Real-time ultrasound to predict rabbit carcass composition and volume of longissimus dorsi muscle

Real-time ultrasonography (RTU) was used to measure the longissimus dorsi muscle (LM) volume in vivo and to predict the carcass composition of rabbits. For this, 63 New Zealand White × Californian rabbits with 2093±63 g live weight were used. Animals were scanned between the 6th and 7th lumbar vertebrae using an RTU equipment with a 7.5 MHz probe. Measurements of LM volume were obtianed both in vivo and on carcass. Regression equations were used for the prediction of carcass composition and LM volume using the LM volume measured obtained with RTU (LMVU) as independent variable. Carcass meat, bone and total dissectible fat weights represented 780, 164 and 56 g/kg of the reference carcass weight, respectively. Regression equations showed a strong relationship between LMVU and the correspondent volume in carcass. Furthermore, LMVU was also useful in predicting the amounts of carcass tissues. It is possible to predict LM volume in the carcass using the LM volume measured in vivo by RTU. The amount of carcass tissues can be predicted by the LM volume measured in vivo by RTU.

In silico network topology-based prediction of gene essentiality

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance. (C) 2007 Elsevier B.V. All rights reserved.

Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A machine learning approach for genome-wide prediction of morbid and druggable human genes based on systems-level data

Background: The genome-wide identification of both morbid genes, i.e., those genes whose mutations cause hereditary human diseases, and druggable genes, i.e., genes coding for proteins whose modulation by small molecules elicits phenotypic effects, requires experimental approaches that are time-consuming and laborious. Thus, a computational approach which could accurately predict such genes on a genome-wide scale would be invaluable for accelerating the pace of discovery of causal relationships between genes and diseases as well as the determination of druggability of gene products.Results: In this paper we propose a machine learning-based computational approach to predict morbid and druggable genes on a genome-wide scale. For this purpose, we constructed a decision tree-based meta-classifier and trained it on datasets containing, for each morbid and druggable gene, network topological features, tissue expression profile and subcellular localization data as learning attributes. This meta-classifier correctly recovered 65% of known morbid genes with a precision of 66% and correctly recovered 78% of known druggable genes with a precision of 75%. It was than used to assign morbidity and druggability scores to genes not known to be morbid and druggable and we showed a good match between these scores and literature data. Finally, we generated decision trees by training the J48 algorithm on the morbidity and druggability datasets to discover cellular rules for morbidity and druggability and, among the rules, we found that the number of regulating transcription factors and plasma membrane localization are the most important factors to morbidity and druggability, respectively.Conclusions: We were able to demonstrate that network topological features along with tissue expression profile and subcellular localization can reliably predict human morbid and druggable genes on a genome-wide scale. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing morbidity and druggability.

Effects of egg storage time on spread of hatch, chick quality, and chick juvenile growth

A total of 1,800 incubating eggs produced by a commercial flock of Cobb broiler breeders was used to determine the effects of storage duration (3 or 18 d) on spread of hatch and chick quality. Chick relative growth (RG) at the end of 7 d of rearing was also determined as a measure of the chick performance. Chick quality was defined to encompass several qualitative characteristics and scored according to their importance. Eggs stored for 3 d hatched earlier than those stored for 18 d (P < 0.05). Hatching was normally distributed in both categories of eggs, and the spread of hatch was not affected by storage time (P = 0.69). Storage duration of 18 d reduced the percentage of day-old chick with high quality as well as average chick quality score (P < 0.05). RG varied with length of egg storage, quality of day-old chick, and the incubation duration (P < 0.05). Eighteen-day storage of eggs not only resulted in longer incubation duration and lower quality score but also depressed RG. Chick quality as defined in this study was correlated to RG and storage time. It was concluded that day-old chick quality may be a relatively good indicator of broiler performance. The results suggest however that in order to improve performance prediction power of chick quality, it would be better to define it as a combination of several qualitative aspects of the day-old chick and the juvenile growth to 7 d.

Predicting beef carcass retail products of Mediterranean buffaloes by real-time ultrasound measures

Twenty eight Mediterranean buffaloes bulls were scanned with real-time ultrasound (RTU), slaughtered, and fabricated into retail cuts to determine the potential for ultrasound measures to predict carcass retail yield. Ultrasound measures of fat thickness, ribeye area and rump fat thickness were recorded three to five days prior to slaughter. Carcass measurements were taken, and one side of each carcass was fabricated into retail cuts. Stepwise regression analysis was used to compare possible models for prediction of either kilograms or percent retail product from carcass mesaurements and ultrasound measures. Results indicate that possible prediction models for percent or kilograms of retail products using RTU measures were similar in their predictive power and accuracy when compared to models derived from carcass measurements. Both fat thickness and ribeye area were over-predicted when measured ultrasonically compared to measurements taken on the carcass in the cooler. The mean absolute differences for both traits are larger than the mean differences, indicating that some images were interpreted to be larger and some smaller than actual carcass measurements. Ultrasound measurements of REA and FT had positive correlations with carcass measures of the same traits (r=.96 for REA and r=.99 for FT). Standard errors of prediction currently are being used as the standard to certify ultrasound technicians for accuracy. Regression equations using live weight (LW), rib eye area (REAU) and subcutaneous fat thickness (FTU) between 12(th) and 13 (th) ribs and also over the biceps femoris muscle (FTP8) by ultrasound explained 95% of the variation in the hot carcass weight when measure immediately before slaughter.

Structural real time control using fluid magnetorheologic damper

In the last decades there was a great development in the study of control systems to attenuate the harmful effect of natural events in great structures, as buildings and bridges. Magnetorheological fluid (MR), that is an intelligent material, has been considered in many proposals of project for these controllers. This work presents the controller design using feedback of states through LMI (Linear Matrix Inequalities) approach. The experimental test were carried out in a structure with two degrees of freedom with a connected shock absorber MR. Experimental tests were realized in order to specify the features of this semi-active controller. In this case, there exist states that are not measurable, so the feedback of the states involves the project of an estimator. The coupling of the MR damper causes a variation in dynamics properties, so an identification methods, based on experimental input/output signal was used to compare with the numerical application. The identification method of Prediction Error Methods - (PEM) was used to find the physical characteristics of the system through realization in modal space of states. This proposal allows the project of a semi-active control, where the main characteristic is the possibility of the variation of the damping coefficient.

Cluster analysis and artificial neural network on the superovulatory response prediction in mice

Complex biological systems require sophisticated approach for analysis, once there are variables with distinct measure levels to be analyzed at the same time in them. The mouse assisted reproduction, e.g. superovulation and viable embryos production, demand a multidisciplinary control of the environment, endocrinologic and physiologic status of the animals, of the stressing factors and the conditions which are favorable to their copulation and subsequently oocyte fertilization. In the past, analyses with a simplified approach of these variables were not well succeeded to predict the situations that viable embryos were obtained in mice. Thereby, we suggest a more complex approach with association of the Cluster Analysis and the Artificial Neural Network to predict embryo production in superovulated mice. A robust prediction could avoid the useless death of animals and would allow an ethic management of them in experiments requiring mouse embryo.

Spin stabilized satellite's attitude analytical prediction

An analytical approach for spin stabilized attitude propagation is presented, considering the coupled effect of the aerodynamic torque and the gravity gradient torque. A spherical coordination system fixed in the satellite is used to locate the satellite spin axis in relation to the terrestrial equatorial system. The spin axis direction is specified by its right ascension and the declination angles and the equation of motion are described by these two angles and the magnitude of the spin velocity. An analytical averaging method is applied to obtain the mean torques over an orbital period. To compute the average components of both aerodynamic torque and the gravity gradient torque in the satellite body frame reference system, an average time in the fast varying orbit element, the mean anomaly, is utilized. Afterwards, the inclusion of such torques on the rotational motion differential equations of spin stabilized satellites yields conditions to derive an analytical solution. The pointing deviation evolution, that is, the deviation between the actual spin axis and the computed spin axis, is also availed. In order to validate the analytical approach, the theory developed has been applied for spin stabilized Brazilian satellite SCD1, which are quite appropriated for verification and comparison of the data generated and processed by the Satellite Control Center of the Brazil National Research Institute (INPE). Numerical simulations performed with data of Brazilian Satellite SCD1 show the period that the analytical solution can be used to the attitude propagation, within the dispersion range of the attitude determination system performance of Satellite Control Center of the Brazilian Research Institute.

Rubber tree early selection for yield stability in time and among locations

Rubber production in the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg.] can be expressed differently in different environments. Thus the objective of the present study was to select productive progenies, stable and responsive in time and among locations. Thirty progenies were assessed by early yield tests at three ages and in three locations. A randomized block design was used with three replications and ten plants per plot, in 3 × 3 m spacing. The procedure of the mixed linear Reml/Blup model-restricted maximum likelihood/best non-biased linear prediction was used in the genetic statistical analyses. In all the individual analyses, the values observed for the progeny average heritability (ĥpa 2) were greater than those of the additive effect based on single individuals (ĥa 2) and within plot additive (ĥad 2). In the joint analyses in time, there was genotype × test interaction in the three locations. When 20 % of the best progenies were selected the predicted genetic gains were: Colina GG = 24.63 %, Selvíria GG = 13.63 %, and Votuporanga GG = 25.39 %. Two progenies were among the best in the analyses in the time and between locations. In the joint analysis among locations there was only genotype × location interaction in the first early test. In this test, selecting 20 %, the general predicted genetic gain was GG = 25.10 %. Identifying progenies with high and stable yield over time and among locations contributes to the efficiency of the genetic breeding program. The relative performance of the progenies varies depending of the age of early selection test. © 2013 Springer Science+Business Media Dordrecht.

Damage detection in nonlinear structures using discrete-time volterra series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinear procedures are not used currently in practical applications due to the complexity and difficulty for implementation of such techniques. Therefore, the development of techniques that consider the nonlinear behavior of structures for damage detection is a research of major importance since nonlinear dynamical effects can be erroneously treated as damage in the structure by classical metrics. This paper proposes the discrete-time Volterra series for modeling the nonlinear convolution between the input and output signals in a benchmark nonlinear system. The prediction error of the model in an unknown structural condition is compared with the values of the reference structure in healthy condition for evaluating the method of damage detection. Since the Volterra series separate the response of the system in linear and nonlinear contributions, these indexes are used to show the importance of considering the nonlinear behavior of the structure. The paper concludes pointing out the main advantages and drawbacks of this damage detection methodology. © (2013) Trans Tech Publications.

Analytical prediction of the spin stabilized satellite's attitude using the solar radiation torque

The aim of this paper is to present an analytical solution for the spin motion equations of spin-stabilized satellite considering only the influence of solar radiation torque. The theory uses a cylindrical satellite on a circular orbit and considers that the satellite is always illuminated. The average components of this torque were determined over an orbital period. These components are substituted in the spin motion equations in order to get an analytical solution for the right ascension and declination of the satellite spin axis. The time evolution for the pointing deviation of the spin axis was also analyzed. These solutions were numerically implemented and compared with real data of the Brazilian Satellite of Data Collection - SCD1 an SCD2. The results show that the theory has consistency and can be applied to predict the spin motion of spin-stabilized artificial satellites.

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Spatial and Temporal Prediction Model of Corn Grain Yield as a Function of Soil Attributes

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.