Hydrological data and site feature information for a forest clearcutting and residual biomass removal study at the Marcell Experimental Forest

Plant-based feedstocks have long been considered viable, potential sources for biofuels. However, concerns regarding production effects may outweigh gains like carbon savings. Additional information is needed to understand environmental effects of growing feedstocks, including effects on wildlife communities and populations. We used a randomized and replicated experimental design to examine initial effects of biofuel feedstock treatment options, including removal of woody biomass after clearcutting and intercropping switchgrass (Panicum virgatum), on rodents to 2 years post-treatment in regenerating pine plantations in North Carolina, USA. Rodent community composition did not change with switchgrass production or residual biomass removal treatments. Further, residual biomass removal had no influence on rodent population abundances. However,Peromyscus leucopuswas found in the greatest abundance and had the greatest survival in treatments without switchgrass. In contrast, abundance of invasiveMus musculuswas greatest in switchgrass treatments. Other native species, such asSigmodon hispidus, were not influenced by the presence of switchgrass. Our results suggest that planting of switchgrass, but not biomass removal, had species-specific effects on rodents at least 2 years post-planting in an intensively managed southern pine system. Determining ecological mechanisms underlying our observed species associations with switchgrass will be integral for understanding long-term sustainability of biofuels production in southern pine forest.

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Quality of Pellets from Olive Grove Residual Biomass

Renewable Energy and Power Quality Journal ◽

10.24084/repqj08.463 ◽

2010 ◽

Vol 1 (08) ◽

pp. 751-756 ◽

Cited By ~ 1

Author(s):

A. García Maraver ◽

A.F. Ramos Ridao ◽

D.P. Ruiz ◽

M. Zamorano

Keyword(s):

Olive Grove ◽

Residual Biomass

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Using Convolutional Neural Networks to measure the contribution of visual features to the representation of object animacy in the brain

10.31237/osf.io/fxz4q ◽

2019 ◽

Cited By ~ 1

Author(s):

Sushrut Thorat

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Temporal Cortex ◽

Large Degree ◽

Visual Features ◽

Visual Feature ◽

Visual Stream ◽

Feature Information ◽

Ventral Visual Stream ◽

Animal Images

A mediolateral gradation in neural responses for images spanning animals to artificial objects is observed in the ventral temporal cortex (VTC). Which information streams drive this organisation is an ongoing debate. Recently, in Proklova et al. (2016), the visual shape and category (“animacy”) dimensions in a set of stimuli were dissociated using a behavioural measure of visual feature information. fMRI responses revealed a neural cluster (extra-visual animacy cluster - xVAC) which encoded category information unexplained by visual feature information, suggesting extra-visual contributions to the organisation in the ventral visual stream. We reassess these findings using Convolutional Neural Networks (CNNs) as models for the ventral visual stream. The visual features developed in the CNN layers can categorise the shape-matched stimuli from Proklova et al. (2016) in contrast to the behavioural measures used in the study. The category organisations in xVAC and VTC are explained to a large degree by the CNN visual feature differences, casting doubt over the suggestion that visual feature differences cannot account for the animacy organisation. To inform the debate further, we designed a set of stimuli with animal images to dissociate the animacy organisation driven by the CNN visual features from the degree of familiarity and agency (thoughtfulness and feelings). Preliminary results from a new fMRI experiment designed to understand the contribution of these non-visual features are presented.

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Reconnaissance Borehole Geophysical, Geological, and Hydrological Data from the Proposed Hydrodynamic Compartments of the Culpeper Basin in Loudoun, Prince William, Culpeper, Orange, and Fairfax Counties, Virginia (Version 1.0)

Open-File Report ◽

10.3133/ofr20061203 ◽

2006 ◽

Author(s):

Michael P. Ryan ◽

Herbert A. Pierce ◽

Carole D. Johnson ◽

David M. Sutphin ◽

David L. Daniels ◽

...

Keyword(s):

Hydrological Data

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Identification of Anti-cancer Peptides Based on Multi-classifier System

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666191203141102 ◽

2020 ◽

Vol 22 (10) ◽

pp. 694-704 ◽

Cited By ~ 2

Author(s):

Wanben Zhong ◽

Bineng Zhong ◽

Hongbo Zhang ◽

Ziyi Chen ◽

Yan Chen

Keyword(s):

Machine Learning ◽

Side Effect ◽

Learning Models ◽

Normal Cells ◽

Classifier System ◽

Prediction Rate ◽

Anti Cancer ◽

Feature Information ◽

Machine Learning Models ◽

Better Than

Aim and Objective: Cancer is one of the deadliest diseases, taking the lives of millions every year. Traditional methods of treating cancer are expensive and toxic to normal cells. Fortunately, anti-cancer peptides (ACPs) can eliminate this side effect. However, the identification and development of new anti Materials and Methods: In our study, a multi-classifier system was used, combined with multiple machine learning models, to predict anti-cancer peptides. These individual learners are composed of different feature information and algorithms, and form a multi-classifier system by voting. Results and Conclusion: The experiments show that the overall prediction rate of each individual learner is above 80% and the overall accuracy of multi-classifier system for anti-cancer peptides prediction can reach 95.93%, which is better than the existing prediction model.

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Variation in regrowth ability in relation to land-use intensity in three common grassland herbs

Journal of Plant Ecology ◽

10.1093/jpe/rtab001 ◽

2021 ◽

Author(s):

Anna Kirschbaum ◽

Oliver Bossdorf ◽

J F Scheepens

Keyword(s):

Land Use ◽

Phenotypic Plasticity ◽

Temporal Variation ◽

Temporal Stability ◽

Common Garden ◽

Grassland Management ◽

Land Use Intensity ◽

Evolutionary Changes ◽

Biomass Removal ◽

Reproductive Biomass

Abstract Aims Plant populations in managed grasslands are subject to strong selection exerted by grazing, mowing and fertilization. Many previous studies showed that this can cause evolutionary changes in mean trait values, but little is known about the evolution of phenotypic plasticity in response to land use. In this study, we aimed to elucidate the relationships between phenotypic plasticity – specifically, regrowth ability after biomass removal – and the intensity of grassland management and levels of temporal variation therein. Methods We conducted an outdoor common garden experiment to test if plants from more intensively mown and grazed sites showed an increased ability to regrow after biomass removal. We used three common plant species from temperate European grasslands, with seed material from 58 – 68 populations along gradients of land-use intensity, ranging from extensive (only light grazing) to very intensive management (up to four cuts per year). Important findings In two out of three species, we found significant population differentiation in regrowth ability after clipping. While variation in regrowth ability was unrelated to the mean land-use intensity of populations of origin, we found a relationship with its temporal variation in P. lanceolata, where plants experiencing less variable environmental conditions over the last 11 years showed stronger regrowth in reproductive biomass after clipping. Therefore, while mean grazing and mowing intensity may not select for regrowth ability, the temporal stability of the environmental heterogeneity created by land use may have caused its evolution in some species.

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Influences of the Pretreatments of Residual Biomass on Gasification Processes: Experimental Devolatilizations Study in a Fluidized Bed

Applied Sciences ◽

10.3390/app11125722 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5722

Author(s):

Stefania Lucantonio ◽

Andrea Di Giuliano ◽

Katia Gallucci

Keyword(s):

Fluidized Bed ◽

Molar Ratio ◽

Thermochemical Conversion ◽

Chemical Looping ◽

Carbon Conversion ◽

European Research ◽

Residual Biomass ◽

General Improvement ◽

Straight Lines ◽

Integral Average

The European research project CLARA (chemical looping gasification for sustainable production of biofuels, G.A. 817841) investigated chemical looping gasification of wheat straw pellets. This work focuses on pretreatments for this residual biomass, i.e., torrefaction and torrefaction-washing. Devolatilizations of individual pellets were performed in a laboratory-scale fluidized bed made of sand, at 700, 800, and 900 °C, to quantify and analyze the syngas released from differently pretreated biomasses; experimental data were assessed by integral-average parameters: gas yield, H2/CO molar ratio, and carbon conversion. A new analysis of devolatilization data was performed, based on information from instantaneous peaks of released syngas, by simple regressions with straight lines. For all biomasses, the increase of devolatilization temperature between 700 and 900 °C enhanced the thermochemical conversion in terms of gas yield, carbon conversion, and H2/CO ratio in the syngas. Regarding pretreatments, the main evidence is the general improvement of syngas quality (i.e., composition) and quantity, compared to those of untreated pellets; only slighter differentiations were observed concerning different pretreatments, mainly thanks to peak quantities, which highlighted an improvement of the H2/CO molar ratio in correlation with increased torrefaction temperature from 250 to 270 °C. The proposed methods emerged as suitable straightforward tools to investigate the behavior of biomasses and the effects of process parameters and biomass nature.

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