Uniqueness of isometric immersions with the same mean curvature

Motivated by the quasi-local mass problem in general relativity, we study the rigidity of isometric immersions with the same mean curvature into a warped product space. As a corollary of our main result, two star-shaped hypersurfaces in a spatial Schwarzschild or AdS-Schwarzschild manifold with nonzero mass differ only by a rotation if they are isometric and have the same mean curvature. We also give similar results if the mean curvature condition is replaced by an $\sigma_2$-curvature condition.Comment: 23 pages, introduction revised, references update

HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence

A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming

In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future

Construction and Evaluation of the Brucella Double Gene Knock-out Vaccine Strain MB6 Δbp26ΔwboA (RM6)

Brucellosis is a serious zoonotic infection worldwide. To date, vaccination is the most effective measure against brucellosis. This study was aimed at obtaining a vaccine strain that has high protective efficacy and low toxicity, and allows vaccination to be differentiated from infection. Using homologous recombination, we constructed a double gene-deletion Brucella strain MB6 Δbp26ΔwboA (RM6) and evaluated its characteristics, safety and efficacy. The RM6 strain had good proliferative ability and stable biological characteristics in vivo and in vitro. Moreover, it had a favorable safety profile and elicited specific immune responses in mice and sheep. The RM6 strain may have substantial practical application value

Development and Efficacy Evaluation of an SP01-adjuvanted Inactivated Escherichia Coli Mutant Vaccine Against Bovine Coliform Mastitis

Escherichia coli ( E. coli ) is one of the most common pathogens causing clinical mastitis in cattle, but no vaccine is available to prevent this disease in China. Therefore, development of an E. coli vaccine against bovine clinical mastitis is urgently needed. The candidate vaccine (Ch-O111-1) and challenge (LZ06) strains were screened from milk samples of cows with clinical mastitis. To extend the cross-protection of the Ch-O111-1 strain, we deleted the galE gene fragment of the Ch-O111-1 strain through homologous recombination between the Ch-O111-1 strain and pCVD442/ΔgalE plasmid, which was identified through conventional methods, including PCR, SDS-PAGE and sequencing. The Ch-O111-1/ΔgalE (Z9) strain was characterized by extensive cross-reactivity and attenuated virulence. We prepared inactivated Z9 vaccines with different adjuvants. Immunization of inactivated Z9 antigen induced adjuvant-, dosage- and inoculation time-dependent antibody titers in cows and mice. Furthermore, immunization with SP01-adjuvanted inactivated Z9 vaccine protected cows against severe clinical mastitis caused by LZ06 and protected mice against death due to LZ06. An SP01-adjuvanted inactivated Z9 vaccine was successfully developed and found to protect cows against severe mastitis caused by Escherichia coli

Rapid diagnosis of duck Tembusu virus and goose astrovirus with TaqMan-based duplex real-time PCR

The mixed infection of duck Tembusu virus (DTMUV) and goose astrovirus (GoAstV) is an important problem that endangers the goose industry. Although quantitative PCR has been widely used in monitoring these two viruses, there is no reliable method to detect them at the same time. In this study, by analyzing the published genomes of DTMUV and goose astrovirus genotype 2 (GoAstV-2) isolated in China, we found that both viruses have high conservation, showing 96.5 to 99.5% identities within different strains of DTMUV and GoAstV, respectively. Subsequently, PCR primers and TaqMan probes were designed to identify DTMUV and GoAstV-2, and different fluorescent reporters were given to two probes for differential diagnosis. Through the optimization and verification, this study finally developed a duplex TaqMan qPCR method that can simultaneously detect the above two viruses. The lower limits of detection were 100 copies/μL and 10 copies/μL for DTMUV and GoAstV-2 under optimal condition. The assay was also highly specific in detecting one or two viruses in various combinations in specimens, and provide tool for clinical diagnosis of mixed infections of viruses in goose

Concomitant mutation status of ALK-rearranged non-small cell lung cancers and its prognostic impact on patients treated with crizotinib

Background: In non-small cell lung cancer (NSCLC), anaplastic lymphoma kinase (ALK) rearrangement characterizes a subgroup of patients who show sensitivity to ALK tyrosine kinase inhibitors (TKIs). However, the prognoses of these patients are heterogeneous. A better understanding of the genomic alterations occurring in these tumors could explain the prognostic heterogeneity observed in these patients. Methods: We retrospectively analyzed 96 patients with NSCLC with ALK detected by immunohistochemical staining (VENTANA anti-ALK(D5F3) Rabbit Monoclonal Primary Antibody). Cancer tissues were subjected to next-generation sequencing using a panel of 520 cancer-related genes. The genomic landscape, distribution of ALK fusion variants, and clinicopathological characteristics of the patients were evaluated. The correlations of genomic alterations with clinical outcomes were also assessed. Results: Among the 96 patients with immunohistochemically identified ALK fusions, 80 (83%) were confirmed by next-generation sequencing. TP53 mutation was the most commonly co-occurring mutation with ALK rearrangement. Concomitant driver mutations [2 Kirsten rat sarcoma viral oncogene homolog (KRAS) G12, 1 epidermal growth factor receptor (EGFR) 19del, and 1 MET exon 14 skipping] were also observed in 4 adenocarcinomas. Echinoderm microtubule associated protein-like 4 (EML4)-ALK fusions were identified in 95% of ALK-rearranged patients, with 16.2% of them also harboring additional non-EML4- ALK fusions. Nineteen non-EML4 translocation partners were also discovered, including 10 novel ones. Survival analyses revealed that patients concurrently harboring PIK3R2 alterations showed a trend toward shorter progression-free survival (6 vs. 13 months, P=0.064) and significantly shorter overall survival (11 vs. 32 months, P=0.004) than did PIK3R2-wild-type patients. Patients with concomitant alterations in PI3K the signaling pathway also had a shorter median overall survival than those without such alterations (23 vs. 32 months, P=0.014), whereas progression-free survival did not differ significantly. Conclusions: The spectrum of ALK-fusion variants and the landscape of concomitant genomic alterations were delineated in 96 NSCLC patients. Our study also demonstrated the prognostic value of concomitant alterations in crizotinib-treated patients, which could facilitate improved stratification of ALK-rearranged NSCLC patients in the selection of candidates who could optimally benefit from therapy