Distribution of Prochlorococcus Ecotypes in the Red Sea Basin Based on Analyses of rpoC1 Sequences

KAUST Repository

Shibl, Ahmed A.; Haroon, Mohamed; Ngugi, David; Thompson, Luke R.; Stingl, Ulrich

2016-01-01

The marine picocyanobacteria Prochlorococcus represent a significant fraction of the global pelagic bacterioplankton community. Specifically, in the surface waters of the Red Sea, they account for around 91% of the phylum Cyanobacteria. Previous work suggested a widespread presence of high-light (HL)-adapted ecotypes in the Red Sea with the occurrence of low-light (LL)-adapted ecotypes at intermediate depths in the water column. To obtain a more comprehensive dataset over a wider biogeographical scope, we used a 454-pyrosequencing approach to analyze the diversity of the Prochlorococcus rpoC1 gene from a total of 113 samples at various depths (up to 500 m) from 45 stations spanning the Red Sea basin from north to south. In addition, we analyzed 45 metagenomes from eight stations using hidden Markov models based on a set of reference Prochlorococcus genomes to (1) estimate the relative abundance of Prochlorococcus based on 16S rRNA gene sequences, and (2) identify and classify rpoC1 sequences as an assessment of the community structure of Prochlorococcus in the northern, central and southern regions of the basin without amplification bias. Analyses of metagenomic data indicated that Prochlorococcus occurs at a relative abundance of around 9% in samples from surface waters (25, 50, 75 m), 3% in intermediate waters (100 m) and around 0.5% in deep-water samples (200–500 m). Results based on rpoC1 sequences using both methods showed that HL II cells dominate surface waters and were also present in deep-water samples. Prochlorococcus communities in intermediate waters (100 m) showed a higher diversity and co-occurrence of low-light and high-light ecotypes. Prochlorococcus communities at each depth range (surface, intermediate, deep sea) did not change significantly over the sampled transects spanning most of the Saudi waters in the Red Sea. Statistical analyses of rpoC1 sequences from metagenomes indicated that the vertical distribution of Prochlorococcus in the water

Distribution of Prochlorococcus Ecotypes in the Red Sea Basin Based on Analyses of rpoC1 Sequences

KAUST Repository

Shibl, Ahmed A.

2016-06-25

The marine picocyanobacteria Prochlorococcus represent a significant fraction of the global pelagic bacterioplankton community. Specifically, in the surface waters of the Red Sea, they account for around 91% of the phylum Cyanobacteria. Previous work suggested a widespread presence of high-light (HL)-adapted ecotypes in the Red Sea with the occurrence of low-light (LL)-adapted ecotypes at intermediate depths in the water column. To obtain a more comprehensive dataset over a wider biogeographical scope, we used a 454-pyrosequencing approach to analyze the diversity of the Prochlorococcus rpoC1 gene from a total of 113 samples at various depths (up to 500 m) from 45 stations spanning the Red Sea basin from north to south. In addition, we analyzed 45 metagenomes from eight stations using hidden Markov models based on a set of reference Prochlorococcus genomes to (1) estimate the relative abundance of Prochlorococcus based on 16S rRNA gene sequences, and (2) identify and classify rpoC1 sequences as an assessment of the community structure of Prochlorococcus in the northern, central and southern regions of the basin without amplification bias. Analyses of metagenomic data indicated that Prochlorococcus occurs at a relative abundance of around 9% in samples from surface waters (25, 50, 75 m), 3% in intermediate waters (100 m) and around 0.5% in deep-water samples (200–500 m). Results based on rpoC1 sequences using both methods showed that HL II cells dominate surface waters and were also present in deep-water samples. Prochlorococcus communities in intermediate waters (100 m) showed a higher diversity and co-occurrence of low-light and high-light ecotypes. Prochlorococcus communities at each depth range (surface, intermediate, deep sea) did not change significantly over the sampled transects spanning most of the Saudi waters in the Red Sea. Statistical analyses of rpoC1 sequences from metagenomes indicated that the vertical distribution of Prochlorococcus in the water

The role of ecotypic variation and the environment on biomass and nitrogen in a dominant prairie grass.

Science.gov (United States)

Mendola, Meredith L; Baer, Sara G; Johnson, Loretta C; Maricle, Brian R

2015-09-01

Knowledge of the relative strength of evolution and the environment on a phenotype is required to predict species responses to environmental change and decide where to source plant material for ecological restoration. This information is critically needed for dominant species that largely determine the productivity of the central U.S. grassland. We established a reciprocal common garden experiment across a longitudinal gradient to test whether ecotypic variation interacts with the environment to affect growth and nitrogen (N) storage in a dominant grass. We predicted plant growth would increase from west to east, corresponding with increasing precipitation, but differentially among ecotypes due to local adaptation in all ecotypes and a greater range of growth response in ecotypes originating from west to east. We quantified aboveground biomass, root biomass, belowground net primary production (BNPP), root C:N ratio, and N storage in roots of three ecotypes of Andropogon gerardii collected from and reciprocally planted in central Kansas, eastern Kansas, and s6uthern Illinois. Only the ecotype from the most mesic region (southern Illinois) exhibited more growth from west to east. There was evidence for local adaptation in the southern Illinois ecotype by means of the local vs. foreign contrast within a site and the home vs. away contrast when growth in southern Illinois was compared to the most distant 'site in central Kansas. Root biomass of the eastern Kansas ecotype was higher at home than at either away site. The ecotype from the driest region, central Kansas, exhibited the least response across the environmental gradient, resulting in a positive relationship between the range of biomass response and precipitation in ecotype region of origin. Across all sites, ecotypes varied in root C:N ratio (highest in the driest-origin ecotype) and N storage in roots (highest in the most mesic-origin ecotype). The low and limited range of biomass, higher C:N ratio of roots

Reproductive, morphological, and phytochemical responses of Arabidopsis thaliana ecotypes to enhanced UV-B radiation

Energy Technology Data Exchange (ETDEWEB)

Trumbull, V.L.; McCloud, E.S.; Paige, K.N. (Univ. of Illinois, Urbana, IL (United States))

1994-06-01

Two ecotypes of Arabidopsis thaliana, collected from Libya and Norway, were grown in the greenhouse under. UV-B doses of 0 and 10.5 kJ m[sup [minus]2] UV-B[sub BE]. The high UV-B dose simulated midsummer ambient conditions over Libya and a 40% reduction in stratospheric ozone over Norway. The Libyan ectotype, which originated from latitudes where solar UV-B is high, showed no UV-B induced damage to plant growth. However the Norwegian ecotype, which originated from latitudes where solar UV-B is low, showed a significant reduction in plant height, inflorescence weight, and rosette weight in response to enhanced UV-B. Although fruit and seed number for both ecotypes were unaffected by enhanced UV-B radiation the germination success of the seeds harvested from the irradiated Norwegian plants were significantly reduced. The two ecotypes also differed with respect to their accumulation of kaempferol, a putative UV-B protective filter. The Libyan ecotype increased kaempferol concentration by 38% over the 0 kJ treatment whereas the Norwegian ecotype increased by only 15%. These data suggest that, for these ecotypes, variation in UV-B sensitivity may be explained by the differential induction of UV-absorbing leaf pigments.

Evaluation of Freezing Tolerance of Three Ajowan (Trachyspermum ammi (Linn. Sprague Ecotypes in Controlled Conditions

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Z Boroumand Rezazadeh

2013-08-01

Full Text Available Ajowan is one of the endemic plants in Khorasan province, and there is a little information on its tolerance to cold stress. In order to study freezing tolerance of ajowan, an experiment was conducted in faculty of agriculture, Ferdowsi University of Mashhad, based on factorial-completely randomized design with three replications and three ecotypes of ajowan (Neishabour, Birjand and Torbat-e-Heidarieh were imposed on eight freezing temperatures (0 (control, -1.5, -3, -4.5, -6,-7.5, -9 and -10.5 °C. Plants were grown in natural environment till 4-5 leaf stage, then for freezing treatments transferred to thermo-gradient freezer. The cell membrane stability was evaluated by electrolyte leakage index (EL and temperature for killing 50% of samples according to the electrolyte leakage (LT50el was determined. Furthermore, survival percentage, leaf number and dry weight, temperature for killing 50% of samples according to survival (LT50su and reduced dry matter temperature 50 (RDMT50 were determined after three weeks recovery in the glasshouse. Response of ajowan ecotypes for electrolyte leakage was different and birjand ecotype had the lowest %EL, whereas the slope of %EL in mentioned ecotype was lower than two other ecotypes. However there were no significant differences among ajowan ecotypes on LT50su. Decreasing temperature to -7.5 °C reduced survival percentage of Neishabour and Torbat-e-Heidarieh ecotypes to lower than 20 percent, whiles in this temperature Birjand’s survival percentage was about 60 percent. It seems that Birjand ecotype with the lowest electrolyte leakage, the highest survival and dry matter and the lowest LT50su was more tolerant than two other ecotypes.

Growth performance and stomatal behavior in relation to ecotypic adaptations in cynodon dactylon (L.) pers

International Nuclear Information System (INIS)

Tufail, A.; Ahmad, F.; Hameed, M.; Ahmad, R.

2017-01-01

Evolution has great ecological significance in terms of plant morphological and stomatal characteristics that must have been genetically fixed during the long evolutionary period. Impact of environmental conditions on growth and stomatal features of twelve ecotypes of Cynodon dactylon that were collected from ecologically different habitats in the Punjab, Pakistan were evaluated. The collected ecotypes Derawar Fort-saline desert (DF-SD), Muzaffar garh-River bank (M-RB), Khabbeki Lake-hyper saline (KL-HS), Ucchali Lake-hyper saline (UL-HS), Kalar Kahar Lake-saline (KKL-S), Treemu-saline wetland (T-SW), Sahianwala-saline wetland (S-SW), Sahianwala-hyper saline (S-HS), Pakka Anna-hyper saline (PA-HS), Pakka Anna-reclaimed field (PA-RF), Botanic Garden-non saline (BG-NS) and Gatwala-saline semiarid (G-SSA) were grown in controlled environments at University of Agriculture, Faisalabad till their acclimatization to evaluate genetically fixed characteristics. After 6-month growth in soil, the plants were transferred to half-strength Hoagland's nutrient medium. There was a huge variation in all morphological characteristics recorded during the investigation, which were due to environmental heterogeniety to which these ecotypes were originally adapted. An exclusive feature of the DF-SD ecotypes is the long and numerous roots, and tillering capacity that surpassed all other ecotypes. Leaves per plant were also exceptionally high that may improve the photosymthetic efficiency of the plant. It showed a good potential of overall growth and biomass production. The robust growth was also recorded in the KKL-S ecotypes, and this can be related to the complete dominance of these two ecotypes in their respective habitats. Small stomata were recorded in the three ecotypes (DF-SD, KL-HS and PA-HS), which are of great ecological significance. Stomatal shape, however, is different in different ecotypes, but its contribution towards stress tolerance is still to be investigated. (author)

Identification and Selection for Salt Tolerance in Alfalfa (Medicago sativa L. Ecotypes via Physiological Traits

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Hassan MONIRIFAR

2009-12-01

Full Text Available Salt stress is a serious environmental problem throughout the world which may be partially relieved by breeding cultivars that can tolerate salt stress. Plant breeding may provide a relatively cost effective short-term solution to the salinity problem by producing cultivars able to remain productive at low to moderate levels of salinity. Five alfalfa cultivars, �Seyah-Roud�, �Ahar-Hourand�, �Oskou�, �Malekan� and �Sefida-Khan� were assessed for salt tolerance at mature plant stage. A greenhouse screening system was used to evaluate individual alfalfa plants grown in perlit medium, and irrigated with water containing different amounts of NaCl. Three salt levels were achieved by adding 0, 100 and 200 mM NaCl to Hoagland nutrient solution, respectively. Forage yield, sodium and potassium contents and K/Na ratio was determined. Also, leaf samples were analyzed for proline and chlorophyll contents. The ecotypes Seyha-Roud and �Sefida-Khan� had comparatively less sodium contents than �Oskou�, �Ahar-Hourand� and �Malekan� ecotypes, also potassium content increased under saline condition. Forage yield of different alfalfa ecotypes was significantly influenced by the salinity. The ecotypes �Malekan�, Ahar- Hourand and �Oskou� were successful in maintaining forage yield under salinity stress. Sodium contents increased due to salinity in all alfalfa ecotypes however ecotypes �Ahar-Hourand� and �Malekan� maintained the highest leaf Na concentration. They showed higher content of K than other ecotypes but had lower K/Na ratio. It was concluded that, two ecotypes �Malekan� and �Ahar-Hourand� were better.

Ecotypic variation in population dynamics of reintroduced bighorn sheep

Science.gov (United States)

Bleich, Vernon C.; Sargeant, Glen A.; Wiedmann, Brett P.

2018-01-01

Selection of bighorn sheep (Ovis canadensis) for translocation historically has been motivated by preservation of subspecific purity rather than by adaptation of source stocks to similar environments. Our objective was to estimate cause‐specific, annual, and age‐specific mortality of introduced bighorn sheep that originated at low elevations in southern British Columbia, Canada (BC ecotype), or in the Missouri River Breaks region of central Montana, USA (MT ecotype). In North Dakota, USA, mortality was similar and typically low for adult female bighorn sheep from Montana (0.09 ± 0.029 [SE]) and British Columbia (0.08 ± 0.017) during 2000–2016. Median life expectancy was 11 years for females that reached adulthood (2 yrs old); however, mortality accelerated with age and reached 86% by age 16. Mortalities resulted primarily from low rates of predation, disease, accidents, and unknown natural causes (recruitment of bighorn sheep from Montana, resulted in a greater projected rate of increase for the MT ecotype (λ = 1.21) than for the BC ecotype (1.02), and a more youthful age structure. These results support translocation of bighorn sheep from areas that are environmentally similar to areas that will be stocked. Potential benefits include more rapid population growth, greater resilience to and more rapid recovery from density‐independent losses, an increased possibility that rapidly growing populations will expand into adjacent habitat, increased hunter opportunity, increased connectivity among herds, and a more complete restoration of ecosystem processes.

Assessment of total flavonoid content and antioxidant activity of Mullein (Verbascum songaricum ecotypes

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2017-11-01

Full Text Available Background and objectives: The Mullein genus is the largest genus of Scrophulariaceae family which has extensive natural habitat in southwest of Iran. Mullein contains compounds such as phenolic compounds, mucilage, saponins and anthocyanin. The aim of this study was to evaluate the total flavonoid content and antioxidant activity of mullein ecotypes in Iran. Methods: Six ecotypes of the Verbascum songaricum were evaluated. Determination of total flavonoid content was performed bythealuminium chloride colourimetric method. The antioxidant activity of the flower extracts was measured using the DPPH method. Results: The results showed that total flavonoid content and antioxidant activity were different among ecotypes.  The highest and lowest amounts of total flavonoidwas obtained  from Shermard ecotype (13.42 mg rutin /g DW and Klar ecotypes(10.10 mg rutin /g DW, respectively. The highest amounts of antioxidant activity were obtained from the Shermard ecotype (IC50 246.35 μg/mL. The correlation analysis showed that a significant relation between flavonoid, antioxidant activity and habitat elevation. Conclusion: Total flavonoid content and antioxidant activity of the samples were affected by habitat climatic.  The present data indicated that the highest antioxidant activity may be due to higher flavonoid content and the habitat elevation was effective on the flavonoid content. Due to the high amounts of flavonoid and antioxidant activity of mullein extract, it seems to be a good herbal option as an antioxidant in complementary therapies.

Photosynthetic pigments and stomatal conductance in ecotypes of copoazu (Theobroma grandi orum Willd. Ex. Spreng K. Schum..

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Juan Carlos Suárez-Salazar

2016-12-01

Full Text Available The objective of this work was to evaluate the variability of photosynthetic pigment content and daily stomatal conductance was evaluated in relation to environmental variables in Copoazú (Theobroma grandi orum ecotypes. The ecotypes used were part of the germoplasm bank of the University of the Amazon (Colombia. The study was carried out during the year 2015. Four leaves of the average stratum of four plants were collected for each ecotype, to extract and read at different levels of absorbance and determine the content of photosynthetic pigments. During the hours of 04:00 a.m. to 6:00 p.m., the stomatal conductance (gs was monitored for environmental variables (relative humidity, air temperature, radiation and vapor pressure de cit (VPD. An analysis of variance was made using the Tukey test, correlations and regressions were made between gs and environmental variables. The contents of chlorophyll a, b, total and carotenoids among ecotypes were different (P<0.0001, the ecotype UA-31 presented the highest values, contrasting with the ecotype UA-37. Concerning gs, the interaction ecotype*hour showed signi cant differences (P<0.0001 .The ecotypes that presented the highest values of gs were UA-67 and UA-039, (P<0.0001, radiation (-0.91, P<0.0001 and DPV (-0.94; P<0.0001 0.0001.The results suggest that ecotypes UA-039 and UA-31 were the most suitable in terms of gaseous exchange and content of photosynthetic pigments.

Phytostabilization potential of two ecotypes of Vetiveria zizanioides in cadmium-contaminated soils: greenhouse and field experiments.

Science.gov (United States)

Phusantisampan, Theerawut; Meeinkuirt, Weeradej; Saengwilai, Patompong; Pichtel, John; Chaiyarat, Rattanawat

2016-10-01

Soil contamination by cadmium (Cd) poses a serious environmental and public health concern. Phytoremediation, i.e., the use of plants to remove contaminants from soil, has been proposed for treatment of Cd-contaminated ecosystems. In this study, we demonstrated the potential of Vetiveria zizanioides, commonly known as vetiver, to serve as an effective phytoremediation agent. Two ecotypes, i.e., India and Sri Lanka, were grown in greenhouse pots and in the field. Soils were amended with cow manure, pig manure, bat manure, and an organic fertilizer. Among all amendments, pig manure performed best in both greenhouse and field studies in terms of increasing total V. zizanioides biomass production in both ecotypes. In both greenhouse and in the field, tissue of the Sri Lanka ecotype had higher Cd concentrations than did the India ecotype. In the greenhouse, the presence of Cd did not affect total biomass production or root dry weight. The Sri Lanka ecotype had 2.7 times greater adventitious root numbers and 3.6 times greater Cd accumulation in roots than did the India ecotype. In the field study, the Sri Lanka ecotype offers potential as an excluder species, as it accumulated Cd primarily in roots, with translocation factor values 1 for all experiments except for the pig manure amendment. In addition, the highest Cd concentration in the Sri Lanka ecotype root (71.3 mg kg(-1)) was consistent with highest Cd uptake (10.4 mg plant(-1)) in the cow manure treatment. The India ecotype contained lower root Cd concentrations, and Cd accumulation was slightly higher in shoots compared to roots, with translocation factor (TF) values >1. The India ecotype was therefore not considered as an excluder in the Cd-contaminated soil. With the use of excluder species combined with application of organic amendments, soil contamination by Cd may be treated by alternative remediation methods such as phytostabilization.

A comparison of the phenolic profile and antioxidant activity of different Cichorium spinosum L. ecotypes.

Science.gov (United States)

Petropoulos, Spyridon A; Fernandes, Ângela; Barros, Lillian; Ferreira, Isabel Cfr

2018-01-01

Wild greens are considered a rich source of phenolic compounds and antioxidants and an essential part of the so-called Mediterranean diet. In the present study, Cichorium spinosum L. ecotypes, cultivated or collected in situ from wild plants from the eastern Mediterranean, were evaluated regarding their phenolic composition and antioxidant activity. Significant differences were observed among the various studied ecotypes regarding their phenolic compound content and profile, especially between wild and cultivated ecotypes, as well as the phenolic acid content between commercial products and cultivated plants. The antioxidant activity also varied among the various studied ecotypes and growing conditions, with commercial products having the highest antioxidant activity, whereas wild ecotypes showed lower antioxidant activity. Cichorium spinosum leaves are a rich source of chicoric and 5-O-caffeoylquinic acid, while significant differences in total phenolic acids, flavonoids and phenolic compound content and in antioxidant activity were observed among the studied ecotypes, as well as between the tested growing conditions. According to the results of the present study, further valorization of C. spinosum species has great potential, since it could be used as a new alternative species in the food industry. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Screening of sesame ecotypes (Sesamum indicum L. for salinity tolerance under field conditions: 1-Phenological and morphological characteristics

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F. Fazeli Kakhki

2016-05-01

Full Text Available Salinity is one of the most restrictions in plant growth in dry and semi dry land which effects production of many crops such as sesame. In order to study the phenology and morphology characteristics of 43 ecotypes and line of sesame (Sesamum indicum L. under salinity of irrigation water (5.2 dS.m-1 a field experiment was conducted at research farm of center of excellence for special crops, Ferdowsi University of Mashhad, Iran, during growing season of 2009-2010 based on a randomized complete block design with three replications. Results showed that four sesame ecotypes could not emerge, 14 sesame ecotypes had appropriate emergence but died before reproductive stage and only 58 % of sesame ecotypes could alive until maturity. There was significant difference between sesame ecotypes for phenological stages and were varied from 64 to 81 days for vegetative and 60 to 65 days for reproductive stages. Plant height, number and length of branches also were different between sesame ecotypes. The highest and the lowest plant height were observed in MSC43 and MSC12 ecotypes, respectively. Number of branches per plant was from 1 to 8 and length of branches in 32 percent of ecotypes was more than 100 cm. There was a considerable correlation between seed weight in plant with reproductive growth (r=0.38** and plant height (r=0.25. In addition different response of sesame ecotypes to saline water and also better morphological indices in some sesame ecotypes may be show the tolerance of these accessions to salinity. More studies may be useful for selection of sesame salt tolerance resources.

Genome-scale cold stress response regulatory networks in ten Arabidopsis thaliana ecotypes

DEFF Research Database (Denmark)

Barah, Pankaj; Jayavelu, Naresh Doni; Rasmussen, Simon

2013-01-01

available from Arabidopsis thaliana 1001 genome project, we further investigated sequence polymorphisms in the core cold stress regulon genes. Significant numbers of non-synonymous amino acid changes were observed in the coding region of the CBF regulon genes. Considering the limited knowledge about......BACKGROUND: Low temperature leads to major crop losses every year. Although several studies have been conducted focusing on diversity of cold tolerance level in multiple phenotypically divergent Arabidopsis thaliana (A. thaliana) ecotypes, genome-scale molecular understanding is still lacking....... RESULTS: In this study, we report genome-scale transcript response diversity of 10 A. thaliana ecotypes originating from different geographical locations to non-freezing cold stress (10°C). To analyze the transcriptional response diversity, we initially compared transcriptome changes in all 10 ecotypes...

Ecotypic variation in population dynamics of reintroduced bighorn sheep

Science.gov (United States)

Bleich, Vernon C.; Sargeant, Glen A.; Wiedmann, Brett P.

2018-01-01

Selection of bighorn sheep (Ovis canadensis) for translocation historically has been motivated by preservation of subspecific purity rather than by adaptation of source stocks to similar environments. Our objective was to estimate cause‐specific, annual, and age‐specific mortality of introduced bighorn sheep that originated at low elevations in southern British Columbia, Canada (BC ecotype), or in the Missouri River Breaks region of central Montana, USA (MT ecotype). In North Dakota, USA, mortality was similar and typically low for adult female bighorn sheep from Montana (0.09 ± 0.029 [SE]) and British Columbia (0.08 ± 0.017) during 2000–2016. Median life expectancy was 11 years for females that reached adulthood (2 yrs old); however, mortality accelerated with age and reached 86% by age 16. Mortalities resulted primarily from low rates of predation, disease, accidents, and unknown natural causes (<0.04 [upper 90% CI]). Similar survival rates of female bighorn sheep from female bighorn sheep from British Columbia and Montana, coupled with greater recruitment of bighorn sheep from Montana, resulted in a greater projected rate of increase for the MT ecotype (λ = 1.21) than for the BC ecotype (1.02), and a more youthful age structure. These results support translocation of bighorn sheep from areas that are environmentally similar to areas that will be stocked. Potential benefits include more rapid population growth, greater resilience to and more rapid recovery from density‐independent losses, an increased possibility that rapidly growing populations will expand into adjacent habitat, increased hunter opportunity, increased connectivity among herds, and a more complete restoration of ecosystem processes.

Differences in thermal acclimation of chloroplast functioning in two ecotypes of Valonia utricularis (Chlorophyta)

NARCIS (Netherlands)

Eggert, A.; van Hasselt, P.R; Breeman, Arno

Chloroplast functioning in two temperature ecotypes of the tropical to warm-temperate green macrophyte Valonia ultricularis was monitored by measuring chlorophyll a fluorescence parameters. One ecotype from the Mediterranean Sea is, with respect to growth and survival, more cold-adapted and

Variable but persistent coexistence of Prochlorococcus ecotypes along temperature gradients in the ocean's surface mixed layer.

Science.gov (United States)

Chandler, Jeremy W; Lin, Yajuan; Gainer, P Jackson; Post, Anton F; Johnson, Zackary I; Zinser, Erik R

2016-04-01

The vast majority of the phytoplankton communities in surface mixed layer of the oligotrophic ocean are numerically dominated by one of two ecotypes of Prochlorococcus, eMIT9312 or eMED4. In this study, we surveyed large latitudinal transects in the Atlantic and Pacific Ocean to determine if these ecotypes discretely partition the surface mixed layer niche, or if populations exist as a continuum along key environmental gradients, particularly temperature. Transitions of dominance occurred at approximately 19-21°C, with the eMED4 ecotype dominating the colder, and eMIT9312 ecotype dominating the warmer regions. Within these zones of regional dominance, however, the minority ecotype was not competed to extinction. Rather, a robust log-linear relationship between ecotype ratio and temperature characterized this stabilized coexistence: for every 2.5°C increase in temperature, the eMIT9312:eMED4 ratio increased by an order of magnitude. This relationship was observed in both quantitative polymerase chain reaction and in pyrosequencing assays. Water column stratification also contributed to the ecotype ratio along the basin-scale transects, but to a lesser extent. Finally, instances where the ratio of the eMED4 and eMIT9312 abundances did not correlate well with temperature were identified. Such occurrences are likely due to changes in water temperatures outpacing changes in community structure. © 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.

Phenotypic plasticity in response to the social environment: effects of density and sex ratio on mating behaviour following ecotype divergence.

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Kristina Karlsson

Full Text Available The ability to express phenotypically plastic responses to environmental cues might be adaptive in changing environments. We studied phenotypic plasticity in mating behaviour as a response to population density and adult sex ratio in a freshwater isopod (Asellus aquaticus. A. aquaticus has recently diverged into two distinct ecotypes, inhabiting different lake habitats (reed Phragmites australis and stonewort Chara tomentosa, respectively. In field surveys, we found that these habitats differ markedly in isopod population densities and adult sex ratios. These spatially and temporally demographic differences are likely to affect mating behaviour. We performed behavioural experiments using animals from both the ancestral ecotype ("reed" isopods and from the novel ecotype ("stonewort" isopods population. We found that neither ecotype adjusted their behaviour in response to population density. However, the reed ecotype had a higher intrinsic mating propensity across densities. In contrast to the effects of density, we found ecotype differences in plasticity in response to sex ratio. The stonewort ecotype show pronounced phenotypic plasticity in mating propensity to adult sex ratio, whereas the reed ecotype showed a more canalised behaviour with respect to this demographic factor. We suggest that the lower overall mating propensity and the phenotypic plasticity in response to sex ratio have evolved in the novel stonewort ecotype following invasion of the novel habitat. Plasticity in mating behaviour may in turn have effects on the direction and intensity of sexual selection in the stonewort habitat, which may fuel further ecotype divergence.

Genetic variation between ecotypic populations of Chloris ...

African Journals Online (AJOL)

Genetic variation between ecotypic populations of Chloris roxburghiana grass detected through RAPD analysis. ... frequency indicated that the four populations of C. roxburghiana were genetically distinct, probably as a result of variation in soil fertility, geographical isolation and socio-ecological history of the study sites.

Effect of Irrigation Intervals on Some Morphophysiological Traits of Basil (Ocimum basilicum L. Ecotypes

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M Goldani

2012-10-01

Full Text Available In order to determine the effect of different irrigation intervals on some morphophysiological traits of basil (Ocimum basilicum L., an experiment was conducted as factorial based on randomized complete block design with three replications under greenhouse conditions during 2010. Treatments included five irrigation intervals with 4, 8, 12, 16 and 20 days intervals and two ecotypes of basil (green and purple. The results showed that by increasing irrigation interval plant height, spike number, spike weight and shoot dry weight between irrigation intervals decreased. Purple basil was more tolerant than basil green ecotype to drought stress. Interaction between irrigation intervals and ecotypes showed that the best treatment related to four days irrigation interval and purple basil ecotype. The effect of irrigation intervals on root area, root diameter mean, total length, root volume and dry weight of root was significant. In all irrigation intervals, purple basil had better performance compared to green ecotype. The results showed that by increasing in irrigation interval decreased root surface area, but increased total root length. It was concluded that increasing irrigation interval up to 12 days decreased shoot and root surface areas. Increasing irrigation interval decreased chlorophyll- a, b and increased prolin amino acid content of basil leaf.

Genetic diversity of indigenous chicken ecotypes in Jordan

African Journals Online (AJOL)

Jane

2010-10-11

Oct 11, 2010 ... DNA polymorphism of 4 indigenous chicken ecotypes was assessed in Jordan using random amplified ... Such technology is random amplified polymorphic DNA. (RAPD) ..... ping from genetics lab for animal and plant at MU.

Quantification of caffeic acid content in 4 species of mullein (Verbascum sp. ecotypes from southwest Iran

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F. Jamshidi kia

2017-11-01

Full Text Available Background and objectives: The Verbascum genus is the largest genus of Scrophulariaceae family which has extensive natural habitat in southwest of Iran. Phenolic acids are one of the most important chemical compounds that have different biological activities including anti-inflammatory, antibacterial, antiviral, anti-tumor and antioxidant. Therefore, this study was conducted with the aim of caffeic acid quantification of 4 species of Verbascum sp. ecotypes from southwest Iran. Methods: Nine ecotypes of the 4 species (V. macrophyllus, V. pseudo- digitalis, V. sinatum, V. songaricum were collected from the southwest of Iran. Quantification of caffeic acid contentusing reversed-phase high-performance liquid chromatography (RP-HPLC with UV PDA 2800 detector, a C18 column with dimensions of 250×4.6 mm was performed. Results: The results showed that Verbascum sp. contained caffeic acid compound and there was a difference among species and ecotypes. The results showed the highest and lowest content of caffeic acid obtained from the V. sinatum species and ecotype Sepidan (7.76 μg/mg extract and V. songaricum species and ecotype Farokhshahr (0.54 μg/mg extract, respectively. Conclusion: The results revealed a high level of variation in caffeic acid among Verbascum sp. which was affected by habitat and climatic. The pattern of habitats of suitable ecotypes superior in terms of composition to should be selected and used for breeding and cropping mullein.

Evaluation of Fall Planting Dates of Cumin (Cuminum cyminum L. Ecotypes in Mashhad Conditions

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Z Khorasani

2012-07-01

Full Text Available In order to study the effects of fall planting dates on yield and yield components of six cumin (Cuminum cyminum L. ecotypes an experiment was arranged in a randomized complete block design as a split-plot with three replications during 2007-08 growing season at the Agricultural Research Station of Ferdowsi University of Mashhad. Three planting dates (18 Oct. (first, 8 Nov. (second and 29 Dec. (third and six cumin ecotypes (Torbat heydarieh, Khaf, Sabzevar, Ghaen, Ghoochan and RZ19 were allocated to main and sub plots, respectively. Results showed that the effects of planting dates, ecotypes and interaction effects of planting dates and cumin ecotypes were significant for yield components (winter survival percentage, number of umbel per plant, number of seeds per umbel and 1000-seed weight and seed yield and biological yield. There was a reduction on yield components (number of umbel per plant, number of seeds per umbel and 1000-seed weight, seed yield and biological yield due to delay planting date from 18 Oct. to 29 Dec. The highest winter survival percentage was achieved on the third planting date. The highest and lowest amount for all of the traits, were achieved in Ghaen and RZ19 ecotypes, respectively. According to the useful results and for the deployment of cumin fall planting in other locations of province, continuation of this study to recommended.

Sensitivity of two ecotypes of Arabidopsis Thaliana (Cvi and Te) towards UV-B irradiation

International Nuclear Information System (INIS)

Velichkova, M.; Stanoeva, D.; Popova, A.

2013-01-01

he susceptibility of Arabidopsis thaliana towards the detrimental effect of UV-B irradiation was investigated using two ecotypes, Cvi and Te. The effect of UV-B treatment on primary photosynthetic reactions - energy interaction between the main pigment-protein complexes and oxygen evolution, was evaluated at low (4 0 C) and at room (22 0 C) temperature. UV-B-induced alterations of investigated photosynthetic reactions are better expressed at 22 0 C than at 4 0 C for Cvi. For Te ecotype the energy interaction was suppressed to higher extent at 22 0 C, while oxygen evolving activity was affected similarly at both temperatures. At low and room temperature, the energy interaction in the complex PSII-core antenna is affected stronger by UV-B treatment than the energy distribution between both photosystems, as revealed by fluorescence ratios of 77 K spectra. The results presented indicate that the Arabidopsis thaliana ecotype Cvi (Cape Verde Islands) is less affected by UV-B irradiation in respect to the investigated primary photosynthetic reactions than the ecotype Te (Finland)

Effects of environmental biomass-producing factors on Cd uptake in two Swedish ecotypes of Pinus sylvestris

Energy Technology Data Exchange (ETDEWEB)

Ekvall, Lars; Greger, Maria

2003-03-01

Cadmium uptake in Scots pine seedlings was mainly regulated by biomass production. - A factorial design was used to study direct effects of external biomass-producing factors such as light, temperature and photoperiod on cadmium (Cd) uptake and indirect effects, via change in biomass production in two ecotypes of Scots pine (Pinus silvestris). The aim was to find out if the external factors affect the Cd uptake directly or via change in biomass production, and if the effect differs between ecotypes. Seedlings were grown under 10 combinations of external factors, i.e. temperature (15 and 20 deg. C), light intensity (50 and 200 {mu}mol photons m{sup -2} s{sup -1}), photoperiod (18 h light/8 h darkness and continuous light) and external Cd concentration (totally 1.88 and 7.50 {mu}mol). The treatment lasted for 18 days and Cd concentrations in roots and shoots were determined by AAS. The results showed that an increased biomass production increased the total Cd uptake but had a dilution effect on the Cd concentration, especially in the root tissues. The external factors tested did not have any direct effects on the Cd uptake, only in the case of Cd translocation to the shoot did the higher temperature show a direct increase, but only in the southern ecotype. The two ecotypes reacted differently in Cd uptake and translocation to the external factors studied. The relative Cd uptake increased with increasing photoperiod in the northern but not in the southern ecotype. The southern ecotype decreased the Cd concentration in the shoot with increased light intensity caused by a dilution effect due to extensive shoot growth of this ecotype. The conclusion is that the uptake in pine seedlings is mainly regulated via biomass production, and not directly by light and temperature and that resulting plant Cd contents to a certain extent depend on plant origin.

Replaying Evolution to Test the Cause of Extinction of One Ecotype in an Experimentally Evolved Population.

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Caroline B Turner

Full Text Available In a long-term evolution experiment with Escherichia coli, bacteria in one of twelve populations evolved the ability to consume citrate, a previously unexploited resource in a glucose-limited medium. This innovation led to the frequency-dependent coexistence of citrate-consuming (Cit+ and non-consuming (Cit- ecotypes, with Cit-bacteria persisting on the exogenously supplied glucose as well as other carbon molecules released by the Cit+ bacteria. After more than 10,000 generations of coexistence, however, the Cit-lineage went extinct; cells with the Cit-phenotype dropped to levels below detection, and the Cit-clade could not be detected by molecular assays based on its unique genotype. We hypothesized that this extinction was a deterministic outcome of evolutionary change within the population, specifically the appearance of a more-fit Cit+ ecotype that competitively excluded the Cit-ecotype. We tested this hypothesis by re-evolving the population from a frozen population sample taken within 500 generations of the extinction and from another sample taken several thousand generations earlier, in each case for 500 generations and with 20-fold replication. To our surprise, the Cit-type did not go extinct in any of these replays, and Cit-cells also persisted in a single replicate that was propagated for 2,500 generations. Even more unexpectedly, we showed that the Cit-ecotype could reinvade the Cit+ population after its extinction. Taken together, these results indicate that the extinction of the Cit-ecotype was not a deterministic outcome driven by competitive exclusion by the Cit+ ecotype. The extinction also cannot be explained by demographic stochasticity alone, as the population size of the Cit-ecotype should have been many thousands of cells even during the daily transfer events. Instead, we infer that the extinction must have been caused by a rare chance event in which some aspect of the experimental conditions was inadvertently perturbed.

Effects of environmental biomass-producing factors on Cd uptake in two Swedish ecotypes of Pinus sylvestris

International Nuclear Information System (INIS)

Ekvall, Lars; Greger, Maria

2003-01-01

Cadmium uptake in Scots pine seedlings was mainly regulated by biomass production. - A factorial design was used to study direct effects of external biomass-producing factors such as light, temperature and photoperiod on cadmium (Cd) uptake and indirect effects, via change in biomass production in two ecotypes of Scots pine (Pinus silvestris). The aim was to find out if the external factors affect the Cd uptake directly or via change in biomass production, and if the effect differs between ecotypes. Seedlings were grown under 10 combinations of external factors, i.e. temperature (15 and 20 deg. C), light intensity (50 and 200 μmol photons m -2 s -1 ), photoperiod (18 h light/8 h darkness and continuous light) and external Cd concentration (totally 1.88 and 7.50 μmol). The treatment lasted for 18 days and Cd concentrations in roots and shoots were determined by AAS. The results showed that an increased biomass production increased the total Cd uptake but had a dilution effect on the Cd concentration, especially in the root tissues. The external factors tested did not have any direct effects on the Cd uptake, only in the case of Cd translocation to the shoot did the higher temperature show a direct increase, but only in the southern ecotype. The two ecotypes reacted differently in Cd uptake and translocation to the external factors studied. The relative Cd uptake increased with increasing photoperiod in the northern but not in the southern ecotype. The southern ecotype decreased the Cd concentration in the shoot with increased light intensity caused by a dilution effect due to extensive shoot growth of this ecotype. The conclusion is that the uptake in pine seedlings is mainly regulated via biomass production, and not directly by light and temperature and that resulting plant Cd contents to a certain extent depend on plant origin

Study of Ecotype and Sowing Date Interaction in Cumin (Cuminum cyminum L. using Different Univariate Stability Parameters

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J Ghanbari

2017-06-01

Full Text Available Introduction Cumin is one of the most important medicinal plants in Iran and today, it is in the second level of popularity between spices in the world after black pepper. Cumin is an aromatic plant used as flavoring and seasoning agent in foods. Cumin seeds have been found to possess significant biological and have been used for treatment of toothache, dyspepsia, diarrhoea, epilepsy and jaundice. Knowledge of GEI is advantageous to have a cultivar that gives consistently high yield in a broad range of environments and to increase efficiency of breeding program and selection of best genotypes. A genotype that has stable trait expression across environments contributes little to GEI and its performance should be more predictable from the main several statistical methods have been proposed for stability analysis, with the aim of explaining the information contained in the GEI. Regression technique was proposed by Finlay and Wilkinson (1963 and was improved by Eberhart and Russell (1966. Generally, genotype stability was estimated by the slope of and deviation from the regression line for each of the genotypes. This is a popular method in stability analysis and has been applied in many crops. Non-parametric methods (rank mean (R, standard deviation rank (SDR and yield index ratio (YIR, environmental variance (S2i and genotypic variation coefficient (CVi Wricke's ecovalence and Shukla's stability variance (Shukla, 1972 have been used to determine genotype-by-environment interaction in many studies. This study was aimed to evaluate the ecotype × sowing date interaction in cumin and to evaluation of genotypic response of cumin to different sowing dates using univariate stability parameters. Materials and Methods In order to study of ecotype × sowing date interaction, different cumin ecotypes: Semnan, Fars, Yazd, Golestan, Khorasan-Razavi, Khorasan-Shomali, Khorasan-Jonoubi, Isfahan and Kerman in 5 different sowing dates (26th December, 10th January

Distribution and diversity of Prochlorococcus ecotypes in the Red Sea

KAUST Repository

Shibl, Ahmed A.; Thompson, Luke R.; Ngugi, David; Stingl, Ulrich

2014-01-01

Photosynthetic prokaryotes of the genus Prochlorococcus play a major role in global primary production in the world's oligotrophic oceans. A recent study on pelagic bacterioplankton communities in the northern and central Red Sea indicated that the predominant cyanobacterial 16S rRNA gene sequence types were from Prochlorococcus cells belonging to a high-light-adapted ecotype (HL II). In this study, we analyzed microdiversity of Prochlorococcus sp. at multiple depths within and below the euphotic zone in the northern, central, and southern regions of the Red Sea, as well as in surface waters in the same locations, but in a different season. Prochlorococcus dominated the communities in clone libraries of the amplified 16S-23S rRNA internal transcribed spacer (ITS) region. Almost no differences were found between samples from coastal or open-water sites, but a high diversity of Prochlorococcus ecotypes was detected at 100-meter depth in the water column. In addition, an unusual dominance of HL II-related sequences was observed in deeper waters. Our results indicate that the Red Sea harbors diverse Prochlorococcus lineages, but no novel ecotypes, despite its unusual physicochemical properties. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Distribution and diversity of Prochlorococcus ecotypes in the Red Sea

KAUST Repository

Shibl, Ahmed A.

2014-06-19

Photosynthetic prokaryotes of the genus Prochlorococcus play a major role in global primary production in the world\\'s oligotrophic oceans. A recent study on pelagic bacterioplankton communities in the northern and central Red Sea indicated that the predominant cyanobacterial 16S rRNA gene sequence types were from Prochlorococcus cells belonging to a high-light-adapted ecotype (HL II). In this study, we analyzed microdiversity of Prochlorococcus sp. at multiple depths within and below the euphotic zone in the northern, central, and southern regions of the Red Sea, as well as in surface waters in the same locations, but in a different season. Prochlorococcus dominated the communities in clone libraries of the amplified 16S-23S rRNA internal transcribed spacer (ITS) region. Almost no differences were found between samples from coastal or open-water sites, but a high diversity of Prochlorococcus ecotypes was detected at 100-meter depth in the water column. In addition, an unusual dominance of HL II-related sequences was observed in deeper waters. Our results indicate that the Red Sea harbors diverse Prochlorococcus lineages, but no novel ecotypes, despite its unusual physicochemical properties. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Distribution and diversity of Prochlorococcus ecotypes in the Red Sea.

Science.gov (United States)

Shibl, Ahmed A; Thompson, Luke R; Ngugi, David K; Stingl, Ulrich

2014-07-01

Photosynthetic prokaryotes of the genus Prochlorococcus play a major role in global primary production in the world's oligotrophic oceans. A recent study on pelagic bacterioplankton communities in the northern and central Red Sea indicated that the predominant cyanobacterial 16S rRNA gene sequence types were from Prochlorococcus cells belonging to a high-light-adapted ecotype (HL II). In this study, we analyzed microdiversity of Prochlorococcus sp. at multiple depths within and below the euphotic zone in the northern, central, and southern regions of the Red Sea, as well as in surface waters in the same locations, but in a different season. Prochlorococcus dominated the communities in clone libraries of the amplified 16S-23S rRNA internal transcribed spacer (ITS) region. Almost no differences were found between samples from coastal or open-water sites, but a high diversity of Prochlorococcus ecotypes was detected at 100-meter depth in the water column. In addition, an unusual dominance of HL II-related sequences was observed in deeper waters. Our results indicate that the Red Sea harbors diverse Prochlorococcus lineages, but no novel ecotypes, despite its unusual physicochemical properties. © 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved.

Application of otolith shape analysis in identifying different ecotypes of Coilia ectenes in the Yangtze Basin, China

Digital Repository Service at National Institute of Oceanography (India)

Radhakrishnan, K.V.; Li, Y.; Jayalakshmy, K.V.; Liu, M.; Murphy, B.R.; Xie, S.

The variability in otolith shape of the tapertail anchovy Coilia ectenes was investigated as a tool for identifying its different ecotypes. The outlines of 350 sagittal otoliths of known ecotypes collected from seven sampling areas, covering most...

Diverse, rare microbial taxa responded to the Deepwater Horizon deep-sea hydrocarbon plume.

Science.gov (United States)

Kleindienst, Sara; Grim, Sharon; Sogin, Mitchell; Bracco, Annalisa; Crespo-Medina, Melitza; Joye, Samantha B

2016-02-01

The Deepwater Horizon (DWH) oil well blowout generated an enormous plume of dispersed hydrocarbons that substantially altered the Gulf of Mexico's deep-sea microbial community. A significant enrichment of distinct microbial populations was observed, yet, little is known about the abundance and richness of specific microbial ecotypes involved in gas, oil and dispersant biodegradation in the wake of oil spills. Here, we document a previously unrecognized diversity of closely related taxa affiliating with Cycloclasticus, Colwellia and Oceanospirillaceae and describe their spatio-temporal distribution in the Gulf's deepwater, in close proximity to the discharge site and at increasing distance from it, before, during and after the discharge. A highly sensitive, computational method (oligotyping) applied to a data set generated from 454-tag pyrosequencing of bacterial 16S ribosomal RNA gene V4-V6 regions, enabled the detection of population dynamics at the sub-operational taxonomic unit level (0.2% sequence similarity). The biogeochemical signature of the deep-sea samples was assessed via total cell counts, concentrations of short-chain alkanes (C1-C5), nutrients, (colored) dissolved organic and inorganic carbon, as well as methane oxidation rates. Statistical analysis elucidated environmental factors that shaped ecologically relevant dynamics of oligotypes, which likely represent distinct ecotypes. Major hydrocarbon degraders, adapted to the slow-diffusive natural hydrocarbon seepage in the Gulf of Mexico, appeared unable to cope with the conditions encountered during the DWH spill or were outcompeted. In contrast, diverse, rare taxa increased rapidly in abundance, underscoring the importance of specialized sub-populations and potential ecotypes during massive deep-sea oil discharges and perhaps other large-scale perturbations.

Extracellular polysaccharide production by a novel osmotolerant marine strain of Alteromonas macleodii and its application towards biomineralization of silver.

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Ananya Mehta

Full Text Available The present study demonstrates exopolysaccharide production by an osmotolerant marine isolate and also describes further application of the purified polysaccharide for production of colloidal suspension of silver nanoparticles with narrow size distribution. Phylogenetic analysis based on 16S r RNA gene sequencing revealed close affinity of the isolate to Alteromonas macleodii. Unlike earlier reports, where glucose was used as the carbon source, lactose was found to be the most suitable substrate for polysaccharide production. The strain was capable of producing 23.4 gl(-1 exopolysaccharide with a productivity of 7.8 gl(-1 day(-1 when 15% (w/v lactose was used as carbon source. Furthermore, the purified polysaccharide was able to produce spherical shaped silver nanoparticles of around 70 nm size as characterized by Uv-vis spectroscopy, Dynamic light scattering and Transmission electron microscopy. These observations suggested possible commercial potential of the isolated strain for production of a polysaccharide which has the capability of synthesizing biocompatible metal nanoparticle.

Seasonal adaptations to day length in ecotypes of Diorhabda spp. (Coleoptera: Chrysomelidae) inform selection of agents against saltcedars (Tamarix spp.).

Science.gov (United States)

Dalin, Peter; Bean, Daniel W; Dudley, Tom L; Carney, Vanessa A; Eberts, Debra; Gardner, Kevin T; Hebertson, Elizabeth; Jones, Erin N; Kazmer, David J; Michels, G J; O'Meara, Scott A; Thompson, David C

2010-10-01

Seasonal adaptations to daylength often limit the effective range of insects used in biological control of weeds. The leaf beetle Diorhabda carinulata (Desbrochers) was introduced into North America from Fukang, China (latitude 44° N) to control saltcedars (Tamarix spp.), but failed to establish south of 38° N latitude because of a mismatched critical daylength response for diapause induction. The daylength response caused beetles to enter diapause too early in the season to survive the duration of winter at southern latitudes. Using climate chambers, we characterized the critical daylength response for diapause induction (CDL) in three ecotypes of Diorhabda beetles originating from 36, 38, and 43° N latitudes in Eurasia. In a field experiment, the timing of reproductive diapause and voltinism were compared among ecotypes by rearing the insects on plants in the field. CDL declined with latitude of origin among Diorhabda ecotypes. Moreover, CDL in southern (42° N latitude) ecotypes, however, CDL was relatively insensitive to temperature. The southern ecotypes produced up to four generations when reared on plants in the field at sites south of 38° N, whereas northern ecotypes produced only one or two generations. The study reveals latitudinal variation in how Diorhabda ecotypes respond to daylength for diapause induction and how these responses affect insect voltinism across the introduced range.

Cross-cultural and cross-ecotype production of a killer whale `excitement' call suggests universality

Science.gov (United States)

Rehn, Nicola; Filatova, Olga A.; Durban, John W.; Foote, Andrew D.

2011-01-01

Facial and vocal expressions of emotion have been found in a number of social mammal species and are thought to have evolved to aid social communication. There has been much debate about whether such signals are culturally inherited or are truly biologically innate. Evidence for the innateness of such signals can come from cross-cultural studies. Previous studies have identified a vocalisation (the V4 or `excitement' call) associated with high arousal behaviours in a population of killer whales in British Columbia, Canada. In this study, we compared recordings from three different socially and reproductively isolated ecotypes of killer whales, including five vocal clans of one ecotype, each clan having discrete culturally transmitted vocal traditions. The V4 call was found in recordings of each ecotype and each vocal clan. Nine independent observers reproduced our classification of the V4 call from each population with high inter-observer agreement. Our results suggest the V4 call may be universal in Pacific killer whale populations and that transmission of this call is independent of cultural tradition or ecotype. We argue that such universality is more consistent with an innate vocalisation than one acquired through social learning and may be linked to its apparent function of motivational expression.

Comparative Mapping of Seed Dormancy Loci Between Tropical and Temperate Ecotypes of Weedy Rice (Oryza sativa L.

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Lihua Zhang

2017-08-01

Full Text Available Genotypic variation at multiple loci for seed dormancy (SD contributes to plant adaptation to diverse ecosystems. Weedy rice (Oryza sativa was used as a model to address the similarity of SD genes between distinct ecotypes. A total of 12 quantitative trait loci (QTL for SD were identified in one primary and two advanced backcross (BC populations derived from a temperate ecotype of weedy rice (34.3°N Lat.. Nine (75% of the 12 loci were mapped to the same positions as those identified from a tropical ecotype of weedy rice (7.1°N Lat.. The high similarity suggested that the majority of SD genes were conserved during the ecotype differentiation. These common loci are largely those collocated/linked with the awn, hull color, pericarp color, or plant height loci. Phenotypic correlations observed in the populations support the notion that indirect selections for the wild-type morphological characteristics, together with direct selections for germination time, were major factors influencing allelic distributions of SD genes across ecotypes. Indirect selections for crop-mimic traits (e.g., plant height and flowering time could also alter allelic frequencies for some SD genes in agroecosystems. In addition, 3 of the 12 loci were collocated with segregation distortion loci, indicating that some gametophyte development genes could also influence the genetic equilibria of SD loci in hybrid populations. The SD genes with a major effect on germination across ecotypes could be used as silencing targets to develop transgene mitigation (TM strategies to reduce the risk of gene flow from genetically modified crops into weed/wild relatives.

Metabolism of [ 14 C]GA 19 and [ 14 C]GA 53 by ecotypes of Betula ...

African Journals Online (AJOL)

Continuing with this line of research, we studied the metabolism of 14C-labelled GA19 and GA53. [14C]GA19 and [14C] A53 were applied to the apices of the northern ecotype (67º N) and to the leaves of the southern ecotype (64º N) of Betula pendula Roth. under different photoperiods and at different times in order to ...

Genetic structure among the local chicken ecotypes of Tanzania ...

African Journals Online (AJOL)

A study was conducted to evaluate the genetic structure of local chicken ecotypes of Tanzania using 20 polymorphic microsatellite DNA markers. A standard PCR was followed by manual genotyping (6% native polyacrylamide gel visualized by silver staining). Phylogenetic analysis of 13 individuals from each of the nine ...

Asymmetric impacts of two herbivore ecotypes on similar host plants

Science.gov (United States)

Ecotypes may arise following allopatric separation from source populations. The simultaneous transfer of an exotic plant to a novel environment, along with its stenophagous herbivore, may complicate more traditional patterns of divergence from the plant and insect source populations. We evaluated ...

Comparison of the spaceflight transcriptome of four commonly used Arabidopsis thaliana ecotypes

Data.gov (United States)

National Aeronautics and Space Administration — This experiment compared the spaceflight transcriptomes of four commonly used natural variants (ecotypes) of Arabidopsis thaliana using RNAseq. In nature Arabidopsis...

Association between minor loading vein architecture and light- and CO2-saturated rates of photosynthetic oxygen evolution among Arabidopsis thaliana ecotypes from different latitudes

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Christopher M Cohu

2013-07-01

Full Text Available Through microscopic analysis of veins and assessment of light- and CO2-saturated rates of photosynthetic oxygen evolution, we investigated the relationship between minor loading vein anatomy and photosynthesis of mature leaves in three ecotypes of Arabidopsis thaliana grown under four different combinations of temperature and photon flux density (PFD. All three ecotypes exhibited greater numbers and cross-sectional area of phloem cells as well as higher photosynthesis rates in response to higher PFD and especially lower temperature. The Swedish ecotype exhibited the strongest response to these conditions, the Italian ecotype the weakest response, and the Col-0 ecotype exhibited an intermediate response. Among all three ecotypes, strong linear relationships were found between light- and CO2-saturated rates of photosynthetic oxygen evolution and the number and area of either sieve elements or of companion and phloem parenchyma cells in foliar minor loading veins, with the Swedish ecotype showing the highest number of cells in minor loading veins (and largest minor veins coupled with unprecedented high rates of photosynthesis. Linear, albeit less significant, relationships were also observed between number and cross-sectional area of tracheids per minor loading vein versus light- and CO2-saturated rates of photosynthetic oxygen evolution. We suggest that sugar distribution infrastructure in the phloem is co-regulated with other features that set the upper limit for photosynthesis. The apparent genetic differences among Arabidopsis ecotypes should allow for future identification of the gene(s involved in augmenting sugar-loading and -transporting phloem cells and maximal rates of photosynthesis.

Scaling-up permafrost thermal measurements in western Alaska using an ecotype approach

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W. L. Cable

2016-10-01

Full Text Available Permafrost temperatures are increasing in Alaska due to climate change and in some cases permafrost is thawing and degrading. In areas where degradation has already occurred the effects can be dramatic, resulting in changing ecosystems, carbon release, and damage to infrastructure. However, in many areas we lack baseline data, such as subsurface temperatures, needed to assess future changes and potential risk areas. Besides climate, the physical properties of the vegetation cover and subsurface material have a major influence on the thermal state of permafrost. These properties are often directly related to the type of ecosystem overlaying permafrost. In this paper we demonstrate that classifying the landscape into general ecotypes is an effective way to scale up permafrost thermal data collected from field monitoring sites. Additionally, we find that within some ecotypes the absence of a moss layer is indicative of the absence of near-surface permafrost. As a proof of concept, we used the ground temperature data collected from the field sites to recode an ecotype land cover map into a map of mean annual ground temperature ranges at 1 m depth based on analysis and clustering of observed thermal regimes. The map should be useful for decision making with respect to land use and understanding how the landscape might change under future climate scenarios.

Varied growth response of cogongrass ecotypes to elevated CO2

Science.gov (United States)

Cogongrass [Imperata cylindrica (L.) P. Beauv] is an invasive C4 perennial grass which is listed as one of the top ten worst weeds in the world and is a major problem in the Southeast US. Five cogongrass ecotypes (Florida, Hybrid, Louisiana, Mobile, and North Alabama) collected across the Southeast ...

Preliminary Evaluation of Yield and Yield Components of Some Khorasanian Sesame Ecotypes (Sesamum indicum L.

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A. Nezami

2014-09-01

Full Text Available In most region of Khorasan, sesame ecotypes have been planted for many years, but there is little information about seed yield and yield components of them. Therefore a field experimental was conducted to investigation of yield parameters of 14 sesame ecotypes (MSC1, MSC2, MSC3, MSC4, MSC5, MSC6, MSC7, MSC8, MSC9, MSC10, MSC11, MSC12, MSC13 and MSC14 in randomized complete block design with three replications at experimental station, Collage of Agriculture, Ferdowsi University of Mashhad during 2009. Results showed that there were significant difference (P

Ecotype variability in growth and secondary metabolite profile in Moringa oleifera: impact of sulfur and water availability.

Science.gov (United States)

Förster, Nadja; Ulrichs, Christian; Schreiner, Monika; Arndt, Nick; Schmidt, Reinhard; Mewis, Inga

2015-03-25

Moringa oleifera is widely cultivated in plantations in the tropics and subtropics. Previous cultivation studies with M. oleifera focused primarily only on leaf yield. In the present study, the content of potentially health-promoting secondary metabolites (glucosinolates, phenolic acids, and flavonoids) were also investigated. Six different ecotypes were grown under similar environmental conditions to identify phenotypic differences that can be traced back to the genotype. The ecotypes TOT4880 (origin USA) and TOT7267 (origin India) were identified as having the best growth performance and highest secondary metabolite production, making them an ideal health-promoting food crop. Furthermore, optimal cultivation conditions-exemplarily on sulfur fertilization and water availability-for achieving high leaf and secondary metabolite yields were investigated for M. oleifera. In general, plant biomass and height decreased under water deficiency compared to normal cultivation conditions, whereas the glucosinolate content increased. The effects depended to a great extent on the ecotype.

Genetic variations between two ecotypes of Egyptian clover by inter ...

African Journals Online (AJOL)

Aghomotsegin

2015-06-10

Jun 10, 2015 ... Four Egyptian clover (Trifolium alexandrinum L) cultivars representing two ecotypes were used in the present study. Fahl cultivar is prevalent in whole Egypt and is good for single cut as it has poor regeneration ability, whereas Serw1, Giza6 and Gemmiza1 give 5-6 cuts of good fodder. Techniques based ...

Agronomic behaviour of some Cynodon dactylon ecotypes for turfgrass use in the Mediterranean climate

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Roberto Viggiani

2015-02-01

Full Text Available In Italy, the expansion of turfgrasses is limited by the lack of suitable species for cultivation in the Mediterranean climate. With this view, Mi.Te.A.Med. (Turfgrass improvement in the Mediterranean climate research project was developed with the main purpose to find out and agronomically characterise native turfgrass species of Southern and Central Italy and to compare them with some commercial cultivars. During the first step of the research, 11 sites from 6 regions of Southern and Central Italy were identified. In these sites 24 ecotypes of Cynodon dactylon L. (Pers. were collected and their habitus, phenology plus some biometric parameters have been determined. During the two years of research both botanic and agronomic characterisation of the collected C. dactylon ecotypes and their comparison with 3 commercial cultivars (Panama, Transcontinental and Yukon was carried out. In the first year the colour loss interval was assessed. In the second year, colour index was measured by an electronic colorimeter, weekly growth rate was measured by a turfmeter, turf quality and ground cover percentage were assessed by visual estimate. Some native accessions showed behaviour similar to commercial cultivars while an ecotype from the Abruzzo region showed better results compared to the commercial cultivars for several quality indices.

Ecotypes as a concept for exploring responses to climate change in fish assemblages

DEFF Research Database (Denmark)

Engelhard, George H.; Ellis, Jim R.; Payne, Mark

2011-01-01

How do species-rich fish assemblages respond to climate change or to other anthropogenic or environmental drivers? To explore this, a categorization concept is presented whereby species are assigned with respect to six ecotype classifications, according to biogeography, horizontal and vertical...... or combinations of them. The post-1989 warm biological regime appears to have favoured pelagic species more than demersal species. These community-level patterns agree with the expected responses of ecotypes to climate change and also with anticipated vulnerability to fishing pressure....... habitat preference, trophic guild, trophic level, or body size. These classification schemes are termed ecotypology, and the system is applied to fish in the North Sea using International Bottom Trawl Survey data. Over the period 1977–2008, there were changes in the North Sea fish community that can...

Problems with the claim of ecotype and taxon status of the wolf in the Great Lakes region

Science.gov (United States)

Cronin, Matthew A.; Mech, L. David

2009-01-01

Koblmuller et al. (2009) analysed molecular genetic data of the wolf in the Great Lakes (GL) region of the USA and concluded that the animal was a unique ecotype of grey wolf and that genetic data supported the population as a discrete wolf taxon. However, some of the literature that the researchers used to support their position actually did not, and additional confusion arises from indefinite use of terminology. Herein, we discuss the problems with designation of a wolf population as a taxon or ecotype without proper definition and assessment of criteria.

In vitro regeneration of Amazonian pineapple (Ananas comosus plants ecotype Gobernadora

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Héctor Alexander Blanco Flores

2017-01-01

Full Text Available There are a number of pineapple (Ananas comosus cultivars and ecotypes of local commercial importance in Venezuela, among them the Amazonian ones, cultivated mainly by the aboriginal Piaroa, are of relevance. They sow the propagules, which restricts the availability of material for large-scale cultivation. This limitation was approached by plant tissue culture for in vitro propagation of Amazonian pineapple plants, Gobernadora ecotype, through somatic embryogenesis (ES and adventitious organogenesis (OA. Basal and intermediate sections of leaves were tested. Only the leaf base sections (FBS were morphogenically induced. The highest number of vitroplants (1.58 plants / explant was obtained from the embryogenic callus induced in MS medium with Picloram 10 mg.L-1 + Thidiazuron 2 mg.L-1, transferred to MS medium without hormones. In the organogenic process, the highest number of plants/explants (5 was obtained directly in MS with naphthaleneacetic acid 5 mg.L-1 + benzylaminopurine 0.25 mg.L-1, transferred to MS. The latter being the best in vitro culture system due to its productivity and for being a method that minimizes somaclonal variation.

The Effect of Plant Density on Photosynthesis and Growth Indices of Henna (Lowsonia inermis L. Ecotypes

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A Pasandi Pour

2018-05-01

Full Text Available Introduction One of the most important factors to obtain the maximum performance or yield in every climatic condition and for each plant varieties is determining the optimum plant density. Henna (Lowsonia inermis L. is a perennial plant with high value in terms of having medicinal properties and industrial applications. The dye which is derived from green leaves of henna is used for decorating the body with intricate designs and the principle coloring matter is lawsone, 2-hydroxy-1, 4-naphthoqunone. The main purpose of this study was to evaluate the agro-physiological reaction of different henna ecotypes to different planting densities in Kerman weather conditions. Materials and Methods The study was carried out as a factorial experiment based on complete randomized block design with three replications in Shahid Bahonar University in 2015. The experiment consisted of four plant densities (25, 33, 50 and 100 plants m-2 and three ecotypes (Shahdad, Roodbar and Bam. Due to its small seeds and germination problems the planting method used was transplanting. In this study, growth indices such as leaf area index (LAI, crop growth rate (CGR, relative growth rate (RGR, leaf area ratio (LAR, specific leaf area (SLA, specific leaf weight (SLW, leaf area duration (LAD and biomass duration (BMD were calculated. The net photosynthesis, stomatal conductance and transpiration rate were measured in the middle of growing period by photosynthesis meter (CI-340 model, CID Bio- Science companies, USA. At the end, the results were analyzed using the SAS v. 9.1 and MSTATC software’s and diagrams were drawn by Excel software. Results and Discussion The results showed that the studied ecotypes were significantly different in terms of CGR, RGR and stomatal conductance. The highest average of CGR belonged to Shahdad ecotype while there was no significant difference between Roodbar and Bam ecotypes in this case. Shahdad ecotype with the RGR of 0.018 g.g.day had the

Genetic diversity among Korean bermudagrass (Cynodon spp.) ecotypes characterized by morphological, cytological and molecular approaches.

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Kang, Si-Yong; Lee, Geung-Joo; Lim, Ki Byung; Lee, Hye Jung; Park, In Sook; Chung, Sung Jin; Kim, Jin-Baek; Kim, Dong Sub; Rhee, Hye Kyung

2008-04-30

The genus Cynodon comprises ten species. The objective of this study was to evaluate the genetic diversity of Korean bermudagrasses at the morphological, cytological and molecular levels. Morphological parameters, the nuclear DNA content and ploidy levels were observed in 43 bermudagrass ecotypes. AFLP markers were evaluated to define the genetic diversity, and chromosome counts were made to confirm the inferred cytotypes. Nuclear DNA contents were in the ranges 1.42-1.56, 1.94-2.19, 2.54, and 2.77-2.85 pg/2C for the triploid, tetraploid, pentaploid, and hexaploid accessions, respectively. The inferred cytotypes were triploid (2n = 3x = 27), tetraploid (2n = 4x = 36), pentaploid (2n = 5x = 45), and hexaploid (2n = 6x = 54), but the majority of the collections were tetraploid (81%). Mitotic chromosome counts verified the corresponding ploidy levels. The fast growing fine-textured ecotypes had lower ploidy levels, while the pentaploids and hexaploids were coarse types. The genetic similarity ranged from 0.42 to 0.94 with an average of 0.64. UPGMA cluster analysis and principle coordinate analysis separated the ecotypes into 6 distinct groups. The genetic similarity suggests natural hybridization between the different cytotypes, which could be useful resources for future breeding and genetic studies.

Effects of Irrigation Levels on Growth Characteristics and Yield of Four Ecotypes of Sesame (Sesamum indicum L.

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A Koocheki

2015-09-01

Full Text Available In order to study the effects of irrigation levels on growth criteria, yield components and seed yield of four ecotypes of sesame (Sesamum indicum L., a field experiment was conducted as factorial based on a randomized complete block design with three replications at the Agricultural Research Station, Ferdowsi University of Mashhad, during growing season 2010-2011. Three irrigation levels (2000, 3000 and 4000 m3 ha-1 and four ecotypes (Darab, Sabzevar, Kashmar and Kalat were allocated as treatments. Criteria such as leaf are index (LAI, dry matter (DM accumulation, yield components (branch number, capsule number, seed number and 1000-seed weight, biological yield and seed yield of sesame were measured, accordingly. Results indicated that the simple effects of irrigation levels and ecotypes were significant (p≤0.05 on yield and yield components of sesame. Interaction between irrigation levels and ecotypes for yield components, biological yield and seed yield were significant (p≤0.01. By increasing water level from 2000 to 4000 m3 ha-1 enhanced branch number, capsule number, seed number and 1000-seed weight up to 57, 55 and 36%, respectively. Seed yield of Kalat was higher than Darab, Sabzevar and Kashmar with 1, 7 and 11%, respectively. By enhancing irrigation from 2000 to 4000 m3.ha-1 seed yield of Darab, Sabzevar and Kashmar and Kalat increased with 15, 67T 62 and 34%, respectively. There was a positive and significant relationship between yield and yield components. The highest correlation coefficient was observed for 1000-seed weight (r=0.87**.

Study on homologous series of induced early mutants in Indica rice Ⅱ. the relationship between the homologous series of early mutants induced and the ecotype in Indica rice

International Nuclear Information System (INIS)

Chen Xiulan; Yang Hefeng; He Zhentian; Han Yuepeng; Liu Xueyu

2001-01-01

The induced mutation in light sensitivity of the Indica rice leads to induction of the homologous series of early mutants along with the variation of ecological character and the ecoclimate. The induction of mutants was closely related to the ecotype of Indica rice, the homologous series of early mutants in different level were derived from the different ecotype of the Indica rice, otherwise, the similar homologous series of early mutants were derived from the same ecotypic variety. The induction of the early ecotypic variety derived from the homologous series of early mutants provides the basis and possibility for accelerating the development of the new cultivars. (authors)

Kompetisi Antara Ekotipe Echinochloa Crus-galli Pada Beberapa Tingkat Populasi Dengan Padi Sawah (Competition of Echinochloa Crus-galli Ecotypes at Several Populations Against Lowland Rice)

OpenAIRE

Guntoro, Dwi; Chozin, Muhamad Achmad; Santosa, Edi; Tjitrosemito, Soekisman; Burhan, Abdul Harris

2009-01-01

Echinochloa crus-galli is a major weed in paddy field that reduces rice yield. The objective of the research was to study the effect of E. crus-galli ecotypes and populations on rice growth and production. The research was conducted in a green house using split plot design with three replications. The main plot consisted of three E. crus-galli ecotypes i.e ecotype from Karawang, Cikampek, and Sukabumi. E. crus-galli population as sub plot consisted of 0, 1, 2, 3, and 4 E. crus-galli per po...

Ecotype evolution in Glossina palpalis subspecies, major vectors of sleeping sickness.

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Thierry De Meeûs

2015-03-01

Full Text Available The role of environmental factors in driving adaptive trajectories of living organisms is still being debated. This is even more important to understand when dealing with important neglected diseases and their vectors.In this paper, we analysed genetic divergence, computed from seven microsatellite loci, of 614 tsetse flies (Glossina palpalis gambiensis and Glossina palpalis palpalis, major vectors of animal and human trypanosomes from 28 sites of West and Central Africa. We found that the two subspecies are so divergent that they deserve the species status. Controlling for geographic and time distances that separate these samples, which have a significant effect, we found that G. p. gambiensis from different landscapes (Niayes of Senegal, savannah and coastal environments were significantly genetically different and thus represent different ecotypes or subspecies. We also confirm that G. p. palpalis from Ivory Coast, Cameroon and DRC are strongly divergent.These results provide an opportunity to examine whether new tsetse fly ecotypes might display different behaviour, dispersal patterns, host preferences and vectorial capacities. This work also urges a revision of taxonomic status of Glossina palpalis subspecies and highlights again how fast ecological divergence can be, especially in host-parasite-vector systems.

Variable response of three Trifolium repens ecotypes to soil flooding by seawater.

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White, Anissia C; Colmer, Timothy D; Cawthray, Greg R; Hanley, Mick E

2014-08-01

Despite concerns about the impact of rising sea levels and storm surge events on coastal ecosystems, there is remarkably little information on the response of terrestrial coastal plant species to seawater inundation. The aim of this study was to elucidate responses of a glycophyte (white clover, Trifolium repens) to short-duration soil flooding by seawater and recovery following leaching of salts. Using plants cultivated from parent ecotypes collected from a natural soil salinity gradient, the impact of short-duration seawater soil flooding (8 or 24 h) on short-term changes in leaf salt ion and organic solute concentrations was examined, together with longer term impacts on plant growth (stolon elongation) and flowering. There was substantial Cl(-) and Na(+) accumulation in leaves, especially for plants subjected to 24 h soil flooding with seawater, but no consistent variation linked to parent plant provenance. Proline and sucrose concentrations also increased in plants following seawater flooding of the soil. Plant growth and flowering were reduced by longer soil immersion times (seawater flooding followed by drainage and freshwater inputs), but plants originating from more saline soil responded less negatively than those from lower salinity soil. The accumulation of proline and sucrose indicates a potential for solute accumulation as a response to the osmotic imbalance caused by salt ions, while variation in growth and flowering responses between ecotypes points to a natural adaptive capacity for tolerance of short-duration seawater soil flooding in T. repens. Consequently, it is suggested that selection for tolerant ecotypes is possible should the predicted increase in frequency of storm surge flooding events occur. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Establishment of an Indirect Genetic Transformation Method for Arabidopsis thaliana ecotype Bangladesh

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Bulbul AHMED

2011-11-01

Full Text Available Arabidopsis thaliana is a small flowering plant belonging to the Brassicaceae family, which is adopted as a model plant for genetic research. Agrobacterium tumifaciensmediated transformation method for A. thaliana ecotype Bangladesh was established. Leaf discs of A. thaliana were incubated with A. tumefaciens strain LBA4404 containing chimeric nos. nptII. nos and intron-GUS genes. Following inoculation and co-cultivation, leaf discs were cultured on selection medium containing 50 mg/l kanamycin + 50 mg/l cefotaxime + 1.5 mg/l NAA and kanamycin resistant shoots were induced from the leaf discs after two weeks. Shoot regeneration was achieved after transferring the tissues onto fresh medium of the same combination. Finally, the shoots were rooted on MS medium containing 50 mg/l kanamycin. Incorporation and expression of the transgenes were confirmed by PCR analysis. Using this protocol, transgenic A. thaliana plants can be obtained and indicates that genomic transformation in higher plants is possible through insertion of desired gene. Although Agrobacterium mediated genetic transformation is established for A. thaliana, this study was the conducted to transform A. thaliana ecotype Bangladesh.

'Fagiolo a Formella', an Italian lima bean ecotype: biochemical and nutritional characterisation of dry and processed seeds.

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Piergiovanni, Angela R; Sparvoli, Francesca; Zaccardelli, Massimo

2012-08-30

An ecotype of the lima bean, named 'fagiolo a Formella', which, to the best of our knowledge, is the only example of an Italian lima bean (Phaseolus lunatus L.) ecotype, is cultivated in the Campania region of southern Italy. Physical, nutritional and processing traits of dry seeds were evaluated for two consecutive growing seasons (2009 and 2010). The canning quality was also investigated, but only for the harvest of 2010. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis of total seed proteins allowed the attribution of 'fagiolo a Formella' to the Mesoamerican gene pool and Sieva morphotype. Seeds have a trapezoid shape, white coat and 100-seed weight greater than 42 g. Yield, protein, trypsin inhibitor and phytic acid values were found comparable with those reported for lima bean varieties cultivated in sub-tropical areas. Moreover, we found that this ecotype is devoid of lectin. The good adaptation to growing environment is indicated by the fact that 'fagiolo a Formella' seed quality is comparable to that of lima beans grown in America. Overall the canning quality was found satisfactory and canning significantly destroys the main anti-nutritional compounds present in dry seeds. Copyright © 2012 Society of Chemical Industry.

Evaluation of Effect of Chemical and Organic Fertilizers on Growth Characteristics, Yield and Yield components of three Sesame Ecotypes (Sesamum indicum L.

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M Goldani

2014-07-01

Full Text Available Using organic fertilizers is cause increase soil fertility, improving crop growth and production. For this purpose a greenhouse experiment was carried out in factorial arrangement based on a completely randomized design with three replications during 2011 year. First factor included: three sesame ecotype (MSC3, MSC6, MSC7 and second factor was 6 fertilizer treatments that included: Incorporation manure and chemical fertilizer (216 g manure and 1 gram chemical fertilizer NPK, Chemical fertilizer (2 g NPK, Vermicompost (192 g, Manure ( 228 g, Compost Sulfur granules (192 g per vase and Control (without any manure or fertilizer. Results indicated that different manure treatments had significant effect on morphological and yield components traits, as the most number and length branch per plant was obtained from incorporation manure and chemical fertilizer treatment. Appling incorporation manure and chemical fertilizer treatment had the most biomass in MSC3 ecotype that in comparison of control treatment was increased almost 73 percent. Consuming incorporation manure and chemical fertilizer treatment in MSC3 ecotype was also obtained the most capsule per plant (21.2, number seed per capsule (54.4, 100-seed weight (0.257 g and seed per plant with (1.95 g. The least seed weight per plant with 0.450 g was observed in MSC7 ecotype from application of control treatment. Response of three sesame ecotype (MSC3, MSC6, MSC7 to applied vermin-compost manure was similar; as the amount of seed weight per plant was increased more than 1 g per plant in all these ecotypes and in others fertilizer treatments was not observed this trend. There was significant positive correlation between seed weight per plant and number of capsule per plant (r=0.83**, height (r=0.68** and biomass (r=0.51**. The results showed that incorporation manure and chemical fertilizer was improved on growth and yield characteristics of sesame plant.

METABOLISM OF [14C]GA19 AND [14C]GA53 BY ECOTYPES OF ...

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hand, metabolism of [14C]GA53 was very limited in all day-length treatments and during both, day and night periods. ... compounds which are formed by covalent coupling of GAs to .... For each ecotype day-time and night-time metabolism of ...

Identifying Differences in Abiotic Stress Gene Networks between Lowland and Upland Ecotypes of Switchgrass (DE-SC0008338)

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Childs, Kevin [Michigan State Univ., East Lansing, MI (United States); Buell, Robin [Michigan State Univ., East Lansing, MI (United States); Zhao, Bingyu [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States); Zhang, Xunzhong [Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

2016-11-10

Switchgrass (Panicum virgatum) is a warm-season C4 grass that is a target lignocellulosic biofuel species for use in the United States due to its local adaption capabilities and high biomass accumulation. Two ecotypes of switchgrass have been described. Members of the lowland ecotype are taller, have narrower leaf blades and generate more biomass compared to individuals from the upland ecotype. Additionally, lowland plants are generally found in the southern United States while upland switchgrass is more typically present in the northern United States. These differences are important as it is envisioned that switchgrass for biofuel production will typically be grown on marginal lands in the northern United States to supplement and diversify farmers' traditional crop incomes. While lowland switchgrass is more productive, it has poor winter survivability in northern latitudes where upland switchgrass is expected to be grown for biofuel use. Abiotic stresses likely to be encountered by switchgrass include drought and salinity. Despite initially being described as preferring wetter environments, members of the lowland ecotype have been characterized as being more drought tolerant than plants of the upland ecotype. Nonetheless, direct trials have indicated that variation for drought tolerance exists in both ecotypes, but prior to this project, only a relatively small number of switchgrass lines had been tested for drought responses. Similarly, switchgrass cultivars have not been widely tested for salt tolerance, but a few studies have shown that even mild salt stress can inhibit growth. The effects of drought and salt stress on plant growth are complex. Both drought and salinity affect the osmotic potential of plant cells and negatively affect plant growth due to reduced water potential and reduced photosynthesis that results from lower stomatal conductance of CO₂. Plants respond to drought and salt stress by activating genes that directly attempt to

Identifying the transition to the maturation zone in three ecotypes of Arabidopsis thaliana roots.

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Cajero Sánchez, Wendy; García-Ponce, Berenice; Sánchez, María de la Paz; Álvarez-Buylla, Elena R; Garay-Arroyo, Adriana

2018-01-01

The Arabidopsis thaliana (hereafter Arabidopsis) root has become a useful model for studying how organ morphogenesis emerge from the coordination and balance of cell proliferation and differentiation, as both processes may be observed and quantified in the root at different stages of development. Hence, being able to objectively identify and delimit the different stages of root development has been very important. Up to now, three different zones along the longitudinal axis of the primary root of Arabidopsis, have been identified: the root apical meristematic zone (RAM) with two domains [the proliferative (PD) and the transition domain (TD)], the elongation zone (EZ) and the maturation zone (MZ). We previously reported a method to quantify the length of the cells of the meristematic and the elongation zone, as well as the boundaries or transitions between the root domains along the growing part of the Arabidopsis root. In this study, we provide a more accurate criterion to identify the MZ. Traditionally, the transition between the EZ to the MZ has been established by the emergence of the first root-hair bulge in the epidermis, because this emergence coincides with cell maturation in this cell type. But we have found here that after the emergence of the first root-hair bulge some cells continue to elongate and we have confirmed this in three different Arabidopsis ecotypes. We established the limit between the EZ and the MZ by looking for the closest cortical cell with a longer length than the average cell length of 10 cells after the cortical cell closest to the epidermal cell with the first root-hair bulge in these three ecotypes. In Col-0 and Ws this cell is four cells above the one with the root hair bulge and, in the Ler ecotype, this cell is five cells above. To unambiguously identifying the site at which cells stop elongating and attain their final length and fate at the MZ, we propose to calculate the length of completely elongated cortical cells counting 10

Which plant trait explains the variations in relative growth rate and its response to elevated carbon dioxide concentration among Arabidopsis thaliana ecotypes derived from a variety of habitats?

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Oguchi, Riichi; Ozaki, Hiroshi; Hanada, Kousuke; Hikosaka, Kouki

2016-03-01

Elevated atmospheric carbon dioxide (CO2) concentration ([CO2]) enhances plant growth, but this enhancement varies considerably. It is still uncertain which plant traits are quantitatively related to the variation in plant growth. To identify the traits responsible, we developed a growth analysis model that included primary parameters associated with morphology, nitrogen (N) use, and leaf and root activities. We analysed the vegetative growth of 44 ecotypes of Arabidopsis thaliana L. grown at ambient and elevated [CO2] (800 μmol mol(-1)). The 44 ecotypes were selected such that they were derived from various altitudes and latitudes. Relative growth rate (RGR; growth rate per unit plant mass) and its response to [CO2] varied by 1.5- and 1.7-fold among ecotypes, respectively. The variation in RGR at both [CO2]s was mainly explained by the variation in leaf N productivity (LNP; growth rate per leaf N),which was strongly related to photosynthetic N use efficiency (PNUE). The variation in the response of RGR to [CO2] was also explained by the variation in the response of LNP to [CO2]. Genomic analyses indicated that there was no phylogenetic constraint on inter-ecotype variation in the CO2 response of RGR or LNP. We conclude that the significant variation in plant growth and its response to [CO2] among ecotypes reflects the variation in N use for photosynthesis among ecotypes, and that the response of PNUE to CO2 is an important target for predicting and/or breeding plants that have high growth rates at elevated [CO2].

A Proteome Translocation Response to Complex Desert Stress Environments in Perennial Phragmites Sympatric Ecotypes with Contrasting Water Availability.

Science.gov (United States)

Li, Li; Chen, Xiaodan; Shi, Lu; Wang, Chuanjing; Fu, Bing; Qiu, Tianhang; Cui, Suxia

2017-01-01

After a long-term adaptation to desert environment, the perennial aquatic plant Phragmites communis has evolved a desert-dune ecotype. The desert-dune ecotype (DR) of Phragmites communis showed significant differences in water activity and protein distribution compared to its sympatric swamp ecotype (SR). Many proteins that were located in the soluble fraction of SR translocated to the insoluble fraction of DR, suggesting that membrane-associated proteins were greatly reinforced in DR. The unknown phenomenon in plant stress physiology was defined as a proteome translocation response. Quantitative 2D-DIGE technology highlighted these 'bound' proteins in DR. Fifty-eight kinds of proteins were identified as candidates of the translocated proteome in Phragmites . The majority were chloroplast proteins. Unexpectedly, Rubisco was the most abundant protein sequestered by DR. Rubisco activase, various chaperons and 2-cysteine peroxiredoxin were major components in the translocation response. Conformational change was assumed to be the main reason for the Rubisco translocation due to no primary sequence difference between DR and SR. The addition of reductant in extraction process partially reversed the translocation response, implying that intracellular redox status plays a role in the translocation response of the proteome. The finding emphasizes the realistic significance of the membrane-association of biomolecule for plant long-term adaptation to complex stress conditions.

Effect of Drought Stress on Growth and Morphological Characteristics of Two Garlic (Allium sativum L. Ecotypes in Different Planting Densities

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shiva akbari

2017-10-01

Full Text Available Introduction Plants may be exposed to various stresses and water deficit is the most important limiting factor of growth and yield in many parts of the world and Iran. Stress induced growth decrement can be because of cell development decrease due to decrement of turgor pressure and meiosis and photosynthesis decrease due to stomata closure. Determination of desired planting density is one of the success factors of plant growth and production. Garlic (Allium sativum has been an important medicinal plant over centuries in human life. According to the importance of medicinal plants and studying the effects of drought stress on them, the goal of this research is to investigate the effect of drought stress and planting density on growth and morphological characteristics of two ecotypes of garlic and determining the preferable ecotype and density from the perspective of these traits. Materials and methods This experiment was performed in 2012 in a farm in south east of Semnan. It was conducted on a split-plot factorial arrangement based on randomized complete blocks design with three replications. Three levels of drought stress with 60, 80 and 100 percent of crop evapotranspiration (ETc were the main plot factors and factorial combination of three planting density (30, 40 and 50 plants.m-2 and two ecotypes of Tabas and Toroud were the levels of sub plot factors. To estimate water requirement of garlic, daily measured meteorology parameters of Semnan synoptic station were used and water requirement was calculated based on FAO-56 instructions. From mid-January, the sampling of leaf area, bulb and leaf fresh and dry weight was started with destructive method every other week and continued until middle of Jun. three plant were selected randomly from each plot in each turn. From middle of May, height and number of leaves were measured. Leaf area measurement was done by leaf area meter (Delta-T. To estimate growth indices, dry weight of aerial and

Hybrid Sterility over Tens of Meters Between Ecotypes Adapted to Serpentine and Non-Serpentine Soils

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Leonie Moyle; Levine Mia; Stanton Maureen; Jessica Wright

2012-01-01

The development of hybrid sterility is an important step in the process of speciation, however the role of adaptive evolution in triggering these postzygotic barriers is poorly understood. We show that, in the California endemic plant Collinsia sparsiflora ecotypic adaptation to two distinct soil types is associated with the expression of...

Effects of cadmium on ultrastructure and antioxidative defense system in hyperaccumulator and non-hyperaccumulator ecotypes of Sedum alfredii Hance

International Nuclear Information System (INIS)

Jin Xiaofen; Yang Xiaoe; Islam, Ejazul; Liu Dan; Mahmood, Qaisar

2008-01-01

Plant growth, ultrastructural and antioxidant adaptations and glutathione biosynthesis in Cd-hyperaccumulating ecotype Sedum alfredii Hance (HE) countering high Cd environment were investigated and compared with its non Cd-hyperaccumulating ecotype (NHE). Cadmium exposure resulted in significant ultrastructural changes in root meristem and leaf mesophyll cells of S. alfredii, but damage was more pronounced in NHE even when Cd concentrations were one-tenth of those applied to HE. Cadmium stress damaged chloroplasts causing imbalanced lamellae formation coupled with early leaf senescence. Histochemical results revealed that glutathione (GSH) biosynthesis inhibition led to overproduction of hydrogen peroxide (H 2 O 2 ) and superoxide radical (O 2 · - ) in HE but not in NHE. Differences were noted in both HE and NHE for catalase (CAT), guaiacol peroxidase (GPX), ascorbate peroxidase (APX) and glutathione reductase (GR) activities under various Cd stress levels. No relationship was found between antioxidative defense capacity including activities of superoxide dismutase (SOD), CAT, GPX, APX and GR as well as ascorbic acid (AsA) contents and Cd tolerance in the two ecotypes of S. alfredii. The GSH biosynthesis induction in root and shoot exposed to elevated Cd conditions may be involved in Cd tolerance and hyperaccumulation in HE of S. alfredii H

Impact of nitrogen source and supply level on growth, yield and nutritional value of two contrasting ecotypes of Cichorium spinosum L. grown hydroponically.

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Chatzigianni, Martina; Alkhaled, Bara'a; Livieratos, Ioannis; Stamatakis, Aristidis; Ntatsi, Georgia; Savvas, Dimitrios

2018-03-01

In the present study, two contrasting stamnagathi (Cichorium spinosum L.) ecotypes originating either from a mountainous or from a seaside habitat were grown hydroponically and supplied with a nutrient solution differing in the total-N level (4 or 16 mmol L -1 ) and the N source (NH 4 + -N/total-N: 0.05, 0.25 or 0.50). The aim was to search for genotypic differences in nitrogen nutrition. At commercial maturity, the dry weight of mountainous plants was higher than that of seaside plants. The shoot mineral concentrations were higher in seaside plants than in mountainous plants in both harvests. The leaf nitrate concentration was influenced by the levels of both total-N and NH 4 + -N/total-N at both harvests, whereas plants with a seaside origin exhibited higher nitrate concentrations than those originating from a mountainous site in all total-N and NH 4 + -N/total-N treatments. The two stamnagathi ecotypes differed considerably in their responses to nitrogen nutrition and tissue nitrate content. The mountainous ecotype was superior in terms of growth, tissue nitrate concentration and antioxidant capacity, whereas the seaside ecotype accumulated more nutrient microcations in leaves. A low total-N concentration (up to 4 mmol L -1 ) combined with a high NH 4 + -N/total-N ratio (up to 0.05) could minimize tissue NO 3 - concentrations without compromising yield. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Comparison of synthetic chelators and low molecular weight organic acids in enhancing phytoextraction of heavy metals by two ecotypes of Sedum alfredii Hance.

Science.gov (United States)

Liu, Dan; Islam, Ejazul; Li, Tingqiang; Yang, Xiaoe; Jin, Xiaofen; Mahmood, Qaisar

2008-05-01

Lab scale and pot experiments were conducted to compare the effects of synthetic chelators and low molecular weight organic acids (LMWOA) on the phytoextraction of multi-contaminated soils by two ecotypes of Sedum alfredii Hance. Through lab scale experiments, the treatment dosage of 5 and 10 mM for synthetic chelators and LMWOA, respectively, and the treatment time of 10 days were selected for pot experiment. In pot experiment, the hyperaccumulating ecotype (HE) was found more tolerant to the metal toxicity compared with the non-hyperaccumulating ecotype (NHE). EDTA for Pb, EDDS for Cu, and DTPA for Cu and Cd were found more effective to enhance heavy metal accumulation in the shoots of S. alfredii Hance. Compared with synthetic chelators, the phytoextraction ability of LMWOA was lesser. Considering the strong post-harvest effects of synthetic chelators, it is suggested that higher dosage of LMWOA could be practiced during phytoextraction, and some additional measures could also be taken to lower the potential environmental risks of synthetic chelators in the future studies.

Evaluation of Genetic Diversity in Collection from Iranian Jujube Ecotypes (Ziziphus spp. using ISSR-molecular Marker

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hajar shayesteh

2018-02-01

Full Text Available Introduction Jujube (Zizyphus jujuba Mill. as a valuable medicinal plant and adapted to different climatic conditions is widespread in many parts of Iran. Nowadays, beside the export of its fruit, jujube is also used as an herbal medicine to treat the diseases, so it has a high economic value. Study on genetic diversity is the first step to identify and preservation of germplasm. It is also considered as the basic principles of plant breeding. DNA markers seem to be the best way in determination of the genetic diversity. Inter simple sequence repeats (ISSR markers are highly polymorphic and combine most benefits of Simple Sequence Repeats (SSRs and amplified fragment length polymorphism (AFLP to the generality of random amplified polymorphic DNA (RAPD. Materials and Methods In order to study of the genetic diversity among 31 ecotypes collected from eight Jujube-rich provinces, including South Khorasan, Razavi Khorasan, Mazandaran, Golestan, Qom, Isfahan, Lorestan and Fars. Genomic DNA was extracted by CTAB method and polymerase chain reaction (PCR was performed with 13 ISSR primers in which six most efficient primers were selected. Cluster analysis based on Dice similarity coefficient and Unweighed Pair Group Method with Arithmetic Mean (UPGMA was carried out and POPGENe.3.2 software was used to determine the similarity of populations with each other. Results and Discussion 84 loci were amplified and 70 of them (83% revealed a proper polymorphism with the size between 200 and 2000 base pair. The average number of amplified and polymorphic bands per primer was 14 and 11.6 respectively. Primers with di-nucleotide repeats produced more polymorphic bands than ones with tri-nucleotide repeats. It seems that this is due to a higher frequency of sequences containing di-nucleotide repeats in intergenic regions and higher possibility of mutation revealed in more diversity in comparison to gene coding regions. Anchored primers with 1 or 2 nucleotides at

Effect of Freezing on Spermatozoa from Tigaie Rams Belonging to the Mountain Ecotype

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Vasile Miclea

2011-05-01

Full Text Available Our aim was to study the influence of freezing on the viability and frequency of abnormalities in frozen ram spermatozoa. Sperm was collected form 20 rams belonging to the mountain ecotype of the Tigaie breed using the artificial vagina technique and volume and motility were assessed. Afterward it was diluted with Tryladil (1:4 supplemented with 20% egg yolk and heated at 37°C. Subsequently the temperature decreased at a rate of 0.2°C/minute until reaching 4°C and an equilibration time of 2 hours followed. During this time the diluted sperm was packaged in 0.25 ml straws. After sealing these were kept 6 cm above liquid nitrogen level for 13 minutes (- 120°C and then plunged into nitrogen. Volume, motility and concentration were assessed before freezing. After thawing sperm morphology was assessed using Hancock’s method and at the same time the endurance (at 10, 30 and 60 minutes and HOST tests were performed. The highest motility (0.40 was graded at 30 minutes. It could be correlated with the increased percentage of HOST positive spermatozoa, 27.78%. The percentage of abnormal spermatozoa was also high (47.89%, 38.44% of them having acrosome flaws. Cryopreservation has a negative effect on the characteristics of sperm cells from Tigaie rams belonging to the mountain ecotype.

Adaptive Epigenetic Differentiation between Upland and Lowland Rice Ecotypes Revealed by Methylation-Sensitive Amplified Polymorphism

OpenAIRE

Xia, Hui; Huang, Weixia; Xiong, Jie; Tao, Tao; Zheng, Xiaoguo; Wei, Haibin; Yue, Yunxia; Chen, Liang; Luo, Lijun

2016-01-01

The stress-induced epimutations could be inherited over generations and play important roles in plant adaption to stressful environments. Upland rice has been domesticated in water-limited environments for thousands of years and accumulated drought-induced epimutations of DNA methylation, making it epigenetically differentiated from lowland rice. To study the epigenetic differentiation between upland and lowland rice ecotypes on their drought-resistances, the epigenetic variation was investig...

Evaluation of the defensive behavior of two honeybee ecotypes using a laboratory test

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Cecilia Andere

2002-01-01

Full Text Available Honeybee defensive behavior is a useful selection criterion, especially in areas with Africanized honeybees (Apis mellifera L. In all genetic improvement programs the selected characters must be measured with precision, and because of this we evaluated a metabolic method for testing honeybee defensive behavior in the laboratory for its usefulness in distinguishing between honeybee ecotypes and selecting honeybees based on their level of defensive responses. Ten honeybee colonies were used, five having been produced by feral queens from a subtropical region supposedly colonized by Africanized honeybees and five by queens from a temperate region apparently colonized by European honeybees. We evaluate honeybee defensive behavior using a metabolic test based on oxygen consumption after stimulation with an alarm pheromone, measuring the time to the first response, time to maximum oxygen consumption, duration of activity, oxygen consumption at first response, maximum oxygen consumption and total oxygen consumption, colonies being ranked according to the values obtained for each variable. Significant (p < 0.05 differences were detected between ecotypes for each variable but for all variables the highest rankings were obtained for colonies of subtropical origin, which had faster and more intense responses. All variables were highly associated (p < 0.05. Total oxygen consumption was the best indicator of metabolic activity for defensive behavior because it combined oxygen consumption and the length of the response. This laboratory method may be useful for evaluating the defensive behavior of honey bees in genetic programs designed to select less defensive bees.

Allele-specific physical interactions regulate the heterotic traits in hybrids of Arabidopsis thaliana ecotypes

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Babita Singh

2017-10-01

Full Text Available Heterosis is an important phenomenon for the breeding in agricultural crops as it influences yield related traits such as biomass yield, seed number and weight, adaptive and reproductive traits. However, the level of heterosis greatly varies for different traits and different genotypes. The present study focuses on identification of physical interactions between alleles and their role in transcriptional regulation in heterotic plants. Here, we used two Arabidopsis ecotypes; Col-0 and C24 as parent for crosses. We performed crossing between these ecotypes and screened the F1 hybrids on the basis of different SSR markers. Further, we used Hi-C to capture intra- and inter-chromosomal physical interactions between alleles on genome-wide level. Then, we identified allele-specific chromatin interactions and constructed genome-wide allele-specific contact maps at different resolutions for the entire chromosome. We also performed RNA-seq of hybrids and their parents. RNA-seq analysis identified several differentially expressed genes and non-additively expressed genes in hybrids with respect to their parents. Further, to understand the biological significance of these chromatin interactions, we annotated these interactions and correlated with the transcriptome data. Thus, our study provides alleles-specific chromatin interactions in genome-wide fashion which play a crucial role in regulation of different genes that may be important for heterosis.

Lack of Ecotypic Differentiation: Plant Response to Elevation, Population Origin, and Wind in the Luquillo Mountains, Puerto Rico

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Ned Fetcher; Roberto A. Cordero; Janice Voltzow

2000-01-01

How important is ecotypic differentiation along elevational gradients in the tropics? Reciprocal transplants of two shrubs, Clibadium erosum (Asteraceae) and Psychotria berteriana (Rubiaceae), and a palm, Prestoea acuminata var. montana (Palmaceae), were used to test for the effect of environment and population origin on growth and physiology in the Luquillo...

The Effect of Irrigation Cut-off in Flowering Stage and Foliar Application of Spermidine on Essential Oil Quantity and Quality of Three Ecotypes of Cumin

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Sarah Bakhtari

2017-08-01

Full Text Available Introduction Cumin (Cuminum cyminum L. is an annual plant that commonly cultivated in arid and semiarid regions of Iran. The crop has a wide range of uses including medicinal, cosmetic and food industry. Cumin occupies about 26% of total area devoted to medicinal plants in Iran. However, cumin is seriously affected by the Fusarium wilt and blight diseases. The diseases usually increase under warm and wet conditions. Control of the diseases incidence is a crucial factor for cumin production. Limited control of the diseases is provided by seed pre-sowing with certain fungicides such as benlate. Soil fumigation with methyle bromide can provide a control measure against the disease but may be limited application value for large scale production systems in the open field. In addition, methyle bromide is considered an ozone-depleting compound and has potential risk on the living environment and human health. Considering the environmental limitations of chemical fungicides, it seems appropriate to search for a supplemental control strategy .It was demonstrated that peak of the diseases incidence is occurred at flowering stage and irrigation cut-off in this time may be reduced the diseases density. Materials and methods This experiment was conducted in a split-split-plot arrangement in randomized complete block design with three replications in research farm of Shahid Bahonar University of Kerman at 2014. The experimental treatments were irrigation (complete irrigation and cut-off the irrigation in flowering stage assigned to main plots, foliar application of spermidine (0, 1 and 2 Mm as subplot and cumin ecotypes (Kerman, Khorasan and Esfahan that was randomized in sub-subplot. The seedbed preparation was made based on common practices at the location. Plots size under the trial was 4 m×3 m so as to get 50 cm inter row spacing in six rows. The ideal density of the crops was considered as 120 plant.m-2. As soon as the seeds were sown, irrigation

Effect of Irrigation CutOff on Flowering Stage and Foliar Application of Spermidine on Some Quantitative and Qualitative Characteristics of Various Ecotypes of Cumin

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Sarah Bakhtari

2017-02-01

Full Text Available Introduction: Medicinal plants play major roles in human health. . Cumin (Cuminum cyminum L. is an annual plant that commonly cultivated in arid and semiarid regions of Iran. The crop has a wide range of uses including medicinal, cosmetic and food industry. Cumin occupies about 26% of the total area devoted to medicinal plants in Iran. However, cumin is seriously affected by the Fusarium wilt and blight diseases. The diseases usually increase under warm and wet conditions. It was demonstrated that the peak of the disease incidence is occurring at the flowering stage and irrigation cutoff at this time may be reduced the diseases density. Materials and methods: In order to evaluate the effects of irrigation cutoff in flowering stage and foliar application of spermidine on some characteristics of various ecotype of cumin, an experiment was conducted in a split-split-plot arrangement in randomized complete block design with three replications at the research farm of Shahid Bahonar University of Kerman at 2014. The experimental treatments were irrigation in two levels (complete irrigation and cutoff the irrigation in flowering stage assigned to main plots, foliar application of spermidine in three levels (0, 1 and 2 Mm as a subplot and cumin ecotypes in three levels (Kerman, Khorasan and Esfahan that was randomized in sub-subplot. Plots size under the trial was 4 m × 3 m so as to get 50 cm inter row spacing in six rows. The ideal density of the crops was considered as 120 plant m-2. As soon as the seeds were sown, irrigation was applied every 10 days. Foliar application of spermidine was done at three stages (after thinning, before flowering stage and in the middle of flowering stage. No herbicides and chemical fertilizers were applied during the expriments. Results and discussion: In this study the number of branches, umbels per plant, 1000-seed weight, seed yield per plant and hectare, harvest index, essential oil percentage and yield, infected

Cold-acclimation limits low temperature induced photoinhibition by promoting a higher photochemical quantum yield and a more effective PSII restoration in darkness in the Antarctic rather than the Andean ecotype of Colobanthus quitensis Kunt Bartl (Cariophyllaceae

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Bascuñán-Godoy Luisa

2012-07-01

Full Text Available Abstract Background Ecotypes of Colobanthus quitensis Kunt Bartl (Cariophyllaceae from Andes Mountains and Maritime Antarctic grow under contrasting photoinhibitory conditions, reaching differential cold tolerance upon cold acclimation. Photoinhibition depends on the extent of photodamage and recovery capability. We propose that cold acclimation increases resistance to low-temperature-induced photoinhibition, limiting photodamage and promoting recovery under cold. Therefore, the Antarctic ecotype (cold hardiest should be less photoinhibited and have better recovery from low-temperature-induced photoinhibition than the Andean ecotype. Both ecotypes were exposed to cold induced photoinhibitory treatment (PhT. Photoinhibition and recovery of photosystem II (PSII was followed by fluorescence, CO2 exchange, and immunoblotting analyses. Results The same reduction (25% in maximum PSII efficiency (Fv/Fm was observed in both cold-acclimated (CA and non-acclimated (NA plants under PhT. A full recovery was observed in CA plants of both ecotypes under dark conditions, but CA Antarctic plants recover faster than the Andean ecotype. Under PhT, CA plants maintain their quantum yield of PSII, while NA plants reduced it strongly (50% and 73% for Andean and Antarctic plants respectively. Cold acclimation induced the maintenance of PsaA and Cyt b6/f and reduced a 41% the excitation pressure in Antarctic plants, exhibiting the lowest level under PhT. xCold acclimation decreased significantly NPQs in both ecotypes, and reduced chlorophylls and D1 degradation in Andean plants under PhT. NA and CA plants were able to fully restore their normal photosynthesis, while CA Antarctic plants reached 50% higher photosynthetic rates after recovery, which was associated to electron fluxes maintenance under photoinhibitory conditions. Conclusions Cold acclimation has a greater importance on the recovery process than on limiting photodamage. Cold acclimation determined the

Microbiome and ecotypic adaption of Holcus lanatus (L.) to extremes of its soil pH range, investigated through transcriptome sequencing.

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Young, Ellen; Carey, Manus; Meharg, Andrew A; Meharg, Caroline

2018-03-20

Plants can adapt to edaphic stress, such as nutrient deficiency, toxicity and biotic challenges, by controlled transcriptomic responses, including microbiome interactions. Traditionally studied in model plant species with controlled microbiota inoculation treatments, molecular plant-microbiome interactions can be functionally investigated via RNA-Seq. Complex, natural plant-microbiome studies are limited, typically focusing on microbial rRNA and omitting functional microbiome investigations, presenting a fundamental knowledge gap. Here, root and shoot meta-transcriptome analyses, in tandem with shoot elemental content and root staining, were employed to investigate transcriptome responses in the wild grass Holcus lanatus and its associated natural multi-species eukaryotic microbiome. A full factorial reciprocal soil transplant experiment was employed, using plant ecotypes from two widely contrasting natural habitats, acid bog and limestone quarry soil, to investigate naturally occurring, and ecologically meaningful, edaphically driven molecular plant-microbiome interactions. Arbuscular mycorrhizal (AM) and non-AM fungal colonization was detected in roots in both soils. Staining showed greater levels of non-AM fungi, and transcriptomics indicated a predominance of Ascomycota-annotated genes. Roots in acid bog soil were dominated by Phialocephala-annotated transcripts, a putative growth-promoting endophyte, potentially involved in N nutrition and ion homeostasis. Limestone roots in acid bog soil had greater expression of other Ascomycete genera and Oomycetes and lower expression of Phialocephala-annotated transcripts compared to acid ecotype roots, which corresponded with reduced induction of pathogen defense processes, particularly lignin biosynthesis in limestone ecotypes. Ascomycota dominated in shoots and limestone soil roots, but Phialocephala-annotated transcripts were insignificant, and no single Ascomycete genus dominated. Fusarium-annotated transcripts were

The molecular dimension of microbial species: 3. Comparative genomics of Synechococcus strains with different light responses and in situ diel transcription patterns of associated ecotypes in the Mushroom Spring microbial mat

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Millie T. Olsen

2015-06-01

Full Text Available Genomes were obtained for three closely related strains of Synechococcus that are representative of putative ecotypes that predominate at different depths in the 1 mm-thick, upper-green layer in the 60°C mat of Mushroom Spring, Yellowstone National Park, and exhibit different light adaptation and acclimation responses. The genomes were compared to the published genome of a previously obtained, closely related strain from a neighboring spring, and differences in both gene content and orthologous gene alleles between high-light-adapted and low-light-adapted strains were identified. Evidence of genetic differences that relate to adaptation to light intensity and/or quality, CO2 uptake, nitrogen metabolism, organic carbon metabolism, and uptake of other nutrients were found between strains of the different putative ecotypes. In situ diel transcription patterns of genes, including genes unique to either low-light-adapted or high-light-adapted strains and different alleles of an orthologous photosystem gene, revealed that expression is fine-tuned to the different light environments experienced by ecotypes prevalent at various depths in the mat. This study suggests that strains of closely related putative ecotypes have different genomic adaptations that enable them to inhabit distinct ecological niches while living in close proximity within a microbial community.

Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron.

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Sohm, Jill A; Ahlgren, Nathan A; Thomson, Zachary J; Williams, Cheryl; Moffett, James W; Saito, Mak A; Webb, Eric A; Rocap, Gabrielle

2016-02-01

Marine picocyanobacteria, comprised of the genera Synechococcus and Prochlorococcus, are the most abundant and widespread primary producers in the ocean. More than 20 genetically distinct clades of marine Synechococcus have been identified, but their physiology and biogeography are not as thoroughly characterized as those of Prochlorococcus. Using clade-specific qPCR primers, we measured the abundance of 10 Synechococcus clades at 92 locations in surface waters of the Atlantic and Pacific Oceans. We found that Synechococcus partition the ocean into four distinct regimes distinguished by temperature, macronutrients and iron availability. Clades I and IV were prevalent in colder, mesotrophic waters; clades II, III and X dominated in the warm, oligotrophic open ocean; clades CRD1 and CRD2 were restricted to sites with low iron availability; and clades XV and XVI were only found in transitional waters at the edges of the other biomes. Overall, clade II was the most ubiquitous clade investigated and was the dominant clade in the largest biome, the oligotrophic open ocean. Co-occurring clades that occupy the same regime belong to distinct evolutionary lineages within Synechococcus, indicating that multiple ecotypes have evolved independently to occupy similar niches and represent examples of parallel evolution. We speculate that parallel evolution of ecotypes may be a common feature of diverse marine microbial communities that contributes to functional redundancy and the potential for resiliency.

The Effects of Drought Stress on Yield, Yield Components and Anti-oxidant of Two Garlic (Allium sativum L. Ecotypes with Different Planting Densities

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shiva akbari

2016-07-01

Full Text Available Introduction Drought stress reduces plant growth by affecting various physiological and biochemical processes, such as photosynthesis, respiration, translocation, ion uptake, carbohydrates, nutrient metabolism and growth promoters. Garlic (Allium sativum L. is an annual bulb crop that has been cultivated since ancient times and was used as a spice and condiment for many centuries. Garlic is an important plant because of its pharmaceutical properties. The optimum yield of this bulb crop depends on well-managed irrigation, fertilization and cultivation practices. In the final and middle stages of growth, garlic is sensitive to water stress and low irrigation is unsuitable in these stages. This experiment was established to study the influence of drought stress and planting density on yield and its components and the non-enzymatic anti-oxidant content of two different garlic ecotypes. Materials and methods This study was conducted in 2011-2012 in a farmland at the south east of Semnan city. The experimental layout was a split-plot factorial with a randomized complete block design with three replications. The treatments were comprised of three factors: irrigation rates (60%, 80% and 100% of estimated crop evapotranspiration (ETC as the main plot and the factorial combination of three levels of planting density (30, 40 and 50 plants.m-2 and two ecotypes (Tabas and Toroud as the sub-plots. To estimate the crop water requirement, different meteorological parameters were collected from Semnan weather station and were used based on FAO-56 water irrigation calculation instructions. After harvesting, ten garlic plants were sampled randomly in each plot and bulb yield components were measured. To calculate the leaves anti-oxidant content, DPPH method was used. The statistical significances of mean values were assessed by analysis of variance and LSD tests at p≤0.05. All calculations were performed using SAS and Mstat-C softwares. Results and discussion

Corticosterone and pace of life in two life-history ecotypes of the garter snake Thamnophis elegans.

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Palacios, Maria G; Sparkman, Amanda M; Bronikowski, Anne M

2012-02-01

Glucocorticoids are main candidates for mediating life-history trade-offs by regulating the balance between current reproduction and survival. It has been proposed that slow-living organisms should show higher stress-induced glucocorticoid levels that favor self-maintenance rather than current reproduction when compared to fast-living organisms. We tested this hypothesis in replicate populations of two ecotypes of the garter snake (Thamnophis elegans) that exhibit slow and fast pace of life strategies. We subjected free-ranging snakes to a capture-restraint protocol and compared the stress-induced corticosterone levels between slow- and fast-living snakes. We also used a five-year dataset to assess whether baseline corticosterone levels followed the same pattern as stress-induced levels in relation to pace of life. In accordance with the hypothesis, slow-living snakes showed higher stress-induced corticosterone levels than fast-living snakes. Baseline corticosterone levels showed a similar pattern with ecotype, although differences depended on the year of study. Overall, however, levels of glucocorticoids are higher in slow-living than fast-living snakes, which should favor self-maintenance and survival at the expense of current reproduction. The results of the present study are the first to relate glucocorticoid levels and pace of life in a reptilian system and contribute to our understanding of the physiological mechanisms involved in life-history evolution. Copyright © 2011 Elsevier Inc. All rights reserved.

RAPD-PCR and real-time PCR HRM based genetic variation evaluations of Urtica dioica parts, ecotypes and evaluations of morphotypes in Turkey.

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Uzonur, Irem; Akdeniz, Gamze; Katmer, Zeynep; Ersoy, Seyda Karaman

2013-01-01

Urtica dioica is an ethnobotanically and medicinally important Complementary and Alternative Medicine (CAM) plant worldwide and in Turkey; 90 % of herbal CAM applications depend on it in Turkey. It has a wide range of habitats in nearly all continents. It is found in all three phytogeographical regions in Turkey (Euro-Siberian, Irano-Turanian, Mediterranean) with high adaptivity to heterogeneous geographies such as climate, soil types and altitudes. This fact in relation to the assessment of chemical constituents of the plant and combining with further genetic and morphological variation data can assist and enhance the works for the utility and reliability of CAM applications in effect and activity of this plant species. In this work we have made some preliminary experiments with novel approaches to reveal the ecotypes and genetic variation of mighty ecotypes of Urtica dioica from different phytogeographical regions of Turkey (Euro-Siberian and Mediterranean). The ecotypes have heterogeneity in both its parts (leaf, stem, root) as revealed by Random Amplified Polymorphic DNA-Polymerase Chain Reaction (RAPD-PCR) using random primers and High-resolution Melt (HRM) analysis using Urtica dioica specific primers and universal chloroplast DNA (cpDNA) primers and morphological traits such as phenolic contents and antioxidant capacities of plants' leaf infusions as used in medicinal applications in Turkey. This work will contribute a lot for the development of molecular markers to detect the genetic variation and heterogeneity of Urtica dioica to further relate with expected phenotypes that are most useful and relevant in CAM applications.

Search for possible latitudinal ecotypes in Dumontia contorta (Rhodophyta)

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Rietema, H.; van den Hoek, C.

1984-09-01

Effects of daylength and temperature on the formation of erect fronds (macrothalli) from crusts (microthalli) of Dumontia contorta (S. G. Gmel.) Rupr. from three localities in Nova Scotia and one locality in Southern Iceland were investigated and compared to such effects shown by strains from three different East Atlantic localities (Isle of Man; Zeeland, S. W. Netherlands; and Roscoff, Brittany, France). Although these strains showed small differences in their temperature-daylength responses, these could not be interpreted as latitudinal adaptations, and consequently no latitudinal ecotypes could be found for Dumontia contorta in the N. Atlantic Ocean. Upright fronds are formed at a broad temperature range of about 4°-18°C and at daylengths ≤ 13 h. Only in the southernmost part of its distribution area can high autumnal temperatures be expected to block the reappearance of upright fronds after passage of the critical daylength in September. In the larger part of the distribution area even summer temperatures are not high enough to block formation of uprights and here apparently only short daylengths initiate the reappearance of young upright fronds in autumn. The consequences of these aspects of the life history regulation for the geographic distribution are discussed.

The Effect of Drought Stress and Plant Density on Biochemical and Physiological Characteristics of Two Garlic (Allium sativum L. Ecotypes

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Sh Akbari

2017-03-01

Full Text Available Introduction Drought stress is the most important growth limiting factor for crop production. Sugar accumulation under drought stress strengthens and stabilizes cell membranes and maintains the water absorption and turgid property. Under stress conditions, proline will also maintain the turgor pressure and decreased the damages caused to plant membrane. Although proline concentrations may have undesirable effects on plant growth, because of deflecting photosynthetic resources to the processes that are not involved in plant growth. Chloroplasts and its pigments are also affected by drought stress. Density is one of the factors that has a significant impact on plant growth. Garlic is one of the edible plants which has generated considerable interest throughout human history because of its pharmaceutical properties. This study aimed to determine the effects of drought stress and plant density on some biochemical and physiological treats of two garlic ecotypes and determining the more resistant ecotype. Materials and Methods The study was carried out in 2011-2012 in a farm land at the south east of Semnan city. The experimental layout was a split-plot factorial with a randomized complete block design in three replications. The treatments were comprised of three factors: irrigation regimes (60%, 80% and 100% of estimated crop evapotranspiration (ETC that were assigned as the main plot and the factorial combination of 3 levels of planting density (30, 40 and 50 plants. m-2 and two ecotypes (Tabas and Toroud made up the sub-plots. The water requirement was calculated based on FAO-56 crop water requirements instruction. FAO-56 Penman-Monteith equation was used to calculate evapotranspiration. To calculate the content of soluble sugar, proline and leaves pigment, the samples were collected in a random way from the youngest fully expended leaves one month before the final harvest. Relative water content was estimated by measuring dry weight, fresh weight

Morphological and physiological divergences within Quercus ilex support the existence of different ecotypes depending on climatic dryness.

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Peguero-Pina, José Javier; Sancho-Knapik, Domingo; Barrón, Eduardo; Camarero, Julio Jesús; Vilagrosa, Alberto; Gil-Pelegrín, Eustaquio

2014-08-01

Several studies show apparently contradictory findings about the functional convergence within the Mediterranean woody flora. In this context, this study evaluates the variability of functional traits within holm oak (Quercus ilex) to elucidate whether provenances corresponding to different morphotypes represent different ecotypes locally adapted to the prevaling stress levels. Several morphological and physiological traits were measured at leaf and shoot levels in 9-year-old seedlings of seven Q. ilex provenances including all recognized morphotypes. Plants were grown in a common garden for 9 years under the same environmental conditions to avoid possible biases due to site-specific characteristics. Leaf morphometry clearly separates holm oak provenances into 'ilex' (more elongated leaves with low vein density) and 'rotundifolia' (short and rounded leaves with high vein density) morphotypes. Moreover, these morphotypes represent two consistent and very contrasting functional types in response to dry climates, mainly in terms of leaf area, major vein density, leaf specific conductivity, resistance to drought-induced cavitation and turgor loss point. The 'ilex' and 'rotundifolia' morphotypes correspond to different ecotypes as inferred from their contrasting functional traits. To the best of our knowledge, this is the first time that the combined use of morphological and physiological traits has provided support for the concept of these two holm oak morphotypes being regarded as two different species. © The Author 2014. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A compatible interaction of Alternaria brassicicola with Arabidopsis thaliana ecotype DiG: evidence for a specific transcriptional signature

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Gepstein Shimon

2009-03-01

Full Text Available Abstract Background The interaction of Arabidopsis with Alternaria brassicicola provides a model for disease caused by necrotrophs, but a drawback has been the lack of a compatible pathosystem. Infection of most ecotypes, including the widely-studied line Col-0, with this pathogen generally leads to a lesion that does not expand beyond the inoculated area. This study examines an ecotype, Dijon G (DiG, which is considered sensitive to A. brassicicola. Results We show that the interaction has the characteristics of a compatible one, with expanding rather than limited lesions. To ask whether DiG is merely more sensitive to the pathogen or, rather, interacts in distinct manner, we identified genes whose regulation differs between Col-0 and DiG challenged with A. brassicicola. Suppression subtractive hybridization was used to identify differentially expressed genes, and their expression was verified using semi-quantitative PCR. We also tested a set of known defense-related genes for differential regulation in the two plant-pathogen interactions. Several known pathogenesis-related (PR genes are up-regulated in both interactions. PR1, and a monooxygenase gene identified in this study, MO1, are preferentially up-regulated in the compatible interaction. In contrast, GLIP1, which encodes a secreted lipase, and DIOX1, a pathogen-response related dioxygenase, are preferentially up-regulated in the incompatible interaction. Conclusion The results show that DiG is not only more susceptible, but demonstrate that its interaction with A. brassicicola has a specific transcriptional signature.

Effect of Drought Stress on Leaf Water Status, Electrolyte Leakage, Photosynthesis Parameters and Chlorophyll Fluorescence of Two Kochia Ecotypes (Kochia scoparia Irrigated With Saline Water

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A Masoumi

2012-12-01

Full Text Available Rainfall deficiency and the development of salinity in Iran are the most important factors for using new salt and drought-resistant plants instead of conventional crops. Kochia species have recently attracted the attention of researchers as a forage and fodder crop in marginal lands worldwide due to its drought and salt tolerant characteristics. This field experiment was performed at the Salinity Research Station of Ferdowsi University of Mashhad, Iran, in a split plot based on randomized complete block design with three replications in 2008. Drought stress, including four levels (control, no irrigation in vegetative stage, no irrigation at reproductive stage and no irrigation at maturity stage for four weeks, and two Kochia ecotypes (Birjand and Borujerd were allocated as main and sub plots, respectively. Relative water content, electrolyte leakage, photosynthesis parameters and chlorophyll fluorescence were assayed every two week from late vegetative stage. Results showed that drought stress decreased significantly measured parameters in plants under stress, in all stages. Plants completely recovered after eliminating stress and rewatering and recovered plants did not show significant difference with control. Electrolyte leaking and chlorophyll fluorescence showed the lowest change among the measured parameters. It can emphasize that resistant to stress conditions in this plant and cell wall is not damaged at this level of stress situation. Birjand ecotype from the arid region, revealed a better response than Borujerd ecotype to drought stress. Probably it returns to initial adaptation of Birjand. In general this plant can recover after severe drought stress well. It is possible to introduce this plant as a new fodder in arid and saline conditions.

Carbon source utilization patterns of Bacillus simplex ecotypes do not reflect their adaptation to ecologically divergent slopes in 'Evolution Canyon', Israel.

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Sikorski, Johannes; Pukall, Rüdiger; Stackebrandt, Erko

2008-10-01

The 'Evolution Canyons' I and II in Israel are model habitats to study adaptation and speciation of bacteria in the environment. These canyons represent similar ecological replicates, separated by 40 km, with a strongly sun-exposed and hot 'African' south-facing slope (SFS) vs. a cooler and mesic-lush 'European' north-facing slope (NFS). Previously, among 131 Bacillus simplex isolates, distinct genetic lineages (ecotypes), each specific for either SFS or NFS, were identified, suggesting a temperature-driven slope-specific adaptation. Here, we asked whether the ecological heterogeneity of SFS vs. NFS also affected carbon utilization abilities, as determined using the Biolog assay. Contrary to expectation, a correlation between substrate utilization patterns and the ecological origin of strains was not found. Rather, the patterns split according to the two major phylogenetic lineages each of which contain SFS and NFS ecotypes. We conclude that traits related to the general energy metabolism, as far as assessed here, are neither shaped by the major abiotic features of 'Evolution Canyon', namely solar radiation, temperature, and drought, nor by the soil characteristics. We further conclude that some traits diverge rather neutrally from each other, whereas other, more environmentally related traits are shaped by natural selection and show evolutionary convergence.

Different NaCl-Induced Calcium Signatures in the Arabidopsis thaliana Ecotypes Col-0 and C24

KAUST Repository

Schmöckel, Sandra M.

2015-02-27

A common feature of stress signalling pathways are alterations in the concentration of cytosolic free calcium ([Ca2+]cyt), which allow the specific and rapid transmission of stress signals through a plant after exposure to a stress, such as salinity. Here, we used an aequorin based bioluminescence assay to compare the NaCl-induced changes in [Ca2+]cyt of the Arabidopsis ecotypes Col-0 and C24. We show that C24 lacks the NaCl specific component of the [Ca2+]cyt signature compared to Col-0. This phenotypic variation could be exploited as a screening methodology for the identification of yet unknown components in the early stages of the salt signalling pathway.

Different NaCl-Induced Calcium Signatures in the Arabidopsis thaliana Ecotypes Col-0 and C24

KAUST Repository

Schmö ckel, Sandra M.; Garcia, Alexandre F.; Berger, Bettina; Tester, Mark A.; Webb, Alex A. R.; Roy, Stuart J.

2015-01-01

A common feature of stress signalling pathways are alterations in the concentration of cytosolic free calcium ([Ca2+]cyt), which allow the specific and rapid transmission of stress signals through a plant after exposure to a stress, such as salinity. Here, we used an aequorin based bioluminescence assay to compare the NaCl-induced changes in [Ca2+]cyt of the Arabidopsis ecotypes Col-0 and C24. We show that C24 lacks the NaCl specific component of the [Ca2+]cyt signature compared to Col-0. This phenotypic variation could be exploited as a screening methodology for the identification of yet unknown components in the early stages of the salt signalling pathway.

Description of some characteristics of flowers and seeds of Arabidopsis thaliana - ecotype landsberg erecta and mutant NW4

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Leszek Trząski

2014-01-01

Full Text Available Flowers and seeds of Landsberg erecta (Ler ecotype and NW4 mutant were studied by light microscopy and scanning electron microscopy to reveal characteristic features of their structure. The NW4 mutant flowers differ from Ler mainly in presence of two bract-like sepals with complicated vasculature and a variable number of secondary flowers. In the two outer whorls of NW4 flower, variable number of transformed stamen-, petal-, sepal- and style-like elements also occur. The NW4 mutant seeds are characterized by the absence of mucilage around the surface and a deviating seed coat morphology.

Tissue-Specific Accumulation of Sulfur Compounds and Saponins in Different Parts of Garlic Cloves from Purple and White Ecotypes.

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Diretto, Gianfranco; Rubio-Moraga, Angela; Argandoña, Javier; Castillo, Purificación; Gómez-Gómez, Lourdes; Ahrazem, Oussama

2017-08-20

This study set out to determine the distribution of sulfur compounds and saponin metabolites in different parts of garlic cloves. Three fractions from purple and white garlic ecotypes were obtained: the tunic (SS), internal (IS) and external (ES) parts of the clove. Liquid Chromatography coupled to High Resolution Mass spectrometry (LC-HRMS), together with bioinformatics including Principal Component Analysis (PCA), Hierarchical Clustering (HCL) and correlation network analyses were carried out. Results showed that the distribution of these metabolites in the different parts of garlic bulbs was different for the purple and the white ecotypes, with the main difference being a slightly higher number of sulfur compounds in purple garlic. The SS fraction in purple garlic had a higher content of sulfur metabolites, while the ES in white garlic was more enriched by these compounds. The correlation network indicated that diallyl disulfide was the most relevant metabolite with regards to sulfur compound metabolism in garlic. The total number of saponins was almost 40-fold higher in purple garlic than in the white variety, with ES having the highest content. Interestingly, five saponins including desgalactotigonin-rhamnose, proto-desgalactotigonin, proto-desgalactotigonin-rhamnose, voghieroside D1, sativoside B1-rhamnose and sativoside R1 were exclusive to the purple variety. Data obtained from saponin analyses revealed a very different network between white and purple garlic, thus suggesting a very robust and tight coregulation of saponin metabolism in garlic. Findings in this study point to the possibility of using tunics from purple garlic in the food and medical industries, since it contains many functional compounds which can be exploited as ingredients.

Contribution of Bicarbonate Assimilation to Carbon Pool Dynamics in the Deep Mediterranean Sea and Cultivation of Actively Nitrifying and CO2-Fixing Bathypelagic Prokaryotic Consortia.

Science.gov (United States)

La Cono, Violetta; Ruggeri, Gioachino; Azzaro, Maurizio; Crisafi, Francesca; Decembrini, Franco; Denaro, Renata; La Spada, Gina; Maimone, Giovanna; Monticelli, Luis S; Smedile, Francesco; Giuliano, Laura; Yakimov, Michail M

2018-01-01

Covering two-thirds of our planet, the global deep ocean plays a central role in supporting life on Earth. Among other processes, this biggest ecosystem buffers the rise of atmospheric CO 2 . Despite carbon sequestration in the deep ocean has been known for a long time, microbial activity in the meso- and bathypelagic realm via the " assimilation of bicarbonate in the dark " (ABD) has only recently been described in more details. Based on recent findings, this process seems primarily the result of chemosynthetic and anaplerotic reactions driven by different groups of deep-sea prokaryoplankton. We quantified bicarbonate assimilation in relation to total prokaryotic abundance, prokaryotic heterotrophic production and respiration in the meso- and bathypelagic Mediterranean Sea. The measured ABD values, ranging from 133 to 370 μg C m -3 d -1 , were among the highest ones reported worldwide for similar depths, likely due to the elevated temperature of the deep Mediterranean Sea (13-14°C also at abyssal depths). Integrated over the dark water column (≥200 m depth), bicarbonate assimilation in the deep-sea ranged from 396 to 873 mg C m -2 d -1 . This quantity of produced de novo organic carbon amounts to about 85-424% of the phytoplankton primary production and covers up to 62% of deep-sea prokaryotic total carbon demand. Hence, the ABD process in the meso- and bathypelagic Mediterranean Sea might substantially contribute to the inorganic and organic pool and significantly sustain the deep-sea microbial food web. To elucidate the ABD key-players, we established three actively nitrifying and CO 2 -fixing prokaryotic enrichments. Consortia were characterized by the co-occurrence of chemolithoautotrophic Thaumarchaeota and chemoheterotrophic proteobacteria. One of the enrichments, originated from Ionian bathypelagic waters (3,000 m depth) and supplemented with low concentrations of ammonia, was dominated by the Thaumarchaeota "low-ammonia-concentration" deep-sea ecotype

Contribution of Bicarbonate Assimilation to Carbon Pool Dynamics in the Deep Mediterranean Sea and Cultivation of Actively Nitrifying and CO2-Fixing Bathypelagic Prokaryotic Consortia

Science.gov (United States)

La Cono, Violetta; Ruggeri, Gioachino; Azzaro, Maurizio; Crisafi, Francesca; Decembrini, Franco; Denaro, Renata; La Spada, Gina; Maimone, Giovanna; Monticelli, Luis S.; Smedile, Francesco; Giuliano, Laura; Yakimov, Michail M.

2018-01-01

Covering two-thirds of our planet, the global deep ocean plays a central role in supporting life on Earth. Among other processes, this biggest ecosystem buffers the rise of atmospheric CO2. Despite carbon sequestration in the deep ocean has been known for a long time, microbial activity in the meso- and bathypelagic realm via the “assimilation of bicarbonate in the dark” (ABD) has only recently been described in more details. Based on recent findings, this process seems primarily the result of chemosynthetic and anaplerotic reactions driven by different groups of deep-sea prokaryoplankton. We quantified bicarbonate assimilation in relation to total prokaryotic abundance, prokaryotic heterotrophic production and respiration in the meso- and bathypelagic Mediterranean Sea. The measured ABD values, ranging from 133 to 370 μg C m−3 d−1, were among the highest ones reported worldwide for similar depths, likely due to the elevated temperature of the deep Mediterranean Sea (13–14°C also at abyssal depths). Integrated over the dark water column (≥200 m depth), bicarbonate assimilation in the deep-sea ranged from 396 to 873 mg C m−2 d−1. This quantity of produced de novo organic carbon amounts to about 85–424% of the phytoplankton primary production and covers up to 62% of deep-sea prokaryotic total carbon demand. Hence, the ABD process in the meso- and bathypelagic Mediterranean Sea might substantially contribute to the inorganic and organic pool and significantly sustain the deep-sea microbial food web. To elucidate the ABD key-players, we established three actively nitrifying and CO2-fixing prokaryotic enrichments. Consortia were characterized by the co-occurrence of chemolithoautotrophic Thaumarchaeota and chemoheterotrophic proteobacteria. One of the enrichments, originated from Ionian bathypelagic waters (3,000 m depth) and supplemented with low concentrations of ammonia, was dominated by the Thaumarchaeota “low-ammonia-concentration” deep

Non-destructive flavour evaluation of red onion (Allium cepa L.) ecotypes: an electronic-nose-based approach.

Science.gov (United States)

Russo, Mariateresa; di Sanzo, Rosa; Cefaly, Vittoria; Carabetta, Sonia; Serra, Demetrio; Fuda, Salvatore

2013-11-15

This work reports preliminary results on the potential of a metal oxide sensor (MOS)-based electronic nose, as a non-destructive method to discriminate three "Tropea Red Onion" PGI ecotypes (TrT, TrMC and TrA) from each other and the common red onion (RO), which is usually used to counterfeit. The signals from the sensor array were processed using a canonical discriminant function analysis (DFA) pattern recognition technique. The DFA on onion samples showed a clear separation among the four onion groups with an overall correct classification rate (CR) of 97.5%. Onion flavour is closely linked to pungency and thus to the pyruvic acid content. The e-nose analysis results are in good agreement with pyruvic acid analysis. This work demonstrated that artificial olfactory systems have potential for use as an innovative, rapid and specific non-destructive technique, and may provide a method to protect food products against counterfeiting. Copyright © 2013 Elsevier Ltd. All rights reserved.

Genetic and phenotypic variation along an ecological gradient in lake trout Salvelinus namaycush

Science.gov (United States)

Baillie, Shauna M.; Muir, Andrew M.; Hansen, Michael J.; Krueger, Charles C.; Bentzen, Paul

2016-01-01

BackgroundAdaptive radiation involving a colonizing phenotype that rapidly evolves into at least one other ecological variant, or ecotype, has been observed in a variety of freshwater fishes in post-glacial environments. However, few studies consider how phenotypic traits vary with regard to neutral genetic partitioning along ecological gradients. Here, we present the first detailed investigation of lake trout Salvelinus namaycushthat considers variation as a cline rather than discriminatory among ecotypes. Genetic and phenotypic traits organized along common ecological gradients of water depth and geographic distance provide important insights into diversification processes in a lake with high levels of human disturbance from over-fishing.ResultsFour putative lake trout ecotypes could not be distinguished using population genetic methods, despite morphological differences. Neutral genetic partitioning in lake trout was stronger along a gradient of water depth, than by locality or ecotype. Contemporary genetic migration patterns were consistent with isolation-by-depth. Historical gene flow patterns indicated colonization from shallow to deep water. Comparison of phenotypic (Pst) and neutral genetic variation (Fst) revealed that morphological traits related to swimming performance (e.g., buoyancy, pelvic fin length) departed more strongly from neutral expectations along a depth gradient than craniofacial feeding traits. Elevated phenotypic variance with increasing water depth in pelvic fin length indicated possible ongoing character release and diversification. Finally, differences in early growth rate and asymptotic fish length across depth strata may be associated with limiting factors attributable to cold deep-water environments.ConclusionWe provide evidence of reductions in gene flow and divergent natural selection associated with water depth in Lake Superior. Such information is relevant for documenting intraspecific biodiversity in the largest freshwater lake

Characterization of biochemical traits of dog rose (Rosa canina L.) ecotypes in the central part of Iran.

Science.gov (United States)

Javanmard, Milad; Asadi-Gharneh, Hossein Ali; Nikneshan, Pejman

2018-07-01

Dog rose (Rosa canina L.) is a wild native species in Iran, with a significant genetic diversity. This plant serves as a rich source of vitamin C, anthocyanins, phenolic contents and carotenoids. Rose hips have been used in several food products, as well as perfumery and cosmetics industries. In this research, we investigate biochemical characteristics of five dog rose ecotypes (Kopehjamshid, Zarneh, Miyankish, Aghcheh and Sadeghiyeh), that were collected from the central part of Iran (Isfahan province). Amounts of vitamin C, total carotenoids, total phenolic contents, total anthocyanins, macro and micro minerals were measured. Seed oil are extracted by soxhlet method and analysed by gas chromatography. The macro and micro minerals levels in the fruit vary significantly among these regions. The results of this study demonstrate that dog rose have great diversity and can be used in breeding programmes in order to increase nutrient values as a food resource additive.

Identification of ecotype-specific marker genes for categorization of beer-spoiling Lactobacillus brevis.

Science.gov (United States)

Behr, Jürgen; Geissler, Andreas J; Preissler, Patrick; Ehrenreich, Armin; Angelov, Angel; Vogel, Rudi F

2015-10-01

The tolerance to hop compounds, which is mainly associated with inhibition of bacterial growth in beer, is a multi-factorial trait. Any approaches to predict the physiological differences between beer-spoiling and non-spoiling strains on the basis of a single marker gene are limited. We identified ecotype-specific genes related to the ability to grow in Pilsner beer via comparative genome sequencing. The genome sequences of four different strains of Lactobacillus brevis were compared, including newly established genomes of two highly hop tolerant beer isolates, one strain isolated from faeces and one published genome of a silage isolate. Gene fragments exclusively occurring in beer-spoiling strains as well as sequences only occurring in non-spoiling strains were identified. Comparative genomic arrays were established and hybridized with a set of L. brevis strains, which are characterized by their ability to spoil beer. As result, a set of 33 and 4 oligonucleotide probes could be established specifically detecting beer-spoilers and non-spoilers, respectively. The detection of more than one of these marker sequences according to a genetic barcode enables scoring of L. brevis for their beer-spoiling potential and can thus assist in risk evaluation in brewing industry. Copyright © 2015 Elsevier Ltd. All rights reserved.

Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses.

Science.gov (United States)

Marín-Guirao, Lazaro; Ruiz, Juan M; Dattolo, Emanuela; Garcia-Munoz, Rocio; Procaccini, Gabriele

2016-06-27

The increase in extreme heat events associated to global warming threatens seagrass ecosystems, likely by affecting key plant physiological processes such as photosynthesis and respiration. Understanding species' ability to acclimate to warming is crucial to better predict their future trends. Here, we study tolerance to warming in two key Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa. Stress responses of shallow and deep plants were followed during and after short-term heat exposure in mesocosms by coupling photo-physiological measures with analysis of expression of photosynthesis and stress-related genes. Contrasting tolerance and capacity to heat acclimation were shown by shallow and deep P. oceanica ecotypes. While shallow plants acclimated through respiratory homeostasis and activation of photo-protective mechanisms, deep ones experienced photosynthetic injury and impaired carbon balance. This suggests that P. oceanica ecotypes are thermally adapted to local conditions and that Mediterranean warming will likely diversely affect deep and shallow meadow stands. On the other hand, contrasting mechanisms of heat-acclimation were adopted by the two species. P. oceanica regulates photosynthesis and respiration at the level of control plants while C. nodosa balances both processes at enhanced rates. These acclimation discrepancies are discussed in relation to inherent attributes of the two species.

Physiological and molecular evidence of differential short-term heat tolerance in Mediterranean seagrasses

Science.gov (United States)

Marín-Guirao, Lazaro; Ruiz, Juan M.; Dattolo, Emanuela; Garcia-Munoz, Rocio; Procaccini, Gabriele

2016-06-01

The increase in extreme heat events associated to global warming threatens seagrass ecosystems, likely by affecting key plant physiological processes such as photosynthesis and respiration. Understanding species’ ability to acclimate to warming is crucial to better predict their future trends. Here, we study tolerance to warming in two key Mediterranean seagrasses, Posidonia oceanica and Cymodocea nodosa. Stress responses of shallow and deep plants were followed during and after short-term heat exposure in mesocosms by coupling photo-physiological measures with analysis of expression of photosynthesis and stress-related genes. Contrasting tolerance and capacity to heat acclimation were shown by shallow and deep P. oceanica ecotypes. While shallow plants acclimated through respiratory homeostasis and activation of photo-protective mechanisms, deep ones experienced photosynthetic injury and impaired carbon balance. This suggests that P. oceanica ecotypes are thermally adapted to local conditions and that Mediterranean warming will likely diversely affect deep and shallow meadow stands. On the other hand, contrasting mechanisms of heat-acclimation were adopted by the two species. P. oceanica regulates photosynthesis and respiration at the level of control plants while C. nodosa balances both processes at enhanced rates. These acclimation discrepancies are discussed in relation to inherent attributes of the two species.

Effect of Organic and Chemical Fertilizers on Yield and Essential Oil of Two Ecotypes of Savory (Satureja hortensis L. under Normal and Drought Stress Conditions

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O Akrami nejad

2016-02-01

Full Text Available Introduction Savory (Satureja hortensis L. is an annual and aromatic plant from Labiatae family, which has plenty of essential oil and is important in medicinal, food, health and beauty industries (6. In comparison with chemical fertilizers, organic fertilizers especially manure have lots of organic material sources, and can be used as nutrients, especially Nitrogen, Phosphor and Potassium. Organic fertilizers also keeps more water in the soil (14. Water deficit is one of the most important boundaries of production in arid and semi-arid regions. Drought stress reduces water content, limits plant growth and changes some physiological and metabolic activities (31. This experiment was conducted as there is a global interest for production of medicinal plants with sustainable agriculture system, and with low input and shortage of information about Savory reaction to fertilization in drought stress condition. The objective of this research was to compare the effects of chemical fertilizers and different organic fertilizers on quantitative and qualitative characteristics of two ecotypes of savory under drought stress condition. Materials and Methods In order to study the effects of organic and mineral (N, P and K fertilizers on quantitative and qualitative characteristics of savory in drought stress condition, two separate split plot designs with three replications were carried out in 2012-2013 year, at the research field of Shahid Bahonar University of Kerman, Iran. In each design fertilizers including cow manure (30 ton per hectare, poultry manure (10 ton per hectare, chemical fertilizers (used equally with macro elements existing in both poultry and cow manure and control (no fertilizer were used as main factor. Kerman and Khuzestan ecotypes were sub-factor. One of the experiments was irrigated to 100% and the other to 50% of field capacity. Two experiments were analyzed as a combined design. The important characteristics of Savory such as plant

Significance of the identification in the Horn of Africa of an exceptionally deep branching Mycobacterium tuberculosis clade.

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Yann Blouin

Full Text Available Molecular and phylogeographic studies have led to the definition within the Mycobacterium tuberculosis complex (MTBC of a number of geotypes and ecotypes showing a preferential geographic location or host preference. The MTBC is thought to have emerged in Africa, most likely the Horn of Africa, and to have spread worldwide with human migrations. Under this assumption, there is a possibility that unknown deep branching lineages are present in this region. We genotyped by spoligotyping and multiple locus variable number of tandem repeats (VNTR analysis (MLVA 435 MTBC isolates recovered from patients. Four hundred and eleven isolates were collected in the Republic of Djibouti over a 12 year period, with the other 24 isolates originating from neighbouring countries. All major M. tuberculosis lineages were identified, with only two M. africanum and one M. bovis isolates. Upon comparison with typing data of worldwide origin we observed that several isolates showed clustering characteristics compatible with new deep branching. Whole genome sequencing (WGS of seven isolates and comparison with available WGS data from 38 genomes distributed in the different lineages confirms the identification of ancestral nodes for several clades and most importantly of one new lineage, here referred to as lineage 7. Investigation of specific deletions confirms the novelty of this lineage, and analysis of its precise phylogenetic position indicates that the other three superlineages constituting the MTBC emerged independently but within a relatively short timeframe from the Horn of Africa. The availability of such strains compared to the predominant lineages and sharing very ancient ancestry will open new avenues for identifying some of the genetic factors responsible for the success of the modern lineages. Additional deep branching lineages may be readily and efficiently identified by large-scale MLVA screening of isolates from sub-Saharan African countries followed by

Switchgrass Cultivar/Ecotype Selection and Management for Biofuels in the Upper Southeast USA

Science.gov (United States)

Parrish, David J.; Wolf, Dale D.

2014-01-01

Switchgrass (Panicum virgatum L.), a perennial warm-season grass indigenous to the eastern USA, has potential as a biofuels feedstock. The objective of this study was to investigate the performance of upland and lowland switchgrass cultivars under different environments and management treatments. Four cultivars of switchgrass were evaluated from 2000 to 2001 under two management regimes in plots established in 1992 at eight locations in the upper southeastern USA. Two management treatments included 1) a single annual harvest (in late October to early November) and a single application of 50 kg N/ha/yr and 2) two annual harvests (in midsummer and November) and a split application of 100 kg N/ha/yr. Biomass yields averaged 15 Mg/ha/yr and ranged from 10 to 22 Mg/ha/yr across cultivars, managements, locations, and years. There was no yield advantage in taking two harvests of the lowland cultivars (Alamo and Kanlow). When harvested twice, upland cultivars (Cave-in-Rock and Shelter) provided yields equivalent to the lowland ecotypes. Tiller density was 36% lower in stands cutting only once per year, but the stands appeared vigorous after nine years of such management. Lowland cultivars and a one-cutting management (after the tops have senesced) using low rates of applied N (50 kg/ha) are recommended. PMID:25105170

Eco-physiology of Acer saccharum trees on glade-like sites in central Missouri

Science.gov (United States)

Eric J. Rhodenbaugh; Stephen G. Pallardy

1993-01-01

Although sugar maple (Acer saccharum Marsh.) is not considered drought tolerant, it is common on xeric limestone glade-like sites in central Missouri. Acer saccharum on such sites may be a drought-tolerant ecotype or may have access to deep water supply through bedrock cracks. We investigated these possibilities during the 1990...

DeepPy: Pythonic deep learning

DEFF Research Database (Denmark)

Larsen, Anders Boesen Lindbo

This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

The efficiency of Arabidopsis thaliana floral dip transformation is determined not only by the Agrobacterium strain used but also by the physiology and the ecotype of the dipped plant.

Science.gov (United States)

Ghedira, Rim; De Buck, Sylvie; Nolf, Jonah; Depicker, Ann

2013-07-01

To evaluate the chromosomal background of different Agrobacterium strains on floral dip transformation frequency, eight wild-type Agrobacterium strains, provided by Laboratorium voor Microbiologie Gent (LMG) and classified in different genomic groups, were compared with the commonly used Agrobacterium strains C58C1 Rif(r) (pMP90) and LBA4404 in Arabidopsis thaliana Columbia (Col-0) and C24 ecotypes. The C58C1 Rif(r) chromosomal background in combination with the pMP90 virulence plasmid showed high Col-0 floral dip transformation frequencies (0.76 to 1.57%). LMG201, which is genetically close to the Agrobacterium C58 strain, with the same virulence plasmid showed comparable or even higher transformation frequencies (1.22 to 2.28%), whereas the LBA4404 strain displayed reproducibly lower transformation frequencies (Agrobacterium chromosomal backgrounds had transformation frequencies between those of the C58C1 Rif(r) (pMP90) and LBA4404 reference strains. None of the strains could transform the C24 ecotype with a frequency higher than 0.1%. Strikingly, all Arabidopsis Col-0 floral dip transformation experiments showed a high transformation variability from plant to plant (even more than 50-fold) within and across the performed biological repeats for all analyzed Agrobacterium strains. Therefore, the physiology of the plant and, probably, the availability of competent flowers to be transformed determine, to a large extent, floral dip transformation frequencies.

Complete ecological isolation and cryptic diversity in Polynucleobacter bacteria not resolved by 16S rRNA gene sequences.

Czech Academy of Sciences Publication Activity Database

Hahn, M.W.; Jezberová, Jitka; Koll, U.; Saueressig-Beck, T.; Schmidt, J.

2016-01-01

Roč. 10, č. 7 (2016), s. 1642-1655 ISSN 1751-7362 Institutional support: RVO:60077344 Keywords : fresh-water habitats * microbial species delineation * alteromonas-macleodii * community structure * genome sequence * organic-matter Subject RIV: EE - Microbiology, Virology Impact factor: 9.664, year: 2016

Resistance of a Local Ecotype of Castanea sativa to Dryocosmus kuriphilus (Hymenoptera: Cynipidae in Southern Italy

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Francesco Nugnes

2018-02-01

Full Text Available The cynipid Dryocosmus kuriphilus is the most impactful invasive pest of Castanea sativa copse woods and orchards currently reported from many European countries. A low impact solution for the containment of this pest could be the use of resistant trees. We examined the resistance of the red salernitan ecotype (RSE of C. sativa to D. kuriphilus and carried out a morphological characterization of this ecotype’s plants and fruits. From November 2015 to May 2017 we observed and recorded the percentage of infested buds, healthy leaves and shoots on about 50 chestnut trees, together with the number, size, and position of galls, and the number of eggs laid by the gall wasps into the buds and the number of larvae inside the galls. We showed a progressive mortality of cynipid larvae up to the starting point of galls development when almost total larval mortality was recorded. This suggests that RSE trees have a moderate resistance to D. kuriphilus; however, resistance acts at different levels, resulting in fewer eggs being deposited, a low number of larvae reaching the complete development, and a low number of galls on the branches. Moreover, the galls on resistant trees are smaller than the susceptible ones, so the larvae are more exposed to parasitization.

Seed longevity of red rice ecotypes buried in soil Longevidade de sementes de arroz-vermelho enterradas no solo

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J.A. Noldin

2006-12-01

Full Text Available Red rice is a troublesome weed in irrigated rice production and is spread through contaminated commercial rice seed and machinery. Seed dormancy is a major trait for red rice. Studies were carried out at two locations to determine red rice seed longevity in the soil of several ecotypes from four US states. Five months after burial near Beaumont, Texas only three ecotypes had viable seed (O arroz-vermelho constitui-se na principal planta daninha infestante de lavouras de arroz irrigado e a sua disseminação ocorre, principalmente, pelo uso de sementes comerciais contaminadas e equipamentos agrícolas. A ocorrência de dormência nas sementes é uma das principais características que dificultam o controle do arroz-vermelho em lavouras. O objetivo deste trabalho foi estimar a longevidade no solo de ecótipos de arroz-vermelho provenientes de diferentes áreas de produção de arroz nos Estados Unidos. O estudo foi conduzido em dois locais: Beaumont e College Station, no estado do Texas (TX. Para sementes enterradas a 5 cm de profundidade em Beaumont, apenas três ecótipos apresentaram sementes viáveis (<1%. No entanto, quando as sementes foram enterradas em maior profundidade (25 cm, nove ecótipos tinham sementes viáveis após 2 anos. Trinta e seis meses após o enterrio, cinco ecótipos apresentavam sementes com alguma viabilidade, mas todos inferiores a 1%. Sementes de arroz-vermelho produzidas e enterradas em College Station na profundidade de 12 cm, um dia após a colheita, apresentaram maior longevidade que aquelas mantidas na superfície do solo. Após 17 meses, um dos ecótipos de arroz-preto (TX 4, enterrado a 12 cm, foi o que apresentou maior percentual de viabilidade (2%. Nos dois experimentos, observou-se que os cultivares comerciais, Lemont e Mars, não apresentaram sementes viáveis após cinco meses, independentemente da localização no solo. Os resultados deste estudo sugerem que em áreas com arroz-vermelho deve-se evitar o

Genetic diversity of the causative agent of ice-ice disease of the seaweed Kappaphycus alvarezii from Karimunjawa island, Indonesia

Science.gov (United States)

Syafitri, E.; Prayitno, S. B.; Ma'ruf, W. F.; Radjasa, O. K.

2017-02-01

An essential step in investigating the bacterial role in the occurrence of diseases in Kappaphycus alvarezii is the characterization of bacteria associated with this seaweed. A molecular characterization was conducted on the genetic diversity of the causative agents of ice-ice disease associated with K. alvarezii widely known as the main source of kappa carrageenan. K. alvrezii infected with ice-ice were collected from the Karimunjawa island, North Java Sea, Indonesia. Using Zobell 2216E marine agar medium, nine bacterial species were isolated from the infected seaweed. The molecular characterizations revealed that the isolated bacteria causing ice-ice disease were closely related to the genera of Alteromonas, Bacillus, Pseudomonas, Pseudoalteromonas, Glaciecola, Aurantimonas, and Rhodococcus. In order to identify the symptoms causative organisms, the isolated bacterial species were cultured and were evaluated for their pathogenity. Out of 9 species, only 3 isolates were able to cause the ice-ice symptoms and consisted of Alteromonas macleodii, Pseudoalteromonas issachenkonii and Aurantimonas coralicida. A. macleodii showed the highest pathogenity.

Factors influencing elk recruitment across ecotypes in the Western United States

Science.gov (United States)

Lukacs, Paul M.; Mitchell, Michael S.; Hebblewhite, Mark; Johnson, Bruce K.; Johnson, Heather; Kauffman, Matthew J.; Proffitt, Kelly M.; Zager, Peter; Brodie, Jedediah; Hersey, Kent R.; Holland, A. Andrew; Hurley, Mark; McCorquodale, Scott; Middleton, Arthur; Nordhagen, Matthew; Nowak, J. Joshua; Walsh, Daniel P.; White, P.J.

2018-01-01

Ungulates are key components in ecosystems and economically important for sport and subsistence harvest. Yet the relative importance of the effects of weather conditions, forage productivity, and carnivores on ungulates are not well understood. We examined changes in elk (Cervus canadensis) recruitment (indexed as age ratios) across 7 states and 3 ecotypes in the northwestern United States during 1989–2010, while considering the effects of predator richness, forage productivity, and precipitation. We found a broad‐scale, long‐term decrease in elk recruitment of 0.48 juveniles/100 adult females/year. Weather conditions (indexed as summer and winter precipitation) showed small, but measurable, influences on recruitment. Forage productivity on summer and winter ranges (indexed by normalized difference vegetation index [NDVI] metrics) had the strongest effect on elk recruitment relative to other factors. Relationships between forage productivity and recruitment varied seasonally and regionally. The productivity of winter habitat was more important in southern parts of the study area, whereas annual variation in productivity of summer habitat had more influence on recruitment in northern areas. Elk recruitment varied by up to 15 juveniles/100 adult females across the range of variation in forage productivity. Areas with more species of large carnivores had relatively low elk recruitment, presumably because of increased predation. Wolves (Canis lupus) were associated with a decrease of 5 juveniles/100 adult females, whereas grizzly bears (Ursus arctos) were associated with an additional decrease of 7 juveniles/100 adult females. Carnivore species can have a critical influence on ungulate recruitment because their influence rivals large ranges of variation in environmental conditions. A more pressing concern, however, stems from persistent broad‐scale decreases in recruitment across the distribution of elk in the northwestern United States, irrespective of

Deep Echo State Network (DeepESN): A Brief Survey

OpenAIRE

Gallicchio, Claudio; Micheli, Alessio

2017-01-01

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

Induction of cell death by graphene in Arabidopsis thaliana (Columbia ecotype) T87 cell suspensions

International Nuclear Information System (INIS)

Begum, Parvin; Fugetsu, Bunshi

2013-01-01

Highlights: • This study was set up to explore potential influence of graphene on T87 cells. • Fragmented nuclei, membrane damage, mitochondrial dysfunction were observed. • ROS increased, ROS are key mediators in the cell death signaling pathway. • Translocation of graphene into cells and an endocytosis-like structure was observed. • Graphene entering into the cells by endocytosis. -- Abstract: The toxicity of graphene on suspensions of Arabidopsis thaliana (Columbia ecotype) T87 cells was investigated by examining the morphology, mitochondrial dysfunction, reactive oxygen species generation (ROS), and translocation of graphene as the toxicological endpoints. The cells were grown in Jouanneau and Péaud-Lenoel (JPL) media and exposed to graphene at concentrations 0–80 mg/L. Morphological changes were observed by scanning electron microscope and the adverse effects such as fragmented nuclei, membrane damage, mitochondrial dysfunction was observed with fluorescence microscopy by staining with Hoechst 33342/propidium iodide and succinate dehydrogenase (mitochondrial bioenergetic enzyme). Analysis of intracellular ROS by 2′,7′-dichlorofluorescein diacetate demonstrated that graphene induced a 3.3-fold increase in ROS, suggesting that ROS are key mediators in the cell death signaling pathway. Transmission electron microscopy verified the translocation of graphene into cells and an endocytosis-like structure was observed which suggested graphene entering into the cells by endocytosis. In conclusion, our results show that graphene induced cell death in T87 cells through mitochondrial damage mediated by ROS

Genetic and Developmental Basis for Increased Leaf Thickness in the Arabidopsis Cvi Ecotype

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Viktoriya Coneva

2018-03-01

Full Text Available Leaf thickness is a quantitative trait that is associated with the ability of plants to occupy dry, high irradiance environments. Despite its importance, leaf thickness has been difficult to measure reproducibly, which has impeded progress in understanding its genetic basis, and the associated anatomical mechanisms that pattern it. Here, we used a custom-built dual confocal profilometer device to measure leaf thickness in the Arabidopsis Ler × Cvi recombinant inbred line population and found statistical support for four quantitative trait loci (QTL associated with this trait. We used publically available data for a suite of traits relating to flowering time and growth responses to light quality and show that three of the four leaf thickness QTL coincide with QTL for at least one of these traits. Using time course photography, we quantified the relative growth rate and the pace of rosette leaf initiation in the Ler and Cvi ecotypes. We found that Cvi rosettes grow slower than Ler, both in terms of the rate of leaf initiation and the overall rate of biomass accumulation. Collectively, these data suggest that leaf thickness is tightly linked with physiological status and may present a tradeoff between the ability to withstand stress and rapid vegetative growth. To understand the anatomical basis of leaf thickness, we compared cross-sections of Cvi and Ler leaves and show that Cvi palisade mesophyll cells elongate anisotropically contributing to leaf thickness. Flow cytometry of whole leaves show that endopolyploidy accompanies thicker leaves in Cvi. Overall, our data suggest that mechanistically, an altered schedule of cellular events affecting endopolyploidy and increasing palisade mesophyll cell length contribute to increase of leaf thickness in Cvi. Ultimately, knowledge of the genetic basis and developmental trajectory leaf thickness will inform the mechanisms by which natural selection acts to produce variation in this adaptive trait.

Why & When Deep Learning Works: Looking Inside Deep Learnings

OpenAIRE

Ronen, Ronny

2017-01-01

The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The outp...

KAJIAN ANATOMI KAYU PADA TIGA EKOTIPE Pinus merkusii SUMATERA DAN POTENSINYA SEBAGAI INDIKATOR PERUBAHAN IKLIM

Directory of Open Access Journals (Sweden)

Yulia Sandri

2016-09-01

Full Text Available Recently, climate change is the one of most important environmental issue. Climate variability can be recorded by tree growing through the growth ring. Growth ring formed by cambial activity were examined in wood anatomy. In Sumatra, there are three ecotypes Pinus merkusii, namely ecotypes Kerinci, Tapanuli, and Aceh which can be distinguished morphologically. This study aims to knowing the wood anatomical characteristics of the three ecotypes and determine the potential as climate indicator. This study was conducted in October 2014 until June 2015. Sample of Kerinci ecotype was collected in Kerinci Seblat National Park, Tapanuli ecotype in Dolok Sibualbuali Natural Reserve and Aceh ecotype in Gunung Leuser National Park on a height of 130 cm using increment borer and cut on the main stem 5×5 cm for anatomical sample. Results from this study indicate that ecotype Kerinci and Tapanuli showed earlywood and latewood boundary exposing the clear growth ring, whereas in Aceh ecotype unclear. Tapanuli ecotype have the thickest tracheid diameter than ecotype Kerinci and Aceh. Ecotypes of Kerinci, Tapanuli, and Aceh has homoceluler and uniseriate ray where Aceh ecotype have the longest ray. Furthermore, Kerinci and Tapanuli ecotype have potential as climate indicator eventhough showed negative correlation, that Tapanuli ecotype show the best result and recommended in dendrochronology study.

Otolith morphology and hearing abilities in cave- and surface-dwelling ecotypes of the Atlantic molly, Poecilia mexicana (Teleostei: Poeciliidae)

Science.gov (United States)

Schulz-Mirbach, Tanja; Ladich, Friedrich; Riesch, Rüdiger; Plath, Martin

2010-01-01

Cave fish have rarely been investigated with regard to their inner ear morphology, hearing abilities, and acoustic communication. Based on a previous study that revealed morphological differences in the saccular otolith between a cave and two surface populations of Poecilia mexicana, we checked for additional differences in utricular and lagenar otoliths and tested whether different populations have similar hearing sensitivities. We found pronounced differences in the shape of all three otoliths. Otoliths of the saccule and lagena from cave fish differed from those of surface fish in the features of the face oriented towards the sensory epithelium. In addition, otoliths of the utricle and lagena were significantly heavier in cave fish. Auditory sensitivities were measured between 100 and 1500 Hz, utilizing the auditory evoked potential recording technique. We found similar hearing abilities in cave and surface fish, with greatest sensitivity between 200 and 300 Hz. An acoustic survey revealed that neither ecotype produced species-specific sounds. Our data indicate that cave dwelling altered the otolith morphology in Atlantic mollies, probably due to metabolic differences. Different otolith morphology, however, did not affect general auditory sensitivity or acoustic behavior. PMID:20430090

Adaptive Epigenetic Differentiation between Upland and Lowland Rice Ecotypes Revealed by Methylation-Sensitive Amplified Polymorphism.

Directory of Open Access Journals (Sweden)

Hui Xia

Full Text Available The stress-induced epimutations could be inherited over generations and play important roles in plant adaption to stressful environments. Upland rice has been domesticated in water-limited environments for thousands of years and accumulated drought-induced epimutations of DNA methylation, making it epigenetically differentiated from lowland rice. To study the epigenetic differentiation between upland and lowland rice ecotypes on their drought-resistances, the epigenetic variation was investigated in 180 rice landraces under both normal and osmotic conditions via methylation-sensitive amplified polymorphism (MSAP technique. Great alterations (52.9~54.3% of total individual-locus combinations of DNA methylation are recorded when rice encountering the osmotic stress. Although the general level of epigenetic differentiation was very low, considerable level of ΦST (0.134~0.187 was detected on the highly divergent epiloci (HDE. The HDE detected in normal condition tended to stay at low levels in upland rice, particularly the ones de-methylated in responses to osmotic stress. Three out of four selected HDE genes differentially expressed between upland and lowland rice under normal or stressed conditions. Moreover, once a gene at HDE was up-/down-regulated in responses to the osmotic stress, its expression under the normal condition was higher/lower in upland rice. This result suggested expressions of genes at the HDE in upland rice might be more adaptive to the osmotic stress. The epigenetic divergence and its influence on the gene expression should contribute to the higher drought-resistance in upland rice as it is domesticated in the water-limited environment.

Adaptive Epigenetic Differentiation between Upland and Lowland Rice Ecotypes Revealed by Methylation-Sensitive Amplified Polymorphism.

Science.gov (United States)

Xia, Hui; Huang, Weixia; Xiong, Jie; Tao, Tao; Zheng, Xiaoguo; Wei, Haibin; Yue, Yunxia; Chen, Liang; Luo, Lijun

2016-01-01

The stress-induced epimutations could be inherited over generations and play important roles in plant adaption to stressful environments. Upland rice has been domesticated in water-limited environments for thousands of years and accumulated drought-induced epimutations of DNA methylation, making it epigenetically differentiated from lowland rice. To study the epigenetic differentiation between upland and lowland rice ecotypes on their drought-resistances, the epigenetic variation was investigated in 180 rice landraces under both normal and osmotic conditions via methylation-sensitive amplified polymorphism (MSAP) technique. Great alterations (52.9~54.3% of total individual-locus combinations) of DNA methylation are recorded when rice encountering the osmotic stress. Although the general level of epigenetic differentiation was very low, considerable level of ΦST (0.134~0.187) was detected on the highly divergent epiloci (HDE). The HDE detected in normal condition tended to stay at low levels in upland rice, particularly the ones de-methylated in responses to osmotic stress. Three out of four selected HDE genes differentially expressed between upland and lowland rice under normal or stressed conditions. Moreover, once a gene at HDE was up-/down-regulated in responses to the osmotic stress, its expression under the normal condition was higher/lower in upland rice. This result suggested expressions of genes at the HDE in upland rice might be more adaptive to the osmotic stress. The epigenetic divergence and its influence on the gene expression should contribute to the higher drought-resistance in upland rice as it is domesticated in the water-limited environment.

Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

OpenAIRE

R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

2011-01-01

The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

Deep learning

CERN Document Server

Goodfellow, Ian; Courville, Aaron

2016-01-01

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

A field reciprocal transplant experiment reveals asymmetric costs of migration between lake and river ecotypes of three-spined sticklebacks (Gasterosteus aculeatus).

Science.gov (United States)

Kaufmann, J; Lenz, T L; Kalbe, M; Milinski, M; Eizaguirre, C

2017-05-01

Theory of local adaptation predicts that nonadapted migrants will suffer increased costs compared to local residents. Ultimately this process can result in the reduction of gene flow and culminate in speciation. Here, we experimentally investigated the relative fitness of migrants in foreign habitats, focusing on diverging lake and river ecotypes of three-spined sticklebacks. A reciprocal transplant experiment performed in the field revealed asymmetric costs of migration: whereas mortality of river fish was increased under lake conditions, lake migrants suffered from reduced growth relative to river residents. Selection against migrants thus involved different traits in each habitat but generally contributed to bidirectional reduction in gene flow. Focusing particularly on the parasitic environments, migrant fish differed from resident fish in the parasite community they harboured. This pattern correlated with both cellular phenotypes of innate immunity as well as with allelic variation at the genes of the major histocompatibility complex. In addition to showing the costs of migration in three-spined sticklebacks, this study highlights the role of asymmetric selection particularly from parasitism in genotype sorting and in the emergence of local adaptation. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Backcasting the decline of a vulnerable Great Plains reproductive ecotype: identifying threats and conservation priorities

Science.gov (United States)

Worthington, Thomas A.; Brewer, Shannon K.; Grabowski, Timothy B.; Mueller, Julia

2014-01-01

Conservation efforts for threatened or endangered species are challenging because the multi-scale factors that relate to their decline or inhibit their recovery are often unknown. To further exacerbate matters, the perceptions associated with the mechanisms of species decline are often viewed myopically rather than across the entire species range. We used over 80 years of fish presence data collected from the Great Plains and associated ecoregions of the United States, to investigate the relative influence of changing environmental factors on the historic and current truncated distributions of the Arkansas River shiner Notropis girardi. Arkansas River shiner represent a threatened reproductive ecotype considered especially well adapted to the harsh environmental extremes of the Great Plains. Historic (n = 163 records) and current (n = 47 records) species distribution models were constructed using a vector-based approach in MaxEnt by splitting the available data at a time when Arkansas River shiner dramatically declined. Discharge and stream order were significant predictors in both models; however, the shape of the relationship between the predictors and species presence varied between time periods. Drift distance (river fragment length available for ichthyoplankton downstream drift before meeting a barrier) was a more important predictor in the current model and indicated river segments 375–780 km had the highest probability of species presence. Performance for the historic and current models was high (area under the curve; AUC > 0.95); however, forecasting and backcasting to alternative time periods suggested less predictive power. Our results identify fragments that could be considered refuges for endemic plains fish species and we highlight significant environmental factors (e.g., discharge) that could be manipulated to aid recovery.

Deep Incremental Boosting

OpenAIRE

Mosca, Alan; Magoulas, George D

2017-01-01

This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

Deep Super Learner: A Deep Ensemble for Classification Problems

OpenAIRE

Young, Steven; Abdou, Tamer; Bener, Ayse

2018-01-01

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Nickel-tolerant ectomycorrhizal Pisolithus albus ultramafic ecotype isolated from nickel mines in New Caledonia strongly enhance growth of the host plant Eucalyptus globulus at toxic nickel concentrations.

Science.gov (United States)

Jourand, Philippe; Ducousso, Marc; Reid, Robert; Majorel, Clarisse; Richert, Clément; Riss, Jennifer; Lebrun, Michel

2010-10-01

Ectomycorrhizal (ECM) Pisolithus albus (Cooke & Massee), belonging to the ultramafic ecotype isolated in nickel-rich serpentine soils from New Caledonia (a tropical hotspot of biodiversity) and showing in vitro adaptive nickel tolerance, were inoculated to Eucalyptus globulus Labill used as a Myrtaceae plant-host model to study ectomycorrhizal symbiosis. Plants were then exposed to a nickel (Ni) dose-response experiment with increased Ni treatments up to 60 mg kg( - )(1) soil as extractable Ni content in serpentine soils. Results showed that plants inoculated with ultramafic ECM P. albus were able to tolerate high and toxic concentrations of Ni (up to 60 μg g( - )(1)) while uninoculated controls were not. At the highest Ni concentration tested, root growth was more than 20-fold higher and shoot growth more than 30-fold higher in ECM plants compared with control plants. The improved growth in ECM plants was associated with a 2.4-fold reduction in root Ni concentration but a massive 60-fold reduction in transfer of Ni from root to shoots. In vitro, P. albus strains could withstand high Ni concentrations but accumulated very little Ni in its tissue. The lower Ni uptake by mycorrhizal plants could not be explained by increased release of metal-complexing chelates since these were 5- to 12-fold lower in mycorrhizal plants at high Ni concentrations. It is proposed that the fungal sheath covering the plant roots acts as an effective barrier to limit transfer of Ni from soil into the root tissue. The degree of tolerance conferred by the ultramafic P. albus isolates to growth of the host tree species is considerably greater than previously reported for other ECM. The primary mechanisms underlying this improved growth were identified as reduced Ni uptake into the roots and markedly reduced transfer from root to shoot in mycorrhizal plants. The fact that these positive responses were observed at Ni concentrations commonly observed in serpentinic soils suggests that

Analyses of the deep borehole drilling status for a deep borehole disposal system

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Choi, Heui Joo; Lee, Min Soo; Kim, Geon Young; Kim, Kyung Su [KAERI, Daejeon (Korea, Republic of)

2016-05-15

The purpose of disposal for radioactive wastes is not only to isolate them from humans, but also to inhibit leakage of any radioactive materials into the accessible environment. Because of the extremely high level and long-time scale radioactivity of HLW(High-level radioactive waste), a mined deep geological disposal concept, the disposal depth is about 500 m below ground, is considered as the safest method to isolate the spent fuels or high-level radioactive waste from the human environment with the best available technology at present time. Therefore, as an alternative disposal concept, i.e., deep borehole disposal technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general status of deep drilling technologies was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, very preliminary applicability of deep drilling technology for deep borehole disposal analyzed. In this paper, as one of key technologies of deep borehole disposal system, the general status of deep drilling technologies in oil industry, geothermal industry and geo scientific field was reviewed for deep borehole disposal of high level radioactive wastes. Based on the results of these review, the very preliminary applicability of deep drilling technology for deep borehole disposal such as relation between depth and diameter, drilling time and feasibility classification was analyzed.

Deep Space Telecommunications

Science.gov (United States)

Kuiper, T. B. H.; Resch, G. M.

2000-01-01

The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

Greedy Deep Dictionary Learning

OpenAIRE

Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

2016-01-01

In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning.

Science.gov (United States)

Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek; Li, Xiaolin

2017-09-01

Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end-to-end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set. © 2017 Wiley Periodicals, Inc.

Diversity of Mat-Forming Fungi in Relation to Soil Properties, Disturbance, and Forest Ecotype at Crater Lake National Park, Oregon, USA

Directory of Open Access Journals (Sweden)

James M. Trappe

2012-04-01

Full Text Available In forest ecosystems, fungal mats are functionally important in nutrient and water uptake in litter and wood decomposition processes, in carbon resource allocation, soil weathering and in cycling of soil resources. Fungal mats can occur abundantly in forests and are widely distributed globally. We sampled ponderosa pine/white fir and mountain hemlock/noble fir communities at Crater Lake National Park for mat-forming soil fungi. Fungus collections were identified by DNA sequencing. Thirty-eight mat-forming genotypes were identified; members of the five most common genera (Gautieria, Lepiota, Piloderma, Ramaria, and Rhizopogon comprised 67% of all collections. The mycorrhizal genera Alpova and Lactarius are newly identified as ectomycorrhizal mat-forming taxa, as are the saprotrophic genera Flavoscypha, Gastropila, Lepiota and Xenasmatella. Twelve typical mat forms are illustrated, representing both ectomycorrhizal and saprotrophic fungi that were found. Abundance of fungal mats was correlated with higher soil carbon to nitrogen ratios, fine woody debris and needle litter mass in both forest ecotypes. Definitions of fungal mats are discussed, along with some of the challenges in defining what comprises a fungal “mat”.

Deep learning? What deep learning? | Fourie | South African ...

African Journals Online (AJOL)

In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

Science.gov (United States)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

Deep learning with Python

CERN Document Server

Chollet, Francois

2018-01-01

DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

Deep learning evaluation using deep linguistic processing

OpenAIRE

Kuhnle, Alexander; Copestake, Ann

2017-01-01

We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

Science.gov (United States)

Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

2018-04-01

Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep frying

NARCIS (Netherlands)

Koerten, van K.N.

2016-01-01

Deep frying is one of the most used methods in the food processing industry. Though practically any food can be fried, French fries are probably the most well-known deep fried products. The popularity of French fries stems from their unique taste and texture, a crispy outside with a mealy soft

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene

2018-05-02

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient\\'s phenotype. Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Hot, deep origin of petroleum: deep basin evidence and application

Science.gov (United States)

Price, Leigh C.

1978-01-01

Use of the model of a hot deep origin of oil places rigid constraints on the migration and entrapment of crude oil. Specifically, oil originating from depth migrates vertically up faults and is emplaced in traps at shallower depths. Review of petroleum-producing basins worldwide shows oil occurrence in these basins conforms to the restraints of and therefore supports the hypothesis. Most of the world's oil is found in the very deepest sedimentary basins, and production over or adjacent to the deep basin is cut by or directly updip from faults dipping into the basin deep. Generally the greater the fault throw the greater the reserves. Fault-block highs next to deep sedimentary troughs are the best target areas by the present concept. Traps along major basin-forming faults are quite prospective. The structural style of a basin governs the distribution, types, and amounts of hydrocarbons expected and hence the exploration strategy. Production in delta depocenters (Niger) is in structures cut by or updip from major growth faults, and structures not associated with such faults are barren. Production in block fault basins is on horsts next to deep sedimentary troughs (Sirte, North Sea). In basins whose sediment thickness, structure and geologic history are known to a moderate degree, the main oil occurrences can be specifically predicted by analysis of fault systems and possible hydrocarbon migration routes. Use of the concept permits the identification of significant targets which have either been downgraded or ignored in the past, such as production in or just updip from thrust belts, stratigraphic traps over the deep basin associated with major faulting, production over the basin deep, and regional stratigraphic trapping updip from established production along major fault zones.

Biofilm inhibition activity of compounds isolated from two Eunicea species collected at the Caribbean Sea

Directory of Open Access Journals (Sweden)

Yenny Martínez Díaz

Full Text Available Abstract Biofilm has a primary role in the pathogenesis of diseases and in the attachment of multicellular organisms to a fouled surface. Because of that, the control of bacterial biofilms has been identified as an important target. In the present study, five lipid compounds isolated from soft coral Eunicea sp. and three terpenoids together with a mixture of sterols from Eunicea fusca collected at the Colombian Caribbean Sea showed different effectiveness against biofilm formation by three marine bacteria associated with immersed fouled surfaces, Ochrobactrum pseudogringnonense,Alteromona macleodii and Vibrio harveyi, and against two known biofilm forming bacteria, Pseudomonas aeruginosa ATCC 27853 and Staphylococcus aureus ATCC 25923. The pure compounds were characterized by NMR, HRESI-MS, HRGC-MS and optical rotation. The most effective compounds were batyl alcohol (1 and fuscoside E peracetate (6, acting against four strains without affecting their microbial growth. Compound 1 showed biofilm inhibition greater than 30% against A. macleodii, and up to 60% against O. pseudogringnonense,V. harveyi and S. aureus. Compound 6 inhibited O. pseudogringnonense and V. harveyi between 25 and 50%, and P. aeruginosa or S. aureus up to 60% at 0.5 mg/ml. The results suggest that these compounds exhibit specific biofilm inhibition with lower antimicrobial effect against the bacterial species assayed.

Description of Alteromonas abrolhosensis sp. nov., isolated from sea water of Abrolhos Bank, Brazil.

Science.gov (United States)

Nóbrega, Maria S; Silva, Bruno S; Leomil, Luciana; Tschoeke, Diogo Antonio; Campeão, Mariana E; Garcia, Gizele D; Dias, Graciela A; Vieira, Verônica V; Thompson, Cristiane C; Thompson, Fabiano L

2018-01-18

Two Gram-negative, motile, aerobic bacteria isolated from waters of the Abrolhos Bank were classified through a whole genome-based taxonomy. Strains PEL67E T and PEL68C shared 99% 16S rRNA and dnaK sequence identity with Alteromonas marina SW-47 T and Alteromonas macleodii ATCC 27126 T . In silico DNA-DNA Hybridization, i.e. genome-to-genome distance (GGD), average amino acid identity (AAI) and average nucleotide identity (ANI) showed that PEL67E T and PEL68C had identity values between 33-36, 86-88 and 83-84%, and 85-86 and 83%, respectively, towards their close neighbors A. macleodii ATCC 27126 T and A. marina SW-47 T . The DNA G + C contents of PEL67E T and PEL68C were 44.5%. The phenotypic features that differentiate PEL67E T and PEL68C strains from their close neighbors were assimilation of galactose and activity of phosphatase, and lack of mannitol, maltose, acetate, xylose and glycerol assimilation and lack of lipase, α and β-glucosidase activity. The new species Alteromonas abrolhosensis is proposed. The type strain is PEL67E T (= CBAS 610 T  = CAIM 1925 T ).

Deep learning in bioinformatics.

Science.gov (United States)

Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

2017-09-01

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu

2017-12-23

Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

Deep learning relevance

DEFF Research Database (Denmark)

Lioma, Christina; Larsen, Birger; Petersen, Casper

2016-01-01

train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

The Amazonian Floodplains, an ecotype with challenging questions on volatile organic compound (VOC) emissions

Science.gov (United States)

Kesselmeier, J.

2012-12-01

based on short-term experiments is risky being transferred to an ecotype which is governed under natural conditions by long term flooding. Furthermore, contrasting such experiments with usually young trees (saplings or a few years old) nothing is known about the emission behavior of adult trees under field conditions.

STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2003-06-01

The Department of Energy (DOE) is sponsoring a Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a project to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. Phase 1 was recently completed and consisted of assessing deep gas well drilling activity (1995-2007) and an industry survey on deep gas well stimulation practices by region. Of the 29,000 oil, gas and dry holes drilled in 2002, about 300 were drilled in the deep well; 25% were dry, 50% were high temperature/high pressure completions and 25% were simply deep completions. South Texas has about 30% of these wells, Oklahoma 20%, Gulf of Mexico Shelf 15% and the Gulf Coast about 15%. The Rockies represent only 2% of deep drilling. Of the 60 operators who drill deep and HTHP wells, the top 20 drill almost 80% of the wells. Six operators drill half the U.S. deep wells. Deep drilling peaked at 425 wells in 1998 and fell to 250 in 1999. Drilling is expected to rise through 2004 after which drilling should cycle down as overall drilling declines.

Differentiation of free-ranging chicken using discriminant analysis of phenotypic traits

Directory of Open Access Journals (Sweden)

Raed M. Al-Atiyat

Full Text Available ABSTRACT In this study, we investigated the differentiation of five different chicken ecotypes - Center, North, South, West, and East - of Saudi Arabia using discriminate analysis. The analysis was based on nine important morphological and phenotypic traits: body color, beak color, earlobe color, eye color, shank color, comb color, comb type, comb size, and feather distribution. There was a strong significant relationship between the phenotype and effect of geographic height in terms of comb type and earlobe color in males as well as body, beak, eye, and shank color. In particular, the comb type and earlobe color differentiated the ecotypes of males. Among the females, the beak, earlobe, eye, shank color, and feather distribution had more differentiating power. Moreover, the discriminant analysis revealed that the five ecotypes were grouped into three clusters; the Center and the North in one cluster, the West and the South ecotypes in the second for males, and the East ecotype in the last cluster. The female dendogram branching was similar to the male dendrogram branching, except that the Center ecotype was grouped with the North instead of the South. The East ecotype was highly discriminated from the other ecotypes. Nevertheless, the potential of recent individual migration between ecotypes was also noted. Accordingly, the results of the utilized traits in this study might be effective in characterization and conservation of the genetic resources of the Saudi chicken.

Deep learning in TMVA Benchmarking Benchmarking TMVA DNN Integration of a Deep Autoencoder

CERN Document Server

Huwiler, Marc

2017-01-01

The TMVA library in ROOT is dedicated to multivariate analysis, and in partic- ular oers numerous machine learning algorithms in a standardized framework. It is widely used in High Energy Physics for data analysis, mainly to perform regression and classication. To keep up to date with the state of the art in deep learning, a new deep learning module was being developed this summer, oering deep neural net- work, convolutional neural network, and autoencoder. TMVA did not have yet any autoencoder method, and the present project consists in implementing the TMVA autoencoder class based on the deep learning module. It also includes some bench- marking performed on the actual deep neural network implementation, in comparison to the Keras framework with Tensorflow and Theano backend.

Naturally evolved enhanced Cd tolerance of Dianthus carthusianorum L. is not related to accumulation of thiol peptides and organic acids.

Science.gov (United States)

Wójcik, Małgorzata; Dresler, Sławomir; Plak, Andrzej; Tukiendorf, Anna

2015-05-01

Two contrasting ecotypes of Dianthus carthusianorum L., metallicolous (M) and nonmetallicolous (NM), were cultivated in hydroponics at 0-50 μM Cd for 14 days to compare their Cd accumulation, sensitivity and tolerance mechanisms. While both ecotypes contained similar concentrations of Cd in the shoots and roots, the M ecotype was more Cd-tolerant (as measured by fresh weight production and root and leaf viability). Both ecotypes accumulated phytochelatins (PCs) in response to Cd with a higher amount thereof found in the NM ecotype. Concentrations of PCs remained unchanged with increasing Cd concentrations in the root tissues, but their content in the shoots increased. The addition of L-buthionine-sulfoximine (BSO) diminished glutathione (GSH) accumulation and arrested PC production, which increased the sensitivity to Cd of the NM, but not M ecotype. Organic acids (malate and citrate) as well as proline accumulation did not change significantly after Cd exposition and was at the same level in both ecotypes. The enhanced Cd tolerance of the M ecotype of D. carthusianorum cannot be explained in terms of restricted Cd uptake and differential production of PCs, organic acids or proline; some other mechanisms must be involved in its adaptation to the high Cd content in the environment.

From prediction to function using evolutionary genomics: human-specific ecotypes of Lactobacillus reuteri have diverse probiotic functions.

Science.gov (United States)

Spinler, Jennifer K; Sontakke, Amrita; Hollister, Emily B; Venable, Susan F; Oh, Phaik Lyn; Balderas, Miriam A; Saulnier, Delphine M A; Mistretta, Toni-Ann; Devaraj, Sridevi; Walter, Jens; Versalovic, James; Highlander, Sarah K

2014-06-19

The vertebrate gut symbiont Lactobacillus reuteri has diversified into separate clades reflecting host origin. Strains show evidence of host adaptation, but how host-microbe coevolution influences microbial-derived effects on hosts is poorly understood. Emphasizing human-derived strains of L. reuteri, we combined comparative genomic analyses with functional assays to examine variations in host interaction among genetically distinct ecotypes. Within clade II or VI, the genomes of human-derived L. reuteri strains are highly conserved in gene content and at the nucleotide level. Nevertheless, they share only 70-90% of total gene content, indicating differences in functional capacity. Human-associated lineages are distinguished by genes related to bacteriophages, vitamin biosynthesis, antimicrobial production, and immunomodulation. Differential production of reuterin, histamine, and folate by 23 clade II and VI strains was demonstrated. These strains also differed with respect to their ability to modulate human cytokine production (tumor necrosis factor, monocyte chemoattractant protein-1, interleukin [IL]-1β, IL-5, IL-7, IL-12, and IL-13) by myeloid cells. Microarray analysis of representative clade II and clade VI strains revealed global regulation of genes within the reuterin, vitamin B12, folate, and arginine catabolism gene clusters by the AraC family transcriptional regulator, PocR. Thus, human-derived L. reuteri clade II and VI strains are genetically distinct and their differences affect their functional repertoires and probiotic features. These findings highlight the biological impact of microbe:host coevolution and illustrate the functional significance of subspecies differences in the human microbiome. Consideration of host origin and functional differences at the subspecies level may have major impacts on probiotic strain selection and considerations of microbial ecology in mammalian species. © The Author(s) 2014. Published by Oxford University Press on

Deep subsurface microbial processes

Science.gov (United States)

Lovley, D.R.; Chapelle, F.H.

1995-01-01

Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

Science.gov (United States)

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Pathogenesis of deep endometriosis.

Science.gov (United States)

Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

2017-12-01

The pathophysiology of (deep) endometriosis is still unclear. As originally suggested by Cullen, change the definition "deeper than 5 mm" to "adenomyosis externa." With the discovery of the old European literature on uterine bleeding in 5%-10% of the neonates and histologic evidence that the bleeding represents decidual shedding, it is postulated/hypothesized that endometrial stem/progenitor cells, implanted in the pelvic cavity after birth, may be at the origin of adolescent and even the occasionally premenarcheal pelvic endometriosis. Endometriosis in the adolescent is characterized by angiogenic and hemorrhagic peritoneal and ovarian lesions. The development of deep endometriosis at a later age suggests that deep infiltrating endometriosis is a delayed stage of endometriosis. Another hypothesis is that the endometriotic cell has undergone genetic or epigenetic changes and those specific changes determine the development into deep endometriosis. This is compatible with the hereditary aspects, and with the clonality of deep and cystic ovarian endometriosis. It explains the predisposition and an eventual causal effect by dioxin or radiation. Specific genetic/epigenetic changes could explain the various expressions and thus typical, cystic, and deep endometriosis become three different diseases. Subtle lesions are not a disease until epi(genetic) changes occur. A classification should reflect that deep endometriosis is a specific disease. In conclusion the pathophysiology of deep endometriosis remains debated and the mechanisms of disease progression, as well as the role of genetics and epigenetics in the process, still needs to be unraveled. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

DeepSpark: A Spark-Based Distributed Deep Learning Framework for Commodity Clusters

OpenAIRE

Kim, Hanjoo; Park, Jaehong; Jang, Jaehee; Yoon, Sungroh

2016-01-01

The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that exploits Apache Spark on commodity clusters. To support parallel operation...

DeepPVP: phenotype-based prioritization of causative variants using deep learning

KAUST Repository

Boudellioua, Imene; Kulmanov, Maxat; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert

2018-01-01

phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data.

Science.gov (United States)

Arango-Argoty, Gustavo; Garner, Emily; Pruden, Amy; Heath, Lenwood S; Vikesland, Peter; Zhang, Liqing

2018-02-01

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories. The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

None

2003-09-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a study to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. An assessment of historical deep gas well drilling activity and forecast of future trends was completed during the first six months of the project; this segment of the project was covered in Technical Project Report No. 1. The second progress report covers the next six months of the project during which efforts were primarily split between summarizing rock mechanics and fracture growth in deep reservoirs and contacting operators about case studies of deep gas well stimulation.

DeepQA: improving the estimation of single protein model quality with deep belief networks.

Science.gov (United States)

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-12-05

Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

Stimulation Technologies for Deep Well Completions

Energy Technology Data Exchange (ETDEWEB)

Stephen Wolhart

2005-06-30

The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies conducted a study to evaluate the stimulation of deep wells. The objective of the project was to review U.S. deep well drilling and stimulation activity, review rock mechanics and fracture growth in deep, high-pressure/temperature wells and evaluate stimulation technology in several key deep plays. This report documents results from this project.

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

Science.gov (United States)

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

DeepBase: annotation and discovery of microRNAs and other noncoding RNAs from deep-sequencing data.

Science.gov (United States)

Yang, Jian-Hua; Qu, Liang-Hu

2012-01-01

Recent advances in high-throughput deep-sequencing technology have produced large numbers of short and long RNA sequences and enabled the detection and profiling of known and novel microRNAs (miRNAs) and other noncoding RNAs (ncRNAs) at unprecedented sensitivity and depth. In this chapter, we describe the use of deepBase, a database that we have developed to integrate all public deep-sequencing data and to facilitate the comprehensive annotation and discovery of miRNAs and other ncRNAs from these data. deepBase provides an integrative, interactive, and versatile web graphical interface to evaluate miRBase-annotated miRNA genes and other known ncRNAs, explores the expression patterns of miRNAs and other ncRNAs, and discovers novel miRNAs and other ncRNAs from deep-sequencing data. deepBase also provides a deepView genome browser to comparatively analyze these data at multiple levels. deepBase is available at http://deepbase.sysu.edu.cn/.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

Energy Technology Data Exchange (ETDEWEB)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-10-15

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea.

Deep Borehole Disposal as an Alternative Concept to Deep Geological Disposal

International Nuclear Information System (INIS)

Lee, Jongyoul; Lee, Minsoo; Choi, Heuijoo; Kim, Kyungsu

2016-01-01

In this paper, the general concept and key technologies for deep borehole disposal of spent fuels or HLW, as an alternative method to the mined geological disposal method, were reviewed. After then an analysis on the distance between boreholes for the disposal of HLW was carried out. Based on the results, a disposal area were calculated approximately and compared with that of mined geological disposal. These results will be used as an input for the analyses of applicability for DBD in Korea. The disposal safety of this system has been demonstrated with underground research laboratory and some advanced countries such as Finland and Sweden are implementing their disposal project on commercial stage. However, if the spent fuels or the high-level radioactive wastes can be disposed of in the depth of 3-5 km and more stable rock formation, it has several advantages. Therefore, as an alternative disposal concept to the mined deep geological disposal concept (DGD), very deep borehole disposal (DBD) technology is under consideration in number of countries in terms of its outstanding safety and cost effectiveness. In this paper, the general concept of deep borehole disposal for spent fuels or high level radioactive wastes was reviewed. And the key technologies, such as drilling technology of large diameter borehole, packaging and emplacement technology, sealing technology and performance/safety analyses technologies, and their challenges in development of deep borehole disposal system were analyzed. Also, very preliminary deep borehole disposal concept including disposal canister concept was developed according to the nuclear environment in Korea

Deep Mapping and Spatial Anthropology

Directory of Open Access Journals (Sweden)

Les Roberts

2016-01-01

Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

Deep Vein Thrombosis

African Journals Online (AJOL)

OWNER

Deep Vein Thrombosis: Risk Factors and Prevention in Surgical Patients. Deep Vein ... preventable morbidity and mortality in hospitalized surgical patients. ... the elderly.3,4 It is very rare before the age ... depends on the risk level; therefore an .... but also in the post-operative period. ... is continuing uncertainty regarding.

pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

Science.gov (United States)

Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

2017-12-05

In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

Comparative efficacy of pour-on and subcutaneous injection of ivermectin on Melophagus ovinus (L.) in Darab ecotype goats of Southern Iran.

Science.gov (United States)

Jafari Shoorijeh, S; Noori, A; Tamadon, A

2007-09-01

The efficacy of ivermectin was evaluated against Melophagus ovinus in Darab ecotype goats of Iran. Twenty-four healthy Iranian crossbreed male goats were randomly divided into three equal groups (n = 8). An experimental infestation was induced in all animals of the three groups with 100 M. ovinus on the body of each animal. Groups 1 and 2 were treated with 1% ivermectin solution at a dosage of 0.5 mg/kg of body weight applied as a pour-on along the dorsal midline and 0.2 mg/kg subcutaneously, respectively; while group 3 was kept as control group. Seven days after infestation ivermectin was administered then the goats were observed for a period of 7 days. Body surface of each goat of three groups was inspected daily and decreases in M. ovinus were recorded. The rate of elimination in keds was assessed on the basis of decrease in keds count on the skin and hairs. The results revealed that complete absence of keds were observed in 6 and 7 days post-treatment with injection and pour-on routes, respectively. The results of present study showed that subcutaneous injection of ivermectin more rapidly eliminated M. ovinus than pour-on route. Both routes were 100% effective against this parasite in the goats. Ivermectin can be a drug of choice against M. ovinus in long-hair Iranian goats due to its high efficacy, easy applicability and wide safety margin.

Ecotypic differentiation between urban and rural populations of the grasshopper Chorthippus brunneus relative to climate and habitat fragmentation.

Science.gov (United States)

San Martin Y Gomez, Gilles; Van Dyck, Hans

2012-05-01

Urbanization alters environmental conditions in multiple ways and offers an ecological or evolutionary challenge for organisms to cope with. Urban areas typically have a warmer climate and strongly fragmented herbaceous vegetation; the urban landscape matrix is often assumed to be hostile for many organisms. Here, we addressed the issue of evolutionary differentiation between urban and rural populations of an ectotherm insect, the grasshopper Chorthippus brunneus. We compared mobility-related morphology and climate-related life history traits measured on the first generation offspring of grasshoppers from urban and rural populations reared in a common garden laboratory experiment. We predicted (1) the urban phenotype to be more mobile (i.e., lower mass allocation to the abdomen, longer relative femur and wing lengths) than the rural phenotype; (2) the urban phenotype to be more warm adapted (e.g., higher female body mass); and (3) further evidence of local adaptation in the form of significant interaction effects between landscape of origin and breeding temperature. Both males and females of urban origin had significantly longer relative femur and wing lengths and lower mass allocation to the abdomen (i.e., higher investment in thorax and flight muscles) relative to individuals of rural origin. The results were overall significant but small (2-4%). Body mass and larval growth rate were much higher (+10%) in females of urban origin. For the life history traits, we did not find evidence for significant interaction effects between the landscape of origin and the two breeding temperatures. Our results point to ecotypic differentiation with urbanization for mobility-related morphology and climate-related life history traits. We argue that the warmer urban environment has an indirect effect through longer growth season rather than direct effects on the development.

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Science.gov (United States)

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Deep learning for image classification

Science.gov (United States)

McCoppin, Ryan; Rizki, Mateen

2014-06-01

This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

2017-03-08

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

Deep learning for computational chemistry.

Science.gov (United States)

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Comparative study of growth traits and haematological parameters of Anak and Nigerian heavy ecotype chickens fed with graded levels of mango seed kernel (Mangifera indica) meal.

Science.gov (United States)

Mbunwen, Ndofor-Foleng Harriet; Ngongeh, Lucas Atehmengo; Okolie, Peter Nzeribe; Okoli, Emeka Linus

2015-08-01

One hundred fifty Anak and 120 Nigerian heavy local ecotype (NHLE) chickens were used to study the effects of feeding graded levels of mango seed kernel meal (MKM) replacing maize diet on growth traits and haematological parameters. A 2 × 5 factorial arrangement was employed: two breeds and five diets. The birds were randomly allocated to five finisher diets formulated such that MKM replaced maize at 0, 10, 20, 30 and 40% (T1, T2, T3, T4 and T5) inclusion levels, respectively. The effect of breed and dietary treatments on growth performance and blood characteristics were determined. The results showed a significant (P  0.05) when the breeds and treatments were compared. It was concluded that inclusion of dietary MKM below 30% could replace maize in the diets of Anak and NHLE growing chickens without adverse effect on growth performance and blood constituents. This work suggests that genetic differences exist in growth traits of these breeds of chickens. This advantage could be useful in breed improvement programmes and better feeding managements of the NHLE and Anak chickens.

What Really is Deep Learning Doing?

OpenAIRE

Xiong, Chuyu

2017-01-01

Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspect...

Taoism and Deep Ecology.

Science.gov (United States)

Sylvan, Richard; Bennett, David

1988-01-01

Contrasted are the philosophies of Deep Ecology and ancient Chinese. Discusses the cosmology, morality, lifestyle, views of power, politics, and environmental philosophies of each. Concludes that Deep Ecology could gain much from Taoism. (CW)

deepTools2: a next generation web server for deep-sequencing data analysis.

Science.gov (United States)

Ramírez, Fidel; Ryan, Devon P; Grüning, Björn; Bhardwaj, Vivek; Kilpert, Fabian; Richter, Andreas S; Heyne, Steffen; Dündar, Friederike; Manke, Thomas

2016-07-08

We present an update to our Galaxy-based web server for processing and visualizing deeply sequenced data. Its core tool set, deepTools, allows users to perform complete bioinformatic workflows ranging from quality controls and normalizations of aligned reads to integrative analyses, including clustering and visualization approaches. Since we first described our deepTools Galaxy server in 2014, we have implemented new solutions for many requests from the community and our users. Here, we introduce significant enhancements and new tools to further improve data visualization and interpretation. deepTools continue to be open to all users and freely available as a web service at deeptools.ie-freiburg.mpg.de The new deepTools2 suite can be easily deployed within any Galaxy framework via the toolshed repository, and we also provide source code for command line usage under Linux and Mac OS X. A public and documented API for access to deepTools functionality is also available. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Is Multitask Deep Learning Practical for Pharma?

Science.gov (United States)

Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

2017-08-28

Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian

Directory of Open Access Journals (Sweden)

Chao Li

2017-02-01

Full Text Available Human gait, as a soft biometric, helps to recognize people through their walking. To further improve the recognition performance, we propose a novel video sensor-based gait representation, DeepGait, using deep convolutional features and introduce Joint Bayesian to model view variance. DeepGait is generated by using a pre-trained “very deep” network “D-Net” (VGG-D without any fine-tuning. For non-view setting, DeepGait outperforms hand-crafted representations (e.g., Gait Energy Image, Frequency-Domain Feature and Gait Flow Image, etc.. Furthermore, for cross-view setting, 256-dimensional DeepGait after PCA significantly outperforms the state-of-the-art methods on the OU-ISR large population (OULP dataset. The OULP dataset, which includes 4007 subjects, makes our result reliable in a statistically reliable way.

Variation in plastic responses of a globally distributed picoplankton species to ocean acidification

Science.gov (United States)

Schaum, Elisa; Rost, Björn; Millar, Andrew J.; Collins, Sinéad

2013-03-01

Phytoplankton are the basis of marine food webs, and affect biogeochemical cycles. As CO2 levels increase, shifts in the frequencies and physiology of ecotypes within phytoplankton groups will affect their nutritional value and biogeochemical function. However, studies so far are based on a few representative genotypes from key species. Here, we measure changes in cellular function and growth rate at atmospheric CO2 concentrations predicted for the year 2100 in 16 ecotypes of the marine picoplankton Ostreococcus. We find that variation in plastic responses among ecotypes is on par with published between-genera variation, so the responses of one or a few ecotypes cannot estimate changes to the physiology or composition of a species under CO2 enrichment. We show that ecotypes best at taking advantage of CO2 enrichment by changing their photosynthesis rates most should increase in relative fitness, and so in frequency in a high-CO2 environment. Finally, information on sampling location, and not phylogenetic relatedness, is a good predictor of ecotypes likely to increase in frequency in this system.

Invited talk: Deep Learning Meets Physics

CERN Multimedia

CERN. Geneva

2018-01-01

Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

Deep geothermics

International Nuclear Information System (INIS)

Anon.

1995-01-01

The hot-dry-rocks located at 3-4 km of depth correspond to low permeable rocks carrying a large amount of heat. The extraction of this heat usually requires artificial hydraulic fracturing of the rock to increase its permeability before water injection. Hot-dry-rocks geothermics or deep geothermics is not today a commercial channel but only a scientific and technological research field. The Soultz-sous-Forets site (Northern Alsace, France) is characterized by a 6 degrees per meter geothermal gradient and is used as a natural laboratory for deep geothermal and geological studies in the framework of a European research program. Two boreholes have been drilled up to 3600 m of depth in the highly-fractured granite massif beneath the site. The aim is to create a deep heat exchanger using only the natural fracturing for water transfer. A consortium of german, french and italian industrial companies (Pfalzwerke, Badenwerk, EdF and Enel) has been created for a more active participation to the pilot phase. (J.S.). 1 fig., 2 photos

Stable architectures for deep neural networks

Science.gov (United States)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Deep Seawater Intrusion Enhanced by Geothermal Through Deep Faults in Xinzhou Geothermal Field in Guangdong, China

Science.gov (United States)

Lu, G.; Ou, H.; Hu, B. X.; Wang, X.

2017-12-01

This study investigates abnormal sea water intrusion from deep depth, riding an inland-ward deep groundwater flow, which is enhanced by deep faults and geothermal processes. The study site Xinzhou geothermal field is 20 km from the coast line. It is in southern China's Guangdong coast, a part of China's long coastal geothermal belt. The geothermal water is salty, having fueled an speculation that it was ancient sea water retained. However, the perpetual "pumping" of the self-flowing outflow of geothermal waters might alter the deep underground flow to favor large-scale or long distant sea water intrusion. We studied geochemical characteristics of the geothermal water and found it as a mixture of the sea water with rain water or pore water, with no indication of dilution involved. And we conducted numerical studies of the buoyancy-driven geothermal flow in the deep ground and find that deep down in thousand meters there is favorable hydraulic gradient favoring inland-ward groundwater flow, allowing seawater intrude inland for an unusually long tens of kilometers in a granitic groundwater flow system. This work formed the first in understanding geo-environment for deep ground water flow.

Deep Learning in Neuroradiology.

Science.gov (United States)

Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

2018-02-01

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

Temperature impacts on deep-sea biodiversity.

Science.gov (United States)

Yasuhara, Moriaki; Danovaro, Roberto

2016-05-01

Temperature is considered to be a fundamental factor controlling biodiversity in marine ecosystems, but precisely what role temperature plays in modulating diversity is still not clear. The deep ocean, lacking light and in situ photosynthetic primary production, is an ideal model system to test the effects of temperature changes on biodiversity. Here we synthesize current knowledge on temperature-diversity relationships in the deep sea. Our results from both present and past deep-sea assemblages suggest that, when a wide range of deep-sea bottom-water temperatures is considered, a unimodal relationship exists between temperature and diversity (that may be right skewed). It is possible that temperature is important only when at relatively high and low levels but does not play a major role in the intermediate temperature range. Possible mechanisms explaining the temperature-biodiversity relationship include the physiological-tolerance hypothesis, the metabolic hypothesis, island biogeography theory, or some combination of these. The possible unimodal relationship discussed here may allow us to identify tipping points at which on-going global change and deep-water warming may increase or decrease deep-sea biodiversity. Predicted changes in deep-sea temperatures due to human-induced climate change may have more adverse consequences than expected considering the sensitivity of deep-sea ecosystems to temperature changes. © 2014 Cambridge Philosophical Society.

Designed Surface Residue Substitutions in [NiFe] Hydrogenase that Improve Electron Transfer Characteristics

Directory of Open Access Journals (Sweden)

Isaac T. Yonemoto

2015-01-01

Full Text Available Photobiological hydrogen production is an attractive, carbon-neutral means to convert solar energy to hydrogen. We build on previous research improving the Alteromonas macleodii “Deep Ecotype” [NiFe] hydrogenase, and report progress towards creating an artificial electron transfer pathway to supply the hydrogenase with electrons necessary for hydrogen production. Ferredoxin is the first soluble electron transfer mediator to receive high-energy electrons from photosystem I, and bears an electron with sufficient potential to efficiently reduce protons. Thus, we engineered a hydrogenase-ferredoxin fusion that also contained several other modifications. In addition to the C-terminal ferredoxin fusion, we truncated the C-terminus of the hydrogenase small subunit, identified as the available terminus closer to the electron transfer region. We also neutralized an anionic patch surrounding the interface Fe-S cluster to improve transfer kinetics with the negatively charged ferredoxin. Initial screening showed the enzyme tolerated both truncation and charge neutralization on the small subunit ferredoxin-binding face. While the enzyme activity was relatively unchanged using the substrate methyl viologen, we observed a marked improvement from both the ferredoxin fusion and surface modification using only dithionite as an electron donor. Combining ferredoxin fusion and surface charge modification showed progressively improved activity in an in vitro assay with purified enzyme.

Comprehensive genomic analyses of the OM43 clade including a novel species from Red Sea indicate ecotype differentiation among marine methylotrophs

KAUST Repository

Jimenez Infante, Francy M.; Ngugi, David; Vinu, Manikandan; Alam, Intikhab; Kamau, Allan; Blom, Jochen; Bajic, Vladimir B.; Stingl, Ulrich

2015-01-01

The OM43 clade within the family Methylophilaceae of Betaproteobacteria represents a group of methylotrophs playing important roles in the metabolism of C1 compounds in marine environments and other aquatic environments around the globe. Using dilution-to-extinction cultivation techniques, we successfully isolated a novel species of this clade (designated here as MBRS-H7) from the ultra-oligotrophic open ocean waters of the central Red Sea. Phylogenomic analyses indicate that MBRS-H7 is a novel species, which forms a distinct cluster together with isolate KB13 from Hawaii (H-RS cluster) that is separate from that represented by strain HTCC2181 (from the Oregon coast). Phylogenetic analyses using the robust 16S–23S internal transcribed spacer revealed a potential ecotype separation of the marine OM43 clade members, which was further confirmed by metagenomic fragment recruitment analyses that showed trends of higher abundance in low chlorophyll and/or high temperature provinces for the H-RS cluster, but a preference for colder, highly productive waters for the HTCC2181 cluster. This potential environmentally driven niche differentiation is also reflected in the metabolic gene inventories, which in the case of H-RS include those conferring resistance to high levels of UV irradiation, temperature, and salinity. Interestingly, we also found different energy conservation modules between these OM43 subclades, namely the existence of the NADH:quinone oxidoreductase NUO system in the H-RS and the non-homologous NQR system in HTCC2181, which might have implications on their overall energetic yields.

Comprehensive genomic analyses of the OM43 clade including a novel species from Red Sea indicate ecotype differentiation among marine methylotrophs

KAUST Repository

Jimenez Infante, Francy M.

2015-12-11

The OM43 clade within the family Methylophilaceae of Betaproteobacteria represents a group of methylotrophs playing important roles in the metabolism of C1 compounds in marine environments and other aquatic environments around the globe. Using dilution-to-extinction cultivation techniques, we successfully isolated a novel species of this clade (designated here as MBRS-H7) from the ultra-oligotrophic open ocean waters of the central Red Sea. Phylogenomic analyses indicate that MBRS-H7 is a novel species, which forms a distinct cluster together with isolate KB13 from Hawaii (H-RS cluster) that is separate from that represented by strain HTCC2181 (from the Oregon coast). Phylogenetic analyses using the robust 16S–23S internal transcribed spacer revealed a potential ecotype separation of the marine OM43 clade members, which was further confirmed by metagenomic fragment recruitment analyses that showed trends of higher abundance in low chlorophyll and/or high temperature provinces for the H-RS cluster, but a preference for colder, highly productive waters for the HTCC2181 cluster. This potential environmentally driven niche differentiation is also reflected in the metabolic gene inventories, which in the case of H-RS include those conferring resistance to high levels of UV irradiation, temperature, and salinity. Interestingly, we also found different energy conservation modules between these OM43 subclades, namely the existence of the NADH:quinone oxidoreductase NUO system in the H-RS and the non-homologous NQR system in HTCC2181, which might have implications on their overall energetic yields.

Extreme Longevity in Proteinaceous Deep-Sea Corals

Energy Technology Data Exchange (ETDEWEB)

Roark, E B; Guilderson, T P; Dunbar, R B; Fallon, S J; Mucciarone, D A

2009-02-09

Deep-sea corals are found on hard substrates on seamounts and continental margins world-wide at depths of 300 to {approx}3000 meters. Deep-sea coral communities are hotspots of deep ocean biomass and biodiversity, providing critical habitat for fish and invertebrates. Newly applied radiocarbon age date from the deep water proteinaceous corals Gerardia sp. and Leiopathes glaberrima show that radial growth rates are as low as 4 to 35 {micro}m yr{sup -1} and that individual colony longevities are on the order of thousands of years. The management and conservation of deep sea coral communities is challenged by their commercial harvest for the jewelry trade and damage caused by deep water fishing practices. In light of their unusual longevity, a better understanding of deep sea coral ecology and their interrelationships with associated benthic communities is needed to inform coherent international conservation strategies for these important deep-sea ecosystems.

New optimized drill pipe size for deep-water, extended reach and ultra-deep drilling

Energy Technology Data Exchange (ETDEWEB)

Jellison, Michael J.; Delgado, Ivanni [Grant Prideco, Inc., Hoston, TX (United States); Falcao, Jose Luiz; Sato, Ademar Takashi [PETROBRAS, Rio de Janeiro, RJ (Brazil); Moura, Carlos Amsler [Comercial Perfuradora Delba Baiana Ltda., Rio de Janeiro, RJ (Brazil)

2004-07-01

A new drill pipe size, 5-7/8 in. OD, represents enabling technology for Extended Reach Drilling (ERD), deep water and other deep well applications. Most world-class ERD and deep water wells have traditionally been drilled with 5-1/2 in. drill pipe or a combination of 6-5/8 in. and 5-1/2 in. drill pipe. The hydraulic performance of 5-1/2 in. drill pipe can be a major limitation in substantial ERD and deep water wells resulting in poor cuttings removal, slower penetration rates, diminished control over well trajectory and more tendency for drill pipe sticking. The 5-7/8 in. drill pipe provides a significant improvement in hydraulic efficiency compared to 5-1/2 in. drill pipe and does not suffer from the disadvantages associated with use of 6-5/8 in. drill pipe. It represents a drill pipe assembly that is optimized dimensionally and on a performance basis for casing and bit programs that are commonly used for ERD, deep water and ultra-deep wells. The paper discusses the engineering philosophy behind 5-7/8 in. drill pipe, the design challenges associated with development of the product and reviews the features and capabilities of the second-generation double-shoulder connection. The paper provides drilling case history information on significant projects where the pipe has been used and details results achieved with the pipe. (author)

Deep Reinforcement Learning: An Overview

OpenAIRE

Li, Yuxi

2017-01-01

We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Science.gov (United States)

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Deep Unfolding for Topic Models.

Science.gov (United States)

Chien, Jen-Tzung; Lee, Chao-Hsi

2018-02-01

Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

Docker Containers for Deep Learning Experiments

OpenAIRE

Gerke, Paul K.

2017-01-01

Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

Auxiliary Deep Generative Models

DEFF Research Database (Denmark)

Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2016-01-01

Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

Accelerating Deep Learning with Shrinkage and Recall

OpenAIRE

Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

2016-01-01

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

Consolidated Deep Actor Critic Networks (DRAFT)

NARCIS (Netherlands)

Van der Laan, T.A.

2015-01-01

The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

OpenAIRE

Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

2017-01-01

In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

Deep Feature Consistent Variational Autoencoder

OpenAIRE

Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

2016-01-01

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

How Stressful Is "Deep Bubbling"?

Science.gov (United States)

Tyrmi, Jaana; Laukkanen, Anne-Maria

2017-03-01

Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Evaluation of the DeepWind concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

Simulator Studies of the Deep Stall

Science.gov (United States)

White, Maurice D.; Cooper, George E.

1965-01-01

Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

Deep Learning

DEFF Research Database (Denmark)

Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

2018-01-01

I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

Science.gov (United States)

Wachinger, Christian; Reuter, Martin; Klein, Tassilo

2018-04-15

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep Learning and Its Applications in Biomedicine.

Science.gov (United States)

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Deep Water Acoustics

Science.gov (United States)

2016-06-28

the Deep Water project and participate in the NPAL Workshops, including Art Baggeroer (MIT), J. Beron- Vera (UMiami), M. Brown (UMiami), T...Kathleen E . Wage. The North Pacific Acoustic Laboratory deep-water acoustic propagation experiments in the Philippine Sea. J. Acoust. Soc. Am., 134(4...estimate of the angle α during PhilSea09, made from ADCP measurements at the site of the DVLA. Sim. A B1 B2 B3 C D E F Prof. # 0 4 4 4 5 10 16 20 α

Reduction of sexual dimorphism in stream-resident forms of three-spined stickleback Gasterosteus aculeatus

Science.gov (United States)

Kitano, J.; Mori, S.; Peichel, C. L.

2013-01-01

Sexual dimorphism in geometric body shape and external morphology was compared between marine and stream-resident forms of three-spined stickleback Gasterosteus aculeatus collected from North America and Japan. Some aspects of sexual dimorphism were shared between ecotypes: males had larger heads than females with no significant effect of ecotype on the magnitude of sexual dimorphism. By contrast, a significant sex-by-ecotype interaction was found for body depth. Males tended to have deeper bodies than females in both forms, but the magnitude of sexual dimorphism was reduced in stream-resident forms. Although females were generally larger in standard length and had larger pelvic girdles, significant sexual dimorphism in these traits was not consistently found across populations or ecotypes. These results suggest that some aspects of sexual dimorphism were shared between ecotypes, while others were unique to each population. The results further suggest that ecology may influence the evolution of sexual dimorphism in some external morphological traits, such as body depth. PMID:22220894

The contribution of post-copulatory mechanisms to incipient ecological speciation in sticklebacks.

Science.gov (United States)

Kaufmann, Joshka; Eizaguirre, Christophe; Milinski, Manfred; Lenz, Tobias L

2015-01-01

Ecology can play a major role in species diversification. As individuals are adapting to contrasting habitats, reproductive barriers may evolve at multiple levels. While pre-mating barriers have been extensively studied, the evolution of post-mating reproductive isolation during early stages of ecological speciation remains poorly understood. In diverging three-spined stickleback ecotypes from two lakes and two rivers, we observed differences in sperm traits between lake and river males. Interestingly, these differences did not translate into ecotype-specific gamete precedence for sympatric males in competitive in vitro fertilization experiments, potentially owing to antagonistic compensatory effects. However, we observed indirect evidence for impeded development of inter-ecotype zygotes, possibly suggesting an early stage of genetic incompatibility between ecotypes. Our results show that pre-zygotic post-copulatory mechanisms play a minor role during this first stage of ecotype divergence, but suggest that genetic incompatibilities may arise at early stages of ecological speciation. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Overview of deep learning in medical imaging.

Science.gov (United States)

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

WFIRST: Science from Deep Field Surveys

Science.gov (United States)

Koekemoer, Anton; Foley, Ryan; WFIRST Deep Field Working Group

2018-01-01

WFIRST will enable deep field imaging across much larger areas than those previously obtained with Hubble, opening up completely new areas of parameter space for extragalactic deep fields including cosmology, supernova and galaxy evolution science. The instantaneous field of view of the Wide Field Instrument (WFI) is about 0.3 square degrees, which would for example yield an Ultra Deep Field (UDF) reaching similar depths at visible and near-infrared wavelengths to that obtained with Hubble, over an area about 100-200 times larger, for a comparable investment in time. Moreover, wider fields on scales of 10-20 square degrees could achieve depths comparable to large HST surveys at medium depths such as GOODS and CANDELS, and would enable multi-epoch supernova science that could be matched in area to LSST Deep Drilling fields or other large survey areas. Such fields may benefit from being placed on locations in the sky that have ancillary multi-band imaging or spectroscopy from other facilities, from the ground or in space. The WFIRST Deep Fields Working Group has been examining the science considerations for various types of deep fields that may be obtained with WFIRST, and present here a summary of the various properties of different locations in the sky that may be considered for future deep fields with WFIRST.

TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

OpenAIRE

Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

2015-01-01

The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

DeepSimulator: a deep simulator for Nanopore sequencing

KAUST Repository

Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

2017-01-01

or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments

Building Program Vector Representations for Deep Learning

OpenAIRE

Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

2014-01-01

Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

[Deep vein thrombosis prophylaxis.

Science.gov (United States)

Sandoval-Chagoya, Gloria Alejandra; Laniado-Laborín, Rafael

2013-01-01

Background: despite the proven effectiveness of preventive therapy for deep vein thrombosis, a significant proportion of patients at risk for thromboembolism do not receive prophylaxis during hospitalization. Our objective was to determine the adherence to thrombosis prophylaxis guidelines in a general hospital as a quality control strategy. Methods: a random audit of clinical charts was conducted at the Tijuana General Hospital, Baja California, Mexico, to determine the degree of adherence to deep vein thrombosis prophylaxis guidelines. The instrument used was the Caprini's checklist for thrombosis risk assessment in adult patients. Results: the sample included 300 patient charts; 182 (60.7 %) were surgical patients and 118 were medical patients. Forty six patients (15.3 %) received deep vein thrombosis pharmacologic prophylaxis; 27.1 % of medical patients received deep vein thrombosis prophylaxis versus 8.3 % of surgical patients (p < 0.0001). Conclusions: our results show that adherence to DVT prophylaxis at our hospital is extremely low. Only 15.3 % of our patients at risk received treatment, and even patients with very high risk received treatment in less than 25 % of the cases. We have implemented strategies to increase compliance with clinical guidelines.

Contemporary deep recurrent learning for recognition

Science.gov (United States)

Iftekharuddin, K. M.; Alam, M.; Vidyaratne, L.

2017-05-01

Large-scale feed-forward neural networks have seen intense application in many computer vision problems. However, these networks can get hefty and computationally intensive with increasing complexity of the task. Our work, for the first time in literature, introduces a Cellular Simultaneous Recurrent Network (CSRN) based hierarchical neural network for object detection. CSRN has shown to be more effective to solving complex tasks such as maze traversal and image processing when compared to generic feed forward networks. While deep neural networks (DNN) have exhibited excellent performance in object detection and recognition, such hierarchical structure has largely been absent in neural networks with recurrency. Further, our work introduces deep hierarchy in SRN for object recognition. The simultaneous recurrency results in an unfolding effect of the SRN through time, potentially enabling the design of an arbitrarily deep network. This paper shows experiments using face, facial expression and character recognition tasks using novel deep recurrent model and compares recognition performance with that of generic deep feed forward model. Finally, we demonstrate the flexibility of incorporating our proposed deep SRN based recognition framework in a humanoid robotic platform called NAO.

Towards deep learning with segregated dendrites.

Science.gov (United States)

Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

2017-12-05

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

The deep ocean under climate change

Science.gov (United States)

Levin, Lisa A.; Le Bris, Nadine

2015-11-01

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

NATURAL GAS RESOURCES IN DEEP SEDIMENTARY BASINS

Energy Technology Data Exchange (ETDEWEB)

Thaddeus S. Dyman; Troy Cook; Robert A. Crovelli; Allison A. Henry; Timothy C. Hester; Ronald C. Johnson; Michael D. Lewan; Vito F. Nuccio; James W. Schmoker; Dennis B. Riggin; Christopher J. Schenk

2002-02-05

From a geological perspective, deep natural gas resources are generally defined as resources occurring in reservoirs at or below 15,000 feet, whereas ultra-deep gas occurs below 25,000 feet. From an operational point of view, ''deep'' is often thought of in a relative sense based on the geologic and engineering knowledge of gas (and oil) resources in a particular area. Deep gas can be found in either conventionally-trapped or unconventional basin-center accumulations that are essentially large single fields having spatial dimensions often exceeding those of conventional fields. Exploration for deep conventional and unconventional basin-center natural gas resources deserves special attention because these resources are widespread and occur in diverse geologic environments. In 1995, the U.S. Geological Survey estimated that 939 TCF of technically recoverable natural gas remained to be discovered or was part of reserve appreciation from known fields in the onshore areas and State waters of the United. Of this USGS resource, nearly 114 trillion cubic feet (Tcf) of technically-recoverable gas remains to be discovered from deep sedimentary basins. Worldwide estimates of deep gas are also high. The U.S. Geological Survey World Petroleum Assessment 2000 Project recently estimated a world mean undiscovered conventional gas resource outside the U.S. of 844 Tcf below 4.5 km (about 15,000 feet). Less is known about the origins of deep gas than about the origins of gas at shallower depths because fewer wells have been drilled into the deeper portions of many basins. Some of the many factors contributing to the origin of deep gas include the thermal stability of methane, the role of water and non-hydrocarbon gases in natural gas generation, porosity loss with increasing thermal maturity, the kinetics of deep gas generation, thermal cracking of oil to gas, and source rock potential based on thermal maturity and kerogen type. Recent experimental simulations

Correlates between feeding ecology and mercury levels in historical and modern arctic foxes (Vulpes lagopus.

Directory of Open Access Journals (Sweden)

Natalia Bocharova

Full Text Available Changes in concentration of pollutants and pathogen distribution can vary among ecotypes (e.g. marine versus terrestrial food resources. This may have important implications for the animals that reside within them. We examined 1 canid pathogen presence in an endangered arctic fox (Vulpes lagopus population and 2 relative total mercury (THg level as a function of ecotype ('coastal' or 'inland' for arctic foxes to test whether the presence of pathogens or heavy metal concentration correlate with population health. The Bering Sea populations on Bering and Mednyi Islands were compared to Icelandic arctic fox populations with respect to inland and coastal ecotypes. Serological and DNA based pathogen screening techniques were used to examine arctic foxes for pathogens. THg was measured by atomic absorption spectrometry from hair samples of historical and modern collected arctic foxes and samples from their prey species (hair and internal organs. Presence of pathogens did not correlate with population decline from Mednyi Island. However, THg concentration correlated strongly with ecotype and was reflected in the THg concentrations detected in available food sources in each ecotype. The highest concentration of THg was found in ecotypes where foxes depended on marine vertebrates for food. Exclusively inland ecotypes had low THg concentrations. The results suggest that absolute exposure to heavy metals may be less important than the feeding ecology and feeding opportunities of top predators such as arctic foxes which may in turn influence population health and stability. A higher risk to wildlife of heavy metal exposure correlates with feeding strategies that rely primarily on a marine based diet.

Correlates between feeding ecology and mercury levels in historical and modern arctic foxes (Vulpes lagopus).

Science.gov (United States)

Bocharova, Natalia; Treu, Gabriele; Czirják, Gábor Árpád; Krone, Oliver; Stefanski, Volker; Wibbelt, Gudrun; Unnsteinsdóttir, Ester Rut; Hersteinsson, Páll; Schares, Gereon; Doronina, Lilia; Goltsman, Mikhail; Greenwood, Alex D

2013-01-01

Changes in concentration of pollutants and pathogen distribution can vary among ecotypes (e.g. marine versus terrestrial food resources). This may have important implications for the animals that reside within them. We examined 1) canid pathogen presence in an endangered arctic fox (Vulpes lagopus) population and 2) relative total mercury (THg) level as a function of ecotype ('coastal' or 'inland') for arctic foxes to test whether the presence of pathogens or heavy metal concentration correlate with population health. The Bering Sea populations on Bering and Mednyi Islands were compared to Icelandic arctic fox populations with respect to inland and coastal ecotypes. Serological and DNA based pathogen screening techniques were used to examine arctic foxes for pathogens. THg was measured by atomic absorption spectrometry from hair samples of historical and modern collected arctic foxes and samples from their prey species (hair and internal organs). Presence of pathogens did not correlate with population decline from Mednyi Island. However, THg concentration correlated strongly with ecotype and was reflected in the THg concentrations detected in available food sources in each ecotype. The highest concentration of THg was found in ecotypes where foxes depended on marine vertebrates for food. Exclusively inland ecotypes had low THg concentrations. The results suggest that absolute exposure to heavy metals may be less important than the feeding ecology and feeding opportunities of top predators such as arctic foxes which may in turn influence population health and stability. A higher risk to wildlife of heavy metal exposure correlates with feeding strategies that rely primarily on a marine based diet.

Deep smarts.

Science.gov (United States)

Leonard, Dorothy; Swap, Walter

2004-09-01

When a person sizes up a complex situation and rapidly comes to a decision that proves to be not just good but brilliant, you think, "That was smart." After you watch him do this a few times, you realize you're in the presence of something special. It's not raw brainpower, though that helps. It's not emotional intelligence, either, though that, too, is often involved. It's deep smarts. Deep smarts are not philosophical--they're not"wisdom" in that sense, but they're as close to wisdom as business gets. You see them in the manager who understands when and how to move into a new international market, in the executive who knows just what kind of talk to give when her organization is in crisis, in the technician who can track a product failure back to an interaction between independently produced elements. These are people whose knowledge would be hard to purchase on the open market. Their insight is based on know-how more than on know-what; it comprises a system view as well as expertise in individual areas. Because deep smarts are experienced based and often context specific, they can't be produced overnight or readily imported into an organization. It takes years for an individual to develop them--and no time at all for an organization to lose them when a valued veteran walks out the door. They can be taught, however, with the right techniques. Drawing on their forthcoming book Deep Smarts, Dorothy Leonard and Walter Swap say the best way to transfer such expertise to novices--and, on a larger scale, to make individual knowledge institutional--isn't through PowerPoint slides, a Web site of best practices, online training, project reports, or lectures. Rather, the sage needs to teach the neophyte individually how to draw wisdom from experience. Companies have to be willing to dedicate time and effort to such extensive training, but the investment more than pays for itself.

Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

Science.gov (United States)

Sadeghi, Zahra

2016-09-01

In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

Climate, carbon cycling, and deep-ocean ecosystems.

Science.gov (United States)

Smith, K L; Ruhl, H A; Bett, B J; Billett, D S M; Lampitt, R S; Kaufmann, R S

2009-11-17

Climate variation affects surface ocean processes and the production of organic carbon, which ultimately comprises the primary food supply to the deep-sea ecosystems that occupy approximately 60% of the Earth's surface. Warming trends in atmospheric and upper ocean temperatures, attributed to anthropogenic influence, have occurred over the past four decades. Changes in upper ocean temperature influence stratification and can affect the availability of nutrients for phytoplankton production. Global warming has been predicted to intensify stratification and reduce vertical mixing. Research also suggests that such reduced mixing will enhance variability in primary production and carbon export flux to the deep sea. The dependence of deep-sea communities on surface water production has raised important questions about how climate change will affect carbon cycling and deep-ocean ecosystem function. Recently, unprecedented time-series studies conducted over the past two decades in the North Pacific and the North Atlantic at >4,000-m depth have revealed unexpectedly large changes in deep-ocean ecosystems significantly correlated to climate-driven changes in the surface ocean that can impact the global carbon cycle. Climate-driven variation affects oceanic communities from surface waters to the much-overlooked deep sea and will have impacts on the global carbon cycle. Data from these two widely separated areas of the deep ocean provide compelling evidence that changes in climate can readily influence deep-sea processes. However, the limited geographic coverage of these existing time-series studies stresses the importance of developing a more global effort to monitor deep-sea ecosystems under modern conditions of rapidly changing climate.

The deep ocean under climate change.

Science.gov (United States)

Levin, Lisa A; Le Bris, Nadine

2015-11-13

The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems. Copyright © 2015, American Association for the Advancement of Science.

SEDS: THE SPITZER EXTENDED DEEP SURVEY. SURVEY DESIGN, PHOTOMETRY, AND DEEP IRAC SOURCE COUNTS

International Nuclear Information System (INIS)

Ashby, M. L. N.; Willner, S. P.; Fazio, G. G.; Huang, J.-S.; Hernquist, L.; Hora, J. L.; Arendt, R.; Barmby, P.; Barro, G.; Faber, S.; Guhathakurta, P.; Bell, E. F.; Bouwens, R.; Cattaneo, A.; Croton, D.; Davé, R.; Dunlop, J. S.; Egami, E.; Finlator, K.; Grogin, N. A.

2013-01-01

The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg 2 to a depth of 26 AB mag (3σ) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 μm. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 ± 1.0 and 4.4 ± 0.8 nW m –2 sr –1 at 3.6 and 4.5 μm to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

The deep lymphatic anatomy of the hand.

Science.gov (United States)

Ma, Chuan-Xiang; Pan, Wei-Ren; Liu, Zhi-An; Zeng, Fan-Qiang; Qiu, Zhi-Qiang

2018-04-03

The deep lymphatic anatomy of the hand still remains the least described in medical literature. Eight hands were harvested from four nonembalmed human cadavers amputated above the wrist. A small amount of 6% hydrogen peroxide was employed to detect the lymphatic vessels around the superficial and deep palmar vascular arches, in webs from the index to little fingers, the thenar and hypothenar areas. A 30-gauge needle was inserted into the vessels and injected with a barium sulphate compound. Each specimen was dissected, photographed and radiographed to demonstrate deep lymphatic distribution of the hand. Five groups of deep collecting lymph vessels were found in the hand: superficial palmar arch lymph vessel (SPALV); deep palmar arch lymph vessel (DPALV); thenar lymph vessel (TLV); hypothenar lymph vessel (HTLV); deep finger web lymph vessel (DFWLV). Each group of vessels drained in different directions first, then all turned and ran towards the wrist in different layers. The deep lymphatic drainage of the hand has been presented. The results will provide an anatomical basis for clinical management, educational reference and scientific research. Copyright © 2018 Elsevier GmbH. All rights reserved.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

Science.gov (United States)

Hwang, Bosun; You, Jiwoo; Vaessen, Thomas; Myin-Germeys, Inez; Park, Cheolsoo; Zhang, Byoung-Tak

2018-02-08

Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Deep inelastic electron and muon scattering

International Nuclear Information System (INIS)

Taylor, R.E.

1975-07-01

From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

Fast, Distributed Algorithms in Deep Networks

Science.gov (United States)

2016-05-11

shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

Deep Learning from Crowds

DEFF Research Database (Denmark)

Rodrigues, Filipe; Pereira, Francisco Camara

Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the stateof-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently...... networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels......, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural...

Deep learning methods for protein torsion angle prediction.

Science.gov (United States)

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

Deep Learning in Gastrointestinal Endoscopy.

Science.gov (United States)

Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

2016-01-01

Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

Neuromorphic Deep Learning Machines

OpenAIRE

Neftci, E; Augustine, C; Paul, S; Detorakis, G

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

Science.gov (United States)

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

Energy Technology Data Exchange (ETDEWEB)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of)

2016-06-15

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested.

Preliminary analyses of the deep geoenvironmental characteristics for the deep borehole disposal of high-level radioactive waste in Korea

International Nuclear Information System (INIS)

Lee, Jong Youl; Lee, Min Soo; Choi, Heui Joo; Kim, Geon Young; Kim, Kyung Su

2016-01-01

Spent fuels from nuclear power plants, as well as high-level radioactive waste from the recycling of spent fuels, should be safely isolated from human environment for an extremely long time. Recently, meaningful studies on the development of deep borehole radioactive waste disposal system in 3-5 km depth have been carried out in USA and some countries in Europe, due to great advance in deep borehole drilling technology. In this paper, domestic deep geoenvironmental characteristics are preliminarily investigated to analyze the applicability of deep borehole disposal technology in Korea. To do this, state-of-the art technologies in USA and some countries in Europe are reviewed, and geological and geothermal data from the deep boreholes for geothermal usage are analyzed. Based on the results on the crystalline rock depth, the geothermal gradient and the spent fuel types generated in Korea, a preliminary deep borehole concept including disposal canister and sealing system, is suggested

Toolkits and Libraries for Deep Learning.

Science.gov (United States)

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy; Philbrick, Kenneth

2017-08-01

Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data. In this paper, we will describe some of the libraries and tools that are available to aid in the construction and efficient execution of deep learning as applied to medical images.

Deep-sea coral research and technology program: Alaska deep-sea coral and sponge initiative final report

Science.gov (United States)

Rooper, Chris; Stone, Robert P.; Etnoyer, Peter; Conrath, Christina; Reynolds, Jennifer; Greene, H. Gary; Williams, Branwen; Salgado, Enrique; Morrison, Cheryl L.; Waller, Rhian G.; Demopoulos, Amanda W.J.

2017-01-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska’s marine waters. In some places, such as the central and western Aleutian Islands, deep-sea coral and sponge resources can be extremely diverse and may rank among the most abundant deep-sea coral and sponge communities in the world. Many different species of fishes and invertebrates are associated with deep-sea coral and sponge communities in Alaska. Because of their biology, these benthic invertebrates are potentially impacted by climate change and ocean acidification. Deepsea coral and sponge ecosystems are also vulnerable to the effects of commercial fishing activities. Because of the size and scope of Alaska’s continental shelf and slope, the vast majority of the area has not been visually surveyed for deep-sea corals and sponges. NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) sponsored a field research program in the Alaska region between 2012–2015, referred to hereafter as the Alaska Initiative. The priorities for Alaska were derived from ongoing data needs and objectives identified by the DSCRTP, the North Pacific Fishery Management Council (NPFMC), and Essential Fish Habitat-Environmental Impact Statement (EFH-EIS) process.This report presents the results of 15 projects conducted using DSCRTP funds from 2012-2015. Three of the projects conducted as part of the Alaska deep-sea coral and sponge initiative included dedicated at-sea cruises and fieldwork spread across multiple years. These projects were the eastern Gulf of Alaska Primnoa pacifica study, the Aleutian Islands mapping study, and the Gulf of Alaska fish productivity study. In all, there were nine separate research cruises carried out with a total of 109 at-sea days conducting research. The remaining projects either used data and samples collected by the three major fieldwork projects or were piggy-backed onto existing research programs at the Alaska Fisheries Science Center (AFSC).

Image Captioning with Deep Bidirectional LSTMs

OpenAIRE

Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

2016-01-01

This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

An overview of latest deep water technologies

International Nuclear Information System (INIS)

Anon.

1995-01-01

The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

Deep learning in neural networks: an overview.

Science.gov (United States)

Schmidhuber, Jürgen

2015-01-01

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Comprehensive Genomic Analyses of the OM43 Clade, Including a Novel Species from the Red Sea, Indicate Ecotype Differentiation among Marine Methylotrophs

Science.gov (United States)

Jimenez-Infante, Francy; Ngugi, David Kamanda; Vinu, Manikandan; Alam, Intikhab; Kamau, Allan Anthony; Blom, Jochen; Bajic, Vladimir B.

2015-01-01

The OM43 clade within the family Methylophilaceae of Betaproteobacteria represents a group of methylotrophs that play important roles in the metabolism of C1 compounds in marine environments and other aquatic environments around the globe. Using dilution-to-extinction cultivation techniques, we successfully isolated a novel species of this clade (here designated MBRS-H7) from the ultraoligotrophic open ocean waters of the central Red Sea. Phylogenomic analyses indicate that MBRS-H7 is a novel species that forms a distinct cluster together with isolate KB13 from Hawaii (Hawaii-Red Sea [H-RS] cluster) that is separate from the cluster represented by strain HTCC2181 (from the Oregon coast). Phylogenetic analyses using the robust 16S-23S internal transcribed spacer revealed a potential ecotype separation of the marine OM43 clade members, which was further confirmed by metagenomic fragment recruitment analyses that showed trends of higher abundance in low-chlorophyll and/or high-temperature provinces for the H-RS cluster but a preference for colder, highly productive waters for the HTCC2181 cluster. This potential environmentally driven niche differentiation is also reflected in the metabolic gene inventories, which in the case of the H-RS cluster include those conferring resistance to high levels of UV irradiation, temperature, and salinity. Interestingly, we also found different energy conservation modules between these OM43 subclades, namely, the existence of the NADH:quinone oxidoreductase complex I (NUO) system in the H-RS cluster and the nonhomologous NADH:quinone oxidoreductase (NQR) system in the HTCC2181 cluster, which might have implications for their overall energetic yields. PMID:26655752

Salinity stress and some physiological relationships in Kochia (Kochia scoparia

Directory of Open Access Journals (Sweden)

Jafar Nabati

2018-06-01

Full Text Available Introduction Soil salinity is one of the major abiotic stresses affecting plant growth and production. It is estimated that approximately half of the irrigated lands of Iran are affected by salinity and much of the agricultural lands of Iran especially in the central regions are susceptible to salinity. According to the development of saline soils and water resources, utilization of halophytes as alternatives for cultivation in saline conditions could be a suitable strategy to crop production. In addition to understanding the physiological salinity tolerance pathways, studying such crops could help to plant breeding and transferring these useful traits to crop species and also domestication of these plants. Materials and methods This experiment was conducted in 2009-2010 in Salinity Research Station of faculty of agriculture, Ferdowsi University of Mashhad as split-plot based on Complete Randomized Block Design with three replications. Salinity as the main plot had two levels of 5.2 and 16.5 dSm-1 and five kochia ecotypes including Birjand, Urmia, Borujerd, Esfahan and Sabzevar were allocated as sub-plot. Seedlings were irrigated with saline water having electrical conductivity (EC of 5.2 dSm-1 until the full establishment and thereafter salinity stress was imposed with saline water having EC=16.5 dSm-1. Physiological and biochemical traits were measured in the youngest fully expanded leaf at the beginning of the anthesis and shoot biomass at the end of the growth season. Data analysis was performed using Minitab 16 and means were compared by LSD test at a significance level of 0.05. Results and Discussion Results indicated that biomass was increased in Birjand, Isfahan and Urmia ecotypes as salinity level increased while it was decreased in Sabzevar and Boroujerd ecotypes. A reduction of 34, 31, 11 and 29 percentage and an increase of 4 percentage in seed yield was seen in Sabzevar, Birjand, Boroujerd, Urmia and Isfahan, respectively. Harvest

Combining shallow and deep processing for a robust, fast, deep-linguistic dependency parser

OpenAIRE

Schneider, G

2004-01-01

This paper describes Pro3Gres, a fast, robust, broad-coverage parser that delivers deep-linguistic grammatical relation structures as output, which are closer to predicate-argument structures and more informative than pure constituency structures. The parser stays as shallow as is possible for each task, combining shallow and deep-linguistic methods by integrating chunking and by expressing the majority of long-distance dependencies in a context-free way. It combines statistical and rule-base...

DeepVel: Deep learning for the estimation of horizontal velocities at the solar surface

Science.gov (United States)

Asensio Ramos, A.; Requerey, I. S.; Vitas, N.

2017-07-01

Many phenomena taking place in the solar photosphere are controlled by plasma motions. Although the line-of-sight component of the velocity can be estimated using the Doppler effect, we do not have direct spectroscopic access to the components that are perpendicular to the line of sight. These components are typically estimated using methods based on local correlation tracking. We have designed DeepVel, an end-to-end deep neural network that produces an estimation of the velocity at every single pixel, every time step, and at three different heights in the atmosphere from just two consecutive continuum images. We confront DeepVel with local correlation tracking, pointing out that they give very similar results in the time and spatially averaged cases. We use the network to study the evolution in height of the horizontal velocity field in fragmenting granules, supporting the buoyancy-braking mechanism for the formation of integranular lanes in these granules. We also show that DeepVel can capture very small vortices, so that we can potentially expand the scaling cascade of vortices to very small sizes and durations. The movie attached to Fig. 3 is available at http://www.aanda.org

Deep Learning in Drug Discovery.

Science.gov (United States)

Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

2016-01-01

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Iris Transponder-Communications and Navigation for Deep Space

Science.gov (United States)

Duncan, Courtney B.; Smith, Amy E.; Aguirre, Fernando H.

2014-01-01

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

Intraspecific variation in the use of water sources by the circum-Mediterranean conifer Pinus halepensis.

Science.gov (United States)

Voltas, Jordi; Lucabaugh, Devon; Chambel, Maria Regina; Ferrio, Juan Pedro

2015-12-01

The relevance of interspecific variation in the use of plant water sources has been recognized in drought-prone environments. By contrast, the characterization of intraspecific differences in water uptake patterns remains elusive, although preferential access to particular soil layers may be an important adaptive response for species along aridity gradients. Stable water isotopes were analysed in soil and xylem samples of 56 populations of the drought-avoidant conifer Pinus halepensis grown in a common garden test. We found that most populations reverted to deep soil layers as the main plant water source during seasonal summer droughts. More specifically, we detected a clear geographical differentiation among populations in water uptake patterns even under relatively mild drought conditions (early autumn), with populations originating from more arid regions taking up more water from deep soil layers. However, the preferential access to deep soil water was largely independent of aboveground growth. Our findings highlight the high plasticity and adaptive relevance of the differential access to soil water pools among Aleppo pine populations. The observed ecotypic patterns point to the adaptive relevance of resource investment in deep roots as a strategy towards securing a source of water in dry environments for P. halepensis. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Context and Deep Learning Design

Science.gov (United States)

Boyle, Tom; Ravenscroft, Andrew

2012-01-01

Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across…

Deep Generative Models for Molecular Science

DEFF Research Database (Denmark)

Jørgensen, Peter Bjørn; Schmidt, Mikkel Nørgaard; Winther, Ole

2018-01-01

Generative deep machine learning models now rival traditional quantum-mechanical computations in predicting properties of new structures, and they come with a significantly lower computational cost, opening new avenues in computational molecular science. In the last few years, a variety of deep...... generative models have been proposed for modeling molecules, which differ in both their model structure and choice of input features. We review these recent advances within deep generative models for predicting molecular properties, with particular focus on models based on the probabilistic autoencoder (or...

Too Deep or Not Too Deep?: A Propensity-Matched Comparison of the Analgesic Effects of a Superficial Versus Deep Serratus Fascial Plane Block for Ambulatory Breast Cancer Surgery.

Science.gov (United States)

Abdallah, Faraj W; Cil, Tulin; MacLean, David; Madjdpour, Caveh; Escallon, Jaime; Semple, John; Brull, Richard

2018-07-01

Serratus fascial plane block can reduce pain following breast surgery, but the question of whether to inject the local anesthetic superficial or deep to the serratus muscle has not been answered. This cohort study compares the analgesic benefits of superficial versus deep serratus plane blocks in ambulatory breast cancer surgery patients at Women's College Hospital between February 2014 and December 2016. We tested the joint hypothesis that deep serratus block is noninferior to superficial serratus block for postoperative in-hospital (pre-discharge) opioid consumption and pain severity. One hundred sixty-six patients were propensity matched among 2 groups (83/group): superficial and deep serratus blocks. The cohort was used to evaluate the effect of blocks on postoperative oral morphine equivalent consumption and area under the curve for rest pain scores. We considered deep serratus block to be noninferior to superficial serratus block if it were noninferior for both outcomes, within 15 mg morphine and 4 cm·h units margins. Other outcomes included intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and incidence of postoperative nausea and vomiting. Deep serratus block was associated with postoperative morphine consumption and pain scores area under the curve that were noninferior to those of the superficial serratus block. Intraoperative fentanyl requirements, time to first analgesic request, recovery room stay, and postoperative nausea and vomiting were not different between blocks. The postoperative in-hospital analgesia associated with deep serratus block is as effective (within an acceptable margin) as superficial serratus block following ambulatory breast cancer surgery. These new findings are important to inform both current clinical practices and future prospective studies.

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

Science.gov (United States)

He, Fei; Han, Ye; Wang, Han; Ji, Jinchao; Liu, Yuanning; Ma, Zhiqiang

2017-03-01

Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

Molecular analysis of deep subsurface bacteria

International Nuclear Information System (INIS)

Jimenez Baez, L.E.

1989-09-01

Deep sediments samples from site C10a, in Appleton, and sites, P24, P28, and P29, at the Savannah River Site (SRS), near Aiken, South Carolina were studied to determine their microbial community composition, DNA homology and mol %G+C. Different geological formations with great variability in hydrogeological parameters were found across the depth profile. Phenotypic identification of deep subsurface bacteria underestimated the bacterial diversity at the three SRS sites, since bacteria with the same phenotype have different DNA composition and less than 70% DNA homology. Total DNA hybridization and mol %G+C analysis of deep sediment bacterial isolates suggested that each formation is comprised of different microbial communities. Depositional environment was more important than site and geological formation on the DNA relatedness between deep subsurface bacteria, since more 70% of bacteria with 20% or more of DNA homology came from the same depositional environments. Based on phenotypic and genotypic tests Pseudomonas spp. and Acinetobacter spp.-like bacteria were identified in 85 million years old sediments. This suggests that these microbial communities might have been adapted during a long period of time to the environmental conditions of the deep subsurface

Joint Training of Deep Boltzmann Machines

OpenAIRE

Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

2012-01-01

We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

Deep boreholes; Tiefe Bohrloecher

Energy Technology Data Exchange (ETDEWEB)

Bracke, Guido [Gesellschaft fuer Anlagen- und Reaktorsicherheit gGmbH Koeln (Germany); Charlier, Frank [NSE international nuclear safety engineering gmbh, Aachen (Germany); Geckeis, Horst [Karlsruher Institut fuer Technologie (Germany). Inst. fuer Nukleare Entsorgung; and others

2016-02-15

The report on deep boreholes covers the following subject areas: methods for safe enclosure of radioactive wastes, requirements concerning the geological conditions of possible boreholes, reversibility of decisions and retrievability, status of drilling technology. The introduction covers national and international activities. Further chapters deal with the following issues: basic concept of the storage in deep bore holes, status of the drilling technology, safe enclosure, geomechanics and stability, reversibility of decisions, risk scenarios, compliancy with safe4ty requirements and site selection criteria, research and development demand.

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Pre-cementation of deep shaft

Science.gov (United States)

Heinz, W. F.

1988-12-01

Pre-cementation or pre-grouting of deep shafts in South Africa is an established technique to improve safety and reduce water ingress during shaft sinking. The recent completion of several pre-cementation projects for shafts deeper than 1000m has once again highlighted the effectiveness of pre-grouting of shafts utilizing deep slimline boreholes and incorporating wireline technique for drilling and conventional deep borehole grouting techniques for pre-cementation. Pre-cementation of deep shaft will: (i) Increase the safety of shaft sinking operation (ii) Minimize water and gas inflow during shaft sinking (iii) Minimize the time lost due to additional grouting operations during sinking of the shaft and hence minimize costly delays and standing time of shaft sinking crews and equipment. (iv) Provide detailed information of the geology of the proposed shaft site. Informations on anomalies, dykes, faults as well as reef (gold bearing conglomerates) intersections can be obtained from the evaluation of cores of the pre-cementation boreholes. (v) Provide improved rock strength for excavations in the immediate vicinity of the shaft area. The paper describes pre-cementation techniques recently applied successfully from surface and some conclusions drawn for further considerations.

Applications of Deep Learning in Biomedicine.

Science.gov (United States)

Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

2016-05-02

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

An empirical test of evolutionary theories for reproductive senescence and reproductive effort in the garter snake Thamnophis elegans.

Science.gov (United States)

Sparkman, Amanda M; Arnold, Stevan J; Bronikowski, Anne M

2007-04-07

Evolutionary theory predicts that differential reproductive effort and rate of reproductive senescence will evolve under different rates of external mortality. We examine the evolutionary divergence of age-specific reproduction in two life-history ecotypes of the western terrestrial garter snake, Thamnophis elegans. We test for the signature of reproductive senescence (decreasing fecundity with age) and increasing reproductive effort with age (increasing reproductive productivity per gram female) in replicate populations of two life-history ecotypes: snakes that grow fast, mature young and have shorter lifespans, and snakes that grow slow, mature late and have long lives. The difference between life-history ecotypes is due to genetic divergence in growth rate. We find (i) reproductive success (live litter mass) increases with age in both ecotypes, but does so more rapidly in the fast-growth ecotype, (ii) reproductive failure increases with age in both ecotypes, but the proportion of reproductive failure to total reproductive output remains invariant, and (iii) reproductive effort remains constant in fast-growth individuals with age, but declines in slow-growth individuals. This illustration of increasing fecundity with age, even at the latest ages, deviates from standard expectations for reproductive senescence, as does the lack of increases in reproductive effort. We discuss our findings in light of recent theories regarding the phenomenon of increased reproduction throughout life in organisms with indeterminate growth and its potential to offset theoretical expectations for the ubiquity of senescence.

Genomics of Rapid Incipient Speciation in Sympatric Threespine Stickleback.

Directory of Open Access Journals (Sweden)

David A Marques

2016-02-01

Full Text Available Ecological speciation is the process by which reproductively isolated populations emerge as a consequence of divergent natural or ecologically-mediated sexual selection. Most genomic studies of ecological speciation have investigated allopatric populations, making it difficult to infer reproductive isolation. The few studies on sympatric ecotypes have focused on advanced stages of the speciation process after thousands of generations of divergence. As a consequence, we still do not know what genomic signatures of the early onset of ecological speciation look like. Here, we examined genomic differentiation among migratory lake and resident stream ecotypes of threespine stickleback reproducing in sympatry in one stream, and in parapatry in another stream. Importantly, these ecotypes started diverging less than 150 years ago. We obtained 34,756 SNPs with restriction-site associated DNA sequencing and identified genomic islands of differentiation using a Hidden Markov Model approach. Consistent with incipient ecological speciation, we found significant genomic differentiation between ecotypes both in sympatry and parapatry. Of 19 islands of differentiation resisting gene flow in sympatry, all were also differentiated in parapatry and were thus likely driven by divergent selection among habitats. These islands clustered in quantitative trait loci controlling divergent traits among the ecotypes, many of them concentrated in one region with low to intermediate recombination. Our findings suggest that adaptive genomic differentiation at many genetic loci can arise and persist in sympatry at the very early stage of ecotype divergence, and that the genomic architecture of adaptation may facilitate this.

Deep Complementary Bottleneck Features for Visual Speech Recognition

NARCIS (Netherlands)

Petridis, Stavros; Pantic, Maja

Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

Producing deep-water hydrocarbons

International Nuclear Information System (INIS)

Pilenko, Thierry

2011-01-01

Several studies relate the history and progress made in offshore production from oil and gas fields in relation to reserves and the techniques for producing oil offshore. The intention herein is not to review these studies but rather to argue that the activities of prospecting and producing deep-water oil and gas call for a combination of technology and project management and, above all, of devotion and innovation. Without this sense of commitment motivating men and women in this industry, the human adventure of deep-water production would never have taken place

Analysis of DNA methylation in Arabidopsis thaliana based on methylation-sensitive AFLP markers.

Science.gov (United States)

Cervera, M T; Ruiz-García, L; Martínez-Zapater, J M

2002-12-01

AFLP analysis using restriction enzyme isoschizomers that differ in their sensitivity to methylation of their recognition sites has been used to analyse the methylation state of anonymous CCGG sequences in Arabidopsis thaliana. The technique was modified to improve the quality of fingerprints and to visualise larger numbers of scorable fragments. Sequencing of amplified fragments indicated that detection was generally associated with non-methylation of the cytosine to which the isoschizomer is sensitive. Comparison of EcoRI/ HpaII and EcoRI/ MspI patterns in different ecotypes revealed that 35-43% of CCGG sites were differentially digested by the isoschizomers. Interestingly, the pattern of digestion among different plants belonging to the same ecotype is highly conserved, with the rate of intra-ecotype methylation-sensitive polymorphisms being less than 1%. However, pairwise comparisons of methylation patterns between samples belonging to different ecotypes revealed differences in up to 34% of the methylation-sensitive polymorphisms. The lack of correlation between inter-ecotype similarity matrices based on methylation-insensitive or methylation-sensitive polymorphisms suggests that whatever the mechanisms regulating methylation may be, they are not related to nucleotide sequence variation.

Outlier Loci Detect Intraspecific Biodiversity amongst Spring and Autumn Spawning Herring across Local Scales.

Directory of Open Access Journals (Sweden)

Dorte Bekkevold

Full Text Available Herring, Clupea harengus, is one of the ecologically and commercially most important species in European northern seas, where two distinct ecotypes have been described based on spawning time; spring and autumn. To date, it is unknown if these spring and autumn spawning herring constitute genetically distinct units. We assessed levels of genetic divergence between spring and autumn spawning herring in the Baltic Sea using two types of DNA markers, microsatellites and Single Nucleotide Polymorphisms, and compared the results with data for autumn spawning North Sea herring. Temporally replicated analyses reveal clear genetic differences between ecotypes and hence support reproductive isolation. Loci showing non-neutral behaviour, so-called outlier loci, show convergence between autumn spawning herring from demographically disjoint populations, potentially reflecting selective processes associated with autumn spawning ecotypes. The abundance and exploitation of the two ecotypes have varied strongly over space and time in the Baltic Sea, where autumn spawners have faced strong depression for decades. The results therefore have practical implications by highlighting the need for specific management of these co-occurring ecotypes to meet requirements for sustainable exploitation and ensure optimal livelihood for coastal communities.

Deep inelastic processes and the parton model

International Nuclear Information System (INIS)

Altarelli, G.

The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

Life Support for Deep Space and Mars

Science.gov (United States)

Jones, Harry W.; Hodgson, Edward W.; Kliss, Mark H.

2014-01-01

How should life support for deep space be developed? The International Space Station (ISS) life support system is the operational result of many decades of research and development. Long duration deep space missions such as Mars have been expected to use matured and upgraded versions of ISS life support. Deep space life support must use the knowledge base incorporated in ISS but it must also meet much more difficult requirements. The primary new requirement is that life support in deep space must be considerably more reliable than on ISS or anywhere in the Earth-Moon system, where emergency resupply and a quick return are possible. Due to the great distance from Earth and the long duration of deep space missions, if life support systems fail, the traditional approaches for emergency supply of oxygen and water, emergency supply of parts, and crew return to Earth or escape to a safe haven are likely infeasible. The Orbital Replacement Unit (ORU) maintenance approach used by ISS is unsuitable for deep space with ORU's as large and complex as those originally provided in ISS designs because it minimizes opportunities for commonality of spares, requires replacement of many functional parts with each failure, and results in substantial launch mass and volume penalties. It has become impractical even for ISS after the shuttle era, resulting in the need for ad hoc repair activity at lower assembly levels with consequent crew time penalties and extended repair timelines. Less complex, more robust technical approaches may be needed to meet the difficult deep space requirements for reliability, maintainability, and reparability. Developing an entirely new life support system would neglect what has been achieved. The suggested approach is use the ISS life support technologies as a platform to build on and to continue to improve ISS subsystems while also developing new subsystems where needed to meet deep space requirements.

Ecological and morphological patterns in communities of land snails of the genus Mandarina from the Bonin Islands.

Science.gov (United States)

Chiba, Satoshi

2004-01-01

The land snail genus Mandarina has undergone extensive radiation within the Bonin Islands in the west Pacific. The preferred above-ground vegetation heights of sympatric species were clearly different. They separated into arboreal, semi-arboreal, exposed ground and sheltered ground ecotypes. Shells of species with different ecotypes differ markedly, but shells of species with the same ecotype are very similar to each other. Shell morphologies of some phylogenetically distantly related species with the same ecotype were indistinguishable. Character evolution estimated parsimoniously using a phylogenetic tree suggests that the speciation among sympatric species is accompanied by ecological and morphological diversification. In addition, species coexistence of Mandarina is related to niche differentiation. The above findings suggest that ecological interactions among species contribute to the ecological and morphological diversification and radiation of these land snails in this depauperate environment.

Automatic Segmentation and Deep Learning of Bird Sounds

NARCIS (Netherlands)

Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

2015-01-01

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

Deep Learning: A Primer for Radiologists.

Science.gov (United States)

Chartrand, Gabriel; Cheng, Phillip M; Vorontsov, Eugene; Drozdzal, Michal; Turcotte, Simon; Pal, Christopher J; Kadoury, Samuel; Tang, An

2017-01-01

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data. With the advent of large datasets and increased computing power, these methods can produce models with exceptional performance. These models are multilayer artificial neural networks, loosely inspired by biologic neural systems. Weighted connections between nodes (neurons) in the network are iteratively adjusted based on example pairs of inputs and target outputs by back-propagating a corrective error signal through the network. For computer vision tasks, convolutional neural networks (CNNs) have proven to be effective. Recently, several clinical applications of CNNs have been proposed and studied in radiology for classification, detection, and segmentation tasks. This article reviews the key concepts of deep learning for clinical radiologists, discusses technical requirements, describes emerging applications in clinical radiology, and outlines limitations and future directions in this field. Radiologists should become familiar with the principles and potential applications of deep learning in medical imaging. © RSNA, 2017.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

Science.gov (United States)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Stratification-Based Outlier Detection over the Deep Web.

Science.gov (United States)

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Deep Learning and Bayesian Methods

Directory of Open Access Journals (Sweden)

Prosper Harrison B.

2017-01-01

Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

Eric Davidson and deep time.

Science.gov (United States)

Erwin, Douglas H

2017-10-13

Eric Davidson had a deep and abiding interest in the role developmental mechanisms played in generating evolutionary patterns documented in deep time, from the origin of the euechinoids to the processes responsible for the morphological architectures of major animal clades. Although not an evolutionary biologist, Davidson's interests long preceded the current excitement over comparative evolutionary developmental biology. Here I discuss three aspects at the intersection between his research and evolutionary patterns in deep time: First, understanding the mechanisms of body plan formation, particularly those associated with the early diversification of major metazoan clades. Second, a critique of early claims about ancestral metazoans based on the discoveries of highly conserved genes across bilaterian animals. Third, Davidson's own involvement in paleontology through a collaborative study of the fossil embryos from the Ediacaran Doushantuo Formation in south China.

More Far-Side Deep Moonquake Nests Discovered

Science.gov (United States)

Nakamura, Y.; Jackson, John A.; Jackson, Katherine G.

2004-01-01

As reported last year, we started to reanalyze the seismic data acquired from 1969 to 1977 with a network of stations established on the Moon during the Apollo mission. The reason for the reanalysis was because recent advances in computer technology make it possible to employ much more sophisticated analysis techniques than was possible previously. The primary objective of the reanalysis was to search for deep moonquakes on the far side of the Moon and, if found, to use them to infer the structure of the Moon's deep interior, including a possible central core. The first step was to identify any new deep moonquakes that escaped our earlier search by applying a combination of waveform cross-correlation and single-link cluster analysis, and then to see if any of them are from previously unknown nests of deep moonquakes. We positively identified 7245 deep moonquakes, more than a five-fold increase from the previous 1360. We also found at least 88 previously unknown deep-moonquake nests. The question was whether any of these newly discovered nets were on the far side of the Moon, and we now report that our analysis of the data indicates that some of them are indeed on the far side.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Directory of Open Access Journals (Sweden)

Peter Christiansen

2016-11-01

Full Text Available Convolutional neural network (CNN-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN. In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m than RCNN. RCNN has a similar performance at a short range (0–30 m. However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms = a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit.

Learning Transferable Features with Deep Adaptation Networks

OpenAIRE

Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

2015-01-01

Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

Theory of deep inelastic lepton-hadron scattering

International Nuclear Information System (INIS)

Geyer, B.; Robaschik, D.; Wieczorek, E.

1979-01-01

The description of deep inelastic lepton-nucleon scattering in the lowest order of the electromagnetic and weak coupling constants leads to a study of virtual Compton amplitudes and their absorptive parts. Some aspects of quantum chromodynamics are discussed. Deep inelastic scattering enables a central quantity of quantum field theory, namely the light cone behaviour of the current commutator. The moments of structure functions are used for the description of deep inelastic scattering. (author)

DeepQA: Improving the estimation of single protein model quality with deep belief networks

OpenAIRE

Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

2016-01-01

Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

DeepDive: Declarative Knowledge Base Construction.

Science.gov (United States)

De Sa, Christopher; Ratner, Alex; Ré, Christopher; Shin, Jaeho; Wang, Feiran; Wu, Sen; Zhang, Ce

2016-03-01

The dark data extraction or knowledge base construction (KBC) problem is to populate a SQL database with information from unstructured data sources including emails, webpages, and pdf reports. KBC is a long-standing problem in industry and research that encompasses problems of data extraction, cleaning, and integration. We describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems. The key idea in DeepDive is that statistical inference and machine learning are key tools to attack classical data problems in extraction, cleaning, and integration in a unified and more effective manner. DeepDive programs are declarative in that one cannot write probabilistic inference algorithms; instead, one interacts by defining features or rules about the domain. A key reason for this design choice is to enable domain experts to build their own KBC systems. We present the applications, abstractions, and techniques of DeepDive employed to accelerate construction of KBC systems.

Pathways to deep decarbonization - 2015 report

International Nuclear Information System (INIS)

Ribera, Teresa; Colombier, Michel; Waisman, Henri; Bataille, Chris; Pierfederici, Roberta; Sachs, Jeffrey; Schmidt-Traub, Guido; Williams, Jim; Segafredo, Laura; Hamburg Coplan, Jill; Pharabod, Ivan; Oury, Christian

2015-12-01

In September 2015, the Deep Decarbonization Pathways Project published the Executive Summary of the Pathways to Deep Decarbonization: 2015 Synthesis Report. The full 2015 Synthesis Report was launched in Paris on December 3, 2015, at a technical workshop with the Mitigation Action Plans and Scenarios (MAPS) program. The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. In turn, this will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization'

Text feature extraction based on deep learning: a review.

Science.gov (United States)

Liang, Hong; Sun, Xiao; Sun, Yunlei; Gao, Yuan

2017-01-01

Selection of text feature item is a basic and important matter for text mining and information retrieval. Traditional methods of feature extraction require handcrafted features. To hand-design, an effective feature is a lengthy process, but aiming at new applications, deep learning enables to acquire new effective feature representation from training data. As a new feature extraction method, deep learning has made achievements in text mining. The major difference between deep learning and conventional methods is that deep learning automatically learns features from big data, instead of adopting handcrafted features, which mainly depends on priori knowledge of designers and is highly impossible to take the advantage of big data. Deep learning can automatically learn feature representation from big data, including millions of parameters. This thesis outlines the common methods used in text feature extraction first, and then expands frequently used deep learning methods in text feature extraction and its applications, and forecasts the application of deep learning in feature extraction.

Deep-seated sarcomas of the penis

Directory of Open Access Journals (Sweden)

Alberto A. Antunes

2005-06-01

Full Text Available Mesenchymal neoplasias represent 5% of tumors affecting the penis. Due to the rarity of such tumors, there is no agreement concerning the best method for staging and managing these patients. Sarcomas of the penis can be classified as deep-seated if they derive from the structures forming the spongy body and the cavernous bodies. Superficial lesions are usually low-grade and show a small tendency towards distant metastasis. In contrast, deep-seated lesions usually show behavior that is more aggressive and have poorer prognosis. The authors report 3 cases of deep-seated primary sarcomas of the penis and review the literature on this rare and aggressive neoplasia.

In Brief: Deep-sea observatory

Science.gov (United States)

Showstack, Randy

2008-11-01

The first deep-sea ocean observatory offshore of the continental United States has begun operating in the waters off central California. The remotely operated Monterey Accelerated Research System (MARS) will allow scientists to monitor the deep sea continuously. Among the first devices to be hooked up to the observatory are instruments to monitor earthquakes, videotape deep-sea animals, and study the effects of acidification on seafloor animals. ``Some day we may look back at the first packets of data streaming in from the MARS observatory as the equivalent of those first words spoken by Alexander Graham Bell: `Watson, come here, I need you!','' commented Marcia McNutt, president and CEO of the Monterey Bay Aquarium Research Institute, which coordinated construction of the observatory. For more information, see http://www.mbari.org/news/news_releases/2008/mars-live/mars-live.html.

Deep Learning for Computer Vision: A Brief Review

Science.gov (United States)

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Deep Learning for Computer Vision: A Brief Review

Directory of Open Access Journals (Sweden)

Athanasios Voulodimos

2018-01-01

Full Text Available Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Deep Learning for Computer Vision: A Brief Review.

Science.gov (United States)

Voulodimos, Athanasios; Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein.

Is deep dreaming the new collage?

Science.gov (United States)

Boden, Margaret A.

2017-10-01

Deep dreaming (DD) can combine and transform images in surprising ways. But, being based in deep learning (DL), it is not analytically understood. Collage is an art form that is constrained along various dimensions. DD will not be able to generate collages until DL can be guided in a disciplined fashion.

Density functionals from deep learning

OpenAIRE

McMahon, Jeffrey M.

2016-01-01

Density-functional theory is a formally exact description of a many-body quantum system in terms of its density; in practice, however, approximations to the universal density functional are required. In this work, a model based on deep learning is developed to approximate this functional. Deep learning allows computational models that are capable of naturally discovering intricate structure in large and/or high-dimensional data sets, with multiple levels of abstraction. As no assumptions are ...

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

OpenAIRE

Wang, Haohan; Raj, Bhiksha

2015-01-01

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

Assessment of deep geological environment condition

International Nuclear Information System (INIS)

Bae, Dae Seok; Han, Kyung Won; Joen, Kwan Sik

2003-05-01

The main tasks of geoscientific study in the 2nd stage was characterized focusing mainly on a near-field condition of deep geologic environment, and aimed to generate the geologic input data for a Korean reference disposal system for high level radioactive wastes and to establish site characterization methodology, including neotectonic features, fracture systems and mechanical properties of plutonic rocks, and hydrogeochemical characteristics. The preliminary assessment of neotectonics in the Korean peninsula was performed on the basis of seismicity recorded, Quarternary faults investigated, uplift characteristics studied on limited areas, distribution of the major regional faults and their characteristics. The local fracture system was studied in detail from the data obtained from deep boreholes in granitic terrain. Through this deep drilling project, the geometrical and hydraulic properties of different fracture sets are statistically analysed on a block scale. The mechanical properties of intact rocks were evaluated from the core samples by laboratory testing and the in-situ stress conditions were estimated by a hydro fracturing test in the boreholes. The hydrogeochemical conditions in the deep boreholes were characterized based on hydrochemical composition and isotopic signatures and were attempted to assess the interrelation with a major fracture system. The residence time of deep groundwater was estimated by C-14 dating. For the travel time of groundwater between the boreholes, the methodology and equipment for tracer test were established

Deep Carbon Observatory investigates Carbon from Crust to Core: An Academic Record of the History of Deep Carbon Science

Science.gov (United States)

Mitton, S. A.

2017-12-01

Carbon plays an unparalleled role in our lives: as the element of life, as the basis of most of society's energy, as the backbone of most new materials, and as the central focus in efforts to understand Earth's variable and uncertain climate. Yet in spite of carbon's importance, scientists remain largely ignorant of the physical, chemical, and biological behavior of many of Earth's carbon-bearing systems. The Deep Carbon Observatory (DCO) is a global research program to transform our understanding of carbon in Earth. At its heart, DCO is a community of scientists, from biologists to physicists, geoscientists to chemists, and many others whose work crosses these disciplinary lines, forging a new, integrative field of deep carbon science. As a historian of science, I specialise in the history of planetary science and astronomy since 1900. This is directed toward understanding of the history of the steps on the road to discovering the internal dynamics of our planet. Within a framework that describes the historical background to the new field of Earth System Science, I present the first history of deep carbon science. This project will identifies the key discoveries of deep carbon science. It will assess the impact of new knowledge on geochemistry, geodynamics, and geobiology. The project will lead to publication, in book form in 2019, of an illuminating narrative that will highlight the engaging human stories of many remarkable scientists and natural philosophers from whom we have learned about the complexity of Earth's internal world. On this journey of discovery we will encounter not just the pioneering researchers of deep carbon science, but also their institutions, their instrumental inventiveness, and their passion for exploration. The book is organised thematically around the four communities of the Deep Carbon Observatory: Deep Life, Extreme Physics and Chemistry, Reservoirs and Fluxes, and Deep Energy. The presentation has a gallery and list of Deep Carbon

Deep Energy Retrofit

DEFF Research Database (Denmark)

Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

Deep Energy Retrofit – A Guide to Achieving Significant Energy User Reduction with Major Renovation Projects contains recommendations for characteristics of some of core technologies and measures that are based on studies conducted by national teams associated with the International Energy Agency...... Energy Conservation in Buildings and Communities Program (IEA-EBC) Annex 61 (Lohse et al. 2016, Case, et al. 2016, Rose et al. 2016, Yao, et al. 2016, Dake 2014, Stankevica et al. 2016, Kiatreungwattana 2014). Results of these studies provided a base for setting minimum requirements to the building...... envelope-related technologies to make Deep Energy Retrofit feasible and, in many situations, cost effective. Use of energy efficiency measures (EEMs) in addition to core technologies bundle and high-efficiency appliances will foster further energy use reduction. This Guide also provides best practice...

"Towards practical cadmium phytoextraction with Thlaspi caerulescens"

Science.gov (United States)

During 2005-2007, a series of field trials were conducted to investigate the potential of Thlapsi caerulescens ecotypes derived from southern France to phytoextract localized Cd/Zn contamination in Thailand. Soil treatments included pH variation and fertilization level. T. caerulescens ecotypes w...

Deep-well injection of radioactive waste in Russia

International Nuclear Information System (INIS)

Hoek, J.

1998-01-01

In the Russian federation, deep borehole injection of liquid radioactive waste has been established practice since at least 1963. The liquid is injected into sandy or other formations with high porosity, which are isolated by water-tight layers. This technique has also been used elsewhere for toxic liquid waste and residues from mining operations. Deep-well injection of radioactive waste is not currently used in any of the European Commission (EC) countries. In this paper the results of a EC-funded study were presented. The study is entitled 'Measurements, modelling of migration and possible radiological consequences at deep well injection sites for liquid radioactive waste in Russia', COSU-CT94-0099-UK. The study was carried out jointly by AEA Technology, CAG and the Research Institute for Nuclear Reactors (NIIAR) at Dimitrovgrad. Many scientists have contributed to the results reported here. The aims of the study are: Provision of extensive information on the deep-well injection repositories and their use in the former Soviet Union; Provision of a methodology to assess safety aspects of deep-well injection of liquid radioactive waste in deep geological formations; This will allow evaluation of proposals to use deep-well injection techniques in other regions; Support for Russian regulatory bodies through evaluation of the suitability of the sites, including estimates of the maximum amount of waste that can be safely stored in them; and Provision of a methodology to assess the use of deep-well injection repositories as an alternative disposal technique for EC countries. 7 refs

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.

2014-11-05

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer\\'s properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEEP: a general computational framework for predicting enhancers

KAUST Repository

Kleftogiannis, Dimitrios A.; Kalnis, Panos; Bajic, Vladimir B.

2014-01-01

Transcription regulation in multicellular eukaryotes is orchestrated by a number of DNA functional elements located at gene regulatory regions. Some regulatory regions (e.g. enhancers) are located far away from the gene they affect. Identification of distal regulatory elements is a challenge for the bioinformatics research. Although existing methodologies increased the number of computationally predicted enhancers, performance inconsistency of computational models across different cell-lines, class imbalance within the learning sets and ad hoc rules for selecting enhancer candidates for supervised learning, are some key questions that require further examination. In this study we developed DEEP, a novel ensemble prediction framework. DEEP integrates three components with diverse characteristics that streamline the analysis of enhancer's properties in a great variety of cellular conditions. In our method we train many individual classification models that we combine to classify DNA regions as enhancers or non-enhancers. DEEP uses features derived from histone modification marks or attributes coming from sequence characteristics. Experimental results indicate that DEEP performs better than four state-of-the-art methods on the ENCODE data. We report the first computational enhancer prediction results on FANTOM5 data where DEEP achieves 90.2% accuracy and 90% geometric mean (GM) of specificity and sensitivity across 36 different tissues. We further present results derived using in vivo-derived enhancer data from VISTA database. DEEP-VISTA, when tested on an independent test set, achieved GM of 80.1% and accuracy of 89.64%. DEEP framework is publicly available at http://cbrc.kaust.edu.sa/deep/.

DEWS (DEep White matter hyperintensity Segmentation framework): A fully automated pipeline for detecting small deep white matter hyperintensities in migraineurs.

Science.gov (United States)

Park, Bo-Yong; Lee, Mi Ji; Lee, Seung-Hak; Cha, Jihoon; Chung, Chin-Sang; Kim, Sung Tae; Park, Hyunjin

2018-01-01

Migraineurs show an increased load of white matter hyperintensities (WMHs) and more rapid deep WMH progression. Previous methods for WMH segmentation have limited efficacy to detect small deep WMHs. We developed a new fully automated detection pipeline, DEWS (DEep White matter hyperintensity Segmentation framework), for small and superficially-located deep WMHs. A total of 148 non-elderly subjects with migraine were included in this study. The pipeline consists of three components: 1) white matter (WM) extraction, 2) WMH detection, and 3) false positive reduction. In WM extraction, we adjusted the WM mask to re-assign misclassified WMHs back to WM using many sequential low-level image processing steps. In WMH detection, the potential WMH clusters were detected using an intensity based threshold and region growing approach. For false positive reduction, the detected WMH clusters were classified into final WMHs and non-WMHs using the random forest (RF) classifier. Size, texture, and multi-scale deep features were used to train the RF classifier. DEWS successfully detected small deep WMHs with a high positive predictive value (PPV) of 0.98 and true positive rate (TPR) of 0.70 in the training and test sets. Similar performance of PPV (0.96) and TPR (0.68) was attained in the validation set. DEWS showed a superior performance in comparison with other methods. Our proposed pipeline is freely available online to help the research community in quantifying deep WMHs in non-elderly adults.

Deep-Sea Corals: A New Oceanic Archive

National Research Council Canada - National Science Library

Adkins, Jess

1998-01-01

Deep-sea corals are an extraordinary new archive of deep ocean behavior. The species Desmophyllum cristagalli is a solitary coral composed of uranium rich, density banded aragonite that I have calibrated for several paleoclimate tracers...

Challenging oil bioremediation at deep-sea hydrostatic pressure

Directory of Open Access Journals (Sweden)

Alberto Scoma

2016-08-01

Full Text Available The Deepwater Horizon (DWH accident has brought oil contamination of deep-sea environments to worldwide attention. The risk for new deep-sea spills is not expected to decrease in the future, as political pressure mounts to access deep-water fossil reserves, and poorly tested technologies are used to access oil. This also applies to the response to oil-contamination events, with bioremediation the only (biotechnology presently available to combat deep-sea spills. Many questions about the fate of petroleum-hydrocarbons at deep-sea remain unanswered, as much as the main constraints limiting bioremediation under increased hydrostatic pressures and low temperatures. The microbial pathways fueling oil take up are unclear, and the mild upregulation observed for beta-oxidation-related genes in both water and sediments contrasts with the high amount of alkanes present in the spilled-oil. The fate of solid alkanes (tar and that of hydrocarbons degradation rates was largely overlooked, as the reason why the most predominant hydrocarbonoclastic genera were not enriched at deep-sea, despite being present at hydrocarbon seeps at the Gulf of Mexico. This mini-review aims at highlighting the missing information in the field, proposing a holistic approach where in situ and ex situ studies are integrated to reveal the principal mechanisms accounting for deep-sea oil bioremediation.

Deep Crustal Melting and the Survival of Continental Crust

Science.gov (United States)

Whitney, D.; Teyssier, C. P.; Rey, P. F.; Korchinski, M.

2017-12-01

Plate convergence involving continental lithosphere leads to crustal melting, which ultimately stabilizes the crust because it drives rapid upward flow of hot deep crust, followed by rapid cooling at shallow levels. Collision drives partial melting during crustal thickening (at 40-75 km) and/or continental subduction (at 75-100 km). These depths are not typically exceeded by crustal rocks that are exhumed in each setting because partial melting significantly decreases viscosity, facilitating upward flow of deep crust. Results from numerical models and nature indicate that deep crust moves laterally and then vertically, crystallizing at depths as shallow as 2 km. Deep crust flows en masse, without significant segregation of melt into magmatic bodies, over 10s of kms of vertical transport. This is a major mechanism by which deep crust is exhumed and is therefore a significant process of heat and mass transfer in continental evolution. The result of vertical flow of deep, partially molten crust is a migmatite dome. When lithosphere is under extension or transtension, the deep crust is solicited by faulting of the brittle upper crust, and the flow of deep crust in migmatite domes traverses nearly the entire thickness of orogenic crust in Recognition of the importance of migmatite (gneiss) domes as archives of orogenic deep crust is applicable to determining the chemical and physical properties of continental crust, as well as mechanisms and timescales of crustal differentiation.

Deep Learning and Bayesian Methods

OpenAIRE

Prosper Harrison B.

2017-01-01

A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

The dynamics of biogeographic ranges in the deep sea.

Science.gov (United States)

McClain, Craig R; Hardy, Sarah Mincks

2010-12-07

Anthropogenic disturbances such as fishing, mining, oil drilling, bioprospecting, warming, and acidification in the deep sea are increasing, yet generalities about deep-sea biogeography remain elusive. Owing to the lack of perceived environmental variability and geographical barriers, ranges of deep-sea species were traditionally assumed to be exceedingly large. In contrast, seamount and chemosynthetic habitats with reported high endemicity challenge the broad applicability of a single biogeographic paradigm for the deep sea. New research benefiting from higher resolution sampling, molecular methods and public databases can now more rigorously examine dispersal distances and species ranges on the vast ocean floor. Here, we explore the major outstanding questions in deep-sea biogeography. Based on current evidence, many taxa appear broadly distributed across the deep sea, a pattern replicated in both the abyssal plains and specialized environments such as hydrothermal vents. Cold waters may slow larval metabolism and development augmenting the great intrinsic ability for dispersal among many deep-sea species. Currents, environmental shifts, and topography can prove to be dispersal barriers but are often semipermeable. Evidence of historical events such as points of faunal origin and climatic fluctuations are also evident in contemporary biogeographic ranges. Continued synthetic analysis, database construction, theoretical advancement and field sampling will be required to further refine hypotheses regarding deep-sea biogeography.

Survey on deep learning for radiotherapy.

Science.gov (United States)

Meyer, Philippe; Noblet, Vincent; Mazzara, Christophe; Lallement, Alex

2018-05-17

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Hello World Deep Learning in Medical Imaging.

Science.gov (United States)

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

QTL analysis of seed dormancy in Arabidopsis using recombinant inbred lines and MQM mapping

NARCIS (Netherlands)

Schaar, Wybe van der; Alonso-Blanco, Carlos; Léon-Kloosterziel, Karen M.; Jansen, Ritsert C.; Ooijen, Johan W. van; Koornneef, Maarten

1997-01-01

The genetic differences for seed germination between two commonly used Arabidopsis thaliana ecotypes Ler and Col, both showing a low level of seed dormancy, were investigated. The analysis was performed with 98 recombinant inbred lines (RILs) derived from the cross between the two ecotypes, and

Metabolite profiling of Arabidopsis thaliana (L.) plants transformed with an antisense chalcone synthase gene

DEFF Research Database (Denmark)

Le Gall, G.; Metzdorff, Stine Broeng; Pedersen, Jan W.

2005-01-01

A metabolite profiling study has been carried out on Arabidopsis thaliana (L.) Heynh. ecotype Wassilewskija and a series of transgenic lines of the ecotype transformed with a CHS (chalcone synthase) antisense construct. Compound identifications by LC/MS and H-1 NMR are discussed. The glucosinolate...

Deep Neuromuscular Blockade Improves Laparoscopic Surgical Conditions

DEFF Research Database (Denmark)

Rosenberg, Jacob; Herring, W Joseph; Blobner, Manfred

2017-01-01

INTRODUCTION: Sustained deep neuromuscular blockade (NMB) during laparoscopic surgery may facilitate optimal surgical conditions. This exploratory study assessed whether deep NMB improves surgical conditions and, in doing so, allows use of lower insufflation pressures during laparoscopic cholecys...

Development of Hydro-Mechanical Deep Drawing

DEFF Research Database (Denmark)

Zhang, Shi-Hong; Danckert, Joachim

1998-01-01

The hydro-mechanical deep-drawing process is reviewed in this article. The process principles and features are introduced and the developments of the hydro-mechanical deep-drawing process in process performances, in theory and in numerical simulation are described. The applications are summarized....... Some other related hydraulic forming processes are also dealt with as a comparison....

76 FR 66078 - Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer...

Science.gov (United States)

2011-10-25

...-0087] Notice of Industry Workshop on Technical and Regulatory Challenges in Deep and Ultra-Deep Outer... discussions expected to help identify Outer Continental Shelf (OCS) challenges and technologies associated... structured venue for consultation among offshore deepwater oil and gas industry and regulatory experts in...

Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

OpenAIRE

Lea-Anne Henry; Sankha S. Mukherjee; Neil M. Roberston; Laurence De Clippele; J. Murray Roberts

2016-01-01

The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

Evolutionary process of deep-sea bathymodiolus mussels.

Science.gov (United States)

Miyazaki, Jun-Ichi; de Oliveira Martins, Leonardo; Fujita, Yuko; Matsumoto, Hiroto; Fujiwara, Yoshihiro

2010-04-27

Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI) and NADH dehydrogenase subunit 4 (ND4) genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of symbiosis in that nutritional adaptation to the deep sea proceeded from extracellular

Ultra Deep Wave Equation Imaging and Illumination

Energy Technology Data Exchange (ETDEWEB)

Alexander M. Popovici; Sergey Fomel; Paul Sava; Sean Crawley; Yining Li; Cristian Lupascu

2006-09-30

In this project we developed and tested a novel technology, designed to enhance seismic resolution and imaging of ultra-deep complex geologic structures by using state-of-the-art wave-equation depth migration and wave-equation velocity model building technology for deeper data penetration and recovery, steeper dip and ultra-deep structure imaging, accurate velocity estimation for imaging and pore pressure prediction and accurate illumination and amplitude processing for extending the AVO prediction window. Ultra-deep wave-equation imaging provides greater resolution and accuracy under complex geologic structures where energy multipathing occurs, than what can be accomplished today with standard imaging technology. The objective of the research effort was to examine the feasibility of imaging ultra-deep structures onshore and offshore, by using (1) wave-equation migration, (2) angle-gathers velocity model building, and (3) wave-equation illumination and amplitude compensation. The effort consisted of answering critical technical questions that determine the feasibility of the proposed methodology, testing the theory on synthetic data, and finally applying the technology for imaging ultra-deep real data. Some of the questions answered by this research addressed: (1) the handling of true amplitudes in the downward continuation and imaging algorithm and the preservation of the amplitude with offset or amplitude with angle information required for AVO studies, (2) the effect of several imaging conditions on amplitudes, (3) non-elastic attenuation and approaches for recovering the amplitude and frequency, (4) the effect of aperture and illumination on imaging steep dips and on discriminating the velocities in the ultra-deep structures. All these effects were incorporated in the final imaging step of a real data set acquired specifically to address ultra-deep imaging issues, with large offsets (12,500 m) and long recording time (20 s).

U.V. repair in deep-sea bacteria

International Nuclear Information System (INIS)

Lutz, L.; Yayanos, A.A.

1986-01-01

Exposure of cells to light of less than 320 nanometers wavelengths may lead to lethal lesions and perhaps carcinogenesis. Many organisms have evolved mechanisms to repair U.V. light-induced damage. Organisms such as deep-sea bacteria are presumably never exposed to U.V. light and perhaps occasionally to visible from bioluminescence. Thus, the repair of U.V. damage in deep-sea bacterial DNA might be inefficient and repair by photoreactivation unlikely. The bacteria utilized in this investigation are temperature sensitive and barophilic. Four deep-sea isolates were chosen for this study: PE-36 from 3584 m, CNPT-3 from 5782 m, HS-34 from 5682 m, and MT-41 from 10,476 m, all are from the North Pacific ocean. The deep-sea extends from 1100 m to depths greater than 7000 m. It is a region of relatively uniform conditions. The temperature ranges from 5 to -1 0 C. There is no solar light in the deep-sea. Deep-sea bacteria are sensitive to U.V. light; in fact more sensitive than a variety of terrestrial and sea-surface bacteria. All four isolates demonstrate thymine dimer repair. Photoreactivation was observed in only MT-41. The other strains from shallower depths displayed no photoreactivation. The presence of DNA sequences homologous to the rec A, uvr A, B, and C and phr genes of E. coli have been examined by Southern hybridization techniques

Diabetic retinopathy screening using deep neural network.

Science.gov (United States)

Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

2017-09-07

There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

Extracting Databases from Dark Data with DeepDive.

Science.gov (United States)

Zhang, Ce; Shin, Jaeho; Ré, Christopher; Cafarella, Michael; Niu, Feng

2016-01-01

DeepDive is a system for extracting relational databases from dark data : the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data - scientific papers, Web classified ads, customer service notes, and so on - were instead in a relational database, it would give analysts a massive and valuable new set of "big data." DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.

Deep groundwater chemistry

International Nuclear Information System (INIS)

Wikberg, P.; Axelsen, K.; Fredlund, F.

1987-06-01

Starting in 1977 and up till now a number of places in Sweden have been investigated in order to collect the necessary geological, hydrogeological and chemical data needed for safety analyses of repositories in deep bedrock systems. Only crystalline rock is considered and in many cases this has been gneisses of sedimentary origin but granites and gabbros are also represented. Core drilled holes have been made at nine sites. Up to 15 holes may be core drilled at one site, the deepest down to 1000 m. In addition to this a number of boreholes are percussion drilled at each site to depths of about 100 m. When possible drilling water is taken from percussion drilled holes. The first objective is to survey the hydraulic conditions. Core drilled boreholes and sections selected for sampling of deep groundwater are summarized. (orig./HP)

Preface: Deep Slab and Mantle Dynamics

Science.gov (United States)

Suetsugu, Daisuke; Bina, Craig R.; Inoue, Toru; Wiens, Douglas A.

2010-11-01

We are pleased to publish this special issue of the journal Physics of the Earth and Planetary Interiors entitled "Deep Slab and Mantle Dynamics". This issue is an outgrowth of the international symposium "Deep Slab and Mantle Dynamics", which was held on February 25-27, 2009, in Kyoto, Japan. This symposium was organized by the "Stagnant Slab Project" (SSP) research group to present the results of the 5-year project and to facilitate intensive discussion with well-known international researchers in related fields. The SSP and the symposium were supported by a Grant-in-Aid for Scientific Research (16075101) from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government. In the symposium, key issues discussed by participants included: transportation of water into the deep mantle and its role in slab-related dynamics; observational and experimental constraints on deep slab properties and the slab environment; modeling of slab stagnation to constrain its mechanisms in comparison with observational and experimental data; observational, experimental and modeling constraints on the fate of stagnant slabs; eventual accumulation of stagnant slabs on the core-mantle boundary and its geodynamic implications. This special issue is a collection of papers presented in the symposium and other papers related to the subject of the symposium. The collected papers provide an overview of the wide range of multidisciplinary studies of mantle dynamics, particularly in the context of subduction, stagnation, and the fate of deep slabs.

Harnessing the Deep Web: Present and Future

OpenAIRE

Madhavan, Jayant; Afanasiev, Loredana; Antova, Lyublena; Halevy, Alon

2009-01-01

Over the past few years, we have built a system that has exposed large volumes of Deep-Web content to Google.com users. The content that our system exposes contributes to more than 1000 search queries per-second and spans over 50 languages and hundreds of domains. The Deep Web has long been acknowledged to be a major source of structured data on the web, and hence accessing Deep-Web content has long been a problem of interest in the data management community. In this paper, we report on where...

Zooplankton at deep Red Sea brine pools

KAUST Repository

Kaartvedt, Stein

2016-03-02

The deep-sea anoxic brines of the Red Sea comprise unique, complex and extreme habitats. These environments are too harsh for metazoans, while the brine–seawater interface harbors dense microbial populations. We investigated the adjacent pelagic fauna at two brine pools using net tows, video records from a remotely operated vehicle and submerged echosounders. Waters just above the brine pool of Atlantis II Deep (2000 m depth) appeared depleted of macrofauna. In contrast, the fauna appeared to be enriched at the Kebrit Deep brine–seawater interface (1466 m).

How to study deep roots - and why it matters

OpenAIRE

Maeght, Jean-Luc; Rewald, B.; Pierret, Alain

2013-01-01

The drivers underlying the development of deep root systems, whether genetic or environmental, are poorly understood but evidence has accumulated that deep rooting could be a more widespread and important trait among plants than commonly anticipated from their share of root biomass. Even though a distinct classification of "deep roots" is missing to date, deep roots provide important functions for individual plants such as nutrient and water uptake but can also shape plant communities by hydr...

Benchmarking State-of-the-Art Deep Learning Software Tools

OpenAIRE

Shi, Shaohuai; Wang, Qiang; Xu, Pengfei; Chu, Xiaowen

2016-01-01

Deep learning has been shown as a successful machine learning method for a variety of tasks, and its popularity results in numerous open-source deep learning software tools. Training a deep network is usually a very time-consuming process. To address the computational challenge in deep learning, many tools exploit hardware features such as multi-core CPUs and many-core GPUs to shorten the training time. However, different tools exhibit different features and running performance when training ...

High-Redshift Radio Galaxies from Deep Fields

Indian Academy of Sciences (India)

2016-01-27

Jan 27, 2016 ... High-Redshift Radio Galaxies from Deep Fields ... Here we present results from the deep 150 MHz observations of LBDS-Lynx field, which has been imaged at 327, ... Articles are also visible in Web of Science immediately.

Deep Learning Microscopy

KAUST Repository

Rivenson, Yair; Gorocs, Zoltan; Gunaydin, Harun; Zhang, Yibo; Wang, Hongda; Ozcan, Aydogan

2017-01-01

regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably

Deep-sea fungi

Digital Repository Service at National Institute of Oceanography (India)

Raghukumar, C; Damare, S.R.

significant in terms of carbon sequestration (5, 8). In light of this, the diversity, abundance, and role of fungi in deep-sea sediments may form an important link in the global C biogeochemistry. This review focuses on issues related to collection...

Deep Trawl Dataset

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Otter trawl (36' Yankee and 4-seam net deepwater gear) catches from mid-Atlantic slope and canyons at 200 - 800 m depth. Deep-sea (200-800 m depth) flat otter trawls...

Deep learning with convolutional neural network in radiology.

Science.gov (United States)

Yasaka, Koichiro; Akai, Hiroyuki; Kunimatsu, Akira; Kiryu, Shigeru; Abe, Osamu

2018-04-01

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Photon diffractive dissociation in deep inelastic scattering

International Nuclear Information System (INIS)

Ryskin, M.G.

1990-01-01

The new ep-collider HERA gives us the possibility to study the diffractive dissociation of virtual photon in deep inelastic ep-collision. The process of photon dissociation in deep inelastic scattering is the most direct way to measure the value of triple-pomeron vertex G 3P . It was shown that the value of the correct bare vertex G 3P may more than 4 times exceeds its effective value measuring in the triple-reggeon region and reaches the value of about 40-50% of the elastic pp-pomeron vertex. On the contrary in deep inelastic processes the perpendicular momenta q t of the secondary particles are large enough. Thus in deep inelastic reactions one can measure the absolute value of G 3P vertex in the most direct way and compare its value and q t dependence with the leading log QCD predictions

Assessment of Qualitative and Quantitative Characters of Two Persian Clover Ecotypes Inoculated by Rhizobium leguminosarum biovartrifoli and Pesudomonas putida Bacteria

Directory of Open Access Journals (Sweden)

R Azamei

2016-02-01

Full Text Available Introduction Over the past decades, world attitude has changed towards the reduction of environmental pollutants. Harmful effects of synthetic fertilizers on environment have been identified. Bio-fertilizers are not harmful to the environment, but also they have favorable effects on plant growth processes. Soil biotechnology can be defined as the study of soil organisms and their metabolic processes which may have positive effects on plant yields. The main goal of this study is to asess the biotechnology fertilizers beneficial effects on soil organisms and their subsequently to maximize the yield. It is also our desire conside the soil quality, hygiene and environmental protection along this process. Among the strain of nitrogen-fixing bacteria, symbiotic bacteria such as rhizobium bacteria are important and essential in planning the sustainable farming systems. Several studies have shown that crop varieties which inoculated with rhizobium and pseudomonas were superior in yield production and performance. Material and Methods An experiment was designed as factorial performed in randomized complete block design (RCBD with three replications in Agricultural Research Center of Golpayegan (Isfahan during 2010 – 2011. the purpose of this study was to evaluate the effects of inoculation of two ecotypes of Persian clover by various strains of Rhizobium leguminosarum. Biovar trifoli bacteria accompanied with plant growth promoting rhizobacteria (PGPR Pseudomonas putida was employed to find certain qualitative and quantitative characteristics of clover yield, The main plots included two local ecotypes of Persian clover; Arak Haft Chin (V1 and Isfahan Haft Chin (V2, the subplots included inoculation by two strain of Rhizobium; Rb-3, Rb-13 and one strain of Pseudomonas; PS -168.4 cuts were performed during the experiment and 60 kg/ha seed was used for cultivation based on local knowledge. According to recommendations of the Institute of Soil and Water

Ubiquitous healthy diatoms in the deep sea confirm deep carbon injection by the biological pump

KAUST Repository

Agusti, Susana

2015-07-09

The role of the ocean as a sink for CO2 is partially dependent on the downward transport of phytoplankton cells packaged within fast-sinking particles. However, whether such fast-sinking mechanisms deliver fresh organic carbon down to the deep bathypelagic sea and whether this mechanism is prevalent across the ocean requires confirmation. Here we report the ubiquitous presence of healthy photosynthetic cells, dominated by diatoms, down to 4,000 m in the deep dark ocean. Decay experiments with surface phytoplankton suggested that the large proportion (18%) of healthy photosynthetic cells observed, on average, in the dark ocean, requires transport times from a few days to a few weeks, corresponding to sinking rates (124–732 m d−1) comparable to those of fast-sinking aggregates and faecal pellets. These results confirm the expectation that fast-sinking mechanisms inject fresh organic carbon into the deep sea and that this is a prevalent process operating across the global oligotrophic ocean.

Ubiquitous healthy diatoms in the deep sea confirm deep carbon injection by the biological pump

KAUST Repository

Agusti, Susana; Gonzá lez-Gordillo, J. I.; Vaqué , D.; Estrada, M.; Cerezo, M. I.; Salazar, G.; Gasol, J. M.; Duarte, Carlos M.

2015-01-01

The role of the ocean as a sink for CO2 is partially dependent on the downward transport of phytoplankton cells packaged within fast-sinking particles. However, whether such fast-sinking mechanisms deliver fresh organic carbon down to the deep bathypelagic sea and whether this mechanism is prevalent across the ocean requires confirmation. Here we report the ubiquitous presence of healthy photosynthetic cells, dominated by diatoms, down to 4,000 m in the deep dark ocean. Decay experiments with surface phytoplankton suggested that the large proportion (18%) of healthy photosynthetic cells observed, on average, in the dark ocean, requires transport times from a few days to a few weeks, corresponding to sinking rates (124–732 m d−1) comparable to those of fast-sinking aggregates and faecal pellets. These results confirm the expectation that fast-sinking mechanisms inject fresh organic carbon into the deep sea and that this is a prevalent process operating across the global oligotrophic ocean.

The deep universe

CERN Document Server

Sandage, AR; Longair, MS

1995-01-01

Discusses the concept of the deep universe from two conflicting theoretical viewpoints: firstly as a theory embracing the evolution of the universe from the Big Bang to the present; and secondly through observations gleaned over the years on stars, galaxies and clusters.

Deep Vein Thrombosis

Centers for Disease Control (CDC) Podcasts

2012-04-05

This podcast discusses the risk for deep vein thrombosis in long-distance travelers and ways to minimize that risk.  Created: 4/5/2012 by National Center for Emerging and Zoonotic Infectious Diseases (NCEZID).   Date Released: 4/5/2012.

Deep inelastic scattering

International Nuclear Information System (INIS)

Aubert, J.J.

1982-01-01

Deep inelastic lepton-nucleon interaction experiments are renewed. Singlet and non-singlet structure functions are measured and the consistency of the different results is checked. A detailed analysis of the scaling violation is performed in terms of the quantum chromodynamics predictions [fr

Outcomes of the DeepWind conceptual design

NARCIS (Netherlands)

Paulsen, US; Borg, M.; Madsen, HA; Pedersen, TF; Hattel, J; Ritchie, E.; Simao Ferreira, C.; Svendsen, H.; Berthelsen, P.A.; Smadja, C.

2015-01-01

DeepWind has been presented as a novel floating offshore wind turbine concept with cost reduction potentials. Twelve international partners developed a Darrieus type floating turbine with new materials and technologies for deep-sea offshore environment. This paper summarizes results of the 5 MW

Earthquakes - a danger to deep-lying repositories?

International Nuclear Information System (INIS)

2012-03-01

This booklet issued by the Swiss National Cooperative for the Disposal of Radioactive Waste NAGRA takes a look at geological factors concerning earthquakes and the safety of deep-lying repositories for nuclear waste. The geological processes involved in the occurrence of earthquakes are briefly looked at and the definitions for magnitude and intensity of earthquakes are discussed. Examples of damage caused by earthquakes are given. The earthquake situation in Switzerland is looked at and the effects of earthquakes on sub-surface structures and deep-lying repositories are discussed. Finally, the ideas proposed for deep-lying geological repositories for nuclear wastes are discussed

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Ploughing the deep sea floor.

Science.gov (United States)

Puig, Pere; Canals, Miquel; Company, Joan B; Martín, Jacobo; Amblas, David; Lastras, Galderic; Palanques, Albert

2012-09-13

Bottom trawling is a non-selective commercial fishing technique whereby heavy nets and gear are pulled along the sea floor. The direct impact of this technique on fish populations and benthic communities has received much attention, but trawling can also modify the physical properties of seafloor sediments, water–sediment chemical exchanges and sediment fluxes. Most of the studies addressing the physical disturbances of trawl gear on the seabed have been undertaken in coastal and shelf environments, however, where the capacity of trawling to modify the seafloor morphology coexists with high-energy natural processes driving sediment erosion, transport and deposition. Here we show that on upper continental slopes, the reworking of the deep sea floor by trawling gradually modifies the shape of the submarine landscape over large spatial scales. We found that trawling-induced sediment displacement and removal from fishing grounds causes the morphology of the deep sea floor to become smoother over time, reducing its original complexity as shown by high-resolution seafloor relief maps. Our results suggest that in recent decades, following the industrialization of fishing fleets, bottom trawling has become an important driver of deep seascape evolution. Given the global dimension of this type of fishery, we anticipate that the morphology of the upper continental slope in many parts of the world’s oceans could be altered by intensive bottom trawling, producing comparable effects on the deep sea floor to those generated by agricultural ploughing on land.

Parallel Distributed Processing Theory in the Age of Deep Networks.

Science.gov (United States)

Bowers, Jeffrey S

2017-12-01

Parallel distributed processing (PDP) models in psychology are the precursors of deep networks used in computer science. However, only PDP models are associated with two core psychological claims, namely that all knowledge is coded in a distributed format and cognition is mediated by non-symbolic computations. These claims have long been debated in cognitive science, and recent work with deep networks speaks to this debate. Specifically, single-unit recordings show that deep networks learn units that respond selectively to meaningful categories, and researchers are finding that deep networks need to be supplemented with symbolic systems to perform some tasks. Given the close links between PDP and deep networks, it is surprising that research with deep networks is challenging PDP theory. Copyright © 2017. Published by Elsevier Ltd.

DEEP VADOSE ZONE TREATABILITY TEST PLAN

International Nuclear Information System (INIS)

Chronister, G.B.; Truex, M.J.

2009-01-01

(sm b ullet) Treatability test plan published in 2008 (sm b ullet) Outlines technology treatability activities for evaluating application of in situ technologies and surface barriers to deep vadose zone contamination (technetium and uranium) (sm b ullet) Key elements - Desiccation testing - Testing of gas-delivered reactants for in situ treatment of uranium - Evaluating surface barrier application to deep vadose zone - Evaluating in situ grouting and soil flushing

Deep Learning in Visual Computing and Signal Processing

OpenAIRE

Xie, Danfeng; Zhang, Lei; Bai, Li

2017-01-01

Deep learning is a subfield of machine learning, which aims to learn a hierarchy of features from input data. Nowadays, researchers have intensively investigated deep learning algorithms for solving challenging problems in many areas such as image classification, speech recognition, signal processing, and natural language processing. In this study, we not only review typical deep learning algorithms in computer vision and signal processing but also provide detailed information on how to apply...

Deep Visual Attention Prediction

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing

2018-05-01

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

Insulin-like signaling (IIS) responses to temperature, genetic background, and growth variation in garter snakes with divergent life histories.

Science.gov (United States)

Reding, Dawn M; Addis, Elizabeth A; Palacios, Maria G; Schwartz, Tonia S; Bronikowski, Anne M

2016-07-01

The insulin/insulin-like signaling pathway (IIS) has been shown to mediate life history trade-offs in mammalian model organisms, but the function of this pathway in wild and non-mammalian organisms is understudied. Populations of western terrestrial garter snakes (Thamnophis elegans) around Eagle Lake, California, have evolved variation in growth and maturation rates, mortality senescence rates, and annual reproductive output that partition into two ecotypes: "fast-living" and "slow-living". Thus, genes associated with the IIS network are good candidates for investigating the mechanisms underlying ecological divergence in this system. We reared neonates from each ecotype for 1.5years under two thermal treatments. We then used qPCR to compare mRNA expression levels in three tissue types (brain, liver, skeletal muscle) for four genes (igf1, igf2, igf1r, igf2r), and we used radioimmunoassay to measure plasma IGF-1 and IGF-2 protein levels. Our results show that, in contrast to most mammalian model systems, igf2 mRNA and protein levels exceed those of igf1 and suggest an important role for igf2 in postnatal growth in reptiles. Thermal rearing treatment and recent growth had greater impacts on IGF levels than genetic background (i.e., ecotype), and the two ecotypes responded similarly. This suggests that observed ecotypic differences in field measures of IGFs may more strongly reflect plastic responses in different environments than evolutionary divergence. Future analyses of additional components of the IIS pathway and sequence divergence between the ecotypes will further illuminate how environmental and genetic factors influence the endocrine system and its role in mediating life history trade-offs. Copyright © 2016 Elsevier Inc. All rights reserved.

Deep processes in non-relativistic confining potentials

International Nuclear Information System (INIS)

Fishbane, P.M.; Grisaru, M.T.

1978-01-01

The authors study deep inelastic and hard scattering processes for non-relativistic particles confined in deep potentials. The mechanisms by which the effects of confinement disappear and the particles scatter as if free are useful in understanding the analogous results for a relativistic field theory. (Auth.)

Deep Learning in Medical Image Analysis.

Science.gov (United States)

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

pathways to deep decarbonization - 2014 report

International Nuclear Information System (INIS)

Sachs, Jeffrey; Guerin, Emmanuel; Mas, Carl; Schmidt-Traub, Guido; Tubiana, Laurence; Waisman, Henri; Colombier, Michel; Bulger, Claire; Sulakshana, Elana; Zhang, Kathy; Barthelemy, Pierre; Spinazze, Lena; Pharabod, Ivan

2014-09-01

The Deep Decarbonization Pathways Project (DDPP) is a collaborative initiative to understand and show how individual countries can transition to a low-carbon economy and how the world can meet the internationally agreed target of limiting the increase in global mean surface temperature to less than 2 degrees Celsius (deg. C). Achieving the 2 deg. C limit will require that global net emissions of greenhouse gases (GHG) approach zero by the second half of the century. This will require a profound transformation of energy systems by mid-century through steep declines in carbon intensity in all sectors of the economy, a transition we call 'deep decarbonization.' Successfully transition to a low-carbon economy will require unprecedented global cooperation, including a global cooperative effort to accelerate the development and diffusion of some key low carbon technologies. As underscored throughout this report, the results of the DDPP analyses remain preliminary and incomplete. The DDPP proceeds in two phases. This 2014 report describes the DDPP's approach to deep decarbonization at the country level and presents preliminary findings on technically feasible pathways to deep decarbonization, utilizing technology assumptions and timelines provided by the DDPP Secretariat. At this stage we have not yet considered the economic and social costs and benefits of deep decarbonization, which will be the topic for the next report. The DDPP is issuing this 2014 report to the UN Secretary-General Ban Ki-moon in support of the Climate Leaders' Summit at the United Nations on September 23, 2014. This 2014 report by the Deep Decarbonization Pathway Project (DDPP) summarizes preliminary findings of the technical pathways developed by the DDPP Country Research Partners with the objective of achieving emission reductions consistent with limiting global warming to less than 2 deg. C., without, at this stage, consideration of economic and social costs and benefits. The DDPP is a knowledge

Evolutionary process of deep-sea bathymodiolus mussels.

Directory of Open Access Journals (Sweden)

Jun-Ichi Miyazaki

Full Text Available BACKGROUND: Since the discovery of deep-sea chemosynthesis-based communities, much work has been done to clarify their organismal and environmental aspects. However, major topics remain to be resolved, including when and how organisms invade and adapt to deep-sea environments; whether strategies for invasion and adaptation are shared by different taxa or unique to each taxon; how organisms extend their distribution and diversity; and how they become isolated to speciate in continuous waters. Deep-sea mussels are one of the dominant organisms in chemosynthesis-based communities, thus investigations of their origin and evolution contribute to resolving questions about life in those communities. METHODOLOGY/PRINCIPAL FINDING: We investigated worldwide phylogenetic relationships of deep-sea Bathymodiolus mussels and their mytilid relatives by analyzing nucleotide sequences of the mitochondrial cytochrome c oxidase subunit I (COI and NADH dehydrogenase subunit 4 (ND4 genes. Phylogenetic analysis of the concatenated sequence data showed that mussels of the subfamily Bathymodiolinae from vents and seeps were divided into four groups, and that mussels of the subfamily Modiolinae from sunken wood and whale carcasses assumed the outgroup position and shallow-water modioline mussels were positioned more distantly to the bathymodioline mussels. We provisionally hypothesized the evolutionary history of Bathymodilolus mussels by estimating evolutionary time under a relaxed molecular clock model. Diversification of bathymodioline mussels was initiated in the early Miocene, and subsequently diversification of the groups occurred in the early to middle Miocene. CONCLUSIONS/SIGNIFICANCE: The phylogenetic relationships support the "Evolutionary stepping stone hypothesis," in which mytilid ancestors exploited sunken wood and whale carcasses in their progressive adaptation to deep-sea environments. This hypothesis is also supported by the evolutionary transition of

Deep water recycling through time.

Science.gov (United States)

Magni, Valentina; Bouilhol, Pierre; van Hunen, Jeroen

2014-11-01

We investigate the dehydration processes in subduction zones and their implications for the water cycle throughout Earth's history. We use a numerical tool that combines thermo-mechanical models with a thermodynamic database to examine slab dehydration for present-day and early Earth settings and its consequences for the deep water recycling. We investigate the reactions responsible for releasing water from the crust and the hydrated lithospheric mantle and how they change with subduction velocity ( v s ), slab age ( a ) and mantle temperature (T m ). Our results show that faster slabs dehydrate over a wide area: they start dehydrating shallower and they carry water deeper into the mantle. We parameterize the amount of water that can be carried deep into the mantle, W (×10 5 kg/m 2 ), as a function of v s (cm/yr), a (Myrs), and T m (°C):[Formula: see text]. We generally observe that a 1) 100°C increase in the mantle temperature, or 2) ∼15 Myr decrease of plate age, or 3) decrease in subduction velocity of ∼2 cm/yr all have the same effect on the amount of water retained in the slab at depth, corresponding to a decrease of ∼2.2×10 5 kg/m 2 of H 2 O. We estimate that for present-day conditions ∼26% of the global influx water, or 7×10 8 Tg/Myr of H 2 O, is recycled into the mantle. Using a realistic distribution of subduction parameters, we illustrate that deep water recycling might still be possible in early Earth conditions, although its efficiency would generally decrease. Indeed, 0.5-3.7 × 10 8 Tg/Myr of H 2 O could still be recycled in the mantle at 2.8 Ga. Deep water recycling might be possible even in early Earth conditions We provide a scaling law to estimate the amount of H 2 O flux deep into the mantle Subduction velocity has a a major control on the crustal dehydration pattern.

Stratification-Based Outlier Detection over the Deep Web

OpenAIRE

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribu...

Deep neural networks to enable real-time multimessenger astrophysics

Science.gov (United States)

George, Daniel; Huerta, E. A.

2018-02-01

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field of research, there is a pressing need to increase the depth and speed of the algorithms used to enable these ground-breaking discoveries. We introduce Deep Filtering—a new scalable machine learning method for end-to-end time-series signal processing. Deep Filtering is based on deep learning with two deep convolutional neural networks, which are designed for classification and regression, to detect gravitational wave signals in highly noisy time-series data streams and also estimate the parameters of their sources in real time. Acknowledging that some of the most sensitive algorithms for the detection of gravitational waves are based on implementations of matched filtering, and that a matched filter is the optimal linear filter in Gaussian noise, the application of Deep Filtering using whitened signals in Gaussian noise is investigated in this foundational article. The results indicate that Deep Filtering outperforms conventional machine learning techniques, achieves similar performance compared to matched filtering, while being several orders of magnitude faster, allowing real-time signal processing with minimal resources. Furthermore, we demonstrate that Deep Filtering can detect and characterize waveform signals emitted from new classes of eccentric or spin-precessing binary black holes, even when trained with data sets of only quasicircular binary black hole waveforms. The results presented in this article, and the recent use of deep neural networks for the identification of optical transients in telescope data, suggests that deep learning can facilitate real-time searches of gravitational wave sources and their electromagnetic and astroparticle counterparts. In the subsequent article, the framework introduced herein is directly applied to identify and characterize gravitational wave events in real LIGO data.

Avalanches of sediment form deep-marine depositions

NARCIS (Netherlands)

Pohl, Florian|info:eu-repo/dai/nl/34309424X

2017-01-01

The deep ocean is the largest sedimentary system basin on the planet. It serves as the primary storage point for all terrestrially weathered sediment that makes it beyond the near-shore environment. These deep-marine offshore deposits have become a focus of attention in exploration due to the

Equivalent drawbead performance in deep drawing simulations

NARCIS (Netherlands)

Meinders, Vincent T.; Geijselaers, Hubertus J.M.; Huetink, Han

1999-01-01

Drawbeads are applied in the deep drawing process to improve the control of the material flow during the forming operation. In simulations of the deep drawing process these drawbeads can be replaced by an equivalent drawbead model. In this paper the usage of an equivalent drawbead model in the

Deep web search: an overview and roadmap

NARCIS (Netherlands)

Tjin-Kam-Jet, Kien; Trieschnigg, Rudolf Berend; Hiemstra, Djoerd

2011-01-01

We review the state-of-the-art in deep web search and propose a novel classification scheme to better compare deep web search systems. The current binary classification (surfacing versus virtual integration) hides a number of implicit decisions that must be made by a developer. We make these

Age-dependent mixing of deep-sea sediments

International Nuclear Information System (INIS)

Smith, C.R.; Maggaard, L.; Pope, R.H.; DeMaster, D.J.

1993-01-01

Rates of bioturbation measured in deep-sea sediments commonly are tracer dependent; in particular, shorter lived radiotracers (such as 234 Th) often yield markedly higher diffusive mixing coefficients than their longer-lived counterparts (e.g., 210 Pb). At a single station in the 1,240-m deep Santa Catalina Basin, the authors document a strong negative correlation between bioturbation rate and tracer half-life. Sediment profiles of 234 Th (half-life = 24 days) yield an average mixing coefficient (60 cm 2 y -1 ) two orders of magnitude greater than that for 210 Pb (half-life = 22 y, mean mixing coefficient = 0.4 cm 2 y -1 ). A similar negative relationship between mixing rate and tracer time scale is observed at thirteen other deep-sea sites in which multiple radiotracers have been used to assess diffusive mixing rates. This relationship holds across a variety of radiotracer types and time scales. The authors hypothesize that this negative relationship results from age-dependent mixing, a process in which recently sedimented, food-rich particles are ingested and mixed at higher rates by deposit feeders than are older, food-poor particles. Results from an age-dependent mixing model demonstrate that this process indeed can yield the bioturbation-rate vs. tracer-time-scale correlations observed in deep-sea sediments. Field data on mixing rates of recently sedimented particles, as well as the radiotracer activity of deep-sea deposit feeders, provide strong support for the age-dependent mixing model. The presence of age-dependent mixing in deep-sea sediments may have major implications for diagenetic modeling, requiring a match between the characteristic time scales of mixing tracers and modeled reactants. 102 refs., 6 figs., 5 tabs

Deep-sea Hexactinellida (Porifera) of the Weddell Sea

Science.gov (United States)

Janussen, Dorte; Tabachnick, Konstantin R.; Tendal, Ole S.

2004-07-01

New Hexactinellida from the deep Weddel Sea are described. This moderately diverse hexactinellid fauna includes 14 species belonging to 12 genera, of which five species and one subgenus are new to science: Periphragella antarctica n. sp., Holascus pseudostellatus n. sp., Caulophacus (Caulophacus) discohexactinus n. sp., C. ( Caulodiscus) brandti n. sp., C. ( Oxydiscus) weddelli n. sp., and C. ( Oxydiscus) n. subgen. So far, 20 hexactinellid species have been reported from the deep Weddell Sea, 15 are known from the northern part and 10 only from here, while 10 came from the southern area, and five of these only from there. However, this apparent high "endemism" of Antarctic hexactinellid sponges is most likely the result of severe undersampling of the deep-sea fauna. We find no reason to believe that a division between an oceanic and a more continental group of species exists. The current poor database indicates that a substantial part of the deep hexactinellid fauna of the Weddell Sea is shared with other deep-sea regions, but it does not indicate a special biogeographic relationship with any other ocean.

Deepwater Program: Lophelia II, continuing ecological research on deep-sea corals and deep-reef habitats in the Gulf of Mexico

Science.gov (United States)

Demopoulos, Amanda W.J.; Ross, Steve W.; Kellogg, Christina A.; Morrison, Cheryl L.; Nizinski, Martha S.; Prouty, Nancy G.; Bourque, Jill R.; Galkiewicz, Julie P.; Gray, Michael A.; Springmann, Marcus J.; Coykendall, D. Katharine; Miller, Andrew; Rhode, Mike; Quattrini, Andrea; Ames, Cheryl L.; Brooke, Sandra D.; McClain Counts, Jennifer; Roark, E. Brendan; Buster, Noreen A.; Phillips, Ryan M.; Frometa, Janessy

2017-12-11

The deep sea is a rich environment composed of diverse habitat types. While deep-sea coral habitats have been discovered within each ocean basin, knowledge about the ecology of these habitats and associated inhabitants continues to grow. This report presents information and results from the Lophelia II project that examined deep-sea coral habitats in the Gulf of Mexico. The Lophelia II project focused on Lophelia pertusa habitats along the continental slope, at depths ranging from 300 to 1,000 meters. The chapters are authored by several scientists from the U.S. Geological Survey, National Oceanic and Atmospheric Administration, University of North Carolina Wilmington, and Florida State University who examined the community ecology (from microbes to fishes), deep-sea coral age, growth, and reproduction, and population connectivity of deep-sea corals and inhabitants. Data from these studies are presented in the chapters and appendixes of the report as well as in journal publications. This study was conducted by the Ecosystems Mission Area of the U.S. Geological Survey to meet information needs identified by the Bureau of Ocean Energy Management.

Deep learning quick reference useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

CERN Document Server

Bernico, Michael

2018-01-01

This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and optimize your deep neural networks.

Deep inelastic scattering

International Nuclear Information System (INIS)

Zakharov, V.I.

1977-01-01

The present status of the quark-parton-gluon picture of deep inelastic scattering is reviewed. The general framework is mostly theoretical and covers investigations since 1970. Predictions of the parton model and of the asymptotically free field theories are compared with experimental data available. The valence quark approximation is concluded to be valid in most cases, but fails to account for the data on the total momentum transfer. On the basis of gluon corrections introduced to the parton model certain predictions concerning both the deep inelastic structure functions and form factors are made. The contributions of gluon exchanges and gluon bremsstrahlung are highlighted. Asymptotic freedom is concluded to be very attractive and provide qualitative explanation to some experimental observations (scaling violations, breaking of the Drell-Yan-West type relations). Lepton-nuclear scattering is pointed out to be helpful in probing the nature of nuclear forces and studying the space-time picture of the parton model

The DEEP-South: Scheduling and Data Reduction Software System

Science.gov (United States)

Yim, Hong-Suh; Kim, Myung-Jin; Bae, Youngho; Moon, Hong-Kyu; Choi, Young-Jun; Roh, Dong-Goo; the DEEP-South Team

2015-08-01

The DEep Ecliptic Patrol of the Southern sky (DEEP-South), started in October 2012, is currently in test runs with the first Korea Microlensing Telescope Network (KMTNet) 1.6 m wide-field telescope located at CTIO in Chile. While the primary objective for the DEEP-South is physical characterization of small bodies in the Solar System, it is expected to discover a large number of such bodies, many of them previously unknown.An automatic observation planning and data reduction software subsystem called "The DEEP-South Scheduling and Data reduction System" (the DEEP-South SDS) is currently being designed and implemented for observation planning, data reduction and analysis of huge amount of data with minimum human interaction. The DEEP-South SDS consists of three software subsystems: the DEEP-South Scheduling System (DSS), the Local Data Reduction System (LDR), and the Main Data Reduction System (MDR). The DSS manages observation targets, makes decision on target priority and observation methods, schedules nightly observations, and archive data using the Database Management System (DBMS). The LDR is designed to detect moving objects from CCD images, while the MDR conducts photometry and reconstructs lightcurves. Based on analysis made at the LDR and the MDR, the DSS schedules follow-up observation to be conducted at other KMTNet stations. In the end of 2015, we expect the DEEP-South SDS to achieve a stable operation. We also have a plan to improve the SDS to accomplish finely tuned observation strategy and more efficient data reduction in 2016.

A Modeling Study of Deep Water Renewal in the Red Sea

Science.gov (United States)

Yao, F.; Hoteit, I.

2016-02-01

Deep water renewal processes in the Red Sea are examined in this study using a 50-year numerical simulation from 1952-2001. The deep water in the Red Sea below the thermocline ( 200 m) exhibits a near-uniform vertical structure in temperature and salinity, but geochemical tracer distributions, such as 14C and 3He, and dissolved oxygen concentrations indicate that the deep water is renewed on time scales as short as 36 years. The renewal process is accomplished through a deep overturning cell that consists of a southward bottom current and a northward returning current at depths of 400-600 m. Three sources regions are proposed for the formation of the deep water, including two deep outflows from the Gulfs of Aqaba and Suez and winter deep convections in the northern Red Sea. The MITgcm (MIT general circulation model), which has been used to simulate the shallow overturning circulations in the Red Sea, is configured in this study with increased resolutions in the deep water. During the 50 years of simulation, artificial passive tracers added in the model indicate that the deep water in the Red Sea was only episodically renewed during some anomalously cold years; two significant episodes of deep water renewal are reproduced in the winters of 1983 and 1992, in accordance with reported historical hydrographic observations. During these renewal events, deep convections reaching the bottom of the basin occurred, which further facilitated deep sinking of the outflows from the Gulfs of Aqaba and Suez. Ensuing spreading of the newly formed deep water along the bottom caused upward displacements of thermocline, which may have profound effects on the water exchanges in the Strait of Bab el Mandeb between the Red Sea and the Gulf of Aden and the functioning of the ecosystem in the Red Sea by changing the vertical distributions of nutrients.

Deep Borehole Field Test Research Activities at LBNL

Energy Technology Data Exchange (ETDEWEB)

Dobson, Patrick [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Tsang, Chin-Fu [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Kneafsey, Timothy [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Borglin, Sharon [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Piceno, Yvette [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Andersen, Gary [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nakagawa, Seiji [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Nihei, Kurt [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Rutqvist, Jonny [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Doughty, Christine [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Reagan, Matthew [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

2016-08-19

The goal of the U.S. Department of Energy Used Fuel Disposition’s (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

Deep Borehole Field Test Research Activities at LBNL

International Nuclear Information System (INIS)

Dobson, Patrick; Tsang, Chin-Fu; Kneafsey, Timothy; Borglin, Sharon; Piceno, Yvette; Andersen, Gary; Nakagawa, Seiji; Nihei, Kurt; Rutqvist, Jonny; Doughty, Christine; Reagan, Matthew

2016-01-01

The goal of the U.S. Department of Energy Used Fuel Disposition's (UFD) Deep Borehole Field Test is to drill two 5 km large-diameter boreholes: a characterization borehole with a bottom-hole diameter of 8.5 inches and a field test borehole with a bottom-hole diameter of 17 inches. These boreholes will be used to demonstrate the ability to drill such holes in crystalline rocks, effectively characterize the bedrock repository system using geophysical, geochemical, and hydrological techniques, and emplace and retrieve test waste packages. These studies will be used to test the deep borehole disposal concept, which requires a hydrologically isolated environment characterized by low permeability, stable fluid density, reducing fluid chemistry conditions, and an effective borehole seal. During FY16, Lawrence Berkeley National Laboratory scientists conducted a number of research studies to support the UFD Deep Borehole Field Test effort. This work included providing supporting data for the Los Alamos National Laboratory geologic framework model for the proposed deep borehole site, conducting an analog study using an extensive suite of geoscience data and samples from a deep (2.5 km) research borehole in Sweden, conducting laboratory experiments and coupled process modeling related to borehole seals, and developing a suite of potential techniques that could be applied to the characterization and monitoring of the deep borehole environment. The results of these studies are presented in this report.

The biomass of the deep-sea benthopelagic plankton

Science.gov (United States)

Wishner, K. F.

1980-04-01

Deep-sea benthopelagic plankton samples were collected with a specially designed opening-closing net system 10 to 100 m above the bottom in five different oceanic regions at depths from 1000 to 4700 m. Benthopelagic plankton biomasses decrease exponentially with depth. At 1000 m the biomass is about 1% that of the surface zooplankton, at 5000 m about 0.1%. Effects of differences in surface primary productivity on deep-sea plankton biomass are much less than the effect of depth and are detectable only in a few comparisons of extreme oceanic regions. The biomass at 10 m above the bottom is greater than that at 100 m above the bottom (in a three-sample comparison), which could be a consequence of an enriched near-bottom environment. The deep-sea plankton biomass in the Red Sea is anomalously low. This may be due to increased decomposition rates in the warm (22°C) deep Red Sea water, which prevent much detritus from reaching the deep sea. A model of organic carbon utilization in the benthic boundary layer (bottom 100 m), incorporating results from deep-sea sediment trap and respiration studies, indicates that the benthopelagic plankton use only a small amount of the organic carbon flux. A large fraction of the flux is unaccounted for by present estimates of benthic and benthopelagic respiration.

Evolving Deep Networks Using HPC

Energy Technology Data Exchange (ETDEWEB)

Young, Steven R. [ORNL, Oak Ridge; Rose, Derek C. [ORNL, Oak Ridge; Johnston, Travis [ORNL, Oak Ridge; Heller, William T. [ORNL, Oak Ridge; Karnowski, thomas P. [ORNL, Oak Ridge; Potok, Thomas E. [ORNL, Oak Ridge; Patton, Robert M. [ORNL, Oak Ridge; Perdue, Gabriel [Fermilab; Miller, Jonathan [Santa Maria U., Valparaiso

2017-01-01

While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus often a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.

Desalination Economic Evaluation Program (DEEP). User's manual

International Nuclear Information System (INIS)

2000-01-01

DEEP (formerly named ''Co-generation and Desalination Economic Evaluation'' Spreadsheet, CDEE) has been developed originally by General Atomics under contract, and has been used in the IAEA's feasibility studies. For further confidence in the software, it was validated in March 1998. After that, a user friendly version has been issued under the name of DEEP at the end of 1998. DEEP output includes the levelised cost of water and power, a breakdown of cost components, energy consumption and net saleable power for each selected option. Specific power plants can be modelled by adjustment of input data including design power, power cycle parameters and costs

Characterization of majority and minority carrier deep levels in p-type GaN:Mg grown by molecular beam epitaxy using deep level optical spectroscopy

International Nuclear Information System (INIS)

Armstrong, A.; Caudill, J.; Ringel, S. A.; Corrion, A.; Poblenz, C.; Mishra, U. K.; Speck, J. S.

2008-01-01

Deep level defects in p-type GaN:Mg grown by molecular beam epitaxy were characterized using steady-state photocapacitance and deep level optical spectroscopy (DLOS). Low frequency capacitance measurements were used to alleviate dispersion effects stemming from the deep Mg acceptor. Use of DLOS enabled a quantitative survey of both deep acceptor and deep donor levels, the latter being particularly important due to the limited understanding of minority carrier states for p-type GaN. Simultaneous electron and hole photoemissions resulted in a convoluted deep level spectrum that was decoupled by emphasizing either majority or minority carrier optical emission through control of the thermal filling time conditions. In this manner, DLOS was able to resolve and quantify the properties of deep levels residing near both the conduction and valence bandedges in the same sample. Bandgap states through hole photoemission were observed at E v +3.05 eV, E v +3.22 eV and E v +3.26 eV. Additionally, DLOS revealed levels at E c -3.24 eV and E c -2.97 eV through electron emission to the conduction band with the former attributed to the Mg acceptor itself. The detected deep donor concentration is less than 2% of activated [Mg] and demonstrates the excellent quality of the film

DeepFlavour in CMS

CERN Multimedia

CERN. Geneva

2017-01-01

Flavour-tagging of jets is an important task in collider based high energy physics and a field where machine learning tools are applied by all major experiments. A new tagger (DeepFlavour) was developed and commissioned in CMS that is based on an advanced machine learning procedure. A deep neural network is used to do multi-classification of jets that origin from a b-quark, two b-quarks, a c-quark, two c-quarks or light colored particles (u, d, s-quark or gluon). The performance was measured in both, data and simulation. The talk will also include the measured performance of all taggers in CMS. The different taggers and results will be discussed and compared with some focus on details of the newest tagger.

Studies on the yield and quality characteristics of Myrtle (Myrtus communis L. grown in two different ecologies

Directory of Open Access Journals (Sweden)

Halil İbrahim UZUN

2016-12-01

Full Text Available Myrtle is a typical Mediterranean plant. Myrtles plants with white colored and large fruit sized are cultivated in southern coasts of Turkey and named as Hambeles. Black myrtles are naturally grown in the forests and they have smaller fruit size when compared to Hambeles. Main objective of this study was to investigate the yield and quality parameters of some newly selected 3 black myrtle ecotypes (Yakup, Yumaklar, Islangıc and one white myrtle cultivar (Hambeles in upland and lowland ecological conditions in Antalya. Yields, physical and chemical characters of fruits and essential oil composition of leaves were recorded for all plants. Two experimental orchards were established in coastal and highland conditions in Antalya. Highest fruit weight of black myrtles was measured as 0.76 g fruit-1 in Yakup ecotypes in highland and as 0.92 g fruit-1 in Yumaklar ecotypes in lowland. There were no differences among ecotypes in terms of fruit removal force. Fruit yield per tree increased up to 9.2 kg in black myrtle in lowland. Highest perfect seed numbers in myrtle plants were measured in Hambeles ecotype as 19.83 seeds fruit-1. Fruit juice yield ranged from 29.6 to 35.0%. Amount of malic acid in fruit was higher than that of other organic acids. α-pinene and 1,8-cineole were main essential oil components of myrtle leaves.

Genetic relatedness among indigenous rice varieties in the Eastern Himalayan region based on nucleotide sequences of the Waxy gene.

Science.gov (United States)

Choudhury, Baharul I; Khan, Mohammed L; Dayanandan, Selvadurai

2014-12-29

Indigenous rice varieties in the Eastern Himalayan region of Northeast India are traditionally classified into sali, boro and jum ecotypes based on geographical locality and the season of cultivation. In this study, we used DNA sequence data from the Waxy (Wx) gene to infer the genetic relatedness among indigenous rice varieties in Northeast India and to assess the genetic distinctiveness of ecotypes. The results of all three analyses (Bayesian, Maximum Parsimony and Neighbor Joining) were congruent and revealed two genetically distinct clusters of rice varieties in the region. The large group comprised several varieties of sali and boro ecotypes, and all agronomically improved varieties. The small group consisted of only traditionally cultivated indigenous rice varieties, which included one boro, few sali and all jum varieties. The fixation index analysis revealed a very low level of differentiation between sali and boro (F(ST) = 0.005), moderate differentiation between sali and jum (F(ST) = 0.108) and high differentiation between jum and boro (F(ST) = 0.230) ecotypes. The genetic relatedness analyses revealed that sali, boro and jum ecotypes are genetically heterogeneous, and the current classification based on cultivation type is not congruent with the genetic background of rice varieties. Indigenous rice varieties chosen from genetically distinct clusters could be used in breeding programs to improve genetic gain through heterosis, while maintaining high genetic diversity.

Botanical aspects of the ecological integrity of a radioactive waste disposal site

International Nuclear Information System (INIS)

Hall, A.V.

1986-01-01

Botanical factors play a key role in maintaining the long term integrity of ecosystems. The results of botanical research at the Vaalputs radioactive waste disposal site in Bushmanland, South Africa, are outlined. Vaalputs is in an arid region and its vegetation is a patchy mosaic of low shrub and grass communities. Soil variation from site to site is the main determinant of community structure and erratic precipitation is a major stochastic forcing factor. Management measures to conserve taxa, ecosystems and local genetic biogeography are discussed in relation to natural and artificial disturbances which may occur over long time-scales. Restoration of vegetation over the burial trenches should as far as possible be to the site's former ecotypes except that deep-rooted species should be excluded. Precautions should be taken against the accidental establishment of exotic plant invaders at Vaalputs especially if they are deep-rooted. More is now known about plant ecology at Vaalputs than any other part of Bushmanland. It would be valuable to develop such studies further and establish Vaalputs as a permanent reserve for arid-zone biological research

Deep bite malocclusion: exploration of the skeletal and dental factors

International Nuclear Information System (INIS)

Bhateja, N.K.; Fida, M.; Shaikh, A.

2016-01-01

Correction of deep bite is crucial for maintenance of dental hard and soft tissue structures and for prevention of temporomandibular joint disorders. Exploration of underlying skeletal and dental factors is essential for efficient and individualized treatment planning. To date etiological factors of dental and skeletal deep bite have not been explored in Pakistani orthodontic patients. The objectives of this study were to explore frequencies of dental and skeletal etiological factors in deep bite patients and to determine correlations amongst dental and skeletal etiological factors of deep bite. Methods: The study included a total of 113 subjects (males=35; females=78) with no craniofacial syndromes or prior orthodontic treatment. Pre-treatment orthodontic records were used to evaluate various dental and skeletal parameters. Descriptive statistics of each parameter were calculated. The various study parameters were correlated using Pearson's Correlation. Results: Deep curve of Spee was most frequently seen factor of dental deep bite (72.6%), followed by increased coronal length of upper incisors (28.3%), retroclined upper incisors (17.7%), retroclined lower incisors (8%) and increased coronal length of lower incisors (5.3%). Decreased gonial angle was most commonly found factor of skeletal deep bite (43.4%), followed by decreased mandibular plane angle (27.4%) and maxillary plane's clockwise rotation (26.5%). Frankfort mandibular plane angle and gonial angle showed a strong positive correlation (r=0.66, p=0.000). Conclusions: Reduced gonial angle is most frequently seen skeletal factor, signifying the importance of angulation and growth of ramus in development of deep bite. Deep curve of Spee is most frequently seen dental etiological component in deep bite subjects, hence signifying the importance of intruding the lower anterior teeth. (author)

Preliminary discussion on deep-sourced uranium metallogenesis and deep prospecting

International Nuclear Information System (INIS)

Huang Shijie

2006-01-01

Prospecting for hydrothermal type uranium deposits should be aimed at medium-to large-sized deposits, and be guided by mantle-sourced, superimposed, deep-sourced metallogenic theory and the establishment of a multifactor, composite, deep-sourced metallogenic model. The author suggests that hydrothermal uranium deposits may be classified into three genetic types, i.e. hydrothermal circulation concentration, postmagmatic hydrothermal and mantle fluid concentration. These types of uranium deposits are characterized by their own metallogenic features and are concentrated in the same mineralization-concentrated area forming a metallogenic series. Large-sized uranium ore fields and rich-large uranium deposits are usually closely associated with mantle-sourced metallogenesis and the formation of such uranium ore fields and deposits is characterized by specific and unique regional geologic environments. Recognition criteria of mantle-sourced metallogenesis are preliminarily proposed in the paper. It is pointed out that prospecting in the future should follow the metallogenic model proper for the specific genetic type, and the establishment of operable prospecting model to realize the model-guided prospecting. (authors)

Deep Ocean Contribution to Sea Level Rise

Science.gov (United States)

Chang, L.; Sun, W.; Tang, H.; Wang, Q.

2017-12-01

The ocean temperature and salinity change in the upper 2000m can be detected by Argo floats, so we can know the steric height change of the ocean. But the ocean layers above 2000m represent only 50% of the total ocean volume. Although the temperature and salinity change are small compared to the upper ocean, the deep ocean contribution to sea level might be significant because of its large volume. There has been some research on the deep ocean rely on the very sparse situ observation and are limited to decadal and longer-term rates of change. The available observational data in the deep ocean are too spares to determine the temporal variability, and the long-term changes may have a bias. We will use the Argo date and combine the situ data and topographic data to estimate the temperature and salinity of the sea water below 2000m, so we can obtain a monthly data. We will analyze the seasonal and annual change of the steric height change due to the deep ocean between 2005 and 2016. And we will evaluate the result combination the present-day satellite and in situ observing systems. The deep ocean contribution can be inferred indirectly as the difference between the altimetry minus GRACE and Argo-based steric sea level.

Deep learning for SAR image formation

Science.gov (United States)

Mason, Eric; Yonel, Bariscan; Yazici, Birsen

2017-04-01

The recent success of deep learning has lead to growing interest in applying these methods to signal processing problems. This paper explores the applications of deep learning to synthetic aperture radar (SAR) image formation. We review deep learning from a perspective relevant to SAR image formation. Our objective is to address SAR image formation in the presence of uncertainties in the SAR forward model. We present a recurrent auto-encoder network architecture based on the iterative shrinkage thresholding algorithm (ISTA) that incorporates SAR modeling. We then present an off-line training method using stochastic gradient descent and discuss the challenges and key steps of learning. Lastly, we show experimentally that our method can be used to form focused images in the presence of phase uncertainties. We demonstrate that the resulting algorithm has faster convergence and decreased reconstruction error than that of ISTA.

Sentimen Analisis Tweet Berbahasa Indonesia Dengan Deep Belief Network

Directory of Open Access Journals (Sweden)

Ira zulfa

2017-07-01

Full Text Available Sentiment analysis is a computational research of opinion sentiment and emotion which is expressed in textual mode. Twitter becomes the most popular communication device among internet users. Deep Learning is a new area of machine learning research. It aims to move machine learning closer to its main goal, artificial intelligence. The purpose of deep learning is to change the manual of engineering with learning. At its growth, deep learning has algorithms arrangement that focus on non-linear data representation. One of the machine learning methods is Deep Belief Network (DBN. Deep Belief Network (DBN, which is included in Deep Learning method, is a stack of several algorithms with some extraction features that optimally utilize all resources. This study has two points. First, it aims to classify positive, negative, and neutral sentiments towards the test data. Second, it determines the classification model accuracy by using Deep Belief Network method so it would be able to be applied into the tweet classification, to highlight the sentiment class of training data tweet in Bahasa Indonesia. Based on the experimental result, it can be concluded that the best method in managing tweet data is the DBN method with an accuracy of 93.31%, compared with  Naive Bayes method which has an accuracy of 79.10%, and SVM (Support Vector Machine method with an accuracy of 92.18%.

Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition

OpenAIRE

Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao

2016-01-01

A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...

Research Proposal for Distributed Deep Web Search

NARCIS (Netherlands)

Tjin-Kam-Jet, Kien

2010-01-01

This proposal identifies two main problems related to deep web search, and proposes a step by step solution for each of them. The first problem is about searching deep web content by means of a simple free-text interface (with just one input field, instead of a complex interface with many input

Short-term Memory of Deep RNN

OpenAIRE

Gallicchio, Claudio

2018-01-01

The extension of deep learning towards temporal data processing is gaining an increasing research interest. In this paper we investigate the properties of state dynamics developed in successive levels of deep recurrent neural networks (RNNs) in terms of short-term memory abilities. Our results reveal interesting insights that shed light on the nature of layering as a factor of RNN design. Noticeably, higher layers in a hierarchically organized RNN architecture results to be inherently biased ...

Biodiversity loss from deep-sea mining

OpenAIRE

C. L. Van Dover; J. A. Ardron; E. Escobar; M. Gianni; K. M. Gjerde; A. Jaeckel; D. O. B. Jones; L. A. Levin; H. Niner; L. Pendleton; C. R. Smith; T. Thiele; P. J. Turner; L. Watling; P. P. E. Weaver

2017-01-01

The emerging deep-sea mining industry is seen by some to be an engine for economic development in the maritime sector. The International Seabed Authority (ISA) – the body that regulates mining activities on the seabed beyond national jurisdiction – must also protect the marine environment from harmful effects that arise from mining. The ISA is currently drafting a regulatory framework for deep-sea mining that includes measures for environmental protection. Responsible mining increasingly stri...

Deep Predictive Models in Interactive Music

OpenAIRE

Martin, Charles P.; Ellefsen, Kai Olav; Torresen, Jim

2018-01-01

Automatic music generation is a compelling task where much recent progress has been made with deep learning models. In this paper, we ask how these models can be integrated into interactive music systems; how can they encourage or enhance the music making of human users? Musical performance requires prediction to operate instruments, and perform in groups. We argue that predictive models could help interactive systems to understand their temporal context, and ensemble behaviour. Deep learning...

Recent changes in the deep-water fish populations of Lake Michigan

Science.gov (United States)

Moffett, James W.

1957-01-01

The deep-water fish fauna of Lake Michigan consisted of lake trout (Salvelinus namaycush), burbot (Lota lota maculosa), seven species of chubs or deep-water ciscoes (Leucichthys spp.), and the deep-water sculpin (Myoxocephalus quadricornis). Other species occupied the deep-water zone but were not typically part of the fauna.

Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks.

Science.gov (United States)

Burt, Jeremy R; Torosdagli, Neslisah; Khosravan, Naji; RaviPrakash, Harish; Mortazi, Aliasghar; Tissavirasingham, Fiona; Hussein, Sarfaraz; Bagci, Ulas

2018-04-10

Deep learning has demonstrated tremendous revolutionary changes in the computing industry and its effects in radiology and imaging sciences have begun to dramatically change screening paradigms. Specifically, these advances have influenced the development of computer-aided detection and diagnosis (CAD) systems. These technologies have long been thought of as "second-opinion" tools for radiologists and clinicians. However, with significant improvements in deep neural networks, the diagnostic capabilities of learning algorithms are approaching levels of human expertise (radiologists, clinicians etc.), shifting the CAD paradigm from a "second opinion" tool to a more collaborative utility. This paper reviews recently developed CAD systems based on deep learning technologies for breast cancer diagnosis, explains their superiorities with respect to previously established systems, defines the methodologies behind the improved achievements including algorithmic developments, and describes remaining challenges in breast cancer screening and diagnosis. We also discuss possible future directions for new CAD models that continue to change as artificial intelligence algorithms evolve.

Stable isotope geochemistry of deep sea cherts

Energy Technology Data Exchange (ETDEWEB)

Kolodny, Y; Epstein, S [California Inst. of Tech., Pasadena (USA). Div. of Geological Sciences

1976-10-01

Seventy four samples of DSDP (Deep Sea Drilling Project) recovered cherts of Jurassic to Miocene age from varying locations, and 27 samples of on-land exposed cherts were analyzed for the isotopic composition of their oxygen and hydrogen. These studies were accompanied by mineralogical analyses and some isotopic analyses of the coexisting carbonates. delta/sup 18/0 of chert ranges between 27 and 39 parts per thousand relative to SMOW, delta/sup 18/0 of porcellanite - between 30 and 42 parts per thousand. The consistent enrichment of opal-CT in porcellanites in /sup 18/0 with respect to coexisting microcrystalline quartz in chert is probably a reflection of a different temperature (depth) of diagenesis of the two phases. delta/sup 18/0 of deep sea cherts generally decrease with increasing age, indicating an overall cooling of the ocean bottom during the last 150 m.y. A comparison of this trend with that recorded by benthonic foraminifera (Douglas et al., Initial Reports of the Deep Sea Drilling Project; 32:509(1975)) indicates the possibility of delta/sup 18/0 in deep sea cherts not being frozen in until several tens of millions of years after deposition. Cherts of any Age show a spread of delta/sup 18/0 values, increasing diagenesis being reflected in a lowering of delta/sup 18/0. Drusy quartz has the lowest delta/sup 18/0 values. On land exposed cherts are consistently depleted in /sup 18/0 in comparison to their deep sea time equivalent cherts. Water extracted from deep sea cherts ranges between 0.5 and 1.4 wt%. deltaD of this water ranges between -78 and -95 parts per thousand and is not a function of delta/sup 18/0 of the cherts (or the temperature of their formation).

Distributed deep learning networks among institutions for medical imaging.

Science.gov (United States)

Chang, Ken; Balachandar, Niranjan; Lam, Carson; Yi, Darvin; Brown, James; Beers, Andrew; Rosen, Bruce; Rubin, Daniel L; Kalpathy-Cramer, Jayashree

2018-03-29

Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data. We simulate the distribution of deep learning models across 4 institutions using various training heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The training heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in 3 independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance that was comparable to that of centrally hosted patient data. We also found that there is an improvement in the performance of cyclical weight transfer heuristic with a high frequency of weight transfer. We show that distributing deep learning models is an effective alternative to sharing patient data. This finding has implications for any collaborative deep learning study.

Deep learning for studies of galaxy morphology

Science.gov (United States)

Tuccillo, D.; Huertas-Company, M.; Decencière, E.; Velasco-Forero, S.

2017-06-01

Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.

Deep-Learning-Based Drug-Target Interaction Prediction.

Science.gov (United States)

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

Deep inelastic scattering near the Coulomb barrier

International Nuclear Information System (INIS)

Gehring, J.; Back, B.; Chan, K.

1995-01-01

Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems 124,112 Sn + 58,64 Ni by Wolfs et al. We previously extended these measurements to the system 136 Xe + 64 Ni and currently measured the system 124 Xe + 58 Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring

Deep Phenotyping: Deep Learning For Temporal Phenotype/Genotype Classification

OpenAIRE

Najafi, Mohammad; Namin, Sarah; Esmaeilzadeh, Mohammad; Brown, Tim; Borevitz, Justin

2017-01-01

High resolution and high throughput, genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. Complex developmental phenotypes are observed by imaging a variety of accessions in different environment conditions, however extracting the genetically heritable traits is challenging. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long-Short Term Memories (LSTMs), h...

Deep imitation learning for 3D navigation tasks.

Science.gov (United States)

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Detecting atrial fibrillation by deep convolutional neural networks.

Science.gov (United States)

Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

2018-02-01

Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deep Space Habitat Project

Data.gov (United States)

National Aeronautics and Space Administration — The Deep Space Habitat was closed out at the end of Fiscal Year 2013 (September 30, 2013). Results and select content have been incorporated into the new Exploration...

Deep Water Survey Data

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The deep water biodiversity surveys explore and describe the biodiversity of the bathy- and bentho-pelagic nekton using Midwater and bottom trawls centered in the...

Benchmarking Deep Learning Models on Large Healthcare Datasets.

Science.gov (United States)

Purushotham, Sanjay; Meng, Chuizheng; Che, Zhengping; Liu, Yan

2018-06-04

Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores. We used the Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4) publicly available dataset, which includes all patients admitted to an ICU at the Beth Israel Deaconess Medical Center from 2001 to 2012, for the benchmarking tasks. Our results show that deep learning models consistently outperform all the other approaches especially when the 'raw' clinical time series data is used as input features to the models. Copyright © 2018 Elsevier Inc. All rights reserved.

Investigation of deep levels in GaInNAs

International Nuclear Information System (INIS)

Abulfotuh, F.; Balcioglu, A.; Friedman, D.; Geisz, J.; Kurtz, S.

1999-01-01

This paper presents and discusses the first Deep-Level transient spectroscopy (DLTS) data obtained from measurements carried out on both Schottky barriers and homojunction devices of GaInNAs. The effect of N and In doping on the electrical properties of the GaNInAs devices, which results in structural defects and interface states, has been investigated. Moreover, the location and densities of deep levels related to the presence of N, In, and N+In are identified and correlated with the device performance. The data confirmed that the presence of N alone creates a high density of shallow hole traps related to the N atom and structural defects in the device. Doping by In, if present alone, also creates low-density deep traps (related to the In atom and structural defects) and extremely deep interface states. On the other hand, the co-presence of In and N eliminates both the interface states and levels related to structural defects. However, the device still has a high density of the shallow and deep traps that are responsible for the photocurrent loss in the GaNInAs device, together with the possible short diffusion length. copyright 1999 American Institute of Physics

Investigation of Deep Levels in GaInNas

International Nuclear Information System (INIS)

Balcioglu, A.; Friedman, D.; Abulfotuh, F.; Geisz, J.; Kurtz, S.

1998-01-01

This paper presents and discusses the first Deep-Level transient spectroscopy (DLTS) data obtained from measurements carried out on both Schottky barriers and homojunction devices of GaInNAs. The effect of N and In doping on the electrical properties of the GaNInAs devices, which results in structural defects and interface states, has been investigated. Moreover, the location and densities of deep levels related to the presence of N, In, and N+In are identified and correlated with the device performance. The data confirmed that the presence of N alone creates a high density of shallow hole traps related to the N atom and structural defects in the device. Doping by In, if present alone, also creates low-density deep traps (related to the In atom and structural defects) and extremely deep interface states. On the other hand, the co-presence of In and N eliminates both the interface states and levels related to structural defects. However, the device still has a high density of the shallow and deep traps that are responsible for the photocurrent loss in the GaNInAs device, together with the possible short diffusion length

Predicting Process Behaviour using Deep Learning

OpenAIRE

Evermann, Joerg; Rehse, Jana-Rebecca; Fettke, Peter

2016-01-01

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real da...

Strategic Technologies for Deep Space Transport

Science.gov (United States)

Litchford, Ronald J.

2016-01-01

Deep space transportation capability for science and exploration is fundamentally limited by available propulsion technologies. Traditional chemical systems are performance plateaued and require enormous Initial Mass in Low Earth Orbit (IMLEO) whereas solar electric propulsion systems are power limited and unable to execute rapid transits. Nuclear based propulsion and alternative energetic methods, on the other hand, represent potential avenues, perhaps the only viable avenues, to high specific power space transport evincing reduced trip time, reduced IMLEO, and expanded deep space reach. Here, key deep space transport mission capability objectives are reviewed in relation to STMD technology portfolio needs, and the advanced propulsion technology solution landscape is examined including open questions, technical challenges, and developmental prospects. Options for potential future investment across the full compliment of STMD programs are presented based on an informed awareness of complimentary activities in industry, academia, OGAs, and NASA mission directorates.

Genetic and histological studies on the delayed systemic movement of Tobacco Mosaic Virus in Arabidopsis thaliana

Directory of Open Access Journals (Sweden)

Matus José

2008-09-01

Full Text Available Abstract Background Viral infections and their spread throughout a plant require numerous interactions between the host and the virus. While new functions of viral proteins involved in these processes have been revealed, current knowledge of host factors involved in the spread of a viral infection is still insufficient. In Arabidopsis thaliana, different ecotypes present varying susceptibilities to Tobacco mosaic virus strain U1 (TMV-U1. The rate of TMV-U1 systemic movement is delayed in ecotype Col-0 when compared with other 13 ecotypes. We followed viral movement through vascular tissue in Col-0 plants by electronic microscopy studies. In addition, the delay in systemic movement of TMV-U1 was genetically studied. Results TMV-U1 reaches apical leaves only after 18 days post rosette inoculation (dpi in Col-0, whereas it is detected at 9 dpi in the Uk-4 ecotype. Genetic crosses between Col-0 and Uk-4 ecotypes, followed by analysis of viral movement in F1 and F2 populations, revealed that this delayed movement correlates with a recessive, monogenic and nuclear locus. The use of selected polymorphic markers showed that this locus, denoted DSTM1 (Delayed Systemic Tobamovirus Movement 1, is positioned on the large arm of chromosome II. Electron microscopy studies following the virion's route in stems of Col-0 infected plants showed the presence of curved structures, instead of the typical rigid rods of TMV-U1. This was not observed in the case of TMV-U1 infection in Uk-4, where the observed virions have the typical rigid rod morphology. Conclusion The presence of defectively assembled virions observed by electron microscopy in vascular tissue of Col-0 infected plants correlates with a recessive delayed systemic movement trait of TMV-U1 in this ecotype.

Specialization of Bacillus in the Geochemically Challenged Environment of Death Valley

Science.gov (United States)

Kopac, S.

2014-04-01

Death Valley is the hottest, driest place in North America, a desert with soils containing toxic elements such as boron and lead. While most organisms are unable to survive under these conditions, a diverse community of bacteria survives here. What has enabled bacteria to adapt and thrive in a plethora of extreme and stressful environments where other organisms are unable to grow? The unique environmental adaptations that distinguish ecologically distinct bacterial groups (ecotypes) remain a mystery, in contrast to many animal species (perhaps most notably Darwin's ecologically distinct finch species). We resolve the ecological factors associated with recently diverged ecotypes of the soil bacteria Bacillus subtilis and Bacillus licheniformis, isolated from the dry, geochemically challenging soils of Death Valley, CA. To investigate speciation associated with challenging environmental parameters, we sampled soil transects along a 400m stretch that parallels a decrease in salinity adjacent to a salt flat; transects also encompass gradients in soil B, Cu, Fe, NO3, and P, all of which were quantified in our soil samples. We demarcated strains using Ecotype Simulation, a sequence-based algorithm. Each ecotype's habitat associations were determined with respect to salinity, B, Cu, Fe, NO3, and P. In addition, our sample strains were tested for tolerance of copper, boron and salinity (all known to inhibit growth at high concentrations) by comparing their growth over a 20 hour period. Ecotypes differed in their habitat associations with salinity, boron, copper, iron, and other ecological factors; these environmental dimensions are likely causing speciation of B. subtilis-licheniformis ecotypes at our sample site. Strains also differed in tolerance of boron and copper, providing evidence that our sequence-based demarcations reflect real differences in metabolism. By better understanding the relationship between bacterial speciation and the environment, we can begin to

Transcriptomic analysis of cadmium stress response in the heavy metal hyperaccumulator Sedum alfredii Hance.

Directory of Open Access Journals (Sweden)

Jun Gao

Full Text Available The Sedum alfredii Hance hyperaccumulating ecotype (HE has the ability to hyperaccumulate cadmium (Cd, as well as zinc (Zn and lead (Pb in above-ground tissues. Although many physiological studies have been conducted with these plants, the molecular mechanisms underlying their hyper-tolerance to heavy metals are largely unknown. Here we report on the generation of 9.4 gigabases of adaptor-trimmed raw sequences and the assembly of 57,162 transcript contigs in S. alfredii Hance (HE shoots by the combination of Roche 454 and Illumina/Solexa deep sequencing technologies. We also have functionally annotated the transcriptome and analyzed the transcriptome changes upon Cd hyperaccumulation in S. alfredii Hance (HE shoots. There are 110 contigs and 123 contigs that were up-regulated (Fold Change ≥ 2.0 and down-regulated (Fold Change ecotype (NHE. Our results demonstrated that several genes involved in cell wall modification, metal translocation and remobilization were more induced or constitutively expressed at higher levels in HE shoots than that in NHE shoots in response to Cd exposure. Together, our study provides large-scale expressed sequence information and genome-wide transcriptome profiling of Cd responses in S. alfredii Hance (HE shoots.

A deep learning / neuroevolution hybrid for visual control

DEFF Research Database (Denmark)

Poulsen, Andreas Precht; Thorhauge, Mark; Funch, Mikkel Hvilshj

2017-01-01

This paper presents a deep learning / neuroevolution hybrid approach called DLNE, which allows FPS bots to learn to aim & shoot based only on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature...... representations, while the evolving network takes those features as inputs to infer actions. The results suggest that combining deep learning and neuroevolution in a hybrid approach is a promising research direction that could make complex visual domains directly accessible to networks trained through evolution....

Assisted Diagnosis Research Based on Improved Deep Autoencoder

Directory of Open Access Journals (Sweden)

Ke Zhang-Han

2017-01-01

Full Text Available Deep Autoencoder has the powerful ability to learn features from large number of unlabeled samples and a small number of labeled samples. In this work, we have improved the network structure of the general deep autoencoder and applied it to the disease auxiliary diagnosis. We have achieved a network by entering the specific indicators and predicting whether suffering from liver disease, the network using real physical examination data for training and verification. Compared with the traditional semi-supervised machine learning algorithm, deep autoencoder will get higher accuracy.

Inferring ancestral distribution area and survival vegetation of Caragana (Fabaceae) in Tertiary

Science.gov (United States)

Mingli Zhang; Juanjuan Xue; Qiang Zhang; Stewart C. Sanderson

2015-01-01

Caragana, a leguminous genus mainly restricted to temperate Central and East Asia, occurs in arid, semiarid, and humid belts, and has forest, grassland, and desert ecotypes. Based on the previous molecular phylogenetic tree and dating, biogeographical analyses of extant species area and ecotype were conducted by means of four ancestral optimization approaches: S-DIVA,...

Revised estimate for the radiocarbon age of North Atlantic deep water

International Nuclear Information System (INIS)

Broecker, W.S.

1979-01-01

The extent to which the admixture of water of Antarctic origin influences the 14 C/C ratio in North Atlantic deep water (NADW) has been heretofore underestimated. When this correction is properly made, a ventilation time for the deep western Atlantic is reduced to only about 100 years. The production rate of the northern component of NADW entering the western basin must be of the order of 30 Sv. If this northern component water is assumed to be the major supplier of new 14 C to the deep sea, the carbon isotope ventilation time of the world deep ocean must be of the order of 900 years. However, since the new deep waters formed around the perimeter of the Antarctic are thought to enter the deep sea at a rate of about 20 Sv, the water ventilation time for the deep sea is of the order of 550 years

Decrease of labile Zn and Cd in the rhizosphere of hyperaccumulating Thlaspi caerulescens with time

Energy Technology Data Exchange (ETDEWEB)

Dessureault-Rompre, Jacynthe, E-mail: dessureaultromj@agr.gc.c [Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitaetstrasse 16, CH-8092 Zuerich (Switzerland); Luster, Joerg, E-mail: joerg.luster@wsl.c [Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), Zuercherstrasse 111, CH-8903 Birmensdorf (Switzerland); Schulin, Rainer, E-mail: rainer.schulin@env.ethz.c [Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitaetstrasse 16, CH-8092 Zuerich (Switzerland); Tercier-Waeber, Mary-Lou, E-mail: marie-louise.tercier@unige.c [CABE, Department of Inorganic and Analytical Chemistry, Sciences II, University of Geneva, 30 quai Ernest Ansermet, CH-1211 Geneva 4 (Switzerland); Nowack, Bernd, E-mail: bernd.nowack@empa.c [Institute of Terrestrial Ecosystems (ITES), ETH Zurich, Universitaetstrasse 16, CH-8092 Zuerich (Switzerland); Empa - Swiss Federal Laboratories for Materials Testing and Research, Lerchenfeldstrasse 5, CH-9014 St. Gallen (Switzerland)

2010-05-15

By using a rhizobox micro-suction cup technique we studied in-situ mobilization and complexation of Zn and Cd in the rhizosphere of non-hyperaccumulating Thlaspi perfoliatum and two different Thlaspi caerulescens ecotypes, one of them hyperaccumulating Zn, the other Zn and Cd. The dynamic fraction (free metal ions and small labile complexes) of Zn and Cd decreased with time in the rhizosphere solution of the respective hyperaccumulating T. caerulescens ecotypes, and at the end of the experiment, it was significantly smaller than in the other treatments. Furthermore, the rhizosphere solutions of the T. caerulescens ecotypes exhibited a higher UV absorptivity than the solution of the T. perfoliatum rhizosphere and the plant-free soil. Based on our findings we suggest that mobile and labile metal-dissolved soil organic matter complexes play a key role in the rapid replenishment of available metal pools in the rhizosphere of hyperaccumulating T. caerulescens ecotypes, postulated earlier. - A mechanism that explains the rapid replenishment of metal pools accessible by hyperaccumulator plants for phytoextraction is proposed.

Antioxidant activities of chestnut nut of Castanea sativa Mill. (cultivar 'Judia') as function of origin ecosystem.

Science.gov (United States)

Dinis, Lia-Tânia; Oliveira, Maria Manuela; Almeida, José; Costa, Rita; Gomes-Laranjo, José; Peixoto, Francisco

2012-05-01

The antioxidant properties of different ecotypes of chestnut nut (cv. Judia) were studied. Total phenolics and flavonoids were also determinated. Total phenolics amount ranged from 9.6mg/g of GAE (hottest ecotype, Murça) to 19.4mg/g of GAE (coldest ecotype, Valpaços). Gallic and ellagic acid were the predominant compounds and Valpaços had the highest values while, Murça had the lowest ones. The antioxidant capacity of ethanolic extracts were evaluated through several biochemical essays: ABTS (2,2'-azinobis-(3-ethylbenzothiazoline-6-sulphonic acid)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) radical-scavenging activity, FRAP (ferric reducing/antioxidant power) and inhibition of oxidative haemolysis in erythrocytes. In order to evaluate the antioxidant efficiency of each ecotype, the EC50 values were calculated. Once again Valpaços revealed the best antioxidant properties, presenting much lower EC50 values. Climatic conditions influence seems to be a limiting factor for production of phenolic compounds and consequently for the antioxidant properties of chestnut nuts. Copyright © 2011 Elsevier Ltd. All rights reserved.

Training Deep Spiking Neural Networks Using Backpropagation.

Science.gov (United States)

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

The deep Canary poleward undercurrent

Science.gov (United States)

Velez-Belchi, P. J.; Hernandez-Guerra, A.; González-Pola, C.; Fraile, E.; Collins, C. A.; Machín, F.

2012-12-01

Poleward undercurrents are well known features in Eastern Boundary systems. In the California upwelling system (CalCEBS), the deep poleward flow has been observed along the entire outer continental shelf and upper-slope, using indirect methods based on geostrophic estimates and also using direct current measurements. The importance of the poleward undercurrents in the CalCEBS, among others, is to maintain its high productivity by means of the transport of equatorial Pacific waters all the way northward to Vancouver Island and the subpolar gyre but there is also concern about the low oxygen concentration of these waters. However, in the case of the Canary Current Eastern Boundary upwelling system (CanCEBS), there are very few observations of the poleward undercurrent. Most of these observations are short-term mooring records, or drifter trajectories of the upper-slope flow. Hence, the importance of the subsurface poleward flow in the CanCEBS has been only hypothesized. Moreover, due to the large differences between the shape of the coastline and topography between the California and the Canary Current system, the results obtained for the CalCEBS are not completely applicable to the CanCEBS. In this study we report the first direct observations of the continuity of the deep poleward flow of the Canary Deep Poleward undercurrent (CdPU) in the North-Africa sector of the CanCEBS, and one of the few direct observations in the North-Africa sector of the Canary Current eastern boundary. The results indicate that the Canary Island archipelago disrupts the deep poleward undercurrent even at depths where the flow is not blocked by the bathymetry. The deep poleward undercurrent flows west around the eastern-most islands and north east of the Conception Bank to rejoin the intermittent branch that follows the African slope in the Lanzarote Passage. This hypothesis is consistent with the AAIW found west of Lanzarote, as far as 17 W. But also, this hypothesis would be coherent

Performance of deep geothermal energy systems

Science.gov (United States)

Manikonda, Nikhil

Geothermal energy is an important source of clean and renewable energy. This project deals with the study of deep geothermal power plants for the generation of electricity. The design involves the extraction of heat from the Earth and its conversion into electricity. This is performed by allowing fluid deep into the Earth where it gets heated due to the surrounding rock. The fluid gets vaporized and returns to the surface in a heat pipe. Finally, the energy of the fluid is converted into electricity using turbine or organic rankine cycle (ORC). The main feature of the system is the employment of side channels to increase the amount of thermal energy extracted. A finite difference computer model is developed to solve the heat transport equation. The numerical model was employed to evaluate the performance of the design. The major goal was to optimize the output power as a function of parameters such as thermal diffusivity of the rock, depth of the main well, number and length of lateral channels. The sustainable lifetime of the system for a target output power of 2 MW has been calculated for deep geothermal systems with drilling depths of 8000 and 10000 meters, and a financial analysis has been performed to evaluate the economic feasibility of the system for a practical range of geothermal parameters. Results show promising an outlook for deep geothermal systems for practical applications.

Deep-Sea Microbes: Linking Biogeochemical Rates to -Omics Approaches

Science.gov (United States)

Herndl, G. J.; Sintes, E.; Bayer, B.; Bergauer, K.; Amano, C.; Hansman, R.; Garcia, J.; Reinthaler, T.

2016-02-01

Over the past decade substantial progress has been made in determining deep ocean microbial activity and resolving some of the enigmas in understanding the deep ocean carbon flux. Also, metagenomics approaches have shed light onto the dark ocean's microbes but linking -omics approaches to biogeochemical rate measurements are generally rare in microbial oceanography and even more so for the deep ocean. In this presentation, we will show by combining metagenomics, -proteomics and biogeochemical rate measurements on the bulk and single-cell level that deep-sea microbes exhibit characteristics of generalists with a large genome repertoire, versatile in utilizing substrate as revealed by metaproteomics. This is in striking contrast with the apparently rather uniform dissolved organic matter pool in the deep ocean. Combining the different -omics approaches with metabolic rate measurements, we will highlight some major inconsistencies and enigmas in our understanding of the carbon cycling and microbial food web structure in the dark ocean.

Multiscale deep features learning for land-use scene recognition

Science.gov (United States)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Deep-water subsea lifting operations

Energy Technology Data Exchange (ETDEWEB)

Nestegaard, Arne; Boee, Tormod

2010-07-01

Significant costs are related to marine operations in the installation phase of deep water subsea field developments. In order to establish safe operational criteria and procedures for the installation, detailed planning is necessary, including numerical modelling and analysis of the environmental conditions and hydrodynamic loads on the installed object as well as the installation equipment. This paper presents recommendations for modelling and analysis of deep water subsea lifting operations developed for the new DNV RP-H103 [1]. During installation of subsea structures, the highest dynamic forces are most often encountered in the splash zone. Recommendations for estimation of maximum forces will be presented. For small structures and tools, installation through the moon pool of a small installation vessel is often preferred. Calculation methods for loading on structures installed through a moon pool will be presented. During intervention or installation in deep water a significant amplification of amplitude and forces can be experienced when the frequency range of vertical crane tip motion coincides with the natural vertical oscillation of the lift wire and load. Vertical resonance may reduce the operability of the operation. Simplified calculation methods for such operations are presented. (Author)

Deep-Elaborative Learning of Introductory Management Accounting for Business Students

Science.gov (United States)

Choo, Freddie; Tan, Kim B.

2005-01-01

Research by Choo and Tan (1990; 1995) suggests that accounting students, who engage in deep-elaborative learning, have a better understanding of the course materials. The purposes of this paper are: (1) to describe a deep-elaborative instructional approach (hereafter DEIA) that promotes deep-elaborative learning of introductory management…

Microbially-mediated fluorescent organic matter transformations in the deep ocean

DEFF Research Database (Denmark)

Aparicio, Fran L.; Nieto-Cid, Mar; Borrull, Encarna

2015-01-01

The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also b....... These findings contribute to the understanding of FDOM variability in deep waters and provide valuable information for studies where fluorescent compounds are used in order to track water masses and/or microbial processes.......The refractory nature of marine dissolved organic matter (DOM) increases while it travels from surface waters to the deep ocean. This resistant fraction is in part composed of fluorescent humic-like material, which is relatively difficult to metabolize by deep water prokaryotes, and it can also...

Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

Science.gov (United States)

Ubbens, Jordan R.; Stavness, Ian

2017-01-01

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. PMID:28736569

Deep-brain-stimulation does not impair deglutition in Parkinson's disease.

Science.gov (United States)

Lengerer, Sabrina; Kipping, Judy; Rommel, Natalie; Weiss, Daniel; Breit, Sorin; Gasser, Thomas; Plewnia, Christian; Krüger, Rejko; Wächter, Tobias

2012-08-01

A large proportion of patients with Parkinson's disease develop dysphagia during the course of the disease. Dysphagia in Parkinson's disease affects different phases of deglutition, has a strong impact on quality of life and may cause severe complications, i.e., aspirational pneumonia. So far, little is known on how deep-brain-stimulation of the subthalamic nucleus influences deglutition in PD. Videofluoroscopic swallowing studies on 18 patients with Parkinson's disease, which had been performed preoperatively, and postoperatively with deep-brain-stimulation-on and deep-brain-stimulation-off, were analyzed retrospectively. The patients were examined in each condition with three consistencies (viscous, fluid and solid). The 'New Zealand index for multidisciplinary evaluation of swallowing (NZIMES) Subscale One' for qualitative and 'Logemann-MBS-Parameters' for quantitative evaluation were assessed. Preoperatively, none of the patients presented with clinically relevant signs of dysphagia. While postoperatively, the mean daily levodopa equivalent dosage was reduced by 50% and deep-brain-stimulation led to a 50% improvement in motor symptoms measured by the UPDRS III, no clinically relevant influence of deep-brain-stimulation-on swallowing was observed using qualitative parameters (NZIMES). However quantitative parameters (Logemann scale) found significant changes of pharyngeal parameters with deep-brain-stimulation-on as compared to preoperative condition and deep-brain-stimulation-off mostly with fluid consistency. In Parkinson patients without dysphagia deep-brain-stimulation of the subthalamic nucleus modulates the pharyngeal deglutition phase but has no clinically relevant influence on deglutition. Further studies are needed to test if deep-brain-stimulation is a therapeutic option for patients with swallowing disorders. Copyright © 2012 Elsevier Ltd. All rights reserved.

Deep venous thrombosis of the upper extremity. A review

DEFF Research Database (Denmark)

Klitfod, Lotte; Broholm, R; Baekgaard, N

2013-01-01

Upper extremity deep venous thrombosis (UEDVT) occurs either spontaneously, as a consequence of strenuous upper limb activity (also known as the Paget-Schroetter syndrome) or secondary to an underlying cause. Primary and secondary UEDVT differs in long-term sequelae and mortality. This review...... to the condition. Malignancy and therapeutic interventions are major risk factors for the secondary deep vein thrombosis in combination with the patient's characteristics, comorbidities and prior history of deep vein thrombosis. Complications: recurrent deep venous thrombosis, pulmonary embolism and Post....... Treatment modalities and strategies: the treatment modalities include anticoagulation therapy, catheter-directed thrombolysis, surgical decompression, percutaneous transluminal angioplasty and stenting and they may be combined. However, the optimal treatment and timing of treatment remains controversial...

Approximate Inference and Deep Generative Models

CERN Multimedia

CERN. Geneva

2018-01-01

Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.

Deep Learning for Video Game Playing

OpenAIRE

Justesen, Niels; Bontrager, Philip; Togelius, Julian; Risi, Sebastian

2017-01-01

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces...

Deep Learning For Sequential Pattern Recognition

OpenAIRE

Safari, Pooyan

2013-01-01

Projecte realitzat en el marc d’un programa de mobilitat amb la Technische Universität München (TUM) In recent years, deep learning has opened a new research line in pattern recognition tasks. It has been hypothesized that this kind of learning would capture more abstract patterns concealed in data. It is motivated by the new findings both in biological aspects of the brain and hardware developments which have made the parallel processing possible. Deep learning methods come along with ...

Deep mycoses in Amazon region.

Science.gov (United States)

Talhari, S; Cunha, M G; Schettini, A P; Talhari, A C

1988-09-01

Patients with deep mycoses diagnosed in dermatologic clinics of Manaus (state of Amazonas, Brazil) were studied from November 1973 to December 1983. They came from the Brazilian states of Amazonas, Pará, Acre, and Rondônia and the Federal Territory of Roraima. All of these regions, with the exception of Pará, are situated in the western part of the Amazon Basin. The climatic conditions in this region are almost the same: tropical forest, high rainfall, and mean annual temperature of 26C. The deep mycoses diagnosed, in order of frequency, were Jorge Lobo's disease, paracoccidioidomycosis, chromomycosis, sporotrichosis, mycetoma, cryptococcosis, zygomycosis, and histoplasmosis.

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

Science.gov (United States)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

Deep learning—Accelerating Next Generation Performance Analysis Systems?

Directory of Open Access Journals (Sweden)

Heike Brock

2018-02-01

Full Text Available Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

Plastic microfibre ingestion by deep-sea organisms

Science.gov (United States)

Taylor, M. L.; Gwinnett, C.; Robinson, L. F.; Woodall, L. C.

2016-09-01

Plastic waste is a distinctive indicator of the world-wide impact of anthropogenic activities. Both macro- and micro-plastics are found in the ocean, but as yet little is known about their ultimate fate and their impact on marine ecosystems. In this study we present the first evidence that microplastics are already becoming integrated into deep-water organisms. By examining organisms that live on the deep-sea floor we show that plastic microfibres are ingested and internalised by members of at least three major phyla with different feeding mechanisms. These results demonstrate that, despite its remote location, the deep sea and its fragile habitats are already being exposed to human waste to the extent that diverse organisms are ingesting microplastics.

Deep Learning Microscopy

KAUST Repository

Rivenson, Yair

2017-05-12

We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably better resolution, matching the performance of higher numerical aperture lenses, also significantly surpassing their limited field-of-view and depth-of-field. These results are transformative for various fields that use microscopy tools, including e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, our presented approach is broadly applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.

Deep groundwater quantity and quality in the southwestern US

Science.gov (United States)

Kang, M.; Ayars, J. E.; Jackson, R. B.

2017-12-01

Groundwater demands are growing in many arid regions and adaptation through the use of non-traditional resources during extreme droughts is increasingly common. One such resource is deep groundwater, which we define as deeper than 300 m and up to several kilometer-depths. Although deep groundwater has been studied in the context of oil and gas, geothermal, waste disposal, and other uses, it remains poorly characterized, especially for the purposes of human consumption and irrigation uses. Therefore, we evaluate deep groundwater quantity and quality within these contexts. We compile and analyze data from water management agencies and oil and gas-based sources for the southwestern US, with a detailed look at California's Central Valley. We also use crop tolerance thresholds to evaluate deep groundwater quality for irrigation purposes. We find fresh and usable groundwater volume estimates in California's Central Valley to increase by three- and four-fold respectively when depths of up to 3 km are considered. Of the ten basins in the southwestern US with the most data, we find that the Great Basin has the greatest proportions of fresh and usable deep groundwater. Given the potentially large deep groundwater volumes, it is important to characterize the resource, guard against subsidence where extracted, and protect it for use in decades and centuries to come.

An Ensemble of Deep Support Vector Machines for Image Categorization

NARCIS (Netherlands)

Abdullah, Azizi; Veltkamp, Remco C.; Wiering, Marco

2009-01-01

This paper presents the deep support vector machine (D-SVM) inspired by the increasing popularity of deep belief networks for image recognition. Our deep SVM trains an SVM in the standard way and then uses the kernel activations of support vectors as inputs for training another SVM at the next

Deep water characteristics and circulation in the South China Sea

Science.gov (United States)

Wang, Aimei; Du, Yan; Peng, Shiqiu; Liu, Kexiu; Huang, Rui Xin

2018-04-01

This study investigates the deep circulation in the South China Sea (SCS) using oceanographic observations combined with results from a bottom layer reduced gravity model. The SCS water, 2000 m below the surface, is quite different from that in the adjacent Pacific Ocean, and it is characterized by its low dissolved oxygen (DO), high temperature and low salinity. The horizontal distribution of deep water properties indicates a basin-scale cyclonic circulation driven by the Luzon overflow. The results of the bottom layer reduced gravity model are consistent with the existence of the cyclonic circulation in the deep SCS. The circulation is stronger at the northern/western boundary. After overflowing the sill of the Luzon Strait, the deep water moves broadly southwestward, constrained by the 3500 m isobath. The broadening of the southward flow is induced by the downwelling velocity in the interior of the deep basin. The main deep circulation bifurcates into two branches after the Zhongsha Islands. The southward branch continues flowing along the 3500 m isobath, and the eastward branch forms the sub-basin scale cyclonic circulation around the seamounts in the central deep SCS. The returning flow along the east boundary is fairly weak. The numerical experiments of the bottom layer reduced gravity model reveal the important roles of topography, bottom friction, and the upwelling/downwelling pattern in controlling the spatial structure, particularly the strong, deep western boundary current.

Focused Crawling of the Deep Web Using Service Class Descriptions

Energy Technology Data Exchange (ETDEWEB)

Rocco, D; Liu, L; Critchlow, T

2004-06-21

Dynamic Web data sources--sometimes known collectively as the Deep Web--increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deep Web. To address these challenges, we present DynaBot, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DynaBot has three unique characteristics. First, DynaBot utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DynaBot employs a modular, self-tuning system architecture for focused crawling of the DeepWeb using service class descriptions. Third, DynaBot incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.

Studies upon morhological and biological traits of Festuca rubra, subsp.fallax (Poaceae

Directory of Open Access Journals (Sweden)

Bogusław Sawicki

2013-12-01

Full Text Available Observation and measurements of some traits of Festuca rubra L., subsp. fallax (Thuill. Hack. ecotypes were made in 1995-1997 using samples selected from natural habitats and collected in Grassland Experimental Station in Sosnowica. High differentiation of traits under study and their correlations were found. Valorized ecotypes are good material for new varieties breeding.

Variability of photosynthetic parameters of Pinus sibirica Du Tour needles under changing climatic factors

Directory of Open Access Journals (Sweden)

A.P. Zotikova

2013-12-01

Full Text Available The air temperature and relative humidity and the intensity of photosynthetically active radiation are the basic ecological factors determining geographical distribution of a species. Wood plant adaptation depends on the intensity of physiological and biochemicalprocesses of plants as a response to changing environmental factors. Investigations to reveal (detect the variability of modification andgenetic components of the photosynthetic parameters in needles of the Siberian cedar (Pinus sibirica Du Tour mountain ecotypes, distributed in central part of the Altai Mountains, were carried out. Also, the survey was extended to some experiments with these ecotypes introduced to mild climate and flat regions from south-western of Siberia. The length and thickness of needles, the size of chloroplasts, content of the photosynthetic pigments, and the functional activity of chloroplastsat the level of photo system II were the evaluated traits. Growing under mountainous conditions (at about 2000m elevation, the two-year-old needles were shorter and thicker and contained very large in size chloroplasts while the content of chlorophylls and carotinoids was twice lower than that in the local ecotype growing in the lowlands. On the other hand, more green and yellow pigments were found in needles of mountain ecotypes planted in the lowlands compared to the local lowland ectype trees. A decrease in pool of the photosynthetic pigments in the highlands ecotypes is probably due to decreased biosynthesis andincreased photo-destruction caused by severe light and temperature conditions. These parameters are likely to be associated withmodifications due to intense insolation, low temperature, ozone concentration, UV radiation, and other negative factors that are morepronounced at high elevation. Despite the large pool of accumulated photosynthetic pigments, the functional activity of chloroplasts in themountain ecotype at the level

State of HIV in the US Deep South.

Science.gov (United States)

Reif, Susan; Safley, Donna; McAllaster, Carolyn; Wilson, Elena; Whetten, Kathryn

2017-10-01

The Southern United States has been disproportionately affected by HIV diagnoses and mortality. To inform efforts to effectively address HIV in the South, this manuscript synthesizes recent data on HIV epidemiology, care financing, and current research literature on factors that predispose this region to experience a greater impact of HIV. The manuscript focuses on a specific Southern region, the Deep South, which has been particularly affected by HIV. Epidemiologic data from the Centers from Disease Control and Prevention indicate that the Deep South had the highest HIV diagnosis rate and the highest number of individuals diagnosed with HIV (18,087) in 2014. The percentage of new HIV diagnoses that were female has decreased over time (2008-2014) while increasing among minority MSM. The Deep South also had the highest death rates with HIV as an underlying cause of any US region in 2014. Despite higher diagnosis and death rates, the Deep South received less federal government and private foundation funding per person living with HIV than the US overall. Factors that have been identified as contributors to the disproportionate effects of HIV in the Deep South include pervasive HIV-related stigma, poverty, higher levels of sexually transmitted infections, racial inequality and bias, and laws that further HIV-related stigma and fear. Interventions that address and abate the contributors to the spread of HIV disease and the poorer HIV-related outcomes in the Deep South are warranted. Funding inequalities by region must also be examined and addressed to reduce the regional disparities in HIV incidence and mortality.

Quantitative phenotyping via deep barcode sequencing.

Science.gov (United States)

Smith, Andrew M; Heisler, Lawrence E; Mellor, Joseph; Kaper, Fiona; Thompson, Michael J; Chee, Mark; Roth, Frederick P; Giaever, Guri; Nislow, Corey

2009-10-01

Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or "Bar-seq," outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that approximately 20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene-environment interactions on a genome-wide scale.

Gene expression in the deep biosphere.

Science.gov (United States)

Orsi, William D; Edgcomb, Virginia P; Christman, Glenn D; Biddle, Jennifer F

2013-07-11

Scientific ocean drilling has revealed a deep biosphere of widespread microbial life in sub-seafloor sediment. Microbial metabolism in the marine subsurface probably has an important role in global biogeochemical cycles, but deep biosphere activities are not well understood. Here we describe and analyse the first sub-seafloor metatranscriptomes from anaerobic Peru Margin sediment up to 159 metres below the sea floor, represented by over 1 billion complementary DNA (cDNA) sequence reads. Anaerobic metabolism of amino acids, carbohydrates and lipids seem to be the dominant metabolic processes, and profiles of dissimilatory sulfite reductase (dsr) transcripts are consistent with pore-water sulphate concentration profiles. Moreover, transcripts involved in cell division increase as a function of microbial cell concentration, indicating that increases in sub-seafloor microbial abundance are a function of cell division across all three domains of life. These data support calculations and models of sub-seafloor microbial metabolism and represent the first holistic picture of deep biosphere activities.

Exploration in the Deep water Niger Delta: Technical to Business Perspectives

International Nuclear Information System (INIS)

Feeley, M.H.

2002-01-01

Prolific source rocks, high quality deep water reservoirs and a high technical success rate in finding hydrocarbons make the Nigeria deep water one of the top exploration opportunities in the world. Several major discoveries have resulted from exploration on blocks awarded in 1993. Enthusiastic participation by industry in the 2000 Tender Round clearly indicates the continuing appeal of deep water exploration in Nigeria.Commercially, challenges still exist in the Nigerian deep water. Industry has spent more than $2 Billion USD on exploration and appraisal, yet only a handful of developments are moving forward to development. First oil from the deep water is not expected until 2004, 11 years after acreage award and 8 years after discovery. Tougher economic terms, OPEC quota constraints, an abundance of deep water gas, lengthy approval processes and high up-front bonus and exploration costs challenge the economic returns on deep water gas, lengthy approval processes and high up-front bonus and exploration costs challenge the economic returns on deep water investments. Will deep water exploration, development and production deliver the financial returns industry expected when it signed up for the blocks 10 years ago? What are the indications for the 2000 Tender Round blocks?A good explorer learns form experience. What can be learned technically and commercially by looking back over the results of the last 10 years of exploration in Nigeria's deep water? A perspective is provided on the successes, the failures and the challenges to be overcome in realizing the commercial potential of the basin

Decadal trends in deep ocean salinity and regional effects on steric sea level

Science.gov (United States)

Purkey, S. G.; Llovel, W.

2017-12-01

We present deep (below 2000 m) and abyssal (below 4000 m) global ocean salinity trends from the 1990s through the 2010s and assess the role of deep salinity in local and global sea level budgets. Deep salinity trends are assessed using all deep basins with available full-depth, high-quality hydrographic section data that have been occupied two or more times since the 1980s through either the World Ocean Circulation Experiment (WOCE) Hydrographic Program or the Global Ship-Based Hydrographic Investigations Program (GO-SHIP). All salinity data is calibrated to standard seawater and any intercruise offsets applied. While the global mean deep halosteric contribution to sea level rise is close to zero (-0.017 +/- 0.023 mm/yr below 4000 m), there is a large regional variability with the southern deep basins becoming fresher and northern deep basins becoming more saline. This meridional gradient in the deep salinity trend reflects different mechanisms driving the deep salinity variability. The deep Southern Ocean is freshening owing to a recent increased flux of freshwater to the deep ocean. Outside of the Southern Ocean, the deep salinity and temperature changes are tied to isopycnal heave associated with a falling of deep isopycnals in recent decades. Therefore, regions of the ocean with a deep salinity minimum are experiencing both a halosteric contraction with a thermosteric expansion. While the thermosteric expansion is larger in most cases, in some regions the halosteric compensates for as much as 50% of the deep thermal expansion, making a significant contribution to local sea level rise budgets.

Performance Evaluation of Deep Learning Tools in Docker Containers

OpenAIRE

Xu, Pengfei; Shi, Shaohuai; Chu, Xiaowen

2017-01-01

With the success of deep learning techniques in a broad range of application domains, many deep learning software frameworks have been developed and are being updated frequently to adapt to new hardware features and software libraries, which bring a big challenge for end users and system administrators. To address this problem, container techniques are widely used to simplify the deployment and management of deep learning software. However, it remains unknown whether container techniques brin...

Effect of swift heavy ion irradiation on deep levels in Au /n-Si (100) Schottky diode studied by deep level transient spectroscopy

Science.gov (United States)

Kumar, Sandeep; Katharria, Y. S.; Kumar, Sugam; Kanjilal, D.

2007-12-01

In situ deep level transient spectroscopy has been applied to investigate the influence of 100MeV Si7+ ion irradiation on the deep levels present in Au/n-Si (100) Schottky structure in a wide fluence range from 5×109to1×1012ions cm-2. The swift heavy ion irradiation introduces a deep level at Ec-0.32eV. It is found that initially, trap level concentration of the energy level at Ec-0.40eV increases with irradiation up to a fluence value of 1×1010cm-2 while the deep level concentration decreases as irradiation fluence increases beyond the fluence value of 5×1010cm-2. These results are discussed, taking into account the role of energy transfer mechanism of high energy ions in material.

Active Cooling of Oil after Deep-frying.

Science.gov (United States)

Totani, Nagao; Yasaki, Naoko; Doi, Rena; Hasegawa, Etsuko

2017-10-01

Oil used for deep-frying is often left to stand after cooking. A major concern is oxidation during standing that might be avoidable, especially in the case of oil used repeatedly for commercial deep-frying as this involves large volumes that are difficult to cool in a conventional fryer. This paper describes a method to minimize oil oxidation. French fries were deep-fried and the oil temperature decreased in a manner typical for a commercial deep-fryer. The concentration of polar compounds generated from thermally oxidized oil remarkably increased at temperature higher than 100°C but little oxidation occurred below 60°C. Heating the oil showed that the peroxide and polar compound content did not increase when the oil was actively cooled using a running water-cooled Graham-type condenser system to cool the oil from 180°C to room temperature within 30 min. When French fries were fried and the oil was then immediately cooled using the condenser, the polar compound content during cooling did not increase. Our results demonstrate that active cooling of heated oil is simple and quite effective for inhibiting oxidation.

Deep sedation during pneumatic reduction of intussusception.

Science.gov (United States)

Ilivitzki, Anat; Shtark, Luda Glozman; Arish, Karin; Engel, Ahuva

2012-05-01

Pneumatic reduction of intussusception under fluoroscopic guidance is a routine procedure. The unsedated child may resist the procedure, which may lengthen its duration and increase the radiation dose. We use deep sedation during the procedure to overcome these difficulties. The purpose of this study was to summarize our experience with deep sedation during fluoroscopic reduction of intussusception and assess the added value and complication rate of deep sedation. All children with intussusception who underwent pneumatic reduction in our hospital between January 2004 and June 2011 were included in this retrospective study. Anesthetists sedated the children using propofol. The fluoroscopic studies, ultrasound (US) studies and the childrens' charts were reviewed. One hundred thirty-one attempted reductions were performed in 119 children, of which 121 (92%) were successful and 10 (8%) failed. Two perforations (1.5%) occurred during attempted reduction. Average fluoroscopic time was 1.5 minutes. No complication to sedation was recorded. Deep sedation with propofol did not add any complication to the pneumatic reduction. The fluoroscopic time was short. The success rate of reduction was high,raising the possibility that sedation is beneficial, possibly by smooth muscle relaxation.

Optimizing interplanetary trajectories with deep space maneuvers

Science.gov (United States)

Navagh, John

1993-09-01

Analysis of interplanetary trajectories is a crucial area for both manned and unmanned missions of the Space Exploration Initiative. A deep space maneuver (DSM) can improve a trajectory in much the same way as a planetary swingby. However, instead of using a gravitational field to alter the trajectory, the on-board propulsion system of the spacecraft is used when the vehicle is not near a planet. The purpose is to develop an algorithm to determine where and when to use deep space maneuvers to reduce the cost of a trajectory. The approach taken to solve this problem uses primer vector theory in combination with a non-linear optimizing program to minimize Delta(V). A set of necessary conditions on the primer vector is shown to indicate whether a deep space maneuver will be beneficial. Deep space maneuvers are applied to a round trip mission to Mars to determine their effect on the launch opportunities. Other studies which were performed include cycler trajectories and Mars mission abort scenarios. It was found that the software developed was able to locate quickly DSM's which lower the total Delta(V) on these trajectories.

Deep inelastic scattering near the Coulomb barrier

Energy Technology Data Exchange (ETDEWEB)

Gehring, J.; Back, B.; Chan, K. [and others

1995-08-01

Deep inelastic scattering was recently observed in heavy ion reactions at incident energies near and below the Coulomb barrier. Traditional models of this process are based on frictional forces and are designed to predict the features of deep inelastic processes at energies above the barrier. They cannot be applied at energies below the barrier where the nuclear overlap is small and friction is negligible. The presence of deep inelastic scattering at these energies requires a different explanation. The first observation of deep inelastic scattering near the barrier was in the systems {sup 124,112}Sn + {sup 58,64}Ni by Wolfs et al. We previously extended these measurements to the system {sup 136}Xe + {sup 64}Ni and currently measured the system {sup 124}Xe + {sup 58}Ni. We obtained better statistics, better mass and energy resolution, and more complete angular coverage in the Xe + Ni measurements. The cross sections and angular distributions are similar in all of the Sn + Ni and Xe + Ni systems. The data are currently being analyzed and compared with new theoretical calculations. They will be part of the thesis of J. Gehring.

Applying a punch with microridges in multistage deep drawing processes.

Science.gov (United States)

Lin, Bor-Tsuen; Yang, Cheng-Yu

2016-01-01

The developers of high aspect ratio components aim to minimize the processing stages in deep drawing processes. This study elucidates the application of microridge punches in multistage deep drawing processes. A microridge punch improves drawing performance, thereby reducing the number of stages required in deep forming processes. As an example, the original eight-stage deep forming process for a copper cylindrical cup with a high aspect ratio was analyzed by finite element simulation. Microridge punch designs were introduced in Stages 4 and 7 to replace the original punches. In addition, Stages 3 and 6 were eliminated. Finally, these changes were verified through experiments. The results showed that the microridge punches reduced the number of deep drawing stages yielding similar thickness difference percentages. Further, the numerical and experimental results demonstrated good consistency in the thickness distribution.

Deep inelastic lepton scattering

International Nuclear Information System (INIS)

Nachtmann, O.

1977-01-01

Deep inelastic electron (muon) nucleon and neutrino nucleon scattering as well as electron positron annihilation into hadrons are reviewed from a theoretical point of view. The emphasis is placed on comparisons of quantum chromodynamics with the data. (orig.) [de

Gamma-rays from deep inelastic collisions

International Nuclear Information System (INIS)

Stephens, F.S.

1979-01-01

The γ-rays associated with deep inelastic collisions can give information about the magnitude and orientation of the angular momentum transferred in these events. In this review, special emphasis is placed on understanding the origin and nature of these γ-rays in order to avoid some of the ambiguities that can arise. The experimental information coming from these γ-ray studies is reviewed, and compared briefly with that obtained by other methods and also with the expectations from current models for deep inelastic collisions. 15 figures

Genetic diversity of archaea in deep-sea hydrothermal vent environments.

Science.gov (United States)

Takai, K; Horikoshi, K

1999-08-01

Molecular phylogenetic analysis of naturally occurring archaeal communities in deep-sea hydrothermal vent environments was carried out by PCR-mediated small subunit rRNA gene (SSU rDNA) sequencing. As determined through partial sequencing of rDNA clones amplified with archaea-specific primers, the archaeal populations in deep-sea hydrothermal vent environments showed a great genetic diversity, and most members of these populations appeared to be uncultivated and unidentified organisms. In the phylogenetic analysis, a number of rDNA sequences obtained from deep-sea hydrothermal vents were placed in deep lineages of the crenarchaeotic phylum prior to the divergence of cultivated thermophilic members of the crenarchaeota or between thermophilic members of the euryarchaeota and members of the methanogen-halophile clade. Whole cell in situ hybridization analysis suggested that some microorganisms of novel phylotypes predicted by molecular phylogenetic analysis were likely present in deep-sea hydrothermal vent environments. These findings expand our view of the genetic diversity of archaea in deep-sea hydrothermal vent environments and of the phylogenetic organization of archaea.

Automated analysis of high-content microscopy data with deep learning.

Science.gov (United States)

Kraus, Oren Z; Grys, Ben T; Ba, Jimmy; Chong, Yolanda; Frey, Brendan J; Boone, Charles; Andrews, Brenda J

2017-04-18

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data. © 2017 The Authors. Published under the terms of the CC BY 4.0 license.

Bipolar gene flow in deep-sea benthic foraminifera

DEFF Research Database (Denmark)

Pawlowski, J.; Fahrni, J.; Lecroq, B.

2007-01-01

Despite its often featureless appearance, the deep-ocean floor includes some of the most diverse habitats on Earth. However, the accurate assessment of global deep-sea diversity is impeded by a paucity of data on the geographical ranges of bottom-dwelling species, particularly at the genetic leve...

DeepWind - from Idea to 5 MW Concept

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Aagaard Madsen, Helge; Kragh, Knud Abildgaard

2014-01-01

The DeepWind concept has been described previously on challenges and potentials, this new offshore floating technology can offer to the wind industry [1]. The paper describes state of the art design improvements, new simulation results of the DeepWind floating vertical axis wind turbine concept...

Geotechnical aspects of deep ocean radioactive waste disposal

International Nuclear Information System (INIS)

Freeman, T.J.

1990-01-01

The methods that might be used to bury radioactive waste in the deep ocean, and their likely effect on the sediment barrier, have been the subject of an international research program performed during the last ten years. This paper reviews the geotechnical aspects of deep ocean disposal and discusses how far the research performed has gone towards providing the information needed to assess this form of disposal. Considerable progress has been made during the course of the international program towards understanding the processes involved in the emplacement of heat generating waste (HGW) into the deep ocean bed and the subsequent interactions between the waste and the sediments. These processes do not appear to have a deleterious effect on the barrier properties of the sediments, and it is concluded that it is likely that HGW could be emplaced in the deep ocean in such a way that the seabed would provide an effective containment for the radionuclides

Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

Recursive Monte Carlo method for deep-penetration problems

International Nuclear Information System (INIS)

Goldstein, M.; Greenspan, E.

1980-01-01

The Recursive Monte Carlo (RMC) method developed for estimating importance function distributions in deep-penetration problems is described. Unique features of the method, including the ability to infer the importance function distribution pertaining to many detectors from, essentially, a single M.C. run and the ability to use the history tape created for a representative region to calculate the importance function in identical regions, are illustrated. The RMC method is applied to the solution of two realistic deep-penetration problems - a concrete shield problem and a Tokamak major penetration problem. It is found that the RMC method can provide the importance function distributions, required for importance sampling, with accuracy that is suitable for an efficient solution of the deep-penetration problems considered. The use of the RMC method improved, by one to three orders of magnitude, the solution efficiency of the two deep-penetration problems considered: a concrete shield problem and a Tokamak major penetration problem. 8 figures, 4 tables

Deep Learning in the Automotive Industry: Applications and Tools

OpenAIRE

Luckow, Andre; Cook, Matthew; Ashcraft, Nathan; Weill, Edwin; Djerekarov, Emil; Vorster, Bennie

2017-01-01

Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, ...

Microbiological characterization of deep geological compartments

International Nuclear Information System (INIS)

Barsotti, V.; Sergeant, C.; Vesvres, M.H.; Coulon, S.; Joulian, C.; Garrido, F.; Ollivier, B.

2012-01-01

Document available in extended abstract form only. Microbial life in deep sediments and Earth's crust is now acknowledged by the scientific world. The deep subsurface biosphere contributes significantly to fundamental biogeochemical processes. However, despite great advances in geo-microbiological studies, deep terrestrial ecosystems are microbiologically poorly understood, mainly due to their inaccessibility. The drilling down to the base of the Triassic (1980 meters deep) in the geological formations of the eastern Paris Basin performed by ANDRA (EST433) in 2008 provides us a good opportunity to explore the deep biosphere. We conditioned the samples on the coring site, in as aseptic conditions as possible. In addition to storage at atmospheric pressure, a portion of the four Triassic samples was placed in a 190 bars pressurized chamber to investigate the influence of the conservation pressure factor on the found microflora. In parallel, in order to evaluate a potential bacterial contamination of the cores by the drilling fluids, samples of mud just before each sample drilling were taken and analyzed. The microbial exploration can be divided in two parts: - A cultural approach in different culture media for metabolic groups as methanogens, fermenters and sulphate reducing bacteria to stimulate their growth and to isolate microbial cells still viable. - A molecular approach by direct extraction of genomic DNA from the geological samples to explore a larger biodiversity. The limits are here the difficulties to extract DNA from these low biomass containing rocks. After comparison and optimization of several DNA extraction methods, the bacterial diversity present in rock cores was analyzed using DGGE (Denaturating Gel Gradient Electrophoresis) and cloning. The detailed results of all these investigations will be presented: - Despite all 400 cultural conditions experimented (with various media, salinities, temperatures, conservation pressure, agitation), no viable and

Postoperative deep venous thrombosis in Japan. Incidence and prophylaxis

International Nuclear Information System (INIS)

Inada, K.; Shirai, N.; Hayashi, M.; Matsumoto, K.; Hirose, M.

1983-01-01

The incidence of postoperative deep venous thrombosis was investigated using the iodine-125-fibrinogen method in 256 patients undergoing major surgery. Deep venous thrombosis was found in 49 patients (15.3 percent), and nonfatal pulmonary embolism developed in one of seven patients in whom the thrombus extended to the popliteal vein. The same investigation was performed in 110 patients who wore a graduated compression stocking on one leg, with the other leg serving as a control. Deep venous thrombosis was found in 4 of 110 stockinged legs (3.6 percent) and in 16 of 110 control legs (14.5 percent). The incidence of deep venous thrombosis decreased significantly in patients who wore the stocking. An increase in femoral venous flow velocity was found in the stockinged legs by the Doppler method. The mean velocity of venous return by xenon-133 clearance was significantly greater in the stockinged legs than in the control legs. These findings were considered to support the efficacy of graduated compression stockings for the prevention of deep venous thrombosis

Outcomes of the DeepWind Conceptual Design

DEFF Research Database (Denmark)

Schmidt Paulsen, Uwe; Borg, Michael; Aagaard Madsen, Helge

2015-01-01

DeepWind has been presented as a novel floating offshore wind turbine concept with cost reduction potentials. Twelve international partners developed a Darrieus type floating turbine with new materials and technologies for deep-sea offshore environment. This paper summarizes results of the 5 MW...... the Deepwind floating 1 kW demonstrator. The 5 MW simulation results, loading and performance are compared to the OC3-NREL 5 MW wind turbine. Finally the paper elaborates the conceptual design on cost modelling....... DeepWind conceptual design. The concept was evaluated at the Hywind test site, described on its few components, in particular on the modified Troposkien blade shape and airfoil design. The feasibility of upscaling from 5 MW to 20 MW is discussed, taking into account the results from testing...

Deep Learning in Open Source Learning Streams

DEFF Research Database (Denmark)

Kjærgaard, Thomas

2016-01-01

This chapter presents research on deep learning in a digital learning environment and raises the question if digital instructional designs can catalyze deeper learning than traditional classroom teaching. As a theoretical point of departure the notion of ‘situated learning’ is utilized...... and contrasted to the notion of functionalistic learning in a digital context. The mechanism that enables deep learning in this context is ‘The Open Source Learning Stream’. ‘The Open Source Learning Stream’ is the notion of sharing ‘learning instances’ in a digital space (discussion board, Facebook group......, unistructural, multistructural or relational learning. The research concludes that ‘The Open Source Learning Stream’ can catalyze deep learning and that there are four types of ‘Open Source Learning streams’; individual/ asynchronous, individual/synchronous, shared/asynchronous and shared...

Pathways to deep decarbonization - Interim 2014 Report

International Nuclear Information System (INIS)

2014-01-01

The interim 2014 report by the Deep Decarbonization Pathways Project (DDPP), coordinated and published by IDDRI and the Sustainable Development Solutions Network (SDSN), presents preliminary findings of the pathways developed by the DDPP Country Research Teams with the objective of achieving emission reductions consistent with limiting global warming to less than 2 deg. C. The DDPP is a knowledge network comprising 15 Country Research Teams and several Partner Organizations who develop and share methods, assumptions, and findings related to deep decarbonization. Each DDPP Country Research Team has developed an illustrative road-map for the transition to a low-carbon economy, with the intent of taking into account national socio-economic conditions, development aspirations, infrastructure stocks, resource endowments, and other relevant factors. The interim 2014 report focuses on technically feasible pathways to deep decarbonization

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation.

Science.gov (United States)

Hohman, Fred; Hodas, Nathan; Chau, Duen Horng

2017-05-01

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as "black-boxes" due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user's data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

ShapeShop: Towards Understanding Deep Learning Representations via Interactive Experimentation

Energy Technology Data Exchange (ETDEWEB)

Hohman, Frederick M.; Hodas, Nathan O.; Chau, Duen Horng

2017-05-30

Deep learning is the driving force behind many recent technologies; however, deep neural networks are often viewed as “black-boxes” due to their internal complexity that is hard to understand. Little research focuses on helping people explore and understand the relationship between a user’s data and the learned representations in deep learning models. We present our ongoing work, ShapeShop, an interactive system for visualizing and understanding what semantics a neural network model has learned. Built using standard web technologies, ShapeShop allows users to experiment with and compare deep learning models to help explore the robustness of image classifiers.

Waste disposal in the deep ocean: An overview

International Nuclear Information System (INIS)

O'Connor, T.P.; Kester, D.R.; Burt, W.V.; Capuzzo, J.M.; Park, P.K.; Duedall, I.W.

1985-01-01

Incineration at sea, industrial and sewage waste disposal in the surface mixing zone, and disposal of low-level nuclear wastes, obsolete munitions, and nerve gas onto the seafloor have been the main uses of the deep sea for waste management. In 1981 the wastes disposed of in the deep sea consisted of 48 X 10/sup 4/ t of liquid industrial wastes and 2 X 10/sup 4/ t of sewage sludge by the United States; 1.5 X 10/sup 4/ t (solids) of sewage sludge by the Federal Republic of German; 5300 t of liquid industrial wastes by Denmark; 99 t of solid industrial wastes by the United Kingdom; and 9400 t of low-level radioactive wastes by several European countries. Also in 1981 at-sea incineration of slightly more than 10/sup 5/ t of organic wastes from Belgium, France, the Federal Republic of Germany, the Netherlands, Norway, Sweden, and the United Kingdom was carried out in the North Sea. Unique oceanographic features of the deep sea include its large dilution capacity; the long residence time of deep-sea water (on the order of 10/sup 2/ y); low biological productivity in the surface water of the open ocean (≅50 g m/sup -2/ of carbon per year); the existence of an oxygen minimum zone at several hundred meters deep in the mid-latitudes; and the abyssal-clay regions showing sedimentary records of tens of millions of years of slow, uninterrupted deposition of fine-grained clay. Any deep-sea waste disposal strategy must take into account oceanic processes and current scientific knowledge in order to attain a safe solution that will last for centuries

Deep borehole disposal of high-level radioactive waste.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Freeze, Geoffrey A.; Brady, Patrick Vane; Swift, Peter N.; Rechard, Robert Paul; Arnold, Bill Walter; Kanney, Joseph F.; Bauer, Stephen J.

2009-07-01

Preliminary evaluation of deep borehole disposal of high-level radioactive waste and spent nuclear fuel indicates the potential for excellent long-term safety performance at costs competitive with mined repositories. Significant fluid flow through basement rock is prevented, in part, by low permeabilities, poorly connected transport pathways, and overburden self-sealing. Deep fluids also resist vertical movement because they are density stratified. Thermal hydrologic calculations estimate the thermal pulse from emplaced waste to be small (less than 20 C at 10 meters from the borehole, for less than a few hundred years), and to result in maximum total vertical fluid movement of {approx}100 m. Reducing conditions will sharply limit solubilities of most dose-critical radionuclides at depth, and high ionic strengths of deep fluids will prevent colloidal transport. For the bounding analysis of this report, waste is envisioned to be emplaced as fuel assemblies stacked inside drill casing that are lowered, and emplaced using off-the-shelf oilfield and geothermal drilling techniques, into the lower 1-2 km portion of a vertical borehole {approx}45 cm in diameter and 3-5 km deep, followed by borehole sealing. Deep borehole disposal of radioactive waste in the United States would require modifications to the Nuclear Waste Policy Act and to applicable regulatory standards for long-term performance set by the US Environmental Protection Agency (40 CFR part 191) and US Nuclear Regulatory Commission (10 CFR part 60). The performance analysis described here is based on the assumption that long-term standards for deep borehole disposal would be identical in the key regards to those prescribed for existing repositories (40 CFR part 197 and 10 CFR part 63).

Deep Belief Nets for Topic Modeling

DEFF Research Database (Denmark)

Maaløe, Lars; Arngren, Morten; Winther, Ole

2015-01-01

-formative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep be-lief nets (DBN) that both find low-dimensional latent representations for documents....... Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an on-line publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance...

Un paseo por la Deep Web

OpenAIRE

Ortega Castillo, Carlos

2018-01-01

Este documento busca presentar una mirada técnica e inclusiva a algunas de las tecnologías de interconexión desarrolladas en la DeepWeb, primero desde un punto de vista teórico y después con una breve introducción práctica. La desmitificación de los procesos desarrollados bajo la DeepWeb, brinda herramientas a los usuarios para esclarecer y construir nuevos paradigmas de sociedad, conocimiento y tecnología que aporten al desarrollo responsable de este tipo de redes y contribuyan al crecimi...

AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling.

Science.gov (United States)

Wang, Sheng; Sun, Siqi; Xu, Jinbo

2016-09-01

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This paper presents Deep Convolutional Neural Fields (DeepCNF), an integration of DCNN with Conditional Random Field (CRF), for sequence labeling with an imbalanced label distribution. The widely-used training methods, such as maximum-likelihood and maximum labelwise accuracy, do not work well on imbalanced data. To handle this, we present a new training algorithm called maximum-AUC for DeepCNF. That is, we train DeepCNF by directly maximizing the empirical Area Under the ROC Curve (AUC), which is an unbiased measurement for imbalanced data. To fulfill this, we formulate AUC in a pairwise ranking framework, approximate it by a polynomial function and then apply a gradient-based procedure to optimize it. Our experimental results confirm that maximum-AUC greatly outperforms the other two training methods on 8-state secondary structure prediction and disorder prediction since their label distributions are highly imbalanced and also has similar performance as the other two training methods on solvent accessibility prediction, which has three equally-distributed labels. Furthermore, our experimental results show that our AUC-trained DeepCNF models greatly outperform existing popular predictors of these three tasks. The data and software related to this paper are available at https://github.com/realbigws/DeepCNF_AUC.

Deep-inelastic electron-proton diffraction

International Nuclear Information System (INIS)

Dainton, J.B.

1995-11-01