Dose response relationship of droplet applications for the leaf-specific weed control in vegetable crops

As a prelude to leaf-specific weed control using droplets targeted by a robotic weeder, amounts of herbicide required to control individual weed seedlings were estimated. Roundup Biactive was applied at doses equivalent to 1/128th to four times the recommended rate in addition to undiluted Roundup and water controls. Based on the mean ground cover of the seedlings, the recommended dose (1.5 l ha 1) was estimated and droplets were applied to individual plants by micropipette. All treatments contained 1% AS 500 SL, Agromix (adjuvant). Three weeks after application dry weights (DW) of each seedling was recorded. DW reductions of 50% were achieved in the five species tested at less than the recommended rate whereas only in one species was a 90% reduction obtained at that rate. In Galium aparine for example, 19.3 μg of glyphosate reduced DW per plant by 90% compared to the recommended dose of 8.4 μg.

Coupling liquid MALDI MS to liquid chromatography

Matrix-assisted laser desorption/ionisation (MALDI) coupled with time-of-flight (TOF) mass spectrometry (MS) is a powerful tool for the analysis of biological samples, and nanoflow high-performance liquid chromatography (nanoHPLC) is a useful separation technique for the analysis of complex proteomics samples. The off-line combination of MALDI and nanoHPLC has been extensively investigated and straightforward techniques have been developed, focussing particularly on automated MALDI sample preparation that yields sensitive and reproducible spectra. Normally conventional solid MALDI matrices such as α-cyano-4-hydroxycinnamic acid (CHCA) are used for sample preparation. However, they have limited usefulness in quantitative measurements and automated data acquisition because of the formation of heterogeneous crystals, resulting in highly variable ion yields and desorption/ ionization characteristics. Glycerol-based liquid support matrices (LSM) have been proposed as an alternative to the traditional solid matrices as they provide increased shot-to-shot reproducibility, leading to prolonged and stable ion signals and therefore better results. This chapter focuses on the integration of the liquid LSM MALDI matrices into the LC-MALDI MS/MS approach in identifying complex and large proteomes. The interface between LC and MALDI consists of a robotic spotter, which fractionates the eluent from the LC column into nanoliter volumes, and co-spots simultaneously the liquid matrix with the eluent fractions onto a MALDI target plate via sheath flow. The efficiency of this method is demonstrated through the analysis of trypsin digests of both bovine serum albumin (BSA) and Lactobacillus plantarum WCFS1 proteins.

Regional representativity of AERONET observation sites during the biomass burning season in South America determined by correlation studies with MODIS Aerosol Optical Depth

This paper presents an analysis of ground-based Aerosol Optical Depth (AOD) observations by the Aerosol Robotic Network (AERONET) in South America from 2001 to 2007 in comparison with the satellite AOD product of Moderate Resolution Imaging Spectroradiometer (MODIS), aboard TERRA and AQUA satellites. Data of 12 observation sites were used with primary interest in AERONET sites located in or downwind of areas with high biomass burning activity and with measurements available for the full time range. Fires cause the predominant carbonaceous aerosol emission signal during the dry season in South America and are therefore a special focus of this study. Interannual and seasonal behavior of the observed AOD at different sites were investigated, showing clear differences between purely fire and urban influenced sites. An intercomparison of AERONET and MODIS AOD annual correlations revealed that neither an interannual long-term trend may be observed nor that correlations differ significantly owing to different overpass times of TERRA and AQUA. Individual anisotropic representativity areas for each AERONET site were derived by correlating daily AOD of each site for all years with available individual MODIS AOD pixels gridded to 1 degrees x 1 degrees. Results showed that for many sites a good AOD correlation (R(2) > 0.5) persists for large, often strongly anisotropic, areas. The climatological areas of common regional aerosol regimes often extend over several hundreds of kilometers, sometimes far across national boundaries. As a practical application, these strongly inhomogeneous and anisotropic areas of influence are being implemented in the tropospheric aerosol data assimilation system of the Coupled Chemistry-Aerosol-Tracer Transport Model coupled to the Brazilian Regional Atmospheric Modeling System (CCATT-BRAMS) at the Brazilian National Institute for Space Research (INPE). This new information promises an improved exploitation of local site sampling and, thus, chemical weather forecast.

On the development of summer thunderstorms in the city of Sao Paulo: Mean meteorological characteristics and pollution effect

This study investigates how the summer thunderstorms developed over the city of Sao Paulo and if the pollution might affect its development or characteristics during the austral summer (December-January-February-March, DJFM months). A total of 605 days from December 1999 to March 2004 was separated as 241 thunderstorms days (TDs) and 364 non-thunderstorm days (NTDs). The analyses are performed by using hourly measurements of air temperature (T), web-bulb temperature (Tw), surface atmospheric pressure (P), wind velocity and direction, rainfall and thunder and lightning observations collected at the Meteorological Station of the University of Sao Paulo in conjunction with aerosol measurements obtained by AERONET (Aerosol Robotic Network), and the NCEP-DOE (National Centers for Environmental Prediction Department of Energy) reanalysis and radiosondes. The wind diurnal cycle shows that for TDs the morning flow is from the northwest rotating to the southeast after 16: 00 local time (LT) and it remains from the east until the night. For the NTDs, the wind is well characterized by the sea-breeze circulation that in the morning has the wind blowing from the northeast and in the afternoon from the southeast. The TDs show that the air temperature diurnal cycle presents higher amplitude and the maximum temperature of the day is 3.2 degrees C higher than in NTDs. Another important factor found is the difference between moisture that is higher during TDs. In terms of precipitation, the TDs represent 40% of total of days analyzed and those days are responsible for more than 60% of the total rain accumulation during the summer, for instance 50% of the TDs had more than 15.5mm day(-1) while the NTDs had 4 mm day(-1). Moreover, the rainfall distribution shows that TDs have higher rainfall rate intensities and an afternoon precipitation maximum; while in the NTDs there isn`t a defined precipitation diurnal cycle. The wind and temperature fields from NCEP reanalysis concur with the local weather station and radiosonde observations. The NCEP composites show that TDs are controlled by synoptic circulation characterized by a pre-frontal situation, with a baroclinic zone situated at southern part of Sao Paulo. In terms of pollution, this study employed the AERONET data to obtain the main aerosol characteristics in the atmospheric column for both TDs and NTDs. The particle size distribution and particle volume size distribution have similar concentrations for both TDs and NTDs and present a similar fine and coarse mode mean radius. In respect to the atmospheric loading, the aerosol optical depth (AOD) at different frequencies presented closed mean values for both TDs and NTDs that were statistically significant at 95% level. The spectral dependency of those values in conjunction with the Angstrom parameter reveal the higher concentration of the fine mode particles that are more likely to be hygroscopic and from urban areas. In summary, no significant aerosol effect could be found on the development of summer thunderstorms, suggesting the strong synoptic control by the baroclinic forcing for deep convective development. (C) 2010 Published by Elsevier B. V.

A real-time polymerase chain reaction assay for the identification and quantification of American Leishmania species on the basis of glucose-6-phosphate dehydrogenase

A real-time polymerase chain reaction (PCR) test was developed on the basis of the Leishmania glucose-6-phosphate dehydrogenase locus that enables identification and quantification of parasites. Using two independent pairs of primers in SYBR-Green assays, the test identified etiologic agents of cutaneous leishmaniasis belonging to both subgenera, Leishmania (Viannia) and Leishmania (Leishmania) in the Americas. Furthermore, use of TaqMan probes enables distinction between L. (V.) braziliensis or L. (V.) peruviania from the other L. (Viannia) species. All assays were negative with DNA of related trypanosomatids, humans, and mice. The parasite burden was estimated by normalizing the number of organisms per total amount of DNA in the sample or per host glyceraldehyde-3-phosphate dehydrogenase copies. The real-time PCR assay for L. (Leishmania) subgenus showed a good linear correlation with quantification on the basis of a limiting dilution assay in experimentally infected mice. The test successfully identifies and quantifies Leishmania in human biopsy specimens and represents a new tool to study leishmaniasis.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They Should be able to recognize human beings and each other, and to engage in social, interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction. behavior control and learning from environment. Learning processes described oil Science of Behavior Analysis may lead to the development of promising methods and Structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation. are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction. (C) 2009 Elsevier Inc. All rights reserved.

A Parallel Hardware Architecture for Scale and Rotation Invariant Feature Detection

This paper proposes a parallel hardware architecture for image feature detection based on the Scale Invariant Feature Transform algorithm and applied to the Simultaneous Localization And Mapping problem. The work also proposes specific hardware optimizations considered fundamental to embed such a robotic control system on-a-chip. The proposed architecture is completely stand-alone; it reads the input data directly from a CMOS image sensor and provides the results via a field-programmable gate array coupled to an embedded processor. The results may either be used directly in an on-chip application or accessed through an Ethernet connection. The system is able to detect features up to 30 frames per second (320 x 240 pixels) and has accuracy similar to a PC-based implementation. The achieved system performance is at least one order of magnitude better than a PC-based solution, a result achieved by investigating the impact of several hardware-orientated optimizations oil performance, area and accuracy.

Semantic mapping using mobile robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Characterization of the optical properties of atmospheric aerosols in Amazonia from long-term AERONET monitoring (1993-1995 and 1999-2006)

We present a new climatology of atmospheric aerosols (primarily pyrogenic and biogenic) for the Brazilian tropics on the basis of a high-quality data set of spectral aerosol optical depth and directional sky radiance measurements from Aerosol Robotic Network (AERONET) Cimel Sun-sky radiometers at more than 15 sites distributed across the Amazon basin and adjacent Cerrado region. This network is the only long-term project (with a record including observations from more than 11 years at some locations) ever to have provided ground-based remotely-sensed column aerosol properties for this critical region. Distinctive features of the Amazonian area aerosol are presented by partitioning the region into three aerosol regimes: southern Amazonian forest, Cerrado, and northern Amazonian forest. The monitoring sites generally include measurements from the interval 1999-2006, but some sites have measurement records that date back to the initial days of the AERONET program in 1993. Seasonal time series of aerosol optical depth (AOD), angstrom ngstrom exponent, and columnar-averaged microphysical properties of the aerosol derived from sky radiance inversion techniques (single-scattering albedo, volume size distribution, fine mode fraction of AOD, etc.) are described and contrasted for the defined regions. During the wet season, occurrences of mineral dust penetrating deep into the interior were observed.

Biomechanical effects of environmental and engineered particles on human airway smooth muscle cells

The past decade has seen significant increases in combustion-generated ambient particles, which contain a nanosized fraction (less than 100 nm), and even greater increases have occurred in engineered nanoparticles (NPs) propelled by the booming nanotechnology industry. Although inhalation of these particulates has become a public health concern, human health effects and mechanisms of action for NPs are not well understood. Focusing on the human airway smooth muscle cell, here we show that the cellular mechanical function is altered by particulate exposure in a manner that is dependent upon particle material, size and dose. We used Alamar Blue assay to measure cell viability and optical magnetic twisting cytometry to measure cell stiffness and agonist-induced contractility. The eight particle species fell into four categories, based on their respective effect on cell viability and on mechanical function. Cell viability was impaired and cell contractility was decreased by (i) zinc oxide (40-100 nm and less than 44 mu m) and copper(II) oxide (less than 50 nm); cell contractility was decreased by (ii) fluorescent polystyrene spheres (40 nm), increased by (iii) welding fumes and unchanged by (iv) diesel exhaust particles, titanium dioxide (25 nm) and copper(II) oxide (less than 5 mu m), although in none of these cases was cell viability impaired. Treatment with hydrogen peroxide up to 500 mu M did not alter viability or cell mechanics, suggesting that the particle effects are unlikely to be mediated by particle-generated reactive oxygen species. Our results highlight the susceptibility of cellular mechanical function to particulate exposures and suggest that direct exposure of the airway smooth muscle cells to particulates may initiate or aggravate respiratory diseases.

Evaluation of galling resistance for some selected combinations of tool steels/stainless steel sheet materials/lubricants using pin-on-disc testing

Stainless steels are well known to be prone to cold welding and material transfer in sliding contacts and therefore difficult to cold form unless certain precautions as discussed in this paper are taken. In the present study different combinations of tool steels/stainless steels/lubricants has been evaluated with respect to their galling resistance using pin-on-disc testing. The results show that a high galling resistance is favored by a high stainless steel sheet hardness and a blasted stainless steel sheet surface topography. The effect of type of lubricant was found to be more complex. For example, the chlorinated lubricants failed to prevent metal-to-metal contact on a brushed sheet surface but succeeded on a blasted sheet surface of the same stainless steel material. This is believed to be due to a protective tribofilm which is able to form on the blasted surface, but not on the brushed surface.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.