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A fog-aided wireless network architecture is studied in which edge nodes (ENs), such as base stations, are connected to a cloud processor via dedicated fronthaul links while also being endowed with caches. Cloud processing enables the centralized implementation of cooperative transmission strategies at the ENs, albeit at the cost of an increased latency due to fronthaul transfer. In contrast, the proactive caching of popular content at the ENs allows for the low-latency delivery of the cached files, but with generally limited opportunities for cooperative transmission among the ENs. The interplay between cloud processing and edge caching is addressed from an information-theoretic viewpoint by investigating the fundamental limits of a high signal-to-noise-ratio metric, termed normalized delivery time (NDT), which captures the worst case coding latency for delivering any requested content to the users. The NDT is defined under the assumptions of either serial or pipelined fronthaul-edge transmission, and is studied as a function of fronthaul and cache capacity constraints. Placement and delivery strategies across both fronthaul and wireless, or edge, segments are proposed with the aim of minimizing the NDT. Information-theoretic lower bounds on the NDT are also derived. Achievability arguments and lower bounds are leveraged to characterize the minimal NDT in a number of important special cases, including systems with no caching capabilities, as well as to prove that the proposed schemes achieve optimality within a constant multiplicative factor of 2 for all values of the problem parameters. [ABSTRACT FROM PUBLISHER]

Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in modern content centric wireless networks by leveraging network load-balancing in the form of localized content storage and delivery. In this paper, we consider a cache-aided network, where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models: 1) centralized content delivery, where user demands at each transmission interval are serviced by the central server via multicast transmissions; and 2) device-to-device assisted distributed delivery, where users multicast to each other in order to service file demands. For such cache-aided networks, we present new results on the fundamental cache storage versus transmission rate tradeoff. Specifically, we develop a new technique for characterizing information theoretic lower bounds on the storage-rate tradeoff and show that the new lower bounds are strictly tighter than cut-set bounds from literature. Furthermore, using the new lower bounds, we improve the constant factor approximation of the optimal storage-rate tradeoff for cache-aided systems under both delivery models. [ABSTRACT FROM PUBLISHER]

Conventional spectrum sharing approaches assume that an unlicensed user accessing a licensed spectrum band autonomously operates by sensing (or detecting) the presence of the primary user, which is licensed to use the given band, with minimal coordination from the primary user. However, in this case, incorrect sensing decisions and resource allocation on the unlicensed secondary users' (SU) part can lead to undesired degradation of the primary users' performance. Therefore, there has been a recent paradigm shift to enable cooperative spectrum sharing, in which interaction is allowed between the primary and the secondary network for efficient spectrum utilization while limiting the interference experienced by a primary user. In this paper, we consider a cooperative spectrum sharing architecture in which the SU is assumed to be a cellular network. We then propose a novel fractional-frequency-reuse-based dynamic resource allocation (DRA) algorithm at the secondary (cellular) network. The joint DRA optimization involves simultaneous spectrum allocation across different base stations and then across users within a base station. It is an NP-hard problem and is prohibitively complex to solve for a large cellular network. Therefore, to reduce the complexity of the joint problem and offer a tractable solution, we formulate layered scheduling algorithms for cross-layer resource allocation. Furthermore, the feedback overhead between the layers is kept minimal. Finally, using simulations, we compare our proposed DRA scheme with other resource allocation schemes for sum system throughput, as well as for fairness in allocation. We also present the result for the case when the available primary spectrum varies over time and locations of SUs. [ABSTRACT FROM AUTHOR]

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with poor response to R-CHOP therapy due to remarkable heterogeneity. Based on gene expression, DLBCL cases were divided into two subtypes, i.e. ABC and GCB, where ABC subtype is associated with poor outcomes. Due to its association with clinical outcome, this classification, also known as cell-of-origin (COO), is an efficient way to predict the response to R-CHOP therapy. Previous COO classification methods have some shortcomings, e.g. limited number of samples in the training dataset. These shortcomings challenge the robustness of methods and make it difficult to implicate these methods at clinical level. To overcome the shortcomings of previous methods, we developed a deep learning-based classifier model on a cohort of 381 DLBCL patients using expression data of 20 genes. We implemented multilayer perceptron (MLP) to train deep learning-based classifier, named MLP-COO. MLP-COO achieved accuracy of 99.70% and 94.70% on training and testing datasets, respectively, with 10-fold cross-validation. We also assessed its performance on an independent dataset of 294 DLBCL patients. On independent dataset, we achieved an accuracy of 95.90% with MCC of 0.917. To show its broader applicability, we used this classifier to predict the clinical outcome using survival data from two large cohorts of DLBCL patients. In survival analysis, MLP-COO recapitulates the survival probabilities of DLBCL patients based on their COO in both cohorts. We anticipate that MLP-COO model developed in this study will benefit in the accurate COO prediction of DLBCL patients and their clinical outcomes. [ABSTRACT FROM AUTHOR]

Although the role of G-quadruplex (G4) DNA structures has been suggested in chromosomal looping this was not tested directly. Here, to test causal function, an array of G4s, or control sequence that does not form G4s, were inserted within chromatin in cells. In vivo G4 formation of the inserted G4 sequence array, and not the control sequence, was confirmed using G4-selective antibody. Compared to the control insert, we observed a remarkable increase in the number of 3D chromatin looping interactions from the inserted G4 array. This was evident within the immediate topologically associated domain (TAD) and throughout the genome. Locally, recruitment of enhancer histone marks and the transcriptional coactivator p300/Acetylated-p300 increased in the G4-array, but not in the control insertion. Resulting promoter-enhancer interactions and gene activation were clear up to 5 Mb away from the insertion site. Together, these show the causal role of G4s in enhancer function and long-range chromatin interactions. Mechanisms of 3D topology are primarily based on DNA-bound architectural proteins that induce/stabilize long-range interactions. Involvement of the underlying intrinsic DNA sequence/structure in 3D looping shown here therefore throws new light on how long-range chromosomal interactions might be induced or maintained. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM) is a common type of blood cancer affecting plasma cells originating from the lymphoid B-cell lineage. It accounts for about 10% of all hematological malignancies and can cause significant end-organ damage. The emergence of genomic technologies such as next-generation sequencing and gene expression analysis has opened new possibilities for early detection of multiple myeloma and identification of personalized treatment options. However, there remain significant challenges to overcome in MM research, including integrating multi-omics data, achieving a comprehensive understanding of the disease, and developing targeted therapies and biomarkers. The extensive data generated by these technologies presents another challenge for data analysis and interpretation. To bridge this gap, we have developed a multi-omics open-access database called MyeloDB. It includes gene expression profiling, high-throughput CRISPR-Cas9 screens, drug sensitivity resources profile, and biomarkers. MyeloDB contains 47 expression profiles, 3 methylation profiles comprising a total of 5630 patient samples and 25 biomarkers which were reported in previous studies. In addition to this, MyeloDB can provide significant insight of gene mutations in MM on drug sensitivity. Furthermore, users can download the datasets and conduct their own analyses. Utilizing this database, we have identified five novel genes, i.e., CBFB, MANF, MBNL1, SEPHS2, and UFM1 as potential drug targets for MM. We hope MyeloDB will serve as a comprehensive platform for researchers and foster novel discoveries in MM. MyeloDB Database URL: . [ABSTRACT FROM AUTHOR]