Global prevalence of depression in menopausal women: A systematic review and meta-analysis.

BACKGROUND: An association between the menopause and depression is widely reported. This review aims to determine the global prevalence of depression in menopausal women (this includes women in perimenopause and postmenopause). METHODS: PubMed, Web of Science, Embase, and PsycINFO databases were systematically searched from database inception until March 1, 2024. Studies with validated methods for assessing the prevalence of depression in perimenopausal and postmenopausal women were included. Two authors independently extracted relevant data. Random effects meta-analysis and Meta-regression analysis were performed using Stata software. RESULTS: Total of 55 studies (76,817 participants) were included in the review. A random effects model was used to calculate pooled prevalence. The pooled depression prevalence in menopausal women was 35.6 % (95 % CI: 32.0-39.2 %), with 33.9 % (95 % CI: 27.8-40.0 %) in perimenopausal women, and 34.9 % (95 % CI: 30.7-39.1 %) in postmenopausal women. Subgroup analyses indicated that region, screening tool, study design, and setting moderated the prevalence of depression. Meta-regression indicated that smaller sample sizes and poorer study quality were significantly associated with a higher prevalence. LIMITATIONS: There was a high degree of heterogeneity across the included studies. Only articles published in English were included. There was significant publication bias in this meta-analysis. There is insufficient information about many risk factors of menopausal depression in current meta-analysis. CONCLUSIONS: Depression is common among menopausal women worldwide. To reduce the negative impact of depression on health outcomes in menopausal women, regular screening and the availability of effective prevention and treatment measures should be made available for this population.

The effectiveness of nursing interventions for elderly dementia patients based on virtual reality technology: A systematic review and meta-analysis.

OBJECTIVE: To systematically evaluate the effect of virtual reality technology-based nursing interventions to improve cognitive function, quality of life, activity of daily living, and negative emotions in patients with dementia. METHODS: Computer searches of the VIP Chinese Science and Technology Journal Database, China National Knowledge Infrastructure Database, Wanfang Database, The Cochrane Library, Embase, PubMed, and Web of Science were conducted to include randomized controlled trials and class experimental studies of virtual reality technology-based nursing interventions for patients with dementia, with a search time frame from the date of database creation to March 31, 2023. Two investigators independently screened the literature according to inclusion and exclusion criteria, extracted data, performed risk bias evaluation, and then performed Meta-analysis on the extracted relevant data using Rev Man 5.4 software. RESULTS: A total of 6 randomized controlled trials and 2 quasi-randomized controlled trial with 514 patients with dementia were included. Meta-analysis results showed that compared with conventional cognitive care interventions, virtual reality-based care interventions significantly improved cognitive function [MD = 1.61, 95% CI (0.99, 2.23), Z = 5.12, P < 0.00001], quality of life [SMD = 0.85, 95% CI (0.56, 1.14), Z = 5.70, P < 0.00001] and activity of daily living [MD = 3.75, 95% CI (1.22, 6.28), Z = 2.91, P = 0.004], and alleviate negative emotions [MD = -4.00, 95% CI (-7.26, -0.75), Z = 2.41, P = 0.02]. CONCLUSIONS: The current results suggest that virtual reality-based nursing interventions have a positive effect on improving cognitive function, quality of life, activities of daily living and alleviating negative emotions in patients with dementia. Due to the limitations of the quantity and quality of the included literature, the above findings are yet to be validated by more high-quality studies.

Globally Localized Multisource Domain Adaptation for Cross-Domain Fault Diagnosis With Category Shift.

Deep learning has demonstrated splendid performance in mechanical fault diagnosis on condition that source and target data are identically distributed. In engineering practice, however, the domain shift between source and target domains significantly limits the further application of intelligent algorithms. Despite various transfer techniques proposed, either they focus on single-source domain adaptation (SDA) or they utilize multisource domain globally or locally, which both cannot address the cross-domain diagnosis effectively, especially with category shift. To this end, we propose globally localized multisource DA for cross-domain fault diagnosis with category shift. Specifically, we construct a GlocalNet to fuse multisource information comprehensively, which consists of a feature generator and three classifiers. By optimizing the Wasserstein discrepancy of classifiers locally and accumulative higher order multisource moment globally, multisource DA is achieved from domain and class levels thus to reduce the shift on domain and category. To refine the classifier at sample level, a distilling strategy is presented. Finally, an adaptive weighting policy is employed for reliable result. To evaluate the effectiveness, the proposed method is compared with multiple methods on four bearing vibration datasets. Experimental results indicate the superiority and practicability of the proposed method for cross-domain fault diagnosis.

Self-Supervised Simple Siamese Framework for Fault Diagnosis of Rotating Machinery With Unlabeled Samples.

Fault diagnosis is vital to ensuring the security of rotating machinery operations. While fault data obtained from mechanical equipment for this issue are often insufficient and of no labels. In this case, supervised algorithms cannot come into play. Hence, this article proposes a self-supervised simple Siamese framework (SSF) for bearing fault diagnosis based on the contrastive learning algorithm SimSiam which uses a simplified Siamese network to find the distinguishable features of different fault categories. SSF consists of a weight-sharing encoder applied on two inputs, a nonlinear predictor and a linear classifier. SSF learns invariant characteristics of fault samples via maximizing the similarity between two views of each inputted sample. Several data augmentation (DA) methods for vibration signals, which provide different sample views for the model, are also studied, for it is crucial for contrastive learning. After fine-tuning the learned encoder and a linear layer classifier with a small subset of labeled data (1%-5% of the total samples), the network achieves satisfactory performance for bearing fault diagnosis. A series of experiments based on the data from three different scenarios are used to verify the proposed methods, getting 100%, 99.38%, and 98.87% accuracy separately.

A TaqMan Probe-Based Multiplex Real-Time PCR for Simultaneous Detection of Porcine Epidemic Diarrhea Virus Subtypes G1 and G2, and Porcine Rotavirus Groups A and C.

Porcine viral diarrhea diseases affect the swine industry, resulting in significant economic losses. Porcine epidemic diarrhea virus (PEDV) genotypes G1 and G2, and groups A and C of the porcine rotavirus, are major etiological agents of severe gastroenteritis and profuse diarrhea, particularly among piglets, with mortality rates of up to 100%. Based on the high prevalence rate and frequent co-infection of PEDV, RVA, and RVC, close monitoring is necessary to avoid greater economic losses. We have developed a multiplex TaqMan probe-based real-time PCR for the rapid simultaneous detection and differentiation of PEDV subtypes G1 and G2, RVA, and RVC. This test is highly sensitive, as the detection limits were 20 and 100 copies/µL for the G1 and G2 subtypes of PEDV, respectively, and 50 copies/µL for RVA and RVC, respectively. Eighty-eight swine clinical samples were used to evaluate this new test. The results were 100% in concordance with the standard methods. Since reassortment between porcine and human rotaviruses has been reported, this multiplex test not only provides a basis for the management of swine diarrheal viruses, but also has the potential to impact public health as well.

Copper-catalyzed three-component reaction to synthesize polysubstituted imidazo[1,2-a]pyridines.

An efficient three-component one-pot and operationally simple cascade of 2-aminopyridines with sulfonyl azides and terminal ynones is reported, providing a variety of polysubstituted imidazo[1,2-a]pyridine derivatives in moderate to excellent yields. In particular, the reaction goes a through CuAAC/ring-cleavage process and forms a highly active intermediate α-acyl-N-sulfonyl ketenimine with base free.

Reversal of the Pinning Direction in the Synthetic Spin Valve with a NiFeCr Seed Layer.

The effect of the seed layers on the magnetic properties of the giant magnetoresistance thin films has received a lot of attention. Here, a synthetic spin valve film stack with a wedge-shaped NiFeCr seed layer is deposited and annealed following a zero-field cooling procedure. The film crystallinity and magnetic properties are studied as a function of the NiFeCr seed layer thickness. It is found that the exchange coupling field from the IrMn/CoFe interface and the antiferromagnetic coupling field in the synthetic antiferromagnet both increase as the seed layer thickness increases, indicating the perfection of film texture. In this film, the critical thickness of the NiFeCr seed layer for the formation of the ordered IrMn3 texture is about 9.3 nm. Meanwhile, a reversal of the pinning direction in the film is observed at this critical thickness of NiFeCr. This phenomenon can be explained in a free energy model by the competition effect between the exchange coupling and the interlayer coupling during the annealing process.

Intelligent fault diagnosis scheme via multi-module supervised-learning network with essential features capture-regulation strategy.

The performance of data driven-based intelligent diagnosis method greatly depends on the quantity and quality of data. Nevertheless, due to realistic limitations, failure data is hard to acquire, which makes the training process of numerous intelligent models unsatisfactory and leads to performance degradation Aiming at this problem, considering the local impulse characteristics as minimum diagnosable units, this paper proposes a signal adaptive augmentation network (SAAN) to effectively construct artificial samples for amplifying fault data volume. The SAAN consists of three sub-structures: impulse extractor, regulator, and classifier. The impulse extractor combines inner product matching principle to extract the local impulse features from insufficient samples to construct massive initial artificial samples. The regulator adopts convolution and deconvolution frameworks to regulate and reconstruct the initial artificial samples by specially designed synthetic loss function, which makes artificial samples have same characteristic distribution as real samples. The augmented method is used for validation on three bearing data with some advanced algorithms. Besides, a focal normalized network is designed for classification under small samples. Relevant experiments indicate that the SAAN shows a competitive performance with existing state-of-art diagnostic methods, which can helpfully improve recognition accuracies of various diagnostic models by 5%-35% under small sample prerequisite.

A multi-branch redundant adversarial net for intelligent fault diagnosis of multiple components under drastically variable speeds.

Intelligent fault diagnosis with small training samples plays an important role in the safety of mechanical equipment. However, affected by sharp speed variation, fault feature is extremely weak, which raises difficulty for fault diagnosis. The mutual coupling of multi-component fault features further increases the difficulty. Considering the ability of redundant second generation wavelet transform in non-stationary feature extraction, a multi-branch redundant adversarial net (RedundancyNet) is proposed to address the above issues. The Net consists of discriminator, the generator based on redundant reconstruction, and the classifier based on redundant decomposition. Firstly, through adversarial training process, the generator fuses multi-scale features to generate the signal with varying speeds, thereby expanding training data. Secondly, through layer-by-layer multi-resolution feature enhancement, the classifier boosts weak fault features of vibration signals at variable speeds. Finally, a multi-branch framework is proposed to realize multi-component fault location and damage identification. The proposed method is validated on two cases. The average classification accuracy in the two cases reach 97.14% and 98.33% respectively. However, other end-to-end intelligent fault diagnosis methods for varying speeds or small samples can only reach the highest classification accuracy of 95.14% in Case 1 and 93.59% in Case2, which is much less than RedundancyNet. The analysis results highlight the effectiveness of the net under drastically variable speeds and small faulty training samples. Besides, the proposed classifier is easy to understand, which reveals the process of feature learning and the extracted feature under varying speeds.

Multi-Frequency Augmentation framework via information active capture for machinery intelligent fault diagnosis.

Data-driven methods, especially deep neural network, received increasing attention in machinery fault diagnosis field. Many works focus on how to design effective model while ignoring a fundamental problem, i.e., directly using raw machinery signal as the input of model. In this work, we analyze from two aspects: model mechanism and mechanical monitoring signal, it shows the limitation of learning raw data directly, which led to the research idea of improving the generalization ability of model by multi-frequency information augmentation. In order to make machinery intelligent model capture multi-frequency information more directly and actively, Multi-Frequency Augmentation framework is proposed in this paper. Firstly, we proposed a data augmentation method to split the raw sample into sample pair. And we could choose to further augment the dataset by Frequency Components Recombination, especially under few-shot scenes. Then, Multi-Frequency Capture Network is built to achieve feature augmentation by learning the sample pair. Finally, fault diagnosis is performed on testing set. The effectiveness and compatibility of Multi-Frequency Augmentation framework is verified with two experiments, which also verifies the feasibility of the proposed research idea. In addition, it could also achieve competitive performance with latest literature methods. Further discussion indicate that the proposed framework provides a new perspective to analyze the model and dataset, which has good application potential.

Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this paper. SSMN consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function. Based on attention mechanism, the encoder is able to extract distinct features to generate prototypes and enhance the identification accuracy. With semi-supervised few-shot learning, SSMN utilizes unlabeled data to refine original prototypes for better fault recognition. A combinatorial learning optimizer is designed to optimize SSMN efficiently. The effectiveness of the proposed method is demonstrated through three bearing vibration datasets and the results indicate the outstanding adaptability in different situations. Comparison with other approaches is also made under the same setup and the experimental results prove the superiority of the proposed method for few-shot fault diagnosis.

Subspace Network with Shared Representation learning for intelligent fault diagnosis of machine under speed transient conditions with few samples.

Sharp speed variation leads to a shift of sample distribution domain, which poses a challenge for vibration-based rolling bearing fault diagnosis. Furthermore, the overfitting effects inflicted on the intelligent diagnosis model due to insufficient data will hinder the performance significantly. In this work, a Subspace Network with Shared Representation learning (SNSR) based on meta-learning is constructed for fault diagnosis under speed transient conditions with few samples. Firstly, shared representation learning based on the cross mutual information estimation is designed to promote the encoder to learn the domain invariant features. Meanwhile, we developed non-parameterized adaptive weight allocation to optimize the estimation of the discriminator. Then, the subspace classifiers in the meta-learning paradigm are employed to force the encoder to learn the discriminative features. Finally, the shared representation learning is embedded into the meta-learning and a cross co-training mechanism is designed for optimization. Thus the fusion framework is endowed with the capacity of learning distinguishable and domain invariant features simultaneously for diagnosis under speed transient conditions with few samples. Comparative experiments on two case studies of bearing fault diagnosis validated the superior performance of the proposed method, with an accuracy of 97.72% and 96.46% in 7-way and 9-way learning respectively.

Copper-Catalyzed One-Pot Synthesis of N-Sulfonyl Amidines from Sulfonyl Hydrazine, Terminal Alkynes and Sulfonyl Azides.

N-Sulfonyl amidines are developed from a Cu-catalyzed three-component reaction from sulfonyl hydrazines, terminal alkynes and sulfonyl azides in toluene at room temperature. Particularly, the intermediate N-sulfonylketenimines was generated via a CuAAC/ring-opening procedure and took a nucleophilic addition with the weak nucleophile sulfonyl hydrazines. In addition, the stability of the product was tested by a HNMR spectrometer.

The Copper-Catalyzed Reaction of 2-(1-Hydroxyprop-2-yn-1-yl)phenols with Sulfonyl Azides Leading to C3-Unsubstituted N-Sulfonyl-2-iminocoumarins.

An operationally simple synthesis of Z-configured and C3-unsubstituted N-sulfonyl-2-iminocoumarins (e.g., 8a) that proceeds under mild conditions is achieved by reacting 2-(1-hydroxyprop-2-yn-1-yl)phenols (e.g., 6a) with sulfonyl azides (e.g., 7a). The cascade process involved likely starts with a copper-catalyzed alkyne-azide cycloaddition (CuAAC) reaction. This is followed by ring-opening of the resulting metalated triazole (with accompanying loss of nitrogen), reaction of the ensuing ketenimine with the pendant phenolic hydroxyl group, and finally dehydration of the (Z)-N-(4-hydroxychroman-2-ylidene)sulfonamide so formed.

Copper catalyzed five-component domino strategy for the synthesis of nicotinimidamides.

A library of medicinally and synthetically important nicotinimidamides was synthesized by a copper-catalyzed multicomponent domino reaction of oxime esters, terminal ynones, sulfonyl azides, aryl aldehydes and acetic ammonium. Its synthetic pathway involves the formation of a highly reactive N-sulfonyl acetylketenimine, characterized by high selectivity, combinations of potential nucleophiles and electrophiles, mild reaction conditions and a wide substrate scope, and is a rare five-component example of a CuAAC/ring-opening reaction.

Preparation of 1,2-substituted benzimidazoles via a copper-catalyzed three component coupling reaction.

1,2-Substituted benzimidazoles were prepared by simply stirring a mixture of copper catalysts, N-substituted o-phenylenediamines, sulfonyl azides and terminal alkynes. Particularly, the intermediate N-sulfonylketenimine occurred with two nucleophilic addition and the sulfonyl group was eliminated via cyclization. In a way, sulfonyl azides and copper catalysts activated the terminal alkynes to synthesize benzimidazoles.

Rapid and efficient synthesis of formamidines in a catalyst-free and solvent-free system.

An operationally rapid and efficient synthesis of N-sulfonyl formamidines that proceeds under mild conditions was achieved by reaction of a mixture of an amine, a sulfonyl azide, and a terminal ynone under catalyst-free and solvent-free conditions. Terminal ynones provide the C source to formamidines via complete cleavage of C[triple bond, length as m-dash]C.

Design and Synthesis of Novel Podophyllotoxins Hybrids and the Effects of Different Functional Groups on Cytotoxicity.

Development of novel anticancer therapeutic candidates is one of the key challenges in medicinal chemistry. Podophyllotoxin and its derivatives, as a potent cytotoxic agent, have been at the center of extensive chemical amendment and pharmacological investigation. Herein, a new series of podophyllotoxin-N-sulfonyl amidine hybrids (4a-4v, 5a-5f) were synthesized by a CuAAC/ring-opening procedure. All the synthesized podophyllotoxins derivatives were evaluated for in vitro cytotoxic activity against a panel of human lung (A-549) cancer cell lines. Different substituents', or functional groups' antiproliferative activities were discussed. The -CF3 group performed best (IC50: 1.65 µM) and exhibited more potent activity than etoposide. Furthermore, molecular docking and dynamics studies were also conducted for active compounds and the results were in good agreement with the observed IC50 values.

SDA: Regularization with Cut-Flip and Mix-Normal for machinery fault diagnosis under small dataset.

Data-driven intelligent diagnosis model plays a key role in the monitoring and maintenance of mechanical equipment. However, due to practical limitations, the fault data is difficult to obtain, which makes model training unsatisfactory and results in poor testing performance. Based on the characteristics of 1-D mechanical vibration signal, this paper proposes Supervised Data Augmentation (SDA) as a regularization method to provide more effective training samples, which includes Cut-Flip and Mix-Normal. Cut-Flip is used directly on the raw sample without parameter selection. Mix-Normal mixes the data and labels of a random sample with a random normal sample at a certain ratio. The proposed SDA is verified on two bearing datasets with some popular intelligent diagnosis networks. Besides, we also design a Batch Normalization CNN (BNCNN) to learn the small dataset. Results show that SDA can significantly improve the classification accuracy of BNCNN by 10%-30% under 1-8 samples of each class. The proposed method also shows a competitive performance with existing advanced methods. Finally, we further discuss each data augmentation method through a series of ablation experiments and summarize the advantages and disadvantages of the proposed SDA.

Intelligent fault identification for industrial automation system via multi-scale convolutional generative adversarial network with partially labeled samples.

Rolling bearings are the widely used parts in most of the industrial automation systems. As a result, intelligent fault identification of rolling bearing is important to ensure the stable operation of the industrial automation systems. However, a major problem in intelligent fault identification is that it needs a large number of labeled samples to obtain a well-trained model. Aiming at this problem, the paper proposes a semi-supervised multi-scale convolutional generative adversarial network for bearing fault identification which uses partially labeled samples and sufficient unlabeled samples for training. The network adopts a one-dimensional multi-scale convolutional neural network as the discriminator and a multi-scale deconvolutional neural network as the generator and the model is trained through an adversarial process. Because of the full use of unlabeled samples, the proposed semi-supervised model can detect the faults in bearings with limited labeled samples. The proposed method is tested on three datasets and the average classification accuracy arrived at of 100%, 99.28% and 96.58% respectively Results indicate that the proposed semi-supervised convolutional generative adversarial network achieves satisfactory performance in bearing fault identification when the labeled data are insufficient.