Characterization of Avidin and Case9 Single Protein Molecules by a Solid-state Nanopore Device

The shape and charge of a protein play significant roles in protein dynamics in the biological system of humans and animals. Characterizing and quantifying the shape and charge of a protein at the single-molecule level remains a challenge. Solid-state nanopores made of silicon nitride (SiNx) have emerged as novel platforms for biosensing such as diagnostics for single-molecule detection and DNA sequencing. SSN detection is based on measuring the variations in ionic conductance as charged biomolecules translocate through nanometer-sized channels driven by an external voltage applied across the membrane. In this paper, we observe the translocation of asymmetric cylindrical structure CRISPR-Cas9 protein and symmetric cylindrical structure Avidin protein driven by an electric field through the solid-state nanopore. We also observe how glycerol impacts on the time durations and current blockage amplitudes produced by the translocation of two proteins in nanopore by using different glycerol concentrations

A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT

The proliferation of fake news has emerged as a critical issue in recent years, requiring significant efforts to detect it. However, the existing fake news detection datasets are sourced from human journalists, which are likely to have inherent bias limitations due to the highly subjective nature of this task. In this paper, we revisit the existing fake news dataset verified by human journalists with augmented fact-checking by large language models (ChatGPT), and we name the augmented fake news dataset ChatGPT-FC. We quantitatively analyze the distinctions and resemblances between human journalists and LLM in assessing news subject credibility, news creator credibility, time-sensitive, and political framing. Our findings highlight LLM's potential to serve as a preliminary screening method, offering a promising avenue to mitigate the inherent biases of human journalists and enhance fake news detection

A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation

Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibit a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models often overlook the importance of generating intermediate conclusions with logical consistency from the given facts, leading to inaccurate conclusions and undermining the overall credibility of entailment trees. To address this limitation, we propose the logical pattern memory pre-trained model (LMPM). LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions. Furthermore, to mitigate the influence of logically irrelevant domain knowledge in the Wikipedia-based data, we introduce an entity abstraction approach to construct the dataset for pre-training LMPM. The experimental results highlight the effectiveness of our approach in improving the quality of entailment tree generation. By leveraging logical entailment patterns, our model produces more coherent and reasonable conclusions that closely align with the underlying premises. Code and Data are released at https://github.com/YuanLi95/T5-LMPMComment: Accepted By Coling 202

Application of bioabsorbable screw fixation for anterior cervical decompression and bone grafting

OBJECTIVES: To examine the application of bioabsorbable screws for anterior cervical decompression and bone grafting fixation and to study their clinical effects in the treatment of cervical spondylosis. METHODS: From March 2007 to September 2012, 56 patients, 36 males and 20 females (38-79 years old, average 58.3±9.47 years), underwent a novel operation. Grafts were fixed by bioabsorbable screws (PLLA, 2.7 mm in diameter) after anterior decompression. The bioabsorbable screws were inserted from the midline of the graft bone to the bone surface of the upper and lower vertebrae at 45 degree angles. Patients were evaluated post-operatively to observe the improvement of symptoms and evaluate the fusion of the bone. The Japanese Orthopaedic Association (JOA) score was used to evaluate the recovery of neurological functions. RESULTS: All screws were successfully inserted, with no broken screws. The rate of symptom improvement was 87.5%. All of the grafts fused well with no extrusion. The average time for graft fusion was 3.8±0.55 months (range 3-5 months). Three-dimensional reconstruction of CT scans demonstrated that the grafts fused with adjacent vertebrae well and that the screws were absorbed as predicted. The MRI findings showed that the cerebrospinal fluid was unobstructed. No obvious complications appeared in any of the follow-up evaluations. CONCLUSIONS: Cervical spondylosis with one- or two-level involvement can be effectively treated by anterior decompression and bone grafting with bioabsorbable screw fixation. This operative method is safe and can avoid the complications induced by metal implants

A network‐based variable selection approach for identification of modules and biomarker genes associated with end‐stage kidney disease

AimsIntervention for end‐stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach.MethodsUsing the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi‐stage knowledge discovery process, including identification of modules of genes by weighted gene co‐expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset.ResultsThree clinically important gene modules associated with ESKD, were identified by weighted gene co‐expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor‐β and Wnt signalling, RNA‐splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively.ConclusionNetwork‐based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in‐depth follow‐up research and effective therapy.SUMMARY AT A GLANCEThis gene–gene network analysis to identify genes associated with end‐stage renal disease is an important step, albeit early, towards the discovery of biomarkers using peripheral blood cells. The findings also provide insight on disease pathophysiology at the molecular level, and hence therapeutic targets for future research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162799/2/nep13655.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162799/1/nep13655_am.pd

Curved water flow characteristics and its influence on navigation

The ship movement is mainly affected by the circulation current in curve channel. In this paper, the curve circulation is taken as the research object, 3D model is established and scientific numerical simulation is carried out. In order to study and analyze the difference, three curve models with different bending degrees are established in this simulation. Finally, according to the simulation results, the measures for safe navigation are proposed

Medium access control for inter-gateway handoff support in multi-hop wireless mesh networks

Wireless mesh networks (WMNs) have emerged to be a key wireless technology to support large-scale wireless Internet access. Seamless inter-gateway handoff support is an essential issue to ensure continuous communications in multi-hop WMNs. When the movement of a mobile mesh node (MN) causes its attachment point change in the Internet, the complete handoff process may include two steps: the link-layer handoff and the network-layer handoff. During the network-layer handoff, network- layer signaling packets need to be transmitted between the MN and the Internet via the multi-hop wireless mesh backbone. Due to the multi-hop transmission of network- layer handoff signaling packets, the handoff performance in WMNs can be largely degraded by the long queueing delay and medium access delay at each mesh router, especially when the backbone traffic volume is high. However, this critical issue is ignored in existing handoff solutions of multi-hop WMNs. In addition, the channel contention between data packets and handoff signaling packets is not considered in existing medium access control (MAC) designs. In this research, the seamless handoff support is addressed from a different perspec- tive. By eliminating channel contentions between data and handoff signaling pack- ets, the queueing delay and channel access delay of signaling packets are reduced, while data throughput is maintained. Since various WMNs have different channel resources and hardware cost requirements, four MAC schemes are proposed to im- prove the multi-hop handoff performance in single-channel single-radio, single-channel multi-radio, multi-channel single-radio, and multi-channel multi-radio WMNs. With the proposed MAC schemes, the inter-gateway handoff performance can be improved significantly in multi-hop WMNs

Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue

Large Language Models (LLMs) have been demonstrated to generate illegal or unethical responses, particularly when subjected to "jailbreak." Research on jailbreak has highlighted the safety issues of LLMs. However, prior studies have predominantly focused on single-turn dialogue, ignoring the potential complexities and risks presented by multi-turn dialogue, a crucial mode through which humans derive information from LLMs. In this paper, we argue that humans could exploit multi-turn dialogue to induce LLMs into generating harmful information. LLMs may not intend to reject cautionary or borderline unsafe queries, even if each turn is closely served for one malicious purpose in a multi-turn dialogue. Therefore, by decomposing an unsafe query into several sub-queries for multi-turn dialogue, we induced LLMs to answer harmful sub-questions incrementally, culminating in an overall harmful response. Our experiments, conducted across a wide range of LLMs, indicate current inadequacies in the safety mechanisms of LLMs in multi-turn dialogue. Our findings expose vulnerabilities of LLMs in complex scenarios involving multi-turn dialogue, presenting new challenges for the safety of LLMs.Comment: working in progress 23pages, 18 figure