Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

ACCURACY OF FNAC IN FEMALE BREAST LESIONS

Background: Various breast lesions are common lesions in females with a wide range of variability from inflammatory lesions, benign and malignant breast lesions. FNAC is first diagnostic test, as it has high sensitivity and specificity. Lesions were categorized on FNA into inflammatory lesions, benign neoplastic lesions, malignant neoplastic lesions, and suspicious for malignancy. Methods: This was a retrospective study done in the Department of Pathology, P.D.U. Medical College, Rajkot, Gujarat State, India from Aug-2013 to July 2014. FNAC of 392 cases of breast lesions were done and reported by expert pathologist. The histopathological specimens when available were reported by other pathologist without prior knowledge of FNA diagnosis. Sensitivity, Specificity and Accuracy of FNA diagnosis were then analyzed. Results: A total of 392 cases of breast lesions were diagnosed on FNA, out of them histopathological correlation was available in 87 cases. Benign breast lesions are more common in younger patients in 21-30 yrs age group and malignant lesions are more common in old age group patients of 41-60 yrs with few exceptions. In our setup fibroadenoma is the most common benign breast lesion (26.53%) and ductal carcinoma (17.86%) is the most common malignant lesion. The sensitivity, specificity and accuracy of FNAC for malignant lesions were found to be 91.43%, 100% and 96.25% respectively. Conclusion: FNAC is an effective and valid tool as the first line diagnostic modality in the preoperative diagnosis of the malignant and benign breast lesions.KEYWORDS: FNAC; Breast lesions; Fibroadenoma; Ductal carcinoma

ACCURACY OF FNAC IN FEMALE BREAST LESIONS

Background: Various breast lesions are common lesions in females with a wide range of variability from inflammatory lesions, benign and malignant breast lesions. FNAC is first diagnostic test, as it has high sensitivity and specificity. Lesions were categorized on FNA into inflammatory lesions, benign neoplastic lesions, malignant neoplastic lesions, and suspicious for malignancy. Methods: This was a retrospective study done in the Department of Pathology, P.D.U. Medical College, Rajkot, Gujarat State, India from Aug-2013 to July 2014. FNAC of 392 cases of breast lesions were done and reported by expert pathologist. The histopathological specimens when available were reported by other pathologist without prior knowledge of FNA diagnosis. Sensitivity, Specificity and Accuracy of FNA diagnosis were then analyzed. Results: A total of 392 cases of breast lesions were diagnosed on FNA, out of them histopathological correlation was available in 87 cases. Benign breast lesions are more common in younger patients in 21-30 yrs age group and malignant lesions are more common in old age group patients of 41-60 yrs with few exceptions. In our setup fibroadenoma is the most common benign breast lesion (26.53%) and ductal carcinoma (17.86%) is the most common malignant lesion. The sensitivity, specificity and accuracy of FNAC for malignant lesions were found to be 91.43%, 100% and 96.25% respectively. Conclusion: FNAC is an effective and valid tool as the first line diagnostic modality in the preoperative diagnosis of the malignant and benign breast lesions.KEYWORDS: FNAC; Breast lesions; Fibroadenoma; Ductal carcinoma

“In small places, close to home”: urban environmental impacts on child rights across four global cities

Urban environments influence child behaviours, exposures and experiences and may affect health, development, achievement and realization of fundamental human rights. We examined the status of eleven UN Convention on the Rights of the Child articles, in a multi-case study across four global cities. Within all study cities, children experienced unequal exposure to urban environmental risks and amenities. Many violations of child rights are related to car-based transportation systems and further challenged by pressures on urban systems from rapid population increases in the context of climate change. A child rights framework provides principles for a collective, multi-sectoral re-imagination of urban environments that support the human rights of all citizens

National-Scale Rainfall-Triggered Landslide Susceptibility and Exposure in Nepal

Nepal is one of the most landslide-prone countries in the world, with year-on-year impacts resulting in loss of life and imposing a chronic impediment to sustainable livelihoods. Living with landslides is a daily reality for an increasing number of people, so establishing the nature of landslide hazard and risk is essential. Here we develop a model of landslide susceptibility for Nepal and use this to generate a nationwide geographical profile of exposure to rainfall-triggered landslides. We model landslide susceptibility using a fuzzy overlay approach based on freely-available topographic data, trained on an inventory of mapped landslides, and combine this with high resolution population and building data to describe the spatial distribution of exposure to landslides. We find that whilst landslide susceptibility is highest in the High Himalaya, exposure is highest within the Middle Hills, but this is highly spatially variable and skewed to on average relatively low values. Around 4 × 106 Nepalis (∼15\% of the population) live in areas considered to be at moderate or higher degree of exposure to landsliding (>0.25 of the maximum), and critically this number is highly sensitive to even small variations in landslide susceptibility. Our results show a complex relationship between landslides and buildings, that implies wider complexity in the association between physical exposure to landslides and poverty. This analysis for the first time brings into focus the geography of the landslide exposure and risk case load in Nepal, and demonstrates limitations of assessing future risk based on limited records of previous events

Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma

SummaryWe have profiled promoter DNA methylation alterations in 272 glioblastoma tumors in the context of The Cancer Genome Atlas (TCGA). We found that a distinct subset of samples displays concerted hypermethylation at a large number of loci, indicating the existence of a glioma-CpG island methylator phenotype (G-CIMP). We validated G-CIMP in a set of non-TCGA glioblastomas and low-grade gliomas. G-CIMP tumors belong to the proneural subgroup, are more prevalent among lower-grade gliomas, display distinct copy-number alterations, and are tightly associated with IDH1 somatic mutations. Patients with G-CIMP tumors are younger at the time of diagnosis and experience significantly improved outcome. These findings identify G-CIMP as a distinct subset of human gliomas on molecular and clinical grounds