A cumulative prospect theory-based method for group medical emergency decision-making with interval uncertainty.

BACKGROUND: An emergency response to a medical situation is generally considered to be a risk decision-making problem. When an emergency event occurs, it makes sense to take into account more than one decision maker's opinions and psychological behaviors. The existing research tends to ignore these multidimensional aspects. To fill this literature gap, we propose a multi-attribute model. METHODS: The model is based on cumulative prospect theory (CPT), considering multiple experts' psychological factors. By not assuming full rationality, we extend existing models to allow multiple experts' risk preferences to be incorporated into the decision-making process in the case of an emergency. Then, traditional CPT is extended by allowing for multiple attributes. In addition, rather than using crisp data, interval values are adopted to tackle the usual uncertainties in reality. RESULTS: The multi-attribute CPT based model proposed can deal with the selection of potential emergency alternatives. The model incorporates interval values to allow more uncertainty and the comparative studies show that the optimal solution changes under different scenarios. CONCLUSIONS: Our illustrative example and comparative study show that considering multiple experts and multiple attributes is more reasonable, especially in complicated situations under an emergency. In addition, decision-makers' risk preferences highly affect the selection outcomes, highlighting their importance in the medical decision-making process. Our proposed model can be applied to similar fields with appropriate modifications.

Multi-Task Learning and Improved TextRank for Knowledge Graph Completion.

Knowledge graph completion is an important technology for supplementing knowledge graphs and improving data quality. However, the existing knowledge graph completion methods ignore the features of triple relations, and the introduced entity description texts are long and redundant. To address these problems, this study proposes a multi-task learning and improved TextRank for knowledge graph completion (MIT-KGC) model. The key contexts are first extracted from redundant entity descriptions using the improved TextRank algorithm. Then, a lite bidirectional encoder representations from transformers (ALBERT) is used as the text encoder to reduce the parameters of the model. Subsequently, the multi-task learning method is utilized to fine-tune the model by effectively integrating the entity and relation features. Based on the datasets of WN18RR, FB15k-237, and DBpedia50k, experiments were conducted with the proposed model and the results showed that, compared with traditional methods, the mean rank (MR), top 10 hit ratio (Hit@10), and top three hit ratio (Hit@3) were enhanced by 38, 1.3%, and 1.9%, respectively, on WN18RR. Additionally, the MR and Hit@10 were increased by 23 and 0.7%, respectively, on FB15k-237. The model also improved the Hit@3 and the top one hit ratio (Hit@1) by 3.1% and 1.5% on the dataset DBpedia50k, respectively, verifying the validity of the model.

A ratiometric fluorescent probe for determination of the anthrax biomarker 2,6-pyridinedicarboxylic acid based on a terbium(III)- functionalized UIO-67 metal-organic framework.

Terbium(III)-functionalized zirconium-based MOFs (Tb3+@UIO-67) were synthesized by doping Tb3+ into UIO-67 using a post-synthetic modification. The Tb3+@UIO-67 (solid or aqueous dispersion) shows only blue fluorescence (emission peaks at 420 nm) under an ultraviolet lamp (254 nm). Upon addition of 2,6-pyridinedicarboxylic acid (DPA; an anthrax biomarker), the color of the Tb3+@UIO-67 aqueous dispersion under an ultraviolet lamp changes from blue to green. This is mainly because DPA has a good sensitization effect on Tb3+. DPA can be determined by measurement of the ratio of the fluorescence intensities at 544 nm and 420 nm (excitation at 278 nm). The method allows DPA to be detected in the 0.3 to 6 µM concentration range, with a detection limit of 36 nM. Graphical abstract Schematic representation of a ratiometric fluorescent probe synthesized by doping terbium ions into a zirconium-based MOF (UIO-67) for determination of an anthrax biomarker.

DWL-4-140: A allene small molecule targeting STING that alleviates lupus-like phenotype in Trex1-/- mice.

The innate immune system plays a critical role in the host response against pathogenic microbial infection. However, aberrant activation of the innate immune pathways is a characteristic feature of various diseases. Thus, targeted drugs must be developed based on the understanding of the innate immune signaling pathways. This study demonstrated that an allene small molecule (DWL-4-140) can efficiently and selectively exert regulatory effects on the stimulator of interferon genes (STING), resulting in the downregulation of DNA-induced interferon responses. Mechanistically, DWL-4-140 targeted the cyclized nucleotide-binding domain (CBD) of STING, inhibiting the assembly of the STING multimeric complex and the recruitment of downstream signaling mediators. In addition to downregulating the 10-carboxymethyl-9-acridanone-induced production of inflammatory factors, DWL-4-140 alleviated the pathological features of Trex1 deletion-induced lupus in mice. Thus, this study demonstrated that DWL-4-140 pharmacologically inhibits STING with potential therapeutic applications in auto-inflammatory diseases.

Identification of epilepsy from intracranial EEG signals by using different neural network models.

In this work, a framework is provided for identifying intracranial electroencephalography (iEEG) seizures based on discrete wavelet transform (DWT) analysis of iEEG signals using forward propagation and feedback neural networks. The performance of 5 different data sets combination classifications is studied using the probabilistic neural network (PNN), learning vector quantization neural network (LVQ) and Elman neural network (ENN). Different feature combinations serve as the input vectors of the classifiers to obtain the best outcomes. It has been found that PNN has less running time and provides better classification accuracy (CA) than ENN and LVQ classifiers for all 5 classification problems. It is worth noticing that the CA for the C-D classification task, which shows the status of pre-ictal versus post-ictal, has been greatly improved, and reached 83.13%. Hence, the epilepsy iEEG signals pattern recognition based on DWT statistical features using the PNN classifier is more suitable for forming a reliable, automatic classification system in order to assist doctors in diagnosis.

A novel luminescent hybrid material based on zinc complexes and graphene oxides for detection of Fe3+ in aqueous media.

Graphene oxide sheet-modified zinc complexes named Zn-PMA/GOSs have been synthesized based on the non-covalent interactions of Zn-PMA and graphene oxide sheets (GOSs) via an in situ reaction. Then, the as-synthesized products are characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), thermogravimetric analysis (TGA) and fluorescence spectroscopy. Zn-PMA/GOSs not only show strong luminescence intensity and better thermal stability than pure zinc complexes but also have good dispersibility in aqueous solutions, and they are further used as fluorescence probes for detecting Fe3+. They show relative selectivity and high sensitivity to Fe3+ (Stern-Volmer constant KSV = 4 × 106 L mol-1), suggesting that Zn-PMA/GOSs can be a promising luminescent probe for selectively sensing Fe3+ ions. Moreover, the possible mechanisms of ion-selective detection are also discussed.

Co-pyrolysis of corn cob and waste cooking oil in a fixed bed.

Corn cob (CC) and waste cooking oil (WCO) were co-pyrolyzed in a fixed bed. The effects of various temperatures of 500 °C, 550 °C, 600 °C and CC/WCO mass ratios of 1:0, 1:0.1, 1:0.5, 1:1 and 0:1 were investigated, respectively. Results show that co-pyrolysis of CC/WCO produce more liquid and less bio-char than pyrolysis of CC individually. Bio-oil and bio-char yields were found to be largely dependent on temperature and CC/WCO ratios. GC/MS of bio-oil show it consists of different classes and amounts of organic compounds other than that from CC pyrolysis. Temperature of 550 °C and CC/WCO ratio of 1:1 seem to be the optimum considering high bio-oil yields (68.6 wt.%) and good bio-oil properties (HHV of 32.78 MJ/kg). In this case, bio-char of 24.96 MJ/kg appears attractive as a renewable source, while gas with LHV of 16.06 MJ/Nm(3) can be directly used in boilers as fuel.