: Generalized anxiety disorder (GAD) is one of the most common psychiatric disorders associated with substantial dysfunction and socioeconomic burden. Pharmacotherapy is the first choice for GAD. ...Remission Hamilton Anxiety Scale (HAM-A) score ≤7 is regarded as a crucial treatment goal for patients with GAD. There is no up-to-date evidence to compare remission rate and tolerability of all available drugs by using network meta-analysis. Therefore, the goal of our study is to update evidence and determine the best advantageous drugs for GAD in remission rate and tolerability profiles.
: We performed a systematic review and network meta-analysis of double-blind randomized controlled trials (RCTs). We searched PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Chinese National Knowledge Infrastructure, wanfang data, China Biology Medicine and ClinicalTrials.gov from their inception to March 2020 to identify eligible double-blind, RCTs reporting the outcome of remission in adult patients who received any pharmacological treatment for GAD. Two reviewers independently assessed quality of included studies utilizing the Cochrane Collaboration's risk of bias tool as described in Cochrane Collaboration Handbook and extracted data from all manuscripts. Our outcomes were remission rate (proportion of participants with a final score of seven or less on HAM-A) and tolerability (treatments discontinuations due to adverse events). We calculated summary odds ratios (ORs) and 95% confidence intervals (CIs) of each outcome via pairwise and network meta-analysis with random effects.
: Overall, 30 studies were included, comprising 32 double-blind RCTs, involving 13,338 participants diagnosed as GAD by DSM-IV criteria. Twenty-eight trials were rated as moderate risk of bias, four trials as low. For remission rate, agomelatine (OR 2.70, 95% CI 1.74-4.19), duloxetine (OR 1.88, 95% CI 1.47-2.40), escitalopram (OR 2.03, 95% CI 1.48-2.78), paroxetine (OR 1.74, 95% CI 1.25-2.42), quetiapine (OR 1.88, 95% CI 1.39-2.55), and venlafaxine (OR 2.28, 95% CI 1.69-3.07) were superior to placebo. For tolerability, sertraline, agomelatine, vortioxetine, and pregabalin were found to be comparable to placebo. However, the others were worse than placebo in terms of tolerability, with ORs ranging between 1.86 (95% CI 1.25-2.75) for tiagabine and 5.98 (95% CI 2.41-14.87) for lorazepam. In head-to-head comparisons, agomelatine, duloxetine, escitalopram, quetiapine, and venlafaxine were more efficacious than tiagabine in terms of remission rate, ORs from 1.66 (95% CI 1.04-2.65) for duloxetine to 2.38 (95% CI 1.32-4.31) for agomelatine. We also found that agomelatine (OR 2.08, 95% CI 1.15-3.75) and venlafaxine (OR 1.76, 95% CI 1.08-2.86) were superior to vortioxetine. Lorazepam and quetiapine were poorly tolerated when compared with other drugs.
: Of these interventions, only agomelatine manifested better remission with relatively good tolerability but these results were limited by small sample sizes. Duloxetine, escitalopram, venlafaxine, paroxetine, and quetiapine showed better remission but were poorly tolerated.
The dynamic uncertain relationship among each brain region is a necessary factor that limits EEG-based emotion recognition. It is a thought-provoking problem to availably employ time-varying spatial ...and temporal characteristics from multi-channel electroencephalogram (EEG) signals. Although deep learning has made remarkable achievements in emotion recognition, the biological topological information among brain regions does not fully exploit, which is vital for EEG-based emotion recognition. In response to this problem, we design a hybrid model called ST-GCLSTM, which comprises a spatial-graph convolutional network (SGCN) module and an attention-enhanced bi-directional Long Short-Term Memory (LSTM) module. The main advantage of ST-GCLSTM is that it can consider the biological topology information of each brain region to extract representative spatial-temporal features from multiple EEG channels. Specifically, we construct two layers SGCN by introducing adjacency matrices to adaptively learn the intrinsic connection among different EEG channels. Moreover, an attention-enhanced mechanism is placed into a bi-directional LSTM module to extract the crucial spatial-temporal features from sequential EEG data, and then these features serve as the input layer of the classifier to learn discriminative emotion-related features. Extensive experiments on the DEAP, SEED, and SEED-IV datasets demonstrate the effectiveness of the proposed ST-GCLSTM model, revealing that our model had an absolute performance improvement over state-of-the-art strategies.
Multi-metric learning -a method to learn multiple local metrics to reveal the feature's correlations of samples from different local regions-has become an essential tool to measure the similarities ...between instances from heterogeneous datasets. However, most existing cluster-based MML methods first partition the training data with a predefined metric and then learn multiple metrics via the local instances, leading to these two independent procedures fail to cooperate with each other. In this paper, we propose an Optimal instance Partition-based Multi-Metric Learning (OPM2L) method for heterogeneous dataset classification by unifying the instance partition and multiple local metrics learning into a single objective. In particular, multiple anchor centers together with a global metric are employed to assist the instance partition process. During the training, the shared information contained in local metrics is aggregated into the global metric by a dedicated regularizer, which improves the instance partition process and offers the subsequent multiple local metrics learning with more informative instances. Moreover, an efficient alternating direction technology is employed to seek a feasible solution to the proposed method. We further confirmed that the sub-problems can be settled with closed-form solutions, while the superiority of the proposed method is also proved by experimental results on extensive datasets.
In recent years, we have witnessed a surge of interests in learning distance metrics from various data mining tasks. Most existing methods aim to pull all the similar samples closer while push the ...dissimilar ones as far as possible. However, when some classes of the dataset exhibit multimodal distribution, these goals conflict and thus can hardly be concurrently satisfied. Additionally, to ensure a valid metric, many methods require a repeated eigenvalue decomposition process, which is time-consuming and numerically unstable. Therefore, how to effectively learn an appropriate distance metric from weakly supervised data remains an open but challenging problem. To address this issue, in this paper, we propose a novel weakly supervised metric learning algorithm, named MultimoDal aware weakly supervised Metric Learning (MDaML). MDaML partitions training data into several clusters and allocates the local cluster center and weight for each sample. Then, combining it with the weighted triplet loss can further enhance the local separability, which encourages local dissimilar samples to keep a large distance from local similar samples. Meanwhile, MDaML casts the metric learning problem into an unconstrained optimization on SPD manifold, which can be efficiently solved by Riemannian Conjugate Gradient Descent (RCGD). Extensive experiments conducted on 13 datasets validate the superiority of the proposed method.
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•Sludge-bagasse mixed biochar (SB-BC-900) exhibited its outstanding catalytic performance.•Singlet oxygen (1O2) was the main reactive oxygen species.•Graphitic carbon (sp2 C=C) and ...oxygenated functional groups (OFGs) played a significant role in the SB-BC-900/PMS system.•The enhanced mechanism was elucidated by density functional theory (DFT).
The exploitation of cost-effective and environmentally friendly biochar catalysts is of major importance for the oxidative treatment of toxic organic wastewater and the resource utilization of waste biomass. In this work, sewage sludge and bagasse waste were co-pyrolyzed into a novel biochar catalyst (SB-BC-900) for efficiently activating peroxymonosulfate (PMS) to degrade bisphenol AF (BPAF) in wastewater. Compared with single sludge biochar (S-BC-900), SB-BC-900 had a larger cumulative pore volume, more oxygenated functional groups (OFGs) and higher graphitization degree, with a significantly higher catalytic capacity. SB-BC-900 enabled rapid degradation of BPAF within 10 min at a low PMS dosage (molar ratio PMS: BPAF = 2.67:1). There was mainly the non-free radical pathway dominated by singlet oxygen (1O2) in the SB-BC-900/PMS system. Graphitic carbon (sp2 C=C) and OFGs on SB-BC-900 played a crucial part in the catalytic degradation of BPAF. A green approach is provided to resource waste biomass for efficient wastewater treatment in this work.
Sludge biochar (SBC) modified by humic acid (HA) was used to activate peroxymonosulfate (PMS) for degrading naproxen (NPX). HA-modified biochar (SBC-50HA) boosted the catalytic performance of SBC for ...PMS activation. The SBC-50HA/PMS system had good reusability and structural stability, and was unaffected by complex water bodies. The results of Fourier transform infrared (FTIR) and X-ray diffraction spectroscopy (XPS) indicated that graphitic carbon (CC), graphitic N, and C–O on SBC-50HA played a vital part on the removal of NPX. The key role of non-radical pathways such as singlet oxygen (1O2) and electron transfer in the SBC-50HA/PMS/NPX system was verified by inhibition experiments, electron paramagnetic resonance (EPR), electrochemistry, and PMS consumption. The possible degradation pathway of NPX was proposed by density functional theory (DFT) calculations, and the toxicity of NPX and its degradation intermediates were evaluated.
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•SBC was modified by Humic acid (HA) to enhance its catalytic ability.•CC, graphitic N, and C–O on SBC-50HA played an important role in the catalytic process.•The removal of NPX was attributed to singlet oxygen (1O2) and electron transfer.•The possible degradation pathway of NPX was investigated by density functional theory (DFT) calculations.
As the information era develops rapidly, it’s common to utilize multiple features from different sources to represent one object. Measuring the similarity between multi-view objects is the ...fundamental task in multi-view learning. To effectively measure the similarity between multi-view samples, multi-view metric learning has gained extensive attention recently. Nevertheless, most existing methods merely focus on the closeness of similar pairs and the separability of dissimilar ones inside each view, so that rich consensus properties existing in multi-views data might be ignored to some extent. To mitigate this issue, we come up with a novel method entitled Hierarchical Multi-view Metric learning with HSIC regularization (HM2H). HM2H aims to simultaneously maintain the closeness of similar points and the separability of dissimilar ones in intra-view and inter-view. Since multiple views depict different perspectives of the same object, the shared metric is introduced to capture the consensus information among those views. Moreover, we take advantage of the Hilbert–Schmidt Independence Criterion to seek the maximum distribution agreement of the multi-view dataset. Correspondingly, an algorithm based on Alternating Direction Method is provided to solve the proposed HM2H. Finally, various experimental results on five visual recognition datasets confirm the effectiveness and feasibility of our proposed method.
In this study, Congo red (CR) was degraded by different particle sizes of zero-valent copper (ZVC) activated persulfate (PS) under mild temperature. The CR removal by 50 nm, 500 nm, 15 μm of ZVC ...activated PS was 97%, 72%, and 16%, respectively. The co-existence of SO
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and Cl
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promoted the degradation of CR, and HCO
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and H
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PO
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were detrimental to the degradation. With the reduction of ZVC particle size, the effect of coexisting anions on degradation grew stronger. The high degradation efficiency of 50 nm and 500 nm ZVC was achieved at pH=7.0, while the high degradation of 15 μm ZVC was achieved at pH=3.0. It was more favorable to leach copper ions for activating PS to generate reactive oxygen species (ROS) with the smaller particle size of ZVC. The radical quenching experiment and electron paramagnetic resonance (EPR) analysis indicated that SO
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•, •OH and •O
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existed in the reaction. The mineralization of CR reached 80% and three possible paths were suggested for the degradation. Moreover, the degradation of 50 nm ZVC can still reach 96% in the 5th cycle, indicating promising application potential in dyeing wastewater treatment.
Metric learning has emerged as a critical tool for analyzing the semantic similarities between objects. However, numerous existing methods are incapable of simultaneously maximizing the proximity of ...similar pairs and the separability between dissimilar ones to achieve the largest margin principle. Additionally, most graph Laplacian-based semi-supervised approaches fail to consider the valuable dissimilar information of unlabeled data, and they treat neighborhood graph construction and metric learning as separate procedures, thereby breaking the unified relationship between these two components. To overcome these challenges, this paper proposes a scalable and efficient metric learning framework called Unified metric learNing based on maxIum enTropy (UNIT). UNIT attempts to unify supervised and semi-supervised metric learning into a framework by introducing the maximum entropy regularizer of the eigenvalues of the learned matrix. With the novel regularizer, UNIT can maximize the closeness of similar instances and the separability of dissimilar ones without encountering the trivial solution problem. Furthermore, the adaptive graph Laplacian, formulated by a similar graph as well as a dissimilar graph, is constructed to mine the rich discriminant information of the unlabeled data. We demonstrate that UNIT can be solved efficiently with the alternating direction method, with each sub-problem being solvable using a closed-form solution. To account for nonlinear data distribution, a kernelized version of UNIT is also provided. The effectiveness of the proposed methods is validated through extensive supervised and semi-supervised experiments on various datasets.
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