Adaptive Power Load Balancing in Cellular Networks

Load balancing in mobile cellular networks is an important mechanism that enables distribution of demand across neighboring cells, which is critical for better resource utilization and user satisfaction. Current approaches for load balancing are reactive, redistributing users only when the offered load approaches the cell capacity. This approach can lead to deteriorated network performance and user experience. In order to better cater to users, mobile networks need to be proactive and provision resources based on expected demand. To this end we propose a load balancing mechanism that allows for proactive network configuration based on prediction of traffic load. Our approach makes use of power control mechanisms to reconfigure the coverage of a mobile base station and thus control the amount of users and offered load at that base station. We apply our method on a real-world cellular network in Senegal and show that it enables better distribution of load in Orange Telecom’s network in Senegal

Long-range epigenetic silencing at 2q14.2 affects most human colorectal cancers and may have application as a non-invasive biomarker of disease

Large chromosomal regions can be suppressed in cancer cells as denoted by hypermethylation of neighbouring CpG islands and downregulation of most genes within the region. We have analysed the extent and prevalence of long-range epigenetic silencing at 2q14.2 (the first and best characterised example of coordinated epigenetic remodelling) and investigated its possible applicability as a non-invasive diagnostic marker of human colorectal cancer using different approaches and biological samples. Hypermethylation of at least one of the CpG islands analysed (EN1, SCTR, INHBB) occurred in most carcinomas (90%), with EN1 methylated in 73 and 40% of carcinomas and adenomas, respectively. Gene suppression was a common phenomenon in all the tumours analysed and affected both methylated and unmethylated genes. Detection of methylated EN1 using bisulfite treatment and melting curve (MC) analysis from stool DNA in patients and controls resulted in a predictive capacity of, 44% sensitivity in positive patients (27% of overall sensitivity) and 97% specificity. We conclude that epigenetic suppression along 2q14.2 is common to most colorectal cancers and the presence of a methylated EN1 CpG island in stool DNA might be used as biomarker of neoplastic disease

Epigenomic analysis of aberrantly methylated genes in colorectal cancer identifies genes commonly affected by epigenetic alterations.

Methylation profiling based on bead-chip arrays is an effective method for screening aberrantly methylated genes in CRC. In addition, we identified novel methylated genes that are candidate diagnostic or prognostic markers for CRC

Performance evaluation of stool DNA methylation tests in colorectal cancer screening: a systematic review and meta‐analysis

AIM: There is not sufficient evidence about whether stool DNA methylation tests allow prioritizing patients to colonoscopy. Due to the COVID-19 pandemic, there will be a wait-list for rescheduling colonoscopies once the mitigation is lifted. The aim of this meta-analysis was to evaluate the accuracy of stool DNA methylation tests in detecting colorectal cancer. METHODS: The PubMed, Cochrane Library and MEDLINE via Ovid were searched. Studies reporting the accuracy (Sackett phase 2 or 3) of stool DNA methylation tests to detect sporadic colorectal cancer were included. The DerSimonian-Laird method with random-effects model was utilized for meta-analysis. RESULTS: Forty-six studies totaling 16 149 patients were included in the meta-analysis. The pooled sensitivity and specificity of all single genes and combinations was 62.7% (57.7%, 67.4%) and 91% (89.5%, 92.2%), respectively. Combinations of genes provided higher sensitivity compared to single genes (80.8% [75.1%, 85.4%] vs. 57.8% [52.3%, 63.1%]) with no significant decrease in specificity (87.8% [84.1%, 90.7%] vs. 92.1% [90.4%, 93.5%]). The most accurate single gene was found to be SDC2 with a sensitivity of 83.1% (72.6%, 90.2%) and a specificity of 91.2% (88.6%, 93.2%). CONCLUSIONS: Stool DNA methylation tests have high specificity (92%) with relatively lower sensitivity (81%). Combining genes increases sensitivity compared to single gene tests. The single most accurate gene is SDC2, which should be considered for further research