This work was supported by the Research Executive Agency, through the 7th Framework Programme of the European Union, Support for Training and Career Development of Researchers (Marie Curie - FP7-PEOPLE-2012-ITN), which funded the Initial Training Network (ITN) HYTECH &#34;Hydrodynamic Transport in Ecologically Critical Heterogeneous Interfaces&#34; (number 316546). It was also supported by the Polo Territoriale di Lecco of the Politecnico di Milano. The experiments were performed during a visit by S. S. to the Politecnico di Milano as a visiting scientist. The authors thank Tarcisio Fazzini, Stefania Gherbi, Francesco Mottini (B.Sc. students at the Politecnico di Milano) and Seyed Abbas Hosseini-Sadabadi (fellow of the HYTECH project and Ph.D. student at the Politecnico di Milano) for supporting the experimental activity and the data analysis. The authors gratefully thank Prof. Roger Nokes (University of Canterbury, Christchurch, New Zealand) for providing the Streams software and constant advice. Finally, the authors thank the JoVE managing editor and three anonymous reviewers for their thought-provoking comments and suggestions, thanks to which the manuscript could be significantly improved.

<ol>
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The authors declare that they have no competing financial interests.

Designing a bed-load transport experiment with particle visualization involves several steps, including the choice of an experimental configuration and hardware tools, flow measurement, particle seeding and visualization, and image analysis. Variations at each step have advantages and disadvantages. The key characteristics of the protocol presented in this manuscript are: (i) using a pressurized flow and a fixed rough bed, (ii) seeding a low number of bed-load particles having a contrasting color to the fixed bed color, (iii) using natural light and, (iv) using multiple cameras to obtain independent track sets to be joined to each other.
The experimental method and the data processing enable the bed-load particles to be reliably tracked for the final measurement. The covered flow guarantees an optimal vision of the moving particles. The fixed bed however, prevents observation of some processes (e.g., those linked with vertical displacements of sediment particles within the active bed-load layer), and thus limits the applicability of the technique to weak bed-loads.
The size of the data samples obtained using only 100 s of movie was relatively small. However, the sample size can easily be increased by lengthening the experimental duration of image acquisition and processing. Feeding a limited number of particles requires a longer experimental time than feeding at a substantially higher rate; but it is well worth the effort because of a relatively straightforward particle tracking due to the small concentration of particles in motion and the use of different colors, both of which reduce the probability of tracking mistakes. The use of natural light in the experiment avoids the need for lighting devices; however, a downside is that good lighting depends on the weather conditions.
The CFDs of particle hop length and duration depicted in Figure 4 show the lowest values as the most frequent ones. The largest measured values of hop length and duration were around 600 mm and 7 s, respectively. This was significantly larger compared to analogous values from the literature4,16,30, since measuring longer tracks runs the risk of long particle hops. The benefit of using two cameras is evident considering that a single camera had a focus area length of around 850 mm, which would not be much larger than hop length values to be measured. The measuring protocol using two cameras instead, ensured a satisfactory separation between the length scales of the process and those of the measuring field, thus reducing the risk of biasing the phenomenological results due to experimental limitations. Also, the focus area can be additionally lengthened by increasing the number of cameras placed along the flume.
An alternative procedure compared to the protocol described here is to create overlapped images before the particle identification and tracking. Our protocol (of performing the tracking twice and linking particle tracks) was preferred as the image merging method would have doubled the size of the data files, requiring a memory consumption that was not affordable.
With the processing algorithms described here, several particle tracks that were shorter than the length of the overlapping area were discarded because they prevented a full reconstruction of the sediment tracks. However, the threshold length of 120 mm was one order of magnitude shorter than the track lengths that could be obtained, and the loss of these data was therefore acceptable. Moreover, the track joining seen in the lower 8 cases of Figure 1 would not enable a significant increase in the track length to be obtained. On the other hand, these situations might help in retrieval of long tracks, such as the situation in Figure 5 that could be due to track interruptions. In a similar case, a long track could be reconstructed by iterative joining operations. It is however important to keep in mind that track interruptions like those in Figure 5 are clearly related to the tracking process rather than to the joining process.
This manuscript presented proof-of-concept results for a single experiment in order to demonstrate the capabilities of the adopted protocol. In future experiments, the protocol will be applied to a series of different hydro-dynamic conditions to achieve a detailed analysis of the bed-load sediment transport process.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/55874/55874fig5.jpg" /> 
Figure 5: A Situation of Track Joining in the Presence of Interruptions. The joining of these tracks into a single track is not possible with the protocol described here. As mentioned in the caption of Figure 1 and in step 8.4 of the Protocol, the tracks shorter than the length of the overlapping region are excluded. This eliminates the short red and green tracks; therefore, the remaining long ones cannot be joined because they have no common point. <a href="//cloudflare.jove.com/files/ftp_upload/55874/55874fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Since pioneering works appeared some decades ago1,2, the use of image analysis for the study of river sediment transport has been constantly increasing. This technique indeed proved its capability to provide relatively high-resolution and low-cost data for detailed analyses of physical phenomena3,4,5. With time, significant improvements have been obtained for both hardware and software tools.
The measurement of sediment transport can be performed using a Eulerian approach that targets measurement of sediment fluxes, or a Lagrangian one that aims at measuring trajectories of individual grains as they move. Image processing offers unique possibilities for particle tracking in comparison to other Eulerian methods6,7. However, despite these potentialities, the application of image analysis to bed-load sediment transport suffers from some critical experimental limitations, in terms of spatial/temporal support scales for the measurement and size of data samples. For example, it is difficult to achieve simultaneously an appropriate combination of a large spatial area, long duration of an experiment, and high measuring frequency3,4,8, without compromising the quality and quantity of data. In addition, the particle tracking can be performed manually2,4, which requires a great human effort, or automatically3,8, with the possibility of tracking errors made by the software used for the analysis.
This paper presents a protocol for the experimental investigation of bed-load sediment transport, where long duration was achieved by the type of camera used, large field of view was ensured by simultaneous use of two cameras at different locations, and reliable automatic processing was made possible by ad hoc experimental conditions. The experimental operation was designed and the processing tools were selected based on experience acquired by the authors in several research works dealing with the detailed investigation of sediment transport by image methods3,9,10,11,12,13,14,15,16,17,18.
A sediment transport experiment is described, that was performed releasing particles over a fixed, rough bed. The particle feeding was much less than the transport capacity of the flow to maintain a low concentration of moving grains, thus avoiding the congestion of particles to be tracked. Furthermore, the transported particles were not moving continuously, but intermittent motion was observed. The use of a fixed bed rather than a movable one represents a loss of similarity to natural conditions. However, a fixed bed was frequently used in sediment transport experiments19,20,21 under the assumption that the results are more simple and explanatory than those from complicated scenarios with a variety of acting processes. The use of a fixed bed obviously prevents processes of sediment burial and reappearance from being observed. On the other hand, in the presence of a weak bed load, the transport of sediment takes place in a superficial layer of a loose bed, and in this case, the use of a fixed bed may be adequate. In fact, specific comparisons between the properties of particle motion in experiments run with the two conditions did not present any significant differences3,14. Finally, the experiment presented here was performed with a pressurized flow to ensure an optimal condition for particle visualization through a transparent cover. Sediment transport with a pressurized flow has been experimentally studied in research prototyping ice-covered rivers, showing that the interaction between the near-bed boundary layer and the sediment is analogous to that of open-channel flow22,23. In the following sections, all methods are outlined and some representative results are provided.

<table border="0" cellpadding="0" cellspacing="0" width="635">
	<tbody>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Laser distance sensor</td>
			<td style="width:111px;">METRICA</td>
			<td style="width:119px;">PREXISOX2</td>
			<td style="width:191px;">Used to measure the flume slope</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Two-component polyester resin</td>
			<td style="width:111px;">Gelson</td>
			<td style="width:119px;">MS 65213</td>
			<td style="width:191px;">Used to glue sediment particles onto steel plates</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Water-resistant spray paint</td>
			<td style="width:111px;">Any</td>
			<td style="width:119px;"></td>
			<td style="width:191px;">Used to paint the fixed bed</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Ultrasonic Velocity Profiler</td>
			<td style="width:111px;">Signal Processing</td>
			<td style="width:119px;">DOP 2000</td>
			<td style="width:191px;">Used to measure the water velocity profiles</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Camera</td>
			<td style="width:111px;">Go-Pro</td>
			<td style="width:119px;">Hero 4 Black</td>
			<td style="width:191px;">Used to acquire movies of bed-load particle motion</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">Streams</td>
			<td style="width:111px;">University of Canterbury</td>
			<td style="width:119px;">2.01</td>
			<td style="width:191px;">Used for particle identification and tracking</td>
		</tr>
		<tr height="46">
			<td height="46" style="height:47px;width:216px;">MatLab</td>
			<td style="width:111px;">MathWorks</td>
			<td style="width:119px;">R14</td>
			<td style="width:191px;">Used to develop ad hoc codes for a variety of operations</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:216px;">Plexiglas</td>
			<td style="width:111px;"></td>
			<td style="width:119px;"></td>
			<td style="width:191px;">Transparent acrylic material</td>
		</tr>
	</tbody>
</table>

image-based lagrangian particle tracking in bed-load experiments

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a relatively low cost. This manuscript describes an application of particle tracking velocimetry (PTV) to a bed-load experiment with lightweight sediment. The key characteristics of the investigated sediment transport conditions were the presence of a covered flow and of a fixed rough bed above which particles were released in limited number at the flume inlet. Under the applied flow conditions, the motion of the individual bed-load particles was intermittent, with alternating movement and stillness terms. The flow pattern was preliminarily characterized by acoustic measurements of vertical profiles of the stream-wise velocity. During process visualization, a large field of view was obtained using two action-cameras placed at different locations along the flume. The experimental protocol is described in terms of channel calibration, experiment realization, image pre-processing, automatic particle tracking, and post-processing of particle track data from the two cameras. The presented proof-of-concept results include probability distributions of the particle hop length and duration. The achievements of this work are compared to those of existing literature to demonstrate the validity of the protocol.

Results presented in this section are for an experiment where the flume slope was set to zero (slope values were computed with &#177; 0.05% accuracy). The used sediment was made of PBT particles that were quasi-spherical, with a size d = 3 mm and a density &#961;p = 1,270 kg/m3. The experiment was run with a flow rate Q = 9.7 &#215; 10-3 m3/s resulting in a bulk velocity U = 0.31 m/s.
For the velocity measurements with the UVP, a 2 MHz probe was used at 81&#176; inclination. Velocity data were acquired at 20 Hz for 250 s. A representative velocity profile is depicted in Figure 2. It was taken at the channel axis and at 4.5 m from the flume inlet, where the flow was fully developed. Some values related to invalid elevations measurements were removed. An asymmetric profile resulted from the different roughness of the plastic lid and sediment bed. The plots also show the portion of the profile used for the estimation of the shear velocity, obtaining us = 25.9 &#177; 1.3 mm/s. The particle Reynolds number (Rep = us&#215;d/&#957;, with &#957; as the kinematic viscosity of water) was therefore equal to 78, indicating a transitionally rough regime.
The visualization of sediment transport was performed with two cameras placed at 3.5 m and 4.3 m from the flume inlet. The cameras operated at a frequency of 30 fps and with a resolution of 1,920 x 1,080 pixel. The factor for correction of image distortion was k = 0.6. After removal of distortion, the image calibration was 1 pixel = 0.5 mm. The length of overlap was from 760.15 to 880.11 mm (where the latter was the length of the focus area of the first camera from its upstream edge). The threshold intensity for particle identification was set to 80 and the expected blob size ranged from 0.5 to 8 mm. The search window for particle tracking was as follows: 1 mm upstream and 7 mm downstream, 4 mm laterally. The search window for reconnection of interrupted tracks was as follows: 1 mm upstream and 31 mm downstream, 16 mm laterally along 4 following frames. The threshold value of the square root of the mean squared difference of x and y values between two tracks to be joined was set to 10 mm.
The particle tracks measured using a subset of 3,000 images from each camera (corresponding to 100 s duration) are depicted in Figure 3. The database comprehended 37 and 34 tracks from the upstream and downstream camera, respectively. An overlap of the tracks obtained by the two cameras is first proposed and then the resulting full set of tracks is displayed. It is evident that the overlap in the central portion of the measuring area was satisfactory. 12 links were obtained at the end 59 tracks. The longest track spanned the entire observation window with a total length of approximately 1.6 m (more than 530 particle sizes, 15.2 flow depths or 5.3 flume widths), which is very large in comparison to other literature studies where similar analyses were performed3,4,5,8.
By taking a Lagrangian framework, the key indicators of particle kinematics are here applied in terms of properties of particle hops. Under an intermittent bed-load transport like the one in this experiment, these hops are motions separated by periods of rest. To detect hops within a full track for a single particle, the identification of particle motion and stillness is a necessary preliminary step. In this work, we applied a criterion30 that considers a particle in motion at a certain instant if its x position at that instant is larger than all the previous ones and lower than all the following ones. A total number of 98 hops was obtained from the 59 measured particle tracks. Figure 4 depicts the obtained Cumulative Frequency Distribution (CFD) for the hop length and duration.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55874/55874fig2.jpg" /> 
Figure 2: Measured Velocity Profile. (Top) The time-averaged vertical profile of the stream-wise velocity component. (Bottom) The estimation of the shear velocity by fitting a logarithmic equation to the lower portion of the profile. Note that a vertical axis starting from the top of the channel and oriented downwards is used in the first plot, representing the result from the measurement with the UVP. An axis from the channel bottom and directed upwards is instead used in the second plot, as needed to estimate the shear velocity by equation fitting. <a href="https://cloudflare.jove.com/files/ftp_upload/55874/55874fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55874/55874fig3.jpg" /> 
Figure 3: Plan View of Measured Particle Tracks. (Top) The tracks from the two cameras (upstream camera in red and downstream in black). (Bottom) The sample of joined tracks (changing color for clarity and some tracks highlighted by a thicker line). <a href="https://cloudflare.jove.com/files/ftp_upload/55874/55874fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/55874/55874fig4.jpg" /> 
Figure 4: Cumulative Frequency Distribution (CFD) of Hop Length (Top) and Duration (Bottom). Within each track of Figure 3, the particle was labeled at each time instant to represent if the particle was in motion or at rest at that instant. Particle hops were then extracted from the tracks as portions between particle entrainment (transition from stillness to motion) and disentrainment (transition from motion to stillness). The samples obtained for hop lengths and durations were used to create the distributions depicted here. <a href="https://cloudflare.jove.com/files/ftp_upload/55874/55874fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Code Files: join_cameras.m <a href="http://cloudflare.jove.com/files/ftp_upload/55874/join_cameras.m">Please click here to download this file.</a>

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Image-based Lagrangian Particle Tracking in Bed-load Experiments

The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Image analysis has been increasingly used for the measurement of river flows due to its capabilities to furnish detailed quantitative depictions at a ...

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8.9K Views.  Politecnico di Milano. The manuscript presents a protocol for the conduction of bed-load sediment transport experiments where the moving particles are tracked by image analysis. The experimental facility, the procedures for run realization and data processing, and finally some proof-of-concept results are presented here.

Video: Image-based Lagrangian Particle Tracking in Bed-load Experiments

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently than plants3, but also synthesize advanced biofuels2-4. Generally, atmospheric CO2 is not a sufficient source for supporting maximal algal growth5. On the other hand, the high concentrations of CO2 in industrial exhaust gases have adverse effects on algal physiology. Consequently, both cultivation conditions (such as nutrients and light) and the control of the flue gas flow into the photo-bioreactors are important to develop an efficient &ldquo;flue gas to algae&rdquo; system. Researchers have proposed different photobioreactor configurations4,6 and cultivation strategies7,8 with flue gas. Here, we present a protocol that demonstrates how to use models to predict the microalgal growth in response to flue gas settings. We perform both experimental illustration and model simulations to determine the favorable conditions for algal growth with flue gas. We develop a Monod-based model coupled with mass transfer and light intensity equations to simulate the microalgal growth in a homogenous photo-bioreactor. The model simulation compares algal growth and flue gas consumptions under different flue-gas settings. The model illustrates: 1) how algal growth is influenced by different volumetric mass transfer coefficients of CO2; 2) how we can find optimal CO2 concentration for algal growth via the dynamic optimization approach (DOA); 3) how we can design a rectangular on-off flue gas pulse to promote algal biomass growth and to reduce the usage of flue gas. On the experimental side, we present a protocol for growing Chlorella under the flue gas (generated by natural gas combustion). The experimental results qualitatively validate the model predictions that the high frequency flue gas pulses can significantly improve algal cultivation.

Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype &#34;flue gas to algal cultivation&#34; systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently ...

Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example

Cross contamination of foodborne pathogens in the retail environment is a significant public health issue contributing to an increased risk for foodborne illness. Ready-to-eat (RTE) processed foods such as deli meats, cheese, and in some cases fresh produce, have been involved in foodborne disease outbreaks due to contamination with pathogens such as Listeria monocytogenes. With respect to L. monocytogenes, deli slicers are often the main source of cross contamination. The goal of this study was to use a fluorescent compound to simulate bacterial contamination and track this contamination in a retail setting. A mock deli kitchen was designed to simulate the retail environment. Deli meat was inoculated with the fluorescent compound and volunteers were recruited to complete a set of tasks similar to those expected of a food retail employee. The volunteers were instructed to slice, package, and store the meat in a deli refrigerator. The potential cross contamination was tracked in the mock retail environment by swabbing specific areas and measuring the optical density of the swabbed area with a spectrophotometer. The results indicated that the refrigerator (i.e. deli case) grip and various areas on the slicer had the highest risk for cross contamination. The results of this study may be used to develop more focused training material for retail employees. In addition, similar methodologies could also be used to track microbial contamination in food production environments (e.g.&#160;small farms), hospitals, nursing homes, cruise ships, and hotels.

The overall goal of this study was to demonstrate the potential cross contamination mechanism of foodborne pathogen Listeria monocytogenes in a retail deli setting. This methodology may be applied to a variety of different environments to track pathogen contamination. &#160;

Cross contamination of foodborne pathogens in the retail environment is a significant public health issue contributing to an increased risk for foodborne ...

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

Plant-pollinator interactions have been studied for approximately one hundred years. During that time, many field methods have been developed to clarify the pollination effectiveness of each pollinator for visited flowers. Pollinator observations have been one of the most common methods to identify pollinators, and bagging and cage experiments have been conducted to show the effectiveness of specific pollinators. In a previous study of Lycoris sanguinea var. sanguinea, its effective pollinators, the visitation frequencies of each floral visitor, and its reproductive strategies were not identified. This study reports the observation that small bees visited flowers that were partially opened (breaking buds). To the best of our knowledge, this phenomenon has not been reported previously. Further, this study investigates the hypothesis that small bees can pollinate at that flowering stage. This study demonstrates the basic methods of field experiments in pollination ecology using L. sanguinea var. sanguinea. Pollinator observations and digital video showed the visitation frequencies of each floral visitor. Bagging and cage experiments revealed that these flowers could be pollinated fully and that breaking-bud pollination could be important for the pollination of this plant species. The advantages and disadvantages of each method are discussed, and recent developments, including laboratory experiments, are described.

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

To reveal the pollinator effectiveness of a given plant species, multiple methods of field experiments have been developed. This study demonstrates the basic methods of field experiments for pollination ecology using the case study of Lycoris sanguinea var. sanguinea and the novel pollination mechanism, breaking-bud pollination.

Plant-pollinator interactions have been studied for approximately one hundred years. During that time, many field methods have been developed to clarify ...

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial communities, a relatively precise but effort-intensive technique to assay microbial community composition is phospholipid fatty acid (PLFA) analysis. PLFA was developed to analyze phospholipid biomarkers, which can be used as indicators of microbial biomass and the composition of broad functional groups of fungi and bacteria. It has commonly been used to compare soils under alternative plant communities, ecology, and management regimes. The PLFA method has been shown to be sensitive to detecting shifts in microbial community composition.
An alternative method, fatty acid methyl ester extraction and analysis (MIDI-FA) was developed for rapid extraction of total lipids, without separation of the phospholipid fraction, from pure cultures as a microbial identification technique. This method is rapid but is less suited for soil samples because it lacks an initial step separating soil particles and begins instead with a saponification reaction that likely produces artifacts from the background organic matter in the soil. 
This article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples. The method combines the accuracy achieved through PLFA profiling by extracting and concentrating soil lipids as a first step, and a reduction in effort by saponifying the organic material extracted and processing with the MIDI-FA method as a second step.

The article describes a method that increases throughput while balancing effort and accuracy for extraction of lipids from the cell membranes of microorganisms for use in characterizing both total lipids and the relative abundance of indicator lipids to determine soil microbial community structure in studies with many samples.

Microbial communities are important drivers and regulators of ecosystem processes. To understand how management of ecosystems may affect microbial ...

Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees

The European honey bee, Apis mellifera L., is an economically and agriculturally important pollinator that generates billions of dollars annually. Honey bee colony numbers have been declining in the United States and many European countries since 1947. A number of factors play a role in this decline, including the unintentional exposure of honey bees to pesticides. The development of new methods and regulations are warranted to reduce pesticide exposures to these pollinators. One approach is the use of repellent chemistries that deter honey bees from a recently pesticide-treated crop. Here, we describe a protocol to discern the deterrence of honey bees exposed to select repellent chemistries. Honey bee foragers are collected and starved overnight in an incubator 15 h prior to testing. Individual honey bees are placed into Petri dishes that have either a sugar-agarose cube (control treatment) or sugar-agarose-compound cube (repellent treatment) placed into the middle of the dish. The Petri dish serves as the arena that is placed under a camera in a light box to record the honey bee locomotor activities using video tracking software. A total of 8 control and 8 repellent treatments were analyzed for a 10 min period with each treatment was duplicated with new honey bees. Here, we demonstrate that honey bees are deterred from the sugar-agarose cubes with a compound treatment whereas honey bees are attracted to the sugar-agarose cubes without an added compound.

The loss of honey bee colonies presents a challenge to crop pollination services. Current pollinator protection practices warrant an alternative approach to minimize the contact of honey bees to harmful pesticides using repellent chemistries. Here, we provide detailed methods for a visual tracking protocol to screen deterrents for bees.

The European honey bee, Apis mellifera L., is an economically and agriculturally important pollinator that generates billions of dollars annually. Honey ...

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration experiment conducted at a test site near the Tottori Sand Dune, Japan. The antenna array used in this study consists of 10 transmitting antennas (Tx) and 11 receiving antennas (Rx). For this experiment, the system was configured to use all possible Tx-Rx pairings, resulting in a Multi-Offset Gather (MOG) consisting of 110 Tx-Rx combinations. The array was left stationary at a position directly above the infiltration area and data were collected every 1.5 seconds using a time-based trigger. Common-Offset Gather (COG) and Common Mid-Point (CMP) data cubes were reconstructed from the MOG data during post-processing. There have been few studies that used time-lapse CMP data to estimate changes in velocity of propagation. In this study, electromagnetic (EM) wave velocity was estimated heuristically at 1-minute intervals from the reconstructed CMP data through curve fitting, using the hyperbola equation. We then proceeded to calculate the depth of the wetting front. The evolution of the wetting front over time obtain through this method is consistent with the observations from a soil moisture sensor which was placed at a depth below 20 cm. The results obtained in this study demonstrate the ability of such array GPR system to monitor a subsurface dynamic process like water infiltration accurately and quantitatively.

Here we present a Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array for monitoring the dynamic process of subsurface water infiltration. A time-lapse radar image of the infiltration process allowed estimating the depth of the wetting front during the course of the infiltration process.

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration ...

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the approach more practical than in the past. Image-based surveys allow for initial samples to be revisited and are non-invasive compared to traditional survey methods that typically involve nets or dredges. Protocols for image-based surveys can vary greatly but should be driven by target species behavior and survey objectives. To demonstrate this, we describe our most recent methods for an Atlantic sea scallop (Placopecten magellanicus) drop camera survey to provide a procedural example and representative results. The procedure is divided into three critical steps that include survey design, data collection, and data products. The influence of scallop behavior and the survey goal of providing an independent assessment of the U.S. sea scallop resource on the survey procedure are then discussed in the context of generalizing the method. Overall, the broad applicability and flexibility of the University of Massachusetts Dartmouth School for Marine Science and Technology (SMAST) drop camera survey demonstrates the method could be generalized and applied to a variety of sessile invertebrates or habitat focused research.

Image based surveying is an increasingly practical, non-invasive method to sample the marine environment. We present the protocol of a drop camera survey that estimates the abundance and distribution of the Atlantic sea scallop (Placopecten magellanicus). We discuss how this protocol can be generalized for application to other benthic macroinvertebrates.

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the ...

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard for evaluating the mining value of shale gas. Currently, studies on the correlation between particle size and methane adsorption are controversial. In this study, an isothermal adsorption apparatus, the gravimetric sorption analyzer, is used to test the adsorption capacity of different particle sizes in shale to determine the relationship between the particle size and the adsorption capacity of shale. Thegravimetric method requires fewer parameters and produces better results in terms of accuracy and consistency than methods like the volumetric method. Gravimetric measurements are performed in four steps: a blank measurement, preprocessing, a buoyancy measurement, and adsorption and desorption measurements. Gravimetric measurement is presently considered to be a more scientific and accurate method of measuring the amount of adsorption; however, it is time-consuming and requires a strict measuring technique. A Magnetic Suspension Balance (MSB) is the key to verify the accuracy and consistency of this method. Our results show that adsorption capacity and particle size are correlated, but not a linear correlation, and the adsorptions in particles sieved into 40 - 60 and 60 - 80 meshes tend to be larger. We propose that the maximum adsorption corresponding to the particle size is approximately 250 &#181;m (60 mesh) in the shale gas fracturing.

We use an isothermal adsorption apparatus, the gravimetric sorption analyzer, to test the adsorption capacity of different particle sizes of shale, in order to find out the relationship between particle size and the adsorption capacity of shale.

The amount of adsorbed shale gas is a key parameter used in shale gas resource evaluation and target area selection, and it is also an important standard ...

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The purpose of this manuscript is to provide detail on how to rapidly sample soil, rhizosphere, and endosphere of replicated field trials and analyze changes that may occur in the microbial communities due to sample type, treatment, and plant genotype. The experiment used to demonstrate these methods consists of replicated field plots containing two, pure, warm-season grasses (Panicum virgatum and Andropogon gerardii) and a low-diversity grass mixture (A. gerardii, Sorghastrum nutans, and Bouteloua curtipendula). Briefly, plants are excavated, a variety of roots are cut and placed in phosphate buffer, and then shaken to collect the rhizosphere. Roots are brought to the laboratory on ice and surface sterilized with bleach and ethanol (EtOH). The rhizosphere is filtered and concentrated by centrifugation. Excavated soil from around the root ball is placed into plastic bags and brought to the lab where a small amount of soil is taken for DNA extractions. DNA is extracted from roots, soil, and rhizosphere and then amplified with primers for the V4 region of the 16S rRNA gene. Amplicons are sequenced, then analyzed with open access bioinformatics tools. These methods allow researchers to test how the microbial community diversity and composition varies due to sample type, treatment, and plant genotype. Using these methods along with statistical models, the representative results demonstrate there are significant differences in microbial communities of roots, rhizosphere, and soil. Methods presented here provide a complete set of steps for how to collect field samples, isolate, extract, quantify, amplify, and sequence DNA, and analyze microbial community diversity and composition in replicated field trials.

Excavation of plant roots from the field as well as processing of samples into endosphere, rhizosphere, and soil are described in detail, including DNA extraction and data analysis methods. This paper is designed to enable other laboratories to use these techniques for the study of soil, endosphere, and rhizosphere microbiomes.

Plant and soil microbiome studies are becoming increasingly important for understanding the roles microorganisms play in agricultural productivity. The ...

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...

Image-based Lagrangian Particle Tracking in Bed-load Experiments

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Chapters in this video

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Tracking Microbial Contamination in Retail Environments Using Fluorescent Powder - A Retail Delicatessen Environment Example

Field Experiments of Pollination Ecology: The Case of Lycoris sanguinea var. sanguinea

A Lipid Extraction and Analysis Method for Characterizing Soil Microbes in Experiments with Many Samples

Video Tracking Protocol to Screen Deterrent Chemistries for Honey Bees

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

Isolation and Analysis of Microbial Communities in Soil, Rhizosphere, and Roots in Perennial Grass Experiments

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment