Central Complex Research Articles

Availability analysis of redundant and replicated cloud services with Bayesian networks

AbstractDue to the growing complexity of modern data centers, failures are not uncommon any more. Therefore, fault tolerance mechanisms play a vital role in fulfilling the availability requirements. Multiple availability models have been proposed to assess compute systems, among which Bayesian network models have gained popularity in industry and research due to its powerful modeling formalism. In particular, this work focuses on assessing the availability of redundant and replicated cloud computing services with Bayesian networks. So far, research on availability has only focused on modeling either infrastructure or communication failures in Bayesian networks, but have not considered both simultaneously. This work addresses practical modeling challenges of assessing the availability of large‐scale redundant and replicated services with Bayesian networks, including cascading and common‐cause failures from the surrounding infrastructure and communication network. In order to ease the modeling task, this paper introduces a high‐level modeling formalism to build such a Bayesian network automatically. Performance evaluations demonstrate the feasibility of the presented Bayesian network approach to assess the availability of large‐scale redundant and replicated services. This model is not only applicable in the domain of cloud computing it can also be applied for general cases of local and geo‐distributed systems.

A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images.

Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images. Over the years, automatic strategies were proposed for such purpose, with deep learning methods achieving the current state-of-the-art results. However, these strategies typically ignore the segmentation of the internal structures of the kidney. Moreover, they were evaluated in different private datasets, hampering the direct comparison of results, and making it difficult to determination the optimal strategy for this task. In this study, we perform a comparative analysis of 7 deep learning networks for the segmentation of the kidney and internal structures (Capsule, Central Echogenic Complex (CEC), Cortex and Medulla) in 2D US images in an open access multi-class kidney US dataset. The dataset includes 514 images, acquired in multiple clinical centers using different US machines and protocols. The dataset contains the annotation of two experts, but 321 images with complete segmentation of all 4 classes were used. Overall, the results demonstrate that the DeepLabV3+ network outperformed the inter-rater variability with a Dice score of 78.0% compared to 75.6% for inter-rater variability. Specifically, DeepLabV3Plus achieved mean Dice scores of 94.2% for the Capsule, 85.8% for the CEC, 62.4% for the Cortex, and 69.6% for the Medulla. These findings suggest the potential of deep learning-based methods in improving the accuracy of kidney segmentation in US images.Clinical Relevance- This study shows the potential of DL for improving accuracy of kidney segmentation in US, leading to increased diagnostic efficiency, and enabling new applications such as computer-aided diagnosis and treatment, ultimately resulting in improved patient outcomes and reduced healthcare costs.1.

Genomic basis for the unique phenotype of the alkaliphilic purple nonsulfur bacterium Rhodobaca bogoriensis.

Although several species of purple sulfur bacteria inhabit soda lakes, Rhodobaca bogoriensis is the first purple nonsulfur bacterium cultured from such highly alkaline environments. Rhodobaca bogoriensis strain LBB1T was isolated from Lake Bogoria, a soda lake in the African Rift Valley. The phenotype of Rhodobaca bogoriensis is unique among purple bacteria; the organism is alkaliphilic but not halophilic, produces carotenoids absent from other purple nonsulfur bacteria, and is unable to grow autotrophically or fix molecular nitrogen. Here we analyze the draft genome sequence of Rhodobaca bogoriensis to gain further insight into the biology of this extremophilic purple bacterium. The strain LBB1T genome consists of 3.91 Mbp with no plasmids. The genome sequence supports the defining characteristics of strain LBB1T, including its (1) production of a light-harvesting 1-reaction center (LH1-RC) complex but lack of a peripheral (LH2) complex, (2) ability to synthesize unusual carotenoids, (3) capacity for both phototrophic (anoxic/light) and chemotrophic (oxic/dark) energy metabolisms, (4) utilization of a wide variety of organic compounds (including acetate in the absence of a glyoxylate cycle), (5) ability to oxidize both sulfide and thiosulfate despite lacking the capacity for autotrophic growth, and (6) absence of a functional nitrogen-fixation system for diazotrophic growth. The assortment of properties in Rhodobaca bogoriensis has no precedent among phototrophic purple bacteria, and the results are discussed in relation to the organism's soda lake habitat and evolutionary history.

Comparative morphology of serotonin-immunoreactive neurons innervating the central complex in the brain of dicondylian insects.

Serotonin (5-hydroxytryptamine) acts as a widespread neuromodulator in the nervous system of vertebrates and invertebrates. In insects, it promotes feeding, enhances olfactory sensitivity, modulates aggressive behavior, and, in the central complex of Drosophila, serves a role in sleep homeostasis. In addition to a role in sleep-wake regulation, the central complex has a prominent role in spatial orientation, goal-directed locomotion, and navigation vector memory. To further understand the role of serotonergic signaling in this brain area, we analyzed the distribution and identity of serotonin-immunoreactive neurons across a wide range of insect species. While one bilateral pair of tangential neurons innervating the central body was present in all species studied, a second type was labeled in all neopterans but not in dragonflies and firebrats. Both cell types show conserved major fiber trajectories but taxon-specific differences in dendritic targets outside the central body and axonal terminals in the central body, noduli, and lateral accessory lobes. In addition, numerous tangential neurons of the protocerebral bridge were labeled in all studied polyneopteran species except for Phasmatodea, but not in Holometabola. Lepidoptera and Diptera showed additional labeling of two bilateral pairs of neurons of a third type. The presence of serotonin in systems of columnar neurons apparently evolved independently in dragonflies and desert locusts. The data suggest distinct evolutionary changes in the composition of serotonin-immunolabeled neurons of the central complex and provides a promising basis for a phylogenetic study in a wider range of arthropod species.

Organization of descending neurons in the brain of the desert locust.

In most animals, multiple external and internal signals are integrated by the brain, transformed and, finally, transmitted as commands to motor centers. In insects, the central complex is a motor control center in the brain, involved in decision-making and goal-directed navigation. In desert locusts, it encodes celestial cues in a compass-like fashion indicating a role in sky-compass navigation. While several descending brain neurons (DBNs) including two neurons transmitting sky compass signals have been identified in the locust, a complete analysis of DBNs and their relationship to the central complex is still lacking. As a basis for further studies, we used Neurobiotin tracer injections into a neck connective to map the organization of DBNs in the brain. Cell counts revealed a maximum of 324 bilateral pairs of DBNs with somata distributed in 14 ipsilateral and nine contralateral groups. These neurons invaded most brain neuropils, especially the posterior slope, posterior and ventro-lateral protocerebrum, the antennal mechanosensory and motor center, but less densely the lateral accessory lobes that are targeted by central-complex outputs. No arborizations were found in the central complex and only few processes in the mushroom body, antennal lobe, lobula, medulla, and superior protocerebrum. Double label experiments provide evidence for the presence of GABA, dopamine, tyramine, but not serotonin, in small sets of DBNs. The data show that some DBNs may be targeted directly by central-complex outputs, but many others are likely only indirectly influenced by central-complex networks, in addition to input from multiple other brain areas.

Exploring the relationship between amygdala subnuclei volumes and cognitive performance in left-lateralized temporal lobe epilepsy with and without hippocampal sclerosis

Cognitive disruption is a debilitating comorbidity in Temporal Lobe Epilepsy (TLE). Despite recent advances, the amygdala is often neglected in studies that explore cognition in TLE. Amygdala subnuclei are differently engaged in TLE with hippocampal sclerosis (TLE-HS) compared to non-lesional TLE (TLE-MRIneg), with predominant atrophy in the first and increased volume in the latter. Herein, we aim to explore the relationship between the volumes of the amygdala and its substructures with respect to cognitive performances in a population of left-lateralized TLE with and without HS. Twenty-nine TLEs were recruited (14 TLE-HS; 15 TLE-MRIneg). After investigating the differences in the subcortical amygdalae and hippocampal volumes compared to a matched healthy control population, we explored the associations between the subnuclei of the amygdala and the hippocampal subfields with the cognitive scores in TLE patients, according to their etiology. In TLE-HS, a reduced volume of the basolateral and cortical amygdala complexes joined with whole hippocampal atrophy, was related to poorer scores in verbal memory tasks, while in TLE-MRIneg, poorer performances in attention and processing speed tasks were associated with a generalized amygdala enlargement, particularly of the basolateral and central complexes. The present findings extend our knowledge of amygdala involvement in cognition and suggest structural amygdala abnormalities as useful disease biomarkers in TLE.

Poster 237: Menisco-Tibial Ligament Complex Anatomy and Biomechanics – Implications for Meniscus Repairs, Ramp Lesions and Transplants

Poster 237: Menisco-Tibial Ligament Complex Anatomy and Biomechanics – Implications for Meniscus Repairs, Ramp Lesions and Transplants

Four-Point Refixation for In-the-Bag Intraocular Lens Dislocation Into Vitreous Cavity With Implantable Capsular Hooks.

Four-Point Refixation for In-the-Bag Intraocular Lens Dislocation Into Vitreous Cavity With Implantable Capsular Hooks.

A model of cue integration as vector summation in the insect brain.

Ball-rolling dung beetles are known to integrate multiple cues in order to facilitate their straight-line orientation behaviour. Recent work has suggested that orientation cues are integrated according to a vector sum, that is, compass cues are represented by vectors and summed to give a combined orientation estimate. Further, cue weight (vector magnitude) appears to be set according to cue reliability. This is consistent with the popular Bayesian view of cue integration: cues are integrated to reduce or minimize an agent's uncertainty about the external world. Integration of orientation cues is believed to occur at the input to the insect central complex. Here, we demonstrate that a model of the head direction circuit of the central complex, including plasticity in input synapses, can act as a substrate for cue integration as vector summation. Further, we show that cue influence is not necessarily driven by cue reliability. Finally, we present a dung beetle behavioural experiment which, in combination with simulation, strongly suggests that these beetles do not weight cues according to reliability. We suggest an alternative strategy whereby cues are weighted according to relative contrast, which can also explain previous results.

Ring neurons in the Drosophila central complex act as a rheostat for sensory modulation of aging.

Sensory perception modulates aging, yet we know little about how. An understanding of the neuronal mechanisms through which animals orchestrate biological responses to relevant sensory inputs would provide insight into the control systems that may be important for modulating lifespan. Here, we provide new awareness into how the perception of dead conspecifics, or death perception, which elicits behavioral and physiological effects in many different species, affects lifespan in the fruit fly, Drosophila melanogaster. Previous work demonstrated that cohousing Drosophila with dead conspecifics decreases fat stores, reduces starvation resistance, and accelerates aging in a manner that requires both sight and the serotonin receptor 5-HT2A. In this manuscript, we demonstrate that a discrete, 5-HT2A-expressing neural population in the ellipsoid body (EB) of the Drosophila central complex, identified as R2/R4 neurons, acts as a rheostat and plays an important role in transducing sensory information about the presence of dead individuals to modulate lifespan. Expression of the insulin-responsive transcription factor foxo in R2/R4 neurons and insulin-like peptides dilp3 and dilp5, but not dilp2, are required, with the latter likely altered in median neurosecretory cells (MNCs) after R2/R4 neuronal activation. These data generate new insights into the neural underpinnings of how perceptive events may impact aging and physiology across taxa.

A Visual Pathway into Central Complex for High-Frequency Motion-Defined Bars in Drosophila.

Relative motion breaks a camouflaged target from a same-textured background, thus eliciting discrimination of a motion-defined object. Ring (R) neurons are critical components in the Drosophila central complex, which has been implicated in multiple visually guided behaviors. Using two-photon calcium imaging with female flies, we demonstrated that a specific population of R neurons that innervate the superior domain of bulb neuropil, termed superior R neurons, encoded a motion-defined bar with high spatial frequency contents. Upstream superior tuberculo-bulbar (TuBu) neurons transmitted visual signals by releasing acetylcholine within synapses connected with superior R neurons. Blocking TuBu or R neurons impaired tracking performance of the bar, which reveals their importance in motion-defined feature encoding. Additionally, the presentation of a low spatial frequency luminance-defined bar evoked consistent excitation in R neurons of the superior bulb, whereas either excited or inhibited responses were evoked in the inferior bulb. The distinct properties of the responses to the two bar stimuli indicate there is a functional division between the bulb subdomains. Moreover, physiological and behavioral tests with restricted lines suggest that R4d neurons play a vital role in tracking motion-defined bars. We conclude that the central complex receives the motion-defined features via a visual pathway from superior TuBu to R neurons and might encode different visual features via distinct response patterns at the population level, thereby driving visually guided behaviors.SIGNIFICANCE STATEMENT Animals could discriminate a motion-defined object that is indistinguishable with a same-textured background until it moves, but little is known about the underlying neural mechanisms. In this study, we identified that R neurons and their upstream partners, TuBu neurons, innervating the superior bulb of Drosophila central brain are involved in the discrimination of high-frequency motion-defined bars. Our study provides new evidence that R neurons receive multiple visual inputs from distinct upstream neurons, indicating a population coding mechanism for the fly central brain to discriminate diverse visual features. These results build progress in unraveling neural substrates for visually guided behaviors.

LIM-kinase 1 effects on memory abilities and male courtship song in Drosophila depend on the neuronal type.

The signal pathway of actin remodeling, including LIM-kinase 1 (LIMK1) and its substrate cofilin, regulates multiple processes in neurons of vertebrates and invertebrates. Drosophila melanogaster is widely used as a model object for studying mechanisms of memory formation, storage, retrieval and forgetting. Previously, active forgetting in Drosophila was investigated in the standard Pavlovian olfactory conditioning paradigm. The role of specific dopaminergic neurons (DAN) and components of the actin remodeling pathway in different forms of forgetting was shown. In our research, we investigated the role of LIMK1 in Drosophila memory and forgetting in the conditioned courtship suppression paradigm (CCSP). In the Drosophila brain, LIMK1 and p-cofilin levels appeared to be low in specific neuropil structures, including the mushroom body (MB) lobes and the central complex. At the same time, LIMK1 was observed in cell bodies, such as DAN clusters regulating memory formation in CCSP. We applied GAL4 × UAS binary system to induce limk1 RNA interference in different types of neurons. The hybrid strain with limk1 interference in MB lobes and glia showed an increase in 3-h short-term memory (STM), without significant effects on long-term memory. limk1 interference in cholinergic neurons (CHN) impaired STM, while its interference in DAN and serotoninergic neurons (SRN) also dramatically impaired the flies' learning ability. By contrast, limk1 interference in fruitless neurons (FRN) resulted in increased 15-60 min STM, indicating a possible LIMK1 role in active forgetting. Males with limk1 interference in CHN and FRN also showed the opposite trends of courtship song parameters changes. Thus, LIMK1 effects on the Drosophila male memory and courtship song appeared to depend on the neuronal type or brain structure.

Recurrence quantification analysis during a mental calculation task.

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

The structure and assembly of reaction centre-light-harvesting 1 complexes in photosynthetic bacteria.

Chlorophototrophic organisms have a charge-separating reaction centre (RC) complex that receives energy from a dedicated light-harvesting (LH) antenna. In the purple phototrophic bacteria these two functions are embodied by the 'core' photosynthetic component, the RC-LH1 complex. RC-LH1 complexes sit within a membrane bilayer, with the central RC wholly or partly surrounded by a curved array of LH1 subunits that bind a series of bacteriochlorophyll (BChl) and carotenoid pigments. Decades of research have shown that the absorption of light initiates a cascade of energy, electron, and proton transfers that culminate in the formation of a quinol, which is subsequently oxidized by the cytochrome bc1 complex. However, a full understanding of all these processes, from femtosecond absorption of light to millisecond quinone diffusion, requires a level of molecular detail that was lacking until the remarkable recent upsurge in the availability of RC-LH1 structures. Here, we survey thirteen recently determined RC-LH1 assemblies, and we compare the precise molecular arrangements of pigments and proteins that allow efficient light absorption and the transfer of energy, electrons and protons. We highlight shared structural features, as well as differences that span the bound pigments and cofactors, the structures of individual subunits, the overall architecture of the complexes, and the roles of additional subunits newly identified in just one or a few species. We discuss RC-LH1 structures in the context of prior biochemical and spectroscopic investigations, which together enhance our understanding of the molecular mechanisms of photosynthesis in the purple phototrophic bacteria. A particular emphasis is placed on...

The Kidneys Are Not All Normal: Transplanted Kidneys and Their Speckle Distributions.

Modelling ultrasound speckle to characterise tissue properties has generated considerable interest. As speckle is dependent on the underlying tissue architecture, modelling it may aid in tasks such as segmentation or disease detection. For the transplanted kidney, where ultrasound is used to investigate dysfunction, it is unknown which statistical distribution best characterises such speckle. This applies to the regions of the transplanted kidney: the cortex, the medulla and the central echogenic complex. Furthermore, it is unclear how these distributions vary by patient variables such as age, sex, body mass index, primary disease or donor type. These traits may influence speckle modelling given their influence on kidney anatomy. We investigate these two aims. B-mode images from n=821 kidney transplant recipients (one image per recipient) were automatically segmented into the cortex, medulla and central echogenic complex using a neural network. Seven distinct probability distributions were fitted to each region's histogram, and statistical analysis was performed. The Rayleigh and Nakagami distributions had model parameters that differed significantly between the three regions (p ≤ 0.05). Although both had excellent goodness of fit, the Nakagami had higher Kullbeck-Leibler divergence. Recipient age correlated weakly with scale in the cortex (Ω: ρ=0.11, p=0.004), while body mass index correlated weakly with shape in the medulla (m: ρ=0.08, p=0.04). Neither sex, primary disease nor donor type exhibited any correlation. We propose the Nakagami distribution be used to characterize transplanted kidneys regionally independent of disease etiology and most patient characteristics.

Integration of optic flow into the sky compass network in the brain of the desert locust.

Flexible orientation through any environment requires a sense of current relative heading that is updated based on self-motion. Global external cues originating from the sky or the earth's magnetic field and local cues provide a reference frame for the sense of direction. Locally, optic flow may inform about turning maneuvers, travel speed and covered distance. The central complex in the insect brain is associated with orientation behavior and largely acts as a navigation center. Visual information from global celestial cues and local landmarks are integrated in the central complex to form an internal representation of current heading. However, it is less clear how optic flow is integrated into the central-complex network. We recorded intracellularly from neurons in the locust central complex while presenting lateral grating patterns that simulated translational and rotational motion to identify these sites of integration. Certain types of central-complex neurons were sensitive to optic-flow stimulation independent of the type and direction of simulated motion. Columnar neurons innervating the noduli, paired central-complex substructures, were tuned to the direction of simulated horizontal turns. Modeling the connectivity of these neurons with a system of proposed compass neurons can account for rotation-direction specific shifts in the activity profile in the central complex corresponding to turn direction. Our model is similar but not identical to the mechanisms proposed for angular velocity integration in the navigation compass of the fly Drosophila.

The influence of stimulus history on directional coding in the monarch butterfly brain.

The central complex is a brain region in the insect brain that houses a neural network specialized to encode directional information. Directional coding has traditionally been investigated with compass cues that revolve in full rotations and at constant angular velocities around the insect's head. However, these stimulus conditions do not fully simulate an insect's sensory perception of compass cues during navigation. In nature, an insect flight is characterized by abrupt changes in moving direction as well as constant changes in velocity. The influence of such varying cue dynamics on compass coding remains unclear. We performed long-term tetrode recordings from the brain of monarch butterflies to study how central complex neurons respond to different stimulus velocities and directions. As these butterflies derive directional information from the sun during migration, we measured the neural response to a virtual sun. The virtual sun was either presented as a spot that appeared at random angular positions or was rotated around the butterfly at different angular velocities and directions. By specifically manipulating the stimulus velocity and trajectory, we dissociated the influence of angular velocity and direction on compass coding. While the angular velocity substantially affected the tuning directedness, the stimulus trajectory influenced the shape of the angular tuning curve. Taken together, our results suggest that the central complex flexibly adjusts its directional coding to the current stimulus dynamics ensuring a precise compass even under highly demanding conditions such as during rapid flight maneuvers.

Neural dynamics and architecture of the heading direction circuit in zebrafish

Animals generate neural representations of their heading direction. Notably, in insects, heading direction is topographically represented by the activity of neurons in the central complex. Although head direction cells have been found in vertebrates, the connectivity that endows them with their properties is unknown. Using volumetric lightsheet imaging, we find a topographical representation of heading direction in a neuronal network in the zebrafish anterior hindbrain, where a sinusoidal bump of activity rotates following directional swims of the fish and is otherwise stable over many seconds. Electron microscopy reconstructions show that, although the cell bodies are located in a dorsal region, these neurons arborize in the interpeduncular nucleus, where reciprocal inhibitory connectivity stabilizes the ring attractor network that encodes heading. These neurons resemble those found in the fly central complex, showing that similar circuit architecture principles may underlie the representation of heading direction across the animal kingdom and paving the way to an unprecedented mechanistic understanding of these networks in vertebrates.

3D atlas of cerebral neuropils with previously unknown demarcations in the honey bee brain.

Honey bees (Apis mellifera) express remarkable social interactions and cognitive capabilities that have been studied extensively. In many cases, behavioral studies were accompanied by neurophysiological and neuroanatomical investigations. While most studies have focused on primary sensory neuropils, such as the optic lobes or antennal lobes, and major integration centers, such as the mushroom bodies or the central complex, many regions of the cerebrum (the central brain without the optic lobes) of the honey bee are only poorly explored so far, both anatomically and physiologically. To promote studies of these brain regions, we used anti-synapsin immunolabeling and neuronal tract tracings followed by confocal imaging and 3D reconstructions to demarcate all neuropils in the honey bee cerebrum and close this gap at the anatomical level. We demarcated 35 neuropils and 25 fiber tracts in the honey bee cerebrum, most of which have counterparts in the fly (Drosophila melanogaster) and other insect species that have been investigated so far at this level of detail. We discuss the role of cerebral neuropils in multisensory integration in the insect brain, emphasize the importance of this brain atlas for comparative studies, and highlight specific architectural features of the honey bee cerebrum.

How bumblebees coordinate path integration and body orientation at the start of their first learning flight.

The start of a bumblebee's first learning flight from its nest provides an opportunity to examine the bee's learning behaviour during its initial view of the nest's unfamiliar surroundings. Like many other hymenopterans, bumblebees store views of their nest surroundings while facing their nest. We found that a bumblebee's first fixation of the nest is a coordinated manoeuvre in which the insect faces the nest with its body oriented towards a particular visual feature within its surroundings. This conjunction of nest fixation and body orientation is preceded and reached by means of a translational scan during which the bee flies perpendicularly to its preferred body orientation. The utility of the coordinated manoeuvre is apparent during the bees' first return flight after foraging. Bees then adopt a similar preferred body orientation when close to the nest. How does a bee, unacquainted with its surroundings, know when it is facing its nest? A likely answer is through path integration, which gives bees continuously updated information about the current direction of their nest. Path integration also gives bees the possibility to fixate the nest when their body points in a desired direction. The three components of this coordinated manoeuvre are discussed in relation to current understanding of the central complex in the insect brain, noting that nest fixation is egocentric, whereas the preferred body orientation and flight direction that the bee adopts within the visual surroundings of the nest are geocentric.