Traffic Offloading/Onloading in Multi-RAT Cellular Networks

We analyze next generation cellular networks, offering connectivity to mobile users through multiple radio access technologies (RATs), namely LTE and WiFi. We develop a framework based on the Markovian agent formalism, which can model several aspects of the system, including user traffic dynamics and radio resource allocation. In particular, through a mean-field solution, we show the ability of our framework to capture the system behavior in flash-crowd scenarios, i.e., when a burst of traffic requests takes place in some parts of the network service area. We consider a distributed strategy for the user RAT selection, which aims at ensuring high user throughput, and investigate its performance under different resource allocation scheme

Channel Secondary Random Process for Robust Secret Key Generation

The broadcast nature of wireless communications imposes the risk of information leakage to adversarial users or unauthorized receivers. Therefore, information security between intended users remains a challenging issue. Most of the current physical layer security techniques exploit channel randomness as a common source between two legitimate nodes to extract a secret key. In this paper, we propose a new simple technique to generate the secret key. Specifically, we exploit the estimated channel to generate a secondary random process (SRP) that is common between the two legitimate nodes. We compare the estimated channel gain and phase to a preset threshold. The moving differences between the locations at which the estimated channel gain and phase exceed the threshold are the realization of our SRP. We simulate an orthogonal frequency division multiplexing (OFDM) system and show that our proposed technique provides a drastic improvement in the key bit mismatch rate (BMR) between the legitimate nodes when compared to the techniques that exploit the estimated channel gain or phase directly. In addition to that, the secret key generated through our technique is longer than that generated by conventional techniques

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma:leveraging proteomics and machine learning for biomarker discovery

BACKGROUND: The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.METHODS: Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.FINDINGS: A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.INTERPRETATION: Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.FUNDING: Cancer Research UK, Blood Cancer UK, National Institute for Health Research.</p

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma:leveraging proteomics and machine learning for biomarker discovery

BACKGROUND: The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.METHODS: Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.FINDINGS: A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91-0.98), sensitivity of 91% (83%-98%), and specificity of 86% (78%-95%). By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81-0.93), sensitivity of 75% (64%-86%), and specificity of 84% (75%-93%). The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87-0.97) and 0.88 (0.82-0.95) respectively.INTERPRETATION: Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.FUNDING: Cancer Research UK, Blood Cancer UK, National Institute for Health Research.</p

A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

From MDPI via Jisc Publications RouterHistory: accepted 2021-11-04, pub-electronic 2021-11-08Publication status: PublishedFunder: Medical Research Council; Grant(s): MR/M008959Funder: CRUK Manchester Centre award; Grant(s): C5759/A25254Prostate cancer is the most frequent form of cancer in men, accounting for more than one-third of all cases. Current screening techniques, such as PSA testing used in conjunction with routine procedures, lead to unnecessary biopsies and the discovery of low-risk tumours, resulting in overdiagnosis. SWATH-MS is a well-established data-independent (DI) method requiring prior knowledge of targeted peptides to obtain valuable information from SWATH maps. In response to the growing need to identify and characterise protein biomarkers for prostate cancer, this study explored a spectrum source for targeted proteome analysis of blood samples. We created a comprehensive prostate cancer serum spectral library by combining data-dependent acquisition (DDA) MS raw files from 504 patients with low, intermediate, or high-grade prostate cancer and healthy controls, as well as 304 prostate cancer-related protein in silico assays. The spectral library contains 114,684 transitions, which equates to 18,479 peptides translated into 1227 proteins. The robustness and accuracy of the spectral library were assessed to boost confidence in the identification and quantification of prostate cancer-related proteins across an independent cohort, resulting in the identification of 404 proteins. This unique database can facilitate researchers to investigate prostate cancer protein biomarkers in blood samples. In the real-world use of the spectrum library for biomarker detection, using a signature of 17 proteins, a clear distinction between the validation cohort’s pre- and post-treatment groups was observed. Data are available via ProteomeXchange with identifier PXD028651

Demo: AIML-as-a-service for SLA management of a digital twin virtual network service

Proceedings of: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).This demonstration presents an AI/ML platform that is offered as a service (AIMLaaS) and integrated in the management and orchestration (MANO) workflow defined in the project 5Growth following the recommendations of various standardization organizations. In such a system, SLA management decisions (scaling, in this demo) are taken at runtime by AI/ML models that are requested and downloaded by the MANO stack from the AI/ML platform at instantiation time, according to the service definition. Relevant metrics to be injected into the model are also automatically configured so that they are collected, ingested, and consumed along the deployed data engineering pipeline. The use case to which it is applied is a digital twin service, whose control and motion planning function has stringent latency constraints (directly linked to its CPU consumption), eventually determining the need for scaling out/in to fulfill the SLA.Work supported in part by EU Commission H2020 5Growth project (Grant No. 856709) and H2020 Europe/Taiwan 5G-Dive project (Grant No. 859881)

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BackgroundA non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.MethodsThis was a prospective case–control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.ResultsThe top discriminatory proteins individually showed moderate accuracy (AUC &gt; 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs &gt; 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96–0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86–0.9)).ConclusionA patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted

Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid

From MDPI via Jisc Publications RouterHistory: accepted 2021-07-23, pub-electronic 2021-07-28Publication status: PublishedFunder: Cancer Research UK Manchester Centre; Grant(s): C147/A25254Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. This unique resource enables the study of protein biomarkers for endometrial cancer detection in cervico-vaginal fluid. Data are available via ProteomeXchange with unique identifier PXD025925

Characterization of Brain Lysosomal Activities in GBA-Related and Sporadic Parkinson’s Disease and Dementia with Lewy Bodies

Mutations in the GBA gene, encoding the lysosomal hydrolase glucocerebrosidase (GCase), are the most common known genetic risk factor for Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). The present study aims to gain more insight into changes in lysosomal activity in different brain regions of sporadic PD and DLB patients, screened for GBA variants. Enzymatic activities of GCase, β-hexosaminidase, and cathepsin D were measured in the frontal cortex, putamen, and substantia nigra (SN) of a cohort of patients with advanced PD and DLB as well as age-matched non-demented controls (n = 15/group) using fluorometric assays. Decreased activity of GCase (− 21%) and of cathepsin D (− 15%) was found in the SN and frontal cortex of patients with PD and DLB compared to controls, respectively. Population stratification was applied based on GBA genotype, showing substantially lower GCase activity (~ − 40%) in GBA variant carriers in all regions. GCase activity was further significantly decreased in the SN of PD and DLB patients without GBA variants in comparison to controls without GBA variants. Our results show decreased GCase activity in brains of PD and DLB patients with and without GBA variants, most pronounced in the SN. The results of our study confirm findings from previous studies, suggesting a role for GCase in GBA-associated as well as sporadic PD and DLB

Alpha-synuclein targets GluN2A NMDA receptor subunit causing striatal synaptic dysfunction and visuospatial memory alteration

Parkinson's disease is a progressive neurodegenerative disorder characterized by altered striatal dopaminergic signalling that leads to motor and cognitive deficits. Parkinson's disease is also characterized by abnormal presence of soluble toxic forms of \u3b1-synuclein that, when clustered into Lewy bodies, represents one of the pathological hallmarks of the disease. However, \u3b1-synuclein oligomers might also directly affect synaptic transmission and plasticity in Parkinson's disease models. Accordingly, by combining electrophysiological, optogenetic, immunofluorescence, molecular and behavioural analyses, here we report that \u3b1-synuclein reduces N-methyl-d-aspartate (NMDA) receptor-mediated synaptic currents and impairs corticostriatal long-term potentiation of striatal spiny projection neurons, of both direct (D1-positive) and indirect (putative D2-positive) pathways. Intrastriatal injections of \u3b1-synuclein produce deficits in visuospatial learning associated with reduced function of GluN2A NMDA receptor subunit indicating that this protein selectively targets this subunit both in vitro and ex vivo. Interestingly, this effect is observed in spiny projection neurons activated by optical stimulation of either cortical or thalamic glutamatergic afferents. We also found that treatment of striatal slices with antibodies targeting \u3b1-synuclein prevents the \u3b1-synuclein-induced loss of long-term potentiation and the reduced synaptic localization of GluN2A NMDA receptor subunit suggesting that this strategy might counteract synaptic dysfunction occurring in Parkinson's disease