Dendrites Of Layer Research Articles

TSOM: Small object motion detection neural network inspired by avian visual circuit

Detecting small moving objects in complex backgrounds from an overhead perspective is a highly challenging task for machine vision systems. As an inspiration from nature, the avian visual system is capable of processing motion information in various complex aerial scenes, and the Retina-OT-Rt visual circuit of birds is highly sensitive to capturing the motion information of small objects from high altitudes. However, more needs to be done on small object motion detection algorithms based on the avian visual system. In this paper, we conducted mathematical description based on extensive studies of the biological mechanisms of the Retina-OT-Rt visual circuit. Based on this, we proposed a novel tectum small object motion detection neural network (TSOM). The TSOM neural network includes the retina, SGC dendritic, SGC Soma, and Rt layers, each corresponding to neurons in the visual pathway for precise topographic projection, spatial–temporal encoding, motion feature selection, and multi-directional motion integration. Extensive experiments on pigeon neurophysiological experiments and image sequence data showed that the TSOM is biologically interpretable and effective in extracting reliable small object motion features from complex high-altitude backgrounds.

Inhibitory Postsynaptic Potentials Participate in Intracellular and Extracellular Theta Rhythms intheHippocampus: A Personal Narrative.

The hypothesis that the hippocampal theta rhythm consists of inhibitory postsynaptic potentials (IPSPs) was critical for understanding the theta rhythm. The dominant views in the early 1980s were that intracellularly recorded theta consisted of excitatory postsynaptic potentials (EPSPs) with little participation by IPSPs, and that IPSPs generated a closed monopolar field in the hippocampus. I (Leung) conceived of a new model for generation of the hippocampal theta rhythm, with theta-rhythmic IPSPs as an essential component, and thus sought to reinvestigate the relation between theta and IPSPs quantitatively with intracellular and extracellular recordings. The intracellular recordings were performed by Leung and Yim in the laboratory of Kris Krnjević at McGill University. Using protocols of passing steady-state holding currents and injection of chloride ions, the intracellular theta and IPSP in a CA1 neuron typically showed the same reversal potential and correlated change in amplitude. Low-intensity stimulation of the alveus evoked an antidromic action potential in CA1 neurons, identifying them as pyramidal cells with output axons in the alveus, which then activated a feedback IPSP with almost no excitatory component. Theta-rhythmic somatic inhibition, together with phase-shifted theta-rhythmic distal apical dendritic excitation were proposed as the two dipoles that generate a gradual extracellular theta phase shift in the CA1 apical dendritic layer. The distal apical excitation driven by the entorhinal cortex was proposed to be atropine-resistant and dominated during walking in rats. Other than serving a conventional role in limiting excitation, rhythmic proximal inhibition and distal dendritic excitation provide varying phasic modulation along the soma-dendritic axis of pyramidal cells, resulting in theta phase-dependent synaptic plasticity and gamma oscillations, which are likely involved in cognitive processing.

Analysis on dendritic deep learning model for AMR task

This study introduces a novel hybrid deep learning model featuring a dendritic layer for enhancing the performance of automatic modulation recognition (AMR). By replacing the fully connected layer, the proposed model demonstrates superior classification accuracy in AMR tasks. Comparative experiments with nine state-of-the-art deep learning models on the RadioML2016.10a dataset reveal its consistent superiority. Statistical analyses, including the Friedman test and Wilcoxon signed-rank test, confirm the significant advantage of the HDM-D model.

Oxynitrided Ti-6Al-4V Coatings Deposited by Twin Wire Arc Spray for Protection of Aluminum Die-Casting Molds

Steel molds used for aluminum die-casting often fail due to excessive wear or cracking phenomena associated with the soldering effect in contact with molten aluminum, which leads to the formation of iron-based intermetallic compounds and causes problems in the cast components. One solution is to apply protective coatings whose composition is less reactive with the molten aluminum and improve its hardness, toughness, wear, and corrosion resistance, thus prolonging its service life. This work evaluates the effectiveness of Ti-6Al-4V coatings deposited by twin wire arc spraying in an air or nitrogen atmosphere. Nitrogen was used as the carrier and shielding gas for the in-flight molten particles. Coatings were deposited by varying the stand-off distance, the nitrogen gas pressures, and the substrate temperature. The microstructure of the coatings is interlayered, one porous layer of dendrites and one highly densified layer. The presence of TiN, TiO2, α-Ti, and β-Ti phases was confirmed by different characterization methods. For instance, x-ray photoelectron spectroscopy measurements confirmed the presence of N-Ti, O-Ti, and N-O bonds, with the oxygen/nitrogen/titanium percentage associated with the formation of a non-stoichiometric (Ti, Al, V)NxOy phase. Finally, the reactivity of selected oxynitrided Ti-6Al-4V coating in contact with molten aluminum showed a low reaction rate compared to the coarse reaction layer suffered by the uncoated steel substrates.

An electrodiffusive network model with multicompartmental neurons and synaptic connections.

Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.

Vertical Profile Climatology of Polarimetric Radar Variables and Retrieved Microphysical Parameters in Synoptic and Lake Effect Snowstorms

AbstractThis study derives polarimetric radar vertical profiles and microphysical retrievals for 25 Synoptic Snow (SS) and 23 Lake Effect Snow (LES) cases using the Range‐Defined Quasi‐Vertical Profiles (RD‐QVP), Columnar Vertical Profiles (CVP), and Process‐oriented Vertical Profiles (POVP) methods. For all vertical profile techniques, SS cases exhibit a near‐linear increase in reflectivity from −30 to 0°C whereas ZDR and Kdp locally peak in the dendritic growth layer. LES cases universally exhibit negative ZDR, rather high Z, negligible Kdp, and near‐unity ρhv. Ground measurements from the past OWLeS campaign provide direct evidence that conical graupel may strongly affect these polarimetric measurements in LES bands. Aggregation efficiencies for SS cases are estimated by optimizing the theoretical number concentration (Nt) and mean volume diameter (Dm) steady‐state vertical profiles against radar‐retrieved profiles derived from 20 of the 25 synoptic storm RD‐QVPs. The median estimated aggregation efficiency is approximately 0.15 with a relatively narrow interquartile range that spans from 0.1 to just over 0.2. Values of optimized aggregation efficiencies are nearly independent of the assumed gamma distribution shape parameter. These results are used to derive temperature‐dependent, climatological steady‐state relations for vertical profiles of Nt, Dm, and liquid‐equivalent snowfall rates. These results can be used in numerical weather prediction model aggregation parameterizations and can also provide climatologically representative vertical profiles of radar and microphysical quantities.

Unraveling ice multiplication in winter orographic clouds via in-situ observations, remote sensing and modeling

Recent years have shown that secondary ice production (SIP) is ubiquitous, affecting all clouds from polar to tropical regions. SIP is not described well in models and may explain biases in warm mixed-phase cloud ice content and structure. Through modeling constrained by in-situ observations and its synergy with radar we show that SIP in orographic clouds exert a profound impact on the vertical distribution of hydrometeors and precipitation, especially in seeder-feeder cloud configurations. The mesoscale model simulations coupled with a radar simulator strongly support that enhanced aggregation and SIP through ice-ice collisions contribute to observed spectral bimodalities, skewing the Doppler spectra toward the slower-falling side at temperatures within the dendritic growth layer, ranging from −20 °C to −10 °C. This unique signature provides an opportunity to infer long-term SIP occurrences from the global cloud radar data archive, particularly for this underexplored temperature regime.

Subcellular pathways through VGluT3-expressing mouse amacrine cells provide locally tuned object-motion-selective signals in the retina

VGluT3-expressing mouse retinal amacrine cells (VG3s) respond to small-object motion and connect to multiple types of bipolar cells (inputs) and retinal ganglion cells (RGCs, outputs). Because these input and output connections are intermixed on the same dendrites, making sense of VG3 circuitry requires comparing the distribution of synapses across their arbors to the subcellular flow of signals. Here, we combine subcellular calcium imaging and electron microscopic connectomic reconstruction to analyze how VG3s integrate and transmit visual information. VG3s receive inputs from all nearby bipolar cell types but exhibit a strong preference for the fast type 3a bipolar cells. By comparing input distributions to VG3 dendrite responses, we show that VG3 dendrites have a short functional length constant that likely depends on inhibitory shunting. This model predicts that RGCs that extend dendrites into the middle layers of the inner plexiform encounter VG3 dendrites whose responses vary according to the local bipolar cell response type.

Speech Emotion Recognition using Hybrid Architectures

The detection of human emotions from speech signals remains a challenging frontier in audio processing and human-computer interaction domains. This study introduces a novel approach to Speech Emotion Recognition (SER) using a Dendritic Layer combined with a Capsule Network (DendCaps). A Convolutional Neural Network (NN) and a Long Short-Time Neural Network (CLSTM) hybrid model are used to create a baseline which is then compared to the DendCap model. Integrating dendritic layers and capsule networks for speech emotion detection can harness the unique advantages of both architectures, potentially leading to more sophisticated and accurate models. Dendritic layers, inspired by the nonlinear processing properties of dendritic trees in biological neurons, can handle the intricate patterns and variabilities inherent in speech signals, while capsule networks, with their dynamic routing mechanisms, are adept at preserving hierarchical spatial relationships within the data, enabling the model to capture more refined emotional subtleties in human speech. The main motivation for using DendCaps is to bridge the gap between the capabilities of biological neural systems and artificial neural networks. This combination aims to capitalize on the hierarchical nature of speech data, where intricate patterns and dependencies can be better captured. Finally, two ensemble methods namely stacking and boosting are used for evaluating the CLSTM and DendCaps networks and the experimental results show that stacking of the CLSTM and DendCaps networks gives the superior result with a 75% accuracy.

A realistic computational model for the formation of a Place Cell

Hippocampal Place Cells (PCs) are pyramidal neurons showing spatially localized firing when an animal gets into a specific area within an environment. Because of their obvious and clear relation with specific cognitive functions, Place Cells operations and modulations are intensely studied experimentally. However, although a lot of data have been gathered since their discovery, the cellular processes that interplay to turn a hippocampal pyramidal neuron into a Place Cell are still not completely understood. Here, we used a morphologically and biophysically detailed computational model of a CA1 pyramidal neuron to show how, and under which conditions, it can turn into a neuron coding for a specific cue location, through the self-organization of its synaptic inputs in response to external signals targeting different dendritic layers. Our results show that the model is consistent with experimental findings demonstrating PCs stability within the same spatial context over different trajectories, environment rotations, and place field remapping to adapt to changes in the environment. To date, this is the only biophysically and morphologically accurate cellular model of PCs formation, which can be directly used in physiologically accurate microcircuits and large-scale model networks to study cognitive functions and dysfunctions at cellular level.

Retinal clues for selective neuronal loss in multiple sclerosis.

The relationship between the cell body layer and the dendritic network layer of the retina and cognitive performance (CP) in MS patients has not been examined separately. The objective of this study is to predict cognitive impairment (CI) in RRMS patients and to examine the relationship between CP and ganglion cell layer (GCL), inner plexiform layer (IPL), and GCL divided by IPL (GCL/IPL). Ophthalmological evaluation, retinal segmentation, and Symbol Digit Modalities Test (SDMT) were performed on 102 RRMS patients and 54 healthy subjects. The relationships of GCL, IPL, and GCL/IPL with CP in eyes without a history of optic neuritis were investigated using Spearman's correlation. Models were created by accepting 1 standard deviation less of the SDMT mean of the control group as the limit for CI. The cutoff value of the GCL/IPL variable that could predict CI was calculated by ROC analysis, and the ability to accurately predict CI was tested with binary logistic regression. No correlation was found between OCT parameters and CP in healthy subjects. Correlation was found between GCL thickness and GCL/IPL variable and CP in RRMS patients (r=0.235, r=0.667 respectively). A GCL/IPL value of 1.255 was able to identify CI with 81.8% sensitivity and 75.9% specificity (AUC=0.844, LR=3.38) and predicted CI with 74.5% accuracy (Nagelkerke R2=0.439). In RRMS patients, the IPL thickness is unrelated to CP. Therewithal, the GCL/IPL-CP relationship is stronger than the GCL-CP relationship and GCL/IPL can predict CI.

Mineralogy, geochemistry and genesis of the polymetallic crusts from the Chagos-Laccadive Ridge, Arabian Sea, India

Recent offshore exploration by the Geological Survey of India (GSI) located ferromanganese crusts (FeMn) along the Chagos-Laccadive Ridge (CLR) offshore western continental margin of India. CLR FeMn crusts grew on phosphorite substrate rock (phosphorite cemented sand/silt with moderate carbonate fluorapatite (CFA) cement and goethite). Phosphorite precipitation is the product of intense upwelling and phosphorus regeneration due to organic matter degradation and fish debris dissolution. CLR FeMn crusts are of hydrogenetic origin precipitated in an oxic environment. As a result of hydrogenetic processes under oxic conditions, Fe-vernadite dominated the mineral phase of FeMn crusts. CLR FeMn crusts formed when the North Indian deep-water layer mixed with oxygen-depleted waters from the Arabian Sea, Persian Gulf, and Red Sea. Three genetic layers are identified in the FeMn crusts; Type-I, Type-II, and Type-III developed in response to fluctuation in dissolved oxygen during periods of intensified suboxic bottom waters and monsoon maxima associated with past climatic events in the Arabian Sea. Type-I layers are columnar showing low Mn/Fe ratio (∼1), reflectivity, Mg (average 1.24%), and Ni (average 0.29%). Dendritic Type-II layers show high Mn/Fe ratios (average 12.29), reflectivity, Mg (average 3.51%) and Ni (average 1.69%). Type-III layers show very low Mn/Fe ratios (average 0.21), reflectivity, Mg (average 1.4%) and Ni (average 0.01%) and contain a substantial amount of terrigenous debris of aeolian origin. Elements of economic interest such as Mn, Co, Ni, Cu, Mo, Li, Te, and REY in CLR FeMn crusts are depleted owing to terrigenous contribution.

Novel Fe3O4 nanoparticles encapsulated in and loaded on hollow carbon nanotubes wrapped dendritic carbon layers architecture for water decomposition

In this work, a new type of Fe3O4 nanoparticles encapsulated in and loaded on hollow carbon nanotubes (HCNTs) wrapped dendritic carbon layers (DCL) architecture were synthesized successfully by a simple method. In the preparation process, hollow polypyrrole (PPy) tubes were used as the base material, iron phthalocyanine (FePc) was applied as iron and carbon source, and the iron in FePc was successfully reduced to Fe3O4 by the reducibility of hydrogen peroxide which were successfully embedded in the hollow PPy tubes and loaded on the outer wall of the PPy tubes. Meanwhile, phthalocyanine, as a carbon source, was wrapped in the outer layer of hollow PPy. After calcination, a carbon layer coated with Fe3O4 nanoparticles was formed. Moreover, iron as a precursor catalyzed to take shape dendritic carbon layers structure during high temperature calcination. Ultimately, the novel Fe3O4 nanoparticles coated in HCNTs and encapsulated in dendritic carbon layers have been successfully prepared which have a relatively unique morphology and structure by an innovative, green and simple preparation method. Fe3O4 nanoparticles are not only embedded in the interior of HCNTs, but also loaded on the outer wall of HCNTs. Greater specific surface area and better electrocatalytic performance are displayed in acquired 2Fe3O4@N-HCNT@2Fe3O4@DCL in oxygen evolution reactions (OER) and hydrogen evolution reactions (HER). In this work, a novel method of metal oxide embedding into void internal structure is developed, and a relatively new morphology is obtained, which provides a new train of thought for the progress of material preparation.

Pruning method for dendritic neuron model based on dendrite layer significance constraints

AbstractThe dendritic neural model (DNM) mimics the non‐linearity of synapses in the human brain to simulate the information processing mechanisms and procedures of neurons. This enhances the understanding of biological nervous systems and the applicability of the model in various fields. However, the existing DNM suffers from high complexity and limited generalisation capability. To address these issues, a DNM pruning method with dendrite layer significance constraints is proposed. This method not only evaluates the significance of dendrite layers but also allocates the significance of a few dendrite layers in the trained model to a few dendrite layers, allowing the removal of low‐significance dendrite layers. The simulation experiments on six UCI datasets demonstrate that our method surpasses existing pruning methods in terms of network size and generalisation performance.

Enhancing mechanical properties and creep performance of 304H and inconel 617 superalloy dissimilar welds for Advanced Ultra Super Critical power plants

This investigation is conducted to find improvements in the mechanical properties of the dissimilar welds between austenitic stainless steel and Nickel superalloy used in the manufacturing of Advanced Ultra Super Critical (AUSC) power plants boiler components. Nickel superalloys (IN617) have excellent creep rupture properties, so they are employed in higher temperature regions (above 700 °C) of the boiler. In contrast, austenitic stainless steel (304H) is used in moderate temperature regions (up to 650 °C) part of the boiler, also striking off the economic balance need. The welding is carried out with manual multipass Gas Tungsten Arc Welding using an IN617 filler to compare 304H and IN617 weld without and with a buttering layer. Multiple attenuated buttering material layers are deposited at the 304H side, further machining the butter weld layer to the weld groove and closure weld to the IN617 part. The buttering technique improves mechanical properties and high-temperature creep rupture life through microstructure transformation. The dissimilar weld characterization shows a supplementary dendritic growth layer for the buttering deposition. The tensile strength is found to be 728.214 MPa with buttering higher than without buttering weld (620.25 MPa). The V-notch Charpy impact toughness value of the buttered weld sample is 66 J, greater than the non-buttered sample, i.e., 63.25 J. The creep rupture duration significantly increased with buttering. Strain hardening at higher applied stress helps to resist creep failure even at higher temperatures.

Alteration in the Synaptic and Extrasynaptic Organization of AMPA Receptors in the Hippocampus of P301S Tau Transgenic Mice.

Tau pathology is a hallmark of Alzheimer's disease (AD) and other tauopathies, but how pathological tau accumulation alters the glutamate receptor dynamics driving synaptic dysfunction is unclear. Here, we determined the impact of tau pathology on AMPAR expression, density, and subcellular distribution in the hippocampus of P301S mice using immunoblot, histoblot, and quantitative SDS-digested freeze-fracture replica labeling (SDS-FRL). Histoblot and immunoblot showed differential regulation of GluA1 and GluA2 in the hippocampus of P301S mice. The GluA2 subunit was downregulated in the hippocampus at 3 months while both GluA1 and GluA2 subunits were downregulated at 10 months. However, the total amount of GluA1-4 was similar in P301S mice and in age-matched wild-type mice. Using quantitative SDS-FRL, we unraveled the molecular organization of GluA1-4 in various synaptic connections at a high spatial resolution on pyramidal cell spines and interneuron dendrites in the CA1 field of the hippocampus in 10-month-old P301S mice. The labeling density for GluA1-4 in the excitatory synapses established on spines was significantly reduced in P301S mice, compared to age-matched wild-type mice, in the strata radiatum and lacunosum-moleculare but unaltered in the stratum oriens. The density of synaptic GluA1-4 established on interneuron dendrites was significantly reduced in P301S mice in the three strata. The labeling density for GluA1-4 at extrasynaptic sites was significantly reduced in several postsynaptic compartments of CA1 pyramidal cells and interneurons in the three dendritic layers in P301S mice. Our data demonstrate that the progressive accumulation of phospho-tau is associated with alteration of AMPARs on the surface of different neuron types, including synaptic and extrasynaptic membranes, leading to a decline in the trafficking and synaptic transmission, thereby likely contributing to the pathological events taking place in AD.

Radar Retrieval Evaluation and Investigation of Dendritic Growth Layer Polarimetric Signatures in a Winter Storm

Abstract This study evaluates ice particle size distribution and aspect ratio φ Multi-Radar Multi-Sensor (MRMS) dual-polarization radar retrievals through a direct comparison with two legs of observational aircraft data obtained during a winter storm case from the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign. In situ cloud probes, satellite, and MRMS observations illustrate that the often-observed Kdp and ZDR enhancement regions in the dendritic growth layer can either indicate a local number concentration increase of dry ice particles or the presence of ice particles mixed with a significant number of supercooled liquid droplets. Relative to in situ measurements, MRMS retrievals on average underestimated mean volume diameters by 50% and overestimated number concentrations by over 100%. IWC retrievals using ZDR and Kdp within the dendritic growth layer were minimally biased relative to in situ calculations where retrievals yielded −2% median relative error for the entire aircraft leg. Incorporating φ retrievals decreased both the magnitude and spread of polarimetric retrievals below the dendritic growth layer. While φ radar retrievals suggest that observed dendritic growth layer particles were nonspherical (0.1 ≤ φ ≤ 0.2), in situ projected aspect ratios, idealized numerical simulations, and habit classifications from cloud probe images suggest that the population mean φ was generally much higher. Coordinated aircraft radar reflectivity with in situ observations suggests that the MRMS systematically underestimated reflectivity and could not resolve local peaks in mean volume diameter sizes. These results highlight the need to consider particle assumptions and radar limitations when performing retrievals. significance statement Developing snow is often detectable using weather radars. Meteorologists combine these radar measurements with mathematical equations to study how snow forms in order to determine how much snow will fall. This study evaluates current methods for estimating the total number and mass, sizes, and shapes of snowflakes from radar using images of individual snowflakes taken during two aircraft legs. Radar estimates of snowflake properties were most consistent with aircraft data inside regions with prominent radar signatures. However, radar estimates of snowflake shapes were not consistent with observed shapes estimated from the snowflake images. Although additional research is needed, these results bolster understanding of snow-growth physics and uncertainties between radar measurements and snow production that can improve future snowfall forecasting.

Recent advances based on Mg anodes and their interfacial modulation in Mg batteries

Magnesium (Mg) batteries (MBs), as post-lithium-ion batteries, have received great attention in recent years due to their advantages of high energy density, low cost, and safety insurance. However, the formation of passivation layers on the surface of Mg metal anode and the poor compatibility between Mg metal and conventional electrolytes during charge-discharge cycles seriously affect the performance of MBs. The great possibility of generating Mg dendrites has also caused controversy among researchers. Moreover, the regulation of Mg deposition and the enhancement of battery cycle stability is largely limited by interfacial stability between Mg metal anode and electrolyte. In this review, recent advances in interfacial science and engineering of MBs are summarized and discussed. Special attention is given to interfacial chemistry including passivation layer formation, incompatibilities, ion transport, and dendrite growth. Strategies for building stable electrode/interfaces, such as anode designing and electrolyte modification, construction of artificial solid electrolyte interphase (SEI) layers, and development of solid-state electrolytes to improve interfacial contacts and inhibit Mg dendrite and passivation layer formation, are reviewed. Innovative approaches, representative examples, and challenges in developing high-performance anodes are described in detail. Based on the review of these strategies, reference is provided for future research to improve the performance of MBs, especially in terms of interface and anode design.

Ice microphysical processes in the dendritic growth layer: a statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations

Abstract. The dendritic growth layer (DGL), defined as the temperature region between −20 and −10 ∘C, plays an important role for ice depositional growth, aggregation and potentially secondary ice processes. The DGL has been found in the past to exhibit specific observational signatures in polarimetric and vertically pointing radar observations. However, consistent conclusions about their physical interpretation have often not been reached. In this study, we exploit a unique 3-months dataset of mid-latitude winter clouds observed with vertically pointing triple-frequency (X-, Ka-, W-band) and polarimetric W-band Doppler radars. In addition to standard radar moments, we also analyse the multi-wavelength and polarimetric Doppler spectra. New variables, such as the maximum of the spectral differential reflectivity (ZDR) (sZDRmax), allows us to analyse the ZDR signal of asymmetric ice particles independent of the presence of low ZDR producing aggregates. This unique dataset enables us to investigate correlations between enhanced aggregation and evolution of small ice particles in the DGL. For this, the multi-frequency observations are used to classify all profiles according to their maximum average aggregate size within the DGL. The strong correlation between aggregate class and specific differential phase shift (KDP) confirms the expected link between ice particle concentration and aggregation. Interestingly, no correlation between aggregation class and sZDRmax is visible. This indicates that aggregation is rather independent of the aspect ratio and density of ice crystals. A distinct reduction of mean Doppler velocity in the DGL is found to be strongest for cases with largest aggregate sizes. Analyses of spectral edge velocities suggest that the reduction is the combined result of the formation of new ice particles with low fall velocity and a weak updraft. It appears most likely that this updraft is the result of latent heat released by enhanced depositional growth. Clearly, the strongest correlations of aggregate class with other variables are found inside the DGL. Surprisingly, no correlation between aggregate class and concentration or aspect ratio of particles falling from above into the DGL could be found. Only a weak correlation between the mean particle size falling into the DGL and maximum aggregate size within the DGL is apparent. In addition to the correlation analysis, the dataset also allows study of the evolution of radar variables as a function of temperature. We find the ice particle concentration continuously increasing from −18 ∘C towards the bottom of the DGL. Aggregation increases more rapidly from −15 ∘C towards warmer temperatures. Surprisingly, KDP and sZDRmax are not reduced by the intensifying aggregation below −15 ∘C but rather reach their maximum values in the lower half of the DGL. Also below the DGL, KDP and sZDRmax remain enhanced until −4 ∘C. Only there, additional aggregation appears to deplete ice crystals and therefore reduce KDP and sZDRmax. The simultaneous increase of aggregation and particle concentration inside the DGL necessitates a source mechanism for new ice crystals. As primary ice nucleation is expected to decrease towards warmer temperatures, secondary ice processes are a likely explanation for the increase in ice particle concentration. Previous laboratory experiments strongly point towards ice collisional fragmentation as a possible mechanism for new particle generation. The presence of an updraft in the temperature region of maximum depositional growth might also suggest an important positive feedback mechanism between ice microphysics and dynamics which might further enhance ice particle growth in the DGL.

Different modes of synaptic and extrasynaptic NMDA receptor alteration in the hippocampus of P301S tau transgenic mice.

N-methyl-d-aspartate receptors (NMDARs) are pivotal players in the synaptic transmission and synaptic plasticity underlying learning and memory. Accordingly, dysfunction of NMDARs has been implicated in the pathophysiology of Alzheimer disease (AD). Here, we used histoblot and sodium dodecylsulphate-digested freeze-fracture replica labelling (SDS-FRL) techniques to investigate the expression and subcellular localisation of GluN1, the obligatory subunit of NMDARs, in the hippocampus of P301S mice. Histoblots showed that GluN1 expression was significantly reduced in the hippocampus of P301S mice in a laminar-specific manner at 10 months of age but was unaltered at 3 months. Using the SDS-FRL technique, excitatory synapses and extrasynaptic sites on spines of pyramidal cells and interneuron dendrites were analysed throughout all dendritic layers in the CA1 field. Our ultrastructural approach revealed a high density of GluN1 in synaptic sites and a substantially lower density at extrasynaptic sites. Labelling density for GluN1 in excitatory synapses established on spines was significantly reduced in P301S mice, compared with age-matched wild-type mice, in the stratum oriens (so), stratum radiatum (sr) and stratum lacunosum-moleculare (slm). Density for synaptic GluN1 on interneuron dendrites was significantly reduced in P301S mice in the so and sr but unaltered in the slm. Labelling density for GluN1 at extrasynaptic sites showed no significant differences in pyramidal cells, and only increased density in the interneuron dendrites of the sr. This differential alteration of synaptic versus extrasynaptic NMDARs supports the notion that the progressive accumulation of phospho-tau is associated with changes in NMDARs, in the absence of amyloid-β pathology, and may be involved in the mechanisms causing abnormal network activity of the hippocampal circuit.