Clinical significance of epithelial-to-mesenchymal transition in laryngeal carcinoma: Its role in the different subsites

Background: During epithelial-to-mesenchymal transition, cancer cells lose adhesion capacity gaining migratory properties. The role of the process on prognosis has been evaluated in 50 cases of laryngeal carcinoma. Methods: E-cadherin, N-cadherin, β-catenin, α-catenin, γ-catenin, caveolin-1, and vimentin immunohistochemical expression were evaluated using a double score based on staining intensity and cellular localization. Results: Cytoplasmic E-cadherin and α/γ catenin staining were associated with a decrease in survival, cytoplasmic β-catenin was associated with advanced stage, and N-cadherin and vimentin expression were associated with poor differentiation and tumor relapse. On the basis of cancer cells, epithelial or mesenchymal morphological and immunophenotypic similarity we identified 4 main subgroups correlated with a transition to a more undifferentiated phenotype, which have a different pattern of relapse and survival. Conclusion: The negative prognostic role of epithelial-to-mesenchymal transition has been confirmed and a predictive role in glottic tumors has been suggested, leading us to propose epithelial-to-mesenchymal transition as an additional adverse feature in laryngeal carcinoma

Suzaku observations of ‘bare’ active galactic nuclei

We present an X-ray spectral analysis of a large sample of 25 ‘bare’ active galactic nuclei (AGN), sources with little or no complicating intrinsic absorption, observed with Suzaku. Our work focuses on studying the potential contribution from relativistic disc reflection and examining the implications of this interpretation for the intrinsic spectral complexities frequently displayed by AGN in the X-ray bandpass. During the analysis, we take the unique approach of attempting to simultaneously undertake a systematic analysis of the whole sample, as well as a detailed treatment of each individual source, and find that disc reflection has the required flexibility to successfully reproduce the broad-band spectrum observed for all of the sources considered. Where possible, we use the reflected emission to place constraints on the black hole spin for this sample of sources. Our analysis suggests a general preference for rapidly rotating black holes, which if taken at face value is most consistent with the scenario in which supermassive black hole growth is dominated by prolonged, ordered accretion. However, there may be observational biases towards AGN with high spin in the compiled sample, limiting our ability to draw strong conclusions for the general population at this stage. Finally, contrary to popular belief, our analysis also implies that the dichotomy between radio-loud/radio-quiet AGN is not solely related to black hole spin

Kinetic theory for non-equilibrium stationary states in long-range interacting systems

We study long-range interacting systems perturbed by external stochastic forces. Unlike the case of short-range systems, where stochastic forces usually act locally on each particle, here we consider perturbations by external stochastic fields. The system reaches stationary states where external forces balance dissipation on average. These states do not respect detailed balance and support non-vanishing fluxes of conserved quantities. We generalize the kinetic theory of isolated long-range systems to describe the dynamics of this non-equilibrium problem. The kinetic equation that we obtain applies to plasmas, self-gravitating systems, and to a broad class of other systems. Our theoretical results hold for homogeneous states, but may also be generalized to apply to inhomogeneous states. We obtain an excellent agreement between our theoretical predictions and numerical simulations. We discuss possible applications to describe non-equilibrium phase transitions.Comment: 11 pages, 2 figures; v2: small changes, close to the published versio

Interpretable Ranking Using LambdaMART (Abstract)

In this talk we present the main results of a short paper appearing at SIGIR 2022 [1]. Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this talk we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models, as Neural RankGAM [2], address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, we propose Interpretable LambdaMART, an interpretable LtR solution based on LambdaMART that is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that our approach outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%

Electrospun silk fibroin fibers for storage and controlled release of human platelet lysate

Human platelet lysate (hPL) is a pool of growth factors and cytokines able to induce regeneration of different tissues. Despite its good potentiality as therapeutic tool for regenerative medicine applications, hPL has been only moderately exploited in this field. A more widespread adoption has been limited because of its rapid degradation at room temperature that decreases its functionality. Another limiting factor for its extensive use is the difficulty of handling the hPL gels. In this work, silk fibroin-based patches were developed to address several points: improving the handling of hPL, enabling their delivery in a controlled manner and facilitating their storage by creating a device ready to use with expanded shelf life. Patches of fibroin loaded with hPL were synthesized by electrospinning to take advantage of the fibrous morphology. The release kinetics of the material was characterized and tuned through the control of fibroin crystallinity. Cell viability assays, performed with primary human dermal fibroblasts, demonstrated that fibroin is able to preserve the hPL biological activity and prolong its shelf-life. The strategy of storing and preserving small active molecules within a naturally-derived, protein-based fibrous scaffold was successfully implemented, leading to the design of a biocompatible device, which can potentially simplify the storage and the application of the hPL on a human patient, undergoing medical procedures such as surgery and wound care. Statement of Significance: Human platelets lysate (hPL) is a mixture of growth factors and cytokines able to induce the regeneration of damaged tissues. This study aims at enclosing hPL in a silk fibroin electrospun matrix to expand its utilization. Silk fibroin showed the ability to preserve the hPL activity at temperature up to 60 \ub0C and the manipulation of fibroin's crystallinity provided a tool to modulate the hPL release kinetic. This entails the possibility to fabricate the hPL silk fibroin patches in advance and store them, resulting in an easy and fast accessibility and an expanded use of hPL for wound healing

Quality versus efficiency in document scoring with learning-to-rank models

Learning-to-Rank (LtR) techniques leverage machine learning algorithms and large amounts of training data to induce high-quality ranking functions. Given a set of docu- ments and a user query, these functions are able to precisely predict a score for each of the documents, in turn exploited to effectively rank them. Although the scoring efficiency of LtR models is critical in several applications – e.g., it directly impacts on response time and throughput of Web query processing – it has received relatively little attention so far. The goal of this work is to experimentally investigate the scoring efficiency of LtR models along with their ranking quality. Specifically, we show that machine-learned ranking mod- els exhibit a quality versus efficiency trade-off. For example, each family of LtR algorithms has tuning parameters that can influence both effectiveness and efficiency, where higher ranking quality is generally obtained with more complex and expensive models. Moreover, LtR algorithms that learn complex models, such as those based on forests of regression trees, are generally more expensive and more effective than other algorithms that induce simpler models like linear combination of features. We extensively analyze the quality versus efficiency trade-off of a wide spectrum of state- of-the-art LtR, and we propose a sound methodology to devise the most effective ranker given a time budget. To guarantee reproducibility, we used publicly available datasets and we contribute an open source C++ framework providing optimized, multi-threaded imple- mentations of the most effective tree-based learners: Gradient Boosted Regression Trees (GBRT), Lambda-Mart (λ-MART), and the first public-domain implementation of Oblivious Lambda-Mart (λ-MART), an algorithm that induces forests of oblivious regression trees. We investigate how the different training parameters impact on the quality versus effi- ciency trade-off, and provide a thorough comparison of several algorithms in the quality- cost space. The experiments conducted show that there is not an overall best algorithm, but the optimal choice depends on the time budget

Learning Early Exit Strategies for Additive Ranking Ensembles

Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the average number of trees traversed by documents to accumulate the scores, thus reducing the overall query response time. LEAR exploits a classifier that predicts whether a document can early exit the ensemble because it is unlikely to be ranked among the final top-k results. The early exit decision occurs at a sentinel point, i.e., after having evaluated a limited number of trees, and the partial scores are exploited to filter out non-promising documents. We evaluate LEAR by deploying it in a production-like setting, adopting a state-of-the-art algorithm for ensembles traversal. We provide a comprehensive experimental evaluation on two public datasets. The experiments show that LEAR has a significant impact on the efficiency of the query processing without hindering its ranking quality. In detail, on a first dataset, LEAR is able to achieve a speedup of 3x without any loss in NDCG@10, while on a second dataset the speedup is larger than 5x with a negligible NDCG@10 loss (< 0.05%)

Numerical solution of the two-dimensional Helmholtz equation with variable coefficients by the radial integration boundary integral and integro-differential equation methods

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Taylor & Francis.This paper presents new formulations of the boundary–domain integral equation (BDIE) and the boundary–domain integro-differential equation (BDIDE) methods for the numerical solution of the two-dimensional Helmholtz equation with variable coefficients. When the material parameters are variable (with constant or variable wave number), a parametrix is adopted to reduce the Helmholtz equation to a BDIE or BDIDE. However, when material parameters are constant (with variable wave number), the standard fundamental solution for the Laplace equation is used in the formulation. The radial integration method is then employed to convert the domain integrals arising in both BDIE and BDIDE methods into equivalent boundary integrals. The resulting formulations lead to pure boundary integral and integro-differential equations with no domain integrals. Numerical examples are presented for several simple problems, for which exact solutions are available, to demonstrate the efficiency of the proposed methods

Can Embeddings Analysis Explain Large Language Model Ranking?

Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP)

Numerical solution of the two-dimensional Helmholtz equation with variable coefficients by the radial integration boundary integral and integro-differential equation methods

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 Taylor & Francis.This paper presents new formulations of the boundary–domain integral equation (BDIE) and the boundary–domain integro-differential equation (BDIDE) methods for the numerical solution of the two-dimensional Helmholtz equation with variable coefficients. When the material parameters are variable (with constant or variable wave number), a parametrix is adopted to reduce the Helmholtz equation to a BDIE or BDIDE. However, when material parameters are constant (with variable wave number), the standard fundamental solution for the Laplace equation is used in the formulation. The radial integration method is then employed to convert the domain integrals arising in both BDIE and BDIDE methods into equivalent boundary integrals. The resulting formulations lead to pure boundary integral and integro-differential equations with no domain integrals. Numerical examples are presented for several simple problems, for which exact solutions are available, to demonstrate the efficiency of the proposed methods