Effect of nematic order on the low-energy spin fluctuations in detwinned BaFe1.935_{1.935}Ni0.065_{0.065}As2_2

The origin of nematic order remains one of the major debates in iron-based superconductors. In theories based on spin nematicity, one major prediction is that the spin-spin correlation length at (0,$\pi$) should decrease with decreasing temperature below the structural transition temperature $T_s$. Here we report inelastic neutron scattering studies on the low-energy spin fluctuations in BaFe$_{1.935}$Ni$_{0.065}$As$_2$ under uniaxial pressure. Both intensity and spin-spin correlation start to show anisotropic behavior at high temperature, while the reduction of the spin-spin correlation length at (0,$\pi$) happens just below $T_s$, suggesting strong effect of nematic order on low-energy spin fluctuations. Our results favor the idea that treats the spin degree of freedom as the driving force of the electronic nematic order.Comment: 5 pages, 4 figure

Binaural Sound Source Localization Based on Convolutional Neural Network

Binaural sound source localization (BSSL) in low signal-to-noise ratio (SNR) and high reverberation environment is still a challenging task. In this paper, a novel BSSL algorithm is proposed by introducing convolutional neural network (CNN). The proposed algorithm first extracts the spatial feature of each sub-band from binaural sound signal, and then combines the features of all sub-bands within one frame to assemble a two-dimensional feature matrix as a grey image. To fully exploit the advantage of the CNN in extracting high-level features from the grey image, the spatial feature matrix of each frame is used as input to train the CNN model. The CNN is then used to predict azimuth of sound source. The experiments show that the proposed algorithm significantly improves the localization performance of BSSL in various acoustic environments, especially to deal with low SNR and high reverberation conditions

Comparison of the efficacy of half ticagrelor loading doses and clopidogrel in elderly acute coronary syndrome patients in China

Purpose: To evaluate the effects of half-load doses (HLD) of ticagrelor and clopidogrel on elderly acute coronary syndrome patients (ACS) over a period of 90 days. Methods: Seventy-four patients diagnosed as ACS were included in this trial. The patients were randomly distributed into group 1 (treated with HLD ticagrelor, 90 mg LD) and group 2 (treated with clopidogrel, 300 mg LD). The interaction of treatment effect was evaluated using Multivariate Cox proportional hazards regression models. Results: Within three months, a total of 12 patients (16.21 %) died of myocardial infarction or stroke. The endpoint of HLD ticagrelor-treated elderly ACS patients was 20 %, and the incidence of clopidogreltreated endpoints was 14.81 %. Conclusion: In the first 45 patients treated with HLD ticagrelor, their cumulative incidence of cardiac events was relatively high. However, there were no considerable changes in the therapeutic benefits of these two drugs in elderly ACS patients. Keywords: Elder patients, Acute coronary syndrome, Ticagrelor, Clopidogre

On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations

Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the continuous improvement in performance, we find that existing DocRE models which initially perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names. To this end, we systematically investigate the robustness of DocRE models to entity name variations in this work. We first propose a principled pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata. By applying the pipeline to DocRED and Re-DocRED datasets, we construct two novel benchmarks named Env-DocRED and Env-Re-DocRED for robustness evaluation. Experimental results show that both three representative DocRE models and two in-context learned large language models consistently lack sufficient robustness to entity name variations, particularly on cross-sentence relation instances and documents with more entities. Finally, we propose an entity variation robust training method which not only improves the robustness of DocRE models but also enhances their understanding and reasoning capabilities. We further verify that the basic idea of this method can be effectively transferred to in-context learning for DocRE as well.Comment: Accepted to ACL 2024 Finding

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component modifications to the previous SQL statements, which could be further extracted as the modification patterns. Since these modification patterns could also be combined with other SQL statements, the models are supposed to have the compositional generalization to these novel combinations. This work is the first exploration of compositional generalization in context-dependent Text-to-SQL scenarios. To facilitate related studies, we constructed two challenging benchmarks named \textsc{CoSQL-CG} and \textsc{SParC-CG} by recombining the modification patterns and existing SQL statements. The following experiments show that all current models struggle on our proposed benchmarks. Furthermore, we found that better aligning the previous SQL statements with the input utterance could give models better compositional generalization ability. Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models. Further experiments validate the effectiveness of our method. Source code and data are available.Comment: Accepted to ACL 2023 (Findings), Long Paper, 11 page

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for addressing the pervasive data scarcity problem in real-world scenarios. Metric-based meta-learning is an effective framework widely adopted for FSDLRE, which constructs class prototypes for classification. However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype. 2) They use a set of generic NOTA (none-of-the-above) prototypes across all tasks, neglecting that the NOTA semantics differs in tasks with different target relation types. In this paper, we propose a relation-aware prototype learning method for FSDLRE to strengthen the relational semantics of prototype representations. By judiciously leveraging the relation descriptions and realistic NOTA instances as guidance, our method effectively refines the relation prototypes and generates task-specific NOTA prototypes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches by average 2.61% $F_1$ across various settings of two FSDLRE benchmarks.Comment: Accepted to EMNLP 202

The analgesic effect of green light on neuropathic pain: a mini-review of the literature and a proposal for future work

Medically refractory, severe, and unrelenting neuropathic pain remains a public health challenge worldwide. Green light has been found to have an analgesic effect on neuropathic pain. Interestingly, this analgesic effect is prolonged even after green light exposure. Peripheral and central mechanisms include the inhibition of the inflammatory response and the activation of the endogenous cannabinoid system and nerve circuits between the lateral geniculate nucleus and other brain regions, such as the dorsal raphe nucleus and the rostral ventromedial medulla, which may mediate the analgesic effect of green light. An increasing number of clinical studies highlight the side effects of traditional analgesics. The antinociceptive effect of green light has been proven in fibromyalgia and migraine patients. However, the effect of green light on neuropathic pain has not been reported in clinical settings. Here, we review the cellular and molecular mechanisms of the antinociceptive effect of green light. Furthermore, the green light parameters (intensity, duration, and wavelength) used in clinical trials are also summarized

Survey of COVID-19 outbreaks linked to imported frozen food in China’s Mainland

ObjectiveSince the global pandemic of coronavirus disease 2019 （COVID-19） broken， a series of local outbreaks caused by the contamination of imported cold chain food by SARS-CoV-2 had been reported in China’s Mainland. To provide corresponding suggestions for the prevention and control of similar epidemics in the future， the characteristics and spread of such epidemics was analyzed.MethodsAll the literature， official news reports and other materials on the local COVID-19 outbreak caused by SARS-CoV-2 contaminated imported cold chain food was collected， and the corresponding data was sort out and analyzed.ResultsFrom June 2020 to November 2022， a total of 20 local COVID-19 outbreaks related to imported cold chain products were reported， with a total of 1 646 cases， involving 9 provinces. Among the 20 outbreaks， there were 10 outbreaks in 2020， 3 outbreaks in 2021， and seven outbreaks in 2022， and 3 outbreaks had 200 or more cases.ConclusionSince the global pandemic of COVID-19 broken， the 20 local outbreaks related to imported cold-chain food traced domestically were caused by cold-chain workers who were infected by contact with imported cold-chain food or their overpacks， and most of local outbreaks （75%） caused subsequent community transmission. But under various effective control measures such as the construction of centralized supervision warehouses in China， the number and duration of local outbreaks related to imported cold chain food have shown a downward trend

Screening and risk factors of exocrine pancreatic insufficiency in critically ill adult patients receiving enteral nutrition

INTRODUCTION: Malnutrition is a frequent problem associated with detrimental clinical outcomes in critically ill patients. To avoid malnutrition, most studies focus on the prevention of inadequate nutrition delivery, whereas little attention is paid to the potential role of exocrine pancreatic insufficiency (EPI). In this trial, we aim to evaluate the prevalence of EPI and identify its potential risk factors in critically ill adult patients without preexisting pancreatic diseases. METHODS: In this prospective cross-sectional study, we recruited 563 adult patients with critical illnesses. All details of the patients were documented, stool samples were collected three to five days following the initiation of enteral nutrition, and faecal elastase 1 (FE-1) concentrations were assayed using an enzyme-linked immunosorbent assay kit. Blood samples were also taken to determine serum amylase and lipase activity. RESULTS: The percentages of recruited patients with EPI (FE-1 concentration <200 μg/g) and severe EPI (FE-1 concentration <100 μg/g) were 52.2% and 18.3%, respectively. The incidences of steatorrhea were significantly different (P < 0.05) among the patients without EPI, with moderate EPI (FE-1 concentration = 100 to 200 μg/g) and severe EPI (FE-1 concentration < 100 μg/g). Both multivariate logistic regression analysis and z-tests indicated that the occurrence of EPI was closely associated with shock, sepsis, diabetes, cardiac arrest, hyperlactacidemia, invasive mechanical ventilation and haemodialysis. CONCLUSIONS: More than 50% of critically ill adult patients without primary pancreatic diseases had EPI, and nearly one-fifth of them had severe EPI. The risk factors for EPI included shock, sepsis, diabetes, cardiac arrest, hyperlactacidemia, invasive mechanical ventilation and haemodialysis. TRIAL REGISTRATION: NCT0175302