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26 results for “Process conditions”
Impact of processing and storage conditions on color stability of strawberry puree: the role of PPO reactions revisited
<p>The effect of pre-heating fresh strawberries (hot break) and the use of refrigerated temperatures prior to pasteurization on the stability of anthocyanins, vitamin C, color and polyphenol oxidase (PPO) activity during storage (42 days at 35 ºC) of strawberry puree was studied. Hot break resulted in 20% residual PPO activity and caused 10% anthocyanin degradation, whereas vitamin C was unaffected. After mashing, purees were stored at 4 ºC and 25 ºC for 3 hours. During this period, anthocyanins and PPO activity remained constant independently of the processing history but ascorbic acid was oxidized faster at 25 ºC. Pasteurization caused complete inactivation of PPO, reduction of anthocyanins (25%), of <em>a*</em> value (6%) and of vitamin C (50%). Neither partial inactivation of PPO early in the processing (hot break) nor the use of refrigeration prior to pasteurization had a positive effect on color and anthocyanin stability of strawberry puree during subsequent storage, suggesting that PPO-derived reaction products formed during processing have a very limited impact on color degradation during shelf-life.</p>
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet.
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
<p>Understanding how niche-based and neutral processes contribute to the spatial variation in plant-pollinator interactions is central to designing effective pollination conservation schemes. Such schemes are needed to reverse declines of wild bees and other pollinating insects and to promote pollination services to wild and cultivated plants. We used data on wild bee interactions with plants belonging to the four tribes Loteae, Trifolieae, Anthemideae, and either spring- or summer-flowering Cichorieae, sampled systematically along a 682km latitudinal gradient to build models that allowed us to (a) predict occurrences of pairwise bee-flower interactions across 115 sampling locations, and (b) estimate the contribution of variables hypothesized to be related to niche-based assembly structuring processes (viz. annual mean temperature, landscape diversity, bee sociality, bee phenology, and flower preferences of bees) and neutral processes (viz. regional commonness and dispersal distance to conspecifics). While neutral processes were important predictors of plant-pollinator distributions, niche-based processes were reflected in the contrasting distributions of solitary bee and bumble bees along the temperature gradient, and in the influence of bee flower preferences on the distribution of bee species across plant types. In particular, bee flower preferences separated bees into three main groups, albeit with some overlap: visitors to spring-flowering Cichorieae; visitors to Anthemideae and summer-flowering Cichorieae; and visitors to Trifolieae and Loteae. Our findings suggest that both neutral and niche-based processes are significant contributors to the spatial distribution of plant-pollinator interactions so that conservation actions in our region should be directed towards areas: near high concentrations of known occurrences of regionally rare bees; in mild climatic conditions; and that are surrounded by heterogeneous landscapes. Given the observed niche-based differences, the proportion of functionally distinct plants in flower-mixes could be chosen to target bee species, or guilds, of conservation concern.</p>
Data for thesis titled: The impact of processing conditions on enzymatic protein hydrolysis performance from sardine (Sardina pilchardus) by-products using Alcalase 2.4L, and the influence on final spray dried hydrolysate powder properties
<p>The data answer the objectives that focused on:</p> <ol> <li>determining the substrate-specific optimum hydrolysis temperature and pH for the particular enzyme-substrate (Alcalase-sardine by-product) combination,</li> <li>investigating the effect of mixing speed, solids concentration and enzyme dosage on dry solids yield and protein recovery during enzymatic hydrolysis of sardine processing by-products,</li> <li>evaluating the influence of solids concentration on emulsion formation during enzymatic hydrolysis,</li> <li>determining the effect of solids concentration and emulsion formation on molecular weight distribution of protein hydrolysates,</li> <li>investigating the effect of mixing speed and solids concentration on the viscosity and mixing regime of material during enzymatic hydrolysis,</li> <li>establishing the role played by processing conditions (degree of hydrolysis (DH), maltodextrin addition and inlet air temperature) on powder recovery during spray drying, and</li> <li>investigating the role of DH, maltodextrin concentration and spray drying temperature on handling and storage properties of spray dried protein hydrolysates.</li> </ol> <p>This data also appears in journal papers with the following titles:</p> <p>Chiodza, K. & Goosen, N.J. 2023a. Evaluation of handling and storage stability of spray dried protein hydrolysates from sardine (Sardina pilchardus) processing by-products: Effect of enzymatic hydrolysis time, spray drying temperature and maltodextrin concentration. <em>Food and Bioproducts Processing</em>. (June, 30). DOI: <a href="https://www.sciencedirect.com/science/article/pii/S0960308523000743?via%3Dihub">https://doi.org/10.1016/j.fbp.2023.06.009</a>.</p> <p>Chiodza, K. & Goosen, N.J. 2023b. Influence of mixing speed, solids concentration and enzyme dosage on dry solids yield and protein recovery during enzymatic hydrolysis of sardine (Sardina pilchardus) processing by-products using Alcalase 2.4L: a multivariable optimisation approach. <em>Biomass Conversion and Biorefinery</em>. 1:1–23. DOI: <a href="https://link.springer.com/article/10.1007/s13399-023-03829-2">https://doi.org/10.1007/s13399-023-03829-2</a>. </p> <p>Chiodza, K. & Goosen, N.J. 2023c. Emulsion formation during enzymatic protein hydrolysis and its effect on protein recovery and molecular weight distribution of protein hydrolysates from sardine (Sardina pilchardus) by-products. <em>Biomass Conversion and Biorefinery</em>. 1:1–12. DOI: <a href="https://link.springer.com/article/10.1007/s13399-023-04438-9">https://doi.org/10.1007/s13399-023-04438-9</a>.</p>
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
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Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Hearing Aid Processing and Working Memory in Realistic Spatial Conditions
ClinicalTrials.gov study NCT04521166. IPD Sharing: NO. Countries: 1. Publications: 1.
Detection and Estimation of Inundation and Associated Risks Using Traffic and Monitoring Cameras and Image Processing Under Extreme Flooding Conditions
<p>The main objective of this project is to develop an inundation detection and evaluation framework using images from traffic monitoring cameras and reliable flood monitoring under extreme precipitation conditions. This study presents a comparative assessment of image enhancement and segmentation techniques to automatically identify the flash flooding from the low-resolution images taken by traffic-monitoring cameras. Due to inaccurate equipment in severe weather conditions (e.g., raindrops or light refraction on camera lenses), low-resolution images are subject to noises that degrade the quality of information. De-noising procedures are carried out for the enhancement of images by removing different types of noises. After the de-noising, image segmentation is implemented to detect the inundation from the images automatically. In addition, the detection of the inundation using the image segmentation with and without de-noising techniques are compared. The results indicate that among de-noising methods, the Bayes shrink with the thresholding discrete wavelet transform shows the most reliable result. For the image segmentation, the Bayesian segmentation is superior to the others. The results demonstrate that the proposed image enhancement and segmentation methods can be effectively used to identify the inundation from low-resolution images taken in severe weather conditions. A new Bayesian filtering method will be devised and applied to estimate the inundation from low-resolution images that will allow traffic engineers to take preventive or proactive actions to improve the safety of drivers and protect and preserve the transportation infrastructure. This new observation with improved accuracy will enhance our understanding of dynamic urban flooding by filling an information gap in the locations where conventional observations have limitations.</p>
Data from: Proteomic evidence of a paedomorphic evolutionary process within a marine snail species: a strategy for adapting to extreme ecological conditions?
The exposed and sheltered ecotypes of the marine snail Littorina saxatilis from European rocky shores are considered a key model system to study adaptation and ecological speciation. Previous studies showed that two ecotypes (RB and SU) of this species in NW Spain have differently adapted to different shore levels and microhabitats. In order to understand how this divergent adaptive process has been accomplished, we followed a quantitative proteomic approach to investigate the proteome variation in a number of different biological factors, i.e. ecotype, ontogeny and their interactions. This approach allowed testing the hypothesis that one of the ecotypes has evolved by paedomorphosis, and also whether or not the molecular mechanisms related to ecotype differentiation are set up in early developmental stages. Additionally the identification of some candidate proteins by mass spectrometry provides some functional insights about these evolutionary processes. Results from this study provided evidence of higher ontogenetic differentiation at proteome level in the RB (metamorphic) than in SU (paedomorphic) ecotype that point to the possibility of juvenile stage retention in this latter ecotype. The level of protein expression (proteome) differences between ecotypes maintained nearly constant from late embryonic stages to adulthood, although some proteins involved in these changes considerably differed in embryonic compared to other ontogenetic stages. Paedomorphosis may be the evolutionary response of the SU ecotype of solving the trade-off during sexually immaturity that is caused by the evolution of small size arising from adaptation to the wave exposed habitat. Some potential candidate genes of adaptation related to energetic metabolism have been identified, providing a promising baseline for future functional analyses.
Dataset for "Machine Learning-Aided High Throughput Examination of Block Copolymer Processing Conditions"
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Data and analysis for "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"
<p>This contains all of the necessary data and code to reproduce the results of the manuscript submitted to the Journal of</p> <p>Hydrometeorology entitled "A process-conditioned and spatially consistent method for reducing systematic biases in modeled streamflow"</p>
Dataset - Observation of photon-photon thermodynamic processes under negative optical temperature conditions
<p>Experimental data and simulation results associated with the article: Observation of photon-photon thermodynamic processes under negative optical temperature conditions</p>
Hydrothermal carbonization of vegetable-tanned leather shavings (HTC-VTS) for environmental remediation: optimization of process conditions
<p><span>Herein, the response surface methodology (RSM) has been used to study simultaneously the effects of carbonization temperature, residence time, and moisture content on the activated hydrochar preparation-based vegetable-tanned leather shavings (VTS) using hydrothermal carbonization method (HTC). Owing to the desirability chosen, three responses were analysed, namely: the hydrochar yield, iodine, and methylene blue numbers. The analysis of experimental results revealed that the hydrochar yield was decreased with an increase in carbonization temperature which led to micropores formation inside the hydrochar network. The optimal preparation conditions retained were: 83.10%, 390.44 mg/g, and 259.63 mg/g for the hydrochar yield, iodine, and methylene blue number respectively. The hydrochar micrograph showed the presence of external pores, whereas the FTIR analysis recorded the presence of acidic functional groups found on hydrochar surface. The findings revealed that the VTS is a good precursor for the hydrochar preparation useful in the removal of organic and inorganic pollutants in aqueous media. </span></p>
The Role of the Glutamatergic System in the Extinction of Conditioned Reinforcement Processes
ClinicalTrials.gov study NCT00926900. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Proteomic evidence of a paedomorphic evolutionary process within a marine snail species: a strategy for adapting to extreme ecological conditions?
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Hydrothermal carbonization of vegetable-tanned leather shavings (HTC-VTS) for environmental remediation: optimization of process conditions
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EXPLORING THE ACTUAL PROCESSES OF CONCEPTUAL FIELD EVOLUTION IN MODERN CONDITIONS.
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Supplementary data to: Unravelling driving conditions of rock and ice avalanches and resulting cascading processes in High Mountain Asia
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EDITORIAL CONDITIONS OF IMPROVING THE MILITARY EDUCATION PROCESS ON THE BASE OF MODERN EDITORIAL TECHNOLOGIES
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Impact of processing and storage conditions on the volatile profile of whole chickpeas (Cicer arietinum L.)
<p>Supplementary table and final data used for figures in the paper: https://doi.org/10.1021/acsfoodscitech.1c00108</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.