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Abstract Backgroud This study aimed to explore the clinical value of long - term electroencephalogram (LTM EEG) in seizure-free individuals taking antiepileptic drugs (AEDs) for more than 2 years. We try to look for clinical factors associated with epileptiform activity on LTM EEG in seizure free patients. We hope that the detection of epileptiform activity by the LTM EEG recording can develop the better treatment strategy. Methods The LTM EEG recordings of 770 individuals with a definite diagnosis of epilepsy were assessed. Two hundred sixty-two individuals accorded with the inclusion criteria and exclusion criteria. We collect the demographic and clinical information and LTM EEG data of these 262 individuals. We analysed the data by one-way analysis of variance and Cox proportional hazards models. Results We found that more epileptiform activity were found with LTM EEG recording than regular EEG recording in seizure-free individuals. We found several clinical factors could be associated with epileptiform activity on LTM EEG in seizure free patients by a one-way analysis: symptomatic or cryptogenic epilepsy [hazard ratio (HR) = 2.6], history of cerebral trauma (HR = 7.5), and abnormal imaging findings (HR = 3.1). The following factors suggested a correlation between history of cerebral trauma (HR = 2.4) and history of cerebral surgery (HR = 3.4) with epileptiform activity on LTM EEG presentation by multivariate logistic regression analysis. Conclusions The study indicated a correlation of a number of factors with abnormal LTM EEG presentation: symptomatic or cryptogenic epilepsy, history of cerebral trauma, history of cerebral surgery, and abnormal imaging findings. The LTM EEG recording may help find epileptiform activity in high risk seizure-free individuals. The individuals need be reevaluated the therapeutic strateagies, and increase the hope to reach real seizure-free.

With the development of genome sequencing technology, using computing technology to predict grain protein function has become one of the important tasks of bioinformatics. The protein data of four grains, soybean, maize, indica and japonica are selected in this experimental dataset. In this paper, a novel neural network algorithm Chemical-SA-BiLSTM is proposed for grain protein function prediction. The Chemical-SA-BiLSTM algorithm fuses the chemical properties of proteins on the basis of amino acid sequences, and combines the self-attention mechanism with the bidirectional Long Short-Term Memory network. The experimental results show that the Chemical-SA-BiLSTM algorithm is superior to other classical neural network algorithms, and can more accurately predict the protein function, which proves the effectiveness of the Chemical-SA-BiLSTM algorithm in the prediction of grain protein function. The source code of our method is available at https://github.com/HwaTong/Chemical-SA-BiLSTM. [ABSTRACT FROM AUTHOR]

There are a large number of unannotated proteins with unknown functions in rice, which are difficult to be verified by biological experiments. Therefore, computational method is one of the mainstream methods for rice proteins function prediction. Two representative rice proteins, indica protein and japonica protein, are selected as the experimental dataset. In this paper, two feature extraction methods (the residue couple model method and the pseudo amino acid composition method) and the Principal Component Analysis method are combined to design protein descriptive features. Moreover, based on the state-of-the-art MIML algorithm EnMIMLNN, a novel MIML learning framework MK-EnMIMLNN is proposed. And the MK-EnMIMLNN algorithm is designed by learning multiple kernel fusion function neural network. The experimental results show that the hybrid feature extraction method is better than the single feature extraction method. More importantly, the MK-EnMIMLNN algorithm is superior to most classic MIML learning algorithms, which proves the effectiveness of the MK-EnMIMLNN algorithm in rice proteins function prediction. [ABSTRACT FROM AUTHOR]

A series of YNbO4:Ln3+ (Ln3+ = Dy3+ and/or Eu3+) phosphors have been synthesized by a Pechini-type sol-gel method. X-ray diffraction, field-emission scanning electron microscopy, and transmission electron microscopy were utilized to characterize the prepared phosphors. The pure phase of YNbO4 was obtained after annealing at 1000 °C. The morphologies of the YNbO4:Ln3+ (Ln3+ = Dy3+ and/or Eu3+) phosphors are approximately spherical grains with a diameter of around 40-60 nm. The luminescence properties of the YNbO4:Ln3+ (Ln3+ = Dy3+ and/or Eu3+) phosphors were investigated by photo- and cathodoluminescence methods. Under the excitation of UV light and a low-voltage electron beam, the YNbO4:Ln3+ (Ln3+ = Dy3+ and/or Eu3+) phosphors obtained show characteristic blue wide-band emission from the NbO4 group, whitish light emission from Dy3+, and red emission from Eu3+. Energy is transferred from the YNbO4 host lattice to the Dy3+ and Eu3+ ions. Single-phase white-light-emision was observed in YNbO4:Dy3+ phosphors under the excitation of UV light and a low-voltage electron beam. The color rendering index of the YNbO4:Dy3+ phosphor can be further improved by co-doping YNbO4:Dy3+ with the Eu3+ ion. The emission color of the YNbO4:Dy3+,Eu3+ phosphors can be simply tuned by changing the relative ratio of the Dy3+/Eu3+ ions. The prepared YNbO4:Dy3+ and YNbO4:Dy3+,Eu3+ phosphors have potential applications in UV white-light-emitting diodes and other optoelectronics display devices. [ABSTRACT FROM AUTHOR]