Decomposition of ground biomass of secondary forest and yield of annual crops in no tillage

ABSTRACT The objective of this work was to evaluate the dynamics of decomposition process of chopped secondary forest system, previously enriched with legumes Inga velutina Willd. and Stryphnodendron pulcherrimum (Willd.) Hochr. and the contribution of this process to the nutrient input to the cultivation of corn and bean under no-tillage. The experimental design was a randomized block, split plot with four replications. The plots were two species (I. velutina and S. pulcherrimum) and the subplots were seven times of evaluation (0, 7, 28, 63, 189, 252, 294 days after experiment installation). There was no difference (p ≥ 0.05) between the secondary forest systems enriched and no interaction with times for biomass waste, decomposition constant and half-life time. The waste of S. pulcherrimum trees had higher (p < 0.05) C/N ratio than that I. velutina. However, this one was higher (p < 0.05) in lignin content. Nevertheless, the dynamics of residue decomposition was similar. The corn yield was higher (p < 0.05) in cultivation under I.velutina waste. Meanwhile, the beans planted after corn, shows similar (p > 0.05) yield in both areas, regardless of the waste origin.

Growth and physiological characteristics of the weed false johnsongrass ( Sorghum arundinaceum (Desv.) Stapf)

ABSTRACT Sorghum arundinaceum (Desv.) Stapf is a weed that belongs to the Poaceae family and is widespread throughout Brazil. Despite the frequent occurrence, infesting cultivated areas, there is little research concerning the biology and physiology of this species. The objective of this research was to evaluate the growth, carbon partitioning and physiological characteristics of the weed Sorghum arundinaceum in greenhouse. Plants were collected at regular intervals of seven days, from 22 to 113 days after transplanting (DAT). In each sample, we determined plant height, root volume, leaf area and dry matter, and subsequently we perfomed the growth analysis, we have determined the dry matter partitioning among organs, the accumulation of dry matter, the specific leaf area, the relative growth rate and leaf weight ratio. At 36, 78 and 113 DAT, the photosynthetic and transpiration rates, stomatal conductance, CO2 concentration and chlorophyll fluorescence were evaluated. The Sorghum arundinaceum reached 1.91 in height, with slow initial growth and allocated much of the biomass in the roots. The photosynthetic rate and the maximum quantum yield of FSII are similar throughout the growth cycle. At maturity the Sorghum arundinaceum presents higher values of transpiration rate, stomatal conductance and non-photochemical quenching coefficient (NPQ).

Growth and nutrient accumulation in mycorrhized papaya seedlings cultivated in a phosphorus-fertilized substrate

ABSTRACT The indiscriminate use of mineral fertilizers in papaya orchards has increased production costs, and the use of arbuscular mycorrhizal fungi is a promising alternative to reduce such expenses. Therefore, the present research aimed at studying the efficiency of arbuscular mycorrhizal fungi (AMF) on dry matter and nutrient accumulation in Sunrise Solo papaya seedlings, by applying doses of P2O5 (triple superphosphate) that are harmful to the symbiosis. The experiment was carried out in a protected environment and was set up in a randomized block design with four replications, and consisted of four P2O5 doses (0, 672, 1386 and 2100 mg dm-3), three mycorrhizal fungi species (Gigaspora margarita, Entrophospora colombiana and Scutellospora heterogama) and the control treatment (mycorrhiza-free). Shoot and root dry matter as well as nitrogen, phosphorus and potassium contents in leaf and root tissues were assessed. Mycorrhizal inoculation promoted a 30% increase in shoot dry matter in relation to the control treatment. Mycorrhizal fungi promoted increases in leaf and root nitrogen content up to 672 mg dm-3 P2O5. Inoculation of E. colombiana favored the highest gains in root and shoot dry matter. P2O5 fertilization increased foliar and root phosphorus content.

STRATEGIC DECISIONS OF FAMILY FIRMS ON CASH ACCUMULATION

ABSTRACTGiven the particular strengths, weaknesses, and peculiarities of family firms as well as the importance of liquidity in today’s marketplace, we analyze the distinct characteristics and strategies of family businesses related to the amount of cash a firm holds. We look beyond the traditional factors that influence decisions related to cash management to examine factors that are particularly important for family firms. Specifically, we outline the relevance of strategic decisions guided by family firms’ conservatism, flexibility, long-term view, and the active control that they have over family members. To our knowledge, no prior studies exist regarding family firms and their strategic adjustment of cash holding. Therefore, we investigate whether the ownership structure of the firm (through the presence of a controlling family) moderates decisions on cash holding. We found that family firms tend to accumulate cash for strategic reasons and as a result of their own idiosyncrasies. Thus, family firms can achieve optimal cash accumulation more efficiently than non-family firms.

Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Metal accumulation and oxidative stress biomarkers in octopus (Octopus vulgaris) from Northwest Atlantic

Metals are ubiquitous in the environment and accumulate in aquatic organisms and are known for their ability to enhance the production of reactive oxygen species (ROS). In aquatic species, oxidative stress mechanisms have been studied by measuring antioxidant enzyme activities and oxidative damages in tissues. The aim of this study was to apply and validate a set of oxidative stress biomarkers and correlate responses with metal contents in tissues of common octopus (Octopus vulgaris). Antioxidant enzyme activity (catalase — CAT, superoxide dismutase — SOD and glutathione S-transferases — GST), oxidative damages (lipid peroxidation — LPO and protein carbonyl content — PCO) andmetal content (Cu, Zn, Pb, Cd and As) in the digestive gland and armof octopus, collected in the NWPortuguese coast in different periods, were assessed after capture and after 14 days in captivity. CAT and SOD activitieswere highly responsive to fluctuations inmetal concentrations and able to reduce oxidative damage, LPO and PCO in the digestive gland. CAT activity was also positively correlated with SOD and GST activities, which emphasizes that the three enzymes respond in a coordinated way to metal induced oxidative stress. Our results validate the use of oxidative stress biomarkers to assess metal pollution effects in this ecological and commercial relevant species.Moreover, octopus seems to have the ability to control oxidative damage by triggering an antioxidant enzyme coordinated response in the digestive gland.

Mutation accumulation in Tetrahymena

Background - The rate and fitness effects of mutations are key in understanding the evolution of every species. Traditionally, these parameters are estimated in mutation accumulation experiments where replicate lines are propagated in conditions that allow mutations to randomly accumulate without the purging effect of natural selection. These experiments have been performed with many model organisms but we still lack empirical estimates of the rate and effects of mutation in the protists. Results - We performed a mutation accumulation (MA) experiment in Tetrahymena thermophila, a species that can reproduce sexually and asexually in nature, and measured both the mean decline and variance increase in fitness of 20 lines. The results obtained with T. thermophila were compared with T. pyriformis that is an obligate asexual species. We show that MA lines of T. thermophila go to extinction at a rate of 1.25 clonal extinctions per bottleneck. In contrast, populations of T. pyriformis show a much higher resistance to extinction. Variation in gene copy number is likely to be a key factor in explaining these results, and indeed we show that T. pyriformis has a higher mean copy number per cell than T. thermophila. From fitness measurements during the MA experiment, we infer a rate of mutation to copy number variation of 0.0333 per haploid MAC genome of T. thermophila and a mean effect against copy number variation of 0.16. A strong effect of population size in the rate of fitness decline was also found, consistent with the increased power of natural selection. Conclusions - The rate of clonal extinction measured for T. thermophila is characteristic of a mutational degradation and suggests that this species must undergo sexual reproduction to avoid the deleterious effects detected in the laboratory experiments. We also suggest that an increase in chromosomal copy number associated with the phenotypic assortment of amitotic divisions can provide an alternative mechanism to escape the deleterious effect of random chromosomal copy number variation in species like T. pyriformis that lack the resetting mechanism of sexual reproduction. Our results are relevant to the understanding of cell line longevity and senescence in ciliates.

Forest resources in Graciosa Island (Azores) : biomass availability for sustainable energetic use

Dissertação de Mestrado em Ambiente, Saúde e Segurança.

Cleanup of industrial effluents containing heavy metals: a new opportunity of valorising the biomass produced by brewing industry

Heavy metal pollution is a matter of concern in industrialised countries. Contrary to organic pollutants, heavy metals are not metabolically degraded. This fact has two main consequences: its bioremediation requires another strategy and heavy metals can be indefinitely recycled. Yeast cells of Saccharomyces cerevisiae are produced at high amounts as a by-product of brewing industry constituting a cheap raw material. In the present work, the possibility of valorising this type of biomass in the bioremediation of real industrial effluents containing heavy metals is reviewed. Given the autoaggregation capacity (flocculation) of brewing yeast cells, a fast and off-cost yeast separation is achieved after the treatment of metal-laden effluent, which reduces the costs associated with the process. This is a critical issue when we are looking for an effective, eco-friendly, and low-cost technology. The possibility of the bioremediation of industrial effluents linked with the selective recovery of metals, in a strategy of simultaneous minimisation of environmental hazard of industrial wastes with financial benefits from reselling or recycling the metals, is discussed.

Probabilistic consensus clustering using evidence accumulation

Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.

Biomass production of four Cynara cardunculus clones and lignin composition analysis

Four Cynara cardunculus clones, two from Portugal and two from Spain were studied for biomass production and their lignin was characterized. The clones differed in biomass partitioning: Spanish clones produced more capitula (54.5% vs. 43.9%), and Portuguese clones more stalks (37.2% vs. 25.6%). The heating values (HHV0) of the stalks were similar, ranging from 17.1 to 18.4 MJ/kg. Lignin was studied by analytical pyrolysis (Py-GC/MS(FID)), separately in depithed stalks (stalksDP) and pith. StalksDP had in average higher relative proportions of lignin derived compounds than pith (23.9% vs. 21.8%) with slightly different lignin monomeric composition: pith samples were richer in syringyl units as compared to stalksDP (64% vs. 53%), with S/G ratios of 2.1 and 1.3, respectively. The H:G:S composition was 7:40:53 in stalksDP and 7:29:64 in pith. The lignin content ranged from 18.8% to 25.5%, enabling a differentiation between clones and provenances. © 2015 Elsevier Ltd. All rights reserved.

Supply chain optimization of residual forestry biomass for bioenergy production: the case study of Portugal

Within a large set of renewable energies being explored to tackle energy sourcing problems, bioenergy can represent an attractive solution if effectively managed. The supply chain design supported by mathematical programming can be used as a decision support tool to the successful bioenergy production systems establishment. This strategic decision problem is addressed in this paper where we intent to study the design of the residual forestry biomass to bioelectricity production in the Portuguese context. In order to contribute to attain better solutions a mixed integer linear programming (MILP) model is developed and applied in order to optimize the design and planning of the bioenergy supply chain. While minimizing the total supply chain cost the production energy facilities capacity and location are defined. The model also includes the optimal selection of biomass amounts and sources, the transportation modes selection, and links that must be established for biomass transportation and products delivers to markets. Results illustrate the positive contribution of the mathematical programming approach to achieve viable economic solutions. Sensitivity analysis on the most uncertain parameters was performed: biomass availability, transportation costs, fixed operating costs and investment costs. (C) 2015 Elsevier Ltd. All rights reserved.

Accumulation, responses and genotoxicity of trace elements in octopus vulgaris

Dissertação apresentada para obtenção do Grau de Doutor em Bioquímica pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

A MAP approach to evidence accumulation clustering

The Evidence Accumulation Clustering (EAC) paradigm is a clustering ensemble method which derives a consensus partition from a collection of base clusterings obtained using different algorithms. It collects from the partitions in the ensemble a set of pairwise observations about the co-occurrence of objects in a same cluster and it uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. The Probabilistic Evidence Accumulation for Clustering Ensembles (PEACE) algorithm is a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix based on a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters. In this paper we extend the PEACE algorithm by deriving a consensus solution according to a MAP approach with Dirichlet priors defined for the unknown probabilistic cluster assignments. In particular, we study the positive regularization effect of Dirichlet priors on the final consensus solution with both synthetic and real benchmark data.