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Industrial networks rely on standard real-time communication protocols like ProfiNet. These protocols are used for cyclic data exchange between IO devices and controllers. Since continuous monitoring of IO devices is important, a large number of data packets can be observed in such field networks between the IO devices and controllers. Each IO device cyclically reports its data or internal state to a controller at a predefined frequency. However, the reported data of most IO devices are not changing all the time, and thus the same bytes are transmitted multiple times. The majority of data packets is only used for checking the availability of such devices. In this paper, we consider an industrial environment where IO devices are located in an industrial site while controllers are running remotely (e.g., a software PLC in a private or edge cloud), and there is a radio link (e.g., 5G radio) between the two sides. We propose an in-network traffic reduction method that filters out the unnecessary data traffic at the two ends of the radio link, detects failure of devices and the radio link fast, and does not require any modification in the IO devices and controllers. Our solution is based on the cooperation of two P4-programmable networking elements deployed at the two sides of the radio link. Our preliminary measurements with P4-programmable hardware switches and emulated ProfiNet devices show that the method can significantly reduce the load on the radio link, while it could seamlessly be deployed in existing industrial environments.

In this paper, we propose a Quality of Control aware self-reconfiguring factory cell composed of modular robot elements. The planner is a necessary component. We discuss how the planner reacts to certain practical constraints. We prove with the help of domain independent planning that modular robots are viable aid in a factory cell to speed up productivity. We evaluate the proposed system in a physical simulation environment. The screen recordings of all the scenarios are shown on [1].

Digital Twin (DT) is widely used in various industrial sectors to optimize the operations and maintenance of physical assets, system and manufacturing processes. In this paper our goal is to introduce an architecture in which the radio access control happens automatically to minimize the utilized radio resources while still maximizing the production KPIs of the robot cell. To achieve this, we apply Reinforcement Learning (RL) in a simulated environment to explore the environment fast, while the DT ensures that the learned policy can be applied on the real world environment as well. We show that the application of Ultra Reliable Low Latency Communication (URLLC) connection can be reduced to approx. 30% of the total radio time while achieving real-world accurate robot control. The system in action can be seen on [1].

Resource allocation in wireless access networks has been an intensively researched topic recently: many proposed solutions tackle radio channel access and dynamic spectrum allocation, but traditional issues of queuing, bandwidth sharing and packet processing at wireless access points have been targeted as well. In most of the related work the competition for high quality of service is usually solved by central coordination among users via optimizing a specific target aspect of the overall communication. In this paper we take a turn and provide users with the possibility of resource allocation suggestions. We propose a wireless access sharing framework, in which users have a say in optimizing their quality of service on the long term, and we tackle its analysis with the tool set of stochastic game theory. Our findings show that greedy users become polite against their counterparts when the load is relatively low with the goal of preparing for situations with high load.

5G networks provide technology enablers targeting industrial applications. One key enabler is the Ultra Reliable Low Latency Communication (URLLC). This paper studies the performance impact of network delay on closed-loop control for industrial applications. We investigate the performance of the closed-loop control of an UR5 industrial robot arm assuming fix delay. The goal is to stress the system at the upper limit of the possible network delay. We prove that to achieve the maximum speed, URLLC is a must have.

Background and purpose Acute mortality rate of stroke in Hungary is significantly higher than in Western Europe, which is likely to be partially attributable to suboptimal treatment. Methods We examined the use of acute vascular imaging and mechanical thrombectomy for acute ischaemic stroke patients. We collected data on 20 consecutive patients from Hungarian stroke centers before 31st August 2016. Results Out of the reported 410 patients, 166 (40.4%) underwent CT angiography and 44 (10.7%) had mechanical thrombectomy. Conclusion Only about 1/3 of acute ischaemic stroke patients eligible for thrombectomy actually had it. The underlying reasons include long onset-to-door time, low utilization of acute vessel imaging and a limited neuro-intervention capacity needing improvement.

In this paper we discuss our evolved understanding on the meaning of agile robotics. The lessons learned are concluded from the four years of our participation in ARIAC, the Agile Robotics for Industrial Automation Competition, that we managed to win in 2020. After elaborating on ARIAC tasks, challenges and scoring and their consequences on algorithm design, error handling and software development processes and methodologies, we introduce the concept of Best Effort Agile Robotics (BEAR). The conclusions are mapped to agility standardization directions. The concept of Best Effort Robotics (BER) is also proposed and discussed, highlighting the similarities with best effort networking, a proven concept in Internet technology.

The flexibility of future production systems envisioned by Industry 4.0 requires safe but efficient Human-Robot Collaboration (HRC). An important enabler of HRC is a sophisticated collision avoidance mechanism which can detect objects and potential collision events and as a response, it calculates detour trajectories avoiding physical contacts. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. The required computational power can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can digital twins exploit the benefits of these concepts? Can the robots tolerate the delay characteristics coming with the cloud platforms? In this paper, we answer these questions by building on public and private cloud solutions providing different techniques for parallel computation. Our contribution is threefold. First, we introduce a measurement methodology to characterize different approaches in terms of latency. Second, a real HRC use-case is elaborated and a relevant KPI is defined. Third, we evaluate the pros/cons of different solutions and their impact on the performance.

Safe and efficient Human-Robot Collaboration (HRC) is an essential feature of future Industry 4.0 production systems which requires sophisticated collision avoidance mechanisms with intense computation need. Digital twins provide a novel way to test the impact of different control decisions in a simulated virtual environment even in parallel. In addition, Virtual/Augmented Reality (VR/AR) applications can revolutionize future industry environments. Each component requires extreme computational power which can be provided by cloud platforms but at the cost of higher delay and jitter. Moreover, clouds bring a versatile set of novel techniques easing the life of both developers and operators. Can these applications be realized and operated on today’s systems? In this demonstration, we give answers to this question via real experiments.

Digital Twin (DT) is widely used in various industrial sectors to optimize the operations and maintenance of physical assets, system and manufacturing processes. This paper applies the DT concept on analyzing the effects of network on the control of a real robot where its DT runs in a complex robot cell executing agile tasks. We present the design steps applied to reach a realizable DT in the Gazebo environment. We evaluate our proposed method with real network configurations against a fully simulated scenario solving the Agile Robotics for Industrial Automation Competition (ARIAC) [1]. The evaluation results show that for low delay setups the simulation provides accurate results. For high delay setups the DT should be used for network assessment. It is also shown that 5G Ultra-Reliable Low-Latency Communication (URLLC) is required for the best productivity performance of the robot cell, while the evaluated public LTE+ network can also be used with limited overall accuracy. The DT in action can be seen on [2].

Decision making and data management are critical features for companies to leverage their productivity and revenues. With the advancement of Industry 4.0, smart factories are increasing their investment in cyber-physical systems and digital processing. An extensive field of study that allows this growth is the Internet of Things (IoT). As factories desire velocity and impact in their systems, an IoT network connecting devices, sensors, and robots provides planning and managing advantages. In this work, we compare AMQP and MQTT, two mainstreams IoT protocols, in a smart factory environment. We also include in the comparison the popular robotic communication platform named ROS. The results show that, overall, MQTT performed better than AMQP, which is expected, given that MQTT is a lightweight protocol. However, AMQP initial connections between peers are faster. In this work, we discuss such behaviors and other questions regarding the IoT protocols and how and why they should be implemented in a smart factory environment.

Current Internet transport protocols are being revisited in an attempt to respond to traffic growth in size, diversity and the emergence of new applications. Designed to reduce Web TCP latency and connection establishment time, QUIC uses UDP and defines its own congestion control. To verify some of QUIC’s performance claims, we carried out an extensive set of controlled experiments reflecting Internet traffic conditions by configuring various parameters, such as the round trip time (RTT), the packet loss ratio, web pages, and caching. Surprisingly, our experimental results show that QUIC presented worst page load time (PLT) results than TCP. We next present some of our interpretations of these results.

Efficient techniques for pattern matching are essential in a number of networked systems and services, such as intrusion detection systems, application identification and classification services, and traffic management. Most pattern matching applications describe patterns using regular expression and the support engine is Deterministic Finite Automaton (FA). Previous research studies address either performance or space requirements issues. From the original DFA formalism we design and evaluate optimizations to its representation and operation to meet Deep Packet Inspection (DPI) systems' requirements for commodity platforms, such as (i) decreasing the original DFA memory consumption (high compression ratio) and (ii) performing pattern matching as fast as the original DFA. Our approach spans from designing the DFA to developing memory layouts to get the most of the underlying architecture. The contributions of this work are threefold: (i) a new and improved finite automaton model, called Ranged Compressed DFA (RCDFA), (ii) three RCDFA optimizations for achieving more compression and matching speed, and (iii) three advanced layouts for implementing the compressed automaton without memory and performance penalties. The experimental evaluation shows that RCDFA compresses DFA up to 99% without imposing additional memory lookups. The proposed advanced layouts reach memory compression of around 97%. RCDFA together with the advanced layouts outperforms the standard DFA by up to 32 times in terms of processing speed.

The frequent psychiatric comorbidity in epilepsy compromises the quality of life of those affected, further increasing their suicide risk and the high burden of stigmatisation. Out of adulthood epilepsies, mesio-temporal lobe epilepsy carries the highest cognitive and psychiatric risk, making mental health comorbidities its inherent features. The pathomechanism of the epilepsy-related mental health symptoms and conditions appears to be related to undetected subclinical seizures, postictal exhaustion and inhibition, and the non-REM sleep related interictal epileptic activity. In our work we try to present some of the typical psychiatric syndromes and conditions seen in epilepsy. We aim to highlight the difficulties of treatment on the borderline of neurology and psychiatry.

Epilepsies, especially mesiotemporal epilepsy in adulthood, are frequently associated with chronic cognitive loss, psychiatry symptoms and conditions. We aim to present the pathophysiology of interictal psychiatric comorbidities interlocked with cognitive loss; severely compromising the quality of life of epilepsy patients. We will present the mechanism of cognitive harm related to interictal spiking; and the abnormalities of brain networks in epilepsy, especially of the default mode network, briefly looking into psychosocial and pharmacology effects, too. In addition to seizures, interictal epileptic activity, mainly in sleep, may exert chronic cognitive harm, increasing the risk for primarily non-cognitive psychotic conditions as well. Interictal spikes and pathological high frequency oscillations curiously resemble normal memory traces; enabling them to “behave” and be mistaken for engrams by the memory process. Epileptic activity impairs the white and grey matter of the brain; likely contributing to brain network changes. The epileptic network changes resemble those seen in non-epileptic psychiatry conditions, offering a network-interpretation of psychiatric comorbidity.

Salivary glands produce a bicarbonate-rich fluid containing digestive and protective proteins and other components to be delivered into the gastrointestinal tract. Its function is under strict control of the autonomic nervous system. Salivary electrolyte and fluid secretion are primarily controlled by parasympathetic activity, while protein secretion is primaily triggered by sympathetic stimulation. Stress activates the hypothalamic - pituitary - adrenal axis. The peripheral limb of this axis is the efferent sympathetic/adrenomedullary system. Stress reaction, even if it is sustained for long, does not cause obvious damage to salivary glands. However, stress induces dramatic changes in the constituents of secreted saliva. Since salivary protein secretion is strongly dependent on sympathetic control, changes in saliva can be utilized as sensitive stress indicators. Some of the secreted compounds are known for their protective effect in the mouth and the gut, while others may just pass through the glands from blood plasma because of their chemical nature and the presence of transcellular salivary transporting systems. Indeed, most compounds that appear in blood circulation can also be identified in saliva, although at different concentrations. This work overviews the presently recognized salivary stress biosensors, such as amylase, cortisol, heat shock proteins and other compounds. It also demonstrates that saliva is widely recognised as a diagnostic tool for early and sensitive discovery of salivary and systemic conditions and disorders. At present it may be too early to introduce most of these biomarkers in daily routine diagnostic applications, but advances in salivary biomarker standardisation should permit their wide-range utilization in the future including safe, reliable and non-invasive estimation of acute and chronic stress levels in patients.

In this paper, we propose an architecture for network traffic prioritization. This architecture is application and protocol agnostic. We also give an interface for this architecture which enables QoE triggered fast network interface switching without interrupting the communication sessions. We give an algorithm in order to minimize the cost of the frequent network changes (reordering and retransmissions) and evaluate its performance in simulations and demonstrating it on real Wi-Fi and cellular access network. To demonstrating the effectiveness of our QoE triggered switching approach we eliminate the stalling events on YouTube video playback.

We propose an app agnostic QoE deduction method that can be used for on-line traffic prioritization. Our implemented method extracts QoE information directly from the screen of mobile devices and this information is utilized for video traffic prioritization to improve user QoE. We evaluate our method and show that it can realize 80-100% of the achievable gain of the state-of-the- art methods with 50% less traffic that needs to be prioritized to achieve this. We provide deployment alternatives for traffic prioritization and evaluate performance impact on achievable gain.

In this paper we propose evaluation models and calculate range of potential gains for enhancing the average web-browsing QoE of all the users in a mobile network with given capacity. We investigate a method that takes capacity from users during QoE insensitive periods. The gained capacity is distributed among users being in QoE affecting state. Our models of web page network prioritization show that the potential gain for QoE affecting periods highly depends on the correlation between data downloaded during QoE affecting and QoE insensitive periods. The numerical evaluation of wide range of scenarios with web traffic shows that the achievable gain is up to 104% i.e., halving the download time of QoE affecting part. Network prioritization of web pages has good potential to improve web-browsing QoE.

The demonstrated system is an online video streaming service using QUIC protocol as transport. Video buffer status information is gathered at the client side and it is transferred to the video content server via the object priority assignment method of QUIC. Our proposal is to utilize the priority information for congestion control (CC) influence as well. In our realization, the server extracts the priority information and utilizes it to adjust the aggressiveness level of the CC of TCP emulation in QUIC. The measured gain in initial buffering time (IBT) in the demonstrated system is in the range of 6–49% depending on the video properties and network environment. [1] shows the demonstration.

Game traffic depends on two main factors, the game protocol and the gamers' behavior. Based on a few popular real-time multiplayer games this paper investigates the latter factor showing how a set of typical game phases--e.g., player movement, changes in the environment--impacts traffic on different observation levels. The nature of human behavior has such a high impact on traffic characteristics that it influences the traffic both at a macroscopic--e.g., traffic rate--and at a microscopic--payload content--level. First, by understanding the nature of this impact a user behavior detection algorithm is introduced to grab specific events and states from passive traffic measurements. The algorithms focus on the characteristics of the traffic rate, showing what information can be gathered by observing only packet header information. Second, as an application of our method some results, including a detailed analysis of measurements taken from an operational broadband network, are presented. Third, a novel model and an algorithm are introduced to extend the Deep Packet Inspection traffic classification method with the analysis of non-fix byte signatures, which are not considered in current methods. The model captures the variation of the dynamic byte segments and provides parameters for the algorithm. The introduced algorithm exploits the spatial and temporal correlation by examining and extracting the correlation structure of the traffic and constructing signatures based on the observed correlation. The algorithm is evaluated by examining proprietary gaming traffic and also other known non-gaming protocols.

The area of Internet traffic measurement has advanced enormously over the last couple of years. This was mostly due to the increase in network access speeds, due to the appearance of bandwidth-hungry applications, due to the ISPs' increased interest in precise user traffic profile information and also a response to the enormous growth in the number of connected users. These changes greatly affected the work of Internet service providers and network administrators, which have to deal with increasing resource demands and abrupt traffic changes brought by new applications. This survey explains the main techniques and problems known in the field of IP traffic analysis and focuses on application detection. First, it separates traffic analysis into packet-based and flow-based categories and details the advantages and problems for each approach. Second, this work cites the techniques for traffic analysis accessible in the literature, along with the analysis performed by the authors. Relevant techniques include signature-matching, sampling and inference. Third, this work shows the trends in application classification analysis and presents important and recent references in the subject. Lastly, this survey draws the readers' interest to open research topics in the area of traffic analysis and application detection and makes some final remarks.

Background and purpose: Blockade of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptors is a good treatment option for a variety of central nervous system disorders. The present study evaluated the neuroprotective and anticonvulsant effects of EGIS-8332, a non-competitive AMPA receptor antagonist, as a potential drug candidate.

We observed in vitro neuroprotective and AMPA/kainate receptor antagonist effects of the new 2,3-benzodiazepine derivative EGIS-8332 (R,S-1-(4-aminophenyl)-7,8-methylenedioxy-4-cyano-4-methyl-3-N-acetyl-5H-3,4-dihydro-2,3-benzodiazepine) using the lactate dehydrogenase (LDH) release assay and patch clamp recordings on primary cultures of rat embryonic telencephalon neurons exposed to AMPA/kainate receptor agonists. EGIS-8332 potently decreased alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionate (AMPA) and quisqualate induced LDH release (IC(50)=5.2+/-0.4 and 7.4+/-1.3 microM, respectively) from the cells. Whole-cell patch clamp studies carried out on the ionotropic glutamate receptors N-methyl D-aspartate (NMDA), as well as AMPA (and kainate) in cultured telencephalon neurons verified that EGIS-8332 blocked steady state responses to AMPA and kainate (IC(50)=1.7+/-0.4 and 6.2+/-1.6 microM, respectively), but hardly influenced currents evoked by NMDA. EGIS-8332 also inhibited kainate-evoked response in CHO cells expressing the flop variant of GluR1 receptor and, in cerebellar Purkinje cells at similar efficiency. The stereoselectivity of the inhibitory site is established by the clearly dissimilar inhibitory potency of the enantiomer components of EGIS-8332 differing in the configuration of methyl and cyano substituents on carbon C(4): the R(-) enantiomer was found to be the efficient species. This finding suggests that the inhibitory interaction between the channel protein and drug is promoted by presence of the C(4) methyl group. The inhibition of the AMPA/kainate ion channels by EGIS-8332 is non-competitive, not use dependent, and depends neither on the closed/open state of the channel, nor the membrane potential. These findings suggest an allosteric mechanism for the inhibition. These in vitro observations suggest that the compound might be useful in the treatments of certain acute and chronic neurological syndromes initiated by derangements of ionotropic glutamate receptor function.

Commercial implementations of adaptive streaming systems are either too aggressive or too conservative, not being able to efficiently deliver video content to the client. Because of these issues, a great number of adaptive streaming systems have been proposed in the academia. They employ different strategies, to select the proper video level that the server should send to the client. One trend is the use of control theory, because it guarantees system stability, and enables simulation and tuning of the system prior to its implementation. However, the current proposals that employ control theory do not take full advantage of this technique, because they do not thoroughly analyze the system parameters and their influence on the overall system performance. Therefore, it is possible that they use suboptimal solutions. In this paper, we propose a control-theoretic approach for selecting the video quality in a DASH environment. We use a controller added to a state machine, to improve the QoE and to maximize bandwidth utilization. We also present a comparison of several system settings performances, by changing the controller types, their gains, and the state machine parameters. Experimental results show the effectiveness of our proposed system for buffer time control and video level smoothness, and that it outperforms a current proposal in the academia.

The demonstrated system gathers browser rendering status information at the client side and transfers it toward the cellular network. The network can do the traffic scheduling/boosting accordingly achieving enhanced Quality of Experience (QoE) for the user in a congested radio cell. The realized gain in web rendering time by the demonstrated system is in the range of 7–60% depending on the page structure.

Seizure-related injuries have major impact in the excess mortality and morbidity of epilepsy patients. Experimental data suggest that analgesia may develop during seizures contributing to the severity of seizure-related accidents, especially burns. We aimed to identify those seizure-types that may lead to burn-injuries by seizure-related analgesia. In our tertiary epilepsy centre, we asked 100 epilepsy patients having a history of seizure-related injury, to complete our burn-and-pain questionnaire. Fifty-one patients completed the survey; their epileptology data were collected and those with a seizure-related burn were interviewed. Forty-two out of the 51 patients (82%) had partial epilepsy and 9 (18%) had idiopathic generalised epilepsy. Twenty-six persons (51%) reported decreased pain perception during or after seizures in general. Twelve patients (23%) had suffered one or more seizure-related burn. Five of them fell onto a hot surface or fire accidentally, during generalized tonic-clonic seizures. Seven out of the 12 burnt patients (58%) grasped a hot object or reached into boiling fluid during complex partial seizures; without experiencing-, or reacting in response to pain. These patients had temporal lobe epilepsy, 5 of them had left temporal seizure onset. Our hypothesis based on the circumstantial analysis of our patients' burn-injuries; is that temporal lobe seizures may cause ictal/postictal analgesia. It may be caused by the seizure-related epileptic facilitation of the periaqueductal gray matter; the central pain-inhibiting structure of the brain. Seizure-related endogenous opioid-release my have a contributory role in inhibiting pain-perception. Ictal analgesia warrants better burn-prevention in temporal lobe epilepsy patients. Understanding the mechanism of ictal analgesia and specifying those seizures-types prone to cause it; may help indentifying human pain-inhibiting pathways.

Background: Spinal cord herniation and thoracic anterior adhesion syndrome make up the two extremes of a rare condition; characterized by anterior dural adhesion or protrusion of the spinal cord through the arachnoideal and dural membrane into the extradural space, respectively. Summary: We present the main features of the condition by our case series and forward a hypothesis for the consistent anterior, mid-thoracic localization. We surmise the role of an anterior pulling force by the Hofmann (meningo-vertebral) ligaments; acting when the physiologic thoracic kyphosis suddenly increases. The traction may tear the anterior dura; resulting in a dural defect; it allows the nipping/ protrusion of the spinal cord. Key messages: Because the spinal cord compression syndrome caused by adhesion or herniation of the spinal cord may be surgically treatable; the recognition of the condition is essential. The pathogenetic traction effect of the Hofmann ligament in the affected level may have surgical an prognostic implications.

We have synthesized a novel series of N-substituted sarcosines, analogues of NFPS ( N -[3-(biphenyl-4- yloxy)-3-(4-fluorophenyl)propyl]- N -methylglycine), as type-1 glycine transporter (GlyT-1) inhibitors. Several compounds incorporated a diazine ring inhibited recombinant hGlyT-1b expressed permanently in CHO cells and GlyT-1 in rat brain synaptosomal preparations. A structure-activity relationship for the newly synthesized compounds was obtained and discussed on the ground of their GlyT-1 inhibitory potencies. Replacement of the biphenyl-4-yloxy moiety in NFPS with a 5-pyridazinylphenoxy moiety (compounds 3, 4, 5, and 6) or a 2-phenyl-5- pyridazinyloxy moiety (compounds 10, 11, and 12) afforded compounds exhibiting potent inhibition on GlyT-1 activity. The GlyT-1 inhibitory properties of NFPS analogues, in which sarcosine was closed into a ring forming (methylamino)pyridazine-3-(2 H )-one, were markedly reduced (compounds 13 and 14). The pyridazine-containing GlyT-1 inhibitors with in vitro GlyT-1 inhibitory potency also enhanced extracellular glycine concentrations in conscious rat striatum as was measured by microdialysis technique. In contrast to NFPS, sarcosine-based pyridazine containing GlyT-1 inhibitors failed to evoke compulsive running behavior whereas they inhibited phencyclidine- induced hypermotility in mice. It is believed that increase of extracellular concentrations of glycine by inhibition of its reuptake may probably influence positively glutamate N-methyl-D-aspartate (NMDA)-type ionotropic receptors in the central nervous system. This may have importance in the treatment of neuropsychiatric disorders associated with hypofunctional NMDA receptor-mediated glutamatergic neurochemical transmission. Thus, impaired NMDA receptor functions have been shown to be involved in the development of the negative symptoms and the cognitive deficit of schizophrenia and the treatment of these symptoms is the possible clinical indication of GlyT-1 inhibitors including those containing pyridazine moiety.

The nature of the neuroprotection by the competitive α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor antagonist, 6-nitro-7-sulfamoyl-benzo[f]quinoxaline-2,3-dione (NBQX), and the non-competitive AMPA receptor antagonist, 1-(4-aminophenyl)-3-methylcarbamoyl-4-methyl-3,4-dihydro-7,8-methylenedioxy-5H-2,3-benzodiazepine (GYKI 53655), was investigated in mature telencephalic neurone cultures of the rat. NBQX protected cultured neurones against AMPA-induced delayed toxicity in a competitive manner: the AMPA concentration–response curve was shifted to the right in parallel and concentration dependently. In contrast, GYKI 53655 decreased the maximal neurotoxic effect of AMPA considerably but without affecting the EC50 for AMPA toxicity, which indicated the non-competitive mode of its action. Thus we found a clear relationship between the nature of in vitro neuroprotection and the mode of AMPA channel block.

The neuroprotective efficacy of the most potent competitive α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) antagonist 2,3-dihydroxy-6-nitro-7-sulfamoyl-benzo(F)quinoxaline (NBQX) and three recently developed 2,3-benzodiazepine non-competitive AMPA antagonists (GYKI 52466, 53405 and 53655) was investigated in primary cultures of rat telencephalic neurons. NBQX protected cultured neurons against AMPA (20 μM for 21–23 h) induced toxicity with EC 50 of 0.5 μM. In the same test GYKI 52466, 53405 and 53655 had EC 50 values of 10.6, 9.3 and 5.1 μM, respectively. Thus we found the competitive antagonist NBQX to be ten times more effective neuroprotectant in vitro than the most potent non-competitive GYKI compound (GYKI 53655).

Traffic Identification is a crucial task performed by ISP administrators to evaluate and improve network service quality. Deep Packet Inspection (DPI) is a well-known technique used to identify networked traffic. DPI relies mostly on Regular Expressions (REs) evaluated by Finite Automata. Many previous studies have investigated the impacts on the classification accuracy of such systems when inspecting only a portion of the traffic. However, none have discussed the real impacts on the overall system throughput. This work presents a novel technique to perform DPI on Graphics Processing Units (GPU) called Flow-Based Traffic Identification (FBTI) and a proof-of-concept prototype analysis. Basically we want to increase DPI systems? performance on commodity platforms as well as their capacity to identify networked traffic on high speed links. By combining Deterministic Finite Automaton (DFA) for evaluating REs and flow-level packet sampling we achieve a raw performance of over 60 Gbps on GPUs. Our prototype solution could reach a real throughput of over 12 Gbps, measured as the identified volume of flows.

We present the versatile functionality of our novel user behavior based traffic emulation system in this paper. We show the unique feature of the system, i. e., it is capable of working on different platforms (Windows, Android), on different access technologies (wired, WiFi, 3G) and as a remote controlled system on different sites (Europe, Asia, South America). Our examples exhibit some of the manifold traffic analysis possibilities as a result of this key functionality. We have also made our system available to the public [1].

Channel switching in HSPA networks is used to reduce the channel occupancy when there is no data transfer for the given user, this way reducing battery consumption. This paper is the first dealing with another important aspect that is the CPU load on the radio network controller (RNC) caused by channel switching. This is an optimization task, in which both the channel switching and staying on the high-bandwidth channel have costs. In this paper we propose a system to minimize the costs by applying a predictor based method which uses time slice based features in order to reduce the high variance in the feature values. The proposed system is evaluated and compared to other state-of-the-art methods.

We demonstrate a multi-functional and configurable traffic generation framework which is based on accurately capturing user behavior by automatic Graphical User Interface control. The framework includes the emulation of typical user behaviors on remote controlled machines with the recording of their generated traffic. As far as we know this is the first time when a traffic generation tool is demonstrated which is able to compose arbitrary traffic mix by real generated and not simulated traffic. Our examples exhibit some of the manifold traffic analysis possibilities as use cases. The demonstrated system is open to the public in [1].

Current network traffic analysis systems heavily rely on Deep Packet Inspection (DPI) techniques, such as Finite Automata (FA), to detect patterns carried by regular expression (regex). However, traditional Finite Automata cannot keep up with the ever-growing speed of the Internet links. Although there are a number of efficient FA compressing mechanisms for DPIs, there is no standardized or common way to evaluate and compare them. In this scenario, this paper proposes a methodology to evaluate and compare automaton models and the data-structures that materialize them. We also adapt state-of-the-art memory layouts to better fit in today's computer architectures. Finally, we apply our methodology to most important automaton models, memory layouts, and well-known signature sets. The results show us that some memory layouts are not efficient for regexes that represent small automata and other ones which fit only with uncompressed automata. Further, we also found out that theoretical studies about memory usage from memory encodings are not as accurate as they should be.

Traffic Identification is a key function performed by Internet Service Providers' (ISP) administrators to evaluate and improve network services. However, traffic identification needs to be done in real-time and at wire speed to be useful for network tuning. Deep Packet Inspection (DPI) is widely used for identifying normal applications and attacks in the network by looking for well-known patterns within the packets. Such patterns are mostly expressed by Regular Expressions (RE), which are then evaluated by abstract machines known as Deterministic Finite Automata (DFA). Some previous studies grouped DFAs together to evaluate multiple patterns on a single DFA match's run. Efficient grouping algorithms would combine several DFAs without exceeding the available machine's memory. This work proposes and evaluates a new method to combine several DFAs into a single one. Additionally we compared this algorithm to state-of-the-art approaches using a compressed DFA model. Experimental results show that our algorithm generates less groups and transitions than existent algorithms.

We used isolated chicken retina to induce spreading depression by the glutamate receptor agonist N-methyl-d-aspartate. The N-methyl-d-aspartate-induced latency time of spreading depression was extended by the glycine(B) binding site competitive antagonist 7-chlorokynurenic acid. Addition of the glycine transporter type-1 inhibitors NFPS and Org-24461 reversed the inhibitory effect of 7-chlorokynurenic acid on N-methyl-d-aspartate-evoked spreading depression. The glycine uptake inhibitory activity of Org-24461, NFPS, and some newly synthesized analogs of NFPS was determined in CHO cells stably expressing human glycine transporter type-1b isoform. Compounds, which failed to inhibit glycine transporter type-1, also did not have effect on retinal spreading depression. These experiments indicate that the spreading depression model in chicken retina is a useful in vitro test to determine activity of glycine transporter type-1 inhibitors. In addition, our data serve further evidence for the role of glycine transporter type-1 in retinal neurotransmission and light processing.

In this paper we introduce a novel framework for traffic identification that employs machine learning techniques focusing on the estimation of multiple traffic influencing factors. The effect of these factors is handled with the training of several machine learning models. We utilize the outcome of the multiple models via a recombination algorithm to achieve high overall true positive and true negative and low overall false positive and false negative classification ratio. The proposed method can improve the performance of every kind of machine learning based traffic identification engine making them capable of efficient operation in changing network environment i.e., when the probing node is trained and tested in different sites.

Deep Packet Inspection (DPI) systems have been becoming an important element in traffic measurement ever since port-based classification was deemed no longer appropriate, due to protocol tunneling and misuses of well-defined ports. Current DPI systems express application signatures using regular expressions and it is usual to perform pattern matching through the use of Finite Automaton (FA). Although DPI systems are essentially more accurate, they are also resource-intensive and do not scale well with link speeds. Looking to this area of interest, this paper proposes a novel Deterministic Finite Automaton, called Ranged Compressed Deterministic Finite Automaton (RCDFA), that compresses transitions without additional memory lookups. Experimental results show that RCDFA yields space savings of 97% over the original DFA and up to 93% better compression when compared to the DFA's state-of-the-art compression techniques.

Traffic identification and classification are essential tasks performed by Internet Service Provider (ISPs) administrators. Deep Packet Inspection (DPI) is currently playing a key role in traffic identification and classification due to its increased expressive power. To allow fair comparison among different DPI techniques and systems, workload generators should have the following characteristics: (i) synthetic packets with meaningful payloads; (ii) TCP and UDP traffic generation; (iii) a configurable network traffic profile, and (iv) a high-speed sending rate. This paper proposes a workload generator framework which inherits all of the above characteristics. A performance evaluation shows that our flexible workload generator system achieves very high sending rates over a 10Gbps network, using a commodity Linux machine. Additionally, we have configured and tested our workload generator following a real application traffic profile. We then analyzed its results within a DPI system, proving its accuracy and efficiency.

The most dominant hypotheses for the pathogenesis of schizophrenia have focused primarily upon hyperfunctional dopaminergic and hypofunctional glutamatergic neurotransmission in the central nervous system. The therapeutic efficacy of all atypical antipsychotics is explained in part by antagonism of the dopaminergic neurotransmission, mainly by blockade of D(2) dopamine receptors. N-methyl-D-aspartate (NMDA) receptor hypofunction in schizophrenia can be reversed by glycine transporter type-1 (GlyT-1) inhibitors, which regulate glycine concentrations at the vicinity of NMDA receptors. Combined drug administration with D(2) dopamine receptor blockade and activation of hypofunctional NMDA receptors may be needed for a more effective treatment of positive and negative symptoms and the accompanied cognitive deficit in schizophrenia. To investigate this type of combined drug administration, rats were treated with the atypical antipsychotic risperidone together with the GlyT-1 inhibitor Org-24461. Brain microdialysis was applied in the striatum of conscious rats and determinations of extracellular dopamine, DOPAC, HVA, glycine, glutamate, and serine concentrations were carried out using HPLC/electrochemistry. Risperidone increased extracellular concentrations of dopamine but failed to influence those of glycine or glutamate measured in microdialysis samples. Org-24461 injection reduced extracellular dopamine concentrations and elevated extracellular glycine levels but the concentrations of serine and glutamate were not changed. When risperidone and Org-24461 were added in combination, a decrease in extracellular dopamine concentrations was accompanied with sustained elevation of extracellular glycine levels. Interestingly, the extracellular concentrations of glutamate were also enhanced. Our data indicate that coadministration of an antipsychotic with a GlyT-1 inhibitor may normalize hypofunctional NMDA receptor-mediated glutamatergic neurotransmission with reduced dopaminergic side effects characteristic for antipsychotic medication.