Graph metadata dataset from grouped botnet network activities

Over the past two decades, the development of functional imaging methods has greatly promoted our understanding on the changes of neurons following neurodegenerative disorders, such as Parkinson's disease (PD). The application of a spatial covariance analysis on 18F-FDG PET imaging has led to the identification of a distinctive disease-related metabolic pattern. This pattern has proven to be useful in clinical diagnosis, disease progression monitoring as well as assessment of the neuronal changes before and after clinical treatment. It may potentially serve as an objective biomarker on disease progression monitoring, assessment, histological and functional evaluation of related diseases. PD is one of the most common neurodegenerative disorders in the elderly. It is characterized by progressive loss of dopamine neurons in the substantia nigra pars compacta. Throughout the course of disease, the most obvious symptoms are movement-related, such as resting tremor, muscle rigidity, hypokinesia and postural instability (Worth, 2013). Currently, a definite diagnosis of PD is made by clinical evaluation with at least 2 years of follow-up (Hughes et al., 2002; Bhidayasiri and Reichmann, 2013), due to the overlap of motor symptoms between early PD and atypical parkinsonism including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). However, this classic diagnostic criterion does not benefit the early diagnosis of disease. The prognostic outcome and treatment option are substantially different between PD and atypical parkinsonism. Thus it is critical to develop biomarkers for earlier and more accurate diagnosis of PD. Generally, appropriate diagnostic biomarker for PD ought to cover several key characteristics: (i) minimal invasiveness to detect the biomarker in easily accessible body tissue or fluids, (ii) excellent sensitivity to explore the patients with PD, (iii) high specificity to prevent false-positive results in PD-free individuals, and (iv) robustness against potential affecting factors. A PD-related spatial covariance pattern (PDRP) with quantifiable expression on 18F-FDG PET imaging has been gradually detected using a spatial covariance method during the last two decades and it has been demonstrated to be the right diagnostic biomarker for PD (Eidelberg et al., 1994). PDRP has proven not only to be effective in early discrimination of PD from atypical parkinsonian disorders, but also to be able to assess the disease progression and treatment response. Thus it is considered as a multifunctional biomarker. In this review, we aim to provide an overview of the development in pattern-based biomarker for PD.

Neurons express overlapping homeostatic mechanisms to regulate synaptic function and network properties in response to perturbations of neuronal activity. Endocannabinoids (eCBs) are bioactive lipids synthesized in the postsynaptic compartments to regulate synaptic transmission, plasticity, and neuronal excitability primarily through retrograde activation of presynaptic cannabinoid receptor type 1 (CB1). The eCB system is well situated to regulate neuronal network properties and coordinate presynaptic and postsynaptic activity. However, the role of the eCB system in homeostatic adaptations to neuronal hyperactivity is unknown. To address this issue, we used Western blotting and targeted lipidomics to measure adaptations in eCB system to bicuculline (BCC)-induced chronic hyperexcitation in mature cultured rat cortical neurons, and used multielectrode array (MEA) recording and live-cell imaging of glutamate dynamics to test the effects of pharmacological manipulations of eCB on network activities. We show that BCC-induced chronic hyperexcitation triggers homeostatic downscaling and a coordinated adaptation to enhance tonic eCB signaling. Hyperexcitation triggers first the downregulation of fatty acid amide hydrolase (FAAH), the lipase that degrades the eCB anandamide, then an accumulation of anandamide and related metabolites, and finally a delayed upregulation of surface and total CB1. Additionally, we show that BCC-induced downregulation of surface AMPA-type glutamate receptors (AMPARs) and upregulation of CB1 occur through independent mechanisms. Finally, we show that endocannabinoids support baseline network activities before and after downscaling and is engaged to suppress network activity during adaptation to hyperexcitation. We discuss the implications of our findings in the context of downscaling and homeostatic regulation of in vitro oscillatory network activities.

Insulin-like growth factor (IGF)-1 plays an important role in Alzheimer's disease (AD) pathogenesis and increases disease risk. However, prior research examining IGF-1 levels and brain neural network activity is mixed. The present study investigated the relationship between IGF-1 levels and 21 neural networks, as measured by functional magnetic resonance imaging (fMRI) in 13,235 UK Biobank participants. Linear mixed models were used to regress IGF-1 against the intrinsic functional connectivity (i.e., degree of network activity) for each neural network. Interactions between IGF-1 and AD risk factors such as Apolipoprotein E4 (APOE4) genotype, sex, AD family history, and age were also tested. Higher IGF-1 was associated with more network activity in the right Executive Function neural network. IGF-1 interactions with APOE4 or sex implicated motor, primary/extrastriate visual, and executive function related neural networks. Neural network activity trends with increasing IGF-1 were different in different age groups. Higher IGF-1 levels relate to much more network activity in the Sensorimotor Network and Cerebellum Network in early-life participants (40-52 years old), compared with mid-life (52-59 years old) and late-life (59-70 years old) participants. These findings suggest that sex and APOE4 genotype may modify the relationship between IGF-1 and brain network activities related to visual, motor, and cognitive processing. Additionally, IGF-1 may have an age-dependent effect on neural network connectivity.

By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g., transcription factor proteins and miRNAs, and target genes. Network activity is defined as the degree of consistency between a regulatory network architecture and a specific cellular context of gene expression and can also be measured as a score of statistical significance. The gene network activities are closely related to the dynamics of cell states. To evaluate the activity of regulatory events in the form of network, we propose a network activity evaluation (NAE) framework by measuring the consistency between network architecture and gene expression data across specific states based on mathematical programming. NAE firstly employs the dynamic Bayesian network model to formulate the network structure with time series profiling data. For the constraints of prior knowledge about gene regulatory network, NAE introduces an interpretable general loss function with regularization penalties to calculate the degree of consistency between gene network and gene expression data. Moreover, we design a fast and convergent alternating direction method of multipliers algorithm to optimize the regularized constraint programming. The efficiency and advantage of the NAE framework is deduced through numerous experiments and comparison studies. It reflects the possibility and potential of the match between network and data, thereby helping to reveal the network activity and to explain the dynamic responds underlying the network structure caused by changes in molecular environment of living cells. The code of NAE is freely available for academic use (https://github.com/zpliulab/NAE).

This study investigates the effects of entrepreneurial personality traits, background and networking activities on venture growth among 168 Chinese entrepreneurs in small and medium sized businesses in Singapore. Personality traits include need for achievement, internal locus of control, self‐reliance and extroversion; background comprises education and experience; networking activities consist of size and frequency of communication networks. A structural equation modelling technique – partial least squares (PLS) – is used to estimate a path model with latent variables. The results indicate that experience, networking activities, and number of partners as well as internal locus of control and need for achievement all have positive impact on venture growth. Two other personality traits, self‐reliance and extroversion have negative impact on number of partners and positive impact on networking activities, respectively. The impact of education on venture growth, however, is moderated by firm size, positive for larger firms and negative for smaller firms. Our findings indicate that among all the factors that we have considered, an entrepreneur’s industrial and managerial experience is the dominating factor affecting venture growth.

Computing latest starting times of activities in interval-valued networks with minimal time lags

While the ultimate dependence of brain function on its energy supply is evident, how basic neuronal parameters and network activity respond to energy metabolism deviations is unresolved. The resting membrane potential (E(m)) and reversal potential of GABA-induced anionic currents (E(GABA)) are among the most fundamental parameters controlling neuronal excitability. However, alterations of E(m) and E(GABA) under conditions of metabolic stress are not sufficiently documented, although it is well known that metabolic crisis may lead to neuronal hyper-excitability and aberrant neuronal network activities. In this work, we show that in slices, availability of energy substrates determines whether GABA signaling displays an inhibitory or excitatory mode, both in neonatal neocortex and hippocampus. We demonstrate that in the neonatal brain, E(m) and E(GABA) strongly depend on composition of the energy substrate pool. Complementing glucose with ketone bodies, pyruvate or lactate resulted in a significant hyperpolarization of both E(m) and E(GABA), and induced a radical shift in the mode of GABAergic synaptic transmission towards network inhibition. Generation of giant depolarizing potentials, currently regarded as the hallmark of spontaneous neonatal network activity in vitro, was strongly inhibited both in neocortex and hippocampus in the energy substrate enriched solution. Based on these results we suggest the composition of the artificial cerebrospinal fluid, which bears a closer resemblance to the in vivo energy substrate pool. Our results suggest that energy deficits induce unfavorable changes in E(m) and E(GABA), leading to neuronal hyperactivity that may initiate a cascade of pathological events.

Suppressing anxiety and fear memory relies on bidirectional projections between the medial prefrontal cortex and the amygdala. Positive allosteric modulators of mGluR5 improve cognition in animal models of schizophrenia and retrieval of newly formed associations such as extinction of fear-conditioned behaviour. The increase in neuronal network activities of the medial prefrontal cortex is influenced by both mGluR1 and mGluR5; however, it is not well understood how they modulate network activities and downstream information processing. To map mGluR5-mediated network activity in relation to its emergence as a viable cognitive enhancer, we tested group I mGluR compounds on medial prefrontal cortex network activity via multi-electrode array neuronal spiking and whole-cell patch clamp recordings. Results indicate that mGluR5 activation promotes feed-forward inhibition that depends on recruitment of neuronal activity by carbachol-evoked up states. The rate of neuronal spiking activity under the influence of carbachol was reduced by the mGluR5 positive allosteric modulator, N-(1,3-Diphenyl-1H-pyrazolo-5-yl)-4-nitrobenzamide (VU-29), and enhanced by the mGluR5 negative allosteric modulator, 3-((2-methyl-1,3-thiazol-4-yl)ethynyl)pyridine hydrochloride (MTEP). Spontaneous inhibitory post-synaptic currents were increased upon application of carbachol and in combination with VU-29. These results emphasize a bias towards tonic mGluR5-mediated inhibition that might serve as a signal-to-noise enhancer of sensory inputs projected from associated limbic areas onto the medial prefrontal cortex neuronal microcircuit.

Beta-like hippocampal network activity is differentially affected by amyloid beta peptides

This study deals with online personal social networks (i.e., ego-networks) of youth 12-18 years old, in the Netherlands and investigates if and how these networks operate with respect to learning. The online ego-networks of youth, and the potential these networks have for learning, are largely unexplored. What kinds of resources do youth have access to through their networks? With whom do they connect? How can we characterize these relations in terms of the frequency they meet online and offline, emotional closeness, topics of conversation, and geographical dispersion of contacts? What kinds of networks provide learning experiences? How can we predict these networks? This study describes in detail the characteristics of these ego-networks. Furthermore, we tested the claim that learning in online networks is a likely result of frequent network activity. Particularly we questioned if popular social network activities such as sharing links, giving feedback and editing or creating artefacts together online would be related to the discovery of new information. With a multi-level analysis model we were able to differentiate the individual influences and the influence of their ego-networks on the frequency of discovering new information and overall network activity. The results showed that these network activities strongly and positively predicted discovery of new information. With respect to the people with whom youth construct their networked communities, the study show that youth connects online primarily with contacts who are similar, who live close by and who are emotionally close. In contrast to claims in the literature in which innovation and learning is associated with heterogeneous contacts, these results show that youth chooses homogeneous, emotionally close and locally based online relationships to explore their interests, to relate to and to discover new information together. A possible explanation may be that in this age group, youth are still fostering the ties to their immediate community and that being accepted and being similar may allow for a safer exploring of the world. These results suggests that rather than stating how a particular kind of tie or network predicts innovation, or is likely to provide new information, these relations need to be contextualised and understood from their local, specific settings and social dynamics.

According to the tripartite synapse model, astrocytes have a modulatory effect on neuronal signal transmission. More recently, astrocyte malfunction has been associated with psychiatric diseases such as schizophrenia. Several hypotheses have been proposed on the pathological mechanisms of astrocytes in schizophrenia. For example, post-mortem examinations have revealed a reduced astrocytic density in patients with schizophrenia. Another hypothesis suggests that disease symptoms are linked to an abnormality of glutamate transmission, which is also regulated by astrocytes (glutamate hypothesis of schizophrenia). Electrophysiological findings indicate a dispute over whether the disorder causes an increase or a decrease in neuronal and astrocytic activity. Moreover, there is no consensus as to which molecular pathways and network mechanisms are altered in schizophrenia. Computational models can aid the process in finding the underlying pathological malfunctions. The effect of astrocytes on the activity of neuron-astrocyte networks has been analysed with computational models. These can reproduce experimentally observed phenomena, such as astrocytic modulation of spike and burst signalling in neuron-astrocyte networks. Using an established computational neuron-astrocyte network model, we simulate experimental data of healthy and pathological networks by using different neuronal and astrocytic parameter configurations. In our simulations, the reduction of neuronal or astrocytic cell densities yields decreased glutamate levels and a statistically significant reduction in the network activity. Amplifications of the astrocytic ATP release toward postsynaptic terminals also reduced the network activity and resulted in temporarily increased glutamate levels. In contrast, reducing either the glutamate release or re-uptake in astrocytes resulted in higher network activities. Similarly, an increase in synaptic weights of excitatory or inhibitory neurons raises the excitability of individual cells and elevates the activation level of the network. To conclude, our simulations suggest that the impairment of both neurons and astrocytes disturbs the neuronal network activity in schizophrenia.

Cultures of neurons from rat neocortex exhibit spontaneous, temporally patterned, network activity. Such a distributed activity in vitro constitutes a possible framework for combining theoretical and experimental approaches, linking the single-neuron discharge properties to network phenomena. In this work, we addressed the issue of closing the loop, from the identification of the single-cell discharge properties to the prediction of collective network phenomena. Thus, we compared these predictions with the spontaneously emerging network activity in vitro, detected by substrate arrays of microelectrodes. Therefore, we characterized the single-cell discharge properties to Gauss-distributed noisy currents, under pharmacological blockade of the synaptic transmission. Such stochastic currents emulate a realistic input from the network. The mean (m) and variance (s(2)) of the injected current were varied independently, reminiscent of the extended mean-field description of a variety of possible presynaptic network organizations and mean activity levels, and the neuronal response was evaluated in terms of the steady-state mean firing rate (f). Experimental current-to-spike-rate responses f(m, s(2)) were similar to those of neurons in brain slices, and could be quantitatively described by leaky integrate-and-fire (IF) point neurons. The identified model parameters were then used in numerical simulations of a network of IF neurons. Such a network reproduced a collective activity, matching the spontaneous irregular population bursting, observed in cultured networks. We finally interpret such a collective activity and its link with model details by the mean-field theory. We conclude that the IF model is an adequate minimal description of synaptic integration and neuronal excitability, when collective network activities are considered in vitro.

Is systemic innovation compatible with a decentralized “virtual” organization? The current literature clearly suggests that it is not, pointing out that systemic innovation – innovation requiring coordinated adjustments throughout an entire product system – demands vertical integration, and that a decentralized “virtual” organization is the “wrong choice” (Chesbrough and Teece, 1996:65). I present a theoretical framework and empirical data that suggest otherwise. In a process I call value chain orchestration, I how a highly decentralized organization implements systemic innovation via a mechanism which involves extensive alliance and networking activities, linking the value chains of a wide set of dispersed actors to the value chain of the focal company. With this, I also illuminate the consequences of alliance and network activities, an area where researchers have long called for further studies. In sum, this study illuminates the operating mechanisms which enable systemic innovation via highly a flexible, decentralized organizational structure and illustrates the financial consequences of these intense alliance and networking activities.

GABA Action in Immature Neocortical Neurons Directly Depends on the Availability of Ketone Bodies. Rheims S, Holmgren CD, Chazal G, Mulder J, Harkany T, Zilberter T, Zilberter Y. J Neurochem 2009;110(4):1330–1338. In the early postnatal period, energy metabolism in the suckling rodent brain relies to a large extent on metabolic pathways alternate to glucose such as the utilization of ketone bodies (KBs). However, how KBs affect neuronal excitability is not known. Using recordings of single NMDA and GABA-activated channels in neocortical pyramidal cells we studied the effects of KBs on the resting membrane potential ( Em) and reversal potential of GABA-induced anionic currents ( EGABA), respectively. We show that during postnatal development (P3–P19) if neocortical brain slices are adequately supplied with KBs, Em and EGABA are both maintained at negative levels of about −83 and −80 mV, respectively. Conversely, a KB deficiency causes a significant depolarization of both Em (>5 mV) and EGABA (>15 mV). The KB-mediated shift in EGABA is largely determined by the interaction of the NKCC1 cotransporter and Cl-/HCO3 transporter(s). Therefore, by inducing a hyperpolarizing shift in Em and modulating GABA signaling mode, KBs can efficiently control the excitability of neonatal cortical neurons. Energy Substrate Availability as a Determinant of Neuronal Resting Potential, GABA Signaling and Spontaneous Network Activity in the Neonatal Cortex In Vitro. Holmgren CD, Mukhtarov M, Malkov AE, Popova IY, Bregestovski P, Zilberter Y. J Neurochem 2010;112(4):900–912. While the ultimate dependence of brain function on its energy supply is evident, how basic neuronal parameters and network activity respond to energy metabolism deviations is unresolved. The resting membrane potential ( Em) and reversal potential of GABA-induced anionic currents ( EGABA) are among the most fundamental parameters controlling neuronal excitability. However, alterations of Em and EGABA under conditions of metabolic stress are not sufficiently documented, although it is well known that metabolic crisis may lead to neuronal hyper-excitability and aberrant neuronal network activities. In this work, we show that in slices, availability of energy substrates determines whether GABA signaling displays an inhibitory or excitatory mode, both in neonatal neocortex and hippocampus. We demonstrate that in the neonatal brain, Em and EGABA strongly depend on composition of the energy substrate pool. Complementing glucose with ketone bodies, pyruvate or lactate resulted in a significant hyperpolarization of both Em and EGABA, and induced a radical shift in the mode of GABAergic synaptic transmission towards network inhibition. Generation of giant depolarizing potentials, currently regarded as the hallmark of spontaneous neonatal network activity in vitro, was strongly inhibited both in neocortex and hippocampus in the energy substrate enriched solution. Based on these results we suggest the composition of the artificial cerebrospinal fluid, which bears a closer resemblance to the in vivo energy substrate pool. Our results suggest that energy deficits induce unfavorable changes in Em and EGABA, leading to neuronal hyperactivity that may initiate a cascade of pathological events.

The purpose of this study is to analyze the effect of controlling consulting service quality in relation to the factors of success and performances of the social enterprises. In order to achieve the objective of this study, securement of market competitiveness, entrepreneurship and network activity was defined to be the factors of success and the research model and hypothesis was set according to the theoretical basis of the factors of success, performances and the quality of consulting services. The results showed that first, securement of market competitiveness, entrepreneurship and network activities as factors of success all had significant effects on performances pertaining to both profit-making and public interest, having great influence on the securement of market competitiveness of the social enterprises. Secondly, as a result of analyzing the effects of controlling the quality of consulting services in relation to the relationship between the factors of success and management performances of the social enterprises, network activity showed to have significant effect on performances pertaining to both profit-making and public interest. Through this study, the importance and the necessity of the improvement of consulting services and network activities of social enterprises were highlighted and the necessity of a new consulting principle in the consulting industry that can be specialized to social enterprises is proposed.