This paper aims at analysing the application of the gravimetric method in the search for copper ore in the Valley of Curaçá River in northern Bahia, Brazil. The area where this study was carried out is known as Angico Farm, one of the claims of Caraíba S.A., a copper producer in the northern Bahia, Brazil. There are 18 drill holes at the Angico Farm target, drilled in order to investigate the mineralizations in depth. We have obtained information such as geographic coordinates and chemical results from the company in order to test the geophysical response and the correlation with geology. A 3D inverse gravimetric model was generated in order to verify the validity of the method in exploring for copper ore associated with hydrothermally altered mafic and ultramafic. Both mafic/ultramafic rocks and copper ore present high density, therefore the gravity method may not be effective for identification. We have shown, however, that copper ore from the Curaçá Valley presents a fairly good gravity response, and 3D inverse mathematical model pointed out a well‐delimited copper orebody in the regions where drill holes intersect the ore and coincide with the positive gravity anomaly. The ore contents were overlapped on cross‐sections of density extracted from the inverse model and such information helped us to check out the consistency of the gravimetric method in mapping and modelling mineralized bodies associated with mineral occurrence. Additionally, magnetic susceptibility and gammaspectrometric data were acquired along 18 drillcores to investigate their possible correlation with orebodies.
Inflammatory bowel disease (IBD) is a chronic disease for which medical treatment with immunomodulating drugs is increasingly used earlier to prevent disability. Additionally, cancer occurrence in IBD patients is increased for several reasons, either IBD-related or therapy-associated. Doctors are therefore facing the challenge of managing patients with IBD and a past or current malignancy and the need to balance the risk of cancer recurrence associated with immunosuppressive drugs with the potential worsening of IBD activity if they are withdrawn. This review aims to explore the features of different subtypes of cancer occurring in IBD patients to present current evidence on malignancy recurrence risk associated with IBD medical therapy along with the effects of cancer treatment in IBD and finally to discuss current recommendations on the management of these patients. Due to sparse data, a case-by-case multidisciplinary discussion is advised, including inputs from the gastroenterologist, oncologist, and patient.
IntroductionMalaria is an infectious disease that affected nearly 215 million individuals in 2015. In Brazil, there are various information systems targeted to store data from disease notification, including malaria surveillance. However, these databases are identified and difficult to be accessed by researchers due to privacy restrictions. Objectives and ApproachOur goal is to integrate data from two different malaria surveillance systems, as well climate, hydrographics and socioeconomic data, to support ecological and cohort-based analyzes on malaria recurrence, parasitic classification assisted by machine learning methods and epidemics forecasting. Our approach so far was to generate data sets organized by municipality of residence and by municipality of infection and the disposal of these data sets with information aggregated on monthly and annual basis. We expect these databases can be freely used by any researcher intending to conduct studies on malaria using governmental data. ResultsOur current results comprise data sets with information aggregated by municipality of residence, for all municipalities within the Amazonian region (n=772), and by municipalities of infection with active transmission (n=613). For both case, we added variables referring to demographic, socioeconomic, climatological and hydrological data for the period 2010 to 2015 in an annual and monthly form. We also performed a machine learning-based classification to group notifications according to the type of parasite into P. vivax, P. falciparum and mixed cases. Our goal is to identify similar and different characteristics among such groups that can be used to correctly assess recurrence, as well support epidemic forecasting. Conclusion/ImplicationsFrom the databases we created, it will be possible to implement an indexing structure with related metadata, as well publicize these databases to allow for free access by researchers. Currently, we are also running different predictive analytics methods (including visualisation) targeted to generate a forecast model for malaria epidemics.
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