317 research outputs found
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Inverse Modeling to Quantify Irrigation System Characteristics and Operational Management
Remotely sensed (RS) data is a major source to obtain spatial data required for hydrological models. The challenge for the future is to obtain besides the more direct observable data (landcover, leaf area index, digital elevation model and evapotranspiration), non-visible data such as soil characteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inverse modeling to obtain these non-RS-visible data. For a command area in Haryana, India, we applied for the 2000–2001 rabi season a RS-GIS-combined inverse modeling approach to derive non-RS-visible data required in the regional application of hydrological models. A Genetic Algorithm loaded stochastic physically based soil-water-atmosphere-plant model (SWAP) was developed for the inverse problem and used in the study. The results showed good agreement with the inventoried data such as soil hydraulic properties, sowing dates, ground water depths, irrigation practices and water quality. The derived data could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluate operational management strategies
Generalized variational procedure: An application to non-perturbative QCD
We present a generalized variational procedure oriented to the algebraic
solution of many body Hamiltonians expressed in bosonic and fermionic
variables. The method specializes in the non-perturbative regime of the
solutions. As an example, we focus on the application of the method to
non-perturbative QCD
Impact of heavy alcohol consumption and cigarette smoking on sperm DNAÂ integrity
The purposes of the presents study were to investigate the impact of alcohol consumption and cigarette smoking on semen parameters and sperm DNA quality, as well as to determine whether tobacco smoking, or alcohol consumption causes more deterioration of sperm quality. Two hundred and eleven semen samples of men were included in this study. Four groups were studied: heavy smokers (N = 48), heavy drinkers (N = 52), non-smokers (n = 70), and non-drinkers (n = 41). Semen parameters were determined according to WHO guidelines, protamine deficiency assessed by chromomycin (CMA3) staining, and sperm DNA fragmentation (sDF) evaluated by TUNEL assay. Sperm parameters were significantly higher in non-smokers versus smokers and in non-drinkers versus drinkers (p < 0.005). However, protamine deficiency and sDF were significantly lower in non-smokers versus smokers and in non-drinkers versus drinkers (p < 0.0001). No significant difference in the semen analysis parameters was observed between heavy smokers and heavy drinkers (semen volume: 3.20 ± 1.43 vs. 2.81 ± 1.56 ml, semen count: 65.75 ± 31.32 vs. 53.51 ± 32.67 mill/ml, total motility: 24.27 ± 8.18 vs. 23.75 ± 1.75%, sperm vitality: 36.15 ± 18.57 vs. 34.62 ± 16.65%, functional integrity: 41.56 ± 18.57 vs. 45.96 ± 17.98% and the morphologically normal spermatozoa: 28.77 ± 11.82 vs. 27.06 ± 13.13%, respectively). However, protamine deficiency was significantly higher among drinkers than smokers (37.03 ± 9.75 vs. 33.27 ± 8.56%, p = 0.020). The sDF was also significantly higher among drinkers than smokers (22.37 ± 7.60 vs. 15.55 ± 3.33%, p < 0.0001). Thus, cigarette smoking, and heavy alcohol intake can deteriorate sperm quality. However, alcohol consumption deteriorates sperm maturity and damages DNA integrity at significantly higher rates than cigarette smoking
Generalized variational procedure: an application to nonperturbative QCD
We present a generalized variational procedure oriented to the algebraic solution of many-body Hamiltonians expressed in bosonic and fermionic variables. The method specializes in the nonperturbative regime of the solutions. As an example, we focus on the application of the method to nonperturbative QCD.Facultad de Ciencias Exacta
The Impact of Heavy Smoking on Male Infertility and Its Correlation with the Expression Levels of the PTPRN2 and PGAM5 Genes
Smoking has been linked to male infertility by affecting the sperm epigenome and genome.
In this study, we aimed to determine possible changes in the transcript levels of PGAM5 (the phosphoglycerate mutase family member 5), PTPRN2 (protein tyrosine phosphatase, N2-type receptor),
and TYRO3 (tyrosine protein kinase receptor) in heavy smokers compared to non-smokers, and to
investigate their association with the fundamental sperm parameters. In total, 118 sperm samples
(63 heavy-smokers (G1) and 55 non-smokers (G2)) were included in this study. A semen analysis
was performed according to the WHO guidelines. After a total RNA extraction, RT-PCR was used
to quantify the transcript levels of the studied genes. In G1, a significant decrease in the standard
semen parameters in comparison to the non-smokers was shown (p < 0.05). Moreover, PGAM5 and
PTPRN2 were differentially expressed (p ≤ 0.03 and p ≤ 0.01, respectively) and downregulated in the
spermatozoa of G1 compared to G2. In contrast, no difference was observed for TYRO3 (p ≤ 0.3). In
G1, the mRNA expression level of the studied genes was correlated negatively with motility, sperm
count, normal form, vitality, and sperm membrane integrity (p < 0.05). Therefore, smoking may affect
gene expression and male fertility by altering the DNA methylation patterns in the genes associated
with fertility and sperm quality, including PGAM5, PTPRN2, and TYRO3
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Crop growth and soil water balance modeling to explore water management options
The study was on the performance of the decision support system for agrotechnology transfer (DSSAT) and the soil water atmosphere plant (SWAP) under an acid sulphate soil. The comparison of these models was done as a prerequisite to the selection of an appropriate model, which is capable of simulating water management scenarios, water balance and crop growth, to be coupled with an adaptive optimization algorithm that can be used to explore water management options
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Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture
We present an innovative approach to explore water management options in irrigated agriculture considering the constraints of water availability and the heterogeneity of irrigation system properties. The method is two-folds: (i) system characterization using a stochastic data assimilation procedure where the irrigation system properties and operational management practices are estimated using remote sensing (RS) data; and (ii) water management optimization where we explored water management options under various levels of water availability. We set up a soil–water–atmosphere–plant model (SWAP) in a deterministic–stochastic mode for regional modeling. The distributed data, e.g. sowing dates, irrigation practices, soil properties, depth to groundwater and water quality, required as inputs for the regional modeling were estimated by minimizing the residuals between the distributions of field-scale evapotranspiration (ET) simulated by the regional application of SWAP, and by surface energy balance algorithm for land (SEBAL) using two Landsat7 ETM+ images. The derived distributed data were used as inputs in exploring water management options. Genetic algorithm was used in data assimilation and water management optimizations. The case study was conducted in Bata minor (lateral canal), Kaithal, Haryana, India during 2000–2001 rabi (dry) season. Our results showed that under limited water condition, regional wheat yield could improve further if water and crop management practices are considered simultaneously and not independently. Adjusting sowing dates and their distribution in the irrigated area could improve the regional yield, which also complements the practice of deficit irrigation when water availability is largely a constraint. This result was also found in agreement with the scenario that water is non-limited with the exception that the farmers have more degrees of freedom in their agricultural activities. An improvement of the regional yield to 8.5% is expected under the current scenario
Impact of tobacco smoking in association with H2BFWT, PRM1 and PRM2 genes variants on male infertility
Tobacco's genotoxic components can cause a wide range of gene defects in spermatozoa such as single- or double-strand DNA breaks, cross-links, DNA-adducts,
higher frequencies of aneuploidy and chromosomal abnormalities. The aim in this
study was to determine the correlation between sperm quality determined by
standard parameters, sperm DNA maturity tested by Chromomycin A3 (CMA3)
staining, sperm DNA fragmentation tested by TUNEL assay and tobacco smoking
in association with the single nucleotides polymorphisms (SNP) of three nuclear
protein genes in spermatozoa (H2BFWT, PRM1 and PRM2). In this study, semen
samples of 167 male patients were collected and divided into 54 non-smokers
and 113 smokers. The target sequences in the extracted sperm DNA were amplified by PCR followed by Sanger sequencing. The results showed the presence of
three variants: rs7885967, rs553509 and rs578953 in H2BFWT gene in the study
population. Only one variant rs737008 was detected in PRM1 gene, and three
variants were detected in the PRM2 gene: rs2070923, rs1646022 and rs424908.
No significant association was observed between the concentration, progressive
motility, morphology and the occurrence of H2BFWT, PRM1 and PRM2 SNPs.
However, sperm parameters were significantly lower in heavy smokers compared
to controls (p < 0.01) (sperm count: 46.00 vs. 78.50 mill/ml, progressive motility:
15.00% vs. 22.00%, and morphology 4.00% vs. 5.00%, respectively). Moreover,
the heavy smoker individuals exhibited a considerable increase in CMA3 positivity and sDF compared to non-smokers (p < 0.01) (29.50% vs. 20.50% and 24.50%
vs. 12.00%, respectively). In conclusion, smoking altered sperm parameters and
sperm DNA integrity, but did not show a linkage with genetic variants in
H2BFWT, and protamine genes (PRM1 and PRM2)
StrongNet: An International Network to Improve Diagnostics and Access to Treatment for Strongyloidiasis Control
Strongyloidiasis is a disease caused by an infection with a soil-transmitted helminth that affects, according to largely varying estimates, between 30 million and 370 million people worldwide [1,2]. Not officially listed as a neglected tropical disease (NTD), strongyloidiasis stands out as particularly overlooked [3]. Indeed, there is a paucity of research and public health efforts pertaining to strongyloidiasis. Hence, clinical, diagnostic, epidemiologic, treatment, and control aspects are not adequately addressed to allow for an effective management of the disease, both in clinical medicine and in public health programs [4]. The manifold signs and symptoms caused by Strongyloides stercoralis infection, coupled with the helminth’s unique potential to cause lifelong, persistent infection, make strongyloidiasis relevant beyond tropical and subtropical geographic regions, where, however, most of the disease burden is concentrated. Indeed, strongyloidiasis is acquired through contact with contaminated soil, and the infection is, thus, primarily transmitted in areas with poor sanitation, inadequate access to clean water, and lack of hygiene
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