Ocean-wide genomic variation in Gray's beaked whales, Mesoplodon grayi.

The deep oceans of the Southern Hemisphere are home to several elusive and poorly studied marine megafauna. In the absence of robust observational data for these species, genetic data can aid inferences on population connectivity, demography and ecology. A previous investigation of genetic diversity and population structure in Gray's beaked whale (Mesoplodon grayi) from Western Australia and New Zealand found high levels of mtDNA diversity, no geographic structure and stable demographic history. To further investigate phylogeographic and demographic patterns across their range, we generated complete mitochondrial and partial nuclear genomes of 16 of the individuals previously analysed and included additional samples from South Africa (n = 2) and South Australia (n = 4), greatly expanding the spatial range of genomic data for the species. Gray's beaked whales are highly elusive and rarely observed, and our data represents a unique and geographically broad dataset. We find relatively high levels of diversity in the mitochondrial genome, despite an absence of population structure at the mitochondrial and nuclear level. Demographic analyses suggest these whales existed at stable levels over at least the past 1.1 million years, with an approximately twofold increase in female effective population size approximately 250 thousand years ago, coinciding with a period of increased Southern Ocean productivity, sea surface temperature and a potential expansion of suitable habitat. Our results suggest that Gray's beaked whales are likely to be resilient to near-future ecosystem changes, facilitating their conservation. Our study demonstrates the utility of low-effort shotgun sequencing in providing ecological information on highly elusive species.

Reinforcement and backpropagation training for an optical neural network using self-lensing effects.

The optical bench training of an optical feedforward neural network, developed by the authors, is presented. The network uses an optical nonlinear material for neuron processing and a trainable applied optical pattern as the network weights. The nonlinear material, with the applied weight pattern, modulates the phase front of a forward propagating information beam by dynamically altering the index of refraction profile of the material. To verify that the network can be trained in real time, six logic gates were trained using a reinforcement training paradigm. More importantly, to demonstrate optical backpropagation, three gates were trained via optical error backpropagation. The output error is optically backpropagated, detected with a CCD camera, and the weight pattern is updated and stored on a computer. The obtained results lay the ground work for the implementation of multilayer neural networks that are trained using optical error backpropagation and are able to solve more complex problems.

Neural network implementation using self-lensing media.

An all-optical implementation of a feed-forward artificial neural network is presented that uses self-lensing materials in which the index of refraction is irradiance dependent. Many of these types of material have ultrafast response times and permit both weighted connections and nonlinear neuron processing to be implemented with only thin material layers separated by free space. Both neuron processing and weighted interconnections emerge directly from the physical optics of the device. One creates virtual neurons and their connections simply by applying patterns of irradiance to thin layers of the nonlinear media. This is a result of a variation of the refractive-index profile of the self-lensing nonlinear media in response to the applied irradiance. An optical-backpropagation training method for this network is presented. The optical backpropagation is a training method that can be implemented potentially within the same optical device as the forward calculations, although several issues crucial to this po sibility remain to be addressed. Such a network was numerically simulated and trained to solve many benchmark classification problems, and some of these results are presented. To demonstrate the feasibility of building such a network, we also describe experimental work in the construction of an optical network trained to perform a logic XNOR function. This network, as a proof of concept, uses a relatively slow thermal nonlinear material with ~1-s response time.

[Value of combined electro- and vectorcardiography in the estimation of left ventricular mass in the elderly]. / Valor del electro y vectorcardiograma combinados en la estimación de la masa ventricular izquierda en la tercera edad.

Electro- and vectorcardiographic methods describe left ventricle increases only when it has attained a significantly high magnitude, but even in such circumstances the exactitude of such methods is substantially lower than that of echocardiogram (ECHO). On the other hand, in comparison to other age groups, there have been relatively few reports relating electrocardiogram (ECG), vectorcardiogram (VCG) and ECHO with left ventricular mass (LVM) in healthy elderly subjects where increases of the left ventricle mass, if present, would be small or moderate. In this paper LVM as well as LVM index (iLVM) from a group of healthy subjects belonging to a physical training program for elderly, was studied by means of ECHO and computerized ECG and VCG. From ECG, voltage indexes and other LVM associated parameters were extracted; from VCG, planar maximum vectors, areas within VCG loops and maximal spatial magnitude of QRS (SM), were measured. Results of LVM (221 +/- 37.9, g) were higher than figures reported for others groups. Voltage indexes showed normal values, but QRS duration was somewhat prolonged. The best simple linear regression, combining variables from VCG and ECG was maximum horizontal vector (Vmax-Hor) vs Sokolow-Lyon index (SOK) and combining ECHO with ECG or VCG, LVM vs Area inside horizontal loop (AreaHor). A model for estimation of LVM from electrical variables was obtained by multiple linear regression; combining five variables from ECG and VCG. The best model included Sokolow-Lyon index and variables from horizontal and sagittal planes of VCG and spatial magnitude of QRS: LVM = 4.8 SOK-186 VmaxHOR-80 VmaxSAG + 126SM + 340 AreaH + 175.(ABSTRACT TRUNCATED AT 250 WORDS)

The effects of prolonged beta-adrenoceptor blockade on heart weight and cardiac intracellular potentials in rabbits.

Estimates were made in vivo in rabbits of the relative beta-receptor blocking potency and duration of action of propranolol and practolol. In further experiments groups of litter mates were injected twice daily with approximately equi-active amounts of propranolol or practolol, or with saline, for several weeks. The heart weights of the treated animals were significantly lower than those of the controls, the water contents were higher, and the dry weight differences were highly significant; -16.8% after 2 mg/kg bd propranolol for six weeks and -33.8% after 10 mg/kg practolol. (The treated animals grew less rapidly than the controls; when corrected for body weight these figures were -11.9% and -20.4%, respectively.) In the practolol group, but not the propranolol group, the duration of the atrial intracellular potentials was prolonged. There was no evidence that the prolonged treatment with either drug had a negative inotropic effect, or reduced positive inotropic responses to isoprenaline.