Feeding ecology of the Brazilian silverside Atherinella brasiliensis (Atherinopsidae) in a sub-tropical estuarine ecosystem

The feeding ecology of the Brazilian silverside, Atherinella brasiliensis, in a sub-tropical estuary of Brazil was investigated through the gut analysis of 1431 individuals. We described dietary composition and analysed seasonal, estuarine habitat, and body size variations in the diet; trophic level; feeding diversity; and gut fullness indices. Results reveal that A. brasiliensis is a typical, generalistic and opportunistic predator that makes use of a wide array of prey types (at least 89 different types), with zooplankton (mainly calanoids), diatoms, terrestrial insects, and plant detritus making up the bulk of the overall diet. The exotic calanoid Temora turbinata ranked as the primary prey. A wide feeding diversity (mean H` = 2.26), low trophic level (mean TROPH = 2.57), and high gut replenishment were persistent across seasons and habitats. Diet composition varied largely and significantly with respect to habitat, season, and body size. A closer assessment showed that habitat and season had a stronger effect on diet than fish size.

Feeding ecology of the American freshwater goby Ctenogobius shufeldti (Gobiidae, Perciformes) in a sub-tropical estuary

The feeding ecology of the American freshwater goby Ctenogobius shufeldti in a low salinity salt-marsh habitat in the Paranagua Bay estuarine complex (Brazil) was assessed through the gut analysis of 632 individuals. The effects of a set of abiotic factors (type of sediment, salinity, temperature and estuarine reach), season and body size on dietary composition were analysed. Seasonal and size-related changes in feeding strategy, feeding intensity and trophic level were assessed. The effects of gape and body size on prey size use were also analysed. The results showed that C. shufeldti is a typical omnivorous, generalized benthic predator of low trophic levels throughout the seasons and size classes, feeding on 56 dietary items; tanaids, chlorophyte algae, ostracods, gastropods, detritus and benthic diatoms made up the bulk of its diet. The tanaid Kalliapseudes schubarti was the main prey item in both numerical and volumetric terms. The gut fullness was persistently high across the seasons. As expected for a typical generalized, opportunistic omnivorous feeder: (1) seasonal and spatial-temporal variability of abiotic factors had a significant effect on diet structure, (2) season accounted for most of the dietary variation and (3) diet composition and the size of prey consumed did not vary across the size classes.

Seasonal dynamics of cyanobacteria in a eutrophic reservoir (Arcoverde) in a semi-arid region of Brazil

Environmental conditions favor the predominance of dense populations of cyanobacteria in reservoirs in northeastern Brazil. The aim of this study was to understand cyanobacterial population dynamics in the rainy and dry seasons at two depths in the Arcoverde reservoir. Microalgae and cyanobacteria samples were collected during 24 hours with intervals of 4 hours (nycthemeral) at sub-surface and 10 m using a van Dorn bottle and a determined biomass. Physical and chemical variables were obtained and the data were analyzed using the principal component analysis (PCA). No nycthemeral variations in the taxonomic composition or distribution of the populations of cyanobacteria were found between the different times of day in either the rainy or dry season. In both seasons, the greatest biomass of the phytoplankton community was made up of cyanobacteria at two depths and all times of the day. Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju was dominant at all times of the day on both the surface and at the bottom. In the rainy season, the differences in cyanobacterial biomass between the surface and bottom were less significant than in the dry season. The differences in cyanobacterial biomass between surface and bottom were less pronounced than those found in the dry season. We concluded that a) physical variables better explain the alterations of species in the phytoplankton community in an environment dominated by cyanobacteria throughout the year; b) seasonal climatic factors associated to periods of stratification and de-stratification are important for alterations in the community and variations in biomass and, c) the turbidity caused by rainfall favored the emergence and establishment of other cyanobacteria, especially Planktothrix agardhii (Gomont) Anagnostidis & Komarek.

Estimating total body fat using a skinfold prediction equation in Brazilian children

Background: The double burden of obesity and underweight is increasing in developing countries and simple methods for the assessment of fat mass in children are needed. Aim: To develop and validate a new anthropometric predication equation for assessment of fat mass in children. Subjects and methods: Body composition was assessed in 145 children aged 9.8 +/- 1.3 (SD) years from Sao Paulo, Brazil using dual energy X-ray absorptiometry (DEXA) and skinfold measurements. The study sample was divided into development and validation sub-sets to develop a new prediction equation for FM (PE). Results: Using multiple linear regression analyses, the best equation for predicting FM (R-2 - 0.77) included body weight, triceps skinfold, height, gender and age as independent variables. When cross-validated, the new PE was valid in this sample (R-2 = 0.80), while previously published equations were not. Conclusion: The PE was more valid for Brazilian children that existing equations, but further studies are needed to assess the validity of this PE in other populations.

Soil carbon balance in a tropical grassland: Estimation of soil respiration and its partitioning using a semi-empirical model

In savannah and tropical grasslands, which account for 60% of grasslands worldwide, a large share of ecosystem carbon is located below ground due to high root:shoot ratios. Temporal variations in soil CO2 efflux (R-S) were investigated in a grassland of coastal Congo over two years. The objectives were (1) to identify the main factors controlling seasonal variations in R-S and (2) to develop a semi-empirical model describing R-S and including a heterotrophic component (R-H) and an autotrophic component (R-A). Plant above-ground activity was found to exert strong control over soil respiration since 71% of seasonal R-S variability was explained by the quantity of photosynthetically active radiation absorbed (APAR) by the grass canopy. We tested an additive model including a parameter enabling R-S partitioning into R-A and R-H. Assumptions underlying this model were that R-A mainly depended on the amount of photosynthates allocated below ground and that microbial and root activity was mostly controlled by soil temperature and soil moisture. The model provided a reasonably good prediction of seasonal variations in R-S (R-2 = 0.85) which varied between 5.4 mu mol m(-2) s(-1) in the wet season and 0.9 mu mol m(-2) s(-1) at the end of the dry season. The model was subsequently used to obtain annual estimates of R-S, R-A and R-H. In accordance with results reported for other tropical grasslands, we estimated that R-H accounted for 44% of R-S, which represented a flux similar to the amount of carbon brought annually to the soil from below-ground litter production. Overall, this study opens up prospects for simulating the carbon budget of tropical grasslands on a large scale using remotely sensed data. (C) 2012 Elsevier B.V. All rights reserved.

Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins

The goal of this thesis work is to develop a computational method based on machine learning techniques for predicting disulfide-bonding states of cysteine residues in proteins, which is a sub-problem of a bigger and yet unsolved problem of protein structure prediction. Improvement in the prediction of disulfide bonding states of cysteine residues will help in putting a constraint in the three dimensional (3D) space of the respective protein structure, and thus will eventually help in the prediction of 3D structure of proteins. Results of this work will have direct implications in site-directed mutational studies of proteins, proteins engineering and the problem of protein folding. We have used a combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM), the so-called Hidden Neural Network (HNN) as a machine learning technique to develop our prediction method. By using different global and local features of proteins (specifically profiles, parity of cysteine residues, average cysteine conservation, correlated mutation, sub-cellular localization, and signal peptide) as inputs and considering Eukaryotes and Prokaryotes separately we have reached to a remarkable accuracy of 94% on cysteine basis for both Eukaryotic and Prokaryotic datasets, and an accuracy of 90% and 93% on protein basis for Eukaryotic dataset and Prokaryotic dataset respectively. These accuracies are best so far ever reached by any existing prediction methods, and thus our prediction method has outperformed all the previously developed approaches and therefore is more reliable. Most interesting part of this thesis work is the differences in the prediction performances of Eukaryotes and Prokaryotes at the basic level of input coding when ‘profile’ information was given as input to our prediction method. And one of the reasons for this we discover is the difference in the amino acid composition of the local environment of bonded and free cysteine residues in Eukaryotes and Prokaryotes. Eukaryotic bonded cysteine examples have a ‘symmetric-cysteine-rich’ environment, where as Prokaryotic bonded examples lack it.

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

Basic symptoms and the prediction of first-episode psychosis

Recent focus on early detection and intervention in psychosis has renewed interest in subtle psychopathology beyond positive and negative symptoms. Such self-experienced sub-clinical disturbances are described in detail by the basic symptom concept. This review will give an introduction into the concept of basic symptoms and describe the development of the current instruments for their assessment, the Schizophrenia Proneness Instrument, Adult (SPI-A) and Child and Youth version (SPI-CY), as well as of the two at-risk criteria: the at-risk criterion Cognitive-Perceptive Basic Symptoms (COPER) and the high-risk criterion Cognitive Disturbances (COGDIS). Further, an overview of prospective studies using both or either basic symptom criteria and transition rates related to these will be given, and the potential benefit of combining ultra-high risk criteria, particularly attenuated psychotic symptoms, and basic symptom criteria will be discussed. Finally, their prevalence in psychosis patients, i.e. the sensitivity, as well as in general population samples will be described. It is concluded that both COPER and COGDIS are able to identify subjects at a high risk of developing psychosis. Further, they appear to be sufficiently frequent prior to onset of the first psychotic episode as well as sufficiently rare in persons of general population to be considered as valuable for an early detection of psychosis.

Evaluating the usefulness of soil moisture (forecasts) in streamflow prediction for central Texas

The past decade has brought significant advancements in seasonal climate forecasting. However, water resources decision support and management continues to be based almost entirely on historical observations and does not take advantage of climate forecasts. This study builds on previous work that conditioned streamflow ensemble forecasts on observable climate indicators, such as the El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) for use in a decision support model for the Highland Lakes multi-reservoir system in central Texas operated by the Lower Colorado River Authority (LCRA). In the current study, seasonal soil moisture is explored as a climate indicator and predictor of annual streamflow for the LCRA region. The main purpose of this study is to evaluate the correlation of fractional soil moisture with streamflow using the 1950-2000 Variable Infiltration Capacity (VIC) Retrospective Land Surface Data Set over the LCRA region. Correlations were determined by examining different annual and seasonal combinations of VIC modeled fractional soil moisture and observed streamflow. The applicability of the VIC Retrospective Land Surface Data Set as a data source for this study is tested along with establishing and analyzing patterns of climatology for the watershed study area using the selected data source (VIC model) and historical data. Correlation results showed potential for the use of soil moisture as a predictor of streamflow over the LCRA region. This was evident by the good correlations found between seasonal soil moisture and seasonal streamflow during coincident seasons as well as between seasonal and annual soil moisture with annual streamflow during coincident years. With the findings of good correlation between seasonal soil moisture from the VIC Retrospective Land Surface Data Set with observed annual streamflow presented in this study, future research would evaluate the application of NOAA Climate Prediction Center (CPC) forecasts of soil moisture in predicting annual streamflow for use in the decision support model for the LCRA.

Syntheses and characterization of monomeric Mo(VI) complexes with bidentate phosphine oxide ligands and dimeric and tetrameric Mo(V) clusters with benzoic acid and phosphinic acid derivatives, containing MoO2, Mo2O2(μ-O)2 and Mo4O4(μ3-O)4

Mo(VI) oxo complexes have been persistently sought after as epoxidation catalysts. Further, Mo(V) oxo clusters of the form M4(µ3-X)4 (M = transition metal, X = O, S) have been rigorously studied due to their remarkable structures and also their usefulness as models for electronic studies. The syntheses and characterizations of new Mo(VI) and Mo(V) oxo complexes have been described in this dissertation. Two new complexes MoO2Cl2Ph2P(O)CH2COOH and MoO2Cl2Ph2P(O)C6H4tBuS(O) were synthesized from reactions of “MoO2Cl2” with ligands Ph2P(O)CH2COOH and Ph2P(O)C6H4tBuS(O). Tetrameric packing arrangements comprised of hydrogen bonds were obtained for the complex MoO2Cl2Ph2P(O)CH2COOH and the ligand Ph2P(O)CH2COOH. Further the stability of an Mo-O bond was preferred over the Mo-S bond even though this resulted in the formation of a more strained seven membered ring. Tetranuclear Mo(V) complexes of the form [Mo4(µ3-O)4(µ-O2PR2)4O4], (PR2 = PPh2, PMe2) were synthesized using reactions of MoO2(acac)2 with diphenyl and dimethyl phosphinic acids, in ethanol. In the crystal structure of these complexes four Mo=O units are interconnected by four triply bridging oxygen atoms and bridging phosphinate ligands. The complex exhibited fourfold symmetry as evidenced by a single 31P NMR peak for the P atoms in the coordinated ligands. Reaction of WO2(acac)2 with Ph2POOH in methanol resulted in a dimeric W(VI) complex [(CH3O)2(O)W(µ-O)( µ-O2PPh2)2W(O)(CH3O)2] which contained a packing disorder in its crystal structure. Similar reactions of MoO2(acac)2 with benzoic acid derivatives resulted in dimeric complexes of the form [Mo2O2(acac)2(µ-O)(µ-OC2H5)(µ-O2CR)] (R = C6H5, (o-OH)C6H4, (p-Cl)C6H4, (2,4-(OH)2)C6H3, (o-I)C6H4) and one tetrameric complex [Mo2O2(acac)2(µ-O)(µ-OC2H5)(µ-O2C)C6H4(p-µ-O2C)Mo2O2(acac)2(µ-O)(µ-OC2H5)] with terephthalic acid. 1H NMR proved very useful in the prediction of the formation of dimers with the substituted benzoic acids, which were also confirmed by elemental analyses. The reductive capability of ethanol proved instrumental in the syntheses of Mo(V) tetrameric and dimeric clusters. Synthetic details, IR, 1H and 31P NMR spectroscopy and elemental analyses are reported for all new complexes. Further, single crystal X-ray structures of MoO2Cl2Ph2P(O)CH2COOH, MoO2Cl2Ph2P(O)C6H4tBuS(O), [Mo4(µ3-O)4(µ-O2PR2)4O4], (PR2 = PPh2, PMe2), [(CH3O)2(O)W(µ-O)( µ-O2PPh2)2W(O)(CH3O)2] and [Mo2O2(acac)2(µ-O)(µ-OC2H5)(µ-O2CR)] (R = C6H5, (o-OH)C6H4) are also presented.

A 200 Year Sub-Annual Record of Sulfate in West Antarctica, from Sixteen Ice Cores

Sixteen high-resolution ice-core records from West Antarctica and South Pole are used to examine the spatial and temporal distribution of sulfate for the last 200 years. The preservation of seasonal layers throughout the length of each record results in a dating accuracy of better than 1 year based on known global-scale volcanic events. A dual transport source for West Antarctic sea-salt (ss) SO42- and excess (xs) SO42- is observed: lower-tropospheric for areas below 1000m elevation and mid-/upper-tropospheric/stratospheric for areas located above 1000m. Our XsSO(4)(2-) records with volcanic peaks removed do not display any evidence of an anthropogenic impact on West Antarctic SO42- concentrations but do reveal that a major climate transition takes place over West Antarctica at similar to 1940. Global-scale volcanic eruptions appear as significant peaks in the robust-spline residual xsSO(4)(2-) records from sites located above 1000 m elevation but do not appear in the residual records from sites located below 1000 m.

Seasonal Variation in the Capacity for Plant Trait Measures to Predict Grassland Carbon and Water Fluxes

There is a need for accurate predictions of ecosystem carbon (C) and water fluxes in field conditions. Previous research has shown that ecosystem properties can be predicted from community abundance-weighted means (CWM) of plant functional traits and measures of trait variability within a community (FDvar). The capacity for traits to predict carbon (C) and water fluxes, and the seasonal dependency of these trait-function relationships has not been fully explored. Here we measured daytime C and water fluxes over four seasons in grasslands of a range of successional ages in southern England. In a model selection procedure, we related these fluxes to environmental covariates and plant biomass measures before adding CWM and FDvar plant trait measures that were scaled up from measures of individual plants grown in greenhouse conditions. Models describing fluxes in periods of low biological activity contained few predictors, which were usually abiotic factors. In more biologically active periods, models contained more predictors, including plant trait measures. Field-based plant biomass measures were generally better predictors of fluxes than CWM and FDvar traits. However, when these measures were used in combination traits accounted for additional variation. Where traits were significant predictors their identity often reflected seasonal vegetation dynamics. These results suggest that database derived trait measures can improve the prediction of ecosystem C and water fluxes. Controlled studies and those involving more detailed flux measurements are required to validate and explore these findings, a worthwhile effort given the potential for using simple vegetation measures to help predict landscape-scale fluxes.

(Table 4) Seasonal variation of sea spray sodium concentrations during 2004-2007 at Concordia Station, Antarctica

From November 2004 to December 2007, size-segregated aerosol samples were collected all-year-round at Dome C (East Antarctica) by using PM10 and PM2.5 samplers, and multi-stage impactors. The data set obtained from the chemical analysis provided the longest and the most time-resolved record of sea spray aerosol (sea salt Na+) in inner Antarctica. Sea spray showed a sharp seasonal pattern. The highest values measured in winter (Apr-Nov) were about ten times larger than in summer (Dec-Mar). For the first time, a size-distribution seasonal pattern was also shown: in winter, sea spray particles are mainly submicrometric, while their summer size-mode is around 1-2 µm. Meteorological analysis on a synoptic scale allowed the definition of atmospheric conditions leading sea spray to Dome C. An extreme-value approach along with specific environmental based criteria was taken to yield stronger fingerprints linking atmospheric circulation (means and anomalies) to extreme sea spray events. Air mass back-trajectory analyses for some high sea spray events allowed the identification of two major air mass pathways, reflecting different size distributions: micrometric fractions for transport from the closer Indian-Pacific sector, and sub-micrometric particles for longer trajectories over the Antarctic Plateau. The seasonal pattern of the SO4**2- /Na+ ratio enabled the identification of few events depleted in sulphate, with respect to the seawater composition. By using methanesulphonic acid (MSA) profile to evaluate the biogenic SO4**2- contribution, a more reliable sea salt sulphate was calculated. In this way, few events (mainly in April and in September) were identified originating probably from the "frost flower" source. A comparison with daily-collected superficial snow samples revealed that there is a temporal shift between aerosol and snow sea spray trends. This feature could imply a more complex deposition processes of sea spray, involving significant contribution of wet and diamond dust deposition, but further work has to be carried out to rule out the effect of wind re-distribution and to have more statistic significance.

Field measurements of the atmosphere, ocean, sea ice and sub-ice platelet layer at Atka Bay in 2013

Ice shelves strongly interact with coastal Antarctic sea ice and the associated ecosystem by creating conditions favourable to the formation of a sub-ice platelet layer. The close investigation of this phenomenon and its seasonal evolution remain a challenge due to logistical constraints and a lack of suitable methodology. In this study, we characterize the seasonal cycle of Antarctic fast ice adjacent to the Ekström Ice Shelf in the eastern Weddell Sea. We used a thermistor chain with the additional ability to record the temperature response induced by cyclic heating of resistors embedded in the chain. Vertical sea-ice temperature and heating profiles obtained daily between November 2012 and February 2014 were analyzed to determine sea-ice and snow evolution, and to calculate the basal energy budget. The residual heat flux translated into an ice-volume fraction in the platelet layer of 0.18 ± 0.09, which we reproduced by a independent model simulation and agrees with earlier results. Manual drillings revealed an average annual platelet-layer thickness increase of at least 4m, and an annual maximum thickness of 10m beneath second-year sea ice. The oceanic contribution dominated the total sea-ice production during the study, effectively accounting for up to 70% of second-year sea-ice growth. In summer, an oceanic heat flux of 21 W/m**2 led to a partial thinning of the platelet layer. Our results further show that the active heating method, in contrast to the acoustic sounding approach, is well suited to derive the fast-ice mass balance in regions influenced by ocean/ice-shelf interaction, as it allows sub-diurnal monitoring of the platelet-layer thickness.