Complex Network Tools to Understand the Behavior of Criminality in Urban Areas

Complex networks are nowadays employed in several applications. Modeling urban street networks is one of them, and in particular to analyze criminal aspects of a city. Several research groups have focused on such application, but until now, there is a lack of a well-defined methodology for employing complex networks in a whole crime analysis process, i.e. from data preparation to a deep analysis of criminal communities. Furthermore, the "toolset" available for those works is not complete enough, also lacking techniques to maintain up-to-date, complete crime datasets and proper assessment measures. In this sense, we propose a threefold methodology for employing complex networks in the detection of highly criminal areas within a city. Our methodology comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of assessment measures for analyzing intrinsic criminality of communities, especially when considering different crime types. We show our methodology by applying it to a real crime dataset from the city of San Francisco - CA, USA. The results confirm its effectiveness to identify and analyze high criminality areas within a city. Hence, our contributions provide a basis for further developments on complex networks applied to crime analysis.Comment: 7 pages, 2 figures, 14th International Conference on Information Technology : New Generation

Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives.

In the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.CSBC 2009

The potential biomarkers in predicting pathologic response of breast cancer to three different chemotherapy regimens: a case control study

<p>Abstract</p> <p>Background</p> <p>Preoperative chemotherapy (PCT) has become the standard of care in locally advanced breast cancer. The identification of patient-specific tumor characteristics that can improve the ability to predict response to therapy would help optimize treatment, improve treatment outcomes, and avoid unnecessary exposure to potential toxicities. This study is to determine whether selected biomarkers could predict pathologic response (PR) of breast tumors to three different PCT regimens, and to identify a subset of patients who would benefit from a given type of treatment.</p> <p>Methods</p> <p>118 patients with primary breast tumor were identified and three PCT regimens including DEC (docetaxel+epirubicin+cyclophosphamide), VFC (vinorelbine/vincristine+5-fluorouracil+cyclophosphamide) and EFC (epirubicin+5-fluorouracil+cyclophosphamide) were investigated. Expression of steroid receptors, HER2, P-gp, MRP, GST-pi and Topo-II was evaluated by immunohistochemical scoring on tumor tissues obtained before and after PCT. The PR of breast carcinoma was graded according to Sataloff's classification. Chi square test, logistic regression and Cochran-Mantel-Haenszel assay were performed to determine the association between biomarkers and PR, as well as the effectiveness of each regimen on induction of PR.</p> <p>Results</p> <p>There was a clear-cut correlation between the expression of ER and decreased PR to PCT in all three different regimens (<it>p </it>< 0.05). HER2 expression is significantly associated with increased PR in DEC regimen (<it>p </it>< 0.05), but not predictive for PR in EFC and VFC groups. No significant correlation was found between biomarkers PgR, Topo-II, P-gp, MRP or GST-pi and PR to any tested PCT regimen. After adjusted by a stratification variable of ER or HER2, DEC regimen was more effective in inducing PR in comparison with VFC and EFC regimens.</p> <p>Conclusion</p> <p>ER is an independent predictive factor for PR to PCT regimens including DEC, VFC and EFC in primary breast tumors, while HER2 is only predictive for DEC regimen. Expression of PgR, Topo-II, P-gp, MRP and GST-pi are not predictive for PR to any PCT regimens investigated. Results obtained in this clinical study may be helpful for the selection of appropriate treatments for breast cancer patients.</p

Metabolic Control in Type 1 Diabetes: Is Adjunctive Therapy the Way Forward?

Despite advances in insulin therapies, patients with type 1 diabetes (T1DM) have a shorter life span due to hyperglycaemia-induced vascular disease and hypoglycaemic complications secondary to insulin therapy. Restricting therapy for T1DM to insulin replacement is perhaps an over-simplistic approach, and we focus in this work on reviewing the role of adjuvant therapy in this population. Current data suggest that adding metformin to insulin therapy in T1DM temporarily lowers HbA1c and reduces weight and insulin requirements, but this treatment fails to show a longer-term glycaemic benefit. Agents in the sodium glucose co-transporter-2 inhibitor (SGLT-2) class demonstrate the greatest promise in correcting hyperglycaemia, but there are safety concerns in relation to the risk of diabetic ketoacidosis. Glucagon-like peptide-1 agonists (GLP-1) show a modest effect on glycaemia, if any, but significantly reduce weight, which may make them suitable for use in overweight T1DM patients. Treatment with pramlintide is not widely available worldwide, although there is evidence to indicate that this agent reduces both HbA1c and weight in T1DM. A criticism of adjuvant studies is the heavy reliance on HbA1c as the primary endpoint while generally ignoring other glycaemic parameters. Moreover, vascular risk markers and measures of insulin resistance—important considerations in individuals with a longer T1DM duration—are yet to be fully investigated following adjuvant therapies. Finally, studies to date have made the assumption that T1DM patients are a homogeneous group of individuals who respond similarly to adjuvant therapies, which is unlikely to be the case. Future longer-term adjuvant studies investigating different glycaemic parameters, surrogate vascular markers and harder clinical outcomes will refine our understanding of the roles of such therapies in various subgroups of T1DM patients

Bulk Loading the M-Tree to Enhance Query Performance

Abstract. The M-tree is a paged, dynamically balanced metric access method that responds gracefully to the insertion of new objects. Like many spatial access methods, the M-tree’s performance is largely dependent on the degree of overlap between spatial regions represented by nodes in the tree, and minimisation of overlap is key to many of the design features of the M-tree and related structures. We present a novel approach to overlap minimisation using a new bulk loading algorithm, resulting in a query cost saving of between 25 % and 40 % for non-uniform data. The structural basis of the new algorithm suggests a way to modify the M-tree to produce a variant which we call the SM-tree. The SM-tree has the same query performance after bulk loading as the M-tree, but further supports efficient object deletion while maintaining the usual balance and occupancy constraints.