Class I histone deacetylases inhibition reverses memory impairment induced by acute stress in mice

While chronic stress induces learning and memory impairments, acute stress may facilitate or prevent memory consolidation depending on whether it occurs during the learning event or before it, respectively. On the other hand, it has been shown that histone acetylation regulates long-term memory formation. This study aimed to evaluate the effect of two inhibitors of class I histone deacetylases (HDACs), 4-phenylbutyrate (PB) and IN14 (100 mg/kg/day, ip for 2 days), on memory performance in mice exposed to a single 15-min forced swimming stress session. Plasma corticosterone levels were determined 30 minutes after acute swim stress in one group of mice. In another experimental series, independent groups of mice were trained in one of three different memory tasks: Object recognition test, Elevated T maze, and Buried food location test. Subsequently, the hippocampi were removed to perform ELISA assays for histone deacetylase 2 (HDAC2) expression. Acute stress induced an increase in plasma corticosterone levels, as well as hippocampal HDAC2 content, along with an impaired performance in memory tests. Moreover, PB and IN14 treatment prevented memory loss in stressed mice. These findings suggest that HDAC2 is involved in acute stress-induced cognitive impairment. None of the drugs improved memory in non-stressed animals, indicating that HDACs inhibitors are not cognitive boosters, but rather potentially useful drugs for mitigating memory deficits.

Lack of genetic differentiation in yellowfin tuna has conservation implications in the Eastern Pacific Ocean

Yellowfin tuna, Thunnus albacares, is an important global fishery and of particular importance in the Eastern Pacific Ocean (EPO). According to the 2019 Inter-American Tropical Tuna Commission (IATTC) assessment, yellowfin tuna within the EPO is a single stock, and is being managed as one stock. However, previous studies indicate site fidelity, or limited home ranges, of yellowfin tuna which suggests the potential for multiple yellowfin tuna stocks within the EPO, which was supported by a population genetic study using microsatellites. If numerous stocks are present, management at the wrong spatial scales could cause the loss of minor yellowfin tuna populations in the EPO. In this study we used double digestion RADseq to assess the genetic structure of yellowfin tuna in the EPO. A total of 164 yellowfin tuna from Cabo San Lucas, México, and the Galápagos Islands and Santa Elena, Ecuador, were analysed using 18,011 single nucleotide polymorphisms. Limited genetic differentiation (F_ST = 0.00058–0.00328) observed among the sampling locations (México, Ecuador, Peru, and within Ecuador) is consistent with presence of a single yellowfin tuna population within the EPO. Our findings are consistent with the IATTC assessment and provide further evidence of the need for transboundary cooperation for the successful management of this important fishery throughout the EPO.

Diving into the vertical dimension of elasmobranch movement ecology

Knowledge of the three-dimensional movement patterns of elasmobranchs is vital to understand their ecological roles and exposure to anthropogenic pressures. To date, comparative studies among species at global scales have mostly focused on horizontal movements. Our study addresses the knowledge gap of vertical movements by compiling the first global synthesis of vertical habitat use by elasmobranchs from data obtained by deployment of 989 biotelemetry tags on 38 elasmobranch species. Elasmobranchs displayed high intra- and interspecific variability in vertical movement patterns. Substantial vertical overlap was observed for many epipelagic elasmobranchs, indicating an increased likelihood to display spatial overlap, biologically interact, and share similar risk to anthropogenic threats that vary on a vertical gradient. We highlight the critical next steps toward incorporating vertical movement into global management and monitoring strategies for elasmobranchs, emphasizing the need to address geographic and taxonomic biases in deployments and to concurrently consider both horizontal and vertical movements.

Dynamic balance of a bipedal robot using neural network training with simulated annealing

This work proposes using an evolutionary optimization method known as simulated annealing to train artificial neural networks. These neural networks are used to control posture stabilization of a humanoid robot in a simulation. A total of eight multilayer perceptron neural networks are used. Although the control is used mainly for posture stabilization and not displacement, we propose a posture set to achieve this, including right leg lift in sagittal plane and right leg lift in frontal plane. At the beginning, tests are carried out only considering gravitational force and reaction force between the floor and the humanoid; then tests are carried out with two disturbances: tilted ground and adding a mass to the humanoid. We found that using simulated annealing the robot maintains its stability at all times, decreasing the number of epochs needed to converge, and also, showing flexibility and adaptability to disturbances. The way neural networks learn is analyzed; videos of the movements made, and the model for further experimentation are provided.

Defining a no-reference image quality assessment by means of the self-affine analysis

In this paper we propose a novel Blind Image Quality Assessment via Self-Affine Analysis (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes ω _{λ, ϕ} of different spatial frequencies λ and spatial orientations ϕ, and it transforms these wavelet planes into one-dimension vector Ω using a Hilbert scanning. From the vector Ω there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of Image Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H _{24b p p} = 0.0395, H _{1.375b p p} = 0.0551, and H _{0.50b p p} = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.

PhotoId-Whale: Blue whale dorsal fin classification for mobile devices

Photo-identification (photo-id) is a method used in field studies by biologists to monitor animals according to their density, movement patterns and behavior, with the aim of predicting and preventing ecological risks. However, these methods can introduce subjectivity when manually classifying an individual animal, creating uncertainty or inaccuracy in the data as a result of the human criteria involved. One of the main objectives in photo-id is to implement an automated mechanism that is free of biases, portable, and easy to use. The main aim of this work is to develop an autonomous and portable photo-id system through the optimization of image classification algorithms that have high statistical dependence, with the goal of classifying dorsal fin images of the blue whale through offline information processing on a mobile platform. The new proposed methodology is based on the Scale Invariant Feature Transform (SIFT) that, in conjunction with statistical discriminators such as the variance and the standard deviation, fits the extracted data and selects the closest pixels that comprise the edges of the dorsal fin of the blue whale. In this way, we ensure the elimination of the most common external factors that could affect the quality of the image, thus avoiding the elimination of relevant sections of the dorsal fin. The photo-id method presented in this work has been developed using blue whale images collected off the coast of Baja California Sur. The results shown have qualitatively and quantitatively validated the method in terms of its sensitivity, specificity and accuracy on the Jetson Tegra TK1 mobile platform. The solution optimizes classic SIFT, balancing the results obtained with the computational cost, provides a more economical form of processing and obtains a portable system that could be beneficial for field studies through mobile platforms, making it available to scientists, government and the general public.

Navigation of Silver/Carbon Nanoantennas in Organic Fluids Explored by a Two-Wave Mixing

Within this work are analyzed third-order nonlinear optical properties with a potential influence on the dynamic mechanics exhibited by metal/carbon nanofluids. The nanofluids were integrated by multiwall carbon nanotubes decorated with Ag nanoparticles suspended in ethanol or in acetone. Optical third-order nonlinearities were experimentally explored by vectorial two-wave mixing experiments with a Nd-YAG laser system emitting nanosecond pulses at a 532 nm wavelength. An optically induced birefringence in the metal/organic samples seems to be responsible for a significant modification in density and compressibility modulus in the nanosystems. The measured nonlinear refractive index was associated with a thermal process together with changes in density, compressibility modulus and speed of sound in the samples. Nanofluid diffusivity was studied to characterize the dynamic concentration gradients related to the precipitation of nanostructures in the liquid solutions. The evolution of the nanoparticle density suspended in the nanofluids was considered as a temporal-resolved probabilistic system. It is stated that the incorporation of Ag nanoparticles in carbon nanotubes produces strong mechanical changes in carbon-based nanofluids. According to numerical simulations and optical evaluations, immediate applications for developing dynamic nanoantennas optical logic gates and quantum-controlled metal/carbon systems can be contemplated.