OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation

Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations. However, most medications appear in the dataset for limited times, resulting in insufficient learning of their representations. Medical ontologies are the hierarchical classification systems for medical terms where similar terms are in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on benchmark datasets to evaluate the effectiveness of OntoMedRec, and the result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code on https://anonymous.4open.science/r/OntoMedRec-D12

Operation and evaluation of digitalized retail electricity markets under low-carbon transition: recent advances and challenges

With the growth of electricity consumers purchasing green energy and the development of digital energy trading platforms, the role of digitalized retail electricity markets in the low-carbon transition of electric energy systems is becoming increasingly crucial. In this circumstance, the research work on retail electricity markets needs to be further analyzed and expanded, which would facilitate the efficient decision-making of both market players and policymakers. First, this paper introduces the latest developments in the retail electricity market under low-carbon energy transition and analyzes the limitations of the existing research works. Second, from three aspects of power trading strategy, retail pricing methodology, and market risk management, it provides an overview of the existing operation and mechanism design strategies of the retail electricity market; then, it provides a systematic introduction to the evaluation system and monitoring methodology of electricity markets, which is not sufficient for the current digitalized retail electricity markets. Finally, the issues regarding operation evaluation and platform optimization of the current digitalized retail electricity market are summarized, and the research topics worth further investigations are recommended

Evaluation of corneal thickness using a Scheimpflug–Placido disk corneal analyzer and comparison with ultrasound pachymetry in eyes after laser in situ keratomileusis

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Transformer over pre-trained transformer for neural text segmentation with enhanced topic coherence

This paper proposes a transformer over transformer framework, called Transformer$^2$, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learned from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformer$^2$ can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformer$^2$ manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains

Carboxymethyl cellulose-grafted graphene oxide for efficient antitumor drug delivery

A drug delivery system based on carboxymethyl cellulose-grafted graphene oxide loaded by methotrexate (MTX/CMC-GO) with pH-sensitive and controlled drug-release properties was developed in this work. CMC was grafted on graphene oxide by ethylenediamine through hydrothermal treatment. CMC serves as a pH-sensitive trigger, while CMC-GO serves as a drug-carrying vehicle due to the curved layer and large plain surface. Different amounts of drugs could be loaded into CMC-GO nanocarriers by control of the original amount of drug/carrier ratios. Additionally, low cytotoxicity against NIH-3T3 cells and low in vivo toxicity was observed. In vivo tumor growth inhibition assays showed that MTX/CMC-GO demonstrated superior antitumor activity than free MTX against HT-29 cells. Moreover, prolonged survival time of mice was observed after MTX/CMC-GO administration. The MTX/CMC-GO drug delivery system has a great potential in colon cancer therapy

Assessing and optimizing the hydrological performance of Grey-Green infrastructure systems in response to climate change and non-stationary time series

Climate change has led to the increased intensity and frequency of extreme meteorological events, threatening the drainage capacity in urban catchments and densely built-up cities. To alleviate urban flooding disasters, strategies coupled with green and grey infrastructure have been proposed to support urban stormwater management. However, most strategies rely largely on diachronic rainfall data and ignore long-term climate change impacts. This study described a novel framework to assess and to identify the optimal solution in response to uncertainties following climate change. The assessment framework consists of three components: (1) assess and process climate data to generate long-term time series of meteorological parameters under different climate conditions; (2) optimise the design of Grey-Green infrastructure systems to establish the optimal design solutions; and (3) perform a multi-criteria assessment of economic and hydrological performance to support decision-making. A case study in Guangzhou, China was carried out to demonstrate the usability and application processes of the framework. The results of the case study illustrated that the optimised Grey-Green infrastructure could save life cycle costs and reduce total outflow (56-66%), peak flow (22-85%), and TSS (more than 60%) compared to the fully centralised grey infrastructure system, indicating its high superior in economic competitiveness and hydrological performance under climate uncertainties. In terms of spatial configuration, the contribution of green infrastructure appeared not as critical as the adoption of decentralisation of the drainage networks. Furthermore, under extreme drought scenarios, the decentralised infrastructure system exhibited an exceptionally high degree of removal performance for non-point source pollutants.This work was supported by the National Natural Science Foundation of China [grant number 51808137], Natural Science Foundation of Guangdong Province [grant number 2019A1515010873], and Science and Technology Program of Guangzhou, China [grant number 202201010431]