Prognostic Outcomes and Risk Factors for Patients with Renal Cell Carcinoma and Venous Tumor Thrombus after Radical Nephrectomy and Thrombectomy: The Prognostic Significance of Venous Tumor Thrombus Level.

IntroductionTo evaluate the prognostic outcomes and risk factors for renal cell carcinoma (RCC) patients with venous tumor thrombus in China.Materials and methodsWe reviewed the clinical information of 169 patients who underwent radical nephrectomy and thrombectomy. Overall and cancer-specific survival rates were analyzed. Univariate and multivariate analyses were used to investigate the potential prognostic factors.ResultsThe median survival time was 63 months. The five-year overall survival and cancer-specific survival rate were 53.6% and 54.4% for all patients. For all patients, significant survival difference was only observed between early (below hepatic vein) and advanced (above hepatic vein) tumor thrombus. However, significant differences existed between both RV/IVC and early/advanced tumor thrombus groups in N0M0 patients. Multivariate analysis demonstrated that higher tumor thrombus level (p = 0.016, RR = 1.58), N (p = 0.013, RR = 2.60), and M (p < 0.001, RR = 4.14) stages and adrenal gland invasion (p = 0.001, RR = 4.91) were the most significant negative prognostic predictors.ConclusionsIn this study, we reported most cases of RCC patients with venous extension in China. We proved that patients with RCC and venous tumor thrombus may have relative promising long-term survival rate, especially those with early tumor thrombus

Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme

BACKGROUND: Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. RESULTS: In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. CONCLUSION: The effectiveness of proposed algorithm is validated on both small and large protein interaction networks

Applicability of Measurement-based Quantum Computation towards Physically-driven Variational Quantum Eigensolver

Recently variational quantum algorithms have been considered promising quantum computation methods, where the mainstream algorithms are based on the conventional quantum circuit scheme. However, in the Measurement-Based Quantum Computation (MBQC) scheme, multi-qubit rotation operations are implemented with a straightforward approach that only requires a constant number of single-qubit measurements, providing potential advantages in both resource cost and fidelity. The structure of Hamiltonian Variational Ansatz (HVA) aligns well with this characteristic. In this paper, we propose an efficient measurement-based quantum algorithm for quantum many-body system simulation tasks, alluded to as Measurement-Based Hamiltonian Variational Ansatz (MBHVA). We then demonstrate its effectiveness, efficiency, and advantages with two quantum many-body system models. Numerical experiments show that MBHVA is expected to reduce resource overhead compared to the construction of quantum circuits especially in the presence of large-scale multi-qubit rotation operations. Furthermore, when compared to measurement-based Hardware Efficient Ansatz (MBHEA) on quantum many-body system problems, MBHVA also demonstrates superior performance. We conclude that the MBQC scheme is potentially better suited for quantum simulation than the circuit-based scheme in terms of both resource efficiency and error mitigation

GraphMoco:a Graph Momentum Contrast Model that Using Multimodel Structure Information for Large-scale Binary Function Representation Learning

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter several limitations. Firstly, accurately annotating different pairs of functions with appropriate labels poses a significant challenge, thereby making it difficult to employ supervised learning methods without risk of overtraining on erroneous labels. Secondly, while SOTA models often rely on pre-trained encoders or fine-grained graph comparison techniques, these approaches suffer from drawbacks related to time and memory consumption. Thirdly, the momentum update algorithm utilized in graph-based contrastive learning models can result in information leakage. Surprisingly, none of the existing articles address this issue. This research focuses on addressing the challenges associated with large-scale BCSD. To overcome the aforementioned problems, we propose GraphMoco: a graph momentum contrast model that leverages multimodal structural information for efficient binary function representation learning on a large scale. Our approach employs a CNN-based model and departs from the usage of memory-intensive pre-trained models. We adopt an unsupervised learning strategy that effectively use the intrinsic structural information present in the binary code. Our approach eliminates the need for manual labeling of similar or dissimilar information.Importantly, GraphMoco demonstrates exceptional performance in terms of both efficiency and accuracy when operating on extensive datasets. Our experimental results indicate that our method surpasses the current SOTA approaches in terms of accuracy.Comment: 22 pages,7 figure

Single Entanglement Connection Architecture between Multi-Layer HEA for Distributed VQE

Realization of large-scale quantum computing on current noisy intermediate-scale quantum (NISQ) devices is the key to achieving near-term quantum advantage. In this work, we propose the single entanglement connection architecture (SECA) for the multi-layer hardware-efficient ansatz (HEA) in VQE and combine it with the gate cutting technology to construct distributed VQE (DVQE) which can efficiently expand the size of NISQ devices under low overheads. Simulation experiments with the two-dimensional Ising model as well as Heisenberg model are conducted. Our numerical results indicate a superiority of SEAC in expressibility, stability and computational performance at the cost of a little loss in entangling capability compared with the full entanglement connection architecture (FECA). Furthermore, we find evidence that the DVQE also outperforms the FECA in terms of effectiveness. Finally, we discuss the open question about the relationship among expressibility, entangling capability and computational performance with some interesting phenomenon appearing in simulation experiments