: Houston, United StatesInformations: The Society of Thoracic Surgeonseducation@sts.orgE-mail: www.sts.org/annualmeetingSite: Houston, United StatesInformations: Jim CookPhone: (513) 451-7597jcook@yourmembership.comE-mail: Zurich, SwitzerlandInformations: Judith KutnjakPhone: +43 1 867 49 44 22judith.kutnjak@ee-hsec.orgE-mail: http://heart-team.org/2017-Mitral_Valve_Meeting/index.phpSite: Leeds, United KingdomInformations: Linda CatonPhone: +44 11320 68760linda.caton@nhs.netE-mail: http://www.vatscourse.com/Site: London, United KingdomInformations: AmyPhone: 7025085200Fax: 02073518544morphology@rbht.nhs.ukE-mail: www.rbht.nhs.uk/cardiacmorphologySite:


Mucociliary clearance is one of the major lines of defense of the human respiratory system. The mucus layer coating the airways is constantly moved along and out of the lung by the activity of motile cilia, expelling at the same time particles trapped in it. The efficiency of the cilia motion can experimentally be assessed by measuring the velocity of micro-beads traveling through the fluid surrounding the cilia. Here we present a mathematical model of the fluid flow and of the micro-beads motion. The coordinated movement of the ciliated edge is represented as a continuous envelope imposing a periodic moving velocity boundary condition on the surrounding fluid. Vanishing velocity and vanishing shear stress boundary conditions are applied to the fluid at a finite distance above the ciliated edge. The flow field is expanded in powers of the amplitude of the individual cilium movement. It is found that the continuous component of the horizontal velocity at the ciliated edge generates a 2D fluid velocity field with a parabolic profile in the vertical direction, in agreement with the experimental measurements. Conversely, we show than this model can be used to extract microscopic properties of the cilia motion by extrapolating the micro-bead velocity measurement at the ciliated edge. Finally, we derive from these measurements a scalar index providing a direct assessment of the cilia beating efficiency. This index can easily be measured in patients without any modification of the current clinical procedures. Mucociliary clearance is the first line of defense mechanisms of the human airways. The mucus transporting debris, particles, microorganisms and pollutants is carried away by the coordinated motion of cilia beating at the surface of the airway epithelium. We present here a mathematical and numerical model aiming at defining a global index for assessing the efficiency of this beating. Numerical simulations show that the bead velocity parallel to the wall varies according a parabolic profile with the distance to the wall. The velocity extrapolated at the wall is demonstrated to be a measurement of the momentum transfer between cilia and the surrounding fluid. This model allows us to interpret experimental measurements performed in a companion article and to propose a universal index characterizing the beating efficiency, which can be extracted in the current clinical setting. Mucociliary clearance is one of the major defense mechanisms of the respiratory airway system. The mucus layer coating the epithelial surface of the airways filters the inhaled air by trapping potentially harmful material  –4. This 2 +O(ε3)Insertinψ such that:ψ only by taking the y-derivative of the first equation and subtracting it to it the x-derivative of the second equation:Since the flow is two-dimensional and incompressible, a natural formulation of the problem is obtained by introducing the stream function ε, as it was done for the flow field:ψ0 vanishes since the fluid is set into motion only by the cilia beating (ε > 0). The two orders in ε are solved in sequence. The first order of ψ1 and ψ2 through the Robin condition of ε translate into 4 equations at the wall boundary:y = 0 and y = 1.We now expand the stream function to the second order in ψ1 is a periodic function of period 2π/k along x that solves the following system:ψ1 in Fourier series along the x direction, each term corresponding to a wave traveling forward or backward at dimensionless speed 1.ψ1 are determined by the boundary conditions. In real experiments carried out on fluid flow next to ciliated edges [k is much larger than 1 . This enables us to translate the boundary condition at y = 1 (the last two of eiθ and eiθ− (sin θ and cos θ), we only retain the coefficients corresponding to n = 1 and n = −1, which leads to the following expression for ψ1:ψ1 is real valued implies that the coefficients also satisfy ψ1 can be expressed as:ε is then:A1 and B1 are determined from the two boundary conditions at y = 0 in The coefficients of the Fourier decomposition of ed edges , the valψ2, the second order in ε of the stream function, is a periodic function of period 2π/k along x that solves the following system:ψ1 of the stream function contains only terms in eiθ and eiθ−, as seen in the previous section, a rapid analysis of the above boundary conditions shows that the second order ψ2 will only contain terms either constant or proportional to eiθ2 and eiθ−2. We are interested here only in the constant term, which corresponds to the first non vanishing contribution to the steady flow field in the ε-expansion. From ψ2, here called x-dependency of the parentheses appearing in the last line. All products contain terms that are either constant or oscillating in x. Therefore the contribution of the second parenthesis to the steady stream function can only be constant. Its x-derivative then vanishes and can be removed from the equation satisfied by y yields:A2 is an integration constant. Note that ε2/2 × 2A2 = ε2A2 can actually be interpreted as the steady component of the pressure gradient x. From the previous section, we know that ψ1 can be written as:ψ1 into the right hand side parenthesis of x-derivatives that vanish in the steady flow due to the translation invariance of the problem):C stands for the constant contribution of −2β sin θ |y = 0. This constant C is determined using the expression of ψ1 in C can be expressed from the constants A1 and B1 computed in the previous section:A1 and B1 obtained in the previous section gives immediately:B2 and C2 are integration constants. We observe here that the steady contribution to the horizontal fluid velocity appears as the sum of one parabolic and two exponential profiles. The parabolic profile depends on integration constants A2, B2, and C2 that are determined using the boundaries conditions. Velocity and shear stress vanish at y = 1, so that the fluid in the flowing layer matches the stagnant fluid above. The boundary conditions take the following form:G1, G2, and G3 are defined such that:ε. The first non vanishing order in ε for the oscillatory part of the fluid flow is of order 1 while at the second order, a steady contribution appears. This steady flow is the one responsible for the micro-bead motion observed in the experiments. It has essentially a parabolic profile whose coefficients are directly determined from the values of the quantities k, α, β, and ϕ, which are in turn deduced from the fundamental parameters of the cilia motion and the surrounding fluid . In the experiments, fitting the measured micro-bead velocities with a parabolic profile will allow us to extract quantities that are not easily measurable, such as the cilium amplitude a or the transfer parameter ϕ.equation . The modk are larger than 10 (in most cases much larger), and α2 is about 0.1. We can therefore safely assume that ek− ≪ 1 and α ≪ 1. In this situation, G1 and G2 are negligible compared to G3, and the coefficients of the parabolic profile are:y = 1. Moreover, from α4k in G3 can be neglected, leading to:β, ϕ, and k. It has a maximum value, ε2k/(1 + 2ϕ), which is achieved for β = 1, i.e., for a circular motion of the cilia tips. The case β = 0, together with a finite value of ε = a/h corresponds to the limit of the flat horizontal motion of the cilia tips. The dimensionless fluid velocity at the cilia wall in this situation is:β = 0 would lead to a vanishing steady velocity of the fluid, since all cilia undergo the same oscillatory motion, with a left/right symmetry. What we see here is that the metachronal wave  breaks this symmetry and leads to a positive steady contribution to the fluid velocity. When k = 0 (no wave), one recovers a pure oscillatory motion of the fluid, with no net fluid motion.In experiments carried out on real samples, the values of β → +∞ corresponds to a flat vertical motion of the cilia tips. Since ε = a/h is the adimensioned horizontal beating amplitude, the limit has to be taken with ε → 0, βε remaining constant as the adimensioned vertical amplitude b/h. The horizontal component of the steady velocity is then:Ox, in the positive direction for smaller values of β and in the opposite direction for larger values of β, the critical value βc being obtained when (βc − 1)2 = 2  is the crossing time of the micro-bead entering at , and s of the trajectory. The effective speed For each micro-bead entering the simulation window at The fluid velocity field is solved using the Fourier transform decomposition exposed earlier. This field is periodic both in space and time. y-direction for typical values of the input parameters CBF, CBA, λ, ϕ, and h. One observes that the total bead velocity (blue line) is essentially dominated by the steady parabolic contribution (black line) determined by the coefficients A2, B2, and C2 in ε (green line) brings a significant contribution only very close to the ciliated edge, and the exponential contribution in the steady part is negligible everywhere. Consequently, measuring the bead velocity as function of the distance to the ciliated edge essentially amounts to measuring the steady parabolic profile of the flow. x. This force is exactly compensated by the force exerted by the cilia on the fluid, proportional to the dimensionless shear stress ∂u/∂y (see π/k (the dimensionless wavelength) in the Ox direction and dimensionless thickness H/h in the Oz direction thus reads:us)((y) = u(0)(y − 1)2 found in τw applied to the fluid by the cilia:U0 is the velocity extrapolated at y = 0 from the measurements of microbead velocities above the cilia. This shear stress characterizes the momentum transfer between cilia and the surrounding fluid. Since h is also directly measured by fitting the microbead velocity profile with a parabolic profile, it means that τw can be directly deduced using this microbead tracking technique. Consequently, we propose this shear stress as an index for assessing the efficiency of the ciliary beating. One has to stress that we do not intend to reproduce in this model the in vivo condition. The shear stress measured using this micro-bead velocity technique is not assumed to be identical to the shear stress experienced by mucus in the pulmonary airways. It is only a way to assess of the ability of the ciliated edge to transfer momentum into the surrounding fluid, thus defining a usable clinical index.The horizontal component of the steady pressure gradient x(s) see , appliesa (CBF), the the cilium beating frequency ω/2π (CBF), and the metachronal wavelength λ are measured directly by microscopic measurements on the cilia, while h is extracted from the parabolic fitting of the microbead velocity profile. In the approximation of a flat beating (β = 0), this set of measurements therefore provides a way to assess ϕ*, the equivalent sliding length introduced in the boundary condition of Finally, the extrapolated velocity at the wall can be compared to the one predicted from in vivo condition. It reproduces in fact the ex vivo experiments performed on a few ciliated cells obtained from nasal brushing. In these experiments, clusters formed of a few ciliated cells are immersed in water, and observed between two microscope slides by high speed video-microscopy. The ciliated edges are therefore lying horizontally in the simulation plane , which is perpendicular to the optical axis Oz of the microscope.In human airways, the coordinated motion of the cilia covering the epithelium induces a complex displacement of a double layer consisting in a bottom periciliary layer (PCL) and a top mucus layer. Both layers have similar thicknesses, about 5 to 10 μm, but very different viscosities. The situation presented in this paper is however very different from the a an oscillatory motion of the fluid, and at second order a net steady component that pushes the fluid in the direction of the cilia beating. The net motion of the fluid originates from the phase shift between neighboring cilia tips, which breaks the left/right symmetry. More precisely, it creates an unbalance on the envelope between the respective densities of tips moving forward and those moving backwards. This unbalance appears in the second term of The first and foremost difference lies in the fact that the fluid surrounding the cilia is now essentially water, i.e., a homogeneous Newtonian fluid. Secondly, unlike the in vivo situation where mucus is propelled only by the forward trajectory of the cilia tips, here both forward and backward parts of the stroke cycle contribute to the fluid motion. Consequently, as expressed through the mathematical model developed here, the oscillatory motion of the cilia induces at first order in the amplitude ex vivo experiments, the Womersley number α is close to 0.4 while the dimensionless wave vector k = 2πh/λ is at least about 10 and most of the time much larger than that . This my. The steady contribution to the dimensionless horizontal wall velocity is ε2k/2 (see β = 0) and no slip boundary condition at the cilia wall (ϕ = 0), this value is exactly the steady fluid velocity at the wall expressed in β − β2) (ϕ > 0) reduces this velocity.At a distance larger than a fraction of the metachronal wavelength from the ciliated edge , the os2k/2 see . In the  β − β2) . In addiϕ* = hϕ (ϕ), a result that is also obtained in a purely steady model for a parabolic profile. As shown in the companion paper [ϕ will not be a fitting parameter but predicted by the mere measurement of the cilia density.Introducing a partial slip at the cilia wall, through a Robin type boundary condition with an effective sliding length ϕ* = hϕ , reduceson paper , this efThe model proposed in this article relies on several assumptions, hence has a few limitations that we examine now.β representing the ellipse eccentricity. One also has to stress that, since our model reproduces an experimental setup in which cilia are surrounded only by water, one might expect a different motion from the one achieved in in vivo situations where cilia are beating in a periciliary liquid while their tips are entering the bottom of the mucus layer.First, the motion of each individual cilium tip is assumed to be elliptic. Although this is a generally accepted hypothesis, microscopic imaging of the actual motion of the cilium shows a more complicated trajectory . In partEx vivo experiments carried out in [ϕ), or to a perturbed metachronal wave.The model also assumes an exact synchronization of the individual cilia motions, generating a metachronal wave of constant velocity. This implies that the phase shift between two cilia is a linear function of the distance separating them along the direction of the metachronal wave propagation. d out in  show thaIn the model, the ciliated edge is supposed to be flat. This assumption is generally valid in real experiments (see ). Cases h above the edge. The stagnation of the fluid above the cilia is observed experimentally, and originates from the external environment of the measured cell cluster. Various explanations can account for it: it can be due to the presence of other cell clusters at a few hundred microns distance which create a complex fluid flow in the entire system, or by the friction of the fluid on the upper and lower slides, or the appearance of boundary layers. Determining this value would require modeling the full 3D geometry of the system whose geometry is not easily accessible by microscopic observation. The distance h therefore is treated as the only fitting parameter of the model .An important assumption lies in the fact that the fluid surrounding the cilia is stagnant at a distance Finally, the bead velocities are found in the model to be almost always parallel to the cilia wall. In real experiments, a vertical component  might appear, although much smaller than the horizontal component. This is in particular the case when the ciliated edge is not strictly flat, or when the observation window is such that the ciliated edge is on one side of the window and the bead passing on the other side of the same window. The micro-beads are in this situation not set into motion only by the considered ciliated edge, and external influence originating from outside the observation window interfere, contributing to create a more complex fluid flow pattern. Such situations should be discarded from the analysis, either through eye examination or by an automatized procedure.ϕ*), and the distance from the wall to the region of stagnant fluid (h). Polystyrene micro-beads immersed in the fluid act as massless tracers, allowing the measurement of the local fluid velocity, hence the measurement of the velocity profile. All aforementioned parameters can be determined through local measurement by high speed video-microscopy, except for the distance h which remains the only fitting parameter of the model extracted from the velocity profile. The velocity extrapolated from the profile at the wall is found to be linearly related to the shear stress exerted by the cilia on the fluid. This shear stress is proposed as a new index for assessing the efficiency of the ciliary beating. One has to stress that this index can be measured from nasal brushing in the clinical setting without any modification of the current clinical procedures. Preliminary tests , the ciliary beating amplitude (CBA), the metachronal wavelength (λ), the cilia density (through the sliding length sts (see ) have alS1 FileDerivation of shear stress exerted by the cilia motion onto the surrounding fluid.(PDF)Click here for additional data file.


In addition, USP16 inhibition promoted stem-like properties in tumour cells, as evidenced by their spheroid formation and chemo-responsiveness. Furthermore, ectopic expression of USP16 in tumour cells significantly abrogated the tumour promoting activities of the Ct-HBx proteins (HBxΔ35), leading to decreased tumour cell viability and tumour growth. In human HCCs, USP16 was frequently downregulated, and the decreased expression of USP16 was correlated with high tumour stages and poor differentiation status. Taken together, our study suggests that USP16 downregulation is a critical event in Ct-HBx-mediated promotion of HCC tumorigenicity and malignancy.Hepatitis B virus (HBV) infection is a major factor that contributes to the development of hepatocellular carcinoma (HCC). HBV X protein (HBx) has been shown to accelerate HCC progression by promoting tumour growth and metastasis. In the clinic, carboxyl-terminal truncated HBx (Ct-HBx) proteins are frequently present in HCC tumour tissues, but not in non-tumorous tissues. In this study, we analysed deubiquitinase expression profiles in cells with or without ectopic expression of the Ct-HBx proteins and observed that the expression of ubiquitin specific peptidase 16 (USP16) was substantially inhibited by Ct-HBx proteins. Liver tumour cells with forced down-regulation of USP16 exhibited increased capabilities for colony formation and tumour growth  Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. The prognosis of HCC is approximately 12% survival due to the aggressive metastasis and recurrence of tumours24578101112Ink4a and p19Arf expressionDeubiquitinases (DUBs) have emerged as key regulators of tumour development. The human genome encodes approximately 91 active DUBs that regulate homeostasis and multiple cellular processes, including signal transduction, DNA repair and transcriptional activation141516In this study, we have revealed a tumour suppressive function of USP16 in liver tumour cells and demonstrated that USP16 downregulation is a key event that is critical for Ct-HBX-mediated pro-tumorigenic activity. We show that USP16 has the capacity to inhibit tumour formation and tumour cell stemness properties. More importantly, USP16 is frequently downregulated in HCC tissues, and its low expression is associated with advanced malignance. Therefore, our present data have provided evidence that USP16 downregulation contributes to the pro-tumorigenic activities of HBx in the development of HCC.in silico EST profile analysis. Changes in the expression patterns caused by HBxΔ35 overexpression in immortalized liver LO2 cells and HepG2 liver tumour cell line were analysed by real-time RT-PCR (8913232425To address whether oncogenic Ct-HBx may function by regulating the expression of deubiquitinating enzymes (DUBs), we ectopically expressed a truncated form of HBx, HBxΔ35 721e RT-PCR . By the e RT-PCR . The leve RT-PCR . Furthere RT-PCR . Thus, te RT-PCR . Notablyin vitro  cell line were provided by the American Type Culture Collection (ATCC). The immortalized liver cell line LO2 was obtained from the Shanghai Cancer Institute. All cells were maintained in DMEM with high glucose (GIBCO) supplemented with 10% foetal bovine serum, penicillin (100 U/ml) and streptomycin (100 μg/ml). Cells were incubated at 37 °C in a humidified incubator under 5% COTo culture stem-like cells, the serum-free medium was composed of DMEM/F12, 10 μg/ml bFGF, 20 μg/ml EGF and B27 supplement. Cells were suspended to form sphere-like cell aggregates. Full-length and COOH-terminal truncated forms of HBx (HBxΔ35 and HBxΔ14), that were from non-tumoral and liver cancer tissues respectively, were cloned into pLVX-IRES-zsGreen vector; USP16 was cloned into pLVX-IRES-puro and pLVX-IRES-zsGreen vectors. shRNAs targeting the USP16 expression sequence were cloned into the pPRIME plasmid. Lentiviral particles were constructed after transfecting the vector with the packaging plasmid psPAX2 and envelope plasmid pMD2G into HEK293T cells using Lipofectamine 2000 transfection reagent. The indicated cells were infected with lentivirus plus Polybrene. siRNAs used were as follows: usp16 si-1: TAGTGAATGTGGAATGGAA; usp16 si-2: CAGTGAATATGAAGCTGAAin vivo tumour formation, 106 cells were subcutaneously implanted into nude BALB/c mice (5 to 6 weeks of age). Tumour size was serially measured using callipers. Tumour-bearing mice were euthanized, and the tumours were removed for further study.For Immunohistochemical analysis of HCC tissue microarrays using an anti-USP16 antibody (Santa Cruz Biotechnology) was performed as previously describedin vitro and in vivo experiments were assessed using Student’s t-test or a one way analysis of variance (ANOVA) test. A p-value of less than 0.05 was considered statistically significant.Statistical analyses were applied with SPSS 21.0 software and GraphPad Prism 5 software. The statistical significance of USP16 expression in tumour and non-tumorous tissues was determined using the Wilcoxon matched pair’s signed-rank test. The relationship between clinicopathological parameters and USP16 expression was analysed using the Wilcoxon signed-rank test. The data are presented as the mean ± S.D. from at least three independent experiments. All Detailed Materials and Methods are available in the How to cite this article: Qian, Y. et al. USP16 Downregulation by Carboxyl-terminal Truncated HBx Promotes the Growth of Hepatocellular Carcinoma Cells. Sci. Rep.6, 33039; doi: 10.1038/srep33039 (2016).


Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research. Scholars use a range of different types of social web platforms in their professional activities. Twitter is one of the most popular microblogging platforms in many countries, allowing users to broadcast short messages. Academics use Twitter to communicate scientific messages at real-time events, like conferences, but also for more routine information sharing . Since TThe main previous evidence for the value of Tweet counts as impact indicators for academic papers is that they have statistically significant, but small, positive correlations with citation counts in various disciplines, including the medical sciences , papers The search behavior of Twitter users has studied before but only from a general perspective ,11] and and11] aScholarly communication in the digital age includes formal and informal uses of the internet to discuss, publish and disseminate scientific discoveries . In the Twitter seems to be used by a substantial minority of academics, although this varies by country. Nothing is known about the extent to which non-academics use Twitter to share scholarly information. About a third (35%) of higher education professionals in the USA were Twitter users in 2010 . Uptake Few studies have investigated how people discover scholarly or other information on Twitter. A user study with 20 faculty and students found that facts and links helped to make a tweet useful . A surveThere are many reasons that academics cite research on Twitter. A survey of 28 tweeting academics in 2010 found that they cited peer-reviewed publications as an integral part of their activities on Twitter .Scholars also use Twitter for purposes other than sharing articles. An online survey of faculty and students of a UK university found that academics use Twitter to increase their professional reputation . An analAt conferences, Twitter is sometimes used to disseminate quotes, notes, and presentations . An analMicroblogging services like Twitter may provide a new way for scholars to interact with the public since many non-academics use Twitter and it is suitable for information sharing . TwitterThere have been systematic attempts by scholars to communicate with the public, indicating a belief that this is worthwhile and possible. For example, one study demonstrated that scholars used Twitter to educate the public in the Flint water crisis in Michigan .A content analysis of 72,469 tweets related to more than 900 scientific news stories revealed that climate change generated the most discussion and there was a complex Twitter ecosystem of contributors that included mass media, celebrity activists, and politicians. Whilst individual scholars and articles did not seem to be important on Twitter for discussions of these news stories, major scientific organizations like @NASA, scientific projects like @marsCuriosity, and prominent scholarly bloggers like literary and cultural commentator Maria Popova (@brainpicker) were important . Thus, iTwitter and other social media have been successfully used to engage the public as part of The European Space Agency's comet-chasing Rosetta mission. For example, the hashtag #WakeUpRosetta trended on Twitter at one stage . This ilFrom the perspective of journals in a given field, an analysis of academic articles from several conservation studies journals demonstrated that mass media news stories were the most important factor for informing the public about an article, leading to increased sharing on Twitter and Facebook . This coLiking, saving, and retweeting are important parts of the Twitter information ecosystem. Whilst liking and retweeting help to promote tweets so that they are more easily found by other users, saving helps a user to retrieve a tweet later.Retweeting can be used to help disseminate academic information. A study of tweets from 447 active scholars from ten disciplines showed that scholars retweeted more  than typical users and that biochemists were the most active retweeters . A studySome projects have studied motivations for favoriting tweets (replaced in 2015 by liking tweets), although not in a scholarly context. A mainly crowdsourced (Tellwut) survey of 606 tweeters in 2013/4 found that half favorited tweets. Users favorited a tweet to help them to find it later or to show approval of the tweet  33]. Th. Th33]. Information about scholarly uses of Twitter is helpful to interpret tweet counts as impact evidence for tweeted publications. Traditionally, citation-based indicators have been used to help evaluate scholarly outputs, but are slow to accumulate and are unable to reflect non-academic impacts  35]. In. In35]. Many publications are mentioned on Twitter. At least 39% of papers submitted to arXiv.org in 2012 have been tweeted at least once  and 10% The logical first step for evaluating an impact indicator, such as tweet counts, is to assess whether it correlates with citation counts . This isAnother way to investigate the value of tweeting academic research is to apply content analysis to tweets. Using this approach, most tweets reflect the title of the papers or brief summaries, with very little scholarly discussion . This giThis study investigates key aspects of academic information sharing on Twitter to better understand (a) the context in which academic research is tweeted, and (b) the context in which Twitter is used to find academic information. The research questions cover gaps identified by the literature review.What types of people  tweet academic research?How is Twitter used to find academic-related information?Why is Twitter used to communicate scholarly information? Does the answer depend on academic discipline, gender, age, and occupation?What types of scholarly content is shared on Twitter? Does the answer depend on academic discipline, gender, age, and occupation?Why are scholarly tweets liked, saved, or retweeted?Although many previous studies have analyzed published tweets, not all tweets originate from a person. Twitter bots create automatic tweets that are difficult to distinguish from human-authored posts . MachineTwitter had 310 million users in 2017 , making Twitter’s anti-spam filters made it impossible to tweet a large sample of academic tweeting users directly and so an email survey was used instead. To obtain users’ email addresses, the sample was restricted to users with web links in their Twitter profiles. This is likely to bias the sample towards academic users because these seem most likely to have a public page containing contact information. Such pages are automatically generated for academics by many universities, for example. This additional source of unknown bias is undesirable but there does not seem to be a better alternative. The data mining approach rejected above would also need this step.Using the data from Altmetric.com, 1,771,520 web links were harvested from the 4.5 million Twitter accounts . These links included personal or professional web pages as well as irrelevant targets, such as YouTube videos. The 1,771,520 websites were automatically downloaded and emails extracted from them, when present, producing 57,125 email addresses  from a range of different domains . There whttps://figshare.com/s/caaa37d468b7302cb611. The questionnaire was sent to 57,125 Twitter users through Survey Monkey in early July 2016, with a reminder sent to non-responding users in late July. Approximately 94% of the invitations were not opened and 1617 emails bounced, giving a low response rate . Some survey invitation emails may have been marked as spam and not received by the intended recipients. Several users emailed to confirm the genuineness of the survey, suggesting that some recipients were reluctant to open the survey link in case it connected to a malicious site.A questionnaire was developed and refined through numerous pilot tests. This survey received institutional review board (IRB) approval from Northwestern University. A copy of survey questionnaire is available at To increase the response rate, in mid-August 2016, the survey was distributed through Northwestern University, as a trusted academic mail server, to the 53,809 Twitter users who had not opened the Survey Monkey link. Users who had initially opted out of the survey or with bounced email addresses were excluded. A reminder was sent to non-responding invitations in early September. From this, 3,045 additional emails bounced and 113 additional users opted out. This step gave 807 additional responses.Overall, 1,912 (4%) users completed the survey, with 4,662 (8%) email invitations bouncing and 707 (1%) of users opting out . The resDescriptive statistics are presented as counts and percentages. Chi-square tests were used to assess associations between categorical covariates and responses, Cochran-Armitage trend tests between ordinal covariates and responses, and Wilcoxon rank sum tests for comparisons between skewed continuous covariates and responses. All analyses were performed in SAS v. 9.4, and a nominal 5% type I error level was used.The main results are given in the appendices and this section summarizes the key findings.Based on the IP addresses of the participants, the survey respondents were 64% from North America, 17% from the United Kingdom, 10% from Europe, 5% from Asia, 2% from South America, 2% from Oceania, and 1% from Africa. This agrees with the overall geographical distribution of Twitter users . There wMost participants defined their disciplines as social science (37%) or humanities (22%), with smaller numbers from Engineering/Technology (15%), Natural Sciences (13%), Medical/Health Sciences (11%), and Agricultural Sciences (1%). For the non-academic respondents, these disciplines presumably associated with their highest qualification or occupation. People from these broad disciplinary areas presumably share an information culture to some extent, irrespective of whether they are academics or not. From across academia overall, the first two categories are overrepresented. The survey respondents mostly worked in academic institutions (55%), but almost half worked outside, in industry and professional organizations (41%) or government (4%). Thus, the survey has successfully recruited large numbers of non-academic participants see also .The academic faculty members were assistant professor/lecturer or senior lecturer (40%), associate professor/reader (25%), professor (25%), instructor/teaching assistant (8%), and adjunct faculty (3%).Tweets can be found from a user’s Twitter home page, with the Twitter search box and or through general search engines, such as Google and Bing. Most of the respondents  stated that they found tweets by following specific Twitter accounts. Half  used hashtags and some used keyword searching through Twitter  or web search engines . A few used third-party applications, such as TweetDeck , and other feeds  to find scholarly tweets.p = .205), and genders (p = .205). Account following was more likely to be done by researchers , people in academia , faculty , and younger people . Journalists  and older people  were least likely to use it. Thus, following accounts is particularly important in an academic context although it is common for all (see Almost three quarters (73%) of survey participants found academic tweets by following accounts, with insufficient evidence to claim (population) differences between academic disciplines  searched by hashtags, with insufficient evidence to conclude that there are disciplinary (p = .633), researcher status (p = .814), or work status (p = .831) differences. Hashtags are more often used in academia , by females , by younger people , and by people that had joined Twitter a longer time ago .The main disciplinary differences for methods to find tweets concern querying in Twitter or commercial search engines. Humanities scholars used keyword searches in Twitter (48%) much more than did natural scientists (32%). People involved in engineering or technology used commercial search engines (25%) more than did natural scientists (17%). The underlying difference here is that natural scientists are less likely to actively search for tweets than are users from other disciplines.Non-researchers are more likely to search in Twitter (46% vs. 43%) or commercial search engines (22% vs. 18%) than are researchers, but they otherwise find tweets in similar ways.rd party apps. People in academia are the most likely to follow accounts  and hashtags . Government users are most likely to use a commercial search engine , whereas professional/commercial users are most likely to use Twitter keyword searches . Thus, more active searching is more characteristic of non-academic users.There are differences between sectors in all methods used to find tweets, except for 3The only statistically significant difference in methods to find tweets is that females are more likely to follow hashtags . Hashtags probably perform a communicative function often in academia, such as by allowing conference participants to interact online.rd party apps. The biggest difference is that older users (60+: 31%) were almost twice as likely to find tweets with a commercial search engine than were younger users (<21: 16%).There are age differences in all methods used to find tweets, except for 3The survey respondents that gave a reason for using Twitter (n = 1811) used it to obtain real-time information , share real-time information , expand their professional networks , contribute to wider conversations  promote organizations , communicate about academic events , communicate the results of scientific publications to the public  and peers , and for teaching  see .p = .535) or shared (p = .390) real-time information, or used Twitter to expand their professional network (p = .650). In Social Sciences, Twitter is more likely to be used in teaching. In Humanities, it is more likely to be used to contribute to wider conversations, a natural humanities role. In Engineering/Technology, Twitter tends to be used less for everything, particularly for communicating results and about academic events. In Natural Sciences, it is less likely to be used to contribute to wider conversations. In Medical/Health Sciences, Twitter tends to be used more for most things except teaching, and is particularly well used for communicating results to peers and the public and for communicating about academic events . There were no other significant gender differences in the other reasons for using Twitter in scholarly contexts , communicating the results of academic publications to peers (p = .001) and the public (p = .001), communicating about academic events (p = .001), teaching (p = .003) and promoting their organization (p = .001). In general, people aged 31–40 tended to have more reasons for using Twitter and people under 21 tended to have the fewest reasons , research related news , blog posts , lay summaries of research for the public , videos and images , policy announcements , and presentation slides  . Since pSocial scientists tended to share the most, including publications, research-related news and policy announcements. Humanities scholars tended to share the least, including publications, slides, and research-related news. In Engineering/Technology, slides were shared the most, presumably reflecting the importance of conferences in many engineering fields. In Medical/Health Sciences, policy announcements were frequently shared .Unsurprisingly, researchers shared the most publications and research-related news. Interestingly, however, non-researchers shared the most videos and images, blog posts, and lay summaries. Thus, there seems to be a niche role for non-researchers in helping to communicate research in non-standard ways. This is broadly echoed by the work sector results. The government and industry/professional sectors tend to share similarly, except that government workers share more research-related news. In terms of occupation, researchers and faculty tended to share publications, presentations, and research-related news the most and lay summaries, videos, and images the least .There were only minor gender differences in sharing, with males sending to share presentations more , whereas females shared research-related news and lay summaries more. The age range 31–50 was the most active for sharing, including publications, research-related news. People with older Twitter accounts also tended to share more, including presentations, videos and images, and blog posts .Several questions were asked about the use of Twitter in scholarly activities. Most respondents agreed or strongly agreed  that tweeting academic articles disseminates scholarly information to the public, representing a broad consensus , despiteAlthough respondents agreed that Twitter could be used to communicate scientific knowledge, they were split about whether it could be used to measure scholarly impact. Only half  agreed or strongly agreed, with nearly one-third  being neutral.Two thirds of survey respondents (66%) tweeted at least weekly. This was most common in Social Sciences (69%) and least common in Humanities and Engineering/Technology (62%). Journalists were the most likely to tweet at least weekly (80%) and professionals (58%) the least likely. Males (68%) and people aged 51–60 (70%) were more frequent tweeters, as were people with an account over 8 years old (71%) .liking (formerly known as favoriting), saving, and retweeting content. Most respondents  like a tweet to inform the authors that their tweets were interesting. Forty-three percent (n = 725) like tweets to help them to be found in the future. Only a fifth  liked tweets to disseminate them  but also sometimes  to tell the author that their tweet was interesting, for future access  and to inform the author that they had read the tweet .Unsurprisingly, academic tweets were usually The results have several limitations. The survey sample is limited to Twitter users with personal webpage links in their Twitter profiles. These are presumably more likely to be academics or professionals than people that are unemployed or working in government or industry. The sample is also restricted to people that have current email addresses in their webpages that can be extracted using web-mining methods. Thus, the original sample is a limited and probably biased subset of people that have tweeted academic articles. The low response rate is typical of online surveys and is likely to bias the results to the attitudes of people that have a greater interest in the use of Twitter within research. This may explain the high numbers of social sciences and humanities scholars. It probably also biases the results towards more active users.p value is close to 0.05. As with all surveys, some respondents may misinterpret the questions. Moreover, social network sites and typical methods of use change over time, adding extra uncertainty to the results.The numerous statistical tests greatly increase the likelihood that at least one finds spurious evidence of differences when none are present . The results should therefore be interpreted cautiously, especially when the The survey suggests that most people who tweet academic information work in academic institutions, with other groups of users outside academia such professionals , but with many managers and journalists also tweeting academic information. This is consistent with previous research ,13], bu, bu13], Within academia, the level of Twitter use varies by academic rank, with assistant professors among the top users, followed by associate professors, corroborating previous research . Most reIn terms of gender, the greater number of males responding to the survey may reflect similar gender imbalances in science  and perhFollowing specific accounts and hashtags are the primary means used to find academic information on Twitter. Users prefer this to searching on Twitter or via a web search engine. This probably reflects the way in which most people use Twitter  rather tFor the second research question, the primary motivations for using Twitter in scholarly contexts are to get and share real-time information, and to develop connections. Twitter is also used to participate in online conversations, increase the visibility of organizations, disseminate activities related to academic events, share scholarly findings with peers and the public, and for educational purposes. These findings are consistent with previous studies about the reasons for using Twitter in general  and acadScientific publications, academic news, and blog posts are the most common information sources shared on Twitter. Some respondents also share lay summaries of research, videos, images, policy documents, and presentations. These findings are in agreement with an earlier study that used link analysis . There wThe consensus 81%) that tweeting academic articles disseminates scholarly information to the public is given some credence by the substantial number of non-academics responding to this survey. Perhaps related to this, most respondents (52%) thought that Twitter mentions of academic publications can be a research impact metric. Given the low correlations between Twitter mentions of scientific articles and citation counts found in previous studies,[8],59]1% that t1% that tThere was agreement (81%) that Twitter has changed the way that scientific knowledge can be read and spread, including between disciplines (76%). Although previous studies have found evidence of cross-disciplinary knowledge transfer for other social web sites ,62] thi thi62] tMost participants composed, replied to, liked and retweeted tweets. The level of activity varied based on the respondents’ occupation and gender, with males more actively tweeting than females. Researchers and faculty members at academic institutions more actively tweeted scholarly information than other users.The results have several practical implications. For information sharing, Twitter is particularly important in the social sciences and humanities. Thus, researchers in these areas should be encouraged to investigate whether they can benefit from using the service, if they are not already doing so. It is possible that other disciplines rely on effective alternative communication means, such as conferences, preprint archives, or professional online networks such as LinkedIn.Since users rely on social connections to find information on Twitter , people The dissemination of science to wider audiences is an increasingly important task in academia. People that use Twitter to communicate research agree that it can perform this role, and this is supported by the proportion of non-academics responding to the survey  that also do this, despite the likely bias of the survey towards academics . This gives the strongest evidence yet that Twitter may be successful in this role and that Twitter-based indicators may also help to reveal the wider impacts of scholarly publications–although not in formal evaluations .Finally, since messages shared on Twitter are not always conveyed effectively ,42],,[S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Koichi Hirata's affiliations appeared as affiliations j and k, but should have appeared as j, k and l.In the article, “A comparison of the surgical mortality due to colorectal perforation at different hospitals with data from 10,090 cases in the Japanese National Clinical Database”,


To evaluate the concordance between clinic-collected extra-genital specimens and self-collected mailed-in extra-genital specimens among participants seeking sexually transmitted infection testing at a free clinic in Hollywood, CA.Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG). All testing was performed using the GeneXpert® CT/NG .A convenience sample of 210 men who have sex with men were enrolled between February 29, 2016 and December 21, 2016 and received mail-in testing kits for From the 210 mail-in kits distributed, 149 mail-in kits (71.0%) were returned to the laboratory, resulting in 145 pairs (clinic-collected and mail-in) of rectal test results and 148 pairs of pharyngeal test results for both CT and NG detection. The concordance was 95.0% for all CT rectal tests, 99.3% for all CT pharyngeal tests, 95.7% for all NG rectal tests, and 97.2% for all NG pharyngeal tests.Roughly two-thirds of mail-in test kits were returned and concordance was generally high, however more than one-third of positive results were missed in mail-in samples. The prevalence of potential false-negative results among mail-in samples warrants caution when implementing mail-in STI testing strategies. Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections are common and frequently asymptomatic sexually transmitted infections (STIs) and therefore often go undiagnosed and untreated [ntreated –2. The Untreated . Los Angntreated .The CDC recommends anatomic site-specific diagnostic tests for CT/NG. Studies evaluating self-collected CT/NG site-specific swab tests—which are easy for patients to use in non-clinical settings—show promising results in terms of their ability to detect previously undiagnosed and untreated CT/NG infections –5. STI tThe primary aim of this study was to evaluate the concordance between clinic-collected extra-genital specimens and extra-genital specimens sent via United States Postal Service (USPS) from participants seeking STI testing at a free clinic in Hollywood, CA.A convenience sample of men who have sex with men (MSM) who sought free STI testing at a Hollywood, CA clinic were enrolled between February 29, 2016 and December 21, 2016 and received mail-in testing kits for CT and NG. The clientele that visit this clinic were predominantly MSM that engage in high-risk sexual behaviors. Recruitment occurred five days per week for the duration of the study.Clinic-based rectal swab specimens were self-collected by the participant in accordance with the clinic’s standard of care, while pharyngeal swab specimens were collected by trained research associates. For mail-in specimen collection, participants were asked to self-collect rectal and pharyngeal swab specimens at home after leaving the clinic and then mail those samples via USPS to the laboratory at their earliest convenience (within 48 hours). Participants who were projected to receive presumptive treatment while at the clinic  were asked to self-collect the mail-in swabs before leaving the clinic to ensure treatment would not affect the swab specimens. All testing was performed using the GeneXpert® CT/NG  by trained laboratory staff. Multiple studies have successfully evaluated the sensitivity and specificity of the GeneXpert® CT/NG  compared to other nucleic acid amplification test (NAAT) platforms for screening extra-genital infections –14.As part of the self-collection package, participants were provided written and visual instructions, two swabs in sealed containers , one biohazard bag, and one envelope stamped and addressed to the laboratory. During self-collection, participants were instructed to open one container at a time, remove the swab, swab their designated anatomic site, place the used swab back in the container, close the container, and place the container in the biohazard bag. After both swabs were utilized, participants were instructed to seal the biohazard bag, place the biohazard bag in the envelope, seal the envelope, and place the envelope in a USPS mailbox. Participants received a $25.00 electronic gift card once their mail-in test kit was received. For mail-in specimens, participants were each provided dry FLOQSwabs™  to avoid transporting fluid via USPS. Once our lab received the mailed specimens, the FLOQSwabs™ were transferred to a vial from the Xpert® Vaginal/Endocervical Specimen Collection Kit. This vial included liquid transport reagent which hydrated the dry swabs prior to running these samples on the GeneXpert® CT/NG. Xpert® Vaginal/Endocervical Specimen Collection Kit with liquid transport reagent were used for clinic-collected specimens. Studies by Gaydos et al. and Coorevits et al. have validated the concordance of dry swabs to wet swabs in CT/NG screening platforms –16.Pairs of clinic-collected specimens and mail-in specimens were analyzed to determine the concordance (percent agreement), and 95% confidence intervals (CI) were calculated using exact binomial methodology.The GeneXpert® CT/NG assay includes a control mechanism called the sample adequacy control (SAC) that targets a single copy human gene, hydroxymethylbilane synthase, to control for false negative results where no human cells are present by confirming adequate patient sample has been collected. The SAC cycle threshold quantifies the number of cycles required to detect the presence of this human gene target. A lower SAC cycle threshold value indicates an earlier cycle detection threshold and more human cellular target in the specimen. If the target is not present, the SAC will fail and the results will be invalid. We compared the SAC cycle threshold values between mail-in and clinic-collected specimen tests using the Wilcoxon signed rank sum test. All analyses were conducted using SAS version 9.4 .Ethical approval for this study was granted by the Institutional Review Board (IRB) at the University of California Los Angeles (IRB #15–000740). The IRB waived the requirement for signed informed consent for this research under 45 CFR46.117(c)(2). Verbal informed consent was obtained from all participants. Research associates documented that consent was obtained by initialing next to each participant’s assigned study number on the recruitment document. Each participant received a copy of the information sheet describing the study.From the 210 mail-in kits distributed, 149 kits (71.0%) were returned to the laboratory via USPS—however, one returned kit did not contain a pharyngeal swab, and four clinic-collected rectal swabs were lost during transportation from the clinic to the laboratory. There were 145 pairs (clinic-collected and mail-in) of rectal test results and 148 pairs of pharyngeal test results for both CT and NG detection. For NG, the concordance was 95.7%  for all rectal tests, with 66.7%  agreement among positive samples and 100.0%  agreement among negative samples. The concordance was 97.2%  for all pharyngeal tests, with 78.6%  agreement among positive samples and 100.0%  agreement among negative samples.10.5281/zenodo.1069568).We were able to retrieve SAC cycle threshold data for 131 out of 145 pairs of rectal swabs. The median cycle threshold for mail-in rectal specimen was 33.2 (Interquartile range (IQR): 28.8, 36.4) compared to the median cycle threshold for clinic-collected rectal specimen was 24.65  (p<0.0001). We retrieved the SAC cycle threshold data for 135 out of 148 pharyngeal swabs. The median SAC cycle threshold was 29.1  for pharyngeal mail-in samples, while the median cycle threshold for clinic-collected pharyngeal specimen was 24.6  (p<0.0001). All relevant data files are available from Zenodo database (DOI: We evaluated the concordance between clinic-collected specimens and mail-in specimens for detection of CT and NG infections and found that concordance was generally high. However, more than one-third of positive results were missed in self-collected mail-in samples. Similar high rates of discordance among positive results were seen in a study published by Owens et al. evaluating accuracy of home-based STI testing .Receipt of mailed samples showed optimistic results. Roughly two-thirds of mail-in test kits were returned, a return rate higher than those reported in similar studies that evaluated mail-in STI screening strategies ,19. ThisThere were several limitations in this study. First, this study utilized a convenience sample of participants in Hollywood, CA and the results may not be generalizable to other populations. Second, this study could have benefitted from an FDA-approved reference test, however, currently no NAATs are FDA-approved for use with extra-genital specimen types. Third, due to the small sample size of participants who received positive results, our confidence intervals for concordance of positive results are wide. Finally, there is a degree of uncertainty when comparing samples collected with the dry FLOQSwabs™ to those collected with the Xpert® Vaginal/Endocervical Specimen Collection Kit with liquid transport reagent. While previous research has validated their concordance using NAAT based CT/NG screening, none have evaluated their concordance using the Cepheid GeneXpert® CT/NG in extra genital sites –16. It iA study conducted by Bristow et al. found that lower SAC cycle threshold values were associated with CT and NG infections in urine samples, suggesting that increased amounts of human cellular materials were correlated with infection status . The higThe prevalence of the potential for false-negative results among self-collected mail-in samples warrants caution when implementing mail-in STI testing strategies. Given that both clinic-collected and mail-in rectal samples were self-collected by the patients, the seven discordant CT and six discordant NG results for mailed-in rectal samples are likely indicative of an inadequacy of self-collection when patients are collecting the swabs outside of the clinic setting. Additionally, there is a possibility that the process of sending dry swab specimens via USPS yields results less consistent than those maintained in the clinic.While the present study indicates that mail-in STI testing may not be a viable alternative to clinic-based STI testing, focus groups suggest people prefer having the option to test in the comfort of their own homes ,21–24. GFuture research should analyze the cost-effectiveness of mail-in samples versus clinic-collected samples due to the low cost of mailing specimens. In addition, providing easy-to-use in-home self-collected test kits may address access, confidentiality, and stigma-related barriers to STI clinic-based services. As STI rates continue to rise in Los Angeles County and throughout the United States, it is imperative for public health professionals to find novel approaches to ensure high-risk patients partake in regular STI testing.


In this study, we sought to study the dominant directions of tumour expansion/shrinkage pre-treatment. We examined fifty-six GBMs at two time-points: at radiological diagnosis and as part of the pre-operative planning, both with contrast-enhanced T1-weighted MRIs. The tumour volumes were semi-automatically segmented. A non-linear registration resulting in a deformation field characterizing the changes between the two time points was used together with the segmented tumours to determine the dominant directions of tumour change. To compute the degree of alignment between tumour growth vectors and WM fibres, an angle map was calculated. Our results demonstrate that tumours tend to grow predominantly along the WM, as evidenced by the dominant vector population with the maximum alignments. Our findings represent a step forward in investigating the hypothesis that tumour cells tend to migrate preferentially along the WM.Generating MR-derived growth pattern models for glioblastoma multiforme (GBM) has been an attractive approach in neuro-oncology, suggesting a distinct pattern of lesion spread with a tendency in growing along the white matter (WM) fibre direction for the invasive component. However, the direction of growth is not much studied  However, the treatment effectiveness is limited, as the disease continues to be almost universally fatal, with the median overall survival of only 12–18 months2.Derived from glial cells, glioblastoma multiforme (GBM) is an extremely aggressive malignant brain tumour affecting adults. Currently, the standard treatment offered to patients diagnosed with GBM combines surgery with chemo- and radio-therapy3. Already in 1938, Hans Joachim Scherer, a German neuropathologist, suggested from autopsy findings that gliomas migrate along existing brain structures triggered by the interaction of glioma cells with the neural microenvironment4. Glioma cells may migrate along blood vessels in the perivascular space or directly within the brain parenchyma5. Migration along blood vessels6 may be important for ensuring supply of oxygen and nutrition. However, despite promising effects in animal models, inhibition angioneogenesis with the anti–vascular endothelial growth factor (VEGF) antibody bevacizumab has not been able to improve survival in patients8. It has been suggested that glioma cell invasion may instead be triggered by hypoxia, perhaps explaining the disappointing results from clinical studies on angiogenesis inhibition so far9. Most studies on glioma cell migration are based on animal models, but glioma cell migration and invasion is often rather different from what is observed in humans. For example, a single cell migration is seldom observed in animal models10. At present, the mechanisms underlying glioblastoma growth and invasion are not fully understood, and human data are usually based on autopsy data and thus not longitudinal in nature.After treatment, gliomas will usually relapse within 2-3 cm of the surgical resection cavity11. Hence, it seems likely that direct movement is one of the mechanisms by which cancers spread through nearby tissues, resulting in random and heterogeneous growth patterns12. However, empirical evidence indicates that cancer cells can spread into some tissues more easily than into others. For example, large blood vessels characterized by very strong walls and dense tissues, such as cartilage, are harder for cancer cells to penetrate. This finding indicates that cancer cells grow along the path of least resistance, in line with most natural processes14. According to the generally accepted brain tumour development theory, tumour tends to grow along blood vessels or the white matter fibres, because of their anatomical characteristics, including a lubricated route for cancer cell migration14.As an abnormal capacity, the mobility of most of the cancer cells is greater than that of the normal cellsin vivo with the help of mathematical model and diffusion tensor imaging MR sequences. The diffusion tensor imaging (DTI) data demonstrate the direction of maximum diffusivity, providing a unique opportunity to study WM architecture15. Recently, MR-derived growth pattern models for GBM have been developed as an attractive approach in neuro-oncology, as they have revealed a distinct pattern of lesion spread with growth tendency along the white fibre direction for the invasive component. As such, a significant correlation was found between MR-derived tumour location and prognosis, and/or the region-specific genetic profile of tumour cells18.In the brain, water molecule diffuse along the white matter fibres in a directionally dependent manner. Magnetic resonance imaging (MRI) can map this diffusion phenomenon 23. The exact mechanisms behind tumour migration and growth are not completely understood, but from animal models and histopathological studies, it is seen that growth often follows the basal lamina of brain blood vessels or white matter tracts24. Thus, the direction of growth may be dependent on tumour location. Prediction of tumour growth directions may help define boundaries of focal treatments such as surgery or radiosurgery, thus improving the definition of the clinical target volume margins that are most critical for preventing future growth. For example, radiation target volumes should possibly be location specific, not just adding 2-3 cm in all directions as commonly done today. In addition, understanding and predicting growth, invasiveness and regrowth of glioblastoma in various locations of the brain may enhance prognostication, both in terms of predicting the location of tumour recurrences, but perhaps also for predicting future loss of functions as eloquent brain regions are invaded. So-called multifocal glioblastomas are presumably not truly multifocal but instead connected. Better models may be important for both targeting the most important portions of the tumour  and for avoiding both over- and under-treatment. A growth pattern model may further help classify tumours according to their degree of aggressiveness. In addition, the estimation of tumour growth pattern may be used for educational and scientific purposes.Regardless of tumour grade, size or location, brain tumour patients frequently suffer from impairments in various cognitive domains, which are often difficult to explain based solely on the focal structural damage caused by a tumour. Some studies, therefore, highlight the importance of understanding glioma’s spreading patterns for pathophysiological impacts of brain tumours prior and after treatmentIn the current study, we aimed to investigate the prominent directions of GBM tumour growth inside the adult human brain. We also sought to validate the widely accepted theory postulating that brain tumour cells preferentially grow along WM fibre orientation by demonstrating that the disease targets intrinsic brain networks. We hypothesized that by using the MR images from 56 patients to compute the growth vector field derived from two pre-treatment time points, our understanding of generic solid tumour development and its interactions with local microenvironments may improve.p = 0.014).The image analysis pipeline described in the methods section Fig.  were appWe demonstrate the growth directions of 56 GBM tumours with vector fields and show that the dominant growth direction of GBM is along the orientation of white matter fibres. The degree of alignment between the growth direction and white matter fibres was calculated on a voxel-wise basis using a DTI WM atlas. The resulting angle maps demonstrated that 38.6% of the vectors were aligned with the WM fibres. Although tumour growth in glioblastoma is more often seen along or parallel to the white matter tracts than perpendicular to them, a range of growth directions is observed. The percentage of parallel-to-anti-parallel vector population is independent of the variation of determined arbitrary thresholds, as the other angle ranges are equally distributed around both parallel and anti-parallel ranges  components were mathematically modelled using diffusion tensor imaging data26. The findings yielded by these studies indicate that the direction and drift velocity of tumour cells depend on the structure of white matter fibre network and the movement of glioma cells along the gradient of ECM density that surrounds the WM fibres.Our result is in agreement with previous studies in the literature and provides additional support for the theory that cancer cells tend to grow along the path of least resistance. As a result of this growth pattern, GBMs interfere with global functional networks, rather than impacting the site of the lesion only. Therefore, several studies have been conducted in order to characterize network topology alterations in brain tumour patients before and after tumour resectionThis study has some limitations. We used a healthy DTI atlas for estimating the tumour orientations. This may decrease the accuracy of angle measures in some patients with larger tumour mass. A considerable tissue displacement may degrade the WM fibres morphology; therefore, the healthy DTI atlas may not precisely predict the WM orientations in the dataset. This source of error could be eliminated by using the patient-specific DTI. Since the data were acquired from different radiological centres, we unfortunately had no access to DTI images of these patients. However, this potential source of error was reduced by including relatively large patient cohorts in this study. In addition, using a publicly accessible DTI atlas makes the investigation pipeline reproducible for feature studies.In conclusion, the results of the current study suggest that the dominant growth direction of GBM tumour is along of the white matter fibres, as visualized with an angle map and a comparison between the population of parallel and perpendicular vectors. The prediction of tumour growth direction in various locations of the brain may greatly assist in the modeling of tumour growth prior to treatment. A similar study on lower-grade gliomas and a larger cohort can be performed to investigate the feasibility of an accurate modeling of tumour growth direction.27, and comparable image quality and spatial resolution acquired at both time points to ensure the accuracy of deformation field estimation. A total number of 56 patients met the inclusion criteria. The patients were examined at initial diagnosis, and prior to surgery for intraoperative neuronavigation. The study was approved by the Regional Ethics Committee of Central Norway and adhered to the Helsinki Declaration. All patients provided written informed consent to participate.Patients diagnosed with glioblastoma between January 2004 and May 2014 were retrospectively evaluated for inclusion, based on the following eligibility criteria: age ≥18 years, no prior history of brain tumour and having 2 contrast-enhanced T1-weighted (ce-T1) MRI examinations with the time interval >14 days (Mean ± standard deviation (SD) = 25 ± 9 days) to minimize the uncertainty in the growth rate estimates2, 19 slices, and 224 × 246 to 512 × 512 matrix dimension. Pre-operation ce-T1 images acquired with TR/TE = 10000/70 ms, slice thickness of either 1.0 mm (7 patients) or 1.5 mm (49 patients), in-plane resolution of 0.5 × 0.5 mm2, 23 slices, and 512 × 512 matrix size.MRI data were acquired either at a 1 T, 1.5 T or 3 T MR systems. The system’s standard 32-channel phased-array head coil was used for imaging. The diagnostic scans were performed in one of 12 different radiology clinics, located in the geographical region of our neurosurgical department. All preoperative scans were performed at our hospital. Gadolinium-based contrast agents were used for ce-T1 imaging. For the diagnostic scans, the following protocol parameters were used: repetition time (TR) = 4500ms, echo time (TE) = 14ms, slice thickness of 0.6–1.0 mm (54 patients) and 5 mm (12 patients), in-plane resolution from 0.45 × 0.45 to 1.0 × 1.0 mm27. Tumour volume was defined as the contrast-enhancing compartment of the tumour combined with the central non-enhancing compartment enclosed by the contrast .Contrast-enhanced T1-weighted images from both diagnostic and pre-operative examinations were used for tumour delineation and segmentation. The image analysis software BrainVoyager QX  was used for semi-automatic tumour segmentation. One of the observers, who was blinded to the subjects’ identity, performed delineations of Regions of interests (ROIs). Inside these ROIs, the tumors were segmented using a combination of intensity thresholding and manual delineation. The final segmentations were verified by a neuroradiologist (E. M. B.). Tumor volumes were calculated from the voxel volume and the number of voxels segmented. In some cases, the diagnostic images had gaps between slices, and the voxel volume was calculated as voxel-in-plane-resolution × (slice thickness + gap thickness) as previously described in detail28 and MATLAB  were used for registration of MR images. The data analysis pipeline  in both diagnostic and pre-operative MR images. The N4 bias field correction, available in the ANTs package, was applied to all MR data to minimize the field inhomogeneity effects and artificial low-frequency intensity variations across the images28. The N4 bias correction is an improved version of the non-parametric non-uniform intensity normalization (N3) algorithm with an enhanced B-spline least squares fitting routine (which includes multi-resolution capabilities) and a modified optimization formulation for improved bias field correction. MR images were skull-stripped using the Brain Extraction Tool (BET) algorithm29 provided in FSL package (https://fsl.fmrib.ox.ac.uk/fsl/). To estimate the deformation of tumour volumes, we performed both linear and non-linear registrations using ANTs tools.To obtain tumour deformation fields, we used widely adopted registration methods to estimate the tumour changes between the diagnosis and pre-operation time points. Advanced Normalization Tools (ANTs) script packagesine Fig.  commenceFirst, the affine registration from the ANTs package was used to align the diagnostic with the pre-operative image for each patient. The resulting affine transformation matrix was then applied to the segmented tumour from the diagnostic image. Both MR images (diagnostic and pre-operative) with corresponding tumour segmentations (binary masks) were thus aligned in the space of pre-operative MR image. Second, the tumour regions were masked out from the diagnostic and pre-operative images respectively. Using the non-linear registration tool provided by ANTs, which is based on a b-spline function, the tumour region from the diagnostic image was registered to the tumour region from the pre-operative image. This process yielded a localized deformation field, characterizing the changes in tumour shape and volume between the two time points.30. The registration to MNI space was performed using affine registration in ANTs, between the pre-operative image and the MNI template. To investigate the degree of alignment between the individual vector fields and the white matter fibres, a DTI white matter (WM) atlas31 was used. The atlas is constructed from the IXI brain database (http://www.ixi.org.uk) and consists of both tensor intensities and vectors information (https://www.nitrc.org/). An affine transformation was computed to align the DTI volume to MNI space as well. Next, the deformation field derived from all patients were down-sampled to the voxel size of the DTI atlas (1.75 × 1.75 × 2.25 mm3). This allowed us to calculate the angles between the corresponding vectors obtained from each voxel in the deformation field space and the DTI atlas per patient. The angle maps were generated in 3D space, representing voxel-wise θ values using the dot product (equation 1).The resulting deformation fields were projected into a common space using the MNI152 template. The MNI152 template is an average of structural MR images acquired from 152 healthy individuals and registered to a common spaceWe also calculated the spatial frequency of tumour occurrence in our cohort, by adding the tumour volumes extracted from the pre-operative images and co-registered into MNI space. The generated tumour frequency was color-coded to demonstrate the degree of tumor spatial overlap among the patients.p ≤ 0.05.Statistical analyses were performed using GraphPad Prism . Voxel populations in the angle map were compared across the determined thresholds; parallel (│θ│ < 20°) versus perpendicular (│θ│ > 70°), using the non-parametric Wilcoxon signed rank test with the threshold for statistical significance defined as