Boosting the globalization of plant proteomics through INPPO: current developments 3 and future prospects

The International Plant Proteomics Organization (INPPO) is a non-profit-organization consisting of people who are involved or interested in plant proteomics. INPPO is constantly growing in volume and activity, which is mostly due to the realization among plant proteomics researchers worldwide for the need of such a global platform. Their active participation resulted in the rapid growth within the first year of INPPO’s official launch in 2011 via its website (www.inppo.com) and publication of the ‘viewpoint paper’ in a special issue of PROTEOMICS (May 2011). Here, we will be highlighting the progress achieved in the year 2011 and the future targets for the year 2012 and onwards. INPPO has achieved a successful administrative structure, the Core Committee (CC; composed of President, Vice-President, and General Secretaries), Executive Council (EC), and General Body (GB) toward achieving the INPPO objectives by its proposed initiatives. Various committees and subcommittees are in the process of being functionalized via discussion amongst scientists around the globe. INPPO’s primary aim to popularize the plant proteomics research in biological sciences has also been recognized by PROTEOMICS where a new section has been introduced to plant proteomics starting January 2012, following the very first issue of this journal devoted to plant proteomics in May 2011. To disseminate organizational activities to the scientific community, INPPO has launched a biannual (in January & July) newsletter entitled “INPPO Express: News & Views” with the first issue published in January 2012. INPPO is also planning to have several activities in 2012, including programs within the Education Outreach committee in different countries, and the development of research ideas and proposals with priority on crop and horticultural plants, while keeping tight interactions with proteomics programs on model plants such as Arabidopsis thaliana, rice, or Medicago truncatula. Altogether, the INPPO progress and upcoming activities are because of immense support, dedication, and hard work of all members of the INPPO family, and also due to the wide encouragement and support from the communities (scientific and non-scientific).

Boosting systemic and secreted antibody responses in mice orally immunized with recombinant Bacillus subtilis strains following parenteral priming with a DNA vaccine encoding the enterotoxigenic Escherichia coli (ETEC) CFA/I fimbriae B subunit

Recombinant Bacillus subtilis strains, either spores or vegetative cells, may be employed as safe and low cost orally delivered live vaccine vehicles. In this study, we report the use of an orally delivered B. subtilis vaccine strain to boost systemic and secreted antibody responses in mice i.m. primed with a DNA vaccine encoding the structural subunit (CfaB) of the CFA/I fimbriae encoded by enterotoxigenic Escherichia coli (ETEC), an important etiological agent of diarrhea among travelers and children living in endemic regions. DBA/2 female mice submitted to the prime-boost immunization regimen developed synergic serum (IgG) and mucosal (IgA) antibody responses to the target CfaB antigen. Moreover, in contrast to mice immunized only with one vaccine formulation, sera harvested from prime-boosted vaccinated individuals inhibited adhesion of ETEC cells to human red blood cells. Additionally, vaccinated dams conferred full passive protection to suckling newborn mice challenged with a virulent ETEC strain. Taken together the present results further demonstrate the potential use of recombinant B. subtilis strains as an alternative live vaccine vehicle. (C) 2008 Elsevier Ltd. All rights reserved.

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

Regressão Simplex aplicada a delineamentos de mistura e utilização do Algoritmo Boosting

In the composition of this work are present two parts. The first part contains the theory used. The second part contains the two articles. The first article examines two models of the class of generalized linear models for analyzing a mixture experiment, which studied the effect of different diets consist of fat, carbohydrate, and fiber on tumor expression in mammary glands of female rats, given by the ratio mice that had tumor expression in a particular diet. Mixture experiments are characterized by having the effect of collinearity and smaller sample size. In this sense, assuming normality for the answer to be maximized or minimized may be inadequate. Given this fact, the main characteristics of logistic regression and simplex models are addressed. The models were compared by the criteria of selection of models AIC, BIC and ICOMP, simulated envelope charts for residuals of adjusted models, odds ratios graphics and their respective confidence intervals for each mixture component. It was concluded that first article that the simplex regression model showed better quality of fit and narrowest confidence intervals for odds ratio. The second article presents the model Boosted Simplex Regression, the boosting version of the simplex regression model, as an alternative to increase the precision of confidence intervals for the odds ratio for each mixture component. For this, we used the Monte Carlo method for the construction of confidence intervals. Moreover, it is presented in an innovative way the envelope simulated chart for residuals of the adjusted model via boosting algorithm. It was concluded that the Boosted Simplex Regression model was adjusted successfully and confidence intervals for the odds ratio were accurate and lightly more precise than the its maximum likelihood version.

report of the workshop on boosting SME development and competitiveness in the Caribbean

The Economic Commission for Latin America and the Caribbean (ECLAC) Subregional Headquarters for the Caribbean, in collaboration with the Division of Production, Productivity and Management at ECLAC Headquarters in Chile, convened a one-day workshop on “Boosting SME Development and Competitiveness in the Caribbean”, at the Subregional Headquarters for the Caribbean in Port of Spain on 14 May 2009. The workshop was the culmination of country studies that were carried out under an Italian Government-funded project to assess the policies, institutions and instruments for dynamic Small and Medium Enterprises (SME) development and competitiveness in Jamaica, Suriname and Trinidad and Tobago. The aim was to use the lessons learned from the three country studies to inform policy and practice in the other member countries of the Caribbean Development and Cooperation Committee (CDCC). The main objectives of the workshop were to: (a) share and discuss the findings of the country studies and lessons learned; (b) provide a forum for high quality discussion of the policy environment, instruments, business development and support services required for successful SME development in the Caribbean; and (c) map out a strategy for moving from analysis and recommendation to policy implementation and business changes in order to promote a dynamic and competitive SME sector. The workshop aimed to arrive at practical solutions to major constraints and a weighting of key actions in order of priority of implementation, by adopting a problem-solving approach. Participants at the workshop included representatives of key SME support institutions in the region, actual SMEs and academic researchers. The list of participants and provisional programme are annexed to this report.

Economic Survey of Latin America and the Caribbean 2015: Challenges in boosting the investment cycle to reinvigorate growth

The 2015 edition of the Economic Survey of Latin America and the Caribbean consists of three parts. Part I outlines the region’s economic performance in 2014 and analyses trends in the first half of 2015, as well as the outlook for the rest of the year. Part II analyses the dynamics of investment in Latin America and the Caribbean, the relationship between investment and the business cycle, the role of public investment, infrastructure gaps and the challenges in financing private investment. Part III of this publication may be accessed on the web page of the Economic Commission for Latin America and the Caribbean (http://www.cepal.org/en/node/33006). It contains the notes relating to the economic performance of the countries of Latin America and the Caribbean in 2014 and the first half of 2015, together with their respective statistical annexes, which present the main economic indicators of the countries of the region. The cut-off date for updating the statistical information in this publication was 30 June 2015.

Classificação fonética utilizando Boosting e SVM

Para compor um sistema de Reconhecimento Automático de Voz, pode ser utilizada uma tarefa chamada Classificação Fonética, onde a partir de uma amostra de voz decide-se qual fonema foi emitido por um interlocutor. Para facilitar a classificação e realçar as características mais marcantes dos fonemas, normalmente, as amostras de voz são pré- processadas através de um fronl-en'L Um fron:-end, geralmente, extrai um conjunto de parâmetros para cada amostra de voz. Após este processamento, estes parâmetros são insendos em um algoritmo classificador que (já devidamente treinado) procurará decidir qual o fonema emitido. Existe uma tendência de que quanto maior a quantidade de parâmetros utilizados no sistema, melhor será a taxa de acertos na classificação. A contrapartida para esta tendência é o maior custo computacional envolvido. A técnica de Seleção de Parâmetros tem como função mostrar quais os parâmetros mais relevantes (ou mais utilizados) em uma tarefa de classificação, possibilitando, assim, descobrir quais os parâmetros redundantes, que trazem pouca (ou nenhuma) contribuição à tarefa de classificação. A proposta deste trabalho é aplicar o classificador SVM à classificação fonética, utilizando a base de dados TIMIT, e descobrir os parâmetros mais relevantes na classificação, aplicando a técnica Boosting de Seleção de Parâmetros.

Increasing Food Production in Africa by Boosting the Productivity of Understudied Crops

The Green Revolution has enabled Asian countries to boost their crop production enormously. However, Africa has not benefitted from this agricultural revolution since it did not consider local, but important crops grown in the continent. In addition to their versatile adaptation to extreme environmental conditions, African indigenous crops provide income for subsistence farmers and serve as staple food for the vast majority of low-income consumers. These crops, which are composed of cereals, legumes, vegetables and root crops, are commonly known as underutilized or orphan crops. Recently, some of these under-researched crops have received the attention of the national and international research community, and modern improvement techniques including diverse genetic and genomic tools have been applied in order to boost their productivity. The major bottlenecks affecting the productivity of these crops are unimproved genetic traits such as low yield and poor nutritional status and environmental factors such as drought, weeds and pests. Hence, an agricultural revolution is needed to increase food production of these under-researched crops in order to feed the ever-increasing population in Africa. Here, we present both the benefits and drawbacks of major African crops, the efforts being made to improve them, and suggestions for some future directions.

New definitions of margin for multi-class Boosting algorithms

La familia de algoritmos de Boosting son un tipo de técnicas de clasificación y regresión que han demostrado ser muy eficaces en problemas de Visión Computacional. Tal es el caso de los problemas de detección, de seguimiento o bien de reconocimiento de caras, personas, objetos deformables y acciones. El primer y más popular algoritmo de Boosting, AdaBoost, fue concebido para problemas binarios. Desde entonces, muchas han sido las propuestas que han aparecido con objeto de trasladarlo a otros dominios más generales: multiclase, multilabel, con costes, etc. Nuestro interés se centra en extender AdaBoost al terreno de la clasificación multiclase, considerándolo como un primer paso para posteriores ampliaciones. En la presente tesis proponemos dos algoritmos de Boosting para problemas multiclase basados en nuevas derivaciones del concepto margen. El primero de ellos, PIBoost, está concebido para abordar el problema descomponiéndolo en subproblemas binarios. Por un lado, usamos una codificación vectorial para representar etiquetas y, por otro, utilizamos la función de pérdida exponencial multiclase para evaluar las respuestas. Esta codificación produce un conjunto de valores margen que conllevan un rango de penalizaciones en caso de fallo y recompensas en caso de acierto. La optimización iterativa del modelo genera un proceso de Boosting asimétrico cuyos costes dependen del número de etiquetas separadas por cada clasificador débil. De este modo nuestro algoritmo de Boosting tiene en cuenta el desbalanceo debido a las clases a la hora de construir el clasificador. El resultado es un método bien fundamentado que extiende de manera canónica al AdaBoost original. El segundo algoritmo propuesto, BAdaCost, está concebido para problemas multiclase dotados de una matriz de costes. Motivados por los escasos trabajos dedicados a generalizar AdaBoost al terreno multiclase con costes, hemos propuesto un nuevo concepto de margen que, a su vez, permite derivar una función de pérdida adecuada para evaluar costes. Consideramos nuestro algoritmo como la extensión más canónica de AdaBoost para este tipo de problemas, ya que generaliza a los algoritmos SAMME, Cost-Sensitive AdaBoost y PIBoost. Por otro lado, sugerimos un simple procedimiento para calcular matrices de coste adecuadas para mejorar el rendimiento de Boosting a la hora de abordar problemas estándar y problemas con datos desbalanceados. Una serie de experimentos nos sirven para demostrar la efectividad de ambos métodos frente a otros conocidos algoritmos de Boosting multiclase en sus respectivas áreas. En dichos experimentos se usan bases de datos de referencia en el área de Machine Learning, en primer lugar para minimizar errores y en segundo lugar para minimizar costes. Además, hemos podido aplicar BAdaCost con éxito a un proceso de segmentación, un caso particular de problema con datos desbalanceados. Concluimos justificando el horizonte de futuro que encierra el marco de trabajo que presentamos, tanto por su aplicabilidad como por su flexibilidad teórica. Abstract The family of Boosting algorithms represents a type of classification and regression approach that has shown to be very effective in Computer Vision problems. Such is the case of detection, tracking and recognition of faces, people, deformable objects and actions. The first and most popular algorithm, AdaBoost, was introduced in the context of binary classification. Since then, many works have been proposed to extend it to the more general multi-class, multi-label, costsensitive, etc... domains. Our interest is centered in extending AdaBoost to two problems in the multi-class field, considering it a first step for upcoming generalizations. In this dissertation we propose two Boosting algorithms for multi-class classification based on new generalizations of the concept of margin. The first of them, PIBoost, is conceived to tackle the multi-class problem by solving many binary sub-problems. We use a vectorial codification to represent class labels and a multi-class exponential loss function to evaluate classifier responses. This representation produces a set of margin values that provide a range of penalties for failures and rewards for successes. The stagewise optimization of this model introduces an asymmetric Boosting procedure whose costs depend on the number of classes separated by each weak-learner. In this way the Boosting procedure takes into account class imbalances when building the ensemble. The resulting algorithm is a well grounded method that canonically extends the original AdaBoost. The second algorithm proposed, BAdaCost, is conceived for multi-class problems endowed with a cost matrix. Motivated by the few cost-sensitive extensions of AdaBoost to the multi-class field, we propose a new margin that, in turn, yields a new loss function appropriate for evaluating costs. Since BAdaCost generalizes SAMME, Cost-Sensitive AdaBoost and PIBoost algorithms, we consider our algorithm as a canonical extension of AdaBoost to this kind of problems. We additionally suggest a simple procedure to compute cost matrices that improve the performance of Boosting in standard and unbalanced problems. A set of experiments is carried out to demonstrate the effectiveness of both methods against other relevant Boosting algorithms in their respective areas. In the experiments we resort to benchmark data sets used in the Machine Learning community, firstly for minimizing classification errors and secondly for minimizing costs. In addition, we successfully applied BAdaCost to a segmentation task, a particular problem in presence of imbalanced data. We conclude the thesis justifying the horizon of future improvements encompassed in our framework, due to its applicability and theoretical flexibility.

Boosting with recombinant vaccinia increases immunogenicity and protective efficacy of malaria DNA vaccine

To enhance the efficacy of DNA malaria vaccines, we evaluated the effect on protection of immunizing with various combinations of DNA, recombinant vaccinia virus, and a synthetic peptide. Immunization of BALB/c mice with a plasmid expressing Plasmodium yoelii (Py) circumsporozoite protein (CSP) induces H-2Kd-restricted CD8+ cytotoxic T lymphocyte (CTL) responses and CD8+ T cell- and interferon (IFN)-γ-dependent protection of mice against challenge with Py sporozoites. Immunization with a multiple antigenic peptide, including the only reported H-2Kd-restricted CD8+ T cell epitope on the PyCSP (PyCSP CTL multiple antigenic peptide) and immunization with recombinant vaccinia expressing the PyCSP induced CTL but only modest to minimal protection. Mice were immunized with PyCSP DNA, PyCSP CTL multiple antigenic peptide, or recombinant vaccinia expressing PyCSP, were boosted 9 wk later with the same immunogen or one of the others, and were challenged. Only mice immunized with DNA and boosted with vaccinia PyCSP (D-V) (11/16: 69%) or DNA (D-D) (7/16: 44%) had greater protection (P < 0.0007) than controls. D-V mice had significantly higher individual levels of antibodies and class I-restricted CTL activity than did D-D mice; IFN-γ production by ELIspot also was higher in D-V than in D-D mice. In a second experiment, three different groups of D-V mice each had higher levels of protection than did D-D mice, and IFN-γ production was significantly greater in D-V than in D-D mice. The observation that priming with PyCSP DNA and boosting with vaccinia-PyCSP is more immunogenic and protective than immunizing with PyCSP DNA alone supports consideration of a similar sequential immunization approach in humans.