Range-depths tracking of multiple sperm whales over large distances using a two-element vertical array and rhythmic properties of clicks-trains

International audienceSperm whales (Physeter macrocephalus) have followed fishing vessels off the Alaskan coast for decades, in order to remove sablefish ("depredate") from longlines. The Southeast Alaska Sperm Whale Avoidance Project (SEASWAP) has found that whales respond to distinctive acoustic cues made by hauling fishing vessels, as well as to marker buoys on the surface. Between 15-17 August 2010 a simple two-element vertical array was deployed off the continental slope of Southeast Alaska in 1200 m water depth. The array was attached to a longline fishing buoyline at 300 m depth, close to the sound-speed minimum of the deep-water profile. The buoyline also served as a depredation decoy, attracting seven sperm whales to the area. One animal was tagged with both a LIMPET dive depthtransmitting satellite and bioacoustic B-probe tag. Both tag datasets were used as an independent check of a passive acoustic scheme for tracking the whale in depth and range, which exploited the elevation angles and relative arrival times of multiple ray paths recorded on the array. The localization approach doesnt require knowledge of the local bottom bathymetry. Numerical propagation models yielded accurate locations up to at least 35 km range at Beaufort sea state 3. Ongoing work includes combining the arrival angle information with an algorithm developed by Le Bot et al. [1] that uses the rhythmic properties of odontocet click trains to separate interleaved click trains. This approach will improve our localization capabilities in presence of multiple sperm whales. In order to achieve better separation of interleaved click trains it is possible to use machine learning based algorithms. This new concept is based on finding useful information hidden in a large database. This useful information can then be represented by a sparse subspace. The first step of the approach is to extract informative features with a new detector proposed by Dadouchi et al. [2]. Once the dictionary of features is learned, any signal of this considered dataset can be approximated sparsely. By reducing the dimensional space, the sparse representation has the advantage to provide an optimally representation of the data. [Work supported by the North Pacific Research Board, the Alaska SeaLife Center, ONR, NOAA and ANR-12-ASTR-0021-03 "MER CALME"

Phase Separation of Crystal Surfaces: A Lattice Gas Approach

We consider both equilibrium and kinetic aspects of the phase separation (``thermal faceting") of thermodynamically unstable crystal surfaces into a hill--valley structure. The model we study is an Ising lattice gas for a simple cubic crystal with nearest--neighbor attractive interactions and weak next--nearest--neighbor repulsive interactions. It is likely applicable to alkali halides with the sodium chloride structure. Emphasis is placed on the fact that the equilibrium crystal shape can be interpreted as a phase diagram and that the details of its structure tell us into which surface orientations an unstable surface will decompose. We find that, depending on the temperature and growth conditions, a number of interesting behaviors are expected. For a crystal in equilibrium with its vapor, these include a low temperature regime with logarithmically--slow separation into three symmetrically--equivalent facets, and a higher temperature regime where separation proceeds as a power law in time into an entire one--parameter family of surface orientations. For a crystal slightly out of equilibrium with its vapor (slow crystal growth or etching), power--law growth should be the rule at late enough times. However, in the low temperature regime, the rate of separation rapidly decreases as the chemical potential difference between crystal and vapor phases goes to zero.Comment: 16 pages (RevTex 3.0); 12 postscript figures available on request ([email protected]). Submitted to Physical Review E. SFU-JDSDJB-94-0

Catheter-Based Renal Sympathetic Denervation for Resistant Hypertension Durability of Blood Pressure Reduction Out to 24 Months

Renal sympathetic hyperactivity is seminal in the maintenance and progression of hypertension. Catheter-based renal sympathetic denervation has been shown to significantly reduce blood pressure (BP) in patients with hypertension. Durability of effect beyond 1 year using this novel technique has never been reported. A cohort of 45 patients with resistant hypertension (systolic BP GT = 160 mm Hg on GT = 3 antihypertension drugs, including a diuretic) has been originally published. Herein, we report longer-term follow-up data on these and a larger group of similar patients subsequently treated with catheter-based renal denervation in a nonrandomized manner. We treated 153 patients with catheter-based renal sympathetic denervation at 19 centers in Australia, Europe, and the United States. Mean age was 57 +/- 11 years, 39% were women, 31% were diabetic, and 22% had coronary artery disease. Baseline values included mean office BP of 176/98 +/- 17/15 mm Hg, mean of 5 antihypertension medications, and an estimated glomerular filtration rate of 83 +/- 20 mL/min per 1.73 m(2). The median time from first to last radiofrequency energy ablation was 38 minutes. The procedure was without complication in 97% of patients (149 of 153). The 4 acute procedural complications included 3 groin pseudoaneurysms and 1 renal artery dissection, all managed without further sequelae. Postprocedure office BPs were reduced by 20/10, 24/11, 25/11, 23/11, 26/14, and 32/14 mm Hg at 1, 3, 6, 12, 18, and 24 months, respectively. In conclusion, in patients with resistant hypertension, catheter-based renal sympathetic denervation results in a substantial reduction in BP sustained out to GT = 2 years of follow-up, without significant adverse events. (Hypertension. 2011;57:911-917.

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Relationship between sperm whale (Physeter macrocephalus) click structure and size derived from videocamera images of a depredating whale (sperm whale prey acquisition)

International audienceSperm whales have learned to depredate black cod ﰍAnoplopoma fimbriaﰀ from longline deployments in the Gulf of Alaska. On May 31, 2006, simultaneous acoustic and visual recordings were made of a depredation attempt by a sperm whale at 108 m depth. Because the whale was oriented perpendicularly to the camera as it contacted the longline at a known distance from the camera, the distance from the nose to the hinge of the jaw could be estimated. Allometric relationships obtained from whaling data and skeleton measurements could then be used to estimate both the spermaceti organ length and total length of the animal. An acoustic estimate of animal length was obtained by measuring the inter-pulse interval ﰍIPIﰀ of clicks detected from the animal and using empirical formulas to convert this interval into a length estimate. Two distinct IPIs were extracted from the clicks, one yielding a length estimate that matches the visually-derived length to within experimental error. However, acoustic estimates of spermaceti organ size, derived from standard sound production theories, are inconsistent with the visual estimates, and the derived size of the junk is smaller than that of the spermaceti organ, in contradiction with known anatomical relationships

Acoustic tracking of sperm whales in the Gulf of Alaska using a two-element vertical array and tags

International audienceBetween 15 and 17 August 2010, a simple two-element vertical array was deployed off the continental slope of Southeast Alaska in 1200 m water depth. The array was attached to a vertical buoy line used to mark each end of a longline fishing set, at 300 m depth, close to the sound-speed minimum of the deep-water profile. The buoy line also served as a depredation decoy, attracting seven sperm whales to the area. One animal was tagged with both a LIMPET dive depth-transmitting satellite and bioacoustic "B-probe" tag. Both tag datasets were used as an independent check of various passive acoustic schemes for tracking the whale in depth and range, which exploited the elevation angles and relative arrival times of multiple ray paths recorded on the array. Analytical tracking formulas were viable up to 2 km range, but only numerical propagation models yielded accurate locations up to at least 35 km range at Beaufort sea state 3. Neither localization approach required knowledge of the local bottom bathymetry. The tracking system was successfully used to estimate the source level of an individual sperm whale's "clicks" and "creaks" and predict the maximum detection range of the signals as a function of sea state

Challenges in identifying (or not) focal animal sound production in baleen whale acoustic tag datasets

Ascribing sounds on animal-borne tag recordings to individual sound producers is integral to understanding social behavior of animal groups. Previously, sounds recorded on tags have been assigned to the tagged individual (focal animal) based on proximity of other conspecifics, angle of arrival, low frequency artifacts in the sound, or a combination of signal-to-noise ratio (SNR) and received level (RL). However, most acoustic-based methods do not translate well to baleen whales producing low frequency sounds, as the tag often resides in the near field of the sound source. In addition, for social species that spend time in groups with conspecifics in close proximity, sounds produced by nearby animals may have comparably high SNR and RL. Here we discuss the challenges of determining if a tagged whale is calling in baleen whale datasets, using acoustic records from two humpback whales, oqe fin whale, and one blue whale as examples. The datasets include intense song or feeding calls and are from several locations. We compare SNR, RL, harmonic content, and behavioral sensor data in these cases, and discuss the implications of confirming sound production by a tagged individual for measuring communication, behavior, and responses to external stimuli in baleen whales