Utility of Pelvic Computed Tomography Angiography Prior to Prostatic Artery Embolization

Pelvic computed tomography angiography (CTA) prior to prostatic artery embolization is a beneficial tool for preprocedural planning to increase the likelihood of success during what can be a challenging procedure. Additionally, the same CTA images can be used for calculating the baseline prostate volume as well as for intraprocedural anatomic guidance, adding to the value of the scan. This article discusses the technique used for pelvic CTA and its role in preprocedural assessment of the pelvic vasculature prior to prostatic artery embolization

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

Interleaved text/image Deep Mining on a large-scale radiology database

Despite tremendous progress in computer vision, effec-tive learning on very large-scale (> 100K patients) medi-cal image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital’s picture archiv-ing and communication system. Instead of using full 3D medical volumes, we focus on a collection of representa-tive ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector la-bels. Our system interleaves between unsupervised learn-ing (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demon-strated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their cat-egorization, embedded vector labels and sentence descrip-tions can be harnessed to alleviate the deep learning “data-hungry ” obstacle in the medical domain

Metsätalouden vesistökuormitus : nykykäsitys ja tulevaisuuden menetelmäkehitys

Peer reviewe

Lethal effects of waterlogging on Scots pine appear with delay

Poster

Release of Carbon in Different Molecule Size Fractions from Decomposing Boreal Mor and Peat as Affected by Enchytraeid Worms

Terrestrial export of dissolved organic carbon (DOC) to watercourses has increased in boreal zone. Effect of decomposing material and soil food webs on the release rate and quality of DOC are poorly known. We quantified carbon (C) release in CO2, and DOC in different molecular weights from the most common organic soils in boreal zone; and explored the effect of soil type and enchytraeid worms on the release rates. Two types of mor and four types of peat were incubated in laboratory with and without enchytraeid worms for 154 days at + 15 A degrees C. Carbon was mostly released as CO2; DOC contributed to 2-9% of C release. The share of DOC was higher in peat than in mor. The release rate of CO2 was three times higher in mor than in highly decomposed peat. Enchytraeids enhanced the release of CO2 by 31-43% and of DOC by 46-77% in mor. High molecular weight fraction dominated the DOC release. Upscaling the laboratory results into catchment level allowed us to conclude that peatlands are the main source of DOC, low molecular weight DOC originates close to watercourse, and that enchytraeids substantially influence DOC leaching to watercourse and ultimately to aquatic CO2 emissions.Peer reviewe

Metsäisten valuma-alueiden vesistökuormituksen laskenta

Metsät vaikuttavat vesistöjen veden laatuun. Luonnontilaisilta metsä- ja suoalueilta vesistöihin kulkeutuvia ainevirtoja kutsutaan taustakuormaksi. Metsissä tehtävät toimenpiteet kuten päätehakkuut, maanmuokkaukset, lannoitukset ja ojitukset lisäävät vesistöihin tulevaa kuormitusta. Tässä julkaisussa esitellään metsätalousmaalta tulevaan luonnon taustakuormaan ja eri metsätaloustoimenpiteiden aiheuttamaan kuormituksen lisäykseen perustuva typpi-, fosfori- ja kiintoainekuormituksen laskentamenetelmä, KALLE. Laskentamenetelmän kuvauksen yhteydessä esitetään taustakuormitusluvut ja kivennäis- ja turvemaiden metsänuudistamisen ja lannoituksen sekä turvemaiden kunnostusojituksen ominaiskuormitusluvut. Ominaiskuormitusluvut on tuotettu olettaen, että vesiensuojelusta on huolehdittu uudistamishakkuiden yhteydessä jättämällä suojakaistoja vesistöjen varteen ja kunnostusojituksissa tekemällä laskeutusaltaita. Laskentamenetelmä ottaa huomioon toimenpiteiden pitkän vaikutusajan. KALLE-laskentamenetelmä on kehitetty valtakunnallisia, vesistöalueryhmittäisiä ja vesienhoitoalueittaisia laskelmia varten. Julkaisussa esitetään myös esimerkkilaskelmien tuloksia

ANÁLISE DE MERCÚRIO EM CAMARÃO POR ANÁLISE DIRETA EMPREGANDO ESPECTROMETRIA DE ABSORÇÃO ATÔMICA DMA-80 EVO

Mercury (Hg) is a highly teratogenic and carcinogenic non-essential metal, classified as a priority pollutant. Hg is present in the environment both naturally and by anthropogenic origin. The objective of this work was to determine Hg in a shrimp sample using thermal composition amalgamation atomic absorption spectrometry (TDA-AAS) by the direct analysis method. In this work, wild (Farfantepenaeus brasiliensis) and farmed (Litopenaeus vannamei) shrimp purchased on the market in northeastern Brazil were considered. The analysis method applied was the U.S. EPA Method 7473 and is described elsewhere in the literature as a proven alternative to these techniques that provided environmentally friendly sample preparation. The wild shrimp tissue presented 75.47 μg kg−1 Hg and was the highest concentration of Hg presented in the work while the lowest concentration of Hg in the work was from the shrimp exoskeleton which presented a concentration of 3.71 μg kg −1 Hg.O mercúrio (Hg) é um metal não essencial altamente teratogênico e cancerígeno, classificado como poluente prioritário. O Hg está presente no meio ambiente tanto naturalmente quanto por origem antropogênica. O objetivo deste trabalho foi determinar o Hg em uma amostra de camarão utilizando espectrometria de absorção atômica por amálgama de composição térmica (TDA-AAS) pelo método de análise direta. Neste trabalho foram considerados camarões silvestres (Farfantepenaeus brasiliensis) e de viveiro (Litopenaeus vannamei) adquiridos no mercado do Nordeste do Brasil. O método de análise aplicado foi o Método 7473 da EPA dos EUA e é descrito em outras partes da literatura como uma alternativa comprovada a essas técnicas que forneceram preparação de amostras ecologicamente corretas. O tecido do camarão selvagem apresentou 75,47 μg kg−1 Hg e foi a maior concentração de Hg apresentada no trabalho enquanto a menor concentração de Hg no trabalho foi do exoesqueleto do camarão que apresentou concentração de 3,71 μg kg −1 Hg

Reviews and syntheses : Greenhouse gas exchange data from drained organic forest soils - a review of current approaches and recommendations for future research

Drained organic forest soils in boreal and temperate climate zones are believed to be significant sources of the greenhouse gases (GHGs) carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), but the annual fluxes are still highly uncertain. Drained organic soils exemplify systems where many studies are still carried out with relatively small resources, several methodologies and manually operated systems, which further involve different options for the detailed design of the measurement and data analysis protocols for deriving the annual flux. It would be beneficial to set certain guidelines for how to measure and report the data, so that data from individual studies could also be used in synthesis work based on data collation and modelling. Such synthesis work is necessary for deciphering general patterns and trends related to, e.g., site types, climate, and management, and the development of corresponding emission factors, i.e. estimates of the net annual soil GHG emission and removal, which can be used in GHG inventories. Development of specific emission factors also sets prerequisites for the background or environmental data to be reported in individual studies. We argue that wide applicability greatly increases the value of individual studies. An overall objective of this paper is to support future monitoring campaigns in obtaining high-value data. We analysed peer-reviewed publications presenting CO2, CH4 and N2O flux data for drained organic forest soils in boreal and temperate climate zones, focusing on data that have been used, or have the potential to be used, for estimating net annual soil GHG emissions and removals. We evaluated the methods used in data collection and identified major gaps in background or environmental data. Based on these, we formulated recommendations for future research.Peer reviewe