Energy-Aware Topology Evolution Model with Link and Node Deletion in Wireless Sensor Networks

Based on the complex network theory, a new topological evolving model is proposed. In the evolution of the topology of sensor networks, the energy-aware mechanism is taken into account, and the phenomenon of change of the link and node in the network is discussed. Theoretical analysis and numerical simulation are conducted to explore the topology characteristics and network performance with different node energy distribution. We find that node energy distribution has the weak effect on the degree distribution P(k) that evolves into the scale-free state, nodes with more energy carry more connections, and degree correlation is nontrivial disassortative. Moreover, the results show that, when nodes energy is more heterogeneous, the network is better clustered and enjoys higher performance in terms of the network efficiency and the average path length for transmitting data

A Game Theory Approach to Fair and Efficient Resource Allocation in Cloud Computing

On-demand resource management is a key characteristic of cloud computing. Cloud providers should support the computational resource sharing in a fair way to ensure that no user gets much better resources than others. Another goal is to improve the resource utilization by minimizing the resource fragmentation when mapping virtual machines to physical servers. The focus of this paper is the proposal of a game theoretic resources allocation algorithm that considers the fairness among users and the resources utilization for both. The experiments with an FUGA implementation on an 8-node server cluster show the optimality of this algorithm in keeping fairness by comparing with the evaluation of the Hadoop scheduler. The simulations based on Google workload trace demonstrate that the algorithm is able to reduce resource wastage and achieve a better resource utilization rate than other allocation mechanisms

MedChatZH: a Better Medical Adviser Learns from Better Instructions

Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems. However, in specialized domains like traditional Chinese medical QA, these models may perform unsatisfactorily without fine-tuning on domain-specific datasets. To address this, we introduce MedChatZH, a dialogue model designed specifically for traditional Chinese medical QA. Our model is pre-trained on Chinese traditional medical books and fine-tuned with a carefully curated medical instruction dataset. It outperforms several solid baselines on a real-world medical dialogue dataset. We release our model, code, and dataset on https://github.com/tyang816/MedChatZH to facilitate further research in the domain of traditional Chinese medicine and LLMs.Comment: 7 pages, 3 figure

PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications

Large protein language models are adept at capturing the underlying evolutionary information in primary structures, offering significant practical value for protein engineering. Compared to natural language models, protein amino acid sequences have a smaller data volume and a limited combinatorial space. Choosing an appropriate vocabulary size to optimize the pre-trained model is a pivotal issue. Moreover, despite the wealth of benchmarks and studies in the natural language community, there remains a lack of a comprehensive benchmark for systematically evaluating protein language model quality. Given these challenges, PETA trained language models with 14 different vocabulary sizes under three tokenization methods. It conducted thousands of tests on 33 diverse downstream datasets to assess the models' transfer learning capabilities, incorporating two classification heads and three random seeds to mitigate potential biases. Extensive experiments indicate that vocabulary sizes between 50 and 200 optimize the model, whereas sizes exceeding 800 detrimentally affect the model's representational performance. Our code, model weights and datasets are available at https://github.com/ginnm/ProteinPretraining.Comment: 46 pages, 4figures, 9 table

Characterization of Bovine Induced Pluripotent Stem Cells by Lentiviral Transduction of Reprogramming Factor Fusion Proteins

Pluripotent stem cells from domesticated animals have potential applications in transgenic breeding. Here, we describe induced pluripotent stem (iPS) cells derived from bovine fetal fibroblasts by lentiviral transduction of Oct4, Sox2, Klf4 and c-Myc defined-factor fusion proteins. Bovine iPS cells showed typical colony morphology, normal karyotypes, stained positively for alkaline phosphatase (AP) and expressed Oct4, Nanog and SSEA1. The CpG in the promoter regions of Oct4 and Nanog were highly unmethylated in bovine iPS cells compared to the fibroblasts. The cells were able to differentiate into cell types of all three germ layers in vitro and in vivo. In addition, these cells were induced into female germ cells under defined culture conditions and expressed early and late female germ cell-specific genes Vasa, Dazl, Gdf9, Nobox, Zp2, and Zp3. Our data suggest that bovine iPS cells were generated from bovine fetal fibroblasts with defined-factor fusion proteins mediated by lentivirus and have potential applications in bovine transgenic breeding and gene-modified animals

The serum IgG antibody level as a biomarker for clinical outcome in patients with cerebral sparganosis after treatment

IntroductionCerebral sparganosis is a rare parasitic infection of the brain tissue. The remission of MRI change and clinical symptom has been used to evaluate the therapeutic effect. However, there is no study to correlate the serum IgG antibody level of sparganum to the prognosis of disease after treatment. Methods87 patients with cerebral sparganosis were collected from three medical centers. Clinical symptoms and MRI changes were evaluated at 12 months after initial treatment, and serum IgG antibody level of sparganum was evaluated at 2, 6, and 12 months after treatment. The positive cut-off value was based on 2.1 times the optical density (OD) of negative control. The index value was defined as the sample OD divided by the cut-off value.ResultsAmong the 87 patients after treatment, 71 patients had good clinical outcomes, and 16 had poor clinical outcomes. The area under the curve (AUC) showed that the index value measured at 12 months after treatment had the best prediction effect, with a value of 2.014. In the good-outcome group, the index values were less than 2.014 in all 71 patients, and only 8 patients had mildly enhanced residual lesions on MRI. In the poor-outcome group, the index values were more than 2.014 in all 16 patients, and all patients still showed significantly enhanced lesions on MRI. Compared with poor-outcome patients, only 2 patients with good outcomes had disease recurrence after treatment.DiscussionThis study provided evidence that the serum IgG antibody level of sparganum was a promising biomarker to evaluate the prognosis of patients with cerebral sparganosis after treatment

Preparation of Magnetic Carbon Nanotubes (Mag-CNTs) for Biomedical and Biotechnological Applications

Carbon nanotubes (CNTs) have been widely studied for their potential applications in many fields from nanotechnology to biomedicine. The preparation of magnetic CNTs (Mag-CNTs) opens new avenues in nanobiotechnology and biomedical applications as a consequence of their multiple properties embedded within the same moiety. Several preparation techniques have been developed during the last few years to obtain magnetic CNTs: grafting or filling nanotubes with magnetic ferrofluids or attachment of magnetic nanoparticles to CNTs or their polymeric coating. These strategies allow the generation of novel versatile systems that can be employed in many biotechnological or biomedical fields. Here, we review and discuss the most recent papers dealing with the preparation of magnetic CNTs and their application in biomedical and biotechnological fields

Research on the encryption and digital signatures in remote attestation from elliptic curve group

Bilinear pairings based on the Weil and Tate pairings over elliptic curves have been applied for constructive applications in cryptography protocol for years. Most protocol can be proved to be simplified or expanded using the mathematical structures of different types of pairings.In this paper,we applied parings to a remote attestation model,namely cloud based remote attestation(CBA).We give all the detailed algorithms of it and it can guarantee the private of cloud service and solve authorization auditing mechanisms in cloud environment. The bilinear pair can shorten the required key length and reduced bandwidth usage. It meets the requirements of the trusted computing remote attestation and cloud environment at the same time and the virtual TPM structure fulfills the need of standard cloud computing secure measure, such as duty separation. What’s more, we prove the scheme is correct and secure under the LRSW assumption, and give the costs comparison between the classic remote attestation, BPBA and CBA which show CBA costs lowly