Distributed Smoothed Tree Kernel

In this paper we explore the possibility to merge the world of Compositional Distributional Semantic Models (CDSM) with Tree Kernels (TK). In particular, we will introduce a specific tree kernel (smoothed tree kernel, or STK) and then show that is possibile to approximate such kernel with the dot product of two vectors obtained compositionally from the sentences, creating in such a way a new CDSM

Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients

Abdominal aortic aneurysm in patients affected by intermittent claudication: prevalence and clinical predictors

BACKGROUND: Abdominal aortic aneurysm (AAA) is a frequent cause of death among elderly. Patients affected by lower extremity peripheral arterial disease (LE-PAD) seem to be particularly at high risk for AAA. We aimed this study at assessing the prevalence and the clinical predictors of the presence of AAA in a homogeneous cohort of LE-PAD patients affected by intermittent claudication. METHODS: We performed an abdominal ultrasound in 213 consecutive patients with documented LE-PAD (ankle/brachial index ≤ 0.90) attending our outpatient clinic for intermittent claudication. For each patient we registered cardiovascular risk factors and comorbidities, and measured neutrophil count. RESULTS: The ultrasound was inconclusive in 3 patients (1.4%), thus 210 patients (169 males, 41 females, mean age 65.9 ± 9.8 yr) entered the study. Overall, AAA was present in 19 patients (9.0%), with a not significant higher prevalence in men than in women (10.1% vs 4.9%, p = 0.300). Patients with AAA were older (71.2 ± 7.0 vs 65.4 ± 9.9 years, p = 0.015), were more likely to have hypertension (94.7% vs 71.2%, p = 0.027), and greater neutrophil count (5.5 [4.5 - 6.2] vs 4.1 [3.2 - 5.5] x 10(3)/μL, p = 0.010). Importantly, the c-statistic for neutrophil count (0.73, 95% CI 0.60 - 0.86, p = 0.010) was higher than that for age (0.67, CI 0.56-0.78, p = 0.017). The prevalence of AAA in claudicant patients with a neutrophil count ≥ 5.1 x 10(3)/μL (cut-off identified at ROC analysis) was as high as 29.0%. CONCLUSIONS: Prevalence of AAA in claudicant patients is much higher than that reported in the general population. Ultrasound screening should be considered in these patients, especially in those with an elevated neutrophil count

Integrated Antitumor Activities of Cellular Immunotherapy with CIK Lymphocytes and Interferons against KIT/PDGFRA Wild Type GIST

: Gastrointestinal stromal tumors (GISTs) are rare, mesenchymal tumors of the gastrointestinal tract, characterized by either KIT or PDGFRA mutation in about 85% of cases. KIT/PDGFRA wild type gastrointestinal stromal tumors (wtGIST) account for the remaining 15% of GIST and represent an unmet medical need: their prevalence and potential medical vulnerabilities are not completely defined, and effective therapeutic strategies are still lacking. In this study we set a patient-derived preclinical model of wtGIST to investigate their phenotypic features, along with their susceptibility to cellular immunotherapy with cytokine-induced killer lymphocytes (CIK) and interferons (IFN). We generated 11 wtGIST primary cell lines (wtGISTc). The main CIK ligands (MIC A/B; ULBPs), along with PD-L1/2, were expressed by wtGISTc and the expression of HLA-I molecules was preserved. Patient-derived CIK were capable of intense killing in vitro against wtGISTc resistant to both imatinib and sunitinib. We found that CIK produce a high level of granzyme B, IFNα and IFNγ. CIK-conditioned supernatant was responsible for part of the observed tumoricidal effect, along with positive bystander modulatory activities enhancing the expression of PD-L1/2 and HLA-I molecules. IFNα, but not In, had direct antitumor effects on 50% (4/8) of TKI-resistant wtGISTc, positively correlated with the tumor expression of IFN receptors. wtGIST cells that survived IFNα were still sensitive to CIK immunotherapy. Our data support the exploration of CIK immunotherapy in clinical studies for TKI-resistant wtGIST, proposing reevaluation for IFNα within this challenging setting

Future perspectives in melanoma research. Meeting report from the “Melanoma Bridge. Napoli, December 2nd-4th 2012”

Recent insights into the genetic and somatic aberrations have initiated a new era of rapidly evolving targeted and immune-based treatments for melanoma. After decades of unsuccessful attempts to finding a more effective cure in the treatment of melanoma now we have several drugs active in melanoma. The possibility to use these drugs in combination to improve responses to overcome the resistance, to potentiate the action of immune system with the new immunomodulating antibodies, and identification of biomarkers that can predict the response to a particular therapy represent new concepts and approaches in the clinical management of melanoma. The third “Melanoma Research: “A bridge from Naples to the World” meeting, shortened as “Bridge Melanoma Meeting” took place in Naples, December 2 to 4th, 2012. The four topics of discussion at this meeting were: advances in molecular profiling and novel biomarkers, combination therapies, novel concepts toward integrating biomarkers and therapies into contemporary clinical management of patients with melanoma across the entire spectrum of disease stage, and the knowledge gained from the biology of tumor microenvironment across different tumors as a bridge to impact on prognosis and response to therapy in melanoma. This international congress gathered more than 30 international faculty members who in an interactive atmosphere which stimulated discussion and exchange of their experience regarding the most recent advances in research and clinical management of melanoma patients

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Can we explain natural language inference decisions taken with neural networks? Inference rules in distributed representations

Natural Language Inference (NLI) is a key, complex task where machine learning (ML) is playing an important role. However, ML has progressively obfuscated the role of linguistically-motivated inference rules, which should be the core of NLI systems. In this paper, we introduce distributed inference rules as a novel way to encode linguistically-motivated inference rules in learning interpretable NLI classifiers. We propose two encoders: the Distributed Partial Tree Encoder and the Distributed Smoothed Partial Tree Encoder. These encoders allow modeling syntactic and syntactic-semantic inference rules as distributed representations ready to be used in ML models over large datasets. Although far from the state-of-the-art of end-to-end deep learning systems on large datasets, our shallow networks positively exploit inference rules for NLI, improving over baseline systems. This is a first positive step towards interpretable and explainable end-to-end deep learning systems

Have you lost the thread? Discovering ongoing conversations in scattered dialog blocks

Finding threads in textual dialogs is emerging as a need to better organize stored knowledge. We capture this need by introducing the novel task of discovering on-going conversations in scattered dialog blocks. Our aim in this paper is twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of Big Personal Data. In fact, we showed that personal dialogs can be surrogated with theatrical plays. Second, we propose a suite of computationally light learning models that can use syntactic and semantic features. With this suite, we showed that models for this challenging task should include features capturing shifts in language use and, possibly, modeling underlying scripts.Finding threads in textual dialogs is emerging as a need to better organize stored knowledge. We capture this need by introducing the novel task of discovering ongoing conversations in scattered dialog blocks. Our aim in this article is twofold. First, we propose a publicly available testbed for the task by solving the insurmountable problem of privacy of Big Personal Data. In fact, we showed that personal dialogs can be surrogated with theatrical plays. Second, we propose a suite of computationally light learning models that can use syntactic and semantic features. With this suite, we showed that models for this challenging task should include features capturing shifts in language use and, possibly, modeling underlying scripts

A novel wearable for rehabilitation using infrared sensors: A preliminary investigation

In this paper we outline a novel design of a wireless sensor wearable band for tracking patient movements. This technology and design has potential applications for rehabilitation of stroke survivors who suffer from spasticity in their upper extremities. This technology could be used to track patient movement performed in a non-clinical environment, such as inside the comfort of their home. Data on their treatment progress could be transmitted wirelessly both to the clinician and to the patient. This technology could help realize increased monitoring of the patient, quantitative data on patient improvement over time and decreased health care costs. In this paper we demonstrated a preliminary prototype which can track and distinguish classes of movement of a user performing elbow flexion exercises while seated at a table. A study was completed with 6 participants with classification accuracies up to 88%