170,440 research outputs found
The importance of extranodal extension in metastatic head and neck squamous cell carcinoma, in the light of the new AJCC cancer staging system
No full textLette
Replication material - Does the oath enhance truth-telling in eyewitness testimony? Experimental Evidence
No full textJacquemet N., Launay C., Luchini S., Mijovic-Prelec D., Prelec D., Py J., Rosaz J., Shogren. J.F. (2026). Does the oath enhance truth-telling in eyewitness testimony? Experimental Evidence, Journal of Experimental Criminology
Is a plane liquid curtain algebraically absolutely unstable?
No full textThe dispersion relation of capillary waves in a plane moving liquid curtain is critically re-analyzed with an eye to its behavior near the origin of wavenumber space and the large-time asymptotics of the corresponding Green's function. Evidence is found that recent and less recent theories supporting the existence of a zero-wavenumber algebraic absolute instability contain serious inconsistencies. (C) 2004 American Institute of Physics
Extranodal extension of nodal metastasis is the main prognostic moderator in squamous cell carcinoma of the esophagus after neoadjuvant chemoradiotherapy
No full textNo abstract available; Editoria
The importance of tumor microenvironment modulations in the progression of pancreatic intraductal papillary mucinous neoplasms†
No full textIntraductal papillary mucinous neoplasms (IPMNs) of the pancreas have attracted substantial attention since they represent the most prevalent macroscopic precursor of pancreatic cancer. Most lesions show an epithelium with low-grade dysplasia and will remain indolent and unknown to the patient. Notably, a subgroup of IPMNs will progress to invasive cancer through a stepwise process characterized by the accumulation of specific genomic alterations and concomitant modifications of the tumor microenvironment (TME). The manuscript of Jamouss et al, recently published in The Journal of Pathology, expands the current knowledge on TME dynamics in IPMNs. The neoplastic progression of IPMNs is paralleled by a shift toward an immunosuppressive TME, with depletion of cytotoxic T cells, elevated expression of immune checkpoint molecules, including PD-L1 and VISTA, and increased density of macrophages. Overall, TME modifications are crucial in the progression of pancreatic IPMNs, calling for potential therapeutic strategies focused on TME modulations for cancer interception. (c) 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland
Streamwise oscillation of spanwise velocity at the wall of a channel for turbulent drag reduction
No full textSteady forcing at the wall of a channel flow is studied via direct numerical simulation to assess its ability of yielding reductions in turbulent friction drag. The wall forcing consists of a stationary distribution of spanwise velocity that alternates in the streamwise direction. The idea behind the forcing builds on the existing technique of the spanwise wall oscillation and exploits the convective nature of the flow to achieve an unsteady interaction with turbulence. The analysis takes advantage of the equivalent laminar flow, which is solved analytically to show that the energetic cost of the forcing is unaffected by turbulence. In a turbulent flow, the alternate forcing is found to behave similarly to the oscillating wall; in particular an optimal wavelength is found which yields a maximal reduction in turbulent drag. The energetic performance is significantly improved, with more than 50% of maximum friction saving at large intensities of the forcing, and a net energetic saving of 23% for smaller intensities. Such a steady, wall-based forcing may pave the way to passively interacting with the turbulent flow to achieve drag reduction through a suitable distribution of roughness, designed to excite a selected streamwise wavelength. © 2009 American Institute of Physics. ͓doi:10.1063/1.3266945
Marker identification and classification of cancer types using gene expression data and SIMCA
No full textObjectives. High-throughput technologies are radically boosting the understanding of living systems, thus creating enormous opportunities to elucidate the biological processes of cells in different physiological states. In particular, the application of DNA microarrays to monitor expression profiles from tumor cells is improving cancer analysis to levels that classical methods have been unable to reach. However, molecular diagnostics based on expression profiling requires addressing computational issues as the overwhelming number of variables and the complex, multi-class nature of tumor samples. Thus, the objective of the present research has been the development of a computational procedure for feature extraction and classification of gene expression data.Methods. The Soft Independent Modeling of Class Analogy (SIMCA) approach has been implemented in a data mining scheme, which allows the identification of those genes that are most likely to confer robust and accurate classification of samples from multiple tumor types.Results: The proposed method has been tested on two different microarray data sets, namely Golub's analysis of acute human leukemia [1] and the small round blue cell tumors study presented by Khan et al. [2]. The identified features represent a rational and dimensionally reduced base for understanding the biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for classification of pathological states.Conclusions: The analysis of the SIMCA model residuals allows the identification of specific phenotype markers. At the some time, the class analogy approach provides the assignment to multiple classes, such as different pathological conditions or tissue samples, for previously unseen instances
PCA disjoint models for multiclass cancer analysis using gene expression data
No full textMotivation: Microarray expression profiling appears particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostics based on microarray data presents major challenges due to the overwhelming number of variables and the complex, multiclass nature of tumor samples. Thus, the development of marker selection methods, that allow the identification of those genes that are most likely to confer high classification accuracy of multiple tumor types, and of multiclass classification schemes is of paramount importance.Results: A computational procedure for marker identification and for classification of multiclass gene expression data through the application of disjoint principal component models is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets for therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states. The method has been tested on different microarray data sets obtained from various human tumor samples. The results demonstrate that this procedure allows the identification of specific phenotype markers and can classify previously unseen instances in the presence of multiple classes
Disjoint PCA models for marker identification and classification of cancer types using gene expression data
No full textBackground The parallel monitoring of the expression profiles of thousands of genes seems particularly promising for a deeper understanding of cancer biology and to identify molecular signatures supporting the histological classification schemes of neoplastic specimens. However, molecular diagnostic based on microarray data presents major challenges due to the complex, multiclass nature and to the overwhelming number of variables characterizing gene expression databases of multiple tumor samples. Thus, the development of multiclass; classification schemes and of marker selection methods, that allow the simultaneous classification of multiple tumor types and the identification of those genes that are most likely to confer high classification accuracy, is of paramount importance.Methods. A computational procedure for marker identification and classification of multiclass gene expression data through the application of disjoint principal component models, based on the Soft independent Modeling of Class Analogy approach (SIMCA), is described. The identified features represent a rational and dimensionally reduced base for understanding the basic biology of diseases, defining targets of therapeutic intervention, and developing diagnostic tools for the identification and classification of multiple pathological states.Results. The method has been tested on 2 different microarray data sets obtained from various human tumor samples: i) acute leukemias, and ii) small round blue-cell tumors.Conclusions. The results demonstrate that the disjoint PCA modeling procedure allows the identification of specific phenotype markers and provides the assignment to multiple classes for previously unseen instances
Integral space-time scales in turbulent wall flows
No full textA direct numerical simulation of the Navier-Stokes equations is used to compute the space-time correlations of velocity fluctuations in a turbulent channel flow. By examining the autocorrelation R(xi,tau) of the longitudinal wall shear-stress as a function of the streamwise and temporal separations, the effects of the limited extent of the computational domain when (artificial) periodic boundary conditions are used can be described and quantified. A time scale similar to the conventional integral scale but statistically related to the life time of the turbulent structures is computed from spatio-temporal data. The convection velocity, defined as the direction in the xi,tau plane where the autocorrelations have their maximum at vanishingly small time delay, is computed as a function of the distance from the wall, and compared with the data available in the literature. Based on autocorrelations, the accuracy within which Taylor's hypothesis is verified is quantitatively assessed. Last, the effect of the spatial discretization on the statistical characterization of wall turbulence is discussed. (C) 2003 American Institute of Physics
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