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The objective was to compare standard versus on-plate sample preparation protocols for identification of mastitis bacteria by MALDI-TOF MS. A total of 186 bacterial isolates from cows with subclinical mastitis were identified by MALDI-TOF MS after preparation using two extraction protocols. On-plate protocol was performed by applying the bacterial colony directly from the culture plate onto the plate spot. For the standard protocol, lysis of bacterial colonies using reagents was performed in a cryotube, and the resulting extract was applied onto the plate spot for analysis. The on-plate protocol showed a similar bacteria identification rate (91.4%, n = 170/186) in comparison to the standard (94.6%, n = 176/186). Identification was higher for both protocols when scores used for species-level identification (≥ 2.0) was reduced to genus-level (≥ 1.7); genus-level identification score rate increased from 94.6 to 100% when using the standard protocol, and from 91.4 to 94.6% when using the on-plate protocol. However, when compared standard (as gold standard) versus on-plate protocol, genus-level identification score rate ranged from 87.1 to 89.8%. Therefore, when the on-plate protocol fails to identify any specie, the standard extraction may be more suitable as a reference protocol for use. Strategy for increasing identification with the on-plate protocol may include upgrading the reference database library. Choice of protocol for preparation may be influenced by the bacterial type to be identified. Standard and on-plate extraction protocols of bacterial ribosomal proteins associated with MALDI-TOF MS might be alternatives to conventional microbiology methods for identification of subclinical mastitis pathogens.

The LArIAT liquid argon time projection chamber, placed in a tertiary beam of charged particles at the Fermilab Test Beam Facility, has collected large samples of pions, muons, electrons, protons, and kaons in the momentum range 0∼30-0140 MeV/c. This paper describes the main aspects of the detector and beamline, and also reports on calibrations performed for the detector and beamline components.

Subclinical mastitis is a common and easily disseminated disease in dairy herds. Its routine diagnosis via bacterial culture and biochemical identification is a difficult and time-consuming process. In this work, we show that matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) allows bacterial identification with high confidence and speed (1 d for bacterial growth and analysis). With the use of MALDI-TOF MS, 33 bacterial culture isolates from milk of different dairy cows from several farms were analyzed, and the results were compared with those obtained by classical biochemical methods. This proof-of-concept case demonstrates the reliability of MALDI-TOF MS bacterial identification, and its increased selectivity as illustrated by the additional identification of coagulase-negative Staphylococcus species and mixed bacterial cultures. Matrix-assisted laser desorption-ionization mass spectrometry considerably accelerates the diagnosis of mastitis pathogens, especially in cases of subclinical mastitis. More immediate and efficient animal management strategies for mastitis and milk quality control in the dairy industry can therefore be applied.

The work presented here is about employing a theory of updates to study geometrically observable changes that occur in spatial information about image sequences of a dynamic scene. The logical framework consists of a formalism for specifying the geometrical content of a scene, as well as the changes that occur in this geometry, and an algorithm for constructing a description for such changes from logical deductions. In this approach, a database state represents the available sensor data at a particular time instant. Transitions in sensor data are modeled by changes in the database and interpreted based on axioms encoding commonsense spatial reasoning. The main contribution of this work is that it provides the theoretical foundations for symbolically interpreting long sequences of sensor data transitions. For testing the framework and its implementation, the problem of interpreting rotational movements of objects in a sequence of images was used. Our experiments show that the system correctly interprets rotational movements for objects of different colors and provides satisfactory results for interpreting such movements from perceptually indistinguishable objects.

The unprecedented growth of available data nowadays has stimulated the development of new methods for organizing and extracting useful knowledge from this immense amount of data. Automatic Document Classification (ADC) is one of such methods, that uses machine learning techniques to build models capable of automatically associating documents to well-defined semantic classes. ADC is the basis of many important applications such as language identification, sentiment analysis, recommender systems, spam filtering, among others. Recently, the use of meta-features has been shown to substantially improve the effectiveness of ADC algorithms. In particular, the use of meta-features that make a combined use of local information (through kNN-based features) and global information (through category centroids) has produced promising results. However, the generation of these meta-features is very costly in terms of both, memory consumption and runtime since there is the need to constantly call the kNN algorithm. We take advantage of the current manycore GPU architecture and present a massively parallel version of the kNN algorithm for highly dimensional and sparse datasets (which is the case for ADC). Our experimental results show that we can obtain speedup gains of up to 15x while reducing memory consumption in more than 5000x when compared to a state-of-the-art parallel baseline. This opens up the possibility of applying meta-features based classification in large collections of documents, that would otherwise take too much time or require the use of an expensive computational platform.

The aim of this study was to evaluate the effect of subclinical intramammary infection (IMI) caused by coagulase-negative staphylococci (CNS) as a group and by specific CNS species on milk yield and composition and somatic cell count (SCC) of dairy cows. Selection of cows with IMI caused by CNS was performed by microbiological cultures of composite samples collected from 1,242 dairy cows distributed in 21 dairy herds. After selection of cows, milk yield was measured and milk samples were collected at the mammary quarter level (i.e., 1,140 mammary samples collected from 285 cows) for analysis of milk composition and SCC. In total, 108 isolates of CNS were identified at the species level by PCR-RFLP analysis. Forty-one pairs of contralateral mammary quarters, with and without IMI, were used to evaluate the effect of CNS on milk yield and composition. Mammary quarters infected with CNS had higher geometric mean SCC (306,106 cells/mL) than noninfected contralateral mammary quarters (62,807 cells/mL). Intramammary infection caused by CNS had no effect on milk yield or on contents of fat, crude protein, casein, lactose, total solids, and solids-not-fat. Staphylococcus chromogenes was the most prevalent CNS species in this study and the only species that allowed within-cow evaluation. The IMI caused by S. chromogenes increased SCC but had no effect on milk yield and composition at the quarter level. In conclusion, subclinical mastitis caused by CNS increased the SCC but had no effect on milk yield and composition of dairy cows.

An evolutionary system for derivation of fuzzy classification rules is presented. This system uses two populations: one of fuzzy classification rules, and one of membership function definitions. A constrained-syntax genetic programming evolves the first population and a mutation-based evolutionary algorithm evolves the second population. These two populations co-evolve to better classify the underlying dataset. Unlike other approaches that use fuzzification of continuous attributes of the dataset for discovering fuzzy classification rules, the system presented here fuzzifies the relational operators ldquogreater thanrdquo and ldquoless thanrdquo using evolutionary methods. For testing our system, the system is applied to the Iris dataset. Our experimental results show that our system outperforms previous evolutionary and non-evolutionary systems on accuracy of classification and derivation of interrelation between the attributes of the Iris dataset. The resulting fuzzy rules of the system can be directly used in knowledge-based systems.

Frozen bovine semen used in the IVF process can be a potential source of microorganisms that can prevent or disturb embryo development and cause issues with the sanitary certification for bovine embryo commercialization and export. Therefore, the aim of this work is to introduce a novel tool for the fast identification of the pathogens on the frozen semen based on the mass spectra of their ribosomal proteins analysed by matrix-assisted desorption/ionization-mass spectrometry (MALDI-MS). Thirty bovine semen samples, which were aliquots of commercial sealed straws used daily in the commercial IVF routine at In vitro Brasil Ltd. (Mogi Mirim, SP, Brazil), were used for this work. Fifty microlitres of semen were incubated in 10 mL of brain heart infusion broth (BHI) for 24 h at 37°C. If turbidity was observed, the bacterial cultures were submitted to bacterial extraction and mass spectrometric analysis according to Barreiro et al. (2010). The mass spectra were obtained using an AUTOFLEX MALDI TOF/TOF and were analysed with the database library MALDI Biotyper 3.0 software (Bruker Daltonik, Germany) at default settings. For each sample, the result was given by means of a log score with a maximum value of 3.0. In this study, only scores higher than 2.0 were considered, which provide confident species identification. The bacteria identified were Citrobacter freundii (2 samples), Stenotrophomonas maltophilia (4 samples), Enterobacter cloacae (6 samples) complex, Candida parapsilosis (2 samples), and Enterococcus mundtii (2 samples). Note that all the identified bacteria consistently match with the most common contaminants reported in literature for bovine frozen semen (Bielanskia et al. 2003). The capability of the technique to identify the bacteria without the ribosomal extraction (i.e. of bacteria pellets diluted in water and acetonitrile) was successful for the pellet of S. maltophilia, C. freundii, and E. cloacae complex with scores higher than 2.3, indicating a very high probability of the identification of the bacterial genus and the species. This can be explained by considering the capability of the mass spectrometric matrix to lyse the membrane of the bacteria and directly extract and then ionize the ribosomal proteins. In order to exclude the presence of a mixing of bacteria in the pellet, the colonies were properly isolated. The results matched with the ones obtained before the isolation. In order to confirm the MALDI-MS identification, the isolated bacteria from the bovine semen were also submitted to sequencing of region 16SrRNA. In conclusion, MALDI-MS technique was successfully applied for the identification of pathogens in the bovine semen. Experiments to evaluate the presence of microorganisms in media used for in vitro maturation, IVF, and in vitro culture of the bovine oocytes and embryos using this strategy are underway. This robust and fast approach is able to detect early contamination and allows prevention of economic losses and sanitary excellence in the bovine IVF process.

We observed longitudinal single-mode operation in a distributed feedback dye laser consisting of silk fibroin films doped with Rhodamine 6G dye and infiltrated with silica or silver nanoparticles.

We demonstrate random lasing action in a biopolymer that has large potential for medical applications. The novel random laser consists of nanofibers of bacterial cellulose impregnated with silica nanoparticles and Rhodamine 6G.