Impact of using different radiation therapy techniques in breat cancer: contralateral breast dose

RESUMO: O cancro de mama e o mais frequente diagnoticado a indiv duos do sexo feminino. O conhecimento cientifico e a tecnologia tem permitido a cria ção de muitas e diferentes estrat egias para tratar esta patologia. A Radioterapia (RT) est a entre as diretrizes atuais para a maioria dos tratamentos de cancro de mama. No entanto, a radia ção e como uma arma de dois canos: apesar de tratar, pode ser indutora de neoplasias secund arias. A mama contralateral (CLB) e um orgão susceptivel de absorver doses com o tratamento da outra mama, potenciando o risco de desenvolver um tumor secund ario. Nos departamentos de radioterapia tem sido implementadas novas tecnicas relacionadas com a radia ção, com complexas estrat egias de administra ção da dose e resultados promissores. No entanto, algumas questões precisam de ser devidamente colocadas, tais como: E seguro avançar para tecnicas complexas para obter melhores indices de conformidade nos volumes alvo, em radioterapia de mama? O que acontece aos volumes alvo e aos tecidos saudaveis adjacentes? Quão exata e a administração de dose? Quais são as limitações e vantagens das técnicas e algoritmos atualmente usados? A resposta a estas questões e conseguida recorrendo a m etodos de Monte Carlo para modelar com precisão os diferentes componentes do equipamento produtor de radia ção(alvos, ltros, colimadores, etc), a m de obter uma descri cão apropriada dos campos de radia cão usados, bem como uma representa ção geometrica detalhada e a composição dos materiais que constituem os orgãos e os tecidos envolvidos. Este trabalho visa investigar o impacto de tratar cancro de mama esquerda usando diferentes tecnicas de radioterapia f-IMRT (intensidade modulada por planeamento direto), IMRT por planeamento inverso (IMRT2, usando 2 feixes; IMRT5, com 5 feixes) e DCART (arco conformacional dinamico) e os seus impactos em irradia ção da mama e na irradia ção indesejada dos tecidos saud aveis adjacentes. Dois algoritmos do sistema de planeamento iPlan da BrainLAB foram usados: Pencil Beam Convolution (PBC) e Monte Carlo comercial iMC. Foi ainda usado um modelo de Monte Carlo criado para o acelerador usado (Trilogy da VARIAN Medical Systems), no c odigo EGSnrc MC, para determinar as doses depositadas na mama contralateral. Para atingir este objetivo foi necess ario modelar o novo colimador multi-laminas High- De nition que nunca antes havia sido simulado. O modelo desenvolvido est a agora disponí vel no pacote do c odigo EGSnrc MC do National Research Council Canada (NRC). O acelerador simulado foi validado com medidas realizadas em agua e posteriormente com c alculos realizados no sistema de planeamento (TPS).As distribui ções de dose no volume alvo (PTV) e a dose nos orgãos de risco (OAR) foram comparadas atrav es da an alise de histogramas de dose-volume; an alise estati stica complementar foi realizadas usando o software IBM SPSS v20. Para o algoritmo PBC, todas as tecnicas proporcionaram uma cobertura adequada do PTV. No entanto, foram encontradas diferen cas estatisticamente significativas entre as t ecnicas, no PTV, nos OAR e ainda no padrão da distribui ção de dose pelos tecidos sãos. IMRT5 e DCART contribuem para maior dispersão de doses baixas pelos tecidos normais, mama direita, pulmão direito, cora cão e at e pelo pulmão esquerdo, quando comparados com as tecnicas tangenciais (f-IMRT e IMRT2). No entanto, os planos de IMRT5 melhoram a distribuição de dose no PTV apresentando melhor conformidade e homogeneidade no volume alvo e percentagens de dose mais baixas nos orgãos do mesmo lado. A t ecnica de DCART não apresenta vantagens comparativamente com as restantes t ecnicas investigadas. Foram tamb em identi cadas diferen cas entre os algoritmos de c alculos: em geral, o PBC estimou doses mais elevadas para o PTV, pulmão esquerdo e cora ção, do que os algoritmos de MC. Os algoritmos de MC, entre si, apresentaram resultados semelhantes (com dferen cas at e 2%). Considera-se que o PBC não e preciso na determina ção de dose em meios homog eneos e na região de build-up. Nesse sentido, atualmente na cl nica, a equipa da F sica realiza medi ções para adquirir dados para outro algoritmo de c alculo. Apesar de melhor homogeneidade e conformidade no PTV considera-se que h a um aumento de risco de cancro na mama contralateral quando se utilizam t ecnicas não-tangenciais. Os resultados globais dos estudos apresentados confirmam o excelente poder de previsão com precisão na determinação e c alculo das distribui ções de dose nos orgãos e tecidos das tecnicas de simulação de Monte Carlo usados.---------ABSTRACT:Breast cancer is the most frequent in women. Scienti c knowledge and technology have created many and di erent strategies to treat this pathology. Radiotherapy (RT) is in the actual standard guidelines for most of breast cancer treatments. However, radiation is a two-sword weapon: although it may heal cancer, it may also induce secondary cancer. The contralateral breast (CLB) is a susceptible organ to absorb doses with the treatment of the other breast, being at signi cant risk to develop a secondary tumor. New radiation related techniques, with more complex delivery strategies and promising results are being implemented and used in radiotherapy departments. However some questions have to be properly addressed, such as: Is it safe to move to complex techniques to achieve better conformation in the target volumes, in breast radiotherapy? What happens to the target volumes and surrounding healthy tissues? How accurate is dose delivery? What are the shortcomings and limitations of currently used treatment planning systems (TPS)? The answers to these questions largely rely in the use of Monte Carlo (MC) simulations using state-of-the-art computer programs to accurately model the di erent components of the equipment (target, lters, collimators, etc.) and obtain an adequate description of the radiation elds used, as well as the detailed geometric representation and material composition of organs and tissues. This work aims at investigating the impact of treating left breast cancer using di erent radiation therapy (RT) techniques f-IMRT (forwardly-planned intensity-modulated), inversely-planned IMRT (IMRT2, using 2 beams; IMRT5, using 5 beams) and dynamic conformal arc (DCART) RT and their e ects on the whole-breast irradiation and in the undesirable irradiation of the surrounding healthy tissues. Two algorithms of iPlan BrainLAB TPS were used: Pencil Beam Convolution (PBC)and commercial Monte Carlo (iMC). Furthermore, an accurate Monte Carlo (MC) model of the linear accelerator used (a Trilogy R VARIANR) was done with the EGSnrc MC code, to accurately determine the doses that reach the CLB. For this purpose it was necessary to model the new High De nition multileaf collimator that had never before been simulated. The model developed was then included on the EGSnrc MC package of National Research Council Canada (NRC). The linac was benchmarked with water measurements and later on validated against the TPS calculations. The dose distributions in the planning target volume (PTV) and the dose to the organs at risk (OAR) were compared analyzing dose-volume histograms; further statistical analysis was performed using IBM SPSS v20 software. For PBC, all the techniques provided adequate coverage of the PTV. However, statistically significant dose di erences were observed between the techniques, in the PTV, OAR and also in the pattern of dose distribution spreading into normal tissues. IMRT5 and DCART spread low doses into greater volumes of normal tissue, right breast, right lung, heart and even the left lung than tangential techniques (f-IMRT and IMRT2). However,IMRT5 plans improved distributions for the PTV, exhibiting better conformity and homogeneity in target and reduced high dose percentages in ipsilateral OAR. DCART did not present advantages over any of the techniques investigated. Di erences were also found comparing the calculation algorithms: PBC estimated higher doses for the PTV, ipsilateral lung and heart than the MC algorithms predicted. The MC algorithms presented similar results (within 2% di erences). The PBC algorithm was considered not accurate in determining the dose in heterogeneous media and in build-up regions. Therefore, a major e ort is being done at the clinic to acquire data to move from PBC to another calculation algorithm. Despite better PTV homogeneity and conformity there is an increased risk of CLB cancer development, when using non-tangential techniques. The overall results of the studies performed con rm the outstanding predictive power and accuracy in the assessment and calculation of dose distributions in organs and tissues rendered possible by the utilization and implementation of MC simulation techniques in RT TPS.

Social-media monitoring for cold-start recommendations

Generating personalized movie recommendations to users is a problem that most commonly relies on user-movie ratings. These ratings are generally used either to understand the user preferences or to recommend movies that users with similar rating patterns have rated highly. However, movie recommenders are often subject to the Cold-Start problem: new movies have not been rated by anyone, so, they will not be recommended to anyone; likewise, the preferences of new users who have not rated any movie cannot be learned. In parallel, Social-Media platforms, such as Twitter, collect great amounts of user feedback on movies, as these are very popular nowadays. This thesis proposes to explore feedback shared on Twitter to predict the popularity of new movies and show how it can be used to tackle the Cold-Start problem. It also proposes, at a finer grain, to explore the reputation of directors and actors on IMDb to tackle the Cold-Start problem. To assess these aspects, a Reputation-enhanced Recommendation Algorithm is implemented and evaluated on a crawled IMDb dataset with previous user ratings of old movies,together with Twitter data crawled from January 2014 to March 2014, to recommend 60 movies affected by the Cold-Start problem. Twitter revealed to be a strong reputation predictor, and the Reputation-enhanced Recommendation Algorithm improved over several baseline methods. Additionally, the algorithm also proved to be useful when recommending movies in an extreme Cold-Start scenario, where both new movies and users are affected by the Cold-Start problem.

Chagas disease: What is known and what should be improved: a systemic review

This study consists of a broad review on what is known and what should be improved regarding knowledge of Chagas disease, not only through analysis on the main studies published on the topics discussed, but to a large extent based on experience of this subject, acquired over the past 50 years (1961-2011). Among the subjects covered, we highlight the pathogenesis and evolution of infection by Trypanosoma cruzi, drugs in use and new strategies for treating Chagas disease; the serological tests for the diagnosis and the controls of cure the infection; the regional variations in prevalence, morbidity and response to treatment of the disease; the importance of metacyclogenesis of T. cruzi in different species of triatomines and its capacity to transmit Chagas infection; the risks of adaptation of wild triatomines to human dwellings; the morbidity and need for a surveillance and control program for Chagas disease in the Amazon region and the need to prioritize initiatives for controlling Chagas disease in Latin America and Mexico and in non-endemic countries, which is today a major international dilemma. Finally, we raise the need for to create a new initiative for controlling Chagas disease in the Gran Chaco, which involves parts of Argentina, Bolivia and Paraguay.

Barriers preventing the successful integration of mental health services into Primary Health Care in Nigeria : a mixed methods approach

RESUMO: A Nigéria tem uma população estimada em cerca de 170 milhões de pessoas. O número de profissionais de saúde mental é muito diminuto, contando apenas com 150 psiquiatras o que perfaz aproximadamente um rácio de psiquiatra: população de mais de 1:1 milhão de pessoas. O Plano Nacional de Saúde Mental de 1991 reconheceu esta insuficiência e recomendou a integração dos serviços de saúde mental nos cuidados de saúde primários (CSP). Depois de mais de duas décadas, essa política não foi ainda implementada. Este estudo teve como objetivos mapear a estrutura organizacional dos serviços de saúde mental da Nigéria, e explorar os desafios e barreiras que impedem a integração bem-sucedida dos serviços de saúde mental nos cuidados de saúde primários, isto segundo a perspectiva dos profissionais dos cuidados de saúde primários. Com este objetivo, desenvolveu-se um estudo exploratório sequencial e utilizou-se um modelo misto para a recolha de dados. A aplicação em simultâneo de abordagens qualitativas e quantitativas permitiram compreender os problemas relacionados com a integração dos serviços de saúde mental nos CSP na Nigéria. No estudo qualitativo inicial, foram realizadas entrevistas com listagens abertas a 30 profissionais dos CSP, seguidas de dois grupos focais com profissionais dos CSP de duas zonas governamentais do estado de Oyo de forma a obter uma visão global das perspectivas destes profissionais locais sobre os desafios e barreiras que impedem uma integração bem-sucedida dos serviços de saúde mental nos CSP. Subsequentemente, foram realizadas entrevistas com quatro pessoas-chave, especificamente coordenadores e especialistas em saúde mental. Os resultados do estudo qualitativo foram utilizados para desenvolver um questionário para análise quantitativa das opiniões de uma amostra maior e mais representativa dos profissionais dos CSP do Estado de Oyo, bem como de duas zonas governamentais locais do Estado de Osun. As barreiras mais comummente identificadas a partir deste estudo incluem o estigma e os preconceitos sobre a doença mental, a formação inadequada dos profissionais dos CPS sobre saúde mental, a perceção pela equipa dos CSP de baixa prioridade de ação do Governo, o medo da agressão e violência pela equipa dos CSP, bem como a falta de disponibilidade de fármacos. As recomendações para superar estes desafios incluem a melhoria sustentada dos esforços da advocacia à saúde mental que vise uma maior valorização e apoio governamental, a formação e treino organizados dos profissionais dos cuidados primários, a criação de redes de referência e de apoio com instituições terciárias adjacentes, e o engajamento da comunidade para melhorar o acesso aos serviços e à reabilitação, pelas pessoas com doença mental. Estes resultados fornecem indicações úteis sobre a perceção das barreiras para a integração bem sucedida dos serviços de saúde mental nos CSP, enquanto se recomenda uma abordagem holística e abrangente. Esta informação pode orientar as futuras tentativas de implementação da integração dos serviços de saúde mental nos cuidados primários na Nigéria.------------ABSTRACT: Nigeria has an estimated population of about 170 million people but the number of mental health professionals is very small, with about 150 psychiatrists. This roughly translates to a psychiatrist:population ratio of more than 1:1 million people. The National Mental Health Policy of 1991 recognized this deficiency and recommended the integration of mental health into primary health care (PHC) delivery system. After more than two decades, this policy has yet to be implemented. This study aimed to map out the organizational structure of the mental health systems in Nigeria, and to explore the challenges and barriers preventing the successful integration of mental health into primary health care, from the perspective of the primary health care workers. A mixed methods exploratory sequential study design was employed, which entails the use of sequential timing in the combined methods of data collection. A combination of qualitative and uantitative approaches in sequence, were utilized to understand the problems of mental health services integration into PHC in Nigeria. The initial qualitative phase utilized free listing interviews with 30 PHC workers, followed by two focus group discussions with primary care workers from two Local Government Areas (LGA) of Oyo State to gain useful insight into the local perspectives of PHC workers about the challenges and barriers preventing successful integration of mental health care services into PHC. Subsequently, 4 key informant interviews with PHC co-ordinators and mental health experts were carried out. The findings from the qualitative study were utilized to develop a quantitative study questionnaire to understand the opinions of a larger and more representative sample of PHC staff in two more LGAs of Oyo State, as well as 2 LGAs from Osun State. The common barriers identified from this study include stigma and misconceptions about mental illness, inadequate training of PHC staff about mental health, low government priority, fear of aggression and violence by the PHC staff, as well as non-availability of medications. Recommendations for overcoming these challenges include improved and sustained efforts at mental health advocacy to gain governmental attention and support, organized training and retraining for primary care staff, establishment of referral and supportive networks with neighbouring tertiary facilities and community engagement to improve service utilization and rehabilitation of mentally ill persons. These findings provide useful insight into the barriers to the successful integration of mental health into PHC, while recommending a holistic and comprehensive approach. This information can guide future attempts to implement the integration of mental health into primary care in Nigeria.

Impact of an intervention in the use of sequential antibiotic therapy in a Brazilian university hospital

INTRODUCTION: Sequential antibiotic therapy (SAT) is safe and economical. However, the unnecessary use of intravenous (IV) administration usually occurs. The objective of this work was to get to know the effectiveness of an intervention to implement the SAT in a teaching hospital in Brazil. METHODS: This was a prospective and interventional study, historically controlled, and was conducted in the Hospital de Clínicas, Universidade Federal de Uberlândia, State of Minas Gerais, Brazil, a high complexity teaching hospital having 503 beds. In each of the periods, from 04/04/05 to 07/20/05 (pre-intervention) and from 09/24/07 to 12/20/07 (intervention), 117 patients were evaluated. After the pre-intervention period, guidelines were developed which were implemented during the intervention period along with educational measures and a reminder system added to the patients' prescription. RESULTS: In the pre-intervention and intervention periods, the IV antibiotics were used as treatment for a average time of 14.8 and 11.8 days, respectively. Ceftriaxone was the antibiotic most prescribed in both periods (23.4% and 21.6% respectively). Starting from the first prescription of antibiotics, the average length of hospitalization time was 21.8 and 17.5 days, respectively. The SAT occurred only in 4 and 5 courses of treatment, respectively, and 12.8% and 18.8% of the patients died in the respective periods. CONCLUSIONS: Under the presented conditions, the evaluated intervention strategy is ineffective in promoting the exchange of the antibiotic administration from IV to oral treatment (SAT).

EDP - A new estimation algorithm

The aim of this work project is to analyze the current algorithm used by EDP to estimate their clients’ electrical energy consumptions, create a new algorithm and compare the advantages and disadvantages of both. This new algorithm is different from the current one as it incorporates some effects from temperature variations. The results of the comparison show that this new algorithm with temperature variables performed better than the same algorithm without temperature variables, although there is still potential for further improvements of the current algorithm, if the prediction model is estimated using a sample of daily data, which is the case of the current EDP algorithm.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.

Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

Rely-guarantee protocols for safe interference over shared memory

Mutable state can be useful in certain algorithms, to structure programs, or for efficiency purposes. However, when shared mutable state is used in non-local or nonobvious ways, the interactions that can occur via aliases to that shared memory can be a source of program errors. Undisciplined uses of shared state may unsafely interfere with local reasoning as other aliases may interleave their changes to the shared state in unexpected ways. We propose a novel technique, rely-guarantee protocols, that structures the interactions between aliases and ensures that only safe interference is possible. We present a linear type system outfitted with our novel sharing mechanism that enables controlled interference over shared mutable resources. Each alias is assigned separate, local roles encoded in a protocol abstraction that constrains how an alias can legally use that shared state. By following the spirit of rely-guarantee reasoning, our rely-guarantee protocols ensure that only safe interference can occur but still allow many interesting uses of shared state, such as going beyond invariant and monotonic usages. This thesis describes the three core mechanisms that enable our type-based technique to work: 1) we show how a protocol models an alias’s perspective on how the shared state evolves and constrains that alias’s interactions with the shared state; 2) we show how protocols can be used while enforcing the agreed interference contract; and finally, 3) we show how to check that all local protocols to some shared state can be safely composed to ensure globally safe interference over that shared memory. The interference caused by shared state is rooted at how the uses of di↵erent aliases to that state may be interleaved (perhaps even in non-deterministic ways) at run-time. Therefore, our technique is mostly agnostic as to whether this interference was the result of alias interleaving caused by sequential or concurrent semantics. We show implementations of our technique in both settings, and highlight their di↵erences. Because sharing is “first-class” (and not tied to a module), we show a polymorphic procedure that enables abstract compositions of protocols. Thus, protocols can be specialized or extended without requiring specific knowledge of the interference produce by other protocols to that state. We show that protocol composition can ensure safety even when considering abstracted protocols. We show that this core composition mechanism is sound, decidable (without the need for manual intervention), and provide an algorithm implementation.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.

Prevention in underground construction with sequential excavation method

This paper aims to describe the Sequential Excavation Method, used for excava-tion in underground works, as well as the related risks and preventive measures. This method has characteristics that differentiate it from other tunnelling techniques: it uses a larger number of workers and equipment; it has a high concurrency of tasks with various workers and equip-ment quite exposed to hazards; and it uses many potentially aggressive chemicals. Firstly, it is given a broad overview of this issue. Afterwards, it will be presented the results of a survey to a sample of experienced technicians, aimed at gauging the relevance of a set of guidelines relat-ing to the design and work phases, applicable to the domestic market and prepared following technical visits to works abroad.

Modern optimization in earthwork construction

Earthworks tasks are often regarded in transportation projects as some of the most demanding processes. In fact, sequential tasks such as excavation, transportation, spreading and compaction are strongly based on heavy mechanical equipment and repetitive processes, thus becoming as economically demanding as they are time-consuming. Moreover, actual construction requirements originate higher demands for productivity and safety in earthwork constructions. Given the percentual weight of costs and duration of earthworks in infrastructure construction, the optimal usage of every resource in these tasks is paramount. Considering the characteristics of an earthwork construction, it can be looked at as a production line based on resources (mechanical equipment) and dependency relations between sequential tasks, hence being susceptible to optimization. Up to the present, the steady development of Information Technology areas, such as databases, artificial intelligence and operations research, has resulted in the emergence of several technologies with potential application bearing that purpose in mind. Among these, modern optimization methods (also known as metaheuristics), such as evolutionary computation, have the potential to find high quality optimal solutions with a reasonable use of computational resources. In this context, this work describes an optimization algorithm for earthworks equipment allocation based on a modern optimization approach, which takes advantage of the concept that an earthwork construction can be regarded as a production line.

Parallel improved Schnorr-Euchner enumeration SE++ for the CVP and SVP

The Closest Vector Problem (CVP) and the Shortest Vector Problem (SVP) are prime problems in lattice-based cryptanalysis, since they underpin the security of many lattice-based cryptosystems. Despite the importance of these problems, there are only a few CVP-solvers publicly available, and their scalability was never studied. This paper presents a scalable implementation of an enumeration-based CVP-solver for multi-cores, which can be easily adapted to solve the SVP. In particular, it achieves super-linear speedups in some instances on up to 8 cores and almost linear speedups on 16 cores when solving the CVP on a 50-dimensional lattice. Our results show that enumeration-based CVP-solvers can be parallelized as effectively as enumeration-based solvers for the SVP, based on a comparison with a state of the art SVP-solver. In addition, we show that we can optimize the SVP variant of our solver in such a way that it becomes 35%-60% faster than the fastest enumeration-based SVP-solver to date.

Integrating data from heterogeneous DNA microarray platforms

DNA microarrays are one of the most used technologies for gene expression measurement. However, there are several distinct microarray platforms, from different manufacturers, each with its own measurement protocol, resulting in data that can hardly be compared or directly integrated. Data integration from multiple sources aims to improve the assertiveness of statistical tests, reducing the data dimensionality problem. The integration of heterogeneous DNA microarray platforms comprehends a set of tasks that range from the re-annotation of the features used on gene expression, to data normalization and batch effect elimination. In this work, a complete methodology for gene expression data integration and application is proposed, which comprehends a transcript-based re-annotation process and several methods for batch effect attenuation. The integrated data will be used to select the best feature set and learning algorithm for a brain tumor classification case study. The integration will consider data from heterogeneous Agilent and Affymetrix platforms, collected from public gene expression databases, such as The Cancer Genome Atlas and Gene Expression Omnibus.