DynPeak : An algorithm for pulse detection and frequency analysis in hormonal time series

The endocrine control of the reproductive function is often studied from the analysis of luteinizing hormone (LH) pulsatile secretion by the pituitary gland. Whereas measurements in the cavernous sinus cumulate anatomical and technical difficulties, LH levels can be easily assessed from jugular blood. However, plasma levels result from a convolution process due to clearance effects when LH enters the general circulation. Simultaneous measurements comparing LH levels in the cavernous sinus and jugular blood have revealed clear differences in the pulse shape, the amplitude and the baseline. Besides, experimental sampling occurs at a relatively low frequency (typically every 10 min) with respect to LH highest frequency release (one pulse per hour) and the resulting LH measurements are noised by both experimental and assay errors. As a result, the pattern of plasma LH may be not so clearly pulsatile. Yet, reliable information on the InterPulse Intervals (IPI) is a prerequisite to study precisely the steroid feedback exerted on the pituitary level. Hence, there is a real need for robust IPI detection algorithms. In this article, we present an algorithm for the monitoring of LH pulse frequency, basing ourselves both on the available endocrinological knowledge on LH pulse (shape and duration with respect to the frequency regime) and synthetic LH data generated by a simple model. We make use of synthetic data to make clear some basic notions underlying our algorithmic choices. We focus on explaining how the process of sampling affects drastically the original pattern of secretion, and especially the amplitude of the detectable pulses. We then describe the algorithm in details and perform it on different sets of both synthetic and experimental LH time series. We further comment on how to diagnose possible outliers from the series of IPIs which is the main output of the algorithm.Comment: Nombre de pages : 35 ; Nombre de figures : 16 ; Nombre de tableaux :

Use of group records of feed intake to select for feed efficiency in rabbit

Models for genetic evaluation of feed efficiency (FE) for animals housed in groups when they are either fed ad libitum (F) or on restricted (R) feeding were implemented. Definitions of FE on F included group records of feed intake (¯FI_F) and individual records of growth rate (GF) and metabolic weight (MF). Growth rate (GR) as FE measurement on R was used. Data corresponded to 5,336 kits from a rabbit sire line, from 1,255 litters in 14 batches and 667 cages. A five-trait mixed model (also with metabolic weight on R, MR) was implemented including, for each trait, the systematic effects of batch, body weight at weaning, parity order and litter size; and the random effects of litter, additive genetic and individual. A Bayesian analysis was performed. Conditional traits such as ¯FI_F |M_F,G_F and G_F |M_F,¯FI_F were obtained from elements of additive genetics ( ( ¯FI_F |M_F,G_F )_g and ( G_F |M_F,¯FI_F )_g ) or phenotypic (( ¯FI_F |M_F,G_F )_p and ( G_F |M_F,¯FI_F )_p ) (co)variance matrices. In the first case, heritabilities were low (0.07 and 0.06 for ( ¯FI_F |M_F,G_F )_g and ( G_F |M_F,¯FI_F )_g, respectively) but null genetic correlation between the conditional and conditioning traits is guaranteed. In the second case, heritabilities were higher (0.22 and 0.16 for ( ¯FI_F |M_F,G_F )_p and ( G_F |M_F,¯FI_F )_p, respectively) but the genetic correlation between ( ¯FI_F |M_F,G_F )_p and G_F was moderate (0.58). Heritability of GR was low (0.08). This trait was negatively correlated with ( G_F |M_F,¯FI_F )_p and ( G_F |M_F,¯FI_F )_gof animals on F, which indicate a different genetic background. The correlation between GR and GF was also low to moderate (0.48) and the additive variance of GF was almost 4 times that of GR, suggesting the presence of a substantial genotype by feeding regimen interaction.info:eu-repo/semantics/acceptedVersio

Small Wind Turbine Testing and Applications Development

Small wind turbines offer a promising alternative for many remote electrical uses where there is a good wind resource. The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory helps further the role that small turbines can play in supplying remote power needs. The NWTC tests and develops new applications for small turbines. The NWTC also develops components used in conjunction with wind turbines for various applications. This paper describes wind energy research at the NWTC for applications including battery charging stations, water desalination/purification, and health clinics. Development of data acquisition systems and tests on small turbines are also described

Epilepsy Caused by an Abnormal Alternative Splicing with Dosage Effect of the SV2A Gene in a Chicken Model

Photosensitive reflex epilepsy is caused by the combination of an individual's enhanced sensitivity with relevant light stimuli, such as stroboscopic lights or video games. This is the most common reflex epilepsy in humans; it is characterized by the photoparoxysmal response, which is an abnormal electroencephalographic reaction, and seizures triggered by intermittent light stimulation. Here, by using genetic mapping, sequencing and functional analyses, we report that a mutation in the acceptor site of the second intron of SV2A (the gene encoding synaptic vesicle glycoprotein 2A) is causing photosensitive reflex epilepsy in a unique vertebrate model, the Fepi chicken strain, a spontaneous model where the neurological disorder is inherited as an autosomal recessive mutation. This mutation causes an aberrant splicing event and significantly reduces the level of SV2A mRNA in homozygous carriers. Levetiracetam, a second generation antiepileptic drug, is known to bind SV2A, and SV2A knock-out mice develop seizures soon after birth and usually die within three weeks. The Fepi chicken survives to adulthood and responds to levetiracetam, suggesting that the low-level expression of SV2A in these animals is sufficient to allow survival, but does not protect against seizures. Thus, the Fepi chicken model shows that the role of the SV2A pathway in the brain is conserved between birds and mammals, in spite of a large phylogenetic distance. The Fepi model appears particularly useful for further studies of physiopathology of reflex epilepsy, in comparison with induced models of epilepsy in rodents. Consequently, SV2A is a very attractive candidate gene for analysis in the context of both mono- and polygenic generalized epilepsies in humans

Battery Life Prediction Method for Hybrid Power Applications: Preprint

Batteries in hybrid power applications that include intermittent generators, such as wind turbines, experience a very irregular pattern of charge and discharge cycles. Because battery life is dependent on both depth and rate of discharge (and other factors such as temperature, charging strategy, etc.), estimating battery life and optimally sizing batteries for hybrid systems is difficult. Typically, manufacturers give battery life data, if at all, as cycles to failure versus depth of discharge, where all discharge cycles are assumed to be under conditions of constant temperature, current, and depth of discharge. Use of such information directly can lead to gross errors in battery lifetime estimation under actual operating conditions, which may result in either a higher system cost than necessary or an undersized battery bank prone to early failure. Even so, most current battery life estimation algorithms consider only the effect of depth of discharge on cycle life. This paper will discuss a new battery life prediction method, developed to investigate the effects of two primary determinants of battery life in hybrid power applications, varying depths of discharge and varying rates of discharge. A significant feature of the model is that it bases its analysis on battery performance and cycle life data provided by the manufacturer, supplemented by a limited amount of empirical test data, eliminating the need for an electrochemical model of the battery. It performs the analysis for a user-prescribed discharge profile consisting of a series of discharge events of specified average current and duration. Sample analyses are presented to show how the method can be used to select the most economical battery type and size for a given hybrid power system application