Bi-Dbar-Approach for a Coupled Shifted Nonlocal Dispersionless System

We propose a Bi-Dbar approach and apply it to the extended coupled shifted nonlocal dispersionless system. We introduce the nonlocal reduction to solve the coupled shifted nonlocal dispersionless system. Since no enough constraint conditions can be found to curb the norming contants in the Dbar data, the “solutions” obtained by the Dbar dressing method, in general, do not admit the coupled shifted nonlocal dispersionless system. In the Bi-Dbar approach to the extended coupled shifted nonlocal dispersionless system, the norming constants are free. The constraint conditions on the norming constants are determined by the general nonlocal reduction, and the solutions of the coupled shifted nonlocal dispersionless system are derived

BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of harvesting massive KGs of arbitrary relations from pretrained LMs. With minimal input of a relation definition (a prompt and a few shot of example entity pairs), the approach efficiently searches in the vast entity pair space to extract diverse accurate knowledge of the desired relation. We develop an effective search-and-rescore mechanism for improved efficiency and accuracy. We deploy the approach to harvest KGs of over 400 new relations from different LMs. Extensive human and automatic evaluations show our approach manages to extract diverse accurate knowledge, including tuples of complex relations (e.g., "A is capable of but not good at B"). The resulting KGs as a symbolic interpretation of the source LMs also reveal new insights into the LMs' knowledge capacities.Comment: ACL 2023 (Findings); Code available at https://github.com/tanyuqian/knowledge-harvest-from-lm

Safety and efficacy of aspirin after combined cerebral revascularization for ischemic moyamoya disease: A prospective study

ObjectiveTo analyze the safety and efficacy of regular aspirin use after combined cerebral revascularization in patients with ischemic moyamoya disease.MethodsFrom December 2020 to October 2021, a total of 326 patients diagnosed with ischemic moyamoya disease by global cerebral angiography and undergoing first-time combined cerebral revascularization at the Moyamoya Disease Diagnosis and Treatment Research Center of our hospital were selected. Combined cerebral revascularization: superficial temporal artery-middle cerebral artery (STA-MCA) +encephalo-duro-myo-synangiosis (EDMS).Patients were screened by 2 senior physicians according to established inclusion/exclusion criteria. Patients were divided into aspirin and non-aspirin groups based on whether they received regular oral aspirin after surgery. A total of 133 patients were enrolled in the aspirin group. A total of 71 patients (204 cases) were enrolled in the non-aspirin group. Related data were collected before and 1 year after surgery and statistically analyzed to assess the prognosis of both groups.ResultsIn the two groups, the mRS Score was significantly different after one year (P = 0.023). TIA occurred in 26 patients (19.5%) in the aspirin group and 27 patients (38.0%) in the non-aspirin group within one year after surgery, and the difference between the two groups was statistically significant (P = 0.004). There was no significant difference in cerebral perfusion stage, the improvement rate of cerebral perfusion, Matsushima grading, bypass patency, and other complications within one year after the operation (P > 0.05).ConclusionsIn patients with ischemic moyamoya disease who underwent combined cerebral revascularization, postoperative administration of aspirin can reduce the incidence of TIA without increasing the risk of bleeding, but it can not significantly improve the cerebral perfusion of the operation side, Matsushima grading, and bypass patency

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Analysis of the Global Swell and Wind Sea Energy Distribution Using WAVEWATCH III

Over the past several decades, an increasing number of studies have focused on the global view of swell and wind sea climate. However, our understanding of wind sea and swell is still incomplete as is the lack of an integrated description for all the wave components. In this paper, the European Centre for Medium-Range Weather Forecasts (ECMWF) Era-medium wind data is used to run the WAVEWATCH III model and the global wave fields in 2010 are reproduced. Using the spectra energy partition (SEP) method, two-dimensional wave spectra were separated and detailed information for the components of wind sea and swell was obtained. We found that the highest seasonal mean energy of swell and wind sea are distributed in the respective winter hemispheres. In most seas, swell carries a large part of the wave energy with Ws being higher than 50%. Compared to swell, the global distribution of wind sea energy is highly affected by the seasons. We also established a link between inverse wave age and the ratio of swell energy to total wave energy. This study aims to improve our understanding of surface wave energy composition and thus the parameterization of global-scale wind-wave interaction and air-sea momentum flux

A Multi-Information Dissemination Model Based on Cellular Automata

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform

Predictions and Evolution Characteristics of Failure Modes of Degenerate RC Piers

During the service process, piers are often in harsh chloride ion erosion environments. The failure mode evolution of reinforced concrete (RC) piers may occur under the action of continuous corrosion. Accurately identifying the failure mode types and evolution characteristics of corroded RC bridge piers is a prerequisite for the lifetime seismic performance evaluations of bridges. First, based on Fisher’s theory and 174 RC pier columns as the analysis samples, a two-stage discrimination formula for the pier failure modes was established and compared with the existing theoretical discrimination methods. Then, based on Fisher’s discriminant grouping, and combined with Bayes’ formula and chloride erosion theory, a failure mode discrimination method for corrosion-damaged bridge piers that considers probability was developed. Finally, taking a medium-span concrete bridge as an example, the failure modes of the corroded pier in different service periods were predicted, and the influences of the various parameters on the failure mode evolution process of the corroded pier were studied. The results show that the accuracy of the proposed discriminant model was significantly improved compared with those of previous theoretical studies. The development of the failure mode features depends on how the distinct RC pier material qualities degrade under the influence of chloride ions. The degradation of the stirrups and concrete accelerates the nonductile failure of RC bridge piers, while the degradation of the longitudinal reinforcements delays it

CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images

Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease diagnosis. Deep learning technology is one of the most effective methods for processing OCT retinal layer images, which can segment different retinal layers and effectively obtain the topological structure of the boundary. This paper proposes a neural network method for retinal layer segmentation based on the CSWin Transformer (CTS-Net), which can achieve pixel-level segmentation and obtain smooth boundaries. A Dice loss function based on boundary areas (BADice Loss) is proposed to make CTS-Net learn more features of edge regions and improve the accuracy of boundary segmentation. We applied the model to the publicly available dataset of glaucoma retina, and the test results showed that mean absolute distance (MAD), root mean square error (RMSE), and dice-similarity coefficient (DSC) metrics were 1.79 pixels, 2.15 pixels, and 92.79%, respectively, which are better than those of the compared model. In the cross-validation experiment, the ranges of MAD, RMSE, and DSC are 0.05 pixels, 0.03 pixels, and 0.33%, respectively, with a slight difference, which further verifies the generalization ability of CTS-Net

CTS-Net: A Segmentation Network for Glaucoma Optical Coherence Tomography Retinal Layer Images

Optical Coherence Tomography (OCT) technology is essential to obtain glaucoma diagnostic data non-invasively and rapidly. Early diagnosis of glaucoma can be achieved by analyzing the thickness and shape of retinal layers. Accurate retinal layer segmentation assists ophthalmologists in improving the efficiency of disease diagnosis. Deep learning technology is one of the most effective methods for processing OCT retinal layer images, which can segment different retinal layers and effectively obtain the topological structure of the boundary. This paper proposes a neural network method for retinal layer segmentation based on the CSWin Transformer (CTS-Net), which can achieve pixel-level segmentation and obtain smooth boundaries. A Dice loss function based on boundary areas (BADice Loss) is proposed to make CTS-Net learn more features of edge regions and improve the accuracy of boundary segmentation. We applied the model to the publicly available dataset of glaucoma retina, and the test results showed that mean absolute distance (MAD), root mean square error (RMSE), and dice-similarity coefficient (DSC) metrics were 1.79 pixels, 2.15 pixels, and 92.79%, respectively, which are better than those of the compared model. In the cross-validation experiment, the ranges of MAD, RMSE, and DSC are 0.05 pixels, 0.03 pixels, and 0.33%, respectively, with a slight difference, which further verifies the generalization ability of CTS-Net

Opioids modulate an emergent rhythmogenic process to depress breathing.

How mammalian neural circuits generate rhythmic activity in motor behaviors, such as breathing, walking, and chewing, remains elusive. For breathing, rhythm generation is localized to a brainstem nucleus, the preBötzinger Complex (preBötC). Rhythmic preBötC population activity consists of strong inspiratory bursts, which drive motoneuronal activity, and weaker burstlets, which we hypothesize reflect an emergent rhythmogenic process. If burstlets underlie inspiratory rhythmogenesis, respiratory depressants, such as opioids, should reduce burstlet frequency. Indeed, in medullary slices from neonatal mice, the μ-opioid receptor (μOR) agonist DAMGO slowed burstlet generation. Genetic deletion of μORs in a glutamatergic preBötC subpopulation abolished opioid-mediated depression, and the neuropeptide Substance P, but not blockade of inhibitory synaptic transmission, reduced opioidergic effects. We conclude that inspiratory rhythmogenesis is an emergent process, modulated by opioids, that does not rely on strong bursts of activity associated with motor output. These findings also point to strategies for ameliorating opioid-induced depression of breathing