Arguments to Key Points Mapping with Prompt-based Learning

Handling and digesting a huge amount of information in an efficient manner has been a long-term demand in modern society. Some solutions to map key points (short textual summaries capturing essential information and filtering redundancies) to a large number of arguments/opinions have been provided recently (Bar-Haim et al., 2020). To complement the full picture of the argument-to-keypoint mapping task, we mainly propose two approaches in this paper. The first approach is to incorporate prompt engineering for fine-tuning the pre-trained language models (PLMs). The second approach utilizes prompt-based learning in PLMs to generate intermediary texts, which are then combined with the original argument-keypoint pairs and fed as inputs to a classifier, thereby mapping them. Furthermore, we extend the experiments to cross/in-domain to conduct an in-depth analysis. In our evaluation, we find that i) using prompt engineering in a more direct way (Approach 1) can yield promising results and improve the performance; ii) Approach 2 performs considerably worse than Approach 1 due to the negation issue of the PLM.Comment: Accepted at ICNLSP 202

Functional Comparison of HIV-1 Vpu Alleles Derived from Elite Controller and Chronic Progressor Patients

Human Immunodeficiency Virus type 1 (HIV-1) is the major causative agent of the AIDS epidemic. Several independent transmission events from monkeys to humans gave rise to different viral lineages that differ with respect to their ability to encode for accessory gene products that facilitate virus replication in the infected host. In particular, the vpu gene is unique to the HIV-1/SIVcpz lineage and not present in HIV-2 and most SIV isolates. Vpu is not essential for HIV-1 replication but intensively modulaties host immune components such as the HIV-1 primary entry receptor CD4, whose cell surface levels are reduced by Vpu. Uniquely, Vpu promotes the release of mature viral particle from infected cells by antagonizing the host restriction factor CD317/tetherin. Moreover, Vpu interferes with NF-ƙB signalling triggered by CD317/tetherin and reduces the cell surface exposure of MHC class I (MHC-I) and natural killer cells ligand NTB-A. While these Vpu activities have been established ex vivo, their relevance for HIV pathogenesis in the infected host remains unclear. In an attempt to correlate Vpu function with the clinical outcome of HIV-1 infection, we assessed here the functions of vpu alleles derived from two distinct clinical groups of treatment-naïve HIV-1 infected patients. While HIV-1 elite controllers (ECs) naturally control virus replication and keep the viral load below detectable level (<50 copies/ml), chronic progressors (CPs) display viral loads of more than 2 000 copies/ml. Both EC and CP Vpu alleles showed conserved and potent capacities to reduce cell surface levels of CD4 and CD317 molecules and to promote viral particle release. In contrast, EC Vpu alleles were less activitive in MHC-I and NTB-A downregulation than CP Vpu alleles and the antagonism of NF-kB signalling was not conserved in both patient groups. Sequence analysis of our Vpu alleles revealed the enrichment of killer-cell immunoglobulin-like receptor (KIR) KIR2DL2-associated footprints in EC Vpus, this polymorphism however did not explain the functional difference between EC and CP Vpus. These results suggest downregulation of cell surface CD4 and antagonism of the particle release restriction by CD317 as important in vivo functions of Vpu. Since attenuated Vpu alleles were more frequent in ECs than in CPs, at least a subgroup of EC Vpu alleles may be under selection pressure resulting in adaptation of Vpu that is associated with a mild fitness cost. Whether the functional constrains of EC Vpu alleles contribute to the suppression of HIV-1 in these patients warrants further investigation

A Retrospective Study on the Timing of Perioperative Antimicrobial Interventions in Class I Incisions

 This retrospective case-control study was conducted to provide reference for the timing of antimicrobial drug use for clinical prevention. Cases of patients with type I incision surgery of 2019 at a 3A hospital were selected for statistical analysis, and 336 cases each with surgical duration ≥3h and equivalent surgical duration &lt;3h of the same type were selected as the case and control groups, respectively. The focus was on the type of surgery, length of surgery, timing of medication, days of medication, and the occurrence or not of surgical site infection (SSI)in patients. There was a significant difference in the incidence of SSI between the case and control groups (18.15% Vs. 6.15%, P&lt;0.001). The number of cases of intraoperative additional antimicrobial drugs for surgical duration ≥3h was 155 (57.83%), of which the number of cases with SSI was 40 and the number of cases with SSI without additional 113 was 21 (25.81% Vs. 18.58%, P=0.145). Additional intraoperative antimicrobial drugs for surgery ≥3 h were not effective in reducing the incidence of SSI, but significantly reduced the number of days patients were hospitalized. The occurrence of SSI is related to many factors and should not be overly dependent on the use of antimicrobial drugs

Poly[(μ3-camphorato-κ3 O:O′:O′′)(2-methyl-1H-imidazole-κN 3)zinc(II)]

In the title compound, [Zn(C10H14O4)(C4H6N2)]n, each ZnII ion is coordinated by one N atom from one 2-methyl-1H-imidazole ligand and three O atoms from two camphorate (cap) ligands in a distorted tetra­hedral geometry. In one of the cap ligands, one methyl group is disordered between positions 1 and 3 in a 0.518 (12):0.482 (12) ratio. Each cap ligand bridges three ZnII ions, forming two-dimensional layers, which inter­act further via N—H⋯O hydrogen bonds

A Survey of the Evolution of Language Model-Based Dialogue Systems

Dialogue systems, including task-oriented_dialogue_system (TOD) and open-domain_dialogue_system (ODD), have undergone significant transformations, with language_models (LM) playing a central role. This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly. Thus, our survey provides a chronological perspective aligned with LM breakthroughs, offering a comprehensive review of state-of-the-art research outcomes. What's more, we focus on emerging topics and discuss open challenges, providing valuable insights into future directions for LLM-based_dialogue_systems. Through this exploration, we pave the way for a deeper_comprehension of the evolution, guiding future developments in LM-based dialogue_systems