scholarly journals Volatile Chemical Interaction Between Undamaged Plants: Effects at Higher Trophic Levels

2010 ◽  
pp. 87-98 ◽  
Author(s):  
Robert Glinwood
Keyword(s):  
Chemical Interaction ◽  
Trophic Levels

Relationship between morphometrics and trophic levels in deep-sea fishes

2020 ◽  
Vol 637 ◽  
pp. 225-235 ◽  
Author(s):  
MA Ladds ◽  
MH Pinkerton ◽  
E Jones ◽  
LM Durante ◽  
MR Dunn
Keyword(s):  
Stable Isotopes ◽  
Trophic Level ◽  
Deep Sea ◽  
Strong Relationship ◽  
Ecosystem Model ◽  
Trophic Levels ◽  
Fish Size ◽  
Wide Range ◽  
Gape Size ◽  
Morphological Variables

Marine food webs are structured, in part, by predator gape size. Species found in deep-sea environments may have evolved such that they can consume prey of a wide range of sizes, to maximise resource intake in a low-productivity ecosystem. Estimates of gape size are central to some types of ecosystem model that determine which prey are available to predators, but cannot always be measured directly. Deep-sea species are hypothesized to have larger gape sizes than shallower-water species relative to their body size and, because of pronounced adaptive foraging behaviour, show only a weak relationship between gape size and trophic level. Here we present new data describing selective morphological measurements and gape sizes of 134 osteichthyan and chondrichthyan species from the deep sea (200-1300 m) off New Zealand. We describe how gape size (height, width and area) varied with factors including fish size, taxonomy (class and order within a class) and trophic level estimated from stable isotopes. For deep-sea species, there was a strong relationship between gape size and fish size, better predicted by body mass than total length, which varied by taxonomic group. Results show that predictions of gape size can be made from commonly measured morphological variables. No relationship between gape size and trophic level was found, likely a reflection of using trophic level estimates from stable isotopes as opposed to the commonly used estimates from FishBase. These results support the hypothesis that deep-sea fish are generalists within their environment, including suspected scavenging, even at the highest trophic levels.

Solubility Enhancement of Poorly Water-Soluble Antifungal Drug Acyclovir by Nanocrystal Formulation Approach

2018 ◽  
Vol 11 (2) ◽  
pp. 4036-4042
Author(s):  
Prakash Goudanavar ◽  
Ankit Acharya ◽  
Vinay C.H
Keyword(s):  
Chemical Interaction ◽  
Research Work ◽  
Antiviral Drug ◽  
In Vitro Release ◽  
Oral Route ◽  
Water Soluble ◽  
Precipitation Method ◽  
Dsc Studies ◽  
Ft Ir

Administration of an antiviral drug, acyclovir via the oral route leads to low and variable bioavailability (15-30%). Therefore, this research work was aimed to enhance bioavailability of acyclovir by nanocrystallization technique. The drug nanocrystals were prepared by anti-solvent precipitation method in which different stabilizers were used. The formed nanocrystals are subjected to biopharmaceutical characterization including solubility, particle size and in-vitro release. SEM studies showed nano-crystals were crystalline nature with sharp peaks. The formulated drug nanocrystals were found to be in the range of 600-900nm and formulations NC7 and NC8 showed marked improvement in dissolution velocity when compared to pure drug, thus providing greater bioavailability. FT-IR and DSC studies revealed the absence of any chemical interaction between drug and polymers used. 

Fuel-Cladding Chemical Interaction of a Prototype Annular U-10Zr Fuel with Fe-12Cr Ferritic/Martensitic Cladding

2020 ◽  
Author(s):  
Xiang Liu ◽  
Luca Capriotti ◽  
Tiankai Yao ◽  
Jason Harp ◽  
Michael T. Benson ◽  
...  
Keyword(s):  
Chemical Interaction ◽  
Fuel Cladding

ENERGY TRANSFER EFFICIENCIES ON LOWER TROPHIC LEVELS WITH INTENSIVE OYSTER FARMING IN HIROSHIMA BAY, JAPAN

2017 ◽  
Author(s):  
Akira Umehara ◽  
Akira Umehara ◽  
Satoshi Asaoka ◽  
Satoshi Asaoka ◽  
Naoki Fujii ◽  
...  
Keyword(s):  
Energy Transfer ◽  
Primary Production ◽  
Skeletonema Costatum ◽  
Warm Season ◽  
Nutrient Supply ◽  
Trophic Levels ◽  
Phytoplankton Primary Production ◽  
Mean Values ◽  
Hiroshima Prefecture ◽  
Study Species

In enclosed water areas, organic matters are actively produced by phytoplankton due to abundant nutrient supply from the rivers. In our study area of the semi-enclosed Hiroshima Bay, oyster farming consuming high primary production has been developed since the 1950s, and the oyster production of Hiroshima prefecture have had the largest market share (ca. 60%) in Japan. In this study, species composition of phytoplankton, primary production, and secondary production of net zooplanktons and oysters were determined seasonally at seven stations in the bay between November 2014 and August 2015. In the bay, diatoms including Skeletonema costatum dominated during the period of the study. The primary productions markedly increased during summer (August), and its mean values in the northern part of the bay (NB) and the southern part (SB) were 530 and 313 mgC/m2/d, respectively. The productions of net zooplankton and oyster increased during the warm season, and its mean values in the NB were 14 and 1.2 mgC/m2/d, and in SB were 28 and 0.9 mgC/m2/d, respectively. The energy transfer efficiencies from the primary producers to the secondary producers in the NB and SB were 2.8% and 9.1%, respectively. However, the transfer efficiency to the oysters was approximately 0.3% in the bay. This study clearly showed the spatial difference of the productions and transfer efficiencies, and the low contribution of the production of oysters in secondary productions in Hiroshima Bay.

Evaluation and Avoidance of False Alarm by Controlling Rhine Water with Continuously Working Biotests

1994 ◽  
Vol 29 (3) ◽  
pp. 207-209 ◽  
Author(s):  
H. Puzicha
Keyword(s):  
Warning System ◽  
Point Sources ◽  
Trophic Levels ◽  
River Rhine ◽  
Wide Range ◽  
River Test ◽  
Continuous Working ◽  
Range Of Variation ◽  
Contaminant Load ◽  
Rhine Water

Effluents from point sources (industries, communities) and diffuse inputs introduce pollutants into the water of the river Rhine and cause a basic contaminant load. The aim is to establish a biological warning system to detect increased toxicity in addition to the already existing chemical-physical monitoring system. To cover a wide range of biocides, continuous working biotests at different trophic levels (bacteria, algae, mussels, water fleas, fishes) have been developed and proved. These are checked out for sensitivity against toxicants, reaction time, validity of data and practical handling under field conditions at the river. Test-specific appropriate methods are found to differentiate between the normal range of variation and true alarm signals.

Convolutional Neural Networks for ATC Classification

2019 ◽  
Vol 24 (34) ◽  
pp. 4007-4012 ◽  
Author(s):  
Alessandra Lumini ◽  
Loris Nanni
Keyword(s):  
Classification System ◽  
Feature Vector ◽  
Chemical Interaction ◽  
Anatomical Therapeutic Chemical ◽  
World Health ◽  
Therapeutic Effects ◽  
True Rate ◽  
Unknown Compound ◽  
The Absolute ◽  
Learned Features

Background: Anatomical Therapeutic Chemical (ATC) classification of unknown compound has raised high significance for both drug development and basic research. The ATC system is a multi-label classification system proposed by the World Health Organization (WHO), which categorizes drugs into classes according to their therapeutic effects and characteristics. This system comprises five levels and includes several classes in each level; the first level includes 14 main overlapping classes. The ATC classification system simultaneously considers anatomical distribution, therapeutic effects, and chemical characteristics, the prediction for an unknown compound of its ATC classes is an essential problem, since such a prediction could be used to deduce not only a compound’s possible active ingredients but also its therapeutic, pharmacological, and chemical properties. Nevertheless, the problem of automatic prediction is very challenging due to the high variability of the samples and the presence of overlapping among classes, resulting in multiple predictions and making machine learning extremely difficult. Methods: In this paper, we propose a multi-label classifier system based on deep learned features to infer the ATC classification. The system is based on a 2D representation of the samples: first a 1D feature vector is obtained extracting information about a compound’s chemical-chemical interaction and its structural and fingerprint similarities to other compounds belonging to the different ATC classes, then the original 1D feature vector is reshaped to obtain a 2D matrix representation of the compound. Finally, a convolutional neural network (CNN) is trained and used as a feature extractor. Two general purpose classifiers designed for multi-label classification are trained using the deep learned features and resulting scores are fused by the average rule. Results: Experimental evaluation based on rigorous cross-validation demonstrates the superior prediction quality of this method compared to other state-of-the-art approaches developed for this problem. Conclusion: Extensive experiments demonstrate that the new predictor, based on CNN, outperforms other existing predictors in the literature in almost all the five metrics used to examine the performance for multi-label systems, particularly in the “absolute true” rate and the “absolute false” rate, the two most significant indexes. Matlab code will be available at https://github.com/LorisNanni.

Modulation of Gut Microbiota through Dietary Phytochemicals as a Novel Anti-infective Strategy

2020 ◽  
Vol 17 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Pavan K. Mujawdiya ◽  
Suman Kapur
Keyword(s):  
Microbial Community ◽  
Quorum Sensing ◽  
Population Density ◽  
Gut Microbiota ◽  
Biofilm Formation ◽  
Chemical Interaction ◽  
Bacterial Species ◽  
Critical Role ◽  
Bacterial Cells ◽  
Gut Microbes

: Quorum Sensing (QS) is a phenomenon in which bacterial cells communicate with each other with the help of several low molecular weight compounds. QS is largely dependent on population density, and it triggers when the concentration of quorum sensing molecules accumulate in the environment and crosses a particular threshold. Once a certain population density is achieved and the concentration of molecules crosses a threshold, the bacterial cells show a collective behavior in response to various chemical stimuli referred to as “auto-inducers”. The QS signaling is crucial for several phenotypic characteristics responsible for bacterial survival such as motility, virulence, and biofilm formation. Biofilm formation is also responsible for making bacterial cells resistant to antibiotics. : The human gut is home to trillions of bacterial cells collectively called “gut microbiota” or “gut microbes”. Gut microbes are a consortium of more than 15,000 bacterial species and play a very crucial role in several body functions such as metabolism, development and maturation of the immune system, and the synthesis of several essential vitamins. Due to its critical role in shaping human survival and its modulating impact on body metabolisms, the gut microbial community has been referred to as “the forgotten organ” by O`Hara et al. (2006) [1]. Several studies have demonstrated that chemical interaction between the members of bacterial cells in the gut is responsible for shaping the overall microbial community. : Recent advances in phytochemical research have generated a lot of interest in finding new, effective, and safer alternatives to modern chemical-based medicines. In the context of antimicrobial research various plant extracts have been identified with Quorum Sensing Inhibitory (QSI) activities among bacterial cells. This review focuses on the mechanism of quorum sensing and quorum sensing inhibitors isolated from natural sources.

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