Deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance strategy in space division multiplexing elastic optical networks.

Space division multiplexing elastic optical networks (SDM-EONs) with multi-core fiber (MCF) are the promising candidate for future optical networks due to their high transmission capacity and high flexibility. However, the inherent defects of inter-core crosstalk and spectrum fragmentation may have some negative impact on the performance of SDM-EONs. A deep learning and hierarchical graph-assisted crosstalk-aware fragmentation avoidance (DLHGA) strategy is proposed in this paper. Firstly, we introduce a deep learning (DL) model to predict future requests, so as to achieve reasonable scheduling of resource in advance. Then, considering the inter-core crosstalk of MCF, we abstract the core, spectrum and time resource as a three-dimensional (3D) model with the hierarchical graphs. Therefore, the resource allocation process is simplified to be mitigating the crosstalk and fragmentation from the perspective of inter-core and intra-core, respectively. Finally, based on DL traffic prediction and different characteristics of hierarchical graph, we present an adaptive resource allocation algorithm considering relieving core adjacency and downgrading modulation format to achieve efficient resource scheduling. We evaluate our DLHGA strategy in different topologies, and the results show that our strategy can efficiently improve network performance in terms to inter-core crosstalk and bandwidth blocking probability compared to earlier approaches.

Development of a novel ssDNA aptamer targeting neutrophil gelatinase-associated lipocalin and its application in clinical trials.

BACKGROUND: Neutrophil gelatinase-associated lipocalin (NGAL) is a promising biomarker of early diagnosis and prediction for acute kidney injury (AKI). However, the current program for NGAL detection is not extensively applied in clinics due to the high expense of antibodies. Nucleic acid aptamers are single-strand DNAs or RNAs which could bind to targets with high specificity and affinity, and they have been widely used in the diagnosis and therapy for multiple diseases. It is valuable for us to develop a new method for NGAL detection using aptamers instead of antibodies to achieve increased efficiency and decreased cost. METHODS: Nucleic acid aptamers against NGAL were obtained after SELEX process using magnetic beads, and an enzyme-linked aptamer analysis (ELAA), which can be widely used in clinical diagnosis at low cost, were successfully established. The feasibility of ELAA was further validated with urine samples harvested from 43 AKI patients and 30 healthy people. RESULTS: Three candidate aptamers, including NA36, NA42 and NA53, were obtained after 8 rounds of SELEX process with magnetic beads and verified by quantitative polymerase chain reaction (qPCR), and the Kd value of each aptamer was 43.59, 66.55 and 32.52 nM, respectively. Moreover, the linear relationship was consistent at the range of 125-4000 ng/mL, and the detection limit of ELAA assay was 30.45 ng/mL. We also found that NGAL could be exclusively detected with NA53, and no cross-reaction between NA53 and human albumin or globulin occurred, the coefficient of variation (CV) between inner-plate and inter-plate was less than 15%, and the recovery rate was between 80 and 110%. Moreover, the sensitivity and specificity of ELAA assay in this study are 100% and 90%, respectively. Consistently, these results could also diagnose whether the occurrence of AKI in lots of patients, which has been demonstrated with the ELAA method we established after using NA53. CONCLUSIONS: Taken together, NA53, the best candidate aptamer targeting NGAL protein, can be applied in clinical testing.

Multi-omics joint analysis revealed the metabolic profile of retroperitoneal liposarcoma.

Retroperitoneal liposarcoma (RLPS) is the main subtype of retroperitoneal soft sarcoma (RSTS) and has a poor prognosis and few treatment options, except for surgery. The proteomic and metabolic profiles of RLPS have remained unclear. The aim of our study was to reveal the metabolic profile of RLPS. Here, we performed proteomic analysis (n = 10), metabolomic analysis (n = 51), and lipidomic analysis (n = 50) of retroperitoneal dedifferentiated liposarcoma (RDDLPS) and retroperitoneal well-differentiated liposarcoma (RWDLPS) tissue and paired adjacent adipose tissue obtained during surgery. Data analysis mainly revealed that glycolysis, purine metabolism, pyrimidine metabolism and phospholipid formation were upregulated in both RDDLPS and RWDLPS tissue compared with the adjacent adipose tissue, whereas the tricarboxylic acid (TCA) cycle, lipid absorption and synthesis, fatty acid degradation and biosynthesis, as well as glycine, serine, and threonine metabolism were downregulated. Of particular importance, the glycolytic inhibitor 2-deoxy-D-glucose and pentose phosphate pathway (PPP) inhibitor RRX-001 significantly promoted the antitumor effects of the MDM2 inhibitor RG7112 and CDK4 inhibitor abemaciclib. Our study not only describes the metabolic profiles of RDDLPS and RWDLPS, but also offers potential therapeutic targets and strategies for RLPS.