Precision of distances and ordering of microsatellite markers in consensus linkage maps of chromosomes 1, 3 and 4 from two reciprocal chicken populations using bootstrap sampling

Some factors complicate comparisons between linkage maps from different studies. This problem can be resolved if measures of precision, such as confidence intervals and frequency distributions, are associated with markers. We examined the precision of distances and ordering of microsatellite markers in the consensus linkage maps of chromosomes 1, 3 and 4 from two F 2 reciprocal Brazilian chicken populations, using bootstrap sampling. Single and consensus maps were constructed. The consensus map was compared with the International Consensus Linkage Map and with the whole genome sequence. Some loci showed segregation distortion and missing data, but this did not affect the analyses negatively. Several inversions and position shifts were detected, based on 95% confidence intervals and frequency distributions of loci. Some discrepancies in distances between loci and in ordering were due to chance, whereas others could be attributed to other effects, including reciprocal crosses, sampling error of the founder animals from the two populations, F(2) population structure, number of and distance between microsatellite markers, number of informative meioses, loci segregation patterns, and sex. In the Brazilian consensus GGA1, locus LEI1038 was in a position closer to the true genome sequence than in the International Consensus Map, whereas for GGA3 and GGA4, no such differences were found. Extending these analyses to the remaining chromosomes should facilitate comparisons and the integration of several available genetic maps, allowing meta-analyses for map construction and quantitative trait loci (QTL) mapping. The precision of the estimates of QTL positions and their effects would be increased with such information.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Normal Versus Pathological Voice Signals Using Wavelet Analysis and Least Squares Support-Vector Machines

State of Sao Paulo Research Foundation (FAPESP)

Remote control of CNC machines using the CyberOPC communication system over public networks

During the last few years, the evolution of fieldbus and computers networks allowed the integration of different communication systems involving both production single cells and production cells, as well as other systems for business intelligence, supervision and control. Several well-adopted communication technologies exist today for public and non-public networks. Since most of the industrial applications are time-critical, the requirements of communication systems for remote control differ from common applications for computer networks accessing the Internet, such as Web, e-mail and file transfer. The solution proposed and outlined in this work is called CyberOPC. It includes the study and the implementation of a new open communication system for remote control of industrial CNC machines, making the transmission delay for time-critical control data shorter than other OPC-based solutions, and fulfilling cyber security requirements.

Model to predict the chloride penetration in concrete with profile forming peak: verification of its precision

Chloride attack in marine environments or in structures where deicing salts are used will not always show profiles with concentrations that decrease from the external surface to the interior of the concrete. Some profiles show an increase in chloride concentrations from when a peak is formed. This type of profile must be analyzed in a different way from the traditional model of Fick`s second law to generate more precise service life models. A model for forecasting the penetration of chloride ions as a function of time for profiles having formed a peak. To confirm the efficiency of this model, it is necessary to observe the behavior of a chloride profile with peak in a specific structure over a period of time. To achieve this, two chloride profiles with different ages (22 and 27 years) were extracted from the same structure. The profile obtained from the 22-year sample was used to estimate the chloride profile at 27 years using three models: a) the traditional model using Fick`s second law and extrapolating the value of C(S)-external surface chloride concentration; b) the traditional model using Fick`s second law and shifting the x-axis to the peak depth; c) the previously proposed model. The results from these models were compared with the actual profile measured in the 27-year sample and the results were analyzed. The model was presented with good precision for this study of case, requiring to be tested with other structures in use.

Comparison Between Voltage Control Structures of Synchronous Machines

This paper compares the behaviour of two different control structures of automatic voltage regulators of synchronous machines equipped with static excitation systems. These systems have a fully controlled thyristor bridge that supplies DC current to the rotor winding. The rectifier bridge is fed by the stator terminals through a step-down transformer. The first control structure, named ""Direct Control"", has a single proportional-integral (PI) regulator that compares stator voltage setpoint with measured voltage and acts directly on the thyristor bridge`s firing angle. This control structure is usually employed in commercial excitation systems for hydrogenerators. The second structure, named ""Cascade Control"", was inspired on control loops of commercial DC motor drives. Such drives employ two PIs in a cascade arrangement, the external PI deals with the motor speed while the internal one regulates the armature current. In the adaptation proposed, the external PI compares setpoint with the actual stator voltage and produces the setpoint to the internal PI-loop which controls the field current.

Adoption and use of precision agriculture technologies in the sugarcane industry of So Paulo state, Brazil

Precision agriculture (PA) technologies are being applied to crops in Brazil, which are important to ensure Brazil`s position in agricultural production. However, there are no studies available at present to indicate the extent to which PA technologies are being used in the country. Therefore, the main objective of this research was to investigate how the sugar-ethanol industry in So Paulo state, which produces 60% of the domestic sugarcane, is adopting and using these techniques. For this purpose, primary data were used, which were obtained from a questionnaire sent to all companies operating in the sugar-ethanol industry in the region. The aim was to determine to what extent these companies are adopting and using PA technologies, and also to promote a more in-depth discussion of the topic within the sugar-ethanol industry. Information was obtained on the features of the companies, on sources of information that they use for adopting these technologies, on their impacts on these companies and on obstacles hindering their adoption. The main conclusions of this research suggest that companies that adopt and use PA practices reap benefits, such as managerial improvements, higher yields, lower costs, minimization of environmental impacts and improvements in sugarcane quality.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.

Equine chorionic gonadotropin and gonadotropin-releasing hormone enhance fertility in a norgestomet-based, timed artificial insemination protocol in suckled Nelore (Bos indicus) cows

Two experiments were conducted to investigate the effects of equine chorionic gonadotropin (eCG) at progestin removal and gonadotropin-releasing hormone (GnRH) at timed artificial insemination (TA!) on ovarian follicular dynamics (Experiment 1) and pregnancy rates (Experiment 2) in suckled Nelore (Bos indicus) cows. Both experiments were 2 x 2 factorials (eCG or No eCG, and GnRH or No GnRH), with identical treatments. In Experiment 1, 50 anestrous cows, 134.5 +/- 2.3 d postpartum, received a 3 mg norgestomet ear implant se, plus 3 mg norgestomet and 5 mg estradiol valerate im on Day 0. The implant was removed on Day 9, with TAI 54 h later. Cows received 400 IU eCG or no further treatment on Day 9 and GnRH (100 mu g gonadorelin) or no further treatment at TAI. Treatment with eCG increased the growth rate of the largest follicle from Days 9 to 11 (means +/- SEM, 1.53 +/- 0.1 vs. 0.48 +/- 0.1 mm/d; P < 0.0001), its diameter on Day 11(11.4 +/- 0.6 vs. 9.3 +/- 0.7 mm; P = 0.03), as well as ovulation rate (80.8% vs. 50.0%, P = 0.02), whereas GnRH improved the synchrony of ovulation (72.0 +/- 1.1 VS. 71.1 +/- 2.0 h). In Experiment 2 (n = 599 cows, 40 to 120 d postpartum), pregnancy rates differed (P = 0.004) among groups (27.6%, 40.1%, 47.7%, and 55.7% for Control. GnRH, eCG, and eCG + GnRH groups). Both eCG and GnRH improved pregnancy rates (51.7% vs. 318%, P = 0.002; and 48.0% vs 37.6%, P = 0.02, respectively), although their effects were not additive (no significant interaction). In conclusion, eCG at norgestomet implant removal increased the growth rate of the largest follicle (LF) from implant removal to TAI, the diameter of the LF at TAI, and rates of ovulation and pregnancy rates. Furthermore, GnRH at TAI improved the synchrony of ovulations and pregnancy rates in postpartum Nelore cows treated with a norgestomet-based TAI protocol. (C) 2010 Elsevier Inc. All rights reserved.

Effects of gonadotropin-releasing hormone at initiation of the 5-d timed artificial insemination (AI) program and timing of induction of ovulation relative to AI on ovarian dynamics and fertility of dairy heifers

Two experiments evaluated the effects of the first GnRH injection of the 5-d timed artificial insemination (AI) program on ovarian responses and pregnancy per AT (P/AI), and the effect of timing of the final GnRH to induce ovulation relative to AT on P/AI. In experiment 1, 605 Holstein heifers were synchronized for their second insemination and assigned randomly to receive GnRH on study d 0 (n = 298) or to remain as untreated controls (n = 307). Ovaries were scanned on study d 0 and 5. All heifers received a controlled internal drug-release (CIDR) insert containing progesterone on d 0, a single injection of PGF(2 alpha),, and removal of the CIDR on d 5, and GnRH concurrent with timed AT on d 8. Blood was analyzed for progesterone at AI. Pregnancy was diagnosed on d 32 and 60 after AI. Ovulation on study d 0 was greater for GnRH than control (35.4 vs. 10.6%). Presence of a new corpus luteum (CL) at PGF(2 alpha),, injection was greater for GnRH than for control (43.1 vs. 20.8%), although the proportion of heifers with a CL at PGF(2 alpha) did not differ between treatments and averaged 87.1%. Progesterone on the day of AT was greater for GaRH than control (0.50 +/- 0.07 vs. 0.28 +/- 0.07 ng/mL). The proportion of heifers at AI with progesterone <0.5 ng/mL was less for GURH than for control (73.8 vs. 88.2%). The proportion of heifers in estrus at AI did not differ between treatments and averaged 66.8%. Pregnancy per AI was not affected by treatment at d 32 or 60 (GnRH = 52.5 and 49.8% vs. control = 54.1 and 50.0%), and pregnancy loss averaged 6.0%. Responses to GnRH were not influenced by ovarian status on study d 0. In experiment 2, 1,295 heifers were synchronized for their first insemination and assigned randomly to receive a CIDR on d 0, PGF(2 alpha) and removal of the CIDR on d 5, and either GnRH 56 h after PGF(2 alpha) and AI 16 h later (OVS56, n = 644) or GnRH concurrent with AI 72 h after PGF(2 alpha) (COS72; n = 651). Estrus at AI was greater for COS72 than for OVS56 (61.4 vs. 47.5). Treatment did not affect P/AI on d 32 in heifers displaying signs of estrus at AI, but COS72 improved P/AI compared with OVS56 (55.0 vs. 47.6%) in those not in estrus at AI. Similarly, P/AI on d 60 did not differ between treatments for heifers displaying estrus, but COS72 improved P/AI compared with OVS56 (53.0 vs. 44.7%) in those not in estrus at AI. Administration of GnRH on the first day of the 5-d timed AI program resulted in low ovulation rate and no improvement in P/AI when heifers received a single PGF(2 alpha) injection 5 d later. Moreover, extending the proestrus by delaying the finAI GnRH from 56 to 72 h concurrent with AI benefited fertility of dairy heifers that did not display signs of estrus at insemination following the 5-d timed AI protocol.

Effects of equine chorionic gonadotropin and type of ovulatory stimulus in a timed-AI protocol on reproductive responses in dairy cows

The objectives were to evaluate the effects of equine chorionic gonadotropin (eCG) supplementation (with or without eCG) and type of ovulatory stimulus (GnRH or ECP) on ovarian follicular dynamics, luteal function, and pregnancies per AI (P/AI) in Holstein cows receiving timed artificial insemination (TAI). On Day 0, 742 cows in a total of 782 breedings, received 2 mg of estradiol benzoate (EB) and one intravaginal progesterone (P4) insert (CIDR). On Day 8, the CIDR was removed, and all cows were given PGF2 alpha and assigned to one of four treatments in a 2 x 2 factorial arrangement: (1) CG: GnRH 48 h later; (2) CE: ECP; (3) EG: eCG + GnRH 48 It later; (4) EE: eCG + ECP. There were significant interactions for eCG x ovulatory stimulus and eCG x BCS. Cows in the CG group were less likely (28.9% vs. 33.8%; P < 0.05) to become pregnant compared with those in the EG group (odds ratio [OR] = 0.28). There were no differences in P/AI between CE and EE cows (30.9% vs. 29.1%; OR = 0.85; P = 0.56), respectively. Thinner cows not receiving eCG had lower P/AI than thinner cows receiving eCG (15.2% vs. 38.0%; OR = 0.20; P < 0.01). Treatment with eCG tended to increase serum progestesterone concentrations during the diestrus following synchronized ovulation (P < 0.10). However, the treatment used to induce ovulation did not affect CL volume or serum progesterone concentrations. In conclusion, both ECP and GnRH yielded comparable P/AI. However, eCG treatment at CIDR removal increased pregnancy rate in cows induced to ovulate with GnRH and in cows with lower BCS. (C) 2009 Elsevier Inc. All rights reserved.

A new presynchronization system (Double-Ovsynch) increases fertility at first postpartum timed AI in lactating dairy cows

This study evaluated a novel presynchronization method, using Ovsynch prior to the Ovsynch-timed AI protocol (Double-Ovsynch) compared to Presynch-Ovsynch. Lactating Holstein (n = 337) cows, were assigned to two treatment groups: (1) Presynch (n = 180), two injections of PGF 14 d apart, followed by the Ovsynch-timed AI protocol 12 d later; (2) Double-Ovsynch (n = 157), received GnRH, PGF 7 d later, and GnRH 3 d later, followed by the Ovsynch-timed AI protocol 7 d later. All cows received the same Ovsynch-timed AI protocol: GnRH (G1) at 68 +/- 3 DIM (mean +/- SEM), PGF 7 d later, GnRH (G2) 56 h after PGF, and AI 16 to 20 h later. Pregnancy was diagnosed 39-45 d after timed AI. Double-Ovsynch increased the pregnancies per AI (P/AI) compared to Presynch-Ovsynch (49.7% vs 41.7%, P = 0.03). Surprisingly, Double-Ovsynch increased P/AI only in primiparous (65.2% vs 45.2%; P = 0.02) and not multiparous (37.5% vs 39.3%) cows. In a subset of 87 cows, ovarian ultrasonography and progesterone (P4) measurements were performed at G1 and 7 d later. Double-Ovsynch decreased the percentage of cows with low P4 (<1 ng/mL) at G1 (9.4% vs 33.3%) and increased the percentage of cows with high P4 (>= 3 ng/mL) at PGF (78.1% vs 52.3%). Thus, presynchronization of cows with Double-Ovsynch increased fertility in primiparous cows compared to a standard Presynch protocol, perhaps due to induction of ovulation in non-cycling cows and improved synchronization of cycling cows. Future studies are needed, with a larger number of cows, to further test the hypothesis of higher fertility with Double-Ovsynch, and to elucidate the physiological mechanisms that underlie apparent changes in fertility with this protocol. (C) 2008 Elsevier Inc. All rights reserved.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.