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We propose a simple and effective method to inject word-level information into character-aware neural language models. Unlike previous approaches which usually inject word-level information at the input of a long short-term memory (LSTM) network, we inject it into the softmax function. The resultant model can be seen as a combination of character-aware language model and simple word-level language model. Our injection method can also be used together with previous methods. Through the experiments on 14 typologically diverse languages, we empirically show that our injection method, when used together with the previous methods, works better than the previous methods, including a gating mechanism, averaging, and concatenation of word vectors. We also provide a comprehensive comparison of these injection methods.

OBJECTIVE Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis. METHODS We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies. RESULTS Most features were significant for differentiating the above-mentioned groups (value

Recent studies have investigated bilateral gaits based on the causality analysis of kinetic (or kinematic) signals recorded using both feet. However, these approaches have not considered the influence of their simultaneous causation, which might lead to inaccurate causality inference. Furthermore, the causal interaction of these signals has not been investigated within their frequency domain. Therefore, in this study we attempted to employ a causal-decomposition approach to analyze bilateral gait. The vertical ground reaction force (VGRF) signals of Parkinson's disease (PD) patients and healthy control (HC) individuals were taken as an example to illustrate this method. To achieve this, we used ensemble empirical mode decomposition to decompose the left and right VGRF signals into intrinsic mode functions (IMFs) from the high to low frequency bands. The causal interaction strength (CIS) between each pair of IMFs was then assessed through the use of their instantaneous phase dependency. The results show that the CISes between pairwise IMFs decomposed in the high frequency band of VGRF signals can not only markedly distinguish PD patients from HC individuals, but also found a significant correlation with disease progression, while other pairwise IMFs were not able to produce this. In sum, we found for the first time that the frequency specific causality of bilateral gait may reflect the health status and disease progression of individuals. This finding may help to understand the underlying mechanisms of walking and walking-related diseases, and offer broad applications in the fields of medicine and engineering.

Methyleugenol (ME) is a ubiquitous component in spices and other culinary herbal products. A prevailing theory in ME toxicity is its ability to be metabolically activated by P450 enzymes and sulfotransferases, which initiates sequential reactions of the resulting metabolites with functional biomolecules. The present study aimed at a potential interaction between the reactive metabolites of ME and RNA. Cultured mouse primary hepatocytes were incubated with ME followed by RNA extraction and NaOH and alkaline phosphatase-based RNA hydrolysis. Three adenosine adducts were detected in the hydrolytic mixture by LC-MS/MS. The same adenosine adducts were also detected in hepatic tissues harvested from ME-treated mice. These three adducts were chemically synthesized and structurally characterized by 1H NMR. Additionally, two guanosine adducts and one cytidine adduct were detected in the in vivo samples. These results provided solid evidence that the reactive metabolites of ME attacked RNA, resulting in RNA adduction.

Background The R1441G mutation in the leucine-rich repeat kinase 2 (LRRK2) gene results in late-onset Parkinson’s disease (PD). Peripheral inflammation and gut microbiota are closely associated with the pathogenesis of PD. Chronic periodontitis is a common type of peripheral inflammation, which is associated with PD. Porphyromonas gingivalis (Pg), the most common bacterium causing chronic periodontitis, can cause alteration of gut microbiota. It is not known whether Pg-induced dysbiosis plays a role in the pathophysiology of PD. Methods In this study, live Pg were orally administrated to animals, three times a week for 1 month. Pg-derived lipopolysaccharide (LPS) was used to stimulate mononuclear cells in vitro. The effects of oral Pg administration on the gut and brain were evaluated through behaviors, morphology, and cytokine expression. Results Dopaminergic neurons in the substantia nigra were reduced, and activated microglial cells were increased in R1441G mice given oral Pg. In addition, an increase in mRNA expression of tumor necrosis factor (TNF-α) and interleukin-1β (IL-1β) as well as protein level of α-synuclein together with a decrease in zonula occludens-1 (Zo-1) was detected in the colon in Pg-treated R1441G mice. Furthermore, serum interleukin-17A (IL-17A) and brain IL-17 receptor A (IL-17RA) were increased in Pg-treated R1441G mice. Conclusions These findings suggest that oral Pg-induced inflammation may play an important role in the pathophysiology of LRRK2-associated PD.

Many-objective optimization refers to the optimization aiming at four or more objectives. More complex than multi-objective optimization, which copes with two or three objectives, many-objective optimization represents a challenging problem due to the complex trade-off relationships among the optimization objectives. In this study, a four-objective optimization system was proposed for reducing the resistance and the non-uniformity of wake flow of a deep-sea aquaculture vessel at design speed in full load and ballast conditions. The numerical optimization system was mainly composed of the uniform design (UD), free-form deformation method (FFD), radial basis neural network (RBFNN) and a series of genetic algorithms (GAs). During the optimization process, nine design variables were selected to reconstruct the hull geometry. UD and FFD were utilized to generate a series of derivative ship forms. According to CFD calculation results of all derivative ship forms, four surrogate models were constructed, using RBFNN optimized by the particle swarm optimization (PSO), to replace direct numerical calculations. To verify the prediction accuracy advantage of improved RBFNN-based surrogate models based on small sample size, the accuracy comparison was made among surrogate models based on RBFNN, kriging and support vector regression (SVR). Moreover, Sobol' method was employed to analyze the influence of design variables on the optimization objectives. GAs were applied to perform from single-objective to four-objective optimization of the resistance and non-uniformity of wake flow. Eventually, the optimal ship form was selected from the Pareto front of four-objective optimization. Numerical results of the optimized shape showed that the resistance and the non-uniformity of wake flow in full load and ballast conditions were reduced, and the results of the verification and validation (V&V) procedure proved the validation of the many-objective optimization system.

The drag reduction effect of interceptors on planing boats has been widely proven, but the mechanism of the effect has been rarely studied in terms of drag components, especially for spray resistance. The resistance was caused by the high gauge pressure under the boats transformed from the dynamic pressure, and it is the largest drag component in the high-speed planing mode. In this study, numerical simulations of viscous flow fields around a planing boat with and without interceptors were conducted. A two degrees of freedom motion model was employed to simulate the trim and sinkage. The numerical results were validated against the experimental data. The flow details with and without the interceptor were visualized and compared to reveal the underlying physics. A thinner and longer waterline could be achieved by the interceptor, which made the boat push the water away more gradually, and hence, the wave-making resistance could be decreased. The improved waterline also reduced the component of the freestream normal to the hull surface and led to the less transformed dynamic pressure, resulting in the lowAer spray resistance. Furthermore, the suppression of the flow separation could also be benefited from the interceptor; the viscous pressure resistance was therefore decreased.

Benzbromarone (BBR), a uricosuric agent, has been known to induce hepatotoxicity, and its toxicity has a close relation to cytochrome P450-mediated metabolic activation. An oxidative debromination metabolite of BBR has been reported in microsomal incubations. The present study attempted to define the oxidative debromination pathway of BBR in vivo. One urinary mercapturic acid (M1) and one glutathione (GSH) conjugate (M2) derived from the oxidative debromination metabolite were detected in BBR-treated mice after solid phase extraction. M1 and M2 shared the same chromatographic behavior and mass spectral identities as those detected in N-acetylcysteine/GSH- and BBR-fortified microsomal incubations. The structure of M1 was characterized by chemical synthesis, along with mass spectrometry analysis. In addition, hepatic protein modification that occurs at cysteine residues (M'3) was observed in mice given BBR. The observed protein adduction reached its peak 4 hours after administration and occurred in a dose-dependent manner. A GSH conjugate derived from oxidative debromination of BBR was detected in livers of mice treated with BBR, and the formation of the GSH conjugate apparently took place earlier than the protein adduction. In summary, our in vivo work provided strong evidence for the proposed oxidative debromination pathway of BBR, which facilitates the understanding of the mechanisms of BBR-induced hepatotoxicity. SIGNIFICANCE STATEMENT: This study investigated the oxidative debromination pathway of benzbromarone (BBR) in vivo. One urinary mercapturic acid (M1) and one glutathione (GSH) conjugate (M2) derived from the oxidative debromination metabolite were detected in BBR-treated mice. M1 and M2 were also observed in microsomal incubations. The structure of M1 was characterized by chemical synthesis followed by mass spectrometry analyses. More importantly, protein adduction derived from oxidative debromination of BBR (M'3) was observed in mice given BBR, and occurred in dose- and time-dependent manners. The success in detection of GSH conjugate, urinary N-acetylcysteine conjugate, and hepatic protein adduction in mice given BBR provided solid evidence for in vivo oxidative debromination of BBR. The studies allowed a better understanding of the metabolic activation of BBR.

Using a photothrombotic mouse model of single stroke, we show that a single stroke onset increases the nuclear factor-κB (NF-κB), NLR family CARD domain containing protein 4 (NLRC4), and absent in melanoma 2 (AIM2) inflammasomes, as well as the mRNA levels of NLRP3. Next, using a photothrombotic mouse model of recurrent stroke, we found that recurrent strokes increased the activation of NLRP3, exacerbated the brain damage and the pro-inflammatory response in wild type (WT) mice, but not in NLRP3 knockout ( NLRP3 KO) mice. Additionally, we found that apoptosis-associated speck-like protein containing a CARD (ASC) protein level surrounding the infarct area was comparatively increased, but that ASC specks outside of microglia in both the ipsilateral and contralateral of stroke site were decreased in NLRP3 KO mice relative to wild-type (WT) controls, and the number of ASC specks surrounding the second infarct area was positively correlated to the damage scores. Mechanistically, we found that recombinant ASC (RecASC) activated NLRP3 and induced pro-inflammatory responses, exacerbating the outcome of ischemic stroke, in WT mice, but not in NLRP3 KO mice. We therefore conclude that the NLRP3 inflammasome is activated by two attacks of stroke, which act together with ASC to exacerbate recurrent strokes.

In order to improve the accuracy of vehicle detection, this paper proposes a vehicle detection method based on improved YOLOv3. First, the loss function of the YOLOv3 algorithm is improved by introducing the GIoU and focal loss strategies, meanwhile, the detection accuracy of the network is improved. Secondly, by adopting the method of data augmentation for preprocessing, adding label smoothing strategy improves the generalization ability of the model and further improves the average accuracy of detection. During training, multi-scale training is used, and the cosine decay learning rate decay strategy is used to replace the step learning rate decay strategy, which reduces the negative impact of improper learning rate and decay steps, speeds up model training, and reduces the network training cycle. Experimental results show that the improved method can significantly improve the detection accuracy of the original model in fewer training cycles, has good robustness, and can effectively detect road vehicles.

Character-aware neural language models can capture the relationship between words by exploiting character-level information and are particularly effective for languages with rich morphology. However, these models are usually biased towards information from surface forms. To alleviate this problem, we propose a simple and effective method to improve a character-aware neural language model by forcing a character encoder to produce word-based embeddings under Skip-gram architecture in a warm-up step without extra training data. We empirically show that the resulting character-aware neural language model achieves obvious improvements of perplexity scores on typologically diverse languages, that contain many low-frequency or unseen words.

Objective: Previous studies have already shown that electroencephalography (EEG) brain network (BN) can reflect the health status of individuals. However, novel methods are still needed for BN analysis. Therefore, in this study, BNs were constructed based on stable and unstable EEG components, and these may be implemented for disease diagnosis. Methods: Parkinson's disease (PD) was used as an example to illustrate this method. First, EEG signals were decomposed into dynamic modes (DMs). Each DM contains one eigenvalue that can determine not only the stability of that mode, but also its corresponding oscillatory frequency. Second, the stable and unstable components of EEG signals in each frequency band (delta, theta, alpha and beta) were calculated, which are based on the stable and unstable DMs within each respective frequency band. Third, newly developed BNs were constructed, including stable brain network (SBN), unstable brain network (UBN) and inter-connected brain network (IBN). Finally, their topological attributes were extracted in order to differentiate between PD patients and healthy controls (HC). Furthermore, topological attributes were also derived from traditional brain network (TBN) for comparison. Results: Most topological attributes of SBN, UBN and IBN can significantly differentiate between PD patients and HC ( $p$ value $ 0.05). Furthermore, the area under the curve (AUC), precision and recall values of SBN analysis are all significantly higher than TBN. Conclusion: We proposed a new perspective on EEG BN analysis. Significance: These newly developed BNs not only have biological significance, but also could be widely applied in most medical and engineering fields.