Arctigenin-induced reversal of drug resistance in cisplastin-resistant cell line A549/DDP, and the mechanism involved

Purpose: To investigate the drug resistance reversal effect of arctigenin (ARG) on cisplatin-insensitive A549/DDP cancer cells, and to elucidate the underlying mechanism(s). Methods: Four groups of cells: control, DDP, ARG and ADP were used. The degrees of inhibition of proliferation, drug resistance and apoptotic changes were measured using MTT assay, CCK-8 assay and flow cytometry, respectively. Expressions of PTEN and STAT3 proteins were determined by Western blotting. Results: At ARG concentration of 5 μmol/L, A549/DDP cells were significantly inhibited (p < 0.05). The combination therapy was more effective in reversing A549/DDP cells resistance than the single therapy. The expression level of PTEN protein increased with increase in ARG concentration, while STAT3 protein expression decreased with increase in ARG concentration. ADP group up-regulated PTEN but decreased STAT3 expression levels. Conclusion: ARG regulates drug resistance in A549/DDP cells, possibly via a mechanism involving reduction of A549/DDP cell sensitivity to DDP, thereby regulating the stress pathways associated with PTEN and STAT3. The combination of ARG and DDP effectively reduces A549/DDP cells resistance

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation.Comment: Work in progres

Electron-impact high-lying N2− resonant states

Quasibound states of the nitrogen molecular anion are studied by electron scattering from N 2 using ab initio R -matrix theory and a close-coupling model. Scattering calculations are performed using both cc-pVTZ and cc-pVQZ target basis sets involving up to 26 low-lying target states in a complete active space configuration-interaction representation. Complex resonance potential energy curves are characterized as a function of internuclear separation for all eight N 2 − states identified, including the well-known X 2 Π g shape resonance, one 1 2 Σ + g Feshbach resonance, as well as six core-excited resonances involving 1 2 Δ g , 1 2 Π u , 2 2 Π u , 3 2 Π u , 1 2 Σ + u , and 1 2 Σ − u . The 2 Δ g and 2 Σ − u resonant states are identified and characterized. Comparisons are made with the very different resonance structure in the isoelectronic CO − anion. The present resonance analysis provides a starting point for studies of the vibrational excitation, electron-impact dissociation, and other resonance-driven phenomena in N 2

AlpaCare:Instruction-tuned Large Language Models for Medical Application

Large Language Models (LLMs) have demonstrated significant enhancements in instruction-following abilities through instruction tuning, achieving notable performances across various tasks. Previous research has focused on fine-tuning medical domain-specific LLMs using an extensive array of medical-specific data, incorporating millions of pieces of biomedical literature to augment their medical capabilities. However, existing medical instruction-tuned LLMs have been constrained by the limited scope of tasks and instructions available, restricting the efficacy of instruction tuning and adversely affecting performance in the general domain. In this paper, we fine-tune LLaMA-series models using 52k diverse, machine-generated, medical instruction-following data, MedInstruct-52k, resulting in the model AlpaCare. Comprehensive experimental results on both general and medical-specific domain free-form instruction evaluations showcase AlpaCare's strong medical proficiency and generalizability compared to previous instruction-tuned models in both medical and general domains. We provide public access to our MedInstruct-52k dataset and a clinician-crafted free-form instruction test set, MedInstruct-test, along with our codebase, to foster further research and development. Our project page is available at https://github.com/XZhang97666/AlpaCare

Tunneling Magnetoresistance in Noncollinear Antiferromagnetic Tunnel Junctions

Antiferromagnetic (AFM) spintronics has emerged as a subfield of spintronics driven by the advantages of antiferromagnets producing no stray fields and exhibiting ultrafast magnetization dynamics. The efficient method to detect an AFM order parameter, known as the N\'eel vector, by electric means is critical to realize concepts of AFM spintronics. Here, we demonstrate that non-collinear AFM metals, such as Mn3Sn, exhibit a momentum dependent spin polarization which can be exploited in AFM tunnel junctions to detect the N\'eel vector. Using first-principles calculations based on density functional theory, we predict a tunneling magnetoresistance (TMR) effect as high as 300% in AFM tunnel junctions with Mn3Sn electrodes, where the junction resistance depends on the relative orientation of their N\'eel vectors and exhibits four non-volatile resistance states. We argue that the spin-split band structure and the related TMR effect can also be realized in other non-collinear AFM metals like Mn3Ge, Mn3Ga, Mn3Pt, and Mn3GaN. Our work provides a robust method for detecting the N\'eel vector in non-collinear antiferromagnets via the TMR effect, which may be useful for their application in AFM spintronic devices

Sensitivity analysis of a fiber ring resonator based on an air-core photonic-bandgap fiber

a b s t r a c t The fiber ring resonator (FRR) is the core sensing element in a resonator fiber optic gyroscope (R-FOG), and its sensitivity determines the performance of the R-FOG. This paper presents an in-depth analysis of the sensitivity of the FRR which is made of an air-core photonic-bandgap fiber (PBF), in which the characteristics of the FRR using PBF are compared with that of an FRR using a conventional single mode fiber. When using PBF instead of conventional fiber, it is found that the resonance curve is changed, and the sensitivity of the FRR is decreased a little when a narrow spectral linewidth laser is used. However, the degree of the decrease in sensitivity is not big enough to deny the advantages of PBF in improving the performance of the R-FOG considering that PBF is much better than conventional fiber in reducing the drift. Also, the optimal parameters of the directional coupler for sensitivity are discussed. It is found that the optimal intensity coupling coefficient when using PBF is nearly two times larger than that when using conventional fiber, and the optimal coupler intensity loss when using PBF is smaller than that when using conventional fiber

Adaptive Routing Forwarding Strategy Based on Neural Network Algorithm

With the profound changes in global digital media, the focus of Internet users has gradually shifted to how to quickly obtain information without paying attention to where the information is stored. However, the current TCP/IP network protocol architecture cannot adapt to the rapid development of today#39s content applications. In order to adapt to the changes in the Internet, information-centric networking (ICN)has received extensive attention. Besides, the optimization of the user service request scheduling problem is the core issue affecting the performance of the ICN , and it is one of the hot research topics in the ICN network. To solve this problem, this paper proposes an adaptive routing forwarding strategy based on neural network algorithm. Through the modeling of the classic architecture named data networking (NDN) network delay model of ICN network, a neural network algorithm is used to delay prediction, and a forwarding strategy mechanism based on predict delay is designed to innovate in the NDN. The interface information Stat is added to the forwarding information base (FIB) of the network component to implement the dynamic selection of the forwarding routing. In addition, routing dynamic self-adaptation adjustment mechanism and fault rerouting function are designed in consideration of the situation of route congestion and interruption. Simulation results show that this strategy effectively reduces network delay and improves network performance