SUMO vector

One of the most significant advances in medical history is the discovery and development of antibiotics, which in the middle of last century was flourishing and appeared to be the ultimate solution to the treatment of life-threatening human bacterial diseases. However, lately there has been a huge decline in the rate of discovery of new antimicrobial intervention strategies in parallel with an increasing incidence of multidrug-resistant pathogens; if these circumstances do not change we will continue to approach the end of the antibiotic era. Facing this dark future, scientists are considering new strategies for intervention tailored around the appropriate (selective) stimulation of the host's immune system, and particularly rapid acting innate immunity, as an alternative to direct targeting of microbial pathogens. One recent player in such an immunomodulatory strategy is the naturally occurring host defence peptides (HDP) and their synthetic innate defence regulator (IDR) analo...

According to previous studies, there is a prolyl endopeptidase-inhibiting sequence (LNENLLRFFVAPFPEVFG) within a relatively hydrophobic region of αS1 casein (Juillerat-Jeanneret et al., 2011). Recently, sixteen recombinant peptides were designed in assorted lengths, each containing the inhibitory sequence at varied positions within the αS1 casein sequence. The corresponding cDNA sequences were originally ligated into the pET15b vector, which provides an N-terminal poly-histidine tag for purification. The vector was in turn transformed into a BL21(DE3)pLysS E. coli expression host (Vishram et al., 2016). However, the system proved unsuccessful in adequate expression of the hydrophobic peptide. The intention of this study was to assemble a new pET28-derived expression system, kindly provided by Dr. X. Cheng (MD Anderson in Houston, TX) that utilizes a SUMO (small ubiquitin-like modifier) fusion tag and an N-terminal poly-histidine tag. The SUMO tag is known for enhancing expression levels of potentially toxic or insoluble recombinant peptides (Bommarius et al., 2010). The peptides were expressed using the BL21(DE3)pLysS expression host once again. The expressed peptides were purified via Ni-NTA column chromatography, with subsequent UlpI cleavage. This was followed by reversed-phase high performance liquid chromatography (RP-HPLC) to further purify the peptides. Tricine-SDS-PAGE, a useful screening tool for hydrophobic peptides smaller than 30kDa, was used to analyze expression and purification. Western blot analysis was also used to screen for expression of the peptides, using both Anti-Smt3 and Anti-His-Tag antibodies. A comparison was made between SUMO-fused expression and non-fused expression of the hydrophobic casein peptides.

Among bioactive peptides, cationic antimicrobial peptides (AMPs), also referred to as host defence peptides (HDPs), are valuable tools to treat infections, since they are able to kill a wide variety of microbes directly and/or to modulate host immunity. HDPs have great therapeutic potential against antibiotic-resistant bacteria, viruses and even parasites. However, high manufacturing costs have greatly limited their development as drugs, thus highlighting the need to develop novel and competitive production strategies. Here, a cost-effective procedure was established to produce the high amounts of peptides required for basic and clinical research. Firstly, a novel culture medium was designed, which was found to support significantly higher cell densities and recombinant expression levels of peptides under test compared to conventional media. The procedure has been also efficiently scaled up by using a 5 L fermenter, while the costs have been significantly lowered by developing a successful auto-induction strategy, which has been found to support significantly higher yields of target constructs and cell biomass compared to conventional strategies based on expression induction by IPTG. Interestingly, it was estimated that by increasing the production scale from 100 to 1,000 mg/batch, unit costs decreased strongly from 253 to 42 €/mg. These costs appear highly competitive when compared to chemical synthesis strategies. Altogether, the data indicate that the strategy represents an important starting point for the future development of large-scale manufacture of HDPs.

Recent years have witnessed a colossal increase of vehicles on the roads; unfortunately, the infrastructure of roads and traffic systems has not kept pace with this growth, resulting in inefficient traffic management. Owing to this imbalance, traffic jams on roads, congestions, and pollution have shown a marked increase. The management of growing traffic is a major issue across the world. Intelligent Transportation Systems (ITS) have a great potential in offering solutions to such issues by using novel technologies. In this review, the ITS-based solutions for traffic management and control have been categorized as traffic data collection solutions, traffic management solutions, congestion avoidance solutions, and travel time prediction solutions. The solutions have been presented along with their underlying technologies, advantages, and drawbacks. First, important solutions for collecting traffic-related data and road conditions are discussed. Next, ITS solutions for the effective management of traffic are presented. Third, key strategies based on machine learning and computational intelligence for avoiding congestion are outlined. Fourth, important solutions for accurately predicting travel time are presented. Finally, avenues for future work in these areas are discussed.

Innate immunity is triggered by a variety of bacterial molecules, resulting in both protective and potentially harmful pro-inflammatory responses. Further, innate immunity also provides a mechanism for the maintenance of homeostasis between the host immune system and symbiotic or non-pathogenic microorganisms. However, the bacterial factors that mediate these protective effects have been incompletely defined. Here, it was demonstrated that the lantiobiotic nisin Z is able to modulate host immune responses and mediate protective host immunity. Nisin Z induced the secretion of the chemokines MCP-1, IL-8 and Gro-α, and significantly reduced TNF-α induction in response to bacterial LPS in human PBMC. The results correlated with the ability of nisin Z to confer protection against both the Gram-positive organism Staphylococcus aureus, and the Gram-negatives Salmonella enterica sv. Typhimurium and Escherichia coli in murine challenge models. Mechanistic studies revealed that nisin Z modula...

Parkinson’s disease (PD) is characterized by loss of dopaminergic neurons in substantia nigra pars compacta, α-synuclein (α-syn)-rich intraneuronal inclusions (Lewy bodies), and microglial activation. Emerging evidence suggests that CD4+ T lymphocytes contribute to neuroinflammation in PD. Since the mainstay of PD treatment is dopaminergic substitution therapy and dopamine is an established transmitter connecting nervous and immune systems, we examined CD4+ T naive and memory lymphocytes in PD patients and in healthy subjects (HS), with specific regard to dopaminergic receptor (DR) expression. In addition, the in vitro effects of α-syn were assessed on CD4+ T naive and memory cells. Results showed extensive association between DR expression in T lymphocytes and motor dysfunction, as assessed by UPDRS Part III score. In total and CD4+ T naive cells expression of D1-like DR decrease, while in T memory cells D2-like DR increase with increasing score. In vitro, α-syn increased CD4+ T me...

In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic sce...

In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic sce...

Dear reader, You are holding in your hands a volume of the series „Reports of the DLR-Institute of Transportation Systems“. We are publishing in this series fascinating, scientific topics from the Institute of Transportation Systems of the German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt e.V. - DLR) and from his environment. We are providing libraries with a part of the circulation. Outstanding scientific contributions and dissertations are here published as well as projects reports and proceedings of conferences in our house with different contributors from science, economy and politics. With this series we are pursuing the objective to enable a broad access to scientific works and results. We are using the series as well as to promote practically young researchers by the publication of the dissertation of our staff and external doctoral candidates, too. Publications are important milestones on the academic career path. With the series „Reports of the DLR-Institut...

E-commerce businesses have enjoyed drastic growth in sales numbers; subsequently, logistics businesses, who stands at the end of this commercial chain, have been receiving a rising number of service requests. The system utilizes Google Maps and its “multiple destination” function to search for possible routes. The routes undergo a multilayer perceptron model for traffic condition simulation. Finally, the system applies Dijkstra’s algorithm to identify the optimal route. The proposed system bears the following features: (1) a multilayer perceptron model is created using the history of traffic conditions; (2) the route planning considers traffic condition prediction based on the expected time of travel; (3) the optimal route is calculated using Dijkstra’s algorithm based on vehicle speed; (4) the system can include multiple destinations in its calculation, providing comprehensive travel plan for the logistics vehicle; (5) the system allows logistics business operators to keep track of...

The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed ...

Antimicrobial peptides are the upcoming therapeutic molecules as alternative drugs to the existing antibiotics owing to their potent action against pathogenic microorganisms. In this study, to obtain an antimicrobial peptide with a broad range of activity, the synthetic cationic antimicrobial peptide was designed by using in silico tools viz., antimicrobial peptide database, protparam, hierarchical neural network. Later, the peptide was translated back into a core nucleotide sequence and the gene for the peptide was constructed by overlapping PCR. The amplified gene was cloned into pRSET-A vector and transformed into salt inducible expression host E. coli GJ1158. The expression results show high yields of soluble recombinant fusion peptide (0.52 g/L) from salt-inducible E. coli. The recombinant peptide was purified by the IMAC purification system and cleaved by enterokinase. The digested product was further purified and 0.12 g/L of biologically active recombinant cationic antimicrobial peptide was obtained. In vitro analysis of the purified peptide demonstrated high antimicrobial activity against both Gram positive and Gram negative bacteria devoid of hemolytic activity. Therefore, this synthetic cationic antimicrobial peptide could serves as an promising agent over chemical antibiotics. In this study, a synthetic cationic antimicrobial peptide was designed, cloned and expressed from salt-inducible E. coli GJ1158 using cost effective media in the large scale production of antimicrobial peptide and its biological activity was analysed against different Gram positive and negative organisms.

Sensor-based perception on vehicles are becoming prevalent and important to enhance the road safety. Autonomous driving systems use cameras, LiDAR, and radar to detect surrounding objects, while human-driven vehicles use them to assist the driver. However, the environmental perception by individual vehicles has the limitations on coverage and/or detection accuracy. For example, a vehicle cannot detect objects occluded by other moving/static obstacles. In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects. By using the deep reinforcement learning to select the data to transmit, our scheme mitigates the network load in vehicular communication networks and enhances the communication reliability. To design, test, and verify the cooperative perception scheme, we develop a Cooperative & Intelligent Vehicle Simulation (CIVS) Platform, which integrates three software components: traffic simulator, vehicle simulator, and object classifier. We evaluate that our scheme decreases packet loss and thereby increases the detection accuracy by up to 12%, compared to the baseline protocol.

In this study we used a yeast expression system to express a new antimicrobial peptide dybowskin-2CAMa from the skin cDNA library of Rana amurenisis. The entire coding region of the dybowskin-2CAMa was cloned into the plasmid pPICZα-A and then transformed into competent P. pastoris X33. The expressed dybowskin-2CAMa was purified from the culture supernatant by Sephadex G-25 and YMC*GEL ODS-A chromatography followed by C18 reverse phased HPLC. The purified peptide exhibited a single band of about 2 kDa when resolved by Tricine-SDS-PAGE. Its exact molecular weight was 2456.46 Da which was consistent with the value predicted from its deduced amino acid sequence. Antimicrobial activity assay showed that the recombinant dybowskin-2CAMa could inhibit the growth of a broad spectrum of bacteria, while displaying very low level of hemolytic activity (≤4% relative to Triton X-100), even at concentration of up to 500 µg/mL.

Dear reader, You are holding in your hands a volume of the series „Reports of the DLR-Institute of Transportation Systems“. We are publishing in this series fascinating, scientific topics from the Institute of Transportation Systems of the German Aerospace Center (Deutsches Zentrum fur Luft- und Raumfahrt e.V. - DLR) and from his environment. We are providing libraries with a part of the circulation. Outstanding scientific contributions and dissertations are here published as well as projects reports and proceedings of conferences in our house with different contributors from science, economy and politics. With this series we are pursuing the objective to enable a broad access to scientific works and results. We are using the series as well as to promote practically young researchers by the publication of the dissertation of our staff and external doctoral candidates, too. Publications are important milestones on the academic career path. With the series „Reports of the DLR-Institut...

This paper presents a proactive model and tool for traffic analysis and management that integrates deep learning for traffic parameter prediction with microscopic traffic simulation providing traffic analysts with the ability to visualise the traffic network state ahead of time, generate traffic control measures, and visualise the consequences of the applied measure(s). The model adopts an integrated assess-forecastsimulate approach in which traffic flow characteristics are applied to deep Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) stacked autoencoders in order to forecast traffic flow and speed, which is subsequently passed on to a traffic microsimulation tool Simulation of Urban Mobility (SUMO) where the predicted parameters are used to generate a traffic future state simulation. We test our model using sensorcollected traffic and weather data from the geographical area of Greater Manchester, United Kingdom. The empirical results show the benefits of the model for urban traffic analysis.

This paper proposes an alternative approach for determining the most energy efficient route towards a destination. An innovative mesoscopic vehicular consumption model that is based on machine learning functionality is introduced and its application in a case study involving Fully Electric Vehicles (FEVs) is examined. The integration of this model in a routing engine especially designed for FEVs is also analyzed and a software architecture for implementing the proposed routing methodology is defined. In order to verify the robustness and the energy efficiency of this methodology, a system prototype has been developed and a series of field tests have been performed. The results of these tests are reported and significant conclusions are derived regarding the generated energy efficient routes.

Authors’ address: 1 Department of Biotechnology, Bapatla Engineering College, Bapatla 522101, Guntur, Andhra Pradesh, India. 2 Department of Biotechnology, Acharya Nagarjuna University, Guntur 522510, Andhra Pradesh, India. 3 Department of Biotechnology, R.V.R. & J.C. College of Engineering, Chowdavaram, Guntur 522019, Andhra Pradesh, India. 4 Department of Botany, Govt. Arts Colelge, Ariyalur, Tamilnadu, India. 5 CBST, VIT, Vellore, Tamilnadu, India. † JB Perevali and SR Kotra contributed equally to this work.

The cytoplasmic granules of bovine neutrophils naturally possess indolicidin-a promising cationic antimicrobial peptide as it displays inherent inhibitory activities against a broad type of microbial pathogens. In this study, a shake flask level production and expression optimizations of the indolicidin by the recombinant Escherichia coli C41 (DE3) clones (transformed with pET21a(+) plasmid carrying indolicidin gene) were carried out under standard conditions, as to determine the conditions required for maximal production. It was determined that a concentration of 1 mM of IPTG was effective, the 2×YT with salts and LB media at pH 7.5 with 3-6 h of incubation were required for maximal indolicidin expression.

The cytoplasmic granules of bovine neutrophils naturally possess indolicidin - a promising cationic antimicrobial peptide as it displays inherent inhibitory activities against a broad type of microbial pathogens. In this study, a shake flask level production and expression optimizations of the indolicidin by the recombinant Escherichia coli C41 (DE3) clones (transformed with pET21a(+) plasmid carrying indolicidin gene) were carried out under standard conditions, as to determine the conditions required for maximal production. It was determined that a concentration of 1 mM of IPTG was effective, the 2×YT with salts and LB media at pH 7.5 with 3-6 h of incubation were required for maximal indolicidin expression.

Background: Bactenecin is one of the smallest cationic eukaryotic antimicrobial peptides (AMPs) with a length of 12 residues and a beta-turn structure originated from bovine neutrophil cells. Previous studies reported poor capability of this peptide against Gram-negative bacteria. Objectives: The present study aimed at investigating the bactenecin bactericidal activity and determining the effect of increasing the positive charge of amine and carboxyl terminus and increasing the hydrophobicity of the central part of the antimicrobial peptide. Methods: Similar to the native peptide, three designed variants were employed named BM1, BM2, and BM3 that had increased positive charges, hydrophobicity, and a combination of both, respectively. Conditions for purifying and assaying their antibacterial activity against Gram-negative bacteria were predicted and optimized by APD3 and ExPASy servers. The cloned and expressed peptides in Pichia pastoris GS115 were partially purified by anion and cation exchange chromatography. The final purification rate of AMPs by HPLC was reported to be 70% and peptides were further characterized by LC-mass analysis. Finally, a minimum inhibitory concentration test was conducted. Results: The results implied the more significant effect of positive charges on the performance of these peptides against Escherichia coli.

Numerous peptides are available on the market as therapeutic drugs for regulating tumor growth, microorganism proliferation, immune response and/or metabolic disorders. Peptides are produced either by chemical synthesis or heterologous expression. Independent of the method chosen, there are challenges to transferring its production from the bench (~mg/L) to the industrial (~g/L) scale. Thus, the main scale-up pitfalls for the two methods of peptide production are reviewed here, including the advantages of each. Moreover, there will be a special focus on the main challenges for large-scale, heterologous production systems. Peptides that are currently available on the market are also described with an emphasis on how their process optimization has been designed in order to develop a cost-effective product.

In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic sce...

Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs ...

Cecropin is a cationic antibacterial peptide composed of 35-39 residues. This peptide has been identified as possessing strong antibacterial activity and low toxicity against eukaryotic cells, and it has been claimed that some types of the cecropin family of peptides are capable of killing cancer cells. In this study, the host effect of cloning antibacterial peptide cecropinB2 was investigated. Three different host expression systems were chosen, i.e., Escherichia coli, Bacillus subtilis and Pichia pastoris. Two gene constructs, cecropinB2 (cecB2) and intein-cecropinB2 (INT-cecB2), were applied. Signal peptide and propeptide from Armigeres subalbatus were also attached to the gene construct. The results showed that the best host for cloning cecropinB2 was P. pastoris SMD1168 harboring the gene of pGAPzαC-prepro-cecB2 via Western blot confirmation. The cecropinB2 that was purified using immobilized-metal affinity chromatography resin showed strong antibacterial activity against the Gram-negative strains, including the multi-drug-resistant bacteria Acinetobacter baumannii.

Numerous peptides are available on the market as therapeutic drugs for regulating tumor growth, microorganism proliferation, immune response and/or metabolic disorders. Peptides are produced either by chemical synthesis or heterologous expression. Independent of the method chosen, there are challenges to transferring its production from the bench (~mg/L) to the industrial (~g/L) scale. Thus, the main scale-up pitfalls for the two methods of peptide production are reviewed here, including the advantages of each. Moreover, there will be a special focus on the main challenges for large-scale, heterologous production systems. Peptides that are currently available on the market are also described with an emphasis on how their process optimization has been designed in order to develop a cost-effective product.

bor deaux2.fr (C. Cabanne). 1 Abbre vi a tions used: AMP, anti mi cro bial pep tide; MBP, malt ose bind ing pro tein; Leap2, liver-expressed anti mi cro bial pep tide 2; LB, Luria-Ber tan i; IPTG, iso pro pyl b-d-thio ga lac to py ra no side; EDTA, eth yl ene di amine tet ra ace tic acid; PMSF, phen ylmethanesulfo nyl fluo ride; BCA, bi cinch on i nic acid; RP-HPLC, reverse phase high-per for mance liquid chro ma tog ra phy; SDS-PAGE, sodium dode cyl sulfate-poly acryl amide gel elec tro pho re sis; FPLC, fast pro tein liquid chro ma tog ra phy.

Background: Pramipexole is one of a new generation of dopamine agonists. Recently there have been questions regarding its neuroprotective effects. These effects have been tested against various insults, which have yielded conflicting results. Methods: In this study, we introduced a combination of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)/paraquat to induce a severe Parkinson's disease model. The mice, after receiving the combination of toxins, were evaluated using mortality rates and immunohistochemistry for degenerating tyrosine hydroxylase-positive neurons. Results and conclusions: Pramipexole was tested for its capacity to offer protection against neurotoxic the effects of MPTP/paraquat in this model; however, the results showed no improvement with pramipexole therapy.

Derguene MBAYE 1[0000−0002−7490−2731] , Moussa DIALLO 2 1 Abstract. This article presents a traffic control system based on connected traffic lights controlled by machine learning algorithms. Each traffic light has a vehicle detection computer program as well as a connection module allowing it to transmit its information to other traffic lights in order to boost the passage from one color to another depending on traffic volume. The information exchanged between the traffic lights is collected in a remote server embedding another computer program estimating the number of vehicles that must arrive on the main road in order to decide to slow down or to maintain the traffic on the way leading to this road. The aim is to keep traffic as smooth as possible in order to limit the loss of time on the road which can have consequences at various levels for society. The implementation of such a system consists of collecting data on the traffic of a few target areas in order to model it in simulation software and to conduct experiments by deploying the aforementioned computer programs. The results can then be presented to the state authorities responsible for road transport for deployment on-site.

Host defence peptides (HDPs) are innate immune effector molecules found in diverse species. HDPs exhibit a wide range of functions ranging from direct antimicrobial properties to immunomodulatory effects. Research in the last decade has demonstrated that HDPs are critical effectors of both innate and adaptive immunity. Various studies have hypothesized that the antimicrobial property of certain HDPs may be largely due to their immunomodulatory functions. Mechanistic studies revealed that the role of HDPs in immunity is very complex and involves various receptors, signalling pathways and transcription factors. This review will focus on the multiple functions of HDPs in immunity and inflammation, with special reference to cathelicidins, e.g. LL-37, certain defensins and novel synthetic innate defence regulator peptides. We also discuss emerging concepts of specific HDPs in immune-mediated inflammatory diseases, including the potential use of cationic peptides as therapeutics for immune-mediated inflammatory disorders.

In this paper, an approach to reduce the computation steps required by fast neural networks for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small in size sub-images and then each one is tested separately using a fast neural network. The operation of fast neural networks

Experimental autoimmune encephalomyelitis (EAE) is the most used animal model of multiple sclerosis (MS) for the development of new therapies. Dopamine receptors can modulate EAE and MS development, thus highlighting the potential use of dopaminergic agonists in the treatment of MS, which has been poorly explored. Herein, we hypothesized that pramipexole (PPX), a dopamine D2/D3 receptor-preferring agonist commonly used to treat Parkinson's disease (PD), would be a suitable therapeutic drug for EAE. Thus, we report the effects and the underlying mechanisms of action of PPX in the prevention of EAE. PPX (0.1 and 1 mg/kg) was administered intraperitoneally (i.p.) from day 0 to 40 post-immunization (p.i.). Our results showed that PPX 1 mg/kg prevented EAE development, abolishing EAE signs by blocking neuroinflammatory response, demyelin-ation, and astroglial activation in spinal cord. Moreover, PPX inhibited the production of inflammatory cytokines, such as IL-17, IL-1β, and TNF-α in peripheral lymphoid tissue. PPX was also able to restore basal levels of a number of EAE-induced effects in spinal cord and striatum, such as re-active oxygen species, glutathione peroxidase, parkin, and α-synuclein (α-syn). Thus, our findings highlight the usefulness of PPX in preventing EAE-induced motor symptoms, possibly by modulating immune cell responses, such as those found in MS and other T helper cell-mediated inflammatory diseases.

The rise of antibiotic resistant pathogens is one of the most pressing global health issues. Discovery of new classes of antibiotics has not kept pace; new agents often suffer from cross-resistance to existing agents of similar structure. Short, cationic peptides with antimicrobial activity are essential to the host defenses of many organisms and represent a promising new class of antimicrobials. This paper reports the successful in silico screening for potent antibiotic peptides using a combination of QSAR and machine learning techniques. On the basis of initial high-throughput measurements of activity of over 1400 random peptides, artificial neural network models were built using QSAR descriptors and subsequently used to screen an in silico library of approximately 100,000 peptides. In vitro validation of the modeling showed 94% accuracy in identifying highly active peptides. The best peptides identified through screening were found to have activities comparable or superior to tho...

. Scatterplot Matrix (SigmaPlot v12) Illustrating the peptides experimentally verified percent inhibition (0-100%), in combination with the structure activity relationship estimated charge distribution (-16 -84) and hydrophobicity distribution (16 -126). The scales for the two latter once are only relative, and will have no direct meaning outside of this context. When the three properties are plotted against itself it illustrates the distribution of this property amongst all the peptides in the tested library ).

Cationic antimicrobial host defense peptides (HDPs) combat infection by directly killing a wide variety of microbes, and/or modulating host immunity. HDPs have great therapeutic potential against antibiotic-resistant bacteria, viruses and even parasites, but there are substantial roadblocks to their therapeutic application. High manufacturing costs associated with amino acid precursors have limited the delivery of inexpensive therapeutics through industrial-scale chemical synthesis. Conversely, the production of peptides in bacteria by recombinant DNA technology has been impeded by the antimicrobial activity of these peptides and their susceptibility to proteolytic degradation, while subsequent purification of recombinant peptides often requires multiple steps and has not been cost-effective. Here we have developed methodologies appropriate for largescale industrial production of HDPs; in particular, we describe (i) a method, using fusions to SUMO, for producing high yields of intact recombinant HDPs in bacteria without significant toxicity; and (ii) a simplified 2-step purification method appropriate for industrial use. We have used this method to produce seven HDPs to date (IDR1, MX226, LL37, CRAMP, HHC-10, E5 and E6). Using this technology, pilot-scale fermentation (10 L) was performed to produce large quantities of biologically active cationic peptides. Together, these data indicate that this new method represents a cost-effective means to enable commercial enterprises to produce HDPs in large-scale under Good Laboratory Manufacturing Practice (GMP) conditions for therapeutic application in humans.

Numerous peptides are available on the market as therapeutic drugs for regulating tumor growth, microorganism proliferation, immune response and/or metabolic disorders. Peptides are produced either by chemical synthesis or heterologous expression. Independent of the method chosen, there are challenges to transferring its production from the bench (~mg/L) to the industrial (~g/L) scale. Thus, the main scale-up pitfalls for the two methods of peptide production are reviewed here, including the advantages of each. Moreover, there will be a special focus on the main challenges for large-scale, heterologous production systems. Peptides that are currently available on the market are also described with an emphasis on how their process optimization has been designed in order to develop a cost-effective product.

. Scatterplot Matrix (SigmaPlot v12) Illustrating the peptides experimentally verified percent inhibition (0-100%), in combination with the structure activity relationship estimated charge distribution (-16 -84) and hydrophobicity distribution (16 -126). The scales for the two latter once are only relative, and will have no direct meaning outside of this context. When the three properties are plotted against itself it illustrates the distribution of this property amongst all the peptides in the tested library ).

Innate defense regulators (IDRs) are synthetic immunomodulatory versions of natural host defense peptides (HDP). IDRs mediate protection against bacterial challenge in the absence of direct antimicrobial activity, representing a novel approach to anti-infective and anti-inflammatory therapy. Previously, we reported that IDR-1018 selectively induced chemokine responses and suppressed pro-inflammatory responses. As there has been an increasing appreciation for the ability of HDPs to modulate complex immune processes, including wound healing, we characterized the wound healing activities of IDR-1018 in vitro. Further, we investigated the efficacy of IDR-1018 in diabetic and non-diabetic wound healing models. In all experiments, IDR-1018 was compared to the human HDP LL-37 and HDP-derived wound healing peptide HB-107. IDR-1018 was significantly less cytotoxic in vitro as compared to either LL-37 or HB-107. Furthermore, administration of IDR-1018 resulted in a dose-dependent increase in fibroblast cellular respiration. In vivo, IDR-1018 demonstrated significantly accelerated wound healing in S. aureus infected porcine and non-diabetic but not in diabetic murine wounds. However, no significant differences in bacterial colonization were observed. Our investigation demonstrates that in addition to previously reported immunomodulatory activities IDR-1018 promotes wound healing independent of direct antibacterial activity. Interestingly, these effects were not observed in diabetic wounds. It is anticipated that the wound healing activities of IDR-1018 can be attributed to modulation of host immune pathways that are suppressed in diabetic wounds and provide further evidence of the multiple immunomodulatory activities of IDR-1018.

Intelligent Transport Systems (ITS) fall in the framework of cyberphysical systems due to the interaction between physical systems (vehicles) and distributed information acquisition and dissemination infrastructure. With the accelerated development of wireless Vehicle-to-Vehicle (V2V) and Vehicle-to Infrastructure (V2I) communications, the integrated acquiring and processing of information is becoming feasible at an increasingly large scale. Accurate prediction of the traffic information in real time, such as the speed, flow, density has important applications in many areas of Intelligent Transport systems. It is a challenging problem due to the dynamic changes of the traffic states caused by many uncertain factors along a travelling route. In this paper we present a V2V based Speed Profile Prediction approach (V2VSPP) that was developed using neural network learning to predict the speed of selected agents based on the received signal strength values of communications between pairs of vehicles. The V2VSPP was trained and evaluated by using traffic data provided by the Australian Centre for Field Robotics. It contains vehicle state information, vehicle-to-vehicle communications and road maps with high temporal resolution for large numbers of interacting vehicles over a long time period. The experimental results show that the proposed approach (V2VSPP) has the capability of providing accurate predictions of speed profiles in multi-vehicle trajectories setup.

The antibacterial activity of host defense peptides (HDP) is largely mediated by permeabilization of bacterial membranes. The lipid membrane of enveloped viruses might also be a target of antimicrobial peptides. Therefore, we screened a panel of naturally occurring HDPs representing different classes for inhibition of early, Env-independent steps in the HIV replication cycle. A lentiviral vector-based screening assay was used to determine the inhibitory effect of HDPs on early steps in the replication cycle and on cell metabolism. Human LL37 and porcine Protegrin-1 specifically reduced lentiviral vector infectivity, whereas the reduction of luciferase activities observed at high concentrations of the other HDPs is primarily due to modulation of cellular activity and/ or cytotoxicity rather than antiviral activity. A retroviral vector was inhibited by LL37 and Protegrin-1 to similar extent, while no specific inhibition of adenoviral vector mediated gene transfer was observed. Specifi...

Template matching is a basic method in image analysis to extract useful information from images. In this
paper, we suggest a new method for pattern matching. Our method transform the template image from two
dimensional image into one dimensional vector. Also all sub-windows (same size of template) in the
reference image will transform into one dimensional vectors. The three similarity measures SAD, SSD, and
Euclidean are used to compute the likeness between template and all sub-windows in the reference image
to find the best match. The experimental results show the superior performance of the proposed method
over the conventional methods on various template of different sizes.

Increased multiple antibiotic resistance in the face of declining antibiotic discovery is one of society's most pressing health issues. Antimicrobial peptides represent a promising new class of antibiotics. Here we ask whether it is possible to make small broad spectrum peptides employing minimal assumptions, by capitalizing on accumulating chemical biology information. Using peptide array technology, two large random 9-amino-acid peptide libraries were iteratively created using the amino acid composition of the most active peptides. The resultant data was used together with Artificial Neural Networks, a powerful machine learning technique, to create quantitative in silico models of antibiotic activity. On the basis of random testing, these models proved remarkably effective in predicting the activity of 100,000 virtual peptides. The best peptides, representing the top quartile of predicted activities, were effective against a broad array of multidrugresistant "Superbugs" with activities that were equal to or better than four highly used conventional antibiotics, more effective than the most advanced clinical candidate antimicrobial peptide, and protective against Staphylococcus aureus infections in animal models. ARTICLE www.acschemicalbiology.org VOL.4 NO.1 • ACS CHEMICAL BIOLOGY

Cationic antimicrobial host defense peptides (HDPs) combat infection by directly killing a wide variety of microbes, and/or modulating host immunity. HDPs have great therapeutic potential against antibiotic-resistant bacteria, viruses and even parasites, but there are substantial roadblocks to their therapeutic application. High manufacturing costs associated with amino acid precursors have limited the delivery of inexpensive therapeutics through industrial-scale chemical synthesis. Conversely, the production of peptides in bacteria by recombinant DNA technology has been impeded by the antimicrobial activity of these peptides and their susceptibility to proteolytic degradation, while subsequent purification of recombinant peptides often requires multiple steps and has not been cost-effective. Here we have developed methodologies appropriate for largescale industrial production of HDPs; in particular, we describe (i) a method, using fusions to SUMO, for producing high yields of intact recombinant HDPs in bacteria without significant toxicity; and (ii) a simplified 2-step purification method appropriate for industrial use. We have used this method to produce seven HDPs to date (IDR1, MX226, LL37, CRAMP, HHC-10, E5 and E6). Using this technology, pilot-scale fermentation (10 L) was performed to produce large quantities of biologically active cationic peptides. Together, these data indicate that this new method represents a cost-effective means to enable commercial enterprises to produce HDPs in large-scale under Good Laboratory Manufacturing Practice (GMP) conditions for therapeutic application in humans.

Cationic antimicrobial host defense peptides (HDPs) combat infection by directly killing a wide variety of microbes, and/or modulating host immunity. HDPs have great therapeutic potential against antibiotic-resistant bacteria, viruses and even parasites, but there are substantial roadblocks to their therapeutic application. High manufacturing costs associated with amino acid precursors have limited the delivery of inexpensive therapeutics through industrial-scale chemical synthesis. Conversely, the production of peptides in bacteria by recombinant DNA technology has been impeded by the antimicrobial activity of these peptides and their susceptibility to proteolytic degradation, while subsequent purification of recombinant peptides often requires multiple steps and has not been cost-effective. Here we have developed methodologies appropriate for large-scale industrial production of HDPs; in particular, we describe (i) a method, using fusions to SUMO, for producing high yields of intact recombinant HDPs in bacteria without significant toxicity and (ii) a simplified 2-step purification method appropriate for industrial use. We have used this method to produce seven HDPs to date (IDR1, MX226, LL37, CRAMP, HHC-10, E5 and E6). Using this technology, pilot-scale fermentation (10L) was performed to produce large quantities of biologically active cationic peptides. Together, these data indicate that this new method represents a cost-effective means to enable commercial enterprises to produce HDPs in large-scale under Good Laboratory Manufacturing Practice (GMP) conditions for therapeutic application in humans.