Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Minimising total energy requirements in amplified links by optimising amplifier spacing

We investigate the energy optimization (minimization) for amplified links. We show that using the using a well-established analytic nonlinear signal-to-noise ratio noise model that for a simple amplifier model there are very clear, fiber independent, amplifier gains which minimize the total energy requirement. With a generalized amplifier model we establish the spacing for the optimum power per bit as well as the nonlinear limited optimum power. An amplifier spacing corresponding to 13 dB gain is shown to be a suitable compromise for practical amplifiers operating at the optimum nonlinear power. © 2014 Optical Society of America.

Weak form equation–based finite-element modeling of viscoelastic asphalt mixtures

The objective of this study is to demonstrate using weak form partial differential equation (PDE) method for a finite-element (FE) modeling of a new constitutive relation without the need of user subroutine programming. The viscoelastic asphalt mixtures were modeled by the weak form PDE-based FE method as the examples in the paper. A solid-like generalized Maxwell model was used to represent the deforming mechanism of a viscoelastic material, the constitutive relations of which were derived and implemented in the weak form PDE module of Comsol Multiphysics, a commercial FE program. The weak form PDE modeling of viscoelasticity was verified by comparing Comsol and Abaqus simulations, which employed the same loading configurations and material property inputs in virtual laboratory test simulations. Both produced identical results in terms of axial and radial strain responses. The weak form PDE modeling of viscoelasticity was further validated by comparing the weak form PDE predictions with real laboratory test results of six types of asphalt mixtures with two air void contents and three aging periods. The viscoelastic material properties such as the coefficients of a Prony series model for the relaxation modulus were obtained by converting from the master curves of dynamic modulus and phase angle. Strain responses of compressive creep tests at three temperatures and cyclic load tests were predicted using the weak form PDE modeling and found to be comparable with the measurements of the real laboratory tests. It was demonstrated that the weak form PDE-based FE modeling can serve as an efficient method to implement new constitutive models and can free engineers from user subroutine programming.

As relações da tríplice hélice nas fundações de apoio brasileiras: uma avaliação da dinâmica de captação de recursos para projetos de inovação

Foundations support constitute one of the types of legal entities of private law forged with the purpose of supporting research projects, education and extension and institutional, scientific and technological development of Brazil. Observed as links of the relationship between company, university, and government, foundations supporting emerge in the Brazilian scene from the principle to establish an economic platform of development based on three pillars: science, technology and innovation – ST&I. In applied terms, these ones operate as tools of debureaucratisation making the management between public entities more agile, especially in the academic management in accordance with the approach of Triple Helix. From the exposed, the present study has as purpose understanding how the relation of Triple Helix intervenes in the fund-raising process of Brazilian foundations support. To understand the relations submitted, it was used the interaction models University-Company-Government recommended by Sábato and Botana (1968), the approach of the Triple Helix proposed by Etzkowitz and Leydesdorff (2000), as well as the perspective of the national innovation systems discussed by Freeman (1987, 1995), Nelson (1990, 1993) and Lundvall (1992). The research object of this study consists of 26 state foundations that support research associated with the National Council of the State Foundations of Supporting Research - CONFAP, as well as the 102 foundations in support of IES associated with the National Council of Foundations of Support for Institutions of Higher Education and Scientific and Technological Research – CONFIES, totaling 128 entities. As a research strategy, this study is considered as an applied research with a quantitative approach. Primary research data were collected using the e-mail Survey procedure. Seventy-five observations were collected, which corresponds to 58.59% of the research universe. It is considering the use of the bootstrap method in order to validate the use of the sample in the analysis of results. For data analysis, it was used descriptive statistics and multivariate data analysis techniques: the cluster analysis; the canonical correlation and the binary logistic regression. From the obtained canonical roots, the results indicated that the dependency relationship between the variables of relations (with the actors of the Triple Helix) and the financial resources invested in innovation projects is low, assuming the null hypothesis of this study, that the relations of the Triple Helix do not have interfered positively or negatively in raising funds for investments in innovation projects. On the other hand, the results obtained with the cluster analysis indicate that entities which have greater quantitative and financial amounts of projects are mostly large foundations (over 100 employees), which support up to five IES, publish management reports and use in their capital structure, greater financing of the public department. Finally, it is pertinent to note that the power of the classification of the logistic model obtained in this study showed high predictive capacity (80.0%) providing to the academic community replication in environments of similar analysis.

Using individual tracking data to validate the predictions of species distribution models

The authors would like to thank the College of Life Sciences of Aberdeen University and Marine Scotland Science which funded CP's PhD project. Skate tagging experiments were undertaken as part of Scottish Government project SP004. We thank Ian Burrett for help in catching the fish and the other fishermen and anglers who returned tags. We thank José Manuel Gonzalez-Irusta for extracting and making available the environmental layers used as environmental covariates in the environmental suitability modelling procedure. We also thank Jason Matthiopoulos for insightful suggestions on habitat utilization metrics as well as Stephen C.F. Palmer, and three anonymous reviewers for useful suggestions to improve the clarity and quality of the manuscript.

Facial affect processing inbipolar disorder:a magnetoencephalography study

Background: Identifying biological markers to aid diagnosis of bipolar disorder (BD) is critically important. To be considered a possible biological marker, neural patterns in BD should be discriminant from those in healthy individuals (HI). We examined patterns of neuromagnetic responses revealed by magnetoencephalography (MEG) during implicit emotion-processing using emotional (happy, fearful, sad) and neutral facial expressions, in sixteen BD and sixteen age- and gender-matched healthy individuals. Methods: Neuromagnetic data were recorded using a 306-channel whole-head MEG ELEKTA Neuromag System, and preprocessed using Signal Space Separation as implemented in MaxFilter (ELEKTA). Custom Matlab programs removed EOG and ECG signals from filtered MEG data, and computed means of epoched data (0-250ms, 250-500ms, 500-750ms). A generalized linear model with three factors (individual, emotion intensity and time) compared BD and HI. A principal component analysis of normalized mean channel data in selected brain regions identified principal components that explained 95% of data variation. These components were used in a quadratic support vector machine (SVM) pattern classifier. SVM classifier performance was assessed using the leave-one-out approach. Results: BD and HI showed significantly different patterns of activation for 0-250ms within both left occipital and temporal regions, specifically for neutral facial expressions. PCA analysis revealed significant differences between BD and HI for mild fearful, happy, and sad facial expressions within 250-500ms. SVM quadratic classifier showed greatest accuracy (84%) and sensitivity (92%) for neutral faces, in left occipital regions within 500-750ms. Conclusions: MEG responses may be used in the search for disease specific neural markers.

Data to Decision in a Dynamic Ocean: Robust Species Distribution Models and Spatial Decision Frameworks

Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.

For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.

Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.

Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.

In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.

For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.

Advances in Dynamic Modeling and Computation for Count Data

A class of multi-process models is developed for collections of time indexed count data. Autocorrelation in counts is achieved with dynamic models for the natural parameter of the binomial distribution. In addition to modeling binomial time series, the framework includes dynamic models for multinomial and Poisson time series. Markov chain Monte Carlo (MCMC) and Po ́lya-Gamma data augmentation (Polson et al., 2013) are critical for fitting multi-process models of counts. To facilitate computation when the counts are high, a Gaussian approximation to the P ́olya- Gamma random variable is developed.

Three applied analyses are presented to explore the utility and versatility of the framework. The first analysis develops a model for complex dynamic behavior of themes in collections of text documents. Documents are modeled as a “bag of words”, and the multinomial distribution is used to characterize uncertainty in the vocabulary terms appearing in each document. State-space models for the natural parameters of the multinomial distribution induce autocorrelation in themes and their proportional representation in the corpus over time.

The second analysis develops a dynamic mixed membership model for Poisson counts. The model is applied to a collection of time series which record neuron level firing patterns in rhesus monkeys. The monkey is exposed to two sounds simultaneously, and Gaussian processes are used to smoothly model the time-varying rate at which the neuron’s firing pattern fluctuates between features associated with each sound in isolation.

The third analysis presents a switching dynamic generalized linear model for the time-varying home run totals of professional baseball players. The model endows each player with an age specific latent natural ability class and a performance enhancing drug (PED) use indicator. As players age, they randomly transition through a sequence of ability classes in a manner consistent with traditional aging patterns. When the performance of the player significantly deviates from the expected aging pattern, he is identified as a player whose performance is consistent with PED use.

All three models provide a mechanism for sharing information across related series locally in time. The models are fit with variations on the P ́olya-Gamma Gibbs sampler, MCMC convergence diagnostics are developed, and reproducible inference is emphasized throughout the dissertation.

Phase coexistence of cluster crystals: beyond the Gibbs phase rule

We report a study of the phase behavior of multiple-occupancy crystals through simulation. We argue that in order to reproduce the equilibrium behavior of such crystals it is essential to treat the number of lattice sites as a constraining thermodynamic variable. The resulting free-energy calculations thus differ considerably from schemes used for single-occupancy lattices. Using our approach, we obtain the phase diagram and the bulk modulus for a generalized exponential model that forms cluster crystals at high densities. We compare the simulation results with existing theoretical predictions. We also identify two types of density fluctuations that can lead to two sound modes and evaluate the corresponding elastic constants.

Seawater carbonate chemistry and hydrogen ions and carbonate chemistry in calcifying fluid of coral Astrangia poculata during experiments, 2011

A generalized physicochemical model of the response of marine organisms' calcifying fluids to CO2-induced ocean acidification is proposed. The model is based upon the hypothesis that some marine calcifiers induce calcification by elevating pH, and thus Omega aragonite, of their calcifying fluid by removing protons (H+). The model is explored through two end-member scenarios: one in which a fixed number of H+ is removed from their calcifying fluid, regardless of atmospheric pCO2, and another in which a fixed external-internal proton ratio ([H+]E/[H+]I) is maintained. The model is able to generate the full range of calcification response patterns observed in prior ocean acidification experiments and is consistent with the assertion that organisms' calcification response to ocean acidification is more negative for marine calcifiers that exert weaker control over their calcifying fluid pH. The model is empirically evaluated for the temperate scleractinian coral Astrangia poculata with in situ pH microelectrode measurements of the coral's calcifying fluid under control and acidified conditions. These measurements reveal that (1) the pH of the coral's calcifying fluid is substantially elevated relative to its external seawater under both control and acidified conditions, (2) the coral's [H+]E/[H+]I remains constant under control and acidified conditions, and (3) the coral removes fewer H+ from its calcifying fluid under acidified conditions than under control conditions. Thus, the carbonate system dynamics of A. poculata's calcifying fluid appear to be most consistent with the fixed [H+]E/[H+]I end-member scenario. Similar microelectrode experiments performed on additional taxa are required to assess the model's general applicability.

Modeling Hospital Readmissions in Dalarna County, Sweden

Unplanned hospital readmissions increase health and medical care costs and indicate lower the lower quality of the healthcare services. Hence, predicting patients at risk to be readmitted is of interest. Using administrative data of patients being treated in the medical centers and hospitals in the Dalarna County, Sweden, during 2008 – 2016 two risk prediction models of hospital readmission are built. The first model relies on the logistic regression (LR) approach, predicts correctly 2,648 out of 3,392 observed readmission in the test dataset, reaching a c-statistics of 0.69. The second model is built using random forests (RF) algorithm; correctly predicts 2,183 readmission (out of 3,366) and 13,198 non-readmission events (out of 18,982). The discriminating ability of the best performing RF model (c-statistic 0.60) is comparable to that of the logistic model. Although the discriminating ability of both LR and RF risk prediction models is relatively modest, still these models are capable to identify patients running high risk of hospital readmission. These patients can then be targeted with specific interventions, in order to prevent the readmission, improve patients’ quality of life and reduce health and medical care costs.

Spatial modeling of data with excessive zeros applied to reindeer pellet-group counts

We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under- and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.

Long-term changes of the phytoplankton community and biomass in the subtropical shallow Patos Lagoon Estuary, Brazil

Seasonal and interannual changes (1993e2012) of water temperature and transparency, river discharge, salinity, water quality properties, chlorophyll a (chl-a) and the carbon biomass of the main taxonomical phytoplankton groups were evaluated at a shallow station (~2 m) in the subtropical Patos Lagoon Estuary (PLE), Brazil. Large variations in salinity (0e35), due to a complex balance between Patos Lagoon outflow and oceanic inflows, affected significantly other water quality variables and phytoplankton dynamics, masking seasonal and interannual variability. Therefore, salinity effect was filtered out by means of a Generalized Additive Model (GAM). River discharge and salinity had a significant negative relation, with river discharge being highest and salinity lowest during July to October. Diatoms comprised the dominant phytoplankton group, contributing substantially to the seasonal cycle of chl-a showing higher values in austral spring/summer (September to April) and lowest in autumn/winter (May to August). PLE is a nutrient-rich estuary and the phytoplankton seasonal cycle was largely driven by light availability, with few exceptions in winter. Most variables exhibited large interannual variability. When varying salinity effect was accounted for, chl-a concentration and diatom biomass showed less irregularity over time, and significant increasing trends emerged for dinoflagellates and cyanobacteria. Long-term changes in phytoplankton and water quality were strongly related to variations in salinity, largely driven by freshwater discharge influenced by climatic variability, most pronounced for ENSO events. However, the significant increasing trend of the N:P ratio indicates that important environmental changes related to anthropogenic effects are undergoing, in addition to the hydrology in the PLE.

Efeitos dos fatores ambientais na comunidade de macrófitas em pequenos cursos lóticos perenes subtropicais

Neste estudo foi investigado como a distribuição das espécies e a produção de biomassa de macrófitas aquáticas são influenciadas pelas condições físico-químicas do ambiente. Também foi avaliado como uma espécie com maior potencial competitivo pode interferir na diversidade de espécies da comunidade macrofítica. Para tanto, em cada um dos três arroios, foram dispostos seis transecções, perpendiculares à margem. Em cada transecção foram demarcadas três unidades amostrais de 1m², nas quais foram registrados os parâmetros fitossociológicos cobertura e frequência relativas e valor de importância. A diversidade de espécies foi estimada pelo índice de Shannon, utilizando os valores de cobertura de espécies. Para determinar a biomassa das macrófitas aquáticas foram usados quadrats de 0,25m², alocados dentro da unidade amostral de 1m² usadas para quantificar os dados fitossociológicos, nos mesmos pontos onde foi feito o levantamento de cobertura da vegetação. Utilizamos como variáveis preditoras a velocidade da corrente, radiação solar incidente, coeficiente de sombreamento, vegetação ripária arbórea adjacente, nitrogênio orgânico dissolvido, carbono orgânico dissolvido e condutividade elétrica. Foram registradas 32 espécies de macrófitas aquáticas, distribuídas em 19 famílias e 28 gêneros. Conforme Análise de Correspondência Canônica (CCA), as espécies com maiores valores de biomassa foram relacionadas a unidades amostrais com alta incidência luminosa. As unidades amostrais com dominância de Pistia stratiotes apresentaram menor diversidade de espécies indicando que esta espécie, quando encontra condições que permitam sua proliferação, pode excluir espécies de menor potencial competitivo. De acordo com GLM (Generalized Linear Model), a ausência de vegetação ripária ou presente em apenas uma das margens e baixas velocidades de corrente configura-se em condições favoráveis ao estabelecimento e desenvolvimento de macrófitas aquáticas, possibilitando produção maiores valores de biomassa.

Valoração do efluente da indústria processadora de pescado por incorporação de nutrientes em aphanothece microscopica nägeli

Nesta tese procurou-se demonstrar a valoração do efluente do processamento de pescado por incorporação dos nutrientes em Aphanothece microscopica Nägeli a diferentes temperaturas. Para tanto o trabalho é composto de cinco artigos que objetivaram avaliar sob o ponto de vista do tratamento do efluente pela cianobactéria Aphanothece e a separação e avaliação da biomassa gerada. O primeiro artigo intitula-se “Influência da temperatura na remoção de nutrientes do efluente da indústria de pescado por Aphanothece microscopica Nägeli”, e teve por objetivo avaliar a influência da temperatura (10, 20 e 30ºC) em um sistema de tratamento pela cianobactéria Aphanothece na remoção de matéria orgânica, nitrogênio e fósforo do efluente oriundo do processamento de pescado. A análise dos resultados mostrou que a temperatura influenciou significativamente na remoção de DQO, NTK, N-NH4 + e P-PO4 -3 . Para os experimentos a 20 e 30ºC todos os limites estabelecidos para os parâmetros avaliados foram atingidos. O segundo artigo intitulado “Efeito de coagulantes no efluente da indústria da pesca visando à separação de biomassa quando tratado por cianobactéria” avaliou o efeito da concentração e pH de dois tipos de coagulantes, cloreto férrico (FeCl3) e sulfato de alumínio (Al2(SO4)3), na separação da biomassa da cianobactéria Aphanothece microscopica Nägeli cultivada em efluente da indústria da pesca, assim como a remoção de matéria orgânica e nutrientes do efluente. Os resultados indicaram que o coagulante FeCl3 foi mais eficaz na remoção de todos os parâmetros testados. No que concerne à separação da biomassa, com um número de seis lavagens foi removido cerca de 97,6% da concentração de FeCl3 adicionado inicialmente. O terceiro artigo com o título “Caracterização da biomassa de Aphanothece microscopica Nägeli gerada no efluente da indústria da pesca em diferentes temperaturas de cultivo” avaliou a composição química da biomassa da cianobactéria Aphanothece microscopica Nägeli quando desenvolvida em meio de cultivo padrão BG11 e no efluente do processamento de pescado. O quarto artigo teve como título “Influência do meio de cultivo e temperatura em compostos nitrogenados na cianobactéria Aphanothece microscopica Nägeli” objetivou avaliar o teor de compostos nitrogenados presentes na biomassa da cianobactéria Aphanothece microscopica Nägeli quando cultivada em meio padrão e no efluente da indústria da pesca nas diferentes fases de crescimento. Para o estudo da composição química e nitrogenados no efluente foram realizados experimentos nas temperaturas de 10, 20 e 30ºC. As concentrações de proteína, cinzas e pigmentos aumentaram com o aumento da temperatura. Por outro lado, foi observada uma redução do teor de lipídios e carboidratos com o aumento da temperatura. O íon amônio juntamente com os ácidos nucléicos representa uma importante fração do nitrogênio não protéico presente na biomassa da cianobactéria Aphanothece. Ficou demonstrada a influência do meio de cultivo na concentração de nitrogênio, bem como a determinação de proteína pelo método de Kjeldahl superestima a concentração protéica em cianobactérias. O quinto artigo intitulado “Produção de proteína unicelular a partir do efluente do processamento do pescado: modelagem preditiva e simulação” avaliou a produção de proteína unicelular através do cultivo da cianobactéria Aphanothece microscopica Nägeli no efluente da indústria da pesca. Os dados cinéticos de crescimento celular foram ajustados a quatro modelos matemáticos (Logístico, Gompertz, Gompertz Modificado e Baranyi). Os resultados demonstraram que o modelo Logístico foi considerado o mais adequado para descrever a formação de biomassa. A análise preditiva mostrou a possibilidade da obtenção de 1,66, 18,96 e 57,36 kg.m-3.d-1 de biomassa por volume do reator em 1000 h de processo contínuo, para as temperaturas de 10, 20 e 30ºC, respectivamente.