Validation of scientific topic models using graph analysis and corpus metadata

Probabilistic topic modeling algorithms like Latent Dirichlet Allocation (LDA) have become powerful tools for the analysis of large collections of documents (such as papers, projects, or funding applications) in science, technology an innovation (STI) policy design and monitoring. However, selecting an appropriate and stable topic model for a specific application (by adjusting the hyperparameters of the algorithm) is not a trivial problem. Common validation metrics like coherence or perplexity, which are focused on the quality of topics, are not a good fit in applications where the quality of the document similarity relations inferred from the topic model is especially relevant. Relying on graph analysis techniques, the aim of our work is to state a new methodology for the selection of hyperparameters which is specifically oriented to optimize the similarity metrics emanating from the topic model. In order to do this, we propose two graph metrics: the first measures the variability of the similarity graphs that result from different runs of the algorithm for a fixed value of the hyperparameters, while the second metric measures the alignment between the graph derived from the LDA model and another obtained using metadata available for the corresponding corpus. Through experiments on various corpora related to STI, it is shown that the proposed metrics provide relevant indicators to select the number of topics and build persistent topic models that are consistent with the metadata. Their use, which can be extended to other topic models beyond LDA, could facilitate the systematic adoption of this kind of techniques in STI policy analysis and design.

     

An automated treatment planning strategy for highly noncoplanar radiotherapy arc trajectories

Radiation therapy is a technology-driven cancer treatment modality that has experienced significant advances over the last decades, due to multidisciplinary contributions that include engineering and computing. Recent technological developments allow the use of noncoplanar volumetric modulated arc therapy (VMAT), one of the most recent photon treatment techniques, in clinical practice. In this work, an automated noncoplanar arc trajectory optimization framework designed in two modular phases is presented. First, a noncoplanar beam angle optimization algorithm is used to obtain a set of noncoplanar irradiation directions. Then, anchored in these directions, an optimization strategy is proposed to compute an optimal arc trajectory. The computational experiments considered a pool of twelve difficult head-and-neck tumor cases. It was possible to observe that, for some of these cases, the optimized noncoplanar arc trajectories led to significant treatment planning quality improvements, when compared with coplanar VMAT treatment plans. Although these experiments were done in a research environment treatment planning software (matRad), the conclusions can be of interest for a clinical setting: automated procedures can simplify the current treatment workflow, produce high-quality treatment plans, making better use of human resources and allowing for unbiased comparisons between different treatment techniques.     

Microtubule Destabilizing Sulfonamides as an Alternative to Taxane-Based Chemotherapy

Pan-Gyn cancers entail 1 in 5 cancer cases worldwide, breast cancer being the most commonly diagnosed and responsible for most cancer deaths in women. The high incidence and mortality of these malignancies, together with the handicaps of taxanes—first-line treatments—turn the development of alternative therapeutics into an urgency. Taxanes exhibit low water solubility that require formulations that involve side effects. These drugs are often associated with dose-limiting toxicities and with the appearance of multi-drug resistance (MDR). Here, we propose targeting tubulin with compounds directed to the colchicine site, as their smaller size offer pharmacokinetic advantages and make them less prone to MDR efflux. We have prepared 52 new Microtubule Destabilizing Sulfonamides (MDS) that mostly avoid MDR-mediated resistance and with improved aqueous solubility. The most potent compounds, N-methyl-N-(3,4,5-trimethoxyphenyl-4-methylaminobenzenesulfonamide 38, N-methyl-N-(3,4,5-trimethoxyphenyl-4-methoxy-3-aminobenzenesulfonamide 42, and N-benzyl-N-(3,4,5-trimethoxyphenyl-4-methoxy-3-aminobenzenesulfonamide 45 show nanomolar antiproliferative potencies against ovarian, breast, and cervix carcinoma cells, similar or even better than paclitaxel. Compounds behave as tubulin-binding agents, causing an evident disruption of the microtubule network, in vitro Tubulin Polymerization Inhibition (TPI), and mitotic catastrophe followed by apoptosis. Our results suggest that these novel MDS may be promising alternatives to taxane-based chemotherapy in chemoresistant Pan-Gyn cancers.     

ProVoc: An app to train vocabulary depth in order to foster children's reading comprehension

Many studies have demonstrated the crucial role of vocabulary in predicting reading performance in general. More recent work has indicated that one particular facet of vocabulary (its depth) is more closely related to language comprehension, especially inferential comprehension. On this basis, we developed a training application to specifically improve vocabulary depth. The objective of this study was to test the effectiveness of a mobile application designed to improve vocabulary depth. The effectiveness of this training was examined on 3rd and 4th grade children's vocabulary (breadth and depth), decoding and comprehension performances. A randomized waiting-list control paradigm was used in which an experimental group first received the intervention during the first 4 weeks (between pretest and post-test1), thereafter, a waiting control group received the training for the next 4 weeks (between postest1 and posttest2). Results showed that the developed application led to significant improvements in terms of vocabulary depth performance, as well as a significant transfer effect to reading comprehension. However, we did not observe such a beneficial effect on either vocabulary breadth or written word identification. These results are discussed in terms of the links between vocabulary depth and comprehension, and the opportunities the app presents for remedying language comprehension deficits in children.