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

Unknown Quantum States: The Quantum de Finetti Representation

We present an elementary proof of the quantum de Finetti representation theorem, a quantum analogue of de Finetti's classical theorem on exchangeable probability assignments. This contrasts with the original proof of Hudson and Moody [Z. Wahrschein. verw. Geb. 33, 343 (1976)], which relies on advanced mathematics and does not share the same potential for generalization. The classical de Finetti theorem provides an operational definition of the concept of an unknown probability in Bayesian probability theory, where probabilities are taken to be degrees of belief instead of objective states of nature. The quantum de Finetti theorem, in a closely analogous fashion, deals with exchangeable density-operator assignments and provides an operational definition of the concept of an ``unknown quantum state'' in quantum-state tomography. This result is especially important for information-based interpretations of quantum mechanics, where quantum states, like probabilities, are taken to be states of knowledge rather than states of nature. We further demonstrate that the theorem fails for real Hilbert spaces and discuss the significance of this point.Comment: 30 pages, 2 figure

Nomenclature for renal replacement therapy and blood purification techniques in critically ill patients: practical applications

This article reports the conclusions of the second part of a consensus expert conference on the nomenclature of renal replacement therapy (RRT) techniques currently utilized to manage acute kidney injury and other organ dysfunction syndromes in critically ill patients. A multidisciplinary approach was taken to achieve harmonization of definitions, components, techniques, and operations of the extracorporeal therapies. The article describes the RRT techniques in detail with the relevant technology, procedures, and phases of treatment and key aspects of volume management/fluid balance in critically ill patients. In addition, the article describes recent developments in other extracorporeal therapies, including therapeutic plasma exchange, multiple organ support therapy, liver support, lung support, and blood purification in sepsis. This is a consensus report on nomenclature harmonization in extracorporeal blood purification therapies, such as hemofiltration, plasma exchange, multiple organ support therapies, and blood purification in sepsis

The p53 Codon 72 Pro/Pro Genotype Identifies Poor-Prognosis Neuroblastoma Patients: Correlation with Reduced Apoptosis and Enhanced Senescence by the p53-72P Isoform.

The p53 gene is rarely mutated in neuroblastoma, but codon 72 polymorphism that modulates its proapoptotic activity might influence cancer risk and clinical outcome. We investigated whether this polymorphism affects neuroblastoma risk and disease outcome and assessed the biologic effects of the p53-72R and p53-72P isoforms in p53-null cells. Comparison of 288 healthy subjects and 286 neuroblastoma patients revealed that the p53-72 polymorphism had no significant impact on the risk of developing neuroblastoma; however, patients with the Pro/Pro genotype had a shorter survival than those with the Arg/Arg or the Arg/Pro genotypes even in the stage 3 and 4 subgroup without MYCN amplification. By Cox regression analysis, the p53 Pro/Pro genotype seems to be an independent marker of poor prognosis (hazard ratio = 2.74; 95% confidence interval = 1.14\u20136.55, P = .014) together with clinical stage, MYCN status, and age at diagnosis. In vitro, p53-72P was less effective than p53-72R in inducing apoptosis and inhibiting survival of p53-null LAN-1 cells treated with etoposide, topotecan, or ionizing radiation but not taxol. By contrast, p53-72P was more effective in promoting p21-dependent accelerated senescence, alone or in the presence of etoposide. Thus, the p53-72 Pro/Pro genotype might be a marker of poor outcome independent of MYCN amplification, possibly improving risk stratification. Moreover, the lower apoptosis and the enhanced accelerated senescence by the p53-72P isoform in response to DNA damage suggest that patients with neuroblastoma with the p53-72 Pro/Pro genotype may benefit from therapeutic protocols that do not rely only on cytotoxic drugs that function, in part, through p53 activation

Bayesian Probability and Statistics in Management Research: A New Horizon

This special issue is focused on how a Bayesian approach to estimation, inference, and reasoning in organizational research might supplement—and in some cases supplant—traditional frequentist approaches. Bayesian methods are well suited to address the increasingly complex phenomena and problems faced by 21st-century researchers and organizations, where very complex data abound and the validity of knowledge and methods are often seen as contextually driven and constructed. Traditional modeling techniques and a frequentist view of probability and method are challenged by this new reality

Non-invasive mechanical ventilation in patients with diffuse interstitial lung diseases

Background: To evaluate noninvasive ventilation (NIV) in diffuse interstitial lung diseases (DILD) patients with acute respiratory failure (ARF) according to baseline radiological patterns and the etiology of ARF. Methods: In a multicenter, observational, retrospective study, consecutive DILD patients undergoing NIV because of an episode of ARF were evaluated in six Italian high dependency units. Three groups of patients were identified based on the etiology of ARF: those with pneumonia (Group A), those with acute exacerbation of fibrosis, (Group B) and those with other triggers (Group C). Clinical failure was defined as any among in-hospital mortality, endotracheal intubation and extra-corporeal membrane oxygenation use. Results: Among the 60 patients enrolled (63% males; median age: 71 years), pneumonia (42%) and acute exacerbation of fibrosis (39%) were the two most frequent causes of ARF. A significant increase of PaO2/FiO(2) ratio during NIV treatment was detected in Group A (p = 0.010), but not in Group B. No significant difference in PaO2/FiO(2) ratio, PaCO2 and pH values during NIV treatment was detected in patients with a radiological pattern of usual interstitial pneumonia (UIP) and non-specific interstitial pneumonia (NSIP). 22 patients (37%) suffered for a clinical failure. No significant differences in the study outcome were detected in Group A vs. Group B, as well as among patients with a radiological pattern of UIP vs. NSIP. Conclusions: NIV treatment should be individualized in DILD patients with ARF according to the etiology, but not the baseline radiological pattern, in order to improve oxygenation

Deficiency in the autophagy modulator Dram1 exacerbates pyroptotic cell death of Mycobacteria-infected macrophages

DNA damage regulated autophagy modulator 1 (DRAM1) is a stress-inducible regulator of autophagy and cell death. DRAM1 has been implicated in cancer, myocardial infarction, and infectious diseases, but the molecular and cellular functions of this transmembrane protein remain poorly understood. Previously, we have proposed DRAM1 as a host resistance factor for tuberculosis (TB) and a potential target for host-directed anti-infective therapies. In this study, we generated a zebrafish dram1 mutant and investigated its loss-of-function effects during Mycobacterium marinum (Mm) infection, a widely used model in TB research. In agreement with previous knockdown analysis, dram1 mutation increased the susceptibility of zebrafish larvae to Mm infection. RNA sequencing revealed major effects of Dram1 deficiency on metabolic, immune response, and cell death pathways during Mm infection, and only minor effects on proteinase and metabolic pathways were found under uninfected conditions. Furthermore, unchallenged dram1 mutants did not display overt autophagic defects, but autophagic targeting of Mm was reduced in the absence of Dram1. The phagocytic ability of macrophages in dram1 mutants was unaffected, but acidification of Mm-containing vesicles was strongly reduced, indicating that Dram1 is required for phagosome maturation. By in vivo imaging, we observed that Dram1-deficient macrophages fail to restrict Mm during early stages of infection. The resulting increase in bacterial burden could be reverted by knockdown of inflammatory caspase a (caspa) and gasdermin Eb (gsdmeb), demonstrating pyroptosis as the mechanism underlying premature cell death of Mm-infected macrophages in dram1 mutants. Collectively, these data demonstrate that dissemination of mycobacterial infection in zebrafish larvae is promoted in the absence of Dram1 due to reduced maturation of mycobacteria-containing vesicles, failed intracellular containment, and consequent pyroptotic death of infected macrophages. These results provide new evidence that Dram1 plays a central role in host resistance to intracellular infection, acting at the crossroad of autophagy and cell death