Driven by emerging distributed computing and cloud-edge collaborative applications, metro and regional networks have experienced continuous surges in hub-and-spoke (H&S) traffic, posing great challenges to existing point-to-point network infrastructures. While coherent point-to-multipoint optical transceivers (P2MP-TRXs) present a more cost-effective solution for accommodating H&S traffic, existing P2MP networking paradigms fail to address the dynamic and asymmetric nature of traffic prevalent in such networks, and consequently, could lead to subpar resource utilization. In this paper, we fill this gap by investigating dynamic asymmetric subcarrier allocation and reconfiguration in drop-and-continue (D&C) optical networks. In particular, our approach aims at minimizing the operational cost of service provisioning by invoking reactive and coordinated connection consolidation as a remedy for the inability to accommodate traffic demands with in-service or newly activated P2MP-TRXs. We first devise an integer linear programming (ILP) model to solve the multi-objective optimization problem exactly. As the problem is proved to beNP-hard, we further develop a column generation (CG)-based approximation algorithm that can offer guaranteed optimality bounds within reasonable time, as well as a polynomial-time heuristic framework employing priority-queue-based progressive search. Extensive simulations verify the effectiveness of our proposal, demonstrating up to 43.3% reduction in bandwidth blocking ratio and 2.9% improvement in spectrum utilization compared with the state of the art.

Nowadays, the performance of data-center networks (DCNs) has become crucial for advancing large-scale computing applications. Hence, to improve the throughput, energy-efficiency and latency of DCNs, people are trying to replace the electrical packet switching (EPS) based spine switches with optical circuit switching (OCS) based ones. In an OCS-based DCN, topology engineering (TPE) is the key operation to dynamically reconfigure its inter-pod topology for accommodating traffic with optimized resource utilization. TPE consists of two highly-correlated steps, i.e., optimizing the target physical inter-pod topology of the DCN based on a traffic matrix, and planning the procedure of OCS reconfiguration such that hitless transition can be achieved. In this paper, we study how to optimize the two steps jointly to efficiently accelerate the hitless reconfiguration of an OCS-based DCN. We formulate a mixed linear programming model (MILP) to solve the joint optimization exactly. Then, to solve the problem time-efficiently, we propose an approach that optimizes TPE design greedily according to various metrics to minimize the number of stages required in hitless reconfiguration for TPE. Extensive simulations verify the effectiveness of our proposals and demonstrate their benefits over existing benchmark.

OBJECTIVE: The accuracy of deep learning models for many disease prediction problems is affected by time-varying covariates, rare incidence, covariate imbalance and delayed diagnosis when using structured electronic health records data. The situation is further exasperated when predicting the risk of one disease on condition of another disease, such as the hepatocellular carcinoma risk among patients with nonalcoholic fatty liver disease due to slow, chronic progression, the scarce of data with both disease conditions and the sex bias of the diseases. The goal of this study is to investigate the extent to which the aforementioned issues influence deep learning performance, and then devised strategies to tackle these challenges. These strategies were applied to improve hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. METHODS: We evaluated two representative deep learning models in the task of predicting the occurrence of hepatocellular carcinoma in a cohort of patients with nonalcoholic fatty liver disease (n = 220,838) from a national EHR database. The disease prediction task was carefully formulated as a classification problem while taking censorship and the length of follow-up into consideration. RESULTS: We developed a novel backward masking scheme to deal with the issue of delayed diagnosis which is very common in EHR data analysis and evaluate how the length of longitudinal information after the index date affects disease prediction. We observed that modeling time-varying covariates improved the performance of the algorithms and transfer learning mitigated reduced performance caused by the lack of data. In addition, covariate imbalance, such as sex bias in data impaired performance. Deep learning models trained on one sex and evaluated in the other sex showed reduced performance, indicating the importance of assessing covariate imbalance while preparing data for model training. CONCLUSIONS: The strategies developed in this work can significantly improve the performance of hepatocellular carcinoma risk prediction among patients with nonalcoholic fatty liver disease. Furthermore, our novel strategies can be generalized to apply to other disease risk predictions using structured electronic health records, especially for disease risks on condition of another disease.

The T-cell receptor (TCR) repertoire is highly diverse among the population and plays an essential role in initiating multiple immune processes. TCR sequencing (TCR-seq) has been developed to profile the T cell repertoire. Similar to other high-throughput experiments, contamination can happen during several steps of TCR-seq, including sample collection, preparation and sequencing. Such contamination creates artifacts in the data, leading to inaccurate or even biased results. Most existing methods assume 'clean' TCR-seq data as the starting point with no ability to handle data contamination. Here, we develop a novel statistical model to systematically detect and remove contamination in TCR-seq data. We summarize the observed contamination into two sources, pairwise and cross-cohort. For both sources, we provide visualizations and summary statistics to help users assess the severity of the contamination. Incorporating prior information from 14 existing TCR-seq datasets with minimum contamination, we develop a straightforward Bayesian model to statistically identify contaminated samples. We further provide strategies for removing the impacted sequences to allow for downstream analysis, thus avoiding any need to repeat experiments. Our proposed model shows robustness in contamination detection compared with a few off-the-shelf detection methods in simulation studies. We illustrate the use of our proposed method on two TCR-seq datasets generated locally.

Nowadays, the booming of emerging network services have shifted the major traffic pattern in metro-aggregation networks from point-to-point (P2P) to hub-and-spoke (H&S). Hence, it will be promising to plan metro-aggregation networks with point-to-multipoint coherent optical transceivers (P2MP-TRXs). This work studies how to plan a survivable wavelength-switched optical network (WSON) with P2MP-TRXs and shared backup path protection (SBPP) to address single-link failures. We formulate an integer linear programming (ILP) model to place P2MP-TRXs, assign sub-carriers (SCs) to P2MP-TRXs, and calculate routing and spectrum assignment (RSA) for the working/backup lightpath between each hub-leaf P2MP-TRX pair, such that traffic demands can be satisfied with the minimum cost. A heuristic based on adaptive demand grouping (ADG) is also proposed to solve the problem time-efficiently. Extensive simulations confirmed the performance of our proposals.

MOTIVATION: Cross-sectional analyses of primary cancer genomes have identified regions of recurrent somatic copy-number alteration, many of which result from positive selection during cancer formation and contain driver genes. However, no effective approach exists for identifying genomic loci under significantly different degrees of selection in cancers of different subtypes, anatomic sites or disease stages. RESULTS: CNGPLD is a new tool for performing case-control somatic copy-number analysis that facilitates the discovery of differentially amplified or deleted copy-number aberrations in a case group of cancer compared with a control group of cancer. This tool uses a Gaussian process statistical framework in order to account for the covariance structure of copy-number data along genomic coordinates and to control the false discovery rate at the region level. AVAILABILITY AND IMPLEMENTATION: CNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.