An efficient model for mobile network slice embedding under resource uncertainty

The fifth generation (5G) of mobile networks will support several new use cases, like the Internet of Things (IoT), massive Machine Type Communication (mMTC) and Ultra-Reliable and Low Latency Communication (URLLC) as well as significant improvements of the conventional Mobile Broadband (MBB) use case. End-to-end network slicing is a key-feature of 5G since it allows to share and at the same time isolate resources between several different use cases as well as between tenants by providing logical network. The virtual separation of the network slices on a common end-to-end mobile network infrastructure enables an efficient usage of the underlying network resources and provides means for security and safety related isolation of the defined logical networks. A much-discussed challenge is the reuse or overbooking of resources guaranteed by contract. However, there is a consensus that over-provisioning of mobile communication bands is economically infeasible and a certain risk of network overload is acceptable for the majority of the 5G use cases. In this paper, an efficient model for mobile network slice embedding is presented which enables an informed decision on network slice admission. This is based on the guaranteed end-to-end mobile network resources that have to be provided on the one hand and the capacities and capabilities of the underlying network infrastructure on the other hand. The network slice embedding problem is solved in form of a Mixed Integer Linear Program with an uncertainty-aware objective function. Subsequently, the confidence in the availability of each resource is analyzed

The specificity of intermodular recognition in a prototypical nonribosomal peptide synthetase depends on an adaptor domain

In the quest for new bioactive substances, nonribosomal peptide synthetases (NRPS) provide biodiversity by synthesizing nonproteinaceous peptides with high cellular activity. NRPS machinery consists of multiple modules, each catalyzing a unique series of chemical reactions. Incomplete understanding of the biophysical principles orchestrating these reaction arrays limits the exploitation of NRPSs in synthetic biology. Here, we use nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry to solve the conundrum of how intermodular recognition is coupled with loaded carrier protein specificity in the tomaymycin NRPS. We discover an adaptor domain that directly recruits the loaded carrier protein from the initiation module to the elongation module and reveal its mechanism of action. The adaptor domain of the type found here has specificity rules that could potentially be exploited in the design of engineered NRPS machinery.</p

A New PqsR Inverse Agonist Potentiates Tobramycin Efficacy to Eradicate Pseudomonas aeruginosa Biofilms

Pseudomonas aeruginosa (PA) infections can be notoriously difficult to treat and are often accompanied by the development of antimicrobial resistance (AMR). Quorum sensing inhibitors (QSI) acting on PqsR (MvfR) – a crucial transcriptional regulator serving major functions in PA virulence – can enhance antibiotic efficacy and eventually prevent the AMR. An integrated drug discovery campaign including design, medicinal chemistry-driven hit-to-lead optimization and in-depth biological profiling of a new QSI generation is reported. The QSI possess excellent activity in inhibiting pyocyanin production and PqsR reporter-gene with IC50 values as low as 200 and 11 × 10−9 m, respectively. Drug metabolism and pharmacokinetics (DMPK) as well as safety pharmacology studies especially highlight the promising translational properties of the lead QSI for pulmonary applications. Moreover, target engagement of the lead QSI is shown in a PA mucoid lung infection mouse model. Beyond that, a significant synergistic effect of a QSI-tobramycin (Tob) combination against PA biofilms using a tailor-made squalene-derived nanoparticle (NP) formulation, which enhance the minimum biofilm eradicating concentration (MBEC) of Tob more than 32-fold is demonstrated. The novel lead QSI and the accompanying NP formulation highlight the potential of adjunctive pathoblocker-mediated therapy against PA infections opening up avenues for preclinical development

ECRG4 is a candidate tumor suppressor gene frequently hypermethylated in colorectal carcinoma and glioma

<p>Abstract</p> <p>Background</p> <p>Cancer cells display widespread changes in DNA methylation that may lead to genetic instability by global hypomethylation and aberrant silencing of tumor suppressor genes by focal hypermethylation. In turn, altered DNA methylation patterns have been used to identify putative tumor suppressor genes.</p> <p>Methods</p> <p>In a methylation screening approach, we identified <it>ECRG4 </it>as a differentially methylated gene. We analyzed different cancer cells for <it>ECRG4 </it>promoter methylation by COBRA and bisulfite sequencing. Gene expression analysis was carried out by semi-quantitative RT-PCR. The <it>ECRG4 </it>coding region was cloned and transfected into colorectal carcinoma cells. Cell growth was assessed by MTT and BrdU assays. ECRG4 localization was analyzed by fluorescence microscopy and Western blotting after transfection of an <it>ECRG4-eGFP </it>fusion gene.</p> <p>Results</p> <p>We found a high frequency of <it>ECRG4 </it>promoter methylation in various cancer cell lines. Remarkably, aberrant methylation of <it>ECRG4 </it>was also found in primary human tumor tissues, including samples from colorectal carcinoma and from malignant gliomas. <it>ECRG4 </it>hypermethylation associated strongly with transcriptional silencing and its expression could be re-activated <it>in vitro </it>by demethylating treatment with 5-aza-2'-deoxycytidine. Overexpression of <it>ECRG4 </it>in colorectal carcinoma cells led to a significant decrease in cell growth. In transfected cells, ECRG4 protein was detectable within the Golgi secretion machinery as well as in the culture medium.</p> <p>Conclusions</p> <p><it>ECRG4 </it>is silenced via promoter hypermethylation in different types of human cancer cells. Its gene product may act as inhibitor of cell proliferation in colorectal carcinoma cells and may play a role as extracellular signaling molecule.</p

SON function performance prediction in a cognitive SON management system

As a reply to the increasing demand for fast mobile network connections the concept of Self-Organising Networks (SONs) has been developed, reducing the need for humans to execute Operation, Administration and Maintenance (OAM) tasks for mobile networks. However, a SON contains functions which are provided by different vendors as black boxes, making it hard to predict the performance of the network, especially under untested configurations. Since Mobile Network Operators (MNOs) have to fulfil rising mobile network performance demands while reducing costs at the same time, it is crucial to gain a better understanding of the network behaviour to allow a costneutral performance improvement while simultaneously reducing the risk of network misconfiguration and service disturbance. In this paper an approach is introduced to enhance SON Management models with cognitive Machine Learning (ML) methods. Therefore, the simulated behaviour of three different SON Functions is analysed and described by a Linear Regression (LR) Model. In a second step, performance data of network cells are analysed for similarities using k-Means Clustering. The findings of these two steps are then combined by fitting the models onto smaller clusters of cells. Finally, the utility of these models for predicting the performance of the network is evaluated and the different stages of refinement are compared with each other