Key Lab on Wideband Wireless Communications and Sensor Network Technology of Ministry of Education

A fairness-aware resource allocation scheme in a cooperative orthogonal frequency division multiple (OFDM) network is proposed based on jointly optimizing the subcarrier pairing, power allocation, and channel-user assignment. Compared with traditional OFDM relaying networks, the source is permitted to retransfer the same data transmitted by it in the first time slot, further improving the system capacity performance. The problem which maximizes the energy efficiency (EE) of the system with total power constraint and minimal spectral efficiency constraint is formulated into a mixed-integer nonlinear programming (MINLP) problem which has an intractable complexity in general. The optimization model is simplified into a typical fractional programming problem which is testified to be quasiconcave. Thus we can adopt Dinkelbach method to deal with MINLP problem proposed to achieve the optimal solution. The simulation results show that the joint resource allocation method proposed can achieve an optimal EE performance under the minimum system service rate requirement with a good global convergence

Identification of a cellular senescence-related-lncRNA (SRlncRNA) signature to predict the overall survival of glioma patients and the tumor immune microenvironment

Background: Gliomas are brain tumors that arise from glial cells, and they are the most common primary intracranial tumors with a poor prognosis. Cellular senescence plays a critical role in cancer, especially in glioma. In this study, we constructed a senescence-related lncRNA (SRlncRNA) signature to assess the prognosis of glioma.Methods: The Cancer Genome Atlas was used to collect SRlncRNA transcriptome profiles and clinical data about glioma. Patients were randomized to training, testing, and whole cohorts. LASSO and Cox regression analyses were employed to construct the SRlncRNA signature, and Kaplan–Meier (K-M) analysis was performed to determine each cohort’s survival. Receiver operating characteristic (ROC) curves were applied to verify the accuracy of this signature. Gene set enrichment analysis was used to visualize functional enrichment (GSEA). The CIBERSORT algorithm, ESTIMATE and TIMER databases were utilized to evaluate the differences in the infiltration of 22 types of immune cells and their association with the signature. RT–qPCR and IHC were used to identify the consistency of the signature in tumor tissue.Results: An SRlncRNA signature consisting of six long non-coding RNAs (lncRNAs) was constructed, and patients were divided into high-risk and low-risk groups by the median of their riskscore. The KM analysis showed that the high-risk group had worse overall survival, and the ROC curve confirmed that the riskscore had more accurate predictive power. A multivariate Cox analysis and its scatter plot with clinical characteristics confirmed the riskscore as an independent risk factor for overall survival. GSEA showed that the GO and KEGG pathways were mainly enriched in the immune response to tumor cells, p53 signaling pathway, mTOR signaling pathway, and Wnt signaling pathway. Further validation also yielded significant differences in the risk signature in terms of immune cell infiltration, which may be closely related to prognostic differences, and qRT–PCR and IHC confirmed the consistency of the expression differences in the major lncRNAs with those in the prediction model.Conclusion Our findings indicated that the SRlncRNA signature might be used as a predictive biomarker and that there is a link between it and immune infiltration. This discovery is consistent with the present categorization system and may open new avenues for research and personalized therapy

Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area

Gab2 Ablation Reverses the Stemness of HER2-Overexpressing Breast Cancer Cells

Background/Aims: HER2 has been implicated in mammary tumorigenesis as well as aggressive tumor growth and metastasis. Its overexpression is related to a poor prognosis and chemoresistance in breast cancer patients. Although Grb2-associated binding protein 2 (Gab2) is important in the development and progression of human cancer, its effects and mechanisms in HER2-overexpressing breast cancer are unclear. Methods: Clone formation and MTT assays were used to examine cell proliferation. To detect the effect of Gab2 on the stemness of breast cancer cells, we used flow cytometry, a sphere formation assay, real-time PCR, and western blot. An animal model was created to validate the effect of Gab2 on tumor growth in vivo. Tissue slides were analyzed by immunohistochemistry. Results: Knockdown of Gab2 suppressed PI3K/AKT and MAPK/ERK pathway activity. Gab2 ablation also reduced the stemness of HER2-overexpressing breast cancer cells. In vivo, knockdown of Gab2 inhibited tumor growth. Conclusion: This study unveils a potential function of Gab2 in HER2-overexpressing breast cancer cells. Gab2 might be a potential target in the clinical therapy of HER2-overexpressing breast carcinoma

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively

The influences of driving forces on behaviors of Na+ and H2O in cyclic octa-peptide nanotube: investigated by steered molecular dynamics

The behaviors of Na ^+ and H _2 O in cyclic peptide nanotube (CPN) under different conditions are important for their applications. In this study, a series of driving forces has been applied to Na ^+ and H _2 O constrained in the self-assembled nanotube of {cyclo[(-D-Ala-L-Ala) _4 -]} _10 , to understand the influence on the transport properties and behaviors of Na ^+ and H _2 O using steered molecular dynamics (SMD). The results show that H _2 O need less driving force (0.4 kcal mol ^−1 Å ^−1 ) to migrate in the nanotube than that of Na ^+ (2.3 kcal mol ^−1 Å ^−1 ). Under the same driving force, the transport speed of H _2 O is about 135 times faster than that of Na ^+ . The instantaneous velocity curves reveal that water adopts a kind of irregular hopping transport mode which does not change with the driving force, while Na ^+ transports in an obvious hopping mode changing with driving force in three different types. Particularly, the instantaneous velocity curves of Na ^+ under the driving force of 3.2–5.2 kcal mol ^−1 Å are roughly similar to the pulse signal, which is of great significance to the treatment of human diseases and the detection of electrolytes. The transport resistance mainly comes from electrostatic interaction. Results in this work show that cyclic octa-peptide nanotubes have excellent performance sensitive to external driving forces and are good potential materials for drug design, biosensors, ion transmembrane transport and ion probe for the detection of Na ^+ in organisms