Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction. METHOD: This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods. RESULTS: Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques. CONCLUSIONS: This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

Reconstruction of Metabolic-Protein Interaction Integrated Network of Eriocheir sinensis and Analysis of Ecdysone Synthesis.

Integrated networks have become a new interest in genome-scale network research due to their ability to comprehensively reflect and analyze the molecular processes in cells. Currently, none of the integrated networks have been reported for higher organisms. Eriocheir sinensis is a typical aquatic animal that grows through ecdysis. Ecdysone has been identified to be a crucial regulator of ecdysis, but the influence factors and regulatory mechanisms of ecdysone synthesis in E. sinensis are still unclear. In this work, the genome-scale metabolic network and protein-protein interaction network of E. sinensis were integrated to reconstruct a metabolic-protein interaction integrated network (MPIN). The MPIN was used to analyze the influence factors of ecdysone synthesis through flux variation analysis. In total, 236 integrated reactions (IRs) were found to influence the ecdysone synthesis of which 16 IRs had a significant impact. These IRs constitute three ecdysone synthesis routes. It is found that there might be alternative pathways to obtain cholesterol for ecdysone synthesis in E. sinensis instead of absorbing it directly from the feeds. The MPIN reconstructed in this work is the first integrated network for higher organisms. The analysis based on the MPIN supplies important information for the mechanism analysis of ecdysone synthesis in E. sinensis.

Exploring the mechanism of daphne-type diterpenes against gastric cancer cells.

Gastric cancer is one of the common malignant tumors. It is reported that daphne-type diterpenes have inhibitory effects on gastric cancer cells, but the mechanism is still unknown. To explore the detailed mechanism of the anticancer effect of daphne-type diterpenes, we carried out an integrated network pharmacology prediction study and selected an effective component (yuanhuacine, YHC) for the following validation in silico and in vitro. The result showed that daphne-type diterpenes exerted an anti-tumor effect by targeting proto-oncogene tyrosine-protein kinase SRC as well as regulating the Ras/MAPK signaling pathway, which caused the apoptosis and mitochondrial damage in gastric cancer cells.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Risk-Aware Distributionally Robust Optimization for Mobile Edge Computation Task Offloading in the Space-Air-Ground Integrated Network.

As an emerging network paradigm, the space-air-ground integrated network (SAGIN) has garnered attention from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortage of computing and storage resources in mobile devices greatly impacts the quality of experiences for intelligent applications. Hence, we plan to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MECs). To facilitate efficient processing, we need to solve the optimal task offloading decisions. In contrast to existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capabilities for edge computing nodes, the uncertainty of transmission latency caused by heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we first describe the task offloading decision problem in environments characterized by these new challenges. However, we cannot use standard robust optimization and stochastic optimization methods to obtain optimal results under uncertain network environments. In this paper, we propose the 'condition value at risk-aware distributionally robust optimization' algorithm for task offloading, denoted as RADROO, to solve the task offloading decision problem. RADROO combines the distributionally robust optimization and the condition value at risk model to achieve optimal results. We evaluated our approach in simulated SAGIN environments, considering confidence intervals, the number of mobile task offloading instances, and various parameters. We compare our proposed RADROO algorithm with state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the Brute algorithm. The experimental results show that RADROO can achieve a sub-optimal mobile task offloading decision. Overall, RADROO is more robust than others to the new challenges mentioned above in SAGIN.

On-Demand Anonymous Access and Roaming Authentication Protocols for 6G Satellite-Ground Integrated Networks.

Satellite-ground integrated networks (SGIN) are in line with 6th generation wireless network technology (6G) requirements. However, security and privacy issues are challenging with heterogeneous networks. Specifically, although 5G authentication and key agreement (AKA) protects terminal anonymity, privacy preserving authentication protocols are still important in satellite networks. Meanwhile, 6G will have a large number of nodes with low energy consumption. The balance between security and performance needs to be investigated. Furthermore, 6G networks will likely belong to different operators. How to optimize the repeated authentication during roaming between different networks is also a key issue. To address these challenges, on-demand anonymous access and novel roaming authentication protocols are presented in this paper. Ordinary nodes implement unlinkable authentication by adopting a bilinear pairing-based short group signature algorithm. When low-energy nodes achieve fast authentication by utilizing the proposed lightweight batch authentication protocol, which can protect malicious nodes from DoS attacks. An efficient cross-domain roaming authentication protocol, which allows terminals to quickly connect to different operator networks, is designed to reduce the authentication delay. The security of our scheme is verified through formal and informal security analysis. Finally, the performance analysis results show that our scheme is feasible.

A systematic computational analysis of Mycobacterium tuberculosis H37Rv and human CD34+ genomic expression reveals crucial molecular entities involved in infection progression.

The co-evolution of Mycobacterium tuberculosis H37Rv along with its host systems enables the pathogenic bacterium to emerge as a multi-drug resistant form. This creates challenges for a more efficacious treatment strategy that can mitigate the infection. Working towards the same, our study followed a mathematical and statistical approach proposing that mycobacterial transcription factors regulating virulence and adaptation, host cell cytoplasmic component metabolism, oxidoreductase activity and respiratory ETC would be targets for antibiotics against Mycobacterium tuberculosis. Simultaneously, extending the statistical study on Mycobacterium-infected human cord blood CD34+ cells revealed that the human CD34+ genes, S100A8 and FGR (tyrosine-protein kinase, Src2), might be affected in the infection pathogenesis by Mycobacterium. Further, the deduced Mycobacterium-human gene interaction network proposed that mycobacterial coregulators Rv0452 (MarR family regulator) and Rv3862c (WhiB6) triggered genes controlling bacterial metabolism, which influences human immunological pathways involving TLR2 and CXCL8/MAPK8.Communicated by Ramaswamy H. Sarma.

Factors Associated with Remote Patient Monitoring Services Provision by Hospitals and Health Care Systems in the United States.

Purpose: To assess the factors associated with offering remote patient monitoring (RPM) services. Methods: We integrated three datasets: (1) 2019-2020 Area Health Resource Files, (2) 2019 American Community Survey, and (3) 2019 American Hospitals Association annual survey using county Federal Information Processing Standards code to evaluate associations between hospital characteristics and county-level demographic factors with provision of (1) post-discharge, (2) chronic care, (3) other RPM services, and (4) any of these three RPM service categories. These outcomes were analyzed using multi-level, mixed-effects multivariate logistic regression modeling to account for county-level clustering of hospitals. Findings: Among 3,381 hospitals, 1,354 (40.0%) provided any RPM services. Being part of a clinically integrated network (CIN) and private, non-profit (vs. public) ownership were respectively associated with 104.5% (95% confidence interval [CI]: 69.4-146.8%; p < 0.001) and 30.4% (95% CI: 2.5-66.0%; p = 0.031) higher odds of providing any RPM services. Critical access hospital (CAH) designation, for-profit (vs. public) ownership, and location in the South (vs. Northeast) were associated with significantly lowering odds of providing any RPM services by 36.2% (95% CI: 14.2-52.6%; p = 0.003), 70.1% (95% CI: 56.0-79.6%; p < 0.001), and 34.0% (95% CI: 2.8-55.1%; p = 0.035), respectively. Similar trends were found with the various RPM service categories. Conclusions: The factors most associated with provision of any RPM services were hospital-level factors. Specifically, being part of a CIN and private, non-profit ownership had the highest positive associations with offering RPM services whereas location in the South and CAH designation had the strongest negative associations. Further studies are needed to understand the reasons behind these associations.

General population preferences for cancer care in health systems of China: A discrete choice experiment.

BACKGROUND: The increasing incidence of cancer in China has posed considerable challenges for cancer care delivery systems. This study aimed to determine the general population's preferences for cancer care, to provide evidence for building a people-centered integrated cancer care system. METHODS: We conducted a discrete choice experiment that involved 1,200 participants in Shandong Province. Individuals were asked to choose between cancer care scenarios based on the type and level of hospitals, with various out-of-pocket costs, waiting time, and contact working in the hospitals. Individual preferences, willingness to pay, and uptake rate were estimated using a mixed-logit model. RESULTS: This study included 848 respondents (70.67%). Respondents preferred county hospitals with shorter hospitalization waiting times and contact working in hospitals. Compared to the reference levels, the three highest willingness to pay values were related to waiting time for hospitalization (¥97,857.69-¥145411.70-¥212,992.10/$14512.70-$21565.16-$31587.61), followed by the county-level hospital (¥32,545.13/$4826.58). The preferences of the different groups of respondents were diverse. Based on a county-level general hospital with contact in the hospital, 50% out-of-pocket costs and a waiting time of 15 days, the probability of seeking baseline care was 0.37. Reducing the waiting time from 15 to 7, 3, and 0 days, increases the probability of choosing a county-level hospital from 0.37 to 0.58, 0.64, and 0.70, respectively. CONCLUSIONS: This study suggests that there is a substantial interest in attending county-level hospitals and that reducing hospitalization waiting time is the most effective measure to increase the probability of seeking cancer care in county-level hospitals.

[Preliminary Investigation of the Molecular Mechanism of Empagliflozin Suppressing Gastric Cancer Through Mammalian Target of Rapamycin]. / æ©æ ¼åˆ—å‡€é€šè¿‡é›·å¸•éœ‰ç´ é¶ç‚¹è›‹ç™½æŠ‘åˆ¶èƒƒç™Œçš„åˆ†å­æœºåˆ¶åˆæŽ¢.

Objective: To predict the intervention targets of empagliflozin (EMPA), a specific inhibitor of sodium-glucose cotransporter 2 (SGLT2), in gastric adenocarcinoma through comprehensive network pharmacology, and to validate the effects and the molecular mechanisms of EMPA through cellular and molecular biology experiments. Methods: Bioinformatics analysis of gastric adenocarcinoma was conducted to assess the correlation between gastric adenocarcinoma prognosis and SGLT2 expression. Network pharmacology was utilized to identify shared targets of EMPA and gastric adenocarcinoma. AGS cells, a human gastric adenocarcinoma cells line, were incubated with EMPA at different concentrations for 24 h and, then, cell proliferation was assessed using the CCK8 assay. After AGS cells were incubated with EMPA at the doses of 0, 3, and 6 mmol/L, real-time cell analysis (RTCA) and 5-ethynyl-2-deoxyuridine (EdU) incorporation were used to evaluate EMPA's inhibitory effects on the proliferation of the AGS cells. In addition, wound healing and Transwell assays were performed to assess the inhibitory effect of EMPA on the migration and invasion of the APC cells and Western blot analysis was conducted to examine the expression of mammalian target of rapamycin (mTOR) and phosphorylated mTOR (p-mTOR). BALB/c (nu/nu) nude mice were implanted with 5×106 AGS cells in the axilla. The mice were divided into three groups, a control group, a low-dose group, and a high-dose group, each consisting of 7 mice. After one week, the control group received daily intraperitoneal injections of normal saline, while the low-dose group and high-dose group received daily intraperitoneal injections of EMPA at the doses of 3 mg/kg and 5 mg/kg, respectively. The tumor volume was measured one week after the drug intervention started. Results: Gastric adenocarcinoma patients with low expression of SGLT2 exhibited longer survival time and higher survival rate than those with high expression of SGLT2 did. A total of 104 EMPA-related potential targets and 2028 targets associated with gastric adenocarcinoma were identified. Among these, 45 targets associated with gastric adenocarcinoma overlapped with potential targets of EMPA. Further analysis revealed 10 relevant pathways and 4 core genes. The core genes were cyclin-dependent kinase 4 (CDK4), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), mTOR, and cyclin E1 (CCNE1). CCK-8 assay revealed that EMPA at concentrations ranging from 0.39 to 50 mmol/L effectively inhibited the proliferation of AGS cells. RTCA results indicated a downward shift in the cell growth curve. In comparison to the findings for the control group, EdU assay demonstrated that EMPA at the concentrations of 3 mmol/L and 6 mmol/L significantly inhibited AGS cell proliferation (P<0.05). Results from wound healing and Transwell assays indicated a decrease in the levels of cell migration and invasion (P<0.05) and, notably, there was a significant difference between the high and low-dose EMPA groups (P<0.05). Western blot showed no statistically significant difference in the expression of total mTOR protein between the groups. However, the expression of p-mTOR in the 3 mmol/L and 6 mmol/L EMPA groups decreased compared to that of the control group (P<0.05), with the 6 mmol/L EMPA group exhibiting a more pronounced reduction (P<0.05). Nude mice xenograft tumor experiment demonstrated that, compared to that of the control group, the tumor volumes in the EMPA-treatment groups were significantly reduced (P<0.05), with the high-dose group showing a more pronounced reduction (P<0.05). Conclusion: EMPA inhibits the abnormal proliferation and migration of gastric adenocarcinoma cells, potentially through the modulation of mTOR protein activation. This study provides new potential medication and intervention targets for gastric adenocarcinoma treatment.

Electromagnetic Spectrum Allocation Method for Multi-Service Irregular Frequency-Using Devices in the Space-Air-Ground Integrated Network.

The management and allocation of electromagnetic spectrum resources is the inner driving force of the construction of the space-air-ground integrated network. Existing spectrum allocation methods are difficult to adapt to the scenario where the working bandwidth of multi-service frequency-using devices is irregular and the working priorities are different. In this paper, an orthogonal genetic algorithm based on the idea of mixed niches is proposed to transform the problem of frequency allocation into the optimization problem of minimizing the electromagnetic interference between frequency-using devices in the integrated network. At the same time, a system model is constructed that takes the minimum interference effect of low-priority-to-high-priority devices as the objective function and takes the protection frequency and natural frequency as the constraint conditions. In this paper, we not only introduce the thought of niches to improve the diversity of the population but also use an orthogonal uniform crossover operator to improve the search efficiency. At the same time, we use a standard genetic algorithm and a micro genetic algorithm to optimize the model. The global searchability and local search precision of the proposed algorithm are all improved. Simulation results show that compared with the existing methods, the proposed algorithm has the advantages of fast convergence, strong stability and good optimization effect.

Resource Mapping Allocation Scheme in 6G Satellite Twin Network.

The sixth generation (6G) satellite twin network is an important solution to achieve seamless global coverage of 6G. The deterministic geometric topology and the randomness of the communication behaviors of 6G networks limit the realism and transparency of cross-platform and cross-object communication, twin, and computing co-simulation networks. Meanwhile, the parallel-based serverless architecture has a high redundancy of computational resource allocation. Therefore, for the first time, we present a new hypergraph hierarchical nested kriging model, which provides theoretical analysis and modeling of integrated relationships for communication, twin, and computing. We explore the hierarchical unified characterization method which joins heterogeneous topologies. A basis function matrix for local flexible connectivity of the global network is designed for the connection of huge heterogeneous systems to decouple the resource mapping among heterogeneous networks. To improve the efficiency of resource allocation in communication, twin, and computing integrated network, a multi-constraint multi-objective genetic algorithm (MMGA) based on the common requirements of operations, storage, interaction, and multi-layer optimal solution conflict is proposed for the first time. The effectiveness of the algorithm and architecture is verified through simulation and testing.

Identification of Pseudo-R genes in Vitis vinifera and characterization of their role as immunomodulators in host-pathogen interactions.

INTRODUCTION: Duplication events are fundamental to co-evolution in host-pathogen interactions. Pseudogenes (Ψs) are dysfunctional paralogs of functional genes and resistance genes (Rs) in plants are the key to disarming pathogenic invasions. Thus, deciphering the roles of pseudo-R genes in plant defense is momentous. OBJECTIVES: This study aimed to functionally characterize diverse roles of the resistance Ψs as novel gene footprints and as significant gene regulators in the grapevine genome. METHODS: PlantPseudo pipeline and HMM-profiling identified whole-genome duplication-derived (WGD) Ψs associated with resistance genes (Ψ-Rs). Further, novel antifungal and antimicrobial peptides were characterized for fungal associations using protein-protein docking with Erysiphe necator proteins. miRNA and tasiRNA target sites and transcription factor (TF) binding sites were predicted in Ψ-Rs. Finally, differential co-expression patterns in Ψ-Rs-lncRNAs-coding genes were identified using the UPGMA method. RESULTS: 2,746 Ψ-Rs were identified from 31,032 WGD Ψs in the genome of grapevine. 69-antimicrobial and 81-antifungal novel peptides were generated from Ψ-Rs. The putative genic potential was predicted for five novel antifungal peptides which were further characterized by docking against E. necator proteins. 395 out of 527 resistance loci-specific Ψ-Rs were acting as parental gene mimics. Further, to explore the diverse roles of Ψ-Rs in plant-defense, we identified 37,026 TF-binding sites, 208 miRNA, and 99 tasiRNA targeting sites on these Ψ-Rs. 194 Ψ-Rs were exhibiting tissue-specific expression patterns. The co-expression network analysis between Ψs-lncRNA-genes revealed six out of 79 pathogen-responsive Ψ-Rs as significant during pathogen invasion. CONCLUSIONS: Our study provides pathogen responsive Ψ-Rs integral for pathogen invasion, which will offer a useful resource for future experimental validations. In addition, our findings on novel peptide generations from Ψ-Rs offer valuable insights which can serve as a useful resource for predicting novel genes with the futuristic potential of being investigated for their bioactivities in the plant system.

Transcriptome­based drug repositioning identifies TPCA­1 as a potential selective inhibitor of esophagus squamous carcinoma cell viability.

Esophageal squamous cell carcinoma (ESCC) is a cancer type with limited treatment options. The present study aimed to screen for small molecules that may inhibit ESCC cell viability. The small­molecule­perturbed signatures were extrapolated from the library of integrated network­based cellular signatures (LINCS) database. Since LINCS does not include small­molecule­perturbed signatures of ESCC cells, it was hypothesized that non­ESCC cell lines that display transcriptome profiles similar to those of ESCC may have similar small­molecule­perturbated responses to ESCC cells and that identifying small molecules that inhibit the viability of these non­ESCC cells may also inhibit the viability of ESCC cells. The transcriptomes of >1,000 cancer cell lines from the Cancer Cell Line Encyclopedia database were analyzed and 70 non­ESCC cell lines exhibiting similar transcriptome profiles to those of ESCC cells were identified. Among them, six cell lines with transcriptome signatures upon drug perturbation were available in the LINCS, which were used as reference signatures. A total of 20 ESCC datasets were analyzed and 522 downregulated and 461 upregulated differentially expressed genes (DEGs) that were consistently altered across >50% of the datasets were identified. These DEGs together with the reference signatures were then used as inputs of the ZhangScore method to score small molecules that may reverse transcriptome alterations of ESCC. Among the top­ranked 50 molecules identified by the ZhangScore, four candidates that may inhibit ESCC cell viability were experimentally verified. Furthermore, 2­[(aminocarbonyl)amino]­5­(4­fluorophenyl)­3­-thiophenecarboxamide (TPCA­1), an inhibitor of the NF­κB pathway, was able to preferentially inhibit the viability of ESCC cells compared with non­tumorigenic epithelial Het­1A cells. Mechanistically, TPCA­1 induced ESCC KYSE­450 cell apoptosis by inhibiting the phosphorylation of inhibitor of NF­κB kinase subunit ß, leading to IκBα stabilization and NF­κB signaling pathway inhibition. Collectively, these results demonstrated that LINCS­based drug repositioning may facilitate drug discovery and that TPCA­1 may be a promising candidate molecule in the treatment of ESCC.

Proteome-centric cross-omics characterization and integrated network analyses of triple-negative breast cancer.

We report a comprehensive proteomic study of a 90-case cohort of paired samples of triple-negative breast cancer (TNBC) in quantification, phosphorylation, and DNA-binding capacity. Four integrative subtypes (iP-1-4) are stratified on the basis of global proteome and phosphoproteome, each of which exhibits distinct molecular and pathway features. Scaffold and co-expression network analyses of three proteomic datasets, integrated with those from genome and transcriptome of the same cohort, reveal key pathways and master regulators that, characteristic of TNBC subtypes, play important regulatory roles within and between scaffold sub-structures and co-expression communities. We find that NAE1 is a potential drug target for subtype iP-1, and a series of key molecules in fatty acid metabolism, such as AKT1/FASN, are plausible targets for subtype iP-2. Libraries of proteins, pathways and networks of TNBC provide a valuable molecular infrastructure for further clinical exploration and in-depth studies of the molecular mechanisms of the disease.

Drug connectivity mapping and functional analysis reveal therapeutic small molecules that differentially modulate myelination.

Disruption or loss of oligodendrocytes (OLs) and myelin has devastating effects on CNS function and integrity, which occur in diverse neurological disorders, including Multiple Sclerosis (MS), Alzheimer's disease and neuropsychiatric disorders. Hence, there is a need to develop new therapies that promote oligodendrocyte regeneration and myelin repair. A promising approach is drug repurposing, but most agents have potentially contrasting biological actions depending on the cellular context and their dose-dependent effects on intracellular pathways. Here, we have used a combined systems biology and neurobiological approach to identify compounds that exert positive and negative effects on oligodendroglia, depending on concentration. Notably, next generation pharmacogenomic analysis identified the PI3K/Akt modulator LY294002 as the most highly ranked small molecule with both pro- and anti-oligodendroglial concentration-dependent effects. We validated these in silico findings using multidisciplinary approaches to reveal a profoundly bipartite effect of LY294002 on the generation of OPCs and their differentiation into myelinating oligodendrocytes in both postnatal and adult contexts. Finally, we employed transcriptional profiling and signalling pathway activity assays to determine cell-specific mechanisms of action of LY294002 on oligodendrocytes and resolve optimal in vivo conditions required to promote myelin repair. These results demonstrate the power of multidisciplinary strategies in determining the therapeutic potential of small molecules in neurodegenerative disorders.

New Payment Models: The Medicare Access and CHIP Reauthorization Act of 2015, Merit-based Incentive Payment System, Advanced Alternative Payment Models, Bundling, Value-Based Care, Quadruple Aim, and Big Data: What Do They Mean for Otolaryngology?

New payment models have been introduced by the Centers for Medicare and Medicaid Services to move medicine away from volume-based care toward value-based care. Most models focus on changes for primary care, but specialists like otolaryngologists are wise to familiarize themselves with this changing payment landscape to take advantage of the opportunities and avoid the pitfalls associated with each model.

Complexity against current cancer research: Are we on the wrong track?

Cancer genetics has led to major discoveries, including protooncogene and tumor-suppressor concepts, and cancer genomics generated concepts like driver and passenger genes, revealed tumor heterogeneity and clonal evolution. Reconstructing trajectories of tumorigenesis using spatial and single-cell genomics is possible. Patient stratification and prognostic parameters have been improved. Yet, despite these advances, successful translation into targeted therapies has been scarce and mostly limited to kinase inhibitors. Here, we argue that current cancer research may be on the wrong track, by considering cancer more as a "monogenic" disease, trying to extract common information from thousands of patients, while not properly considering complexity and individual diversity. We suggest to empower a systems cancer approach which reconstructs the information network that has been altered by the tumorigenic events, to analyze hierarchies and predict (druggable) key nodes that could interfere with/block the aberrant information transfer. We also argue that the interindividual variability between patients of similar cohorts is too high to extract common polygenic network information from large numbers of patients and argue in favor of an individualized approach. The analysis we propose would require a structured multinational and multidisciplinary effort, in which clinicians, and cancer, developmental, cell and computational biologists together with mathematicians and informaticians develop dynamic regulatory networks which integrate the entire information transfer in and between cells and organs in (patho)physiological conditions, revealing hierarchies and available drugs to interfere with key regulators. Based on this blueprint, the altered information transfer in individual cancers could be modeled and possible targeted (combo)therapies proposed.

Spectrum Sharing in the Sky and Space: A Survey.

In order to achieve the vision of seamless wireless communication coverage, a space-air-ground integrated network is proposed as a key component of the sixth-generation (6G) mobile communication system. However, the spectrum used by aerial networks has become gradually crowded with the increase in wireless devices. Space networks are also in dire need of developing new bands to address spectrum shortages. As an effective way to solve the spectrum shortage problem, spectrum sharing between aerial/space networks and ground networks has been extensively studied. This article summarizes state-of-the-art studies on spectrum sharing between aerial/space networks and ground networks. First, this article provides an overview of aerial networks and space networks and introduces the main application scenarios of aerial networks and space networks. Then, this article summarizes the spectrum sharing techniques between aerial/space networks and ground networks, including existing spectrum utilization rules, spectrum sharing modes and key technologies. Finally, we summarize the challenges of spectrum sharing between aerial/space networks and ground networks. This article provides guidance for spectrum allocation and spectrum sharing of space-air-ground integrated networks.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach-DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of "hybrid" chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug-gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.