Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Propagação de abacateiro via estacas estioladas

Este trabalho objetivou testar um método de propagação de abacateiro a partir de estacas estioladas cultivadas em recipientes de diferentes alturas. O experimento foi desenvolvido em câmara de nebulização intermitente. O delineamento experimental foi em blocos ao acaso, em esquema fatorial 3x4, com três repetições, testando-se dois métodos de obstrução de seiva (anelamento da casca, estrangulamento da casca) e quatro alturas de recipientes para o substrato (10, 15, 20 e 25 cm). Decorridos 250 dias, não se observou efeito da altura do recipiente na sobrevivência, no número de folhas desenvolvidas, na altura e no diâmetro das mudas formadas a partir de estacas estioladas. Houve maior sobrevivência e emissão de folhas nas estacas de casca anelada em relação à testemunha; as estacas de casca estrangulada situaram-se em posição intermediária. O prévio anelamento ou estrangulamento da casca de ramos estiolados aumentaram a sobrevivência e o crescimento de mudas na propagação de abacateiro da seleção Viamão, por estaquia

Somatostatin receptor 2 expression in nasopharyngeal cancer is induced by Epstein Barr virus infection: impact on prognosis, imaging and therapy

Nasopharyngeal cancer (NPC), endemic in Southeast Asia, lacks effective diagnostic and therapeutic strategies. Even in high-income countries the 5-year survival rate for stage IV NPC is less than 40%. Here we report high somatostatin receptor 2 (SSTR2) expression in multiple clinical cohorts comprising 402 primary, locally recurrent and metastatic NPCs. We show that SSTR2 expression is induced by the Epstein-Barr virus (EBV) latent membrane protein 1 (LMP1) via the NF-kappa B pathway. Using cell-based and preclinical rodent models, we demonstrate the therapeutic potential of SSTR2 targeting using a cytotoxic drug conjugate, PEN-221, which is found to be superior to FDA-approved SSTR2-binding cytostatic agents. Furthermore, we reveal significant correlation of SSTR expression with increased rates of survival and report in vivo uptake of the SSTR2-binding Ga-68-DOTA-peptide radioconjugate in PET-CT scanning in a clinical trial of NPC patients (NCT03670342). These findings reveal a key role in EBV-associated NPC for SSTR2 in infection, imaging, targeted therapy and survival. Nasopharyngeal carcinoma (NPC) lacks effective diagnostic and therapeutic strategies, in particular at advanced stages. Here, the authors show that expression of the somatostatin receptor 2 is induced by Epstein-Barr virus in NPC and has a key role in the diagnosis, imaging, targeted therapies and prognosis of NPC