Differentiation of Crystal Cells, Gravity-Sensing Cells in the Placozoan Trichoplax adhaerens

Trichoplax adhaerens are simple animals with no nervous system, muscles or body axis. Nevertheless, Trichoplax demonstrate complex behaviors, including responses to the direction of the gravity vector. They have only six somatic cell types, and one of them, crystal cells, has been implicated in gravity reception. Multiple crystal cells are scattered near the rim of the pancake-shaped animal; each contains a cup-shaped nucleus and an intracellular crystal, which aligns its position according to the gravity force. Little is known about the development of any cell type in Trichoplax, which, in the laboratory, propagate exclusively by binary fission. Electron and light microscopy were used to investigate the stages by which crystal cells develop their mature phenotypes and distributions. Nascent crystal cells, identified by their possession of a small crystal, were located farther from the rim than mature crystal cells, indicating that crystal cells undergo displacement during maturation. They were elongated in shape and their nucleus was rounded. The crystal develops inside a vacuole flanked by multiple mitochondria, which, perhaps, supply molecules needed for the biomineralization process underlying crystal formation. This research sheds light on the development of unique cells with internal biomineralization and poses questions for further research.

Ocular fixations modulate audiovisual semantic congruency when standing in an upright position

Introduction: Multisensory audiovisual semantic congruency is the process by which visual information is perceived as integrated to auditory stimuli, because both coincide in terms of simultaneity and semantic correspondence. This study was aimed at establishing whether visual percepts, which semantically correspond to auditory stimuli, are associated with ocular fixations in modulating bottom-up areas while keeping a body posture alignment between the up-direction and the idiotropic axes, as well as in another orientation corresponding to a vectorial opposition between the up-direction and the head idiotropic axis. Method: Two groups (one for each position) were selected from a sample of 88 people. A bistable image was presented on a screen of a fixed 120 Hz eye-tracker device, providing background auditory stimuli so as to establish semantic congruencies and their relations to ocular fixations. Results: It was found that audiovisual semantic congruency is associated with fixations when idiotropic vectors are aligned with the up direction. Fixations manifested in bottom-up modulating areas are not associated with multisensory audiovisual semantic congruency when the head idiotropic vector is parallel with the gravity vector. Eye fixations decrease significantly if the head idiotropic axis is aligned with the gravity vector. Conclusion: It is concluded that body position can affect visual perceptual processes involved in the occurrence of semantic congruency.

EKF-based self-attitude estimation with DNN learning landscape information

This paper presents an EKF-based self-attitude estimation with a DNN (deep neural network) learning landscape information. The method integrates gyroscopic angular velocity and DNN inference in the EKF. The DNN predicts a gravity vector in a camera frame. The input of the network is a camera image, the outputs are a mean vector and a covariance matrix of the gravity. It is trained and validated with a dataset of images and corresponded gravity vectors. The dataset is collected in a flight simulator because we can easily obtain various gravity vectors, although the method is not only for UAVs. Using a simulator breaks the limitation of amount of collecting data with ground truth. The validation shows the network can predict the gravity vector from only a single shot image. It also shows that the covariance matrix expresses the uncertainty of the inference. The covariance matrix is used for integrating the inference in the EKF. Flight data of a drone is also recorded in the simulator, and the EKF-based method is tested with it. It shows the method suppresses accumulative error by integrating the network outputs.

Surface gravimetry on Dimorphos

<p>In the frame work of HERA mission, the gravimeter for small solar system objects (GRASS) has been developed to measure the local acceleration vector on the surface of the moonlet of the binary asteroid, Dimorphos. GRASS will be onboard Juventas CubeSat which is one of the two daughtercraft of ESA’s Hera spacecraft. Launched in 2024 it will arrive in the binary system in 2026. Following the soft-landing of the Juventas CubeSat, GRASS will record the temporal variation of the surface gravity vector.</p><p>The average gravitational force expected on the Dimorphos surface is around 5 x 10<sup>-5</sup> m s<sup>-2</sup> (or 5 mGal). Apart from the self-gravitation of the body, centrifugal forces and the acceleration due to the main body of the system contribute to the surface acceleration. The temporal variations of local gravity vector at the landing site will be used to constrain the geological substructure (mass anomalies, local depth and lateral variations of regolith) as well as the surface geophysical environment (tides, dynamic sloped and centrifugal forces).</p><p>We will present the GRASS science objectives in the Hera mission the operational concept that is foreseen to reach these objectives, its current status of development including first test results and the by simulation estimated performances of the instrument.</p><p> </p>

The Circadian-clock Regulates the Arabidopsis Gravitropic Response

For long-term space missions, it is necessary to understand how organisms respond to changes in gravity. Plant roots are positively gravitropic; the primary root grows parallel to gravity's pull even after being turned away from the direction of gravity. We examined if this gravitropic response varies depending on the time of day reorientation occurs. When plants were reoriented in relation to the gravity vector or placed in simulated microgravity, the magnitude of the root gravitropic response varied depending on the time of day the initial change in gravity occurred. The response was greatest when plants were reoriented at dusk, just before a period of rapid growth, and were minimal just before dawn as the plants entered a period of reduced root growth. We found that this variation in the magnitude of the gravitropic response persisted in constant light (CL) suggesting the variation is circadian-regulated. Gravitropic responses were disrupted in plants with disrupted circadian clocks, including plants overexpressing Circadian-clock Associated 1 (CCA1) and elf3-2, in the reorientation assay and on a 2D clinostat. These findings indicate that circadian-regulated pathways modulate the gravitropic responses, thus, highlighting the importance of considering and recording the time of day gravitropic experiments are performed.

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment.