Incoming Connections Research Articles

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

SummaryCOVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.

Edge of Chaos and Avalanches in Neural Networks with Heavy-Tailed Synaptic Weight Distribution.

We propose an analytically tractable neural connectivity model with power-law distributed synaptic strengths. When threshold neurons with biologically plausible number of incoming connections are considered, our model features a continuous transition to chaos and can reproduce biologically relevant low activity levels and scale-free avalanches, i.e., bursts of activity with power-law distributions of sizes and lifetimes. In contrast, the Gaussian counterpart exhibits a discontinuous transition to chaos and thus cannot be poised near the edge of chaos. We validate our predictions in simulations of networks of binary as well as leaky integrate-and-fire neurons. Our results suggest that heavy-tailed synaptic distribution may form a weakly informative sparse-connectivity prior that can be useful in biological and artificial adaptive systems.

Implementation of Quick UDP Internet Connections

With the advent of the Internet growth worldwide, we need to have a protocol which is faster and provides a better support for the following problems:
 
 Faster Connection Establishment Time
 Good Congestion Control
 Connection Migration
 Good Error Correction
 
 One of the key aspects taken under consideration was current scenario of connection establishment time whenever a website is requested and poor video buffering over existing Internet Connections.
 The prime objective is to create a proxy server which routes the incoming connection requests to QUIC supported libraries if the client supports QUIC. If the client does not support QUIC then it routes the incoming request to existing web server which can then handle the request using TCP. After creation of the proxy server a website has to be created using which we can test various aspects of the QUIC protocol.
 Keyword: Quick, Protocol, UDP, Network

An Automated Testing Method for PXI Chassis Software Driver

PCI extensions for instrumentation (PXI) chassis is a multi-vendor interoperable device. It can interconnect with many chassis, module and computers. To make sure a device driver is able to function in a specific configuration, it must go through a series of tests. The complexity of PXI software testing has increased when it needs to cover multiple configurations for a single driver. Majority of the automated test systems will execute tests without having the mechanism to verify the test environment. To improve the PXI chassis driver test duration, the current trend of automated software testing only executes a single test configuration. Thus, an automated test software with the capability to execute multi-test configuration is required. In order to develop this tool, a server-client concept is adopted. The advantage of server-client is to centralize the testing when multiple tests are performed. The software tool client will start once it is booted into the operating system; the test system will connect to server and wait for further action from server. When server detects incoming client connection, it will automatically verify and fix the testing environment; if the client fulfills the test suite requirement it will start to execute the test system. All client test summary results will be updated to the server. The results show an average of 17.1% test duration reduction on the planned test configuration when the automated software tool is applied on testing. Besides, the results indicate higher controller hardware performance can reduce the test duration.

Firing rate distributions in spiking networks with heterogeneous connectivity.

Mean-field theory for networks of spiking neurons based on the so-called diffusion approximation has been used to calculate certain measures of neuronal activity which can be compared with experimental data. This includes the distribution of firing rates across the network. However, the theory in its current form applies only to networks in which there is relatively little heterogeneity in the number of incoming and outgoing connections per neuron. Here we extend this theory to include networks with arbitrary degree distributions. Furthermore, the theory takes into account correlations in the in-degree and out-degree of neurons, which would arise, e.g., in the case of networks with hublike neurons. Finally, we show that networks with broad and positively correlated degrees can generate a large-amplitude sustained response to transient stimuli which does not occur in more homogeneous networks.

Intrinsic and Synaptic Dynamics Contribute to Adaptation in the Core of the Avian Central Nucleus of the Inferior Colliculus

The reduction of neuronal responses to repeated stimulus presentation occurs in many sensory neurons, also in the inferior colliculus of birds. The cellular mechanisms that cause response adaptation are not well described. Adaptation must be explicable by changes in the activity of input neurons, short-term synaptic plasticity of the incoming connections, excitability changes of the neuron under consideration or influences of inhibitory or modulatory network connections. Using whole-cell recordings in acute brain slices of the embryonic chicken brain we wanted to understand the intrinsic and synaptic contributions to adaptation in the core of the central nucleus of the inferior colliculus (ICCc). We described two neuron types in the chicken ICCc based on their action potential firing patterns: Phasic/onset neurons showed strong intrinsic adaptation but recovered more rapidly. Tonic/sustained firing neurons had weaker adaptation but often had additional slow components of recovery from adaptation. Morphological analysis suggested two neuron classes, but no physiological parameter aligned with this classification. Chicken ICCc neurons received mostly mixed AMPA- and NMDA-type glutamatergic synaptic inputs. In the majority of ICCc neurons the input synapses underwent short-term depression. With a simulation of the putative population output activity of the chicken ICCc we showed that the different adaptation profiles of the neuron classes could shift the emphasize of stimulus encoding from transients at long intervals to ongoing parts at short intervals. Thus, we report here that description of biophysical and synaptic properties can help to explain adaptive phenomena in central auditory neurons.

Synaptic Failure Differentially Affects Pattern Formation in Heterogenous Networks.

The communication of neurons is primarily maintained by synapses, which play a crucial role in the functioning of the nervous system. Therefore, synaptic failure may critically impair information processing in the brain and may underlie many neurodegenerative diseases. A number of studies have suggested that synaptic failure may preferentially target neurons with high connectivity (i.e., network hubs). As a result, the activity of these highly connected neurons can be significantly affected. It has been speculated that anesthetics regulate conscious state by affecting synaptic transmission at these network hubs and subsequently reducing overall coherence in the network activity. In addition, hubs in cortical networks are shown to be more vulnerable to amyloid deposition because of their higher activity within the network, causing decrease in coherence patterns and eventually Alzheimer’s disease (AD). Here, we investigate how synaptic failure can affect spatio-temporal dynamics of scale free networks, having a power law scaling of number of connections per neuron – a relatively few neurons (hubs) with a lot of emanating or incoming connections and many cells with low connectivity. We studied two types of synaptic failure: activity-independent and targeted, activity-dependent synaptic failure. We defined scale-free network structures based on the dominating direction of the connections at the hub neurons: incoming and outgoing. We found that the two structures have significantly different dynamical properties. We show that synaptic failure may not only lead to the loss of coherence but unintuitively also can facilitate its emergence. We show that this is because activity-dependent synaptic failure homogenizes the activity levels in the network creating a dynamical substrate for the observed coherence increase. Obtained results may lead to better understanding of changes in large-scale pattern formation during progression of neuro-degenerative diseases targeting synaptic transmission.

IF-RSCA: Intelligent Fragmentation-Aware method for Routing, Spectrum and Core Assignment in Space Division Multiplexing Elastic Optical Networks (SDM-EON)

Abstract An Intelligent Fragmentation Aware method is introduced that chooses the best route, spectrum, and core for every incoming connection request in Space Division Multiplexing Elastic Optical Networks (SDM-EON) based on the Multiple Criteria Decision Making (MCDM) methods. The main idea of our presented method, called Intelligent Fragmentation Aware, Routing, Spectrum and Core Allocation (IF-RSCA), is the classification of cores based on holding times of connections and serve them in suitable cores based on their holding times. Besides, we use the remaining times of existing connections to choose the best spectrum for each connection request. In addition, we formulate our core classifications and IF-RSCA method with Integer Linear Programming. The simulation results show that our presented method decreases the blocking probability with an acceptable rate.

Statistical properties of the telephone network

The directed network of telephone subscribers is considered in the article. It can be described as a dynamic network with vertices that correspond to the subscribers of the telephone network and emerging directional edges that correspond to the connections between the respective subscribers. The position of the edge and its direction is determined by the incoming and outgoing calls from the corresponding vertices. The subject of the article is the statistical properties of the connections of a certain subset of telephone network subscribers. Such connections are dynamic in nature due to their appearance and disappearance. The number of outgoing (or incoming) connections occurred during a day at a selected vertex is used as the main characteristic. The distribution density of the number of outgoing (or incoming) connections (or calls) of such a network has been analyzed using the experimental data. It has been shown that such a distribution density over the number of calls obeys the lognormal distribution density, which depends on the two parameters. The values of two parameters, namely the mean value and the variance, determining the lognormal distribution density are established. The reasons for the appearance of a lognormal distribution density over the number of incoming (or outgoing) connections have been discussed. The statistical properties of other groups of subscribers have been considered as well. In particular, the group that makes a large number of outgoing calls to various subscribers of the telephone network has been selected for a separate study. The members of this group, who create and distribute spam can be called spammers. It has been shown that these groups, spammers for example, also obeys the lognormal distribution density over the number of calls but they are characterized by the different mean value and variance.

Model-based Inference of a Directed Network of Circadian Neurons.

The suprachiasmatic nucleus (SCN) is the master clock of the brain. It is a network of neurons that behave like biological oscillators, capable of synchronizing and maintaining daily rhythms. The detailed structure of this network is still unknown, and the role that the connectivity pattern plays in the network's ability to generate robust oscillations has yet to be fully elucidated. In recent work, we used an information theory-based technique to infer the structure of the functional network for synchronization, from bioluminescence reporter data. Here, we propose a computational method to determine the directionality of the connections between the neurons. We find that most SCN neurons have a similar number of incoming connections, but the number of outgoing connections per neuron varies widely, with the most highly connected neurons residing preferentially in the core.

Specificity and robustness of long-distance connections in weighted, interareal connectomes

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of long-distance connections is unknown, the leading hypothesis is that they act to reduce the topological distance between brain areas and increase the efficiency of interareal communication. However, this hypothesis implies a nonspecificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five weighted interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between areal inputs and outputs. Next, we show that-in isolation-areas' long-distance connectivity profiles exhibit nonrandom levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.

Detecting the Use of Anonymous Proxies

The Internet is built atop the Internet Protocol (IP) which has at its heart a unique identifier known as an IP address. Knowing the location of an IP address can be very useful in many situations such as for banks to know if a connection is in progress from online fraud hotspots. IP addresses can be spoofed allowing hackers to bypass geographical IP restrictions and thus render some category of fraud prevention useless. Anonymous proxies (AP) which act as intermediate relays which disguise the source IP addresses can play a large role in cybercrime. There is a need to ascertain whether an incoming IP connection is an original source matched IP address, or one being routed through an anonymising proxy. This article concentrates on various methods used by anonymising proxies, the characteristics of the anonymous proxies and the potential mechanisms available to detect if a proxy is in use.

6LB: Scalable and Application-Aware Load Balancing with Segment Routing

Network load-balancers generally either do not take the application state into account, or do so at the cost of a centralized monitoring system. This paper introduces a load-balancer running exclusively within the IP forwarding plane, i.e., in an application protocol agnostic fashion – yet which still provides application-awareness and makes real-time, decentralized decisions. To that end, IPv6 Segment Routing is used to direct data packets from a new flow through a chain of candidate servers, until one decides to accept the connection, based solely on its local state. This way, applications themselves naturally decide on how to fairly share incoming connections, while incurring minimal network overhead, and no out-of-band signaling. A consistent hashing algorithm, as well as an in-band stickiness protocol, allow for the proposed solution to be able to be reliably distributed across a large number of instances. Performance evaluation by means of an analytical model and actual tests on different workloads (including a Wikipedia replay as a realistic workload) show significant performance benefits in terms of shorter response times, when compared with the traditional random load-balancer. In addition, this paper introduces and compares kernel bypass high-performance implementations of both 6LB and the state-of-the-art load-balancer, showing that the significant system-level benefits of 6LB are achievable with a negligible data-path CPU overhead.

Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers.

State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.

Drivers of Live Cattle Price in the Livestock Trading System of Central Cameroon.

Livestock production and trade are critical for the food security and welfare of rural households in sub-Saharan Africa. In Cameroon, animal trade consists mainly of live cattle commercialized through livestock markets. Identifying the factors contributing to cattle price formation is critical for designing effective policies for sustainable production and for increasing food availability. In this study, we evaluated the influence of a range of individual- and market-level factors on the price of cattle that were sold in all transactions (n = 118,017) recorded over a 12-month period from 31 livestock markets in the main cattle production area of the country. An information-theoretic approach using a generalized additive mixed-effect model was implemented to select the best explanatory model as well as evaluate the robustness of the identified drivers and the predictive ability of the model. The age and gender of the cattle traded were consistently found to be important drivers of the price (p < 0.01). Also, strong, but complex, relationships were found between cattle prices and both local human and bovine population densities. Finally, the model highlighted a positive association between the number of incoming trading connections of a livestock market and the price of the traded live cattle (p < 0.01). Although our analysis did not account for factors informing on specific phenotypic traits nor breed characteristics of cattle traded, nearly 50% of the observed variation in live cattle prices was explained by the final model. Ultimately, our model gives a large scale overview of drivers of cattle price formation in Cameroon and to our knowledge is the first study of this scale in Central Africa. Our findings represent an important milestone in designing efficient and sustainable animal health management programme in Cameroon and ensure livelihood sustainability for rural households.

Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication.

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs-referred to as the rich club-has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

The Hippocampus and Amygdala Are Integrators of Neocortical Influence: A CorticoCortical Evoked Potential Study

Brain stimulation is increasingly viewed as an effective approach to treat neuropsychiatric disease. The brain's organization in distributed networks suggests that the activity of a remote brain structure could be modulated by stimulating cortical areas that strongly connect to the target. Most connections between cerebral areas are asymmetric, and a better understanding of the relative direction of information flow along connections could improve the targeting of stimulation to influence deep brain structures. The hippocampus and amygdala, two deep-situated structures that are crucial to memory and emotions, respectively, have been implicated in multiple neurological and psychiatric disorders. We explored the directed connectivity between the hippocampus and amygdala and the cerebral cortex in patients implanted with intracranial electrodes using corticocortical evoked potentials (CCEPs) evoked by single-pulse electrical stimulation. The hippocampus and amygdala were connected with most of the cortical mantle, either directly or indirectly, with the inferior temporal cortex being most directly connected. Because CCEPs assess the directionality of connections, we could determine that incoming connections from cortex to hippocampus were more direct than outgoing connections from hippocampus to cortex. We found a similar, although smaller, tendency for connections between the amygdala and cortex. Our results support the roles of the hippocampus and amygdala to be integrators of widespread cortical influence. These results can inform the targeting of noninvasive neurostimulation to influence hippocampus and amygdala function.

Transcriptional signatures of connectomic subregions of the human striatum.

Functionally distinct regions of the brain are thought to possess a characteristic connectional fingerprint-a profile of incoming and outgoing connections that defines the function of that area. This observation has motivated efforts to subdivide brain areas using their connectivity patterns. However, it remains unclear whether these connectomically-defined subregions can be distinguished at the molecular level. Here, we combine high-resolution diffusion-weighted magnetic resonance imaging with transcriptomic data to show that connectomically-defined subregions of the striatum carry distinct transcriptional signatures. Using data-driven clustering of diffusion tractography, seeded from the striatum in 100 healthy individuals, we identify a tripartite organization of the caudate and putamen that comprises ventral, dorsal and caudal subregions. We then use microarray data of gene expression levels in 19 343 genes, taken from 98 tissue samples distributed throughout the striatum, to accurately discriminate the three connectomically-defined subregions with 80-90% classification accuracy using linear support vector machines. This classification accuracy was robust at the group and individual level and was superior for our parcellation of the striatum when compared with parcellations based on anatomical boundaries or other criteria. Genes contributing strongly to classification were enriched for gene ontology categories including dopamine signaling, glutamate secretion, response to amphetamine and metabolic pathways, and were implicated in risk for disorders such as schizophrenia, autism and Parkinson's disease. Our findings highlight a close link between regional variations in transcriptional activity and inter-regional connectivity in the brain, and suggest that there may be a strong genomic signature of connectomically-defined subregions of the brain.

On-chip magnetic cooling of a nanoelectronic device

We demonstrate significant cooling of electrons in a nanostructure below 10 mK by demagnetisation of thin-film copper on a silicon chip. Our approach overcomes the typical bottleneck of weak electron-phonon scattering by coupling the electrons directly to a bath of refrigerated nuclei, rather than cooling via phonons in the host lattice. Consequently, weak electron-phonon scattering becomes an advant- age. It allows the electrons to be cooled for an experimentally useful period of time to temperatures colder than the dilution refrigerator platform, the incoming electrical connections, and the host lattice. There are efforts worldwide to reach sub-millikelvin electron temperatures in nanostructures to study coherent electronic phenomena and improve the operation of nanoelectronic devices. On-chip magnetic cooling is a promising approach to meet this challenge. The method can be used to reach low, local electron temperatures in other nanostructures, obviating the need to adapt traditional, large demagnetisation stages. We demonstrate the technique by applying it to a nanoelectronic primary thermometer that measures its internal electron temperature. Using an optimised demagnetisation process, we demonstrate cooling of the on-chip electrons from 9 mK to below 5 mK for over 1000 seconds.

Efficient Spectrum Defragmentation With Holding-Time Awareness in Elastic Optical Networks

Elastic optical networks are prone to spectrum fragmentation, resulting in poor resource utilization and often higher blocking probability. To overcome the spectrum fragmentation, a defragmentation (DF) of the spectrum can be applied by reconfiguring some or all active connections. However, reconfiguration is generally not desirable, as it can interrupt the services of the existing connections. In this paper, we propose two novel connection reconfiguration schemes to efficiently address spectrum DF: (i) a reactive–disruptive scheme and (ii) a proactive– non-disruptive scheme. Both schemes utilize the information of holding times of the existing connections in order to reduce fragmentation and thus improve resource utilization and minimize connection blocking. The reactive– disruptive reconfiguration scheme tries to allocate an incoming connection, which could not be accepted otherwise, by reconfiguring some existing connections. The proactive–non-disruptive reconfiguration method, on the other hand, allows us to reconfigure only such connections, which can be shifted without crossing over the spectrum of other connections. This paves the way for a non-disruptive DF when setting up a new connection, which is a desirable feature. Simulation results show that the reconfiguration schemes with holding-time awareness can be effectively utilized to reduce the spectrum fragmentation and, hence, result in better resource utilization and overall lower connection blocking probability.