700 research outputs found
PALACIN: ESTUDOS SOBRE O PODER EM GOIÁS.
No full textO objetivo desta dissertação é compreender a produção intelectual de Luís
Palacin à luz da história política e intelectual. Relacionado com a temática de poder e
ideologia, tendo como fonte de pesquisa as obras: Quatro tempos de Ideologia e
Subversão e Corrupção. A reflexão destas obras possibilitará perceber que Luís Palacin
como um historicista procurou utilizar outras metodologias durante a sua escrita para
caracterizar as relações de poder na sociedade goiana. O seguinte trabalho proporcionara
uma compreensão sobre as relações de poder na sociedade goiana, luz, do pensamento de
Luís Palacin, historiador espanhol, que veio para Goiânia realizar um projeto de
evangelização, mas também um projeto de pesquisas serias sobre a História Goiás. Suas
obras apresentam característica da nova história política, rompendo com o pensamento
historiográfico tradicional. Palacin pode ser considerado um dos pioneiros em pesquisa
sobre o poder e ideologia em Goiás.The objective of this dissertation is to understand the intellectual output of Luis Palacin the light of political
and intellectual history. Related to the theme of power and ideology, and as a source of research works:
Four times Ideology and Subversion and Corruption. The reflection of these works will enable to realize
that Louis Palacin as a historicist sought to use other methodologies during your writing to characterize the
relations of power in society Goias. The following work provides an understanding of power relations in
society Goias, light, thought of Louis Palacin, Spanish historian, who came to Goiania to undertake a project
of evangelization but also a serious research project on the history Goiás His works have characteristic of
the new political history, breaking with the traditional historiographical thought. Palacin can be considered
one of the pioneers in research on power and ideology in Goiás
Denitrification rates in mountain lake sediments
No full text[Methods] Dr Carlos Palacin-Lizarbe made the datasets.
For further details of the methods see the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020). Further details on the denitrification rate measurement method are provided in the publication: Palacin-Lizarbe, C., Camarero, L. & Catalan, J. Estimating sediment denitrification rates using cores and N2O microsensors. J Vis Exp, e58553, doi:doi:10.3791/58553 (2018). Further details on the sediment molecular descriptors (DNA, 16S, nirS, nirK, nosZ1, and nosZ2) are provided in the publication: Palacin-Lizarbe, C. et al. The DNRA-denitrification dichotomy differentiates nitrogen transformation pathways in mountain lake benthic habitats. Front Microbiol 10, 1229, doi:10.3389/fmicb.2019.01229 (2019).[Usage Notes] Dryad repository - https://doi.org/10.5061/dryad.j6q573n95
This document describes four datasets (tab-separated text format tables) related to measurements of denitrification rates in mountain lake sediments of the Pyrenees. Dr Carlos Palacin-Lizarbe made the datasets, anyone that uses these data should reference this DRYAD repository and the parent publication: Palacin-Lizarbe, C., Camarero, L., Hallin, S., Jones, C. M. & Catalan, J. Denitrification rates in lake sediments of mountains affected by high atmospheric nitrogen deposition. Sci Rep10, 3003, doi:10.1038/s41598-020-59759-w (2020).
See this publication for further details on the methods used.
Dataset 1: Sediment_denitrification_rates_DRYAD.txt
Dataset containing all measured denitrification rates and incubation conditions. The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, denitrification_rate, nitrate_treatment, temperature_insitu, temperature_incubation, nitrate_actual_plus_added, glucose_added, habitat, site.
Dataset 2: table_background_DRYAD_def.txt
Dataset containing all measured denitrification rates and the background descriptors (landscape, water, sediment). This dataset contain missing values. The dataset contains the following variables: id_core, yymmdd_lake_sample, actual_denitrification_rate, nitrate_add_7_denitrification_rate, nitrate_add_14_denitrification_rate, nitrate_add_28_denitrification_rate, high_nitrate_add_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrmicroogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2.
Dataset 3: table_actual_denitrification_rates_modeled_DRYAD.txt
Dataset containing the actual denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, actual_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of actual denitrification rates, we use the variables scaled; before being scaled some variables were square root (actual_denitrification_rate, DOC, DNA, X16S, nirS, and nirK) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nosZ1, and nosZ2) transformed to reduce the influence of extreme values.
Dataset 4: table_potential_denitrification_rates_modeled_DRYAD.txt
Dataset containing the potential (28 microM nitrate added) denitrification rates modelled in Palacin-Lizarbe et al. 2020 Sci Rep and the background descriptors (landscape, water, sediment). The dataset contains the following variables: id_rate, id_core, yymmdd_lake_sample, nitrate_add_28_denitrification_rate, site, latitude, longitude, altitude, lake_area, catchment_area, renewal_time, habitat, temperature_insitu, nitrate, nitrite, ammonium, sulphate, DOC, water_column_depth, sediment_layer_depth, organic_matter, nitrogen, d15N, carbon, d13C, carbon_nitrogen_ratio, dry_weight_wet_weight_ratio, sediment_density, sediment_grain_size, DNA, X16S, nirS, nirK, nosZ1, nosZ2. In the dataset the variables are not transformed. In Palacin-Lizarbe et al. 2020 Sci Rep when developing the models of potential denitrification rates, we use the variables scaled; before being scaled some variables were square root (nitrate_add_28_denitrification_rate, DOC, DNA, X16S, and nirS) or log10 (lake_area, catchment_area, renewal_time, nitrite, ammonium, sulphate, dry_weight_wet_weight_ratio, nirK, nosZ1, and nosZ2) transformed to reduce the influence of extreme values.
Variables contained in the datasets:
id_rate: identification code for each denitrification rate measurement is useful as a link between datasets.
id_core: identification code for each sediment core is useful as a link between datasets.
yymmdd_lake_sample: The datasets are sorted by this variable. Identification code for each sediment core is useful as a link between datasets. Each code corresponds to the date sampled (year, month, and day), and the Lake, and the number of the sediment core. E.g. 130701Llo3 correspond the 3rd core sampled in Llong Lake on 2013, July 1 (13 07 01). The following are the sampled Lakes, with its abbreviation in brackets: Bergus (Be), Bassa de les Granotes (Bgra), Contraix (Co), Gelat de Bergus (GBe), Llebreta (Lle), Llong (Llo), Plan (Pl), Podo (Po), Redon (Re), Redo Aiguestortes (ReAT), and Redon de Vilamos (ReVil). This variable is useful as a link between datasets.
denitrification_rate (micromols N2O m-2 h-1): denitrification rate without identifying the nitrate treatment.
actual_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured without any substrate addition.
nitrate_add_7_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 7 microM nitrate and 1.5 g/L of glucose.
nitrate_add_14_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 14 microM nitrate and 1.5 g/L of glucose.
nitrate_add_28_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding 28 microM nitrate and 1.5 g/L of glucose.
high_nitrate_add_denitrification_rate (micromols N2O m-2 h-1): denitrification rate measured adding a high concentration of nitrate (>300 microM) and 1.5 g/L of glucose.
nitrate_treatment: treatment of nitrate addition in the denitrification measurements.
Treatments: actual (without any nitrate added), Add_7 (7 microM nitrate added), Add_14 (14 microM nitrate added), Add_28 (28 microM nitrate added), High_add (>300 microM nitrate added).
temperature_incubation (Celsius degrees): constant temperature during the incubation of the denitrification measurement.
nitrate_actual_plus_added (microM): actual (in situ) plus added nitrate concentration in the water overlying the sediment core.
glucose_added (mg * L-1): added when nitrate was also added.
site: Lake (code): Bergus (B), Contraix (C), Bassa de les Granotes (G), Gelat de Bergus (GB), Llebreta (Le), Llong (Lo), Plan (P), Podo (Po), Redon (R), Redo Aiguestortes (RA), and Redon de Vilamos (RV).
latitude (N).
longitude (E).
altitude (m a.s.l.).
lake_area (ha).
catchment_area (ha).
renewal_time (months): mean time that water spends in the lake, retention time and residence time are synonyms of renewal time.
habitat: sediment type (code): littoral sediments from rocky areas (R), helophyte Carex rostrata belts (C), beds of isoetid (I) and elodeid (E) macrophytes, and non-vegetated deep (D) sediments.
temperature_insitu (Celsius degrees): temperature measured in the water overlying the sediment core just after the sediment core was retrieved.
nitrate (microM): nitrate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations.
nitrite (microM): nitrite concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations.
ammonium (microM): ammonium concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations.
sulphate (microM): sulphate concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations.
DOC (mg*L-1): dissolved organic carbon concentration measured in the water overlying the sediment core after less than 4 h of the sediment core retrieval and before the denitrification incubations.
water_column_depth (m): depth of the water column in the sediment core sampling point. Coded as 0.5 for littoral habitats.
sediment_layer_depth (cm): depth of the sediment layer described by molecular and abiotic features.
organic_matter (%): sediment organic matter content determined by Lost on Ignition.
nitrogen (%): sediment nitrogen content.
d15N (parts per thousand): sediment delta15N.
carbon (%): sediment carbon content.
d13C (parts per thousand): sediment delta13C.
carbon_nitrogen_ratio (a/a): sediment atomic ratio of the two elements.
dry_weight_wet_weight_ratio: sediment dry weight to wet weight ratio.
sediment_density (g * cm-3).
sediment_grain_size (um): sediment, volume diameter of the mean-sized spherical particle (vol_weighted_mean parameter provided by Mastersizer 2000, Malvern Instruments Ltd, UK).
DNA (ng * m-2): sediment DNA content, ng of DNA per m2 in the sediment layer (0-0.5 cm).
X16S (copies * m-2): 16S rRNA gene copies per m2 in the sediment layer (0-0.5 cm).
nirS (copies * m-2) : nirS gene copies per m2 in the sediment layer (0-0.5 cm).
nirK (copies * m-2) : nirK gene copies per m2 in the sediment layer (0-0.5 cm).
nosZ1 (copies * m-2) : nosZ1 gene copies per m2 in the sediment layer (0-0.5 cm).
nosZ2 (copies * m-2) : nosZ2 gene copies per m2 in the sediment layer (0-0.5 cm).During the last decades, atmospheric nitrogen loading in mountain ranges of the Northern Hemisphere has increased substantially, resulting in high nitrate concentrations in many lakes. Yet, how increased nitrogen has affected denitrification, a key process for nitrogen removal, is poorly understood. We measured actual and potential (nitrate and carbon amended) denitrification rates in sediments of several lake types and habitats in the Pyrenees during the ice-free season. Actual denitrification rates ranged from 0 to 9 μmol N2O m−2 h−1 (mean, 1.5 ± 1.6 SD), whereas potential rates were about 10-times higher. The highest actual rates occurred in warmer sediments with more nitrate available in the overlying water. Consequently, littoral habitats showed, on average, 3-fold higher rates than the deep zone. The highest denitrification potentials were found in more productive lakes located at relatively low altitude and small catchments, with warmer sediments, high relative abundance of denitrification nitrite reductase genes, and sulphate-rich waters. We conclude that increased nitrogen deposition has resulted in elevated denitrification rates, but not sufficiently to compensate for the atmospheric nitrogen loading in most of the highly oligotrophic lakes. However, there is potential for high rates, especially in the more productive lakes and landscape features largely govern this.Ministerio de Ciencia e Innovación, Gobierno de España, Award: CGL2010-19373. Ministerio de Economía, Industria y Competitividad, Gobierno de España, Award: CGL2016–80124-C2-1-P.Peer reviewe
Influence of titania morphology on the electrochemical properties of composite polymer electrolyte membranes
No full textA comparison of the physico-chemical properties of S-PEEK-based composite membranes containing nanometric or mesoporous anatase titania was carried out. The powders were characterized in terms of specific surface area (by B.E.T. apparatus), acidity, and structural features (XRD). Composites containing various amounts of both titania powders (from 1.33 up to 10% wt) were prepared by casting and their water uptake, proton exchange capacity and proton conductivity (EIS) were evaluated. Despite of its lower specific surface area (83 m(2)/g) nanometric titania-based composites clearly exhibited higher water absorption properties and superior electrochemical performance with respect to mesoporous titania (SSA =. 147 m(2)/g) containing systems. The improvement of membranes performance could be related to the larger number of water-adsorbing acidic sites on the nanometric surface
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