A Setup Method of Tide Level Variations at Open Boundary in Estuaries for Numerical Tidal Flow Analysis

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

A root auto tracing and analysis (ARATA): An automatic analysis software for detecting fine roots in images from flatbed optical scanners

1. Buried scanners are often used to study fine root dynamics by continuously observing them from the images taken at a fixed point. Accordingly, software have been developed to support operators to quantitatively analyse fine roots from scanned images. However, image processing is still time-consuming work. 2. Deep learning has achieved impressive results as a method for recognising objects in pixel units. In this study, we attempted to automate the image analysis of fine roots using convolutional neural network. 3. Using a root auto tracing and analysis (ARATA), we succeeded in extracting fine roots from scanned images and calculated projected area of fine roots for long-term dynamics. 4. Our software enables the automatic processing of scanned images acquired at various study sites and accelerates the study of fine root dynamics over extended time periods

Reconstruction of Virtual Neural Circuits in an Insect Brain

The reconstruction of large-scale nervous systems represents a major scientific and engineering challenge in current neuroscience research that needs to be resolved in order to understand the emergent properties of such systems. We focus on insect nervous systems because they represent a good compromise between architectural simplicity and the ability to generate a rich behavioral repertoire. In insects, several sensory maps have been reconstructed so far. We provide an overview over this work including our reconstruction of population activity in the primary olfactory network, the antennal lobe. Our reconstruction approach, that also provides functional connectivity data, will be refined and extended to allow the building of larger scale neural circuits up to entire insect brains, from sensory input to motor output

Neuroethology of the Waggle Dance: How Followers Interact with the Waggle Dancer and Detect Spatial Information

Since the honeybee possesses eusociality, advanced learning, memory ability, and information sharing through the use of various pheromones and sophisticated symbol communication (i.e., the "waggle dance"), this remarkable social animal has been one of the model symbolic animals for biological studies, animal ecology, ethology, and neuroethology. Karl von Frisch discovered the meanings of the waggle dance and called the communication a "dance language." Subsequent to this discovery, it has been extensively studied how effectively recruits translate the code in the dance to reach the advertised destination and how the waggle dance information conflicts with the information based on their own foraging experience. The dance followers, mostly foragers, detect and interact with the waggle dancer, and are finally recruited to the food source. In this review, we summarize the current state of knowledge on the neural processing underlying this fascinating behavior

Spatial registration of neuron morphologies based on maximization of volume overlap

Background: Morphological features are widely used in the study of neuronal function and pathology. Invertebrate neurons are often structurally stereotypical, showing little variance in gross spatial features but larger variance in their fine features. Such variability can be quantified using detailed spatial analysis, which however requires the morphologies to be registered to a common frame of reference. Results: We outline here new algorithms - Reg-MaxS and Reg-MaxS-N - for co-registering pairs and groups of morphologies, respectively. Reg-MaxS applies a sequence of translation, rotation and scaling transformations, estimating at each step the transformation parameters that maximize spatial overlap between the volumes occupied by the morphologies. We test this algorithm with synthetic morphologies, showing that it can account for a wide range of transformation differences and is robust to noise. Reg-MaxS-N co-registers groups of more than two morphologies by iteratively calculating an average volume and registering all morphologies to this average using Reg-MaxS. We test Reg-MaxS-N using five groups of morphologies from the Droshophila melanogaster brain and identify the cases for which it outperforms existing algorithms and produce morphologies very similar to those obtained from registration to a standard brain atlas. Conclusions: We have described and tested algorithms for co-registering pairs and groups of neuron morphologies. We have demonstrated their application to spatial comparison of stereotypic morphologies and calculation of dendritic density profiles, showing how our algorithms for registering neuron morphologies can enable new approaches in comparative morphological analyses and visualization

Inhibitory Pathways for Processing the Temporal Structure of Sensory Signals in the Insect Brain

Insects have acquired excellent sensory information processing abilities in the process of evolution. In addition, insects have developed communication schemes based on the temporal patterns of specific sensory signals. For instance, male moths approach a female by detecting the spatiotemporal pattern of a pheromone plume released by the female. Male crickets attract a conspecific female as a mating partner using calling songs with species-specific temporal patterns. The dance communication of honeybees relies on a unique temporal pattern of vibration caused by wingbeats during the dance. Underlying these behaviors, neural circuits involving inhibitory connections play a critical common role in processing the exact timing of the signals in the primary sensory centers of the brain. Here, we discuss common mechanisms for processing the temporal patterns of sensory signals in the insect brain

Development of a Scheme and Tools to Construct a Standard Moth Brain for Neural Network Simulations

Understanding the neural mechanisms for sensing environmental information and controlling behavior in natural environments is a principal aim in neuroscience. One approach towards this goal is rebuilding neural systems by simulation. Despite their relatively simple brains compared with those of mammals, insects are capable of processing various sensory signals and generating adaptive behavior. Nevertheless, our global understanding at network system level is limited by experimental constraints. Simulations are very effective for investigating neural mechanisms when integrating both experimental data and hypotheses. However, it is still very difficult to construct a computational model at the whole brain level owing to the enormous number and complexity of the neurons. We focus on a unique behavior of the silkmoth to investigate neural mechanisms of sensory processing and behavioral control. Standard brains are used to consolidate experimental results and generate new insights through integration. In this study, we constructed a silkmoth standard brain and brain image, in which we registered segmented neuropil regions and neurons. Our original software tools for segmentation of neurons from confocal images, KNEWRiTE, and the registration module for segmented data, NeuroRegister, are shown to be very effective in neuronal registration for computational neuroscience studies

Development of the photomultiplier tube readout system for the first Large-Sized Telescope of the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) is the next generation ground-based very high energy gamma-ray observatory. The Large-Sized Telescope (LST) of CTA targets 20 GeV -- 1 TeV gamma rays and has 1855 photomultiplier tubes (PMTs) installed in the focal plane camera. With the 23 m mirror dish, the night sky background (NSB) rate amounts to several hundreds MHz per pixel. In order to record clean images of gamma-ray showers with minimal NSB contamination, a fast sampling of the signal waveform is required so that the signal integration time can be as short as the Cherenkov light flash duration (a few ns). We have developed a readout board which samples waveforms of seven PMTs per board at a GHz rate. Since a GHz FADC has a high power consumption, leading to large heat dissipation, we adopted the analog memory ASIC "DRS4". The sampler has 1024 capacitors per channel and can sample the waveform at a GHz rate. Four channels of a chip are cascaded to obtain deeper sampling depth with 4096 capacitors. After a trigger is generated in a mezzanine on the board, the waveform stored in the capacitor array is subsequently digitized with a low speed (33 MHz) ADC and transferred via the FPGA-based Gigabit Ethernet to a data acquisition system. Both a low power consumption (2.64 W per channel) and high speed sampling with a bandwidth of $>$300 MHz have been achieved. In addition, in order to increase the dynamic range of the readout we adopted a two gain system achieving from 0.2 up to 2000 photoelectrons in total. We finalized the board design for the first LST and proceeded to mass production. Performance of produced boards are being checked with a series of quality control (QC) tests. We report the readout board specifications and QC results.Comment: In Proceedings of the 34th International Cosmic Ray Conference (ICRC2015), The Hague, The Netherlands. All CTA contributions at arXiv:1508.0589

How Do Honeybees Attract Nestmates Using Waggle Dances in Dark and Noisy Hives?

It is well known that honeybees share information related to food sources with nestmates using a dance language that is representative of symbolic communication among non-primates. Some honeybee species engage in visually apparent behavior, walking in a figure-eight pattern inside their dark hives. It has been suggested that sounds play an important role in this dance language, even though a variety of wing vibration sounds are produced by honeybee behaviors in hives. It has been shown that dances emit sounds primarily at about 250–300 Hz, which is in the same frequency range as honeybees' flight sounds. Thus the exact mechanism whereby honeybees attract nestmates using waggle dances in such a dark and noisy hive is as yet unclear. In this study, we used a flight simulator in which honeybees were attached to a torque meter in order to analyze the component of bees' orienting response caused only by sounds, and not by odor or by vibrations sensed by their legs. We showed using single sound localization that honeybees preferred sounds around 265 Hz. Furthermore, according to sound discrimination tests using sounds of the same frequency, honeybees preferred rhythmic sounds. Our results demonstrate that frequency and rhythmic components play a complementary role in localizing dance sounds. Dance sounds were presumably developed to share information in a dark and noisy environment