Subspace-based dynamic selection for high-dimensional data

The number of features collected has increased greatly in the past decade, particularly in medicine and life sciences, which brings challenges and opportunities. Making reliable predictions, exploring associations and extracting meaningful information in high-dimensional data are some of the problems that are yet to be solved. Due to intrinsic properties of high-dimensional spaces such as distance concentration and hubness, traditional classification and clustering algorithms face difficult challenges. In general, a Multiple Classifier System (MCS) provides better classification accuracy than individual classifiers. One of the most promising approaches to MCS is Dynamic Selection (DS) methods, which work by selecting classifiers on the fly, according to each unknown test sample. The rationale behind this is that not every classifier is an expert in predicting all samples, rather each classifier or a combination of classifiers is an expert in a different region of the feature space; whose quality can significantly impact the overall performance. This thesis provides three major contributions. First, traditional DS methods fail to perform effectively in high-dimensional data sets due to the use of a k-Nearest Neighbour (k-NN) to define the region competence and, moreover, they do not indicate which are the most important features for classification. Second, two frameworks were proposed the Subspace-Based Dynamic Selection (SBDS) and the Classifier SBDS (cSBDS) which integrate characteristics of DS methods and subspace clustering. Subspace clustering methods localise their search for clusters and are able to uncover clusters that exist in multiple, possible overlapping subspaces of features and/or samples. The subspace clustering approach separates the high-dimensional feature space into small feature spaces with a reduced number of features and samples in each one. The results indicate that the cSBDS framework performs statistically better when compared to DS methods and majority voting on real-world and synthetic datasets. Third, we provide a comparison between the features selected by the cSBDS framework and feature importance methods. The results indicate that for high-dimensional datasets, the cSBDS framework is able to capture the most important features when the number of clusters per class is increased, while traditional feature importance methods lose this capability

Subspace-based dynamic selection for high-dimensional data

The number of features collected has increased greatly in the past decade, particularly in medicine and life sciences, which brings challenges and opportunities. Making reliable predictions, exploring associations and extracting meaningful information in high-dimensional data are some of the problems that are yet to be solved. Due to intrinsic properties of high-dimensional spaces such as distance concentration and hubness, traditional classification and clustering algorithms face difficult challenges. In general, a Multiple Classifier System (MCS) provides better classification accuracy than individual classifiers. One of the most promising approaches to MCS is Dynamic Selection (DS) methods, which work by selecting classifiers on the fly, according to each unknown test sample. The rationale behind this is that not every classifier is an expert in predicting all samples, rather each classifier or a combination of classifiers is an expert in a different region of the feature space; whose quality can significantly impact the overall performance. This thesis provides three major contributions. First, traditional DS methods fail to perform effectively in high-dimensional data sets due to the use of a k-Nearest Neighbour (k-NN) to define the region competence and, moreover, they do not indicate which are the most important features for classification. Second, two frameworks were proposed the Subspace-Based Dynamic Selection (SBDS) and the Classifier SBDS (cSBDS) which integrate characteristics of DS methods and subspace clustering. Subspace clustering methods localise their search for clusters and are able to uncover clusters that exist in multiple, possible overlapping subspaces of features and/or samples. The subspace clustering approach separates the high-dimensional feature space into small feature spaces with a reduced number of features and samples in each one. The results indicate that the cSBDS framework performs statistically better when compared to DS methods and majority voting on real-world and synthetic datasets. Third, we provide a comparison between the features selected by the cSBDS framework and feature importance methods. The results indicate that for high-dimensional datasets, the cSBDS framework is able to capture the most important features when the number of clusters per class is increased, while traditional feature importance methods lose this capability

Lyme borreliosis in Portugal: study on vector(s), agent(s) and risk factor(s)

A situação da Borreliose de Lyme (BL) em Portugal foi avaliada com base na identificação dos principais vectores (carraças) e sua distribuição, taxas de infecção com os agentes do complexo Borrelia burgdorferi sensu lato (s.l.) e os casos humanos com confirmação laboratorial. Ixodes ricinus, o principal vector desta doença, foi estudado durante um período de 5 anos na Tapada Nacional de Mafra (área protegida), durante o qual foi observado um ciclo unimodal para todos os estados de desenvolvimento, por um período de 1 a 1,5 anos. Confirmou-se uma correlação significativa entre a variação sazonal da abundância de carraças e algumas variáveis climáticas, nomeadamente, a temperatura, humidade e precipitação. Além de I. ricinus, foram colhidas outras espécies de carraças tais como Dermacentor marginatus, Haemaphysalis punctata, Rhipicephalus sanguineus e Ixodes hexagonus. As taxas de infecção atingiram valores globais de 11,8% para I. ricinus e de 5,2% para as restantes espécies, com identificação de vários agentes do complexo B. burgdorferi s.l., provavelmente relacionada com a acentuada diversidade de hospedeiros presentes na área investigada. Num estudo a nível nacional, durante 4 anos, foram amostrados 55 pontos para colheita de vectores, tendo-se obtido um total de 2801 carraças distribuídas pelos seguintes géneros/espécies Rhipicephalus spp, D. marginatus, I. ricinus, Hy. marginatum, H. punctata e Ixodes spp, com diferentes taxas de colheita. Todos estes ixodídeos foram encontrados infectados por B. lusitaniae, a principal espécie genómica detectada no vector (até ao momento). Em Portugal, para além da Tapada Nacional de Mafra, foram apenas identificadas estirpes patogénicas de B. garinii, num local perto de Coimbra (Soure). A confirmação laboratorial de casos humanos foi obtida com base no diagnóstico de rotina desta doença, realizado no Instituto de Higiene e Medicina Tropical, quer ao nível serológico por Western-Blot (15.5%), quer por amplificação do espaço intergénico de rRNA 5S-23S (rrf-rrl) de B. burgdorferi s.l. (28%). Neste último caso, foram identificadas duas espécies genómicas patogénicas (B. garinii e B. afzelii), além de B. lusitaniae. A principal proveniência dos doentes com Borreliose de Lyme foi Lisboa, Coimbra, Tomar, Viseu e Almada.Os principais factores envolvidos na distribuição das carraças e consequentemente no ciclo epidemiológico da Borreliose de Lyme em Portugal encontram-se associados com o clima (temperatura, humidade e precpitação) e composição do habitat (áreas expostas, florestas mistas e de caducas). A estrutura da paisagem (ex. fragmentação) foi igualmente considerada como um factor essencial para a presença de carraças numa determinada área. Com base nestas variáveis, mapas de risco foram criados para os três ixodídeos (I. ricinus, D. marginatus, Rhipicephalus spp) potencialmente mais implicados na transmissão dos agentes de BL em Portugal Em conclusão, a Borreliose de Lyme existe em Portugal e apresenta uma epidemiologia complexa, como a seguir se demonstra: i) além do vector Europeu, registaram-se outros potenciais vectores, susceptíveis de estarem associados a uma maior diversidade de hospedeiros reservatórios (ainda por investigar) e biótopos específicos, ii) uma elevada diversidade de espécies genómicas do complexo B. burgdorferi sensu lato, decorrente deste espectro alargado de vectores-reservatórios, iii) e uma distribuição generalizada de doentes de BL, com importantes taxas de infecção, resultante da referida diversidade de agentes patogénicos, não só das duas espécies genómicas mais reconhecidas na Europa (B. garinii e B. afzelii), como da recentemente isolada B. lusitaniae, indutora de um quadro clínico aparentemente diferente e restricto à zona do Mediterrâneo.The status of Lyme Borreliosis (LB) in Portugal was evaluated through identification of the main vectors (ticks), their distribution, infection rates with Borrelia burgdorferi sensu lato species and human disease cases. Ixodes ricinus, the main vector of this disease, was studied extensively in a 5-year focal study in Tapada Nacional de Mafra, a protected area. An unimodal dynamic cycle was found for all developmental stages and a 1-1.5 year developmental cycle was observed. Climatic variables, including temperature, humidity and precipitation were significantly correlated with seasonal variation in I. ricinus abundance. Other tick species, namely Dermacentor marginatus, Haemaphysalis punctata, Rhipicephalus sanguineus and Ixodes hexagonus, were also collected. An overall infection rate of 11.8% for I. ricinus and 5.2% for the other tick species were detected. Several Borrelia species were identified in these ticks, probably due to the great variety of hosts present in this area. In a nationwide study during a 4-years period, 55 sample sites were surveyed and 2801 ticks were collected, including Rhipicephalus spp, D. marginatus, I. ricinus, Hyalomma marginatum, H. punctata and Ixodes spp, with different collection efforts. All of these ticks were found infected with B. lusitaniae, the main strain of Borrelia found in Portugal. Confirmed pathogenic bacterial strains (B. garinii) were only registered in Mafra and near Coimbra (Soure). Detection of human LB cases was achieved through routine diagnosis in Institute of Hygiene and Tropical Medicine, where several diagnostic techniques were applied. Positive cases were confirmed by immunoblotting (15.5%) and/or amplification of B. burgdorferi s.l. intergenic-spacer of rRNA 5S-23S (rrf-rrl) (28%), with identification of two pathogenic genospecies (B. garinii and B. afzelii), besides B. lusitaniae. Lisboa, Coimbra, Tomar, Viseu and Almada were the main geographic origins of LB positive patients. The main environmental determinants of tick distribution and thus in the epidemiological cycle of Lyme Borreliosis in Portugal were related to climate (temperature, humidity and precipitation) and landscape composition (open areas, mixed and deciduous forests). Landscape structure (e.gfragmentation) was also important in determining tick presence in an area. These environmental factors were used to build risk maps were created for the three main tick-species potentially implicated in the transmission of LB agents in Portugal (I. ricinus, D. marginatus and Rhipicephalus spp). In conclusion, Lyme Borreliosis exists in Portugal and presents a complex epidemiology, as follows: i) besides the known European I. ricinus-vector, other potential tick species were found as vectors for LB spirochetes, being susceptibe to be associated with numerous reservoir hosts (still to investigate) and specific biotopes; ii) an higher diversity of genomic species belonging to B. burgdorferi s.l. complex, which resulted from this large amplitude of both vectors and reservoirs; and iii) a generalized distribution of LB patients, with important infection rates associated with the referred diversity of pathogenic agents, not only with the two more prevalent LB genomic species already recognized in Europe (B. garinii and B. afzelii), as with the recently isolated B. lusitaniae, which induces a clinical status apparently restricted to the Mediterranean basin

Effects of Irrigation Rate and Planting Density on Maize Yield and Water Use Efficiency in the Temperate Climate of Serbia

Scarce water resources severely limit maize (Zea mays L.) cultivation in the temperate regions of northern Serbia. A two-year field experiment was conducted to investigate the effects of irrigation and planting density on yield and water use efficiency in temperate climate under sprinkler irrigation. The experiment included five irrigation treatments (full irrigated treatment – FIT; 80% FIT, 60% FIT, 40% FIT, and rainfed) and three planting densities (PD1: 54,900 plants ha–1 ; PD2: 64,900 plants ha–1; PD3: 75,200 plants ha–1). There was increase in yield with the irrigation (1.05–80.00%) as compared to the rainfed crop. Results showed that decreasing irrigation rates resulted in a decrease in yield, crop water use efficiency (WUE), and irrigation water use efficiency (IWUE). Planting density had significant effects on yield, WUE, and IWUE which differed in both years. Increasing planting density gradually increased yield, WUE, and IWUE. For the pooled data, irrigation rate, planting density and their interaction was significant (P < 0.05). The highest two-year average yield, WUE, and IWUE were found for FIT-PD3 (14,612 kg ha–1), rainfed-PD2 (2.764 kg m–3), and 60% FITPD3 (2.356 kg m–3), respectively. The results revealed that irrigation is necessary for maize cultivation because rainfall is insufficient to meet the crop water needs. In addition, if water becomes a limiting factor, 80% FIT-PD3 with average yield loss of 15% would be the best agronomic practices for growing maize with a sprinkler irrigation system in a temperate climate of Serbia

Ultrasensitive detection of toxocara canis excretory-secretory antigens by a nanobody electrochemical magnetosensor assay.

peer reviewedHuman Toxocariasis (HT) is a zoonotic disease caused by the migration of the larval stage of the roundworm Toxocara canis in the human host. Despite of being the most cosmopolitan helminthiasis worldwide, its diagnosis is elusive. Currently, the detection of specific immunoglobulins IgG against the Toxocara Excretory-Secretory Antigens (TES), combined with clinical and epidemiological criteria is the only strategy to diagnose HT. Cross-reactivity with other parasites and the inability to distinguish between past and active infections are the main limitations of this approach. Here, we present a sensitive and specific novel strategy to detect and quantify TES, aiming to identify active cases of HT. High specificity is achieved by making use of nanobodies (Nbs), recombinant single variable domain antibodies obtained from camelids, that due to their small molecular size (15kDa) can recognize hidden epitopes not accessible to conventional antibodies. High sensitivity is attained by the design of an electrochemical magnetosensor with an amperometric readout with all components of the assay mixed in one single step. Through this strategy, 10-fold higher sensitivity than a conventional sandwich ELISA was achieved. The assay reached a limit of detection of 2 and15 pg/ml in PBST20 0.05% or serum, spiked with TES, respectively. These limits of detection are sufficient to detect clinically relevant toxocaral infections. Furthermore, our nanobodies showed no cross-reactivity with antigens from Ascaris lumbricoides or Ascaris suum. This is to our knowledge, the most sensitive method to detect and quantify TES so far, and has great potential to significantly improve diagnosis of HT. Moreover, the characteristics of our electrochemical assay are promising for the development of point of care diagnostic systems using nanobodies as a versatile and innovative alternative to antibodies. The next step will be the validation of the assay in clinical and epidemiological contexts